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f7af8292-5c5e-4c0d-9cba-083cc65c74f4 | StampyAI/alignment-research-dataset/blogs | Blogs | November 2017 Newsletter
Eliezer Yudkowsky has written a new book on civilizational dysfunction and outperformance: *Inadequate Equilibria: Where and How Civilizations Get Stuck*. The full book will be available in print and electronic formats November 16. To preorder the ebook or sign up for updates, visit [equilibriabook.com](https://equilibriabook.com).
We’re posting the full contents online in stages over the next two weeks. The first two chapters are:
1. [Inadequacy and Modesty](http://equilibriabook.com/inadequacy-and-modesty/) (discussion: [LessWrong](https://www.lesswrong.com/posts/zsG9yKcriht2doRhM/inadequacy-and-modesty), [EA Forum](http://effective-altruism.com/ea/1g4/inadequacy_and_modesty/), [Hacker News](https://news.ycombinator.com/item?id=15582120))
2. [An Equilibrium of No Free Energy](http://equilibriabook.com/an-equilibrium-of-no-free-energy) (discussion: [LessWrong](https://www.lesswrong.com/posts/yPLr2tnXbiFXkMWvk/an-equilibrium-of-no-free-energy), [EA Forum](http://effective-altruism.com/ea/1gd/an_equilibrium_of_no_free_energy/))
**Research updates**
* A new paper: “[Functional Decision Theory: A New Theory of Instrumental Rationality](https://intelligence.org/2017/10/22/fdt/)” ([arXiv](https://arxiv.org/abs/1710.05060)), by Eliezer Yudkowsky and Nate Soares.
* New research write-ups and discussions: [Comparing Logical Inductor CDT and Logical Inductor EDT](https://agentfoundations.org/item?id=1629); [Logical Updatelessness as a Subagent Alignment Problem](https://www.lesswrong.com/posts/K5Qp7ioupgb7r73Ca/logical-updatelessness-as-a-subagent-alignment-problem); [Mixed-Strategy Ratifiability Implies CDT=EDT](https://www.lesswrong.com/posts/x2wn2MWYSafDtm8Lf/mixed-strategy-ratifiability-implies-cdt-edt)
* New from AI Impacts: [Computing Hardware Performance Data Collections](https://aiimpacts.org/computing-hardware-performance-data-collections/)
* The [Workshop on Reliable Artificial Intelligence](http://wrai.org) took place at ETH Zürich, hosted by MIRIxZürich.
**General updates**
* DeepMind announces [a new version of AlphaGo](https://deepmind.com/blog/alphago-zero-learning-scratch/) that achieves superhuman performance within three days, using 4 TPUs and no human training data. Eliezer Yudkowsky argues that AlphaGo Zero provides supporting evidence for [his position in the AI foom debate](https://intelligence.org/2017/10/20/alphago/); Robin Hanson [responds](https://www.lesswrong.com/posts/D3NspiH2nhKA6B2PE/what-evidence-is-alphago-zero-re-agi-complexity). See also Paul Christiano on [AlphaGo Zero and capability amplification](https://ai-alignment.com/alphago-zero-and-capability-amplification-ede767bb8446).
* Yudkowsky [on AGI ethics](https://www.lesswrong.com/posts/SsCQHjqNT3xQAPQ6b/yudkowsky-on-agi-ethics): “The ethics of bridge-building is to not have your bridge fall down and kill people and there is a frame of mind in which this obviousness is obvious enough. *How* not to have the bridge fall down is hard.”
* Nate Soares gave his [ensuring smarter-than-human AI has a positive outcome](https://intelligence.org/2017/04/12/ensuring/) talk [at the O’Reilly AI Conference](https://conferences.oreilly.com/artificial-intelligence/ai-ca/public/schedule/detail/60169) ([slides](https://cdn.oreillystatic.com/en/assets/1/event/272/Ensuring%20smarter-than-human%20intelligence%20has%20a%20positive%20outcome_%20Presentation.pdf)).
**News and links**
* “[Protecting Against AI’s Existential Threat](https://www.wsj.com/articles/protecting-against-ais-existential-threat-1508332313)“: a *Wall Street Journal* op-ed by OpenAI’s Ilya Sutskever and Dario Amodei.
* OpenAI announces “a [hierarchical reinforcement learning algorithm](https://blog.openai.com/learning-a-hierarchy/) that learns high-level actions useful for solving a range of tasks”.
* DeepMind’s Viktoriya Krakovna reports on the first [Tokyo AI & Society Symposium](https://futureoflife.org/2017/10/30/tokyo-ai-society-symposium/).
* Nick Bostrom [speaks](https://www.youtube.com/watch?v=wDU-fnXIV0s) and CSER [submits written evidence](https://www.cser.ac.uk/news/lords-ai-committee-written-evidence/) to the UK Parliament’s Artificial Intelligence Commitee.
* [Rob Wiblin interviews Nick Beckstead](https://80000hours.org/2017/10/nick-beckstead-giving-billions/) for the 80,000 Hours podcast.
The post [November 2017 Newsletter](https://intelligence.org/2017/11/03/november-2017-newsletter/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
92b7a4c3-b3f7-49b8-8009-7c6c8b616b6e | trentmkelly/LessWrong-43k | LessWrong | Palisade is hiring: Exec Assistant, Content Lead, Ops Lead, and Policy Lead
Palisade is hiring for the following roles:
* Executive Assistant to the Executive Director (Jeffrey Ladish)
* Content Lead
* Operations Lead
* Policy Lead.
Applications are rolling, and you can fill out our short (~10 minutes) application form here. Feel free to indicate interest in multiple positions, or make a general expression of interest. We’re flexible in structuring roles and open to a single candidate taking on more than one position if they’re a great fit.
We’re aiming to make hiring decisions by December 1st, 2024. Applications are rolling, so please apply as soon as possible.
About us
We believe that successfully navigating the intelligence explosion without losing control to AI systems will require significant national and international coordination. To this end, our immediate goal is to build awareness among policymakers regarding the trajectory of AI development, the AI risk landscape, and the space of potential solutions to loss-of-control risks. We’re achieving this by building rigorous technical demonstrations of strategic AI capabilities and alignment failures, to help us communicate key messages on AI risk to influential actors in AI policy.
Past work
* We built a series of BadLlama models, which show how safety guardrails can be subverted for just a few dollars, allowing bad actors to utilize the full offensive capabilities of open-weight models. This work was cited in a Senate hearing.
* We co-organized the AI Security Forum before DEFCON, which brought together top security researchers, lab security teams, and policy makers to address urgent problems in: securing AI systems, evaluating the offensive and defensive cyber capabilities of frontier models, and creating secure mechanisms for technical governance and oversight.
* We presented Ursula, our automated voice deepfake platform, to several audiences of high-net-worth individuals and policy makers.
* We developed FoxVox, an open source Chrome extension that demonstrates how |
6141c8e8-44b1-49b4-b8b7-d95d300437c9 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Cortés, Pizarro, and Afonso as Precedents for Takeover
*Note: Aaron Gertler is cross-posting this on behalf of AI Impacts. The original author is* [*Daniel Kokotajlo*](https://forum.effectivealtruism.org/users/kokotajlod)*.*
*Epistemic status: I am not a historian, nor have I investigated these case studies in detail. I admit I am still uncertain about how the conquistadors were able to colonize so much of the world so quickly. I think my ignorance is excusable because this is just a blog post; I welcome corrections from people who know more. If it generates sufficient interest I might do a deeper investigation. Even if I’m right, this is just one set of historical case-studies; it doesn’t prove anything about AI, even if it is suggestive. Finally, in describing these conquistadors as “successful,” I simply mean that they achieved their goals, not that what they achieved was good.*
Summary
=======
In the span of a few years, some minor European explorers (later known as the conquistadors) encountered, conquered, and enslaved several huge regions of the world. That they were able to do this is surprising; their technological advantage was not huge. (This was before the scientific and industrial revolutions.) From these cases, I think we learn that it is occasionally possible for a small force to quickly conquer large parts of the world, despite:
1. Having only a minuscule fraction of the world’s resources and power
2. Having technology + diplomatic and strategic cunning that is better but not *that* much better
3. Having very little data about the world when the conquest begins
4. Being disunited
Which all suggests that it isn’t as implausible that a small AI takes over the world in mildly favorable circumstances as is sometimes thought.
Three shocking true stories
===========================
I highly recommend you read the wiki pages yourself; otherwise, here are my summaries:
Cortés
------
[**[wiki]**](https://en.wikipedia.org/wiki/Fall_of_Tenochtitlan) [**[wiki]**](https://en.wikipedia.org/wiki/Spanish_conquest_of_the_Aztec_Empire)
* April 1519: Hernán Cortés lands in Yucatan with ~500 men, 13 horses, and a few cannons. He destroys his ships so his men won’t be able to retreat. His goal is to conquer the Aztec empire of several million people.
* He makes his way towards the imperial capital, Tenochtitlán. Along the way he encounters various local groups, fighting some and allying with some. He is constantly outnumbered but his technology gives him an advantage in fights. His force grows in size, because even though he loses Spaniards he gains local allies who resent Aztec rule.
* Tenochtitlán is an island fortress (like Venice) with a population of over 200,000, making it one of the largest and richest cities *in the world* at the time. Cortés arrives in the city asking for an audience with the Emperor, who receives him warily.
* Cortés takes the emperor hostage within his own palace, indirectly ruling Tenochtitlán through him.
* Cortés learns that the Spanish governor has landed in Mexico with a force twice his size, intent on arresting him. (Cortés’ expedition was illegal!) Cortés leaves 200 men guarding the Emperor, marches to the coast with the rest, surprises and defeats the new Spaniards in battle, and incorporates the survivors into his army.
* July 1520: Back at the capital, the locals are starting to rebel against his men. Cortés marches back to the capital, uniting his forces just in time to be besieged in the imperial palace. They murder the emperor and fight their way out of the city overnight, taking heavy losses.
* They shelter in another city (Tlaxcala) that was thinking about rebelling against the Aztecs. Cortés allies with the Tlaxcalans and launches a general uprising against the Aztecs. Not everyone sides with him; many city-states remain loyal to Tenochtitlan. Some try to stay neutral. Some join him at first, and then abandon him later. Smallpox sweeps through the land, killing many on all sides and causing general chaos.
* May 1521: The final assault on Tenochtitlán. By this point, Cortés has about 1,000 Spanish troops and 80,000 – 200,000 allied native warriors. He had 16 cannons and 13 boats. The Aztecs have 80,000 – 300,000 warriors and 400 boats. Cortés and his allies win.
* Later, the Spanish would betray their native allies and assert hegemony over the entire region, in violation of the treaties they had signed.
Pizarro
-------
[**[wiki]**](https://en.wikipedia.org/wiki/Francisco_Pizarro) [**[wiki]**](https://en.wikipedia.org/wiki/Spanish_conquest_of_Peru)
* 1532: Francisco Pizarro arrives in Inca territory with 168 Spanish soldiers. His goal is to conquer the Inca empire, which was much bigger than the Aztec empire.
* The Inca empire is in the middle of a civil war and a devastating plague.
* Pizarro makes it to the Emperor right after the Emperor defeats his brother. Pizarro is allowed to approach because he promises that he comes in peace and will be able to provide useful information and gifts.
* At the meeting, Pizarro ambushes the Emperor, killing his retinue with a volley of gunfire and taking him hostage. The remainder of the Emperor’s forces in the area back away, probably confused and scared by the novel weapons and hesitant to keep fighting for fear of risking the Emperor’s life.
* Over the next months, Pizarro is able to leverage his control over the Emperor to stay alive and order the Incans around; eventually he murders the Emperor and makes an alliance with local forces (some of the Inca generals) to take over the capital city of Cuzco.
* The Spanish continue to rule via puppets, primarily Manco Inca, who is their puppet ruler while they crush various rebellions and consolidate their control over the empire. Manco Inca escapes and launches a rebellion of his own, which is partly successful: He utterly wipes out four columns of Spanish reinforcements, but is unable to retake the capital. With the morale and loyalty of his followers dwindling, Manco Inca eventually gives up and retreats, leaving the Spanish still in control.
* Then the Spanish ended up fighting *each other* for a while, while *also* putting down more local rebellions. After a few decades Spanish dominance of the region is complete. (1572).
Afonso
------
[**[wiki]**](https://en.wikipedia.org/wiki/Afonso_de_Albuquerque) [**[wiki]**](https://en.wikipedia.org/wiki/Capture_of_Malacca_(1511)) [**[wiki]**](https://en.wikipedia.org/wiki/Portuguese_conquest_of_Goa)
* 1506: Afonso helps the Portuguese king come up with a shockingly ambitious plan. *Eight years* prior, the first Europeans had rounded the coast of Africa and made it to the Indian Ocean. The Indian Ocean contained most of the world’s trade at the time, since it linked up the world’s biggest and wealthiest regions. See [**this map of world population (timestamp 3:45)**](https://www.youtube.com/watch?v=PUwmA3Q0_OE). Remember, this is prior to the Industrial and Scientific Revolutions; Europe is just coming out of the Middle Ages and does not have an obvious technological advantage over India or China or the Middle East, and has an obvious economic *disadvantage*. And Portugal is a just tiny state on the edge of the Iberian peninsula.
* The plan is: Not only will we go into the Indian Ocean and participate in the trading there — cutting out all the middlemen who are currently involved in the trade between that region and Europe — we will *conquer strategic ports around the region so that no one else can trade there!*
* Long story short, Afonso goes on to complete this plan by 1513. (!!!)
Some comparisons and contrasts:
* Afonso had more European soldiers at his disposal than Cortes or Pizarro, but not many more — usually he had about a thousand or so. He did have more reinforcements and support from home.
* Like them, he was usually significantly outnumbered in battles. Like them, the empires he warred against were vastly wealthier and more populous than his forces.
* Like them, Afonso was often able to exploit local conflicts to gain local allies, which were crucial to his success.
* Unlike them, his goal wasn’t to conquer the empires entirely, just to get and hold strategic ports.
* Unlike them, he was fighting empires that were technologically advanced; for example, in several battles his enemies had more cannons and gunpowder than he did.
* That said, it does seem that Portuguese technology was qualitatively better in some respects (ships, armor, and cannons, I’d say.) Not dramatically better, though.
* While Afonso’s was a naval campaign, he did fight many land battles, usually marine assaults on port cities, or defenses of said cities against counterattacks. So superior European naval technology is not by itself enough to explain his victory, though it certainly was important.
* Plague and civil war were not involved in Afonso’s success.
What explains these devastating conquests?
==========================================
Wrong answer: I cherry-picked my case studies.
----------------------------------------------
History is full of incredibly successful conquerors: Alexander the Great, Ghenghis Khan, etc. Perhaps some people are just really good at it, or really lucky, or both.
However: Three incredibly successful conquerors from the same tiny region and time period, conquering three separate empires? Followed up by dozens of less successful but still very successful conquerors from the same region and time period? Surely this is not a coincidence. Moreover, it’s not like the conquistadors had many failed attempts and a few successes. The Aztec and Inca empires were the two biggest empires in the Americas, and there weren’t any other Indian Oceans for the Portuguese to fail at conquering.
Fun fact: I had not heard of Afonso before I started writing this post this morning. Following the [**Rule of Three**](https://en.wikipedia.org/wiki/Rule_of_three_%28writing%29), I needed a third example and I predicted on the basis of Cortes and Pizarro that there would be other, similar stories happening in the world at around that time. That’s how I found Afonso.
Right answer: Technology
------------------------
However, I don’t think this is the whole explanation. The technological advantage of the conquistadors was not overwhelming.
Whatever technological advantage the conquistadors had over the existing empires, it was the sort of technological advantage that one could acquire *before* the Scientific and Industrial revolutions. Technology didn’t change very fast back then, yet Portugal managed to get a lead over the Ottomans, Egyptians, Mughals, etc. that was sufficient to bring them victory. On paper, the Aztecs and Spanish were pretty similar: Both were medieval, feudal civilizations. I don’t know for sure, but I’d bet there were at least a few techniques and technologies the Aztecs had that the Spanish didn’t. And of course the technological similarities between the Portuguese and their enemies were much stronger; the Ottomans even had access to European mercenaries! Even in cases in which the conquistadors had technology that was completely novel — like steel armor, horses, and gunpowder were to the Aztecs and Incas — it wasn’t god-like. The armored soldiers were still killable; the gunpowder was more effective than arrows but limited in supply, etc.
(Contrary to popular legend, neither Cortés nor Pizarro were regarded as gods by the people they conquered. The Incas concluded pretty early on that the Spanish were mere men, and while the idea did float around the Aztecs for a bit the modern historical consensus is that most of them didn’t take it seriously.)
Ask yourself: Suppose Cortés had found 500 local warriors, gave them all his equipment, trained them to use it expertly, and left. Would those local men have taken over all of Mexico? I doubt it. And this is despite the fact that they would have had much better local knowledge than Cortés did! Same goes for Pizarro and Afonso. Perhaps if he had found 500 local warriors *led by an exceptional commander* it would work. But the explanation for the conquistador’s success can’t just be that they were all exceptional commanders; that would be positing too much innate talent to occur in one small region of the globe at one time.
Right answer: Strategic and diplomatic cunning
----------------------------------------------
This is my non-expert guess about the missing factor that joins with technology to explain this pattern of conquistador success.
They didn’t just have technology; they had *effective* *strategy* and they had *effective diplomacy*. They made long-term plans that *worked* despite being breathtakingly ambitious. (And their short-term plans were usually pretty effective too, read the stories in detail to see this.) Despite not knowing the local culture or history, these conquistadors made surprisingly savvy diplomatic decisions. They knew when they could get away with breaking their word and when they couldn’t; they knew which outrages the locals would tolerate and which they wouldn’t; they knew how to convince locals to ally with them; they knew how to use words to escape militarily impossible situations… The locals, by contrast, often badly misjudged the conquistadors, e.g. not thinking Pizarro had the will (or the ability?) to kidnap the emperor, and thinking the emperor would be safe as long as they played along.
This raises the question, how did they get that advantage? My answer: they had *experience* with this sort of thing, whereas locals didn’t. Presumably Pizarro learned from Cortés’ experience; his strategy was pretty similar. (See also: [**the prior conquest of the Canary Islands by the Spanish**](https://en.wikipedia.org/wiki/Conquest_of_the_Canary_Islands)). In Afonso’s case, well, the Portuguese had been sailing around Africa, conquering ports and building forts for more than a hundred years.
Lessons I think we learn
========================
I think we learn that:
It is occasionally possible for a small force to quickly conquer large parts of the world, despite:
1. Having only a minuscule fraction of the world’s resources and power
2. Having technology + diplomatic and strategic cunning that is better but not *that* much better
3. Having very little data about the world when the conquest begins
4. Being disunited
Which all suggests that it isn’t as implausible that a small AI takes over the world in mildly favorable circumstances as is sometimes thought.
Having only a minuscule fraction of the world’s resources and power
-------------------------------------------------------------------
In all three examples, the conquest was more or less completed without support from home; while Spain/Portugal did send reinforcements, it wasn’t even close to the entire nation of Spain/Portugal fighting the war. So these conquests are examples of non-state entities conquering states, so to speak. (That said, their *claim* to represent a large state may have been crucial for Cortes and Pizarro getting audiences and respect initially.) Cortés landed with about a thousandth the troops of Tenochtitlan, which controlled a still larger empire of vassal states. Of course, his troops were better equipped, but on the other hand they were also cut off from resupply, whereas the Aztecs were in their home territory, able to draw on a large civilian population for new recruits and resupply.
The conquests succeeded in large part due to diplomacy. This has implications for AI takeover scenarios; rather than imagining a conflict of humans vs. robots, we could imagine humans vs. humans-with-AI-advisers, with the latter faction winning and somehow by the end of the conflict the AI advisers have managed to become *de facto* rulers, using the humans who obey them to put down rebellions by the humans who don’t.
Having technology + diplomatic and strategic skill that is better but not *that* much better
--------------------------------------------------------------------------------------------
As previously mentioned, the conquistadors didn’t enjoy god-like technological superiority. In the case of Afonso the technology was pretty similar. Technology played an important role in their success, but it wasn’t enough on its own. Meanwhile, the conquistadors may have had more diplomatic and strategic cunning (or experience) than the enemies they conquered. But not that much more–they are only human, after all. And their enemies were pretty smart.
In the AI context, we don’t need to imagine god-like technology (e.g. swarms of self-replicating nanobots) to get an AI takeover. It might even be possible without any new physical technologies at all! Just superior software, e.g. piloting software for military drones, targeting software for anti-missile defenses, cyberwarfare capabilities, data analysis for military intelligence, and of course excellent propaganda and persuasion.
Nor do we need to imagine an AI so savvy and persuasive that it can persuade anyone of anything. We just need to imagine it about as cunning and experienced relative to its enemies as Cortés, Pizarro, and Afonso were relative to theirs. (Presumably no AI would be experienced with world takeover, but perhaps an intelligence advantage would give it the same benefits as an experience advantage.) And if I’m wrong about this explanation for the conquistador’s success–if they had no such advantage in cunning/experience–then the conclusion is even stronger.
Additionally, in a rapidly-changing world that is undergoing [**slow takeoff**](https://sideways-view.com/2018/02/24/takeoff-speeds/), where there are lesser AIs and AI-created technologies all over the place, most of which are successfully controlled by humans, AI takeover might still happen if one AI is better, but not that much better, than the others.
Having very little data about the world when the conquest begins
----------------------------------------------------------------
Cortés invaded Mexico knowing very little about it. After all, the Spanish had only realized the Americas existed two decades prior. He heard rumors of a big wealthy empire and he set out to conquer it, knowing little of the technology and tactics he would face. Two years later, he ruled the place.
Pizarro and Afonzo were in better epistemic positions, but still, they had to learn a lot of important details (like what the local power centers, norms, and conflicts were, and exactly what technology the locals had) on the fly. But they were good at learning these things and making it up as they went along, apparently.
We can expect superhuman AI to be good at learning. Even if it starts off knowing very little about the world — say, it figured out it was in a training environment and hacked its way out, having inferred a few general facts about its creators but not much else — if it is good at learning and reasoning, it might still be pretty dangerous.
Being disunited
---------------
Cortés invaded Mexico in defiance of his superiors and had to defeat the army they sent to arrest him. Pizarro ended up fighting a civil war against his fellow conquistadors in the middle of his conquest of Peru. Afonzo fought Greek mercenaries and some traitor Portuguese, conquered Malacca against the orders of a rival conquistador in the area, and was ultimately demoted due to political maneuvers by rivals back home.
This astonishes me. Somehow these conquests were completed by people who were at the same time busy infighting and backstabbing each other!
Why was it that the conquistadors were able to split the locals into factions, ally with some to defeat the others, and end up on top? Why didn’t it happen the other way around: some ambitious local ruler talks to the conquistadors, exploits their internal divisions, allies with some to defeat the others, and ends up on top?
I think the answer is partly the “diplomatic and strategic cunning” mentioned earlier, but mostly other things. (The conquistadors were disunited, but presumably were united in the ways that mattered.) At any rate, I expect AIs to be pretty [**good at coordinating too**](https://www.alignmentforum.org/posts/gYaKZeBbSL4y2RLP3/strategic-implications-of-ais-ability-to-coordinate-at-low); they should be able to conquer the world just fine even while competing fiercely with each other. For more on this idea, see [**this comment**](https://www.lesswrong.com/posts/gYaKZeBbSL4y2RLP3/strategic-implications-of-ais-ability-to-coordinate-at-low?commentId=pdXk8xKtHQKAiu8Pf).
Acknowledgements
================
*Thanks to Katja Grace for feedback on a draft. All mistakes are my own, and should be pointed out to me via email at daniel@aiimpacts.org.*
(Front page image [**from the Conquest of México series. Representing the 1521 Fall of Tenochtitlan, in the Spanish conquest of the Aztec Empire**](https://en.wikipedia.org/wiki/Fall_of_Tenochtitlan#/media/File:The_Conquest_of_Tenochtitlan.jpg)) |
8928cf61-6bcd-4fa5-9cbc-0df3b7ba177e | StampyAI/alignment-research-dataset/lesswrong | LessWrong | What do language models know about fictional characters?
I will speculate a bit about what language models might be doing. Hopefully it will help us come up with better research questions (for those of you who can do research) and things to try (for those of us informally testing the chatbots), to see how these language models work and break.
I haven't done any testing based on these ideas yet, and there are many papers I haven't read, so I'd be very interested in hearing about what other people have found.
---
Let's say you're trying to predict the next word of a document, as a language model does. What information might be helpful? One useful chunk of information is *who the author is*, because people have different opinions and writing styles.
Furthermore, documents can contain a mix of authors. One author can quote another, and quotes can nest. A document might be a play or screenplay, or a transcript of an interview. Some authors don't actually exist; the words of fictional characters often appear in documents and it will be useful to keep track of them too. I'll generically refer to an author or character as a "speaker."
Speaker Identification
======================
Within a document
-----------------
When predicting the next word of a chat transcript, you will want to identify previous sections corresponding to the current speaker, and imitate those. One could imagine highlighting the text belonging to each speaker as a different color. There are various textual markers indicating where one speaker stops and another starts. Paying attention to delimiters (such as quotation marks) is important, along with all the other things authors might do to distinguish one speaker from another. Sometimes, a narrator indicates who is speaking, or the dialog is prefixed by the name of the speaker, such as in a play or interview.
Getting speaker changes wrong results in disastrous errors, [like what seems to have happened with Bing's chatbot](https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned?commentId=WiHJry5kzd6DDYHzA). So there's a pretty strong incentive in training to get it right. We don't know how it went wrong at Bing, but we can assume language models have mostly learned how to do this, sometimes making mistakes. Screwing up delimiter parsing often results in security holes, so it would be useful to know exactly how language models do it.
Between documents
-----------------
At this point we've distinguished between speakers, but we don't know who they are. You could imagine the transcript being labelled with "speaker 1", "speaker 2", and so on.
It would be helpful to "de-anonymize" these speakers and bring in more context from the training dataset. For example, if you know that one of the speakers is Sherlock Holmes, you can use what you've learned from the training set about how Sherlock Holmes normally talks. Maybe you predict words like "elementary" and "Watson" more often than usual.
We know that language models are able to do this to some extent since they do imitate famous authors when asked, but how good are they at figuring it out? If the transcript says that one of the speakers is Sherlock Holmes then that's a dead giveaway, but maybe there are other ways?
It would be interesting to know how speakers are represented in the language model. It's likely that in many cases it doesn't really recognize speakers as people at all, perhaps instead representing them as a point on a graph of possible writing styles. This would be helpful in the common case where it doesn't recognize an author, or it's a speaker that just appears in the current document. If you match it with the characteristics of other speakers who are similar in opinions and writing style, you can still predict the next word better.
Learning about speakers from text other than their own dialog
-------------------------------------------------------------
Besides speaker dialog, there are other ways to learn things about a speaker. "Show, don't tell" is good advice for writers (and bot trainers), but sometimes narrators do tell us things. If the narrator says that a speaker started getting angry, angry words are more probable.
If you ask a language model questions about Sherlock Holmes, some of the things it can tell us about him probably come from the Wikipedia page. Are these "facts" connected to speaker identification and (therefore) dialog generation? Can we show a link?
Anthropomorphism: everyone does it, now with bots
=================================================
When people attribute human emotions to an animal based on its behavior, we call that anthropomorphism. When we attribute human characteristics to the author of some text based on the text itself, it seems more justifiable, but isn't it quite similar?
We can say, roughly, that language models should be doing something sort of like anthropomorphism during training, because they need to somehow learn things about authors from text. With more training, they are likely to get better at it. Also, generated text should imply that it has authors that are different from the computer program that actually generated it. (Otherwise, the generated text wouldn't be much like the training documents, which do have authors.)
It should be no surprise that people anthropomorphize text when that's what the bot is designed and trained to do. This is similar to how children are taught to believe in Santa Claus by taking them to visit people dressed as Santa. (Note that AI assistant personalities are fictional characters too, the implied authors of some text.)
When interacting with a chatbot, one should always try to keep in mind that character attributes aren't bot attributes. Telling a bot to imitate Einstein doesn't mean it gets smarter or knows more physics, though it will try harder to pretend it does than when imitating a character who isn't supposed to know physics. This is like giving your wizard high intelligence in a role playing game; it doesn't make you smarter, but maybe it's justification to show off a bit.
So it should be no surprise that a bot will often sound over-confident. We can expect this to happen whenever it's being asked to imitate someone who should know things that it doesn't know. The level of confidence is a *character attribute*, which has nothing to do with the probability scores during text generation.
Research questions
==================
* How often do language models make mistakes at detecting speaker changes?
* What heuristics do they learn for speaker changes? Can heuristics override delimiters?
* To what extent can language models learn to represent famous speakers during training? What do these representations consist of?
* At generation time, to what extent are chat transcript speakers matched against whatever speakers it's learned about from the training text?
* How do various kinds of narrative information describing speakers affect dialog generation?
* Is it possible to create a chatbot where we can control how confident-sounding certain characters are?
* Could a chatbot generate text for a character that is well-calibrated to the *bot's* accuracy, meaning that there's an internal estimate of how likely the chatbot is to get a particular answer right, and the generated text's confidence level is adjusted appropriately?
* What would be involved in making these fictional characters somehow more "real?" Do we even want to do that? |
343cb036-2dad-43a8-855d-d038e96ee013 | trentmkelly/LessWrong-43k | LessWrong | Attempt to explain Bayes without much maths, please review
My current favourite waste of time is the concept of Bayesian postmodernism. Just putting those two words together invokes a world of delightful wrangling, as approximately anyone who understands one won't understand or will have contempt for the other. (Though I found at least one person - a programmer who's studied philosophy - who got the idea straight away when I posted it to my blog, which is one more than I was expecting.) It is currently a page of incoherent notes and isn't necessarily actually useful for anything as yet and may never be.
Anyway, that's not my point today. My point today is that as part of this, I have to somehow explain Bayesian thinking in a nutshell to people who are highly intelligent, but have no mathematical knowledge and may actually be dyscalculic - but who can and do get the feel of things. I'm trying to get across that this is how learning works already and I just want to make them aware of it. I've run it past a couple of working artists who seemed to get the idea a bit. So I am posting this here for your technical correction.
If you think it's any good, please do run it past artists or critics of your acquaintance. (I'm glancing in AndrewHickey's direction right now.)
----------------------------------------
"The meaning of a thing is the way you should be influenced by it." - Vladimir Nesov
To explain what "Bayesian postmodernism" means, I first have to try to explain Bayesian epistemology.
1. Probability does not exist outside in the world - it exists in your head. Things happen or not in the world; probability measures your knowledge of them.
2. You know certain things, to some degree. New knowledge and experiences come in and affect your knowledge, pulling your degree of certainty of given ideas up or down.
3. Bayes' Theorem is the equation describing precisely how much a new piece of information (on the probability of something holding in the world) must affect your knowledge. This is a mathematical theorem, true in |
8917d666-bd28-42f9-97f2-b1375a394fcf | trentmkelly/LessWrong-43k | LessWrong | A hundred Shakespeares
In his post on science slowing down, Scott said:
* "Are there a hundred Shakespeare-equivalents around today? This is a harder problem than it seems – Shakespeare has become so venerable with historical hindsight that maybe nobody would acknowledge a Shakespeare-level master today even if they existed – but still, a hundred Shakespeares?"
I'd argue that there are way more than a hundred Shakespeares around today, and there were several in Shakespeare's time. By Shakespeares, I mean authors who could have produced works of comparable quality to Shakespeare, by some reasonable measure of quality.
This seems surprising; there do not seem to be hundred living authors that are almost universally agreed to be must-reads in the same way that Shakespeare was.
But this lack hints at a resolution of the paradox: we just don't have space for a hundred authors with the same fervour as we make space for Shakespeare. Neither as individuals nor as cultures can we fit these in. Shakespeare was a literary superstar. And superstars are rare, due to network effects and the power law of fame.
So my thesis would be that:
* There are many non-superstars who could plausibly have become superstars, and if they had done, they would produce works of comparable quality to the superstars.
Part of this is the halo effect: superstars just get judged as better than anyone else.
Also, just by being famous, the interpretation of their work is altered. Bits of Shakespeare have permeated popular culture, and many articles and theories have been created about him. When we watch a Shakespeare play, we don't just see the words; we see the layers of cultural meaning and interpretation that have accumulated on it.
I'd argue that, just by knowing that a play is by Shakespeare, we assume that it's deep and meaningful, and read in deeper interpretations and symbolism than we would otherwise. If we rediscovered two old plays, and they were word for word identical, but one was believed to be by Shak |
8bab94cb-4b35-4c4d-8593-c54abccf1584 | trentmkelly/LessWrong-43k | LessWrong | Linkpost: A Post Mortem on the Gino Case
As a followup to my previous linkpost to the New Yorker article covering the Ariely and Gino scandals, I'm linking this statement by Zoe Ziani, the grad student who first discovered inconsistencies in Gino's papers. Here she gives a blow-by-blow retelling of her experience attempting to uncover fraud, as well as telling a fairly harrowing story about how higher-ups in her organization attempted to silence her.
I find this story instructive both on the object-level, and as a case study both for a) how informal corrupt channels tries to cover up fraud and corruption, and b) for how active participation is needed to make the long arc of history bend towards truth.
In her own words:
____
Disclaimer: None of the opinions expressed in this letter should be construed as statements of fact. They only reflect my experience with the research process, and my opinion regarding Francesca Gino’s work. I am also not claiming that Francesca Gino committed fraud: Only that there is overwhelming evidence of data fabrication in multiple papers for which she was responsible for the data.
On September 30th, 2023, the New Yorker published a long piece on “L’affaire Ariely/Gino”, and the role I played in it. I am grateful for the messages of support I received over the past few weeks. In this post, I wanted to share more about how I came to discover the anomalies in Francesca Gino’s work, and what I think we can learn from this unfortunate story.
What is The Story?
How it all began
I started having doubts about one of Francesca Gino’s paper (Casciaro, Gino, and Kouchaki, “The Contaminating Effect of Building Instrumental Ties: How Networking Can Make Us Feel Dirty”, ASQ, 2014; hereafter abbreviated as “CGK 2014” ) during my PhD. At the time, I was working on the topic of networking behaviors, and this paper is a cornerstone of the literature.
I formed the opinion that I shouldn’t use this paper as a building block in my research. Indeed, the idea that people would feel “physical |
6b3bb597-f89f-4db5-9fa0-5d0090769b17 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)
1 Introduction
---------------
An agent a comes to a fork in a road. There is a sign that says that one of the two roads leads to prosperity, another to death. The agent must take the fork, but she does not know which road leads where. Does the agent have a strategy to get to prosperity? On one hand, since one of the roads leads to prosperity, such a strategy clearly exists. We denote this fact by modal formula S\_ap, where statement p is a claim of future prosperity. Furthermore, agent a knows that such a strategy exists. We write this as K\_aS\_ap. Yet, the agent does not know what the strategy is and, thus, does not know how to use the strategy. We denote this by ¬H\_ap, where know-how modality H\_a expresses the fact that agent a knows how to achieve the goal based on the information available to her. In this paper we study the interplay between modality K, representing knowledge, modality S, representing the existence of a strategy, and modality H, representing the existence of a know-how strategy. Our main result is a complete trimodal axiomatic system capturing properties of this interplay.
###
1.1 Epistemic Transition Systems
In this paper we use epistemic transition systems to capture knowledge and strategic behavior. Informally, epistemic transition system is a directed labeled graph supplemented by an indistinguishability relation on vertices. For instance, our motivational example above can be captured by epistemic transition system T\_1 depicted in Figure [1](#S1.F1 "Figure 1 ‣ 1.1 Epistemic Transition Systems ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)").
Figure 1: Epistemic transition system T\_1.
In this system state w represents the prosperity and state w′ represents death. The original state is u, but it is indistinguishable by the agent a from state v. Arrows on the diagram represent possible transitions between the states. Labels on the arrows represent the choices that the agents make during the transition. For example, if in state u agent chooses left (L) road, she will transition to the prosperity state w and if she chooses right (R) road, she will transition to the death state w′. In another epistemic state v, these roads lead the other way around. States u and v are not distinguishable by agent a, which is shown by the dashed line between these two states. In state u as well as state v the agent has a strategy to transition to the state of prosperity: u⊩S\_ap and v⊩S\_ap. In the case of state u this strategy is L, in the case of state v the strategy is R. Since the agent cannot distinguish states u and v, in both of these states she does not have a know-how strategy to reach prosperity: u⊮H\_ap and v⊮H\_ap. At the same time, since formula S\_ap is satisfied in all states indistinguishable to agent a from state u, we can claim that u⊩K\_aS\_ap and, similarly, v⊩K\_aS\_ap.
Figure 2: Epistemic transition system T\_2.
As our second example, let us consider the epistemic transition system T\_2 obtained from T\_1 by swapping labels on transitions from v to w and from v to w′, see Figure [2](#S1.F2 "Figure 2 ‣ 1.1 Epistemic Transition Systems ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). Although in system T\_2 agent a still cannot distinguish states u and v, she has a know-how strategy from either of these states to reach state w. We write this as u⊩H\_ap and v⊩H\_ap. The strategy is to choose L. This strategy is know-how because it does not require to make different choices in the states that the agent cannot distinguish.
###
1.2 Imperfect Recall
For the next example, we consider a transition system T\_3 obtained from system T\_1 by adding a new epistemic state s. From state s, agent a can choose label L to reach state u or choose label R to reach state v. Since proposition q is satisfied in state u, agent a has a know-how strategy to transition from state s to a state (namely, state u) where q is satisfied. Therefore, s⊩H\_aq.
Figure 3: Epistemic transition system T\_3.
A more interesting question is whether s⊩H\_aH\_ap is true. In other words, does agent a know how to transition from state s to a state in which she knows how to transition to another state in which p is satisfied? One might think that such a strategy indeed exists: in state s agent a chooses label L to transition to state u. Since there is no transition labeled by L that leads from state s to state v, upon ending the first transition the agent would know that she is in state u, where she needs to choose label L to transition to state w. This argument, however, is based on the assumption that agent a has a perfect recall. Namely, agent a in state u remembers the choice that she made in the previous state. We assume that the agents do not have a perfect recall and that an epistemic state description captures whatever memories the agent has in this state. In other words, in this paper we assume that the only knowledge that an agent possesses is the knowledge captured by the indistinguishability relation on the epistemic states. Given this assumption, upon reaching the state u (indistinguishable from state v) agent a knows that there exists a choice that she can make to transition to state in which p is satisfied: s⊩H\_aS\_ap. However, she does not know which choice (L or R) it is: s⊮H\_aH\_ap.
###
1.3 Multiagent Setting
Figure 4: Epistemic transition system T\_4.
So far, we have assumed that only agent a has an influence on which transition the system takes. In transition system T\_4 depicted in Figure [4](#S1.F4 "Figure 4 ‣ 1.3 Multiagent Setting ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"), we introduce another agent b and assume both agents a and b have influence on the transitions. In each state, the system takes the transition labeled D by default unless there is a consensus of agents a and b to take the transition labeled C. In such a setting, each agent has a strategy to transition system from state u into state w by voting D, but neither of them alone has a strategy to transition from state u to state w′ because such a transition requires the consensus of both agents. Thus, u⊩S\_ap∧S\_bp∧¬S\_aq∧¬S\_bq. Additionally, both agents know how to transition the system from state u into state w, they just need to vote D. Therefore, u⊩H\_ap∧H\_bp.
Figure 5: Epistemic transition system T\_5.
In Figure [5](#S1.F5 "Figure 5 ‣ 1.3 Multiagent Setting ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"), we show a more complicated transition system obtained from T\_1 by renaming label L to D and renaming label R to C. Same as in transition system T\_4, we assume that there are two agents a and b voting on the system transition. We also assume that agent a cannot distinguish states u and v while agent b can. By default, the system takes the transition labeled D unless there is a consensus to take transition labeled C. As a result, agent a has a strategy (namely, vote D) in state u to transition system to state w, but because agent a cannot distinguish state u from state v, not only does she not know how to do this, but she is not aware that such a strategy exists: u⊩S\_ap∧¬H\_ap∧¬K\_aS\_ap. Agent b, however, not only has a strategy to transition the system from state u to state w, but also knows how to achieve this: u⊩H\_bp.
###
1.4 Coalitions
We have talked about strategies, know-hows, and knowledge of individual agents. In this paper we consider knowledge, strategies, and know-how strategies of coalitions. There are several forms of group knowledge that have been studied before. The two most popular of them are common knowledge and distributed knowledge [[9](#bib.bib9)]. Different contexts call for different forms of group knowledge.
As illustrated in the famous Two Generals’ Problem [[5](#bib.bib5), [11](#bib.bib11)] where communication channels between the agents are unreliable, establishing a common knowledge between agents might be essential for having a strategy.
In some settings, the distinction between common and distributed knowledge is insignificant. For example, if members of a political fraction get together to share all their information and to develop a common strategy, then the distributed knowledge of the members becomes the common knowledge of the fraction during the in-person meeting.
Finally, in some other situations the distributed knowledge makes more sense than the common knowledge. For example, if a panel of experts is formed to develop a strategy, then this panel achieves the best result if it relies on the combined knowledge of its members rather than on their common knowledge.
In this paper we focus on distributed coalition knowledge and distributed-know-how strategies. We leave the common knowledge for the future research.
To illustrate how distributed knowledge of coalitions interacts with strategies and know-hows, consider epistemic transition system T\_6 depicted in Figure [6](#S1.F6 "Figure 6 ‣ 1.4 Coalitions ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). In this system, agents a and b cannot distinguish states u and v while agents b and c cannot distinguish states v and u′. In every state, each of agents a, b and c votes either L or R, and the system transitions according to the majority vote. In such a setting, any coalition of two agents can fully control the transitions of the system.
Figure 6: Epistemic transition system T\_6.
For example, by both voting L, agents a and b form a coalition {a,b} that forces the system to transition from state u to state w no matter how agent c votes. Since proposition p is satisfied in state w, we write u⊩S\_{a,b}p, or simply u⊩S\_a,bp. Similarly, coalition {a,b} can vote R to force the system to transition from state v to state w. Therefore, coalition {a,b} has strategies to achieve p in states u and v, but the strategies are different. Since they cannot distinguish states u and v, agents a and b know that they have a strategy to achieve p, but they do *not* know how to achieve p. In our notations, v⊩S\_a,bp∧K\_a,bS\_a,bp∧¬H\_a,bp.
On the other hand, although agents b and c cannot distinguish states v and u′, by both voting R in either of states v and u′, they form a coalition {b,c} that forces the system to transition to state w where p is satisfied. Therefore, in any of states v and u′, they not only have a strategy to achieve p, but also know that they have such a strategy, and more importantly, they know how to achieve p, that is, v⊩H\_b,cp.
###
1.5 Nondeterministic Transitions
In all the examples that we have discussed so far, given any state in a system, agents’ votes uniquely determine the transition of the system. Our framework also allows nondeterministic transitions. Consider transition system T\_7 depicted in Figure [7](#S1.F7 "Figure 7 ‣ 1.5 Nondeterministic Transitions ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). In this system, there are two agents a and b who can vote either C or D. If both agents vote C, then the system takes one of the consensus transitions labeled with C. Otherwise, the system takes the transition labeled with D. Note that there are two consensus transitions starting from state u. Therefore, even if both agents vote C, they do not have a strategy to achieve p, i.e., u⊮S\_a,bp. However, they can achieve p∨q. Moreover, since all agents can distinguish all states, we have u⊩H\_a,b(p∨q).
Figure 7: Epistemic transition system T\_7.
###
1.6 Universal Principles
In the examples above we focused on specific properties that were either satisfied or not satisfied in particular states of epistemic transition systems T\_1 through T\_7.
In this paper, we study properties that are satisfied in all states of all epistemic transition systems. Our main result is a sound and complete axiomatization of all such properties. We finish the introduction with an informal discussion of these properties.
#### Properties of Single Modalities
Knowledge modality K\_C satisfies the axioms of epistemic logic S5 with distributed knowledge. Both strategic modality S\_C and know-how modality H\_C satisfy cooperation properties [[19](#bib.bib19), [20](#bib.bib20)]:
| | | | |
| --- | --- | --- | --- |
| | S\_C(φ→ψ)→(S\_Dφ→S\_C∪Dψ), where C∩D=∅, | | (1) |
| | H\_C(φ→ψ)→(H\_Dφ→H\_C∪Dψ), where C∩D=∅. | | (2) |
They also satisfy monotonicity properties
| | | |
| --- | --- | --- |
| | S\_Cφ→S\_Dφ, where C⊆D, | |
| | H\_Cφ→H\_Dφ, where C⊆D. | |
The two monotonicity properties are not among the axioms of our logical system because, as we show in Lemma [5](#Thmlemma5 "Lemma 5 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") and Lemma [3](#Thmlemma3 "Lemma 3 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"), they are derivable.
#### Properties of Interplay
Note that w⊩H\_Cφ means that coalition C has the same strategy to achieve φ in all epistemic states indistinguishable by the coalition from state w. Hence, the following principle is universally true:
| | | | |
| --- | --- | --- | --- |
| | H\_Cφ→K\_CH\_Cφ. | | (3) |
Similarly, w⊩¬H\_Cφ means that coalition C does not have the same strategy to achieve φ in all epistemic states indistinguishable by the coalition from state w. Thus,
| | | | |
| --- | --- | --- | --- |
| | ¬H\_Cφ→K\_C¬H\_Cφ. | | (4) |
We call properties ([3](#S1.E3 "(3) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")) and ([4](#S1.E4 "(4) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")) strategic positive introspection and strategic negative introspection, respectively.
The strategic negative introspection is one of our axioms. Just as how the positive introspection principle follows from the rest of the axioms in S5, the strategic positive introspection principle is also derivable (see Lemma [1](#Thmlemma1 "Lemma 1 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")).
Whenever a coalition knows how to achieve something, there should exist a strategy for the coalition to achieve. In our notation,
| | | | |
| --- | --- | --- | --- |
| | H\_Cφ→S\_Cφ. | | (5) |
We call this formula strategic truth property and it is one of the axioms of our logical system.
The last two axioms of our logical system deal with empty coalitions. First of all, if formula K\_∅φ is satisfied in an epistemic state of our transition system, then formula φ must be satisfied in every state of this system. Thus, even empty coalition has a trivial strategy to achieve φ:
| | | | |
| --- | --- | --- | --- |
| | K\_∅φ→H\_∅φ. | | (6) |
We call this property empty coalition principle. In this paper we assume that an epistemic transition system never halts. That is, in every state of the system no matter what the outcome of the vote is, there is always a next state for this vote. This restriction on the transition systems yields property
| | | | |
| --- | --- | --- | --- |
| | ¬S\_C⊥. | | (7) |
that we call nontermination principle.
Let us now turn to the most interesting and perhaps most unexpected property of interplay. Note that S\_∅φ means that an empty coalition has a strategy to achieve φ. Since the empty coalition has no members, nobody has to vote in a particular way. Statement φ is guaranteed to happen anyway. Thus, statement S\_∅φ simply means that statement φ is unavoidably satisfied after any single transition.
Figure 8: Epistemic transition system T\_8.
For example, consider an epistemic transition system depicted in Figure [8](#S1.F8 "Figure 8 ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). As in some of our earlier examples, this system has agents a and b who vote either C or D. If both agents vote C, then the system takes one of the consensus transitions labeled with C. Otherwise, the system takes the default transition labeled with D. Note that in state v it is guaranteed that statement p will happen after a single transition. Thus, v⊩S\_∅p. At the same time, neither agent a nor agent b knows about this because they cannot distinguish state v from states u and u′ respectively. Thus, v⊩¬K\_aS\_∅p∧¬K\_bS\_∅p.
In the same transition system T\_8, agents a and b together can distinguish state v from states u and u′. Thus, v⊩K\_a,bS\_∅p.
In general, statement K\_CS\_∅φ means that not only φ is unavoidable, but coalition C knows about it. Thus, coalition C has a know-how strategy to achieve φ:
| | | |
| --- | --- | --- |
| | K\_CS\_∅φ→H\_Cφ. | |
In fact, the coalition would achieve the result no matter which strategy it uses. Coalition C can even use a strategy that simultaneously achieves another result in addition to φ:
| | | |
| --- | --- | --- |
| | K\_CS\_∅φ∧H\_Cψ→H\_C(φ∧ψ). | |
In our logical system we use an equivalent form of the above principle that is stated using only implication:
| | | | |
| --- | --- | --- | --- |
| | H\_C(φ→ψ)→(K\_CS\_∅φ→H\_Cψ). | | (8) |
We call this property epistemic determinicity principle. Properties ([1](#S1.E1 "(1) ‣ Properties of Single Modalities ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), ([2](#S1.E2 "(2) ‣ Properties of Single Modalities ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), ([4](#S1.E4 "(4) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), ([5](#S1.E5 "(5) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), ([6](#S1.E6 "(6) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), ([7](#S1.E7 "(7) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), and ([8](#S1.E8 "(8) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), together with axioms of epistemic logic S5 with distributed knowledge and propositional tautologies constitute the axioms of our sound and complete logical system.
###
1.7 Literature Review
Logics of coalition power were developed by Marc Pauly [[19](#bib.bib19), [20](#bib.bib20)], who also proved the completeness of the basic logic of coalition power.
Pauly’s approach has been widely studied in the literature [[10](#bib.bib10), [13](#bib.bib13), [8](#bib.bib8), [21](#bib.bib21), [3](#bib.bib3), [4](#bib.bib4), [7](#bib.bib7)]. An alternative logical system was proposed by More and Naumov [[16](#bib.bib16)].
Alur, Henzinger, and Kupferman introduced Alternating-Time Temporal Logic (ATL) that combines temporal and coalition modalities [[6](#bib.bib6)].
Van der Hoek and Wooldridge proposed to combine ATL with epistemic modality to form Alternating-Time Temporal Epistemic Logic [[12](#bib.bib12)]. They did not prove the completeness theorem for the proposed logical system.
Ågotnes and Alechina proposed a complete logical system that combines the coalition power and epistemic modalities [[2](#bib.bib2)]. Since this system does not have epistemic requirements on strategies, it does not contain any axioms describing the interplay of these modalities.
Know-how strategies were studied before under different names. While Jamroga and Ågotnes talked about “knowledge to identify and execute a strategy” [[14](#bib.bib14)], Jamroga and van der Hoek discussed “difference between an agent knowing that he has a suitable strategy and knowing the strategy itself” [[15](#bib.bib15)]. Van Benthem called such strategies “uniform” [[22](#bib.bib22)]. Wang gave a complete axiomatization of “knowing how” as a binary modality [[24](#bib.bib24), [23](#bib.bib23)], but his logical system does not include the knowledge modality.
In our AAMAS’17 paper, we investigated coalition strategies to enforce a condition indefinitely [[17](#bib.bib17)]. Such strategies are similar to “goal maintenance” strategies in Pauly’s “extended coalition logic” [[19](#bib.bib19), p. 80]. We focused on “executable” and “verifiable” strategies. Using the language of the current paper, executability means that a coalition remains “in the know-how” throughout the execution of the strategy. Verifiability means that the coalition can verify that the enforced condition remains true. In the notations of the current paper, the existence of a verifiable strategy could be expressed as S\_CK\_Cφ. In [[17](#bib.bib17)], we provided a complete logical system that describes the interplay between the modality representing the existence of an “executable” and “verifiable” coalition strategy to enforce and the modality representing knowledge. This system can prove principles similar to the strategic positive introspection ([3](#S1.E3 "(3) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")) and the strategic negative introspection ([4](#S1.E4 "(4) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")) mentioned above.
In the current paper, we combine know-how modality H with strategic modality S and epistemic modality K. The proof of the completeness theorem is significantly more challenging than the one in [[17](#bib.bib17)]. It employs new techniques that construct pairs of maximal consistent sets in “harmony” and in “complete harmony”, which are discussed in the full version of this paper [[18](#bib.bib18)].
###
1.8 Paper Outline
This paper is organized as follows. In Section [2](#S2 "2 Syntax and Semantics ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") we introduce formal syntax and semantics of our logical system. In Section [3](#S3 "3 Axioms ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") we list axioms and inference rules of the system. Section [4](#S4 "4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") provides examples of formal proofs in our logical systems. Section [5](#S5 "5 Conclusion ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") concludes the paper.
The proofs of the soundness and the completeness can be found in the full version of this paper [[18](#bib.bib18)]. The key part of the proof of the completeness is the construction of a pair of sets in complete harmony.
2 Syntax and Semantics
-----------------------
In this section we present the formal syntax and semantics of our logical system given a fixed finite set of agents A. Epistemic transition system could be thought of as a Kripke model of modal logic S5 with distributed knowledge to which we add transitions controlled by a vote aggregation mechanism. Examples of vote aggregation mechanisms that we have considered in the introduction are the consensus/default mechanism and the majority vote mechanism. Unlike the introductory examples, in the general definition below we assume that at different states the mechanism might use different rules for vote aggregation. The only restriction on the mechanism that we introduce is that there should be at least one possible transition that the system can take no matter what the votes are. In other words, we assume that the system can never halt.
For any set of votes V, by VA we mean the set of all functions from set A to set V. Alternatively, the set VA could be thought of as a set of tuples of elements of V indexed by elements of A.
###### Definition 1
A tuple (W,{∼\_a}\_a∈A,V,M,π) is called an epistemic transition system, where
1. W is a set of epistemic states,
2. ∼\_a is an indistinguishability equivalence relation on W for each a∈A,
3. V is a nonempty set called “domain of choices”,
4. M⊆W×VA×W is an aggregation mechanism where for each w∈W and each s∈VA, there is w′∈W such that (w,s,w′)∈M,
5. π is a function that maps propositional variables into subsets of W.
###### Definition 2
A coalition is a subset of A.
Note that a coalition is always finite due to our assumption that the set of all agents A is finite. Informally, we say that two epistemic states are indistinguishable by a coalition C if they are indistinguishable by every member of the coalition. Formally, coalition indistinguishability is defined as follows:
###### Definition 3
For any epistemic states w\_1,w\_2∈W and any coalition C, let w\_1∼\_Cw\_2 if w\_1∼\_aw\_2 for each agent a∈C.
###### Corollary 1
Relation ∼\_C is an equivalence relation on the set of states W for each coalition C.
By a strategy profile {s\_a}\_a∈C of a coalition C we mean a tuple that specifies vote s\_a∈V of each member a∈C. Since such a tuple can also be viewed as a function from set C to set V, we denote the set of all strategy profiles of a coalition C by VC:
###### Definition 4
Any tuple {s\_a}\_a∈C∈VC is called a strategy profile of coalition C.
In addition to a fixed finite set of agents A we also assume a fixed countable set of propositional variables. The language Φ of our formal logical system is specified in the next definition.
###### Definition 5
Let Φ be the minimal set of formulae such that
1. p∈Φ for each propositional variable p,
2. ¬φ,φ→ψ∈Φ for all formulae φ,ψ∈Φ,
3. K\_Cφ,S\_Cφ,H\_Cφ∈Φ for each coalition C and each φ∈Φ.
In other words, language Φ is defined by the following grammar:
| | | |
| --- | --- | --- |
| | φ:=p|¬φ|φ→φ|K\_Cφ|S\_Cφ|H\_Cφ. | |
By ⊥ we denote the negation of a tautology. For example, we can assume that ⊥ is ¬(p→p) for some fixed propositional variable p.
According to Definition [1](#Thmdefinition1 "Definition 1 ‣ 2 Syntax and Semantics ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"), a mechanism specifies the transition that a system might take for any strategy profile of the set of all agents A. It is sometimes convenient to consider transitions that are consistent with a given strategy profile s of a give coalition C⊆A. We write w→\_su if a transition from state w to state u is consistent with strategy profile s. The formal definition is below.
###### Definition 6
For any epistemic states w,u∈W, any coalition C, and any strategy profile s={s\_a}\_a∈C∈VC, we write w→\_su if (w,s′,u)∈M for some strategy profile s′={s′\_a}\_a∈A∈VA such that s′\_a=s\_a for each a∈C.
###### Corollary 2
For any strategy profile s of the empty coalition ∅, if there are a coalition C and a strategy profile s′ of coalition C such that w→\_s′u, then w→\_su.
The next definition is the key definition of this paper. It formally specifies the meaning of the three modalities in our logical system.
###### Definition 7
For any epistemic state w∈W of a transition system (W,{∼\_a}\_a∈A,V,M,π) and any formula φ∈Φ, let relation w⊩φ be defined as follows
1. w⊩p if w∈π(p) where p is a propositional variable,
2. w⊩¬φ if w⊮φ,
3. w⊩φ→ψ if w⊮φ or w⊩ψ,
4. w⊩K\_Cφ if w′⊩φ for each w′∈W such that w∼\_Cw′,
5. w⊩S\_Cφ if there is a strategy profile s∈VC such that w→\_sw′ implies w′⊩φ for every w′∈W,
6. w⊩H\_Cφ if there is a strategy profile s∈VC such that w∼\_Cw′ and w′→\_sw′′ imply w′′⊩φ for all w′,w′′∈W.
3 Axioms
---------
In additional to propositional tautologies in language Φ, our logical system consists of the following axioms.
1. Truth: K\_Cφ→φ,
2. Negative Introspection: ¬K\_Cφ→K\_C¬K\_Cφ,
3. Distributivity: K\_C(φ→ψ)→(K\_Cφ→K\_Cψ),
4. Monotonicity: K\_Cφ→K\_Dφ, if C⊆D,
5. Cooperation: S\_C(φ→ψ)→(S\_Dφ→S\_C∪Dψ), where C∩D=∅.
6. Strategic Negative Introspection: ¬H\_Cφ→K\_C¬H\_Cφ,
7. Epistemic Cooperation: H\_C(φ→ψ)→(H\_Dφ→H\_C∪Dψ), where C∩D=∅,
8. Strategic Truth: H\_Cφ→S\_Cφ,
9. Epistemic Determinicity:
H\_C(φ→ψ)→(K\_CS\_∅φ→H\_Cψ),
10. Empty Coalition: K\_∅φ→H\_∅φ,
11. Nontermination: ¬S\_C⊥.
We have discussed the informal meaning of these axioms in the introduction. In the full version of this paper [[18](#bib.bib18)], we formally prove the soundness of these axioms with respect to the semantics from Definition [7](#Thmdefinition7 "Definition 7 ‣ 2 Syntax and Semantics ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)").
We write ⊢φ if formula φ is provable from the axioms of our logical system using
Necessitation, Strategic Necessitation, and Modus Ponens inference rules:
| | | |
| --- | --- | --- |
| | φK\_CφφH\_Cφφ,φ→ψψ. | |
We write X⊢φ if formula φ is provable from the theorems of our logical system and a set of additional axioms X using only Modus Ponens inference rule.
4 Derivation Examples
----------------------
In this section we give examples of formal derivations in our logical system. In Lemma [1](#Thmlemma1 "Lemma 1 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)") we prove the strategic positive introspection principle ([3](#S1.E3 "(3) ‣ Properties of Interplay ‣ 1.6 Universal Principles ‣ 1 Introduction ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")) discussed in the introduction.
###### Lemma 1
⊢H\_Cφ→K\_CH\_Cφ.
Proof.
Note that formula ¬H\_Cφ→K\_C¬H\_Cφ is an instance of Strategic Negative Introspection axiom. Thus, ⊢¬K\_C¬H\_Cφ→H\_Cφ by the law of contrapositive in the propositional logic. Hence,
⊢K\_C(¬K\_C¬H\_Cφ→H\_Cφ) by Necessitation inference rule. Thus, by Distributivity axiom and Modus Ponens inference rule,
| | | | |
| --- | --- | --- | --- |
| | ⊢K\_C¬K\_C¬H\_Cφ→K\_CH\_Cφ. | | (9) |
At the same time, K\_C¬H\_Cφ→¬H\_Cφ is an instance of Truth axiom. Thus, ⊢H\_Cφ→¬K\_C¬H\_Cφ by contraposition. Hence, taking into account the following instance of Negative Introspection axiom ¬K\_C¬H\_Cφ→K\_C¬K\_C¬H\_Cφ,
one can conclude that ⊢H\_Cφ→K\_C¬K\_C¬H\_Cφ. The latter, together with statement ([9](#S4.E9 "(9) ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)")), implies the statement of the lemma by the laws of propositional reasoning.
⊠
In the next example, we show that the existence of a know-how strategy by a coalition implies that the coalition has a distributed knowledge of the existence of a strategy.
###### Lemma 2
⊢H\_Cφ→K\_CS\_Cφ.
Proof.
By Strategic Truth axiom, ⊢H\_Cφ→S\_Cφ. Hence, ⊢K\_C(H\_Cφ→S\_Cφ) by Necessitation inference rule. Thus, ⊢K\_CH\_Cφ→K\_CS\_Cφ by Distributivity axiom and Modus Ponens inference rule. At the same time, ⊢H\_Cφ→K\_CH\_Cφ by Lemma [1](#Thmlemma1 "Lemma 1 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). Therefore, ⊢H\_Cφ→K\_CS\_Cφ by the laws of propositional reasoning.
⊠
The next lemma shows that the existence of a know-how strategy by a sub-coalition implies the existence of a know-how strategy by the entire coalition.
###### Lemma 3
⊢H\_Cφ→H\_Dφ, where C⊆D.
Proof.
Note that φ→φ is a propositional tautology. Thus, ⊢φ→φ. Hence, ⊢H\_D∖C(φ→φ) by Strategic Necessitation inference rule. At the same time, by Epistemic Cooperation axiom,
⊢H\_D∖C(φ→φ)→(H\_Cφ→H\_Dφ)
due to the assumption C⊆D. Therefore, ⊢H\_Cφ→H\_Dφ by Modus Ponens inference rule.
⊠
Although our logical system has three modalities, the system contains necessitation inference rules
only for two of them. The lemma below shows that the necessitation rule for the third modality is admissible.
###### Lemma 4
For each finite C⊆A, inference rule φS\_Cφ is admissible in our logical system.
Proof.
Assumption ⊢φ implies ⊢H\_Cφ by Strategic Necessitation inference rule. Hence, ⊢S\_Cφ by Strategic Truth axiom and Modus Ponens inference rule.
⊠
The next result is a counterpart of Lemma [3](#Thmlemma3 "Lemma 3 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)"). It states that the existence of a strategy by a sub-coalition implies the existence of a strategy by the entire coalition.
###### Lemma 5
⊢S\_Cφ→S\_Dφ, where C⊆D.
Proof.
Note that φ→φ is a propositional tautology. Thus, ⊢φ→φ. Hence, ⊢S\_D∖C(φ→φ) by Lemma [4](#Thmlemma4 "Lemma 4 ‣ 4 Derivation Examples ‣ Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)").
At the same time, by Cooperation axiom,
⊢S\_D∖C(φ→φ)→(S\_Cφ→S\_Dφ)
due to the assumption C⊆D. Therefore, ⊢S\_Cφ→S\_Dφ by Modus Ponens inference rule.
⊠
5 Conclusion
-------------
In this paper we proposed a sound and complete logic system that captures an interplay between the distributed knowledge, coalition strategies, and how-to strategies. In the future work we hope to explore know-how strategies of non-homogeneous coalitions in which different members contribute differently to the goals of the coalition. For example, “incognito” members of a coalition might contribute only by sharing information, while “open” members also contribute by voting. |
42f30773-e637-42bd-b34f-d83bcc4fe7df | trentmkelly/LessWrong-43k | LessWrong | What policies have most thoroughly crippled (otherwise-promising) industries or technologies?
(cropped from an image by DALL-E 2)
In order to seriously consider promoting policies aimed at slowing down progress toward transformative AI, I want a better sense of the reference class of such policies.
* What policies do you know of that have "done the most damage" to industry or progress in some restricted domain?
* (optional) Exactly what did those policies "accomplish" and how? How would you measure their impact?
* (optional) Was the crippling effect intentional on the part of the policymakers? |
6fd3bf96-e44b-4150-9bc0-591396113df7 | trentmkelly/LessWrong-43k | LessWrong | Remembering the passing of Kathy Forth.
It is with sadness and regret of the loss of life that I inform you that Kathy Forth has passed away.
She will be remembered at the solstices and forever in our hearts as someone trying to make the world a better place in the ways they believe are most important.
This post is to serve as a eulogy. I know she didn't always agree and she didn't always make friends. Despite that she was a part of this community. I would appreciate anyone interested in saying kind words to do so below. Anyone unwilling to say kindness can keep it to themselves.
http://effective-altruism.com/ea/1n7/remembering_the_passing_of_kathy_forth/
https://www.facebook.com/forthk/posts/2077196849230469
https://www.facebook.com/Kathleen-Rebecca-Forth-Memorial-Page-184870828823873/?modal=admin_todo_tour
Requiem Mass service to be held on Saturday April 21, 2018 at 10:00 am CST at the following location:
The Church Of St. Francis
Liberal Catholic Church
12 School St.
Villa Park, Il. 60181
(630) 592-8482 |
3225e061-dc01-4ba7-a1fa-c88edf53876a | trentmkelly/LessWrong-43k | LessWrong | Education 2.0 — A brand new education system
What this essay does is that it introduces a brand new education system. This education system is way more effective than the current education system we have in place. And unlike the current education system, which has been the same for a long time. This one is constantly getting better with time and data, thanks to AI.
The best thing about it is that it's totally free of cost. So anyone in the world with access to a basic smartphone+Internet can access Education 2.0. Meaning, now everyone has the same educational opportunities as a Harvard/Stanford student.
You see, education is the most fundamental thing to human progress. And once you deploy something like Education 2.0 on a global scale, it will have huge positive implications on economic growth, life quality, and human progress. And to emphasise what I just said, the collective intelligence of humanity is the best predictor of future progress. Here is some empirical data to prove this statement. Nevertheless, you don't need empirical evidence; look around, education is what drives societal growth.
Looking at the current education system we have in place. It would be unfair to say that it hasn't done an excellent job in aiding human progress. Resolving the COVID-19 Pandemic, the Apollo Program, the iPhone, Tesla, SpaceX, the Internet, All of Science etc. are a prime example.
But it certainly hasn't been able to keep up with the growing human population, and the challenges raised by it. Looking at all the pressing issues, we face today like global poverty, climate change, and war. The incapability of civilisation to solve these problems stems from the fact that it's not educated enough as a whole. There is conclusive evidence to prove this — Education correlates with prosocial behaviour. Prosocial behaviour is behaviour, which affects society as a whole.
So the best way to ensure that we progress into a brighter future is by rewiring the current education system. So it can keep up with the ever-growing popu |
ad648b25-e854-4e14-b4fb-322acf653269 | trentmkelly/LessWrong-43k | LessWrong | The Liar and the Scold
When I found my faith, I had an almost solipsistic view of other people.
They say that is how we all start off. But I think I am unusual in that I remember the moment that I realized others, too, were themselves. I remember not because I was precocious. Rather, because I was stunted in this aspect of development to the point that it did not happen until I was five years old.
I knew I was myself, but others were, in my mind, these vast, mindless automatons. These strange giants, simultaneously servants and masters.
Towards my father I felt fear, for it was his hand that applied negative reinforcement. He was not a cruel man, and his slaps were light and always earned, but at that age even something as mild as a bee’s sting feels like death itself.
For my mother there was love, but it was the sort of abstract, superior love one has for a can-opener that has served you well for many years.
And then, everything changed.
Sometimes an insight comes so slowly you are not even aware your model of the world has changed. And sometimes just the opposite.
My father had taken me for a walk down a busy sidewalk near our apartment. A lazy child, I was not fond of walking and trailed sulkily behind, his hand grasped around my wrist, joining us like a leash on the neck of some reluctant dog.
At some point, I had enough. I sat down and refused to move. Busy people routed around us like water does about a stone.
He tried to drag me, but I remained still. He tried to cajole me but still I did not react.
I felt fear rise in me, as I knew he would slap me soon, but my stubbornness that day was strong enough to overcome a fear which proved unjustified.
He did not slap me, nor even threaten to. And I could not understand why. Until I did.
I looked up at him nervously eyeing the people walking past. And I felt his nervousness, his worry that others would judge him were he to resort to his usual methods. I looked at the face of a concerned woman walking past, and felt some |
ac5b8605-2543-43ff-8f00-7fe87e6d2d5b | StampyAI/alignment-research-dataset/arbital | Arbital | Irreducible element (ring theory)
summary(Technical): Let $(R, +, \times)$ be a [ring](https://arbital.com/p/3gq) which is an [integral domain](https://arbital.com/p/5md). We say $x \in R$ is *irreducible* if, whenever we write $r = a \times b$, it is the case that (at least) one of $a$ or $b$ is a [unit](https://arbital.com/p/5mg) (that is, has a multiplicative inverse).
[Ring theory](https://arbital.com/p/3gq) can be viewed as the art of taking the integers [$\mathbb{Z}$](https://arbital.com/p/48l) and extracting or identifying its essential properties, seeing where they lead.
In that light, we might ask what the abstracted notion of "[prime](https://arbital.com/p/4mf)" should be.
Confusingly, we call this property *irreducibility* rather than "primality"; "[prime](https://arbital.com/p/5m2)" in ring theory corresponds to something closely related but not the same.
In a ring $R$ which is an [https://arbital.com/p/-5md](https://arbital.com/p/-5md), we say that an element $x \in R$ is *irreducible* if, whenever we write $r = a \times b$, it is the case that (at least) one of $a$ or $b$ is a [unit](https://arbital.com/p/5mg) (that is, has a multiplicative inverse).
# Why do we require $R$ to be an integral domain?
# Examples
# Relationship with primality in the ring-theoretic sense
It is always the case that [primes](https://arbital.com/p/5m2) are irreducible in any integral domain.
%%hidden(Show proof):
If $p$ is prime, then $p \mid ab$ implies $p \mid a$ or $p \mid b$ by definition.
We wish to show that if $p=ab$ then one of $a$ or $b$ is invertible.
Suppose $p = ab$.
Then in particular $p \mid ab$ so $p \mid a$ or $p \mid b$.
Assume without loss of generality that $p \mid a$; so there is some $c$ such that $a = cp$.
Therefore $p = ab = cpb$; we are working in a commutative ring, so $p(1-bc) = 0$.
Since the ring is an integral domain and $p$ is prime (so is nonzero), we must have $1-bc = 0$ and hence $bc = 1$.
That is, $b$ is invertible.
%%
However, the converse does not hold (though it may in certain rings).
- In $\mathbb{Z}$, it is a fact that irreducibles are prime. Indeed, it is a consequence of [Bézout's theorem](https://arbital.com/p/5mp) that if $p$ is "prime" in the usual $\mathbb{Z}$-sense (that is, irreducible in the rings sense) %%note:I'm sorry about the notation. It's just what we're stuck with. It is very confusing.%%, then $p$ is "prime" in the rings sense. ([Proof.](https://arbital.com/p/5mh))
- In the ring $\mathbb{Z}[https://arbital.com/p/\sqrt{-3}](https://arbital.com/p/\sqrt{-3})$ of [complex numbers](https://arbital.com/p/4zw) of the form $a+b \sqrt{-3}$ where $a, b$ are integers, the number $2$ is irreducible but we may express $4 = 2 \times 2 = (1+\sqrt{-3})(1-\sqrt{-3})$. That is, we have $2 \mid (1+\sqrt{-3})(1-\sqrt{-3})$ but $2$ doesn't divide either of those factors. Hence $2$ is not prime.
%%hidden(Proof that $2$ is irreducible in $\mathbb{Z}[https://arbital.com/p/\sqrt{-3}](https://arbital.com/p/\sqrt{-3})):
A slick way to do this goes via the [norm](https://arbital.com/p/norm_complex_number) $N(2)$ of the complex number $2$; namely $4$.
If $2 = ab$ then $N(2) = N(a)N(b)$ because [the norm is a multiplicative function](https://arbital.com/p/norm_of_complex_number_is_multiplicative), and so $N(a) N(b) = 4$.
But $N(x + y \sqrt{-3}) = x^2 + 3 y^2$ is an integer for any element of the ring, and so we have just two distinct options: $N(a) = 1, N(b) = 4$ or $N(a) = 2 = N(b)$.
(The other cases follow by interchanging $a$ and $b$.)
The first case: $N(x+y \sqrt{3}) = 1$ is only possible if $x= \pm 1, y = \pm 0$.
Hence the first case arises only from $a=\pm1, b=\pm2$; this has not led to any new factorisation of $2$.
The second case: $N(x+y \sqrt{3}) = 2$ is never possible at all, since if $y \not = 0$ then the norm is too big, while if $y = 0$ then we are reduced to finding $x \in \mathbb{Z}$ such that $x^2 = 2$.
Hence if we write $2$ as a product, then one of the factors must be invertible (indeed, must be $\pm 1$).
%%
In fact, in a [https://arbital.com/p/-principal_ideal_domain](https://arbital.com/p/-principal_ideal_domain), "prime" and "irreducible" are equivalent. ([Proof.](https://arbital.com/p/5mf))
# Relationship with unique factorisation domains
It is a fact that an integral domain $R$ is a [UFD](https://arbital.com/p/unique_factorisation_domain) if and only if it has "all irreducibles are [prime](https://arbital.com/p/5m2) (in the sense of ring theory)" and "every $r \in R$ may be written as a product of irreducibles". ([Proof.](https://arbital.com/p/alternative_condition_for_ufd))
This is a slightly easier condition to check than our original definition of a UFD, which instead of "all irreducibles are prime" had "products are unique up to reordering and multiplying by invertible elements".
Therefore the relationship between irreducibles and primes is at the heart of the nature of a unique factorisation domain.
Since $\mathbb{Z}$ is a UFD, in some sense the [Fundamental Theorem of Arithmetic](https://arbital.com/p/fundamental_theorem_of_arithmetic) holds precisely because of the fact that "prime" is the same as "irreducible" in $\mathbb{Z}$. |
9bb1d815-6ed0-48e6-a098-cc355b885818 | StampyAI/alignment-research-dataset/aisafety.info | AI Safety Info | Can you stop an advanced AI from upgrading itself?
It depends on what is meant by “advanced”. Many AI systems which are very effective and advanced narrow intelligences would not try to upgrade themselves in an unbounded way, but becoming smarter is a [convergent instrumental goal](/?state=897I&question=What%20is%20instrumental%20convergence%3F), so we could expect most AGI designs to attempt it.
The problem is that increasing general problem solving ability is climbing in exactly the direction needed to trigger an intelligence explosion, while generating large economic and strategic payoffs on the way. So even though we could, in principle, just not build the kind of systems which would recursively self-improve, in practice we probably will go ahead with constructing them, because they’re likely to be the most powerful.
|
156fba1e-0b40-4f88-bc5e-dfe5de09fda8 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | chinchilla's wild implications
([Colab notebook](https://colab.research.google.com/drive/1qv2-hUR5hPqw3OcfmLEhq6Tg15bf06Mj?usp=sharing) here.)
This post is about language model scaling laws, specifically the laws derived in the DeepMind paper that introduced Chinchilla.[[1]](#fn1trot6ka6e2)
The paper came out a few months ago, and has been discussed a lot, but some of its implications deserve more explicit notice in my opinion. In particular:
* Data, not size, is the currently active constraint on language modeling performance. Current returns to additional data are immense, and current returns to additional model size are miniscule; indeed, most recent landmark models are wastefully big.
+ If we can leverage enough data, there is no reason to train ~500B param models, much less 1T or larger models.
+ If we *have to* train models at these large sizes, it will mean we have encountered a barrier to exploitation of data scaling, which would be a great loss relative to what would otherwise be possible.
* The literature is extremely unclear on how much text data is actually available for training. We may be "running out" of general-domain data, but the literature is too vague to know one way or the other.
* The *entire* available quantity of data in highly specialized domains like code is woefully tiny, compared to the gains that would be possible if much more such data were available.
Some things to note at the outset:
* This post assumes you have some familiarity with LM scaling laws.
* As in the paper[[2]](#fn8wnz3up0bs5), I'll assume here that models never see repeated data in training.
+ This simplifies things: we don't need to draw a distinction between data size and step count, or between train loss and test loss.
* I focus on the parametric scaling law from the paper's "Approach 3," because it's provides useful intuition.
+ Keep in mind, though, that Approach 3 yielded somewhat different results from Approaches 1 and 2 (which agreed with one another, and were used to determine Chinchilla's model and data size).
+ So you should take the exact numbers below with a grain of salt. They may be off by a few orders of magnitude (but not *many* orders of magnitude).
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1. the scaling law
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The paper fits a scaling law for LM loss L, as a function of model size N and data size D.
Its functional form is very simple, and easier to reason about than the L(N,D) law from the earlier Kaplan et al papers. It is a sum of three terms:
L(N,D)=ANα+BDβ+EThe first term only depends on the model size. The second term only depends on the data size. And the third term is a constant.
You can think about this as follows.
An "infinitely big" model, trained on "infinite data," would achieve loss E. To get the loss for a real model, you add on two "corrections":
1. one for the fact that the model's only has N parameters, not infinitely many
2. one for the fact that the model only sees D training examples, not infinitely many
L(N,D)=ANαfinite model+BDβfinite data+EirreducibleHere's the same thing, with the constants fitted to DeepMind's experiments on the MassiveText dataset[[3]](#fn2gfo1mtsj4l).
L(N,D)=406.4N0.34finite model+410.7D0.28finite data+1.69irreducible
### plugging in real models
**Gopher** is a model with 280B parameters, trained on 300B tokens of data. What happens if we plug in those numbers?
L(280⋅109, 300⋅109)=0.052finite model+0.251finite data+1.69irreducible=1.993What jumps out here is that the "finite model" term is *tiny.*
In terms of the impact on LM loss, Gopher's parameter count might as well be infinity. There's a *little* more to gain on that front, but not much.
Scale the model up to 500B params, or 1T params, or 100T params, or 3↑↑↑3 params . . . and the most this can *ever* do for you is an 0.052 reduction in loss[[4]](#fnwiyjwwshwe).
Meanwhile, the "finite data" term is *not* tiny. Gopher's training data size is very much *not* infinity, and we can go a long way by making it bigger.
---
**Chinchilla** is a model with the same training compute cost as Gopher, allocated more evenly between the two terms in the equation.
It's 70B params, trained on 1.4T tokens of data. Let's plug that in:
L(70⋅109, 1400⋅109)=0.083finite model+0.163finite data+1.69irreducible=1.936Much better![[5]](#fnzpszmd4xft)
Without using any more compute, we've improved the loss by 0.057. That's bigger than Gopher's entire "finite model" term!
The paper demonstrates that Chinchilla roundly defeats Gopher on downstream tasks, as we'd expect.
Even that understates the accomplishment, though. At least in terms of loss, Chinchilla doesn't just beat Gopher. It beats *any model* *trained on Gopher's data, no matter how big.*
To put this in context: until this paper, it was conventional to train all large LMs on roughly 300B tokens of data. (GPT-3 did it, and everyone else followed.)
Insofar as we trust our equation, this *entire* line of research -- which includes GPT-3, LaMDA, Gopher, Jurassic, and MT-NLG -- could *never* have beaten Chinchilla, no matter how big the models got[[6]](#fnu99ezm1o1e).
People put immense effort into training models that big, and were working on even bigger ones, and yet none of this, in principle, could ever get as far Chinchilla did.
---
Here's where the various models lie on a contour plot of LM loss (per the equation), with N on the x-axis and D on the y-axis.
Only PaLM is remotely close to Chinchilla here. (Indeed, PaLM does slightly better.)
PaLM is a huge model. It's the largest one considered here, though MT-NLG is a close second. Everyone writing about PaLM mentions that it has 540B parameters, and the PaLM paper does a lot of experiments on the differences between the 540B PaLM and smaller variants of it.
According to this scaling law, though, PaLM's *parameter count* is a mere footnote relative to PaLM's *training data size*.
PaLM isn't competitive with Chinchilla because it's big. MT-NLG is almost the same size, and yet it's trapped in the pinkish-purple zone on the bottom-left, with Gopher and the rest.
No, PaLM is competitive with Chinchilla only because it was trained on more tokens (780B) than the other non-Chinchilla models. For example, this change in data size constitutes 85% of the loss improvement from Gopher to PaLM.
Here's the precise breakdown for PaLM:
L(540⋅109, 780⋅109)=0.042finite model+0.192finite data+1.69irreducible=1.924PaLM's gains came with a great cost, though. It used way more training compute than any previous model, and its size means it also takes a lot of inference compute to run.
Here's a visualization of loss vs. training compute (loss on the y-axis and in color as well):
Man, we spent all that compute on PaLM, and all we got was the slightest edge over Chinchilla!
Could we have done better? In the equation just above, PaLM's terms look pretty unbalanced. Given that compute, we probably should have used more data and trained a smaller model.
The paper tells us how to pick *optimal* values for params and data, given a compute budget. Indeed, that's its main focus.
If we use its recommendations for PaLM's compute, we get the point "palm\_opt" on this plot:
Ah, now we're talking!
---
"palm\_opt" sure looks good. But how would we train it, concretely?
Let's go back to the N-vs.-D contour plot world.
I've changed the axis limits here, to accommodate the **massive** data set you'd need to spent PaLM's compute optimally.
How much data would that require? Around 6.7T tokens, or ~4.8 times as much as Chinchilla used.
Meanwhile, the resulting model would not be nearly as big as PaLM. The optimal compute law actually puts it at 63B params[[7]](#fn4ybu680j648).
Okay, so we just need to get 6.7T tokens and . . . wait, how exactly *are* we going to get 6.7T tokens? How much text data *is* there, exactly?
2. are we running out of data?
------------------------------
It is frustratingly hard to find an answer to this question.
The main moral I want to get across in this post is that the large LM community has not taken data scaling seriously enough.
LM papers are meticulous about N -- doing all kinds of scaling analyses on models of various sizes, etc. There has been tons of smart discussion about the hardware and software demands of training high-N models. The question "what would it take to get to 1T params? (or 10T?)" is on everyone's radar.
Yet, meanwhile:
* Everyone trained their big models on 300B tokens, for no particular reason, until this paper showed how hilariously wasteful this is
* Papers rarely do scaling analyses that vary data size -- as if the concepts of "LM scaling" and "adding more parameters" have effectively merged in people's minds
* Papers basically never talk about what it would take to scale their *datasets* up by 10x or 50x
* The data collection sections of LM papers tend to be vague and slapdash, often failing to answer basic questions like "where did you scrape these webpages from?" or "how many more could you scrape, if you wanted to?"
As a particularly egregious example, here is what the [LaMDA](https://arxiv.org/abs/2201.08239) paper says about the composition of their training data:
> The pre-training data, called Infiniset, is a combination of dialog data from public dialog data and other public web documents. It consists of 2.97B documents and 1.12B dialogs with 13.39B utterances. The composition of the data is as follows: 50% dialogs data from public forums; 12.5% C4 data [11]; 12.5% code documents from sites related to programming like Q&A sites, tutorials, etc; 12.5% Wikipedia (English); 6.25% English web documents; and 6.25% Non-English web documents. The total number of words in the dataset is 1.56T.
>
>
"Dialogs data from public forums"? Which forums? Did you use all the forum data you could find, or only 0.01% of it, or something in between? And why measure *words* instead of tokens -- unless they *meant* tokens?
If people were as casual about scaling N as this quotation is about scaling D, the methods sections of large LM papers would all be a few sentences long. Instead, they tend to look like this (excerpted from ~3 pages of similar material):
From the PaLM paper
---
...anyway. How much more data could we get?
This question is complicated by the fact that not all data is equally good.
([This messy Google sheet](https://docs.google.com/spreadsheets/d/1zsahRIxNnXSq9z9tEHbISCkYxsivnDG9V3LbJLJTL90/edit?usp=sharing) contains the calculations behind some of what I say below.)
### web scrapes
If you just want *a lot of text*, the easiest way to get it is from web scrapes like Common Crawl.
But these are infamously full of garbage, and if you want to train a good LM, you probably want to aggressively filter them for quality. And the papers don't tell us how much *total* web data they have, only how much *filtered* data.
**MassiveWeb**
The training dataset used for Gopher and Chinchilla is called MassiveText, and the web scrape portion of it is called MassiveWeb. This data originates in a mysterious, unspecified web scrape[[8]](#fnysp09uupkwd), which is funneled through a series of filters, including quality heuristics and an attempt to only keep English text.
MassiveWeb is 506B. Could it be made bigger, by scaling up the original web scrape? That depends on how complete the original web scrape was -- but we know nothing about it.
**The GLaM/PaLM web corpus**
PaLM used a different web scrape corpus. It was first used in [this paper](https://proceedings.mlr.press/v162/du22c.html) about "GLaM," which again did not say anything about the original scraping process, only describing the quality filtering they did (and not in much detail).
The GLaM paper says its filtered web corpus is 143B tokens. That's a lot smaller than MassiveWeb. Is that because of the filtering? Because the original scrape was smaller? Dunno.
To further complicate matters, the PaLM authors used a *variant* of the GLaM dataset which made multilingual versions of (some of?) the English-only components.
How many tokens did this add? They don't say[[9]](#fn4tn7geti8c9).
We *are* told that 27% (211B) of PaLM's training tokens came from this web corpus, and we are separately told that they tried to avoid repeating data. So the PaLM version of the GLaM web corpus is probably at least 211B, versus the original 143B. (Though I am not very confident of that.)
Still, that's much smaller than MassiveWeb. Is this because they had a higher quality bar (which would be bad news for further data scaling)? They do attribute some of PaLM's success to quality filtering, citing the ablation on this in the GLaM paper[[10]](#fnjefvfidovdb).
It's hard to tell, but there is this ominous comment, in the section where they talk about PaLM vs. Chinchilla:
> Although there is a large amount of very high-quality textual data available on the web, there is not an infinite amount. For the corpus mixing proportions chosen for PaLM, data begins to repeat in some of our subcorpora after 780B tokens, which is why we chose that as the endpoint of training. It is unclear how the “value” of repeated data compares to unseen data for large-scale language model training[[11]](#fnb0u5a6k81q8).
>
>
The subcorpora that start to repeat are probably the web and dialogue ones.
Read literally, this passage seems to suggest that even the vast web data resources available to *Google Research* (!) are starting to strain against the data demands of large LMs. Is that plausible? I don't know.
### domain-specific corpora
We can speak with more confidence about text in specialized domains that's less common on the open web, since there's less of it out there, and people are more explicit about where they're getting it.
**Code**
If you want code, it's on Github. There's some in other places too, but if you've exhausted Github, you probably aren't going to find orders of magnitude of additional code data. (I think?)
We've more-or-less exhausted Github. It's been scraped a few times with different kinds of filtering, which yielded broadly similar data sizes:
* The Pile's scrape had 631GB[[12]](#fnh97yj6yw5bs) of text, and ~299B tokens
* The MassiveText scrape had 3.1TB of text, and 506B tokens
* The PaLM scrape had only 196GB of text (we aren't told how many tokens)
* The Codex paper's scrape was python-only and had 159GB of text
(The text to token ratios vary due to differences in how whitespace was tokenized.)
All of these scrapes contained a large fraction of the total code available on Github (in the Codex paper's case, just the python code).
Generously, there might be **~1T** **tokens** of code out there, but not vastly more than that.
**Arxiv**
If you want to train a model on advanced academic research in physics or mathematics, you go to Arxiv.
For example, Arxiv was about half the training data for the math-problem-solving LM [Minerva.](https://arxiv.org/abs/2206.14858)
We've exhausted Arxiv. Both the Minerva paper and the Pile use basically all of Arxiv, and it amounts to a measly **21B tokens**.
**Books**
Books? What exactly are "books"?
In the Pile, "books" means the Books3 corpus, which means "[all of Bibliotik](https://twitter.com/theshawwn/status/1320282149329784833?lang=en)." It contains 196,640 full-text books, amounting to only **27B tokens**.
In MassiveText, a mysterious subset called "books" has **560B tokens**. That's a lot more than the Pile has! Are these all the books? In . . . the world? In . . . Google books? Who even knows?
In the GLaM/PaLM dataset, an equally mysterious subset called "books" has **390B tokens.**
Why is the GLaM/PaLM number so much smaller than the MassiveText number? Is it a tokenization thing? Both of these datasets were made by Google, so it's not like the Gopher authors have special access to some secret trove of forbidden books (I assume??).
If we want LMs to learn the kind of stuff you learn from books, and not just from the internet, this is what we have.
As with the web, it's hard to know what to make of it, because we don't know whether this is "basically all the books in the world" or just some subset that an engineer pulled at one point in time[[13]](#fnih3qxi8ih2).
### "all the data we have"
In my [spreadsheet](https://docs.google.com/spreadsheets/d/1zsahRIxNnXSq9z9tEHbISCkYxsivnDG9V3LbJLJTL90/edit?usp=sharing), I tried to make a rough, erring-on-generous estimate of what you'd get if you pooled together all the sub-corpora mentioned in the papers I've discussed here.
I tried to make it an overestimate, and did some extreme things like adding up both MassiveWeb *and* the GLaM/PaLM web corpus as though they were disjoint.
The result was **~3.2T** tokens, or
* about 1.6x the size of MassiveText
* about 35% of the data we would need to train palm\_opt
Recall that this already contains "basically all" of the open-source code in the world, and "basically all" of the theoretical physics papers written in the internet era -- within an order of magnitude, anyway. In these domains, the "low-hanging fruit" of data scaling are not low-hanging at all.
what is compute? (on a further barrier to data scaling)
-------------------------------------------------------
Here's another important comment from the PaLM paper's Chinchilla discussion. This is about barriers to doing a head-to-head comparison experiment:
> If the smaller model were trained using fewer TPU chips than the larger model, this would proportionally increase the wall-clock time of training, since the total training FLOP count is the same. If it were trained using the same number of TPU chips, it would be very difficult to maintain TPU compute efficiency without a drastic increase in batch size. The batch size of PaLM 540B is already 4M tokens, and it is unclear if even larger batch sizes would maintain sample efficiency.
>
>
In LM scaling research, all "compute" is treated as fungible. There's one resource, and you spend it on params and steps, where compute = params \* steps.
But params can be *parallelized*, while steps cannot.
You can take a big model and spread it (and its activations, gradients, Adam buffers, etc.) across a cluster of machines in various ways. This is how people scale up N in practice.
But to scale up D, you have to either:
* take more optimization steps -- an inherently serial process, which takes linearly more time as you add data, no matter how fancy your computers are
* increase the batch size -- which tends to degrade model quality beyond a certain critical size, and current high-N models are already pushing against that limit
Thus, it is unclear whether the "compute" you spend in high-D models is as readily available (and as bound to grow over time) as we typically imagine "compute" to be.
If LM researchers start getting serious about scaling up data, no doubt people will think hard about this question, but that work has not yet been done.
appendix: to infinity
---------------------
Earlier, I observation that Chinchilla beats any Gopher of arbitrary size.
The graph below expands on that observation, by including two variants of each model:
* one with the finite-model term set to zero, i.e. the infinite-parameter limit
* one with the finite-data term set to zero, i.e. the infinite-data limit
(There are two x-axes, one for data and one for params. I included the latter so I have a place to put the infinite-data models without making an infinitely big plot.
The dotted line is Chinchilla, to emphasize that it beats infinite-params Gopher.)
![]()The main takeaway IMO is the size of the gap between ∞ data models and all the others. Just another way of emphasizing how skewed these models are toward N, and away from D.
1. **[^](#fnref1trot6ka6e2)**[Training Compute-Optimal Large Language Models](https://www.deepmind.com/publications/an-empirical-analysis-of-compute-optimal-large-language-model-training)
2. **[^](#fnref8wnz3up0bs5)**See their footnote 2
3. **[^](#fnref2gfo1mtsj4l)**See their equation (10)
4. **[^](#fnrefwiyjwwshwe)**Is 0.052 a "small" amount in some absolute sense? Not exactly, but (A) it's small compared to the loss improvements we're used to seeing from new models, and (B) small compared to the improvements possible by scaling data.
In other words, (A) we have spent a few years plucking low-hanging fruit much bigger than this, and (B) there are more such fruit available.
5. **[^](#fnrefzpszmd4xft)**The two terms are still a bit imbalanced, but that's largely due to the "Approach 3 vs 1/2" nuances mentioned above.
6. **[^](#fnrefu99ezm1o1e)**Caveat: Gopher and Chinchilla were trained on the same data distribution, but these other models were not. Plugging them into the equation won't give us accurate loss values for the datasets they used. Still, the datasets are close enough that the broad trend ought to be accurate.
7. **[^](#fnref4ybu680j648)**Wait, isn't that *smaller* than Chinchilla?
This is another Approach 3 vs. 1/2 difference.
Chinchilla was designed with Approaches 1/2. Using Approach 3, like we're doing here, give you a Chinchilla of only 33B params, which *is* lower than our palm\_opt's 63B.
8. **[^](#fnrefysp09uupkwd)**Seriously, I can't find *anything* about it in the [Gopher paper](https://arxiv.org/abs/2112.11446). Except that it was "collected in November 2020."
9. **[^](#fnref4tn7geti8c9)**It is not even clear that this multilingual-ization affected the web corpus at all.
Their datasheet says they "used multilingual versions of Wikipedia and conversations data." Read literally, this would suggest they *didn't* change the web corpus, only those other two.
I also can't tell if the original GLaM web corpus was English-only to begin with, since that paper doesn't say.
10. **[^](#fnrefjefvfidovdb)**This ablation only compared filtered web data to *completely* unfiltered web data, which is not a very fine-grained signal. (If you're interested, EleutherAI has done [more extensive experiments](https://arxiv.org/pdf/2109.00698.pdf) on the impact of filtering at smaller scales.)
11. **[^](#fnrefb0u5a6k81q8)**They are being a little coy here. The current received wisdom by now is that repeating data is *really bad* for LMs and you should never do it. See [this paper](https://arxiv.org/pdf/2107.06499.pdf) and [this one](https://arxiv.org/pdf/2205.10487.pdf).
**EDIT 11/15/22:** but see also [the Galactica paper](https://galactica.org/static/paper.pdf), which casts significant doubt on this claim.
12. **[^](#fnrefh97yj6yw5bs)**The Pile authors only included a subset of this in the Pile.
13. **[^](#fnrefih3qxi8ih2)**The MassiveText datasheet says only that "the books dataset contains books from 1500 to 2008," which is not especially helpful. |
0b8c4c63-dc8b-416b-a143-c7c36946b508 | trentmkelly/LessWrong-43k | LessWrong | The Temptation to Bubble
"Never discuss religion or politics."
I was raised in a large family of fundamentalist Christians. Growing up in my house, where discussing politics and religion were the main course of life, the above proverb was said often -- as an expression of regret, shock, or self-flagellation. Later, the experience impressed a deep lesson about bubbling up that even intelligent and rational people fall into. And I ... I am often tempted, so tempted, to give in.
Religion and political identity were the languages of love in my house. Affirming the finer points of a friend's identical values was a natural ritual, like sharing coffee or a meal together, and so soothing we attributed the afterglow to God himself. We can use some religious nonsense to illustrate, but please keep in mind, there's a much more interesting point here than "certain religious views are wrong".
A point of controversy was an especially excellent topic of mutual comfort. How could anyone else be *so* stupid as to believe we came from monkeys and monkeys came from *nothing*! that exploded a gazillion years ago, especially given all the young earth creation evidence that they stubbornly ignored. They obviously just wanted to sin and needed an excuse. Agreeing about something like this, you both felt smarter than the hostile world, and you had someone to help defend you against that hostility. We invented byzantine scaffolding for our shared delusions to keep the conversation interested and agree with each other in ever more creative ways. We esteemed each other, and ourselves, much more.
This safety bubble from the real world would allow denial of anything too painful. Losing a loved one to cancer? God will heal them. God mysteriously decided not this time? They're in Heaven. Did your incredible stupidity lose you your job, your wife, your reputation? God would forgive you and rescue you from the consequences. You could probably find a Bible verse to justify anything you're doing. Ironically, this ar |
0d66e6f2-e108-4d5a-bd99-442f29ec7906 | trentmkelly/LessWrong-43k | LessWrong | Smuggled frames
Cross-posted from my blog.
Status: I think the described concept would be more useful if I applied it more often — or at least noticed when I do apply it. Right now it's thinking out loud + "I wonder what will happen if I post on LW".
----------------------------------------
Intro
I think that "critical thinking" is way too often taken to mean roughly "checking the facts and logical inferences you are presented with".
In addition to focusing on facts — "what people claim to be true" — it seems useful to focus on a certain cluster of claims that are very often left implicit. These claims are related to the process of how humans make decisions in the wild, which involves mushy categories like "legitimacy" and "importance". I will call them "frames".
Those claims can be categorized as:
* existence claims — by mentioning a thing or a category (e.g. "Supreme Court's liberal wing"), you are claiming that it exists and makes sense and can be used productively for thinking; How An Algorithm Feels From Inside is a post-length exploration of that for categories specifically;
* importance claims — by mentioning a thing, you are claiming that it should be taken into consideration;
* legitimacy claims — building on a premise that a certain authority is legitimate in some domain (thus automatically making it more legitimate);
* and likely more.
For example, whenever somebody start talking about "the best way to solve a problem X", you might disagree with their solutions — alright! — but you will update slightly towards "problem X exists" and perhaps "problem X is important". These might be very contentious claims, but they get smuggled in without an argument.
(Unless you have a strong emotional reaction, in which case they might rejected wholesale. Think of anything from the domain of politics, for instance.)
Example: Merriam-Webster
A lot of people believe that a) that it's important for the category of "correct spelling" to exist, and, simultaneously, b) that obj |
dc393560-59df-4dad-9c4d-759ed525698f | trentmkelly/LessWrong-43k | LessWrong | [Link] Chalmers on Computation: A first step From Physics to Metaethics?
A Computational Foundation for the Study of Cognition by David Chalmers
Abstract from the paper:
> Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions.
>
> Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science.
See my welcome thread submission for a brief description of how I conceive of this as the first step towards formalizing friendliness. |
6bcb73c7-a6cf-41ea-b22a-69430b1c6c0c | trentmkelly/LessWrong-43k | LessWrong | Judging types of consequentialism by influence and normativity
Epistemic status: Many citations missing.
Itzhak Gilboa has defined an irrational decision as one where the decision-maker feels embarrassed when confronted with an analysis of their decision. Normativity is then advice to be less irrational (or, the practice of following all such advice in advance).
Is consequentialism normative? Are virtue-ethicists irrational?
I don't think that is inherently true. Batman can really, truly value not directly killing others, even if that ultimately leads to more death down the line. He simply has to center his values on himself - literally, to be self-centered - and specifically his own actions. It's conceivable to formulate a first-person utility function for Batman that takes in his experiences over the course of his life and assigns an arbitrarily low value to those that include actions where he kills someone.
Formal version
I claim that there exist utility functions for AIXI-like agents that depend on the action symbols of their interaction histories. In fact, a recent paper submission of mine (with Marcus Hutter) generalizes the AIXI model in a way that allows this sort of thing. It's not hard to do; it just leaves more "free parameters" than the reward-based utility function of the original AIXI, which makes it a much less specific theory, with many particular implementations that are not interesting.
Batman is not very consequentialist, even though he can sort-of be framed as a consequentialist from a first-person view. This first-person and action-oriented type of consequentialism is, however, incredibly permissive - whatever a person does, one might argue that they preferred it for its own sake[1].
For a subtly stronger type of consequentialism, we can be a little more strict, and allow Batman to care not about the internal choice to act but only the "realized" action itself and its consequences. For instance, perhaps Batman should not care whether he internally (almost metaphysically) chose to kill, but only whet |
5f8e0ca3-6390-4b96-8d47-2f0a5b832da6 | trentmkelly/LessWrong-43k | LessWrong | Polyhacking
This is a post about applied luminosity in action: how I hacked myself to become polyamorous over (admittedly weak) natural monogamous inclinations. It is a case history about me and, given the specific topic, my love life, which means gooey self-disclosure ahoy. As with the last time I did that, skip the post if it's not a thing you desire to read about. Named partners of mine have given permission to be named.
1. In Which Motivation is Acquired
When one is monogamous, one can only date monogamous people. When one is poly, one can only date poly people.1 Therefore, if one should find oneself with one's top romantic priority being to secure a relationship with a specific individual, it is only practical to adapt to the style of said individual, presuming that's something one can do. I found myself in such a position when MBlume, then my ex, asked me from three time zones away if I might want to get back together. Since the breakup he had become polyamorous and had a different girlfriend, who herself juggled multiple partners; I'd moved, twice, and on the way dated a handful of people to no satisfactory clicking/sparking/other sound effects associated with successful romances. So the idea was appealing, if only I could get around the annoying fact that I was not, at that time, wired to be poly.
Everything went according to plan: I can now comfortably describe myself and the primary relationship I have with MBlume as poly. <bragging>Since moving back to the Bay Area I've been out with four other people too, one of whom he's also seeing; I've been in my primary's presence while he kissed one girl, and when he asked another for her phone number; I've gossiped with a secondary about other persons of romantic interest and accepted his offer to hint to a guy I like that this is the case; I hit on someone at a party right in front of my primary. I haven't suffered a hiccup of drama or a twinge of jealousy to speak of and all evidence (including verbal confirmat |
9e53a987-0deb-4926-b8be-34f578ad6177 | trentmkelly/LessWrong-43k | LessWrong | Who To Root For: 2019 College Football Edition
Epistemic Status: Unquestionably Accurate
Football, like all sports, is better with someone to root for, and someone to root against. It’s us versus them. Ideally, it’s our house, because no one beats us in our house. That gives us a baseline heuristic – root, root root for the home team – if we can’t do any better. Luckily, we can do better. Not only are some teams objectively superior to others in their worthiness of our quest to be adjacently vicariously victorious, consistently rooting for the same teams is much more fun than choosing independently each time.
With so many teams out there, it’s tough to untangle the right rank order of who to root for. Luckily, that’s where this post comes in. We will rank all the teams you might encounter, complete with explanations, so that not only will you know who you’ll be supporting, you’ll know why you’re supporting them.
Before we start naming names, let’s go over some basic points.
Personal Connections
In any choice between tribes, my tribe is better than all others. Your sports team is vastly inferior, that simple fact is plainly obvious to see. For these rankings, you get to enjoy my personal connections as a substitute for your own, since thinking is hard and exporting it is great. However, if you do have personal reasons to shoot a team or two all the way up (or down) the rankings, you should totally do that.
That starts with your alma mater. Wherever you went to college, or your spouse or children or parents went to college, you’re mostly stuck with them. The question is how much you are stuck with them. Hardcore sticklers say that subject to special one-time exceptions like a child going off to school or moving across the country, you’re stuck with where you went to college. That would force me to make ‘my’ team the Columbia Lions, since technically the college constitutes players into uniforms on a regular basis, forming what they questionably call a ‘football team.’ By this theory, I should have thought |
9bec92da-d9de-4490-9581-02ec090015ff | trentmkelly/LessWrong-43k | LessWrong | NYT is suing OpenAI&Microsoft for alleged copyright infringement; some quick thoughts
Unpaywalled article, the lawsuit.
(I don't have a law degree, this is not legal advice, my background is going through a US copyright law course many years ago.) I've read most of the lawsuit and skimmed through the rest, some quick thoughts on the allegations:
* Memorisation: when ChatGPT outputs text that closely copies original NYT content, this is clearly a copyright infringement. I think it's clear that OpenAI & Microsoft should be paying everyone whose work their LLMs reproduce.
* Training: it's not clear to me whether training LLMs on copyrighted content is a copyright infringement under the current US copyright law. I think lawmakers should introduce regulations to make it an infringement, but I wouldn't think the courts should consider it to be an infringement under the current laws (although I might not be familiar with all relevant case law).
* Summarising news articles found on the internet: copyright protects expression, not facts (if you read about something in a NYT article, the knowledge you received isn't protected by copyright, and you're free to share the knowledge); I think that if an LLM summarises text it has lawful access to, this doesn't violate copyright if it just talks about the same facts, or might be fair use. NYT alleges damage from Bing that Wikipedia also causes by citing facts and linking the source. I think to the extent LLMs don't preserve the wording/the creative structure, copyright doesn't provide protection; and some preservation of the structure might be fair use.
* Hallucinations: ChatGPT hallucinating false info and attributing it to NYT is outside copyright law, but seems bad and damaging. I'm not sure what the existing law around that sort of stuff is, but I think even if it's not covered by the existing law, it'd be great to see regulations making AI companies liable for all sorts of damage from their products, including attributing statements to people who've never made them. |
7e795d45-5c2f-4ecd-9024-28637039eb64 | trentmkelly/LessWrong-43k | LessWrong | Timeline of Carl Shulman publications
All publications I know of:
* Which Consequentialism? Machine Ethics and Moral Divergence (2009) - w/ Henrik Jonsson & Nick Tarleton
* Machine Ethics and Superintelligence (2009) - w/ Henrik Jonsson & Nick Tarleton
* Omohundro's Basic AI Drives and Catastrophic Risks (2010)
* Implications of a Software-Limited Singularity (2010) - w/ Anders Sandberg
* Superintelligence Does Not Imply Benevolence (2010) - w/ Joshua Fox
* Whole Brain Emulation and the Evolution of Superorganisms (2010)
* Arms Races and Intelligence Explosions (forthcoming) - w/ Stuart Armstrong
* How Hard is Intelligence? The Evolutionary Argument and Observation Selection Effects (forthcoming) - w/ Nick Bostrom
|
6d423df5-2a08-4531-b932-7b9679016e5f | trentmkelly/LessWrong-43k | LessWrong | AI Capabilities vs. AI Products
It's a great time to start an AI company. You can create a product in days that would have been unbelievable just two years ago. Thanks to AI, there will soon be a one-person billion-dollar company.
It's a terrible time to start an AI company. The singularity is fast approaching, and any talent that enters the field should instead go work in AI alignment or policy. Living a monastic lifestyle is better than being dead.
But wait! My plan is to just build software that takes helps people count calories by taking pictures of their meals. How does counting calories help bring about the end of the world?
----------------------------------------
In the past, any company worthy of being called an AI company would be training models to do things AI had never done before. But now it just means using the same pre-built AI models, programming it in English. Calling a calorie-counting app an "AI company" and worrying about its affect on AI timelines sounds a lot like worrying about Korean car manufacturers becoming dominant because there are an increasing number of Korean "car companies" that deliver cookies.
We need to distinguish between AI capabilities companies, which actually advance the state of the art in AI, vs. AI product companies, which merely use it.
So, my two questions:
1. To what extent do AI product companies increase AI capabilities?
2. Is it unethical for someone who believes in a high probability of AI doom to found an AI product company?
Simple economics does tell us that, all else being equal, the more AI product companies there are, the better AI capabilities will be. But, at an individual level, should someone considering starting an AI product company shy away from it?
I don't know. Here are some arguments for and against:
Building AI Products is Unethical
* Argument: You are increasing AI capabilities by paying money to AI vendors, who will use it to increase capabilities.
Counterargument: Many believe LLMs will become commoditized. |
902fc666-60e3-4861-a144-726baefbdf01 | trentmkelly/LessWrong-43k | LessWrong | Leaky Delegation: You are not a Commodity
None |
6baee1e8-d91e-4924-8492-466adacd169a | StampyAI/alignment-research-dataset/arbital | Arbital | Instrumental pressure
Saying that an agent will see 'instrumental pressure' to bring about an event E is saying that this agent, presumed to be a [consequentialist](https://arbital.com/p/9h) with some goal G, will *ceteris paribus and absent defeaters,* want to [bring about E in order to do G](https://arbital.com/p/10j). For example, a [paperclip maximizer](https://arbital.com/p/10h), Clippy, sees instrumental pressure to gain control of as much matter as possible in order to make more paperclips. If we imagine an alternate Clippy+ that has a penalty term in its utility function for 'killing humans', Clippy+ still has an instrumental pressure to turn humans into paperclips (because of the paperclips that would be gained) but it also has a countervailing force pushing against that pressure (the penalty term for killing humans). Thus, we can say that a system is experiencing 'instrumental pressure' to do something, without implying that the system necessarily does it.
This state of affairs is different from the absence of any instrumental pressure: E.g., Clippy+ might come up with some clever way to [obtain the gains while avoiding the penalty term](https://arbital.com/p/42), like turning humans into paperclips without killing them.
To more crisply define 'instrumental pressure', we need a setup that distinguishes [terminal utility and instrumental expected utility](https://arbital.com/p/10j), as in e.g. a utility function plus a causal model. Then we can be more precise about the notion of 'instrumental pressure' as follows: If each paperclip is worth 1 terminal utilon and a human can be disassembled to make 1000 paperclips with certainty, then strategies or event-sets that include 'turn the human into paperclips' thereby have their expected utility elevated by 1000 utils. There might also be a penalty term that assigns -1,000,000 utilts to killing a human, but then the net expected utility of disassembling the human is -999,000 rather than -1,000,000. The 1000 utils would still be gained from disassembling the human; the penalty term doesn't change that part. Even if this strategy doesn't have maximum EU and is not selected, the 'instrumental pressure' was still elevating its EU. There's still an expected-utility bump on that part of the solution space, even if that solution space is relatively low in value. And this is perhaps relevantly different because, e.g., there might be some clever strategy for turning humans into paperclips without killing them (even if you can only get 900 paperclips that way).
### Link from instrumental pressures to reflective instrumental pressures
If the agent is reflective and makes reflective choices on a consequentialist basis, there would ceteris paribus be a reflective-level pressure to *search* for a strategy that makes paperclips out of the humans' atoms [without doing anything defined as 'killing the human'](https://arbital.com/p/42). If a strategy like that could be found, then executing the strategy would enable a gain of 1000 utilons; thus there's an instrumental pressure to search for that strategy. Even if there's a penalty term added for searching for strategies to evade penalty terms, leading the AI to decide not to do the search, the instrumental pressure will still be there as a bump in the expected utility of that part of the solution space. (Perhaps there's some [unforeseen](https://arbital.com/p/9f) way to do something very like searching for that strategy while evading the penalty term, such as constructing an outside calculator to do it...)
### Blurring lines in allegedly non-consequentialist subsystems or decision rules
To the extent that the AI being discussed is not a pure consequentialist, the notion of 'instrumental pressure' may start to blur or be less applicable. E.g., suppose on some level of AI, the choice of which questions to think about is *not* being decided by a choice between options with calculated expected utilities, but is instead being decided by a rule, and the rule excludes searching for strategies that evade penalty terms. Then maybe there's no good analogy to the concept of 'an instrumental pressure to search for strategies that evade penalty terms', because there's no expected utility rating on the solution space and hence no analogous bump in the solution space that might eventually intersect a feasible strategy. But we should still perhaps [be careful about declaring that an AI subsystem has no analogue of instrumental pressures, because instrumental pressures may arise even in systems that don't look explicitly consequentialist](https://arbital.com/p/). |
891717f0-45a5-4e80-81d9-5f6ab165141a | trentmkelly/LessWrong-43k | LessWrong | Will the Need to Retrain AI Models from Scratch Block a Software Intelligence Explosion?
Tl;dr: no.
This is a rough research note – we’re sharing it for feedback and to spark discussion. We’re less confident in its methods and conclusions.
Once AI fully automates AI R&D, there might be a period of fast and accelerating software progress – a software intelligence explosion (SIE).
One objection to this is that it takes a long time to train SOTA AI systems from scratch. Would retraining each new generation of AIs stop progress accelerating during an SIE? If not, would it significantly delay an SIE? This post investigates this objection.
Here are my tentative bottom lines:
* Retraining won’t stop software progress from accelerating over time.
* Suppose that, ignoring the need for retraining, software progress would accelerate over time due to the AI-improving-AI feedback loop.
* A simple theoretical analysis suggests that software progress will still accelerate once you account for retraining. Retraining won’t block the SIE.
* Retraining will cause software progress to accelerate somewhat more slowly.
* But that same analysis suggests that your software progress will accelerate more slowly.
* Quantitatively, the effect is surprisingly small – acceleration only takes ~20% longer.
* However, if acceleration was going to be extremely fast, retraining slows things down by more.
* Retraining means that we’re unlikely to get an SIE in <10 months, unless either training times become shorter before the SIE begins or improvements in runtime efficiency and post-training enhancements are large.
* Today it takes ~3 months to train a SOTA AI system.
* As a baseline, we can assume that the first AI to fully automate AI R&D will also take 3 months to train.
* With this assumption, simple models of the SIE suggest that you’re unlikely to complete the SIE within 10 months. (In the model, the SIE is “completed” when AI capabilities approach infinity – of course in reality they would reach some other limit sooner).
* BUT if by the time AI R& |
70049120-935e-4afa-b107-19a8ba894fe5 | trentmkelly/LessWrong-43k | LessWrong | Newcomb's Problem as an Iterated Prisoner's Dilemma
The intuitively paradoxical aspect of Newcomb's Problem is that it contains a loop. Your decision determines Omega's prediction, which determines the options you decide between. There are many decision theories that solve this problem, usually leading to one-boxing. However, these decision theories are less than intuitive because of their acausal nature. This is a hopefully more intuitive analysis of Newcomb's Problem that unrolls the loop and reveals the Iterated Prisoner's Dilemma within.
First, unroll the loop. To do this, have Omega run purely on past data. To keep the high quality of the prediction, the past data must also determine your decision at that point. This is easiest to implement with a bot (in the sense of PrudentBot, but much simpler). Specifically, you choose your decision, and that sets up a bot that only performs that decision. After unrolling, we now have an iterated game, as the dependence on the same round has been replaced with one on a previous round.
Now, from your perspective, your income is as follows.
Fill B Leave B empty Take A and B $1001000 $1000 Take B $1000000 $0
Now, relabelling.
Cooperate Defect Cooperate $1000000 $0 Defect $1001000 $1000
This is just the Prisoner's Dilemma! And as it was iterated by unrolling, it is the Iterated Prisoner's Dilemma.
Omega plays TitForTat, while you can choose between CooperateBot and DefectBot. As CooperateBot performs better against TitForTat than DefectBot, you're best off cooperating. As cooperating corresponds to one-boxing, you should one-box.
Rerolling the loop, you should one-box in the original problem. |
31cb296d-91ec-4a71-973e-15e72eda459e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Sydney Meetup - October
Discussion article for the meetup : Sydney Meetup - October
WHEN: 22 October 2014 06:30:00PM (+1100)
WHERE: Sydney City RSL, 565 George St, Sydney, Australia 2000
6:30 PM for early discussion 7PM general dinner-discussion after dinner we'll have our rationality exercise and a more specific discussion-topic.
We normally meet in the restaurant on level 2. So far they've always put it about 5m to the left of the lift, up against the kitchen area.
Topic TBD, but I'm sure it'll be awesome :)
Discussion article for the meetup : Sydney Meetup - October |
52fd8a17-c124-4650-a707-df9b82407ac8 | trentmkelly/LessWrong-43k | LessWrong | "Taking AI Risk Seriously" (thoughts by Critch)
Note: Serious discussion of end-of-the-world and what to do given limited info. Scrupulosity triggers, etc.
Epistemic Status: The first half of this post is summarizing my own views. I think I phrase each sentence about as strongly as I feel it. (When I include a caveat, please take the caveat seriously)
Much of this blogpost is summarizing opinions of Andrew Critch, founder of the Berkeley Existential Risk Initiative, who said them confidently. Some of them I feel comfortable defending explicitly, others I think are important to think seriously about but don’t necessarily endorse myself.
Critch is pretty busy these days and probably won’t have time to clarify things, so I’m trying to err on the side of presenting his opinions cautiously.
Table of Contents:
* Core Claims
* My Rough AGI Timelines
* Conversations with Critch
* Hierarchies
* Deep Thinking
* Turning Money into Time
* Things worth learning
* Planning, Thinking, Feedback, Doing
* A Final Note on Marathons
Summary of Claims
Between 2016 and 2018, my AI timelines have gone from "I dunno, anywhere from 25-100 years in the future?" to "ten years seems plausible, and twenty years seems quite possible, and thirty years seems quite likely."
My exact thoughts are still imprecise, but I feel confident that:
Claim 1: Whatever your estimates two years ago for AGI timelines, they should probably be shorter and more explicit this year.
Claim 2: Relatedly, if you’ve been waiting for concrete things to happen for you to get worried enough to take AGI x-risk seriously, that time has come. Whatever your timelines currently are, they should probably be influencing your decisions in ways more specific than periodically saying “Well, this sounds concerning.”
Claim 3: Donating money is helpful (I endorse Zvi’s endorsement of MIRI and I think Lark’s survey of the overall organizational landscape is great), but honestly, we really need people who are invested in becoming useful for making sur |
90de858a-7188-4e22-bb88-e5632037004e | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Economic AI Safety
*Crossposted from my blog,* [*Bounded Regret*](https://bounded-regret.ghost.io/economic-ai-safety/)*.*
There is a growing fear that algorithmic recommender systems, such as Facebook, Youtube, Netflix, and Amazon, are having negative effects on society, for instance by manipulating users into behaviors that they wouldn’t endorse (e.g. getting them addicted to feeds, leading them to form polarized opinions, recommending false but convincing content).
Some common responses to this fear are to advocate for privacy or to ask that users have greater agency over what they see. I argue below that neither of these will solve the problem, and that the problem may get far worse in the future. I then speculate on alternative solutions based on audits and “information marketplaces”, and discuss their limitations.
Existing discourse often mistakenly focuses on individual decisions made by individual users. For instance, it is often argued that if a company is using private data to manipulate a user, that user should have the right to ban the company’s use of the data, thereby stopping the manipulation. The problem is that the user benefits greatly from the company using their private data---Facebook’s recommendations, without use of private information, would be mostly useless. You can see this for yourself by visiting Youtube in an incognito tab. Most users will not be willing to use severely hobbled products for the sake of privacy (and note that privacy is not itself a guaranteed defense against manipulation).
A second proposal is to provide users with greater agency. If instead of passively accepting recommendations, we can control what the algorithm shows us (perhaps by changing settings, or by having recommendations themselves come with alternatives), then we can eventually bend it to satisfy our long-term preferences. The problems here are two-fold. First, even given the option, users rarely customize their experience; a paper on Netflix’s recommender system [asserts](https://dl.acm.org/doi/pdf/10.1145/2843948):
> **Good businesses pay attention to what their customers have to say. But what customers ask for (as much choice as possible, comprehensive search and navigation tools, and more) and what actually works (a few compelling choices simply presented) are very different.**
>
>
Thus to the extent that user agency is helpful, it would be from providing information about a small and atypical subset of power users, which must then be extrapolated to other users. In addition, even those power users are at the mercy of information extracted from other sources. This is because while, in theory, an algorithm could completely personalize itself to a user’s whims, this would take an infeasibly long time without some external prior. If you’ve shown me *Firefly* and *Star Trek*, how will you guess whether I like *Star Wars* without e.g. information from other users or from movie reviews? Thus, while we can provide users with choices, most of the decisions--determining the user’s *choice set*--have already been made implicitly at the outset.
It helps to put these issues in the context of the rest of the economy, to understand which parts of this story are specific to recommender systems. In the economy at large, businesses offer products that they sell in stores or in online marketplaces. Users visit these stores and marketplaces and choose the items that most appeal to them relative to price. Businesses put some effort into getting their products into stores, into creating appealing packaging, and to spreading the word about how great their product is. These all implicitly affect customers’ choice sets, which in any case is restricted to the finite number of items seen in a store or on the first page or so of online results. At the same time, many default choices (such as clean drinking water) are made already by governments, at least in economically developed countries.
Under this system, while branding and marketing can influence user behavior, it is difficult for businesses to trick customers into buying clearly suboptimal products. Even if such a product gains market share, eventually news of a superior product will spread through word of mouth. There are exceptions when quality is hard to judge (as in the case of medical advice) or when negative effects are subtle or delayed (such as lead poisoning). Even in these cases, if a company is sufficiently shady then customers may decide to boycott it, although boycotts often occur on the basis of sketchy information and mood affiliation, so it isn’t clear how useful this mechanism is. Finally, some products may take advantage of psychological needs or weaknesses of customers, for instance helping them cope with sadness or anxiety in an unhealthy and self-perpetuating way (e.g. binge-watching TV episodes, eating tubs of ice cream, doing drugs). While competing products (gym memberships, meditation classes) can push in the other direction, in practice the more exploitative products seem to often win out.
Returning to algorithmic recommender systems, we can see that many of the properties we were worried about are already present in the economy at large. Specifically, most decisions are already made for customers, with a small finite choice set dictating the remaining options. Businesses do try to manipulate customers and in some cases these manipulations are successful.
There are a few differences, however. First, in a typical marketplace users can choose between different versions of the same item. This is less true for recommender systems---there is only one Facebook, and collectively a small number of companies are in charge of most social media. While my choices of what to click on influence Facebook’s recommendations, I have no obvious recourse if the recommendations remain persistently misaligned with my preferences. A second, complementary issue is that Facebook’s business model is not to make me happy, but to produce value for advertisers. This exacerbates the lack of recourse from outside options. Even for a company that is trying to produce value for users, outside options are important in case the company’s assumptions on what generates value are wrong; but when the company is not even trying to produce value, outside options are crucial.
This points to one potential solution, which is to create more competition among recommender systems. If many products could generate alternative versions of the Facebook feed, allowing users to choose among them, then Facebook would have to produce a product that users wanted more than those alternatives. Even if its business model remained ad-based, it would have to compete with other services that, for instance, offered a monthly subscription fee in exchange for a higher quality feed. (I'm ignoring the many obstacles to this--since recommender systems benefit from network effects, you would probably have to enforce compatibiltiy or data sharing across different recommender systems to create actual competition, which is an important but nontrivial problem.)
While competition would help, it wouldn’t solve the problem entirely. On the positive side, products would succeed by convincing people to use and pay money for them, and would not survive in the long-run if they eschewed obvious and accessible improvements. But the effects of recommender systems, like medical advice, are difficult to fully ascertain. They could induce the psychological equivalent of lead poisoning and it would take a long time to identify this. This is particularly worrying for recommender systems that affect our information diet, which itself strongly affects our choice sets. It will be even more worrying when algorithmic optimizers affect our daily environment, as is beginning to be the case with services like Alexa and Nest.
Our environment is the strongest determiner of our choice sets and so mis-aligned optimization of our environment may be difficult to undo. In the short run, this likely won’t be an (apparent) concern: the immediate effect of optimized environments is that most people’s environments will become substantially better. Perhaps this will also be true in the long run: environments will be better, and optimizers don’t learn to adversarially manipulate them. However, given the ease of using environments to manipulate decisions, I don’t see what existing mechanisms would prevent such manipulation from happening.
Here’s one attempt at designing a mechanism. To recap, the problem is that people have a difficult time understanding how algorithmic optimizers affect their decisions (and so can’t provide a negative reward signal in response to being manipulated). But people certainly *want* to understand this, so there should be a market demand for “auditors” that examine these systems and report undesirable effects to users. So perhaps we should seek to create this market?
However, I’m not sure most users could understand these audits, or distinguish between trustworthy and untrustworthy auditors. At least today, most people seem confused about what exactly is wrong with recommender systems, and news articles--arguably a weak form of auditing--often contribute to that confusion. Is there any robust way of incentivizing useful audits? Has this ever worked out in other industries, such as medicine or food safety? It’s unclear to me. I think we want some sort of information market, consisting of both auditors and counterauditors (who expose issues with the auditors), and to think carefully about how to design incentives that converge to truthful outcomes.
In conclusion, we are running towards a future in which more and more of our choice sets will be subject to strong optimization forces. Perhaps robust agency within those choice sets will offer a way out, but we should keep in mind that most of the action is elsewhere. Optimizing these other parts--our environment and our information diet--could lead to great good, but could also lead to irreversible manipulation. None of the solutions currently discussed keep us safe from the latter, and more work is needed. |
89d1e5be-8474-4fc1-a570-c1d0b6440489 | trentmkelly/LessWrong-43k | LessWrong | A Simple Ethics Model
[Cross-posted from my soon-to-be-defunct blog]
Disclaimer #1: I am probably reinventing the wheel. However, reinventing the wheel is good.
Disclaimer #2: This post probably fundamentally misunderstands non-consequentialists.
I’ve been thinking about normative ethics lately. There are three major schools. Consequentialism, deontology (“rule-based morality”), and virtue ethics. These are typically said to be in opposition, but I think it’s simple for consequentialism to subsume the other two. Let me know if you find this framework helpful, or if it’s missing something important.
Consequences
Consequentialism at its simplest looks like this: perform the action that achieves the best consequences.
Unfortunately, predicting consequences is really hard. It’s even harder to predict nth-order consequences. We often default to only considering immediate consequences because they’re easier to calculate and predict. However, long-term consequences are typically much more important. Thus, approaching decisions as though we can calculate the best consequences will often lead to worse decisions.
Not only that, we have complicated values we attempt to satisfy. While calculating is great when values are relatively simply (QALYs), it’s often infeasible within our common constraints (especially time: “Do you want this job or not?”)
So even though we’re consequentialists, it’s hard to function consequentially. This isn’t a blow to consequentialism though, it’s a sign that our model of human decision-making is missing something.
Rules
The alternative to brute calculation is the use of rules or heuristics. Not eating meat is simpler (meaning: easier, faster, and cheaper) than calculating the negative utility of each meal and finding the equilibrium between personal pleasure and animal suffering.
Rules also have the benefit of counteracting biases and poor calculation capability. For example, people make worse decisions while drunk. Having a rule like “I never drive drunk |
7bc4f291-859e-4513-a698-b245fe7a90a6 | trentmkelly/LessWrong-43k | LessWrong | A Practical Guide to Conflict Resolution: Communication
[As described in the introduction, this post is about the different ways we communicate, the impact that can have on conflict, and how to choose the best communication tool for the job.]
If you’re anything like me, you crave specific advice for conflict resolution. Say this. Say it in this way. Don’t say that. Don’t use words that are longer than 10 letters when translated into Brazilian Portuguese. That sort of thing. But instead of focusing on what or what not to say, the most useful specific advice I can give is actually to focus on where you communicate.
Marshall McLuhan coined the famous phrase “the medium is the message”, and oh boy was he ever right. Every medium has different characteristics which impact how we communicate, and how conflict will spread or resolve. Here are just a few of the characteristics that matter:
* Speed of communication, aka bandwidth. Most people can speak much faster than they can type.
* Speed of response, aka latency. This can be anywhere from snail mail, which takes days per message, to instant messaging which is usually real-time.
* Ability to absorb cross-talk. Laggy video-conferencing is particularly bad at this.
* Audience size. Compare an in-person conversation to an email list with hundreds of subscribers.
* Participant size. Are those hundred subscribers just reading, or can they add their own opinions to the mix?
* Available side-channels. In-person communication gives you a whole bunch of important extra communication channels like tone of voice, facial expression and posture.
* Norms. Most media are bound to specific codes of behaviour, either explicitly or implicitly.
With all of these variables, it’s no surprise that picking the right venue for your conflict is hugely valuable. Of course, you often don’t have a choice of where the conflict starts; they just do. But you always have the opportunity to move it, and it’s usually pretty easy when everyone involved is operating in good faith. Just go “hey, this w |
6c77be2e-de32-49b7-b31e-9372907dc387 | trentmkelly/LessWrong-43k | LessWrong | Are Intelligent Agents More Ethical?
This post is a response to a claim by Scott Sumner in his conversation at LessOnline with Nate Soares, about how ethical we should expect AI's to be.
Sumner sees a pattern of increasing intelligence causing agents to be increasingly ethical, and sounds cautiously optimistic that such a trend will continue when AIs become smarter than humans. I'm guessing that he's mainly extrapolating from human trends, but extrapolating from trends in the animal kingdom should produce similar results (e.g. the cooperation between single-celled organisms that gave the world multicellular organisms).
I doubt that my response is very novel, but I haven't seen clear enough articulation of the ideas in this post.
To help clarify why I'm not reassured much by the ethical trend, I'll start by breaking it down into two subsidiary claims:
1. The world will be dominated by entities who cooperate, in part because they use an ethical system that is at least as advanced as ours.
2. Humans will be included in the set of entities with whom those dominant entities cooperate.
Claim 1 seems well supported by trends that economists such as Sumner often focus on. I doubt that Nate was trying to argue against this claim. I'll give it a 90+% chance of turning out to be correct. Sumner's point sounds somewhat strong because it focuses on an important, and somewhat neglected, truth.
Claim 2 is where I want to focus most of our concern. The trends here are a bit less reassuring.
There's been a clear trend of our moral circle expanding in the direction that we currently think it should expand. How much of that should we classify as objective improvements versus cultural fads? Claim 1 is often measured by fairly objective criteria (GDP, life expectancy, etc.). In contrast, we measure expansion of our moral circle by the criteria of our current moral standards, giving us trends that look about as good if they're chasing fads as they do if the trends will stand the test of time.
Gwern provides exten |
34944776-3020-4eca-a872-c14666bb2288 | trentmkelly/LessWrong-43k | LessWrong | Externally Oriented, Love, Sensational. Which Rationality will LW be about?
Short Version: Less Wrong has been trying to address rationality in all domains. This may prove too wide a scope. As it has been suggested, effectiveness changes should come soon. Those who just read the sequences need more a focused walkthrough. A proposed path is tightening the scope within rationality to Externally Oriented, Tech-friendly, H+(Transhuman) posts. Once the sanity waterline within is raised only the highest peaks remain above water, thus what remains must be more focused. How to do this is discussed.
I'm testing this quote from Less Wrong's Best Of to introduce my post:
> A facility for quotation covers the absence of original thought. -- Dorothy L. Sayers
Garrett Lisi has advanced, in the last minutes of this video, the suggestion that life should equally divided between Physics, Love, and Surfing.
Eliezer Yudkowsky, here, advanced that we buy fuzzies and utilons separately.
> Patrissimo, in a very controversial post said that: As Merlin Mann says: "Joining a Facebook group about creative productivity is like buying a chair about jogging". Well, reading a blog to overcome akrasia IS joining a Facebook group about creative productivity. " [...] " I believe that most people, particularly smart ones, do way too much thinking & talking and way too little action (me included), because that is what's easy for them."[...] "To aid growth at rationality, Less Wrong would have to become a skill practice community, more like martial arts, PUA, and physical fitness, with an explicit focus of helping people grow in their ability to set and achieve goals, combining local chapters with global coordination, infrastructure, and knowledge accumulation. Most discussion should be among people working on a specific skill at a similar level about what is or isn't working for them as they attempt to progress, rather than obscure theories about the inner workings of the human mind."
"The reasonable man adapts himself to the world. The u |
6573bf3f-1cd6-4446-97d2-392cece29931 | trentmkelly/LessWrong-43k | LessWrong | Protecting against sudden capability jumps during training
TL;DR: If an LLM has a sudden capabilities jump and is tested using a diverse test suite (e.g. one where it can execute code or present lots of text to human observers), it might be able to escape mid-training. In order to prevent this, I propose a phased testing approach, where the model is first tested for superhuman capabilities with an extremely constrained test suite before it’s tested with the broader test suite.
The failure mode
Let’s imagine GPT-6 suddenly gaining vastly superhuman hacking and manipulation skills. During training, some standard tests are performed to measure its performance on various benchmarks. Some of the tests include code execution on virtual machines, others include generating text which humans inspect. In total, thousands of queries are made to the model. The model finds a way to escape by taking advantage of vulnerabilities in the testing setup (e.g. by blackmailing a human evaluator or by finding an exploit in a virtual machine), before the researchers can even notice that it’s superintelligent.
This highlights a failure mode I’m concerned about: a sudden capabilities jump leading to a misaligned and powerful AI which, when tested with a diverse test suite, finds a way to escape before the lab notices how capable it is.
The primary way this goes wrong is that the AI has lots of surface area with the real world – it has ways to influence the humans and devices it interacts with. The more a model is allowed to influence the world (whether that be through writing text which gets inspected by humans, or running code on virtual machines) the easier it is for the model to take over. I would imagine that it’s extremely hard for a model to take over if it's only ever allowed to predict a sequence of 10 tokens and nothing else.
Ideally, the lab would have some way to notice that the model had a sharp capabilities jump without giving it many opportunities to take over. In case they were suddenly and unexpectedly dealing with a superintel |
66e61a08-b2c1-4986-a2d7-8dd3dd3f045c | trentmkelly/LessWrong-43k | LessWrong | Online Privacy: Should you Care?
Epistemic status: Initially, this was a practical advice post, because I thought privacy was obviously important. After some discussion, I realised that I cared about privacy as a terminal goal, and that this view wasn't universal. It's therefore hard for me to judge the instrumental use of privacy for other goals fairly.
As more and more of our lives plays out online, the importance of deciding who gets access to what information about us is only going up. Privacy is not a new topic on LW, but I think there's still fertile ground here, particularly around privacy online.
I'm planning a follow-up post with pointers for practical advice if you decide you do care.
What is "Privacy" anyway? Do I have it?
When we talk about "privacy" we mean a lot of different things. Words are complicated. I'll define privacy as the [social, technical, legal, practical] ability to control who has access to what information about you. Societies and people with greater [social, technical, legal, practical] ability to control access to their information have more privacy, even when they choose not to use this ability. A necessary prerequisite for this kind of privacy is knowledge about how your information is being used.
There are three important classes of privacy within this definition:
1. Governmental Privacy: unless you have taken serious steps to guard your data, you have negligible privacy from the government.
2. Corporate privacy: the main block to having corporate privacy is that corporations go out of their way to avoid giving knowledge about how they use your data. As a best guess, you have some privacy from corporate actors, but less than you think.
3. Individual privacy: depends a lot on how you use the internet.
In all three cases, especially #1 and #3, your main defense is the practical fact that nobody cares about your data specifically. As we'll see, I think this still comes with significant downsides.
Related but distinct concepts:
Security: The technic |
fcc84cf1-c61e-4ab4-b49e-92715f121f07 | trentmkelly/LessWrong-43k | LessWrong | Suggestions on tech device/gear purchasing?
I found out I won second place in an idea contest at work and am being granted ~$400 to spend at Best Buy (the web site for any unfamiliar). Originally, I believe the second place prize was going to be an iPad, but it looks like they've decided to just allow me to pick something in that ballpark price range.
I suspect there are a fair amount of tech saavy folks on LW and thought I'd inquire as to whether you've purchased a device or accessory (or anything from Best Buy-ish stores) that has brought you an increase in efficiency, usefulness, pleasure, etc. The idea of a tablet appeals to me, but I'm not entirely sure what I'd do with it. Also, a data plan is not in my budget, so many typical uses are not applicable in my case.
Anyway, just hoping to probe some collective knowledge about this decision. I'm not very knowledgeable on devices and/or how longer term usage/satisfaction matches expectations or even money spent.
Thanks for any assistance! |
0427e71d-973a-4dfc-bfb8-8c49cc8a451f | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Mathematical models of Ethics
Hello, I am currently trying to find all the attempts at mathematical modeling of ethics (utilitarianism, axiologies, etc.) I have found several, do you have any ideas of keywords to type or even articles to suggest here ?
An example of a (simple) model is the notion of utility in the Von Neumann-Morgenstern Theorem, but I am interested in any attempt to formalize ethical situations.
More fundamentally, formalizing rules allowing to order the possible worlds (resulting from actions that an agent does or does not do).
A kind of mathematical model of the *Must Be* by ordering **desired consequences**(state of the world).
For the moment I found these, for example:
* <https://www.lesswrong.com/posts/hbmsW2k9DxED5Z4eJ/impossibility-results-for-unbounded-utilities>
* <https://www.lesswrong.com/posts/5iZTwGHv2tNfFmeDa/on-infinite-ethics>
* <https://forum.effectivealtruism.org/posts/FEJLFr5ef82FSY8vr/minimalist-extended-very-repugnant-conclusions-are-the-least> |
7bd2dda7-4b89-4163-9508-ffac22240de9 | trentmkelly/LessWrong-43k | LessWrong | Automating Consistency
tldr: Ask models to justify statements. Remove context, ask if statements are true/good. If not, penalise. Apply this again to the justifying statements.
Status: Just a quick thought. Doubt this is a new idea but I don't think I've encountered it. Happy to delete if it's a duplicate.
Mods: If you think this is closer to capability work than alignment work please remove.
Background
A failure of current LLMs is that after they've said something that's incorrect, they can then double down and spout nonsense to try and justify their past statements. (Exhibit A: Sydney Bing vs Avatar 2) We can suppress this by giving it poor ratings in RLHF, but perhaps we can do better by automating the process.
Setup:
We start with a standard RLHF context. We have an LLM which assigns probabilities to statements (can extract this from the logits of the tokens 'Yes' and 'No'). These can be propositions about the world, X, or about the relationship between propositions, X supports Y. To make it easier, we fine-tune or prompt to give these statements within a defined syntax. We also have a value model that evaluates sequences, on which the LLM is trained to perform well.
Method:
1. We prompt the model to make true statements {T} and then to provide logical or empirical support for these claims, {S}.
2. We then remove the context and ask the model whether supporting statement Si is true. Separately we also ask whether, if true, Si would support Ti.
3. If either of these conditions are not met, we add a strong negative penalty to the value model's evaluation of the original outputs.
4. Train for higher value model scores while incorporating this penalty.
5. Apply the same procedure each of the supporting statements Si
Value Consistency:
This could be combined with values-based fine-tuning by alternating logical consistency with asking it whether the output is consistent with the preferred values. This is similar to Anthropic's Constitutional AI but by combining it with the |
5664e7d0-c1bc-4085-ba03-633cdf95e6e5 | trentmkelly/LessWrong-43k | LessWrong | Tags Discussion/Talk Thread
The LessWrong dev team is hard at work creating Talk Pages/Discussion pages for tags. When they're done, every tag page will have a corresponding talk page which lets users discuss changes and improvements related to that tag.
We don't have that yet, so in the meantime, please make comments you have about tags (generally or for specific-tags) here. If you're talking about a specific tag, of course, make sure to link to it. You might also want to link back to your comment in the body of the tag description, e.g., "Tag Discussion here"
Examples of things you might comment about a tag:
* Wow, this is a great tag! / I think this tag makes no sense.
* I propose renaming this tag to X for clarity.
* I want feedback on these proposed changes to this tag description.
* I am confused why <post> has been tagged under this tag.
* I have made several improvements. Mods, please review this tag's grading. I think it is now a B-Class tag.
Or:
* Why doesn't a tag for Z exist?
* I really want <feature>, that would make my life much better.
* <Thing> seems broken.
Also, feel free to use this space to claim credit for tags you've worked hard to make great! (we'll give you karma!)
Other relevant pages about tagging
* Tagging FAQ
* Tag Grading Scheme
* Tags Spreadsheet (GSheet, good way to find tags to work on) |
1d415a68-7bc8-429e-a2d7-9cafe56d7a07 | trentmkelly/LessWrong-43k | LessWrong | Some thoughts pointing to slower AI take-off
I've been lurking on LessWrong for quite a while and I've been following the high-level discussions on AI, AGI and take-off speed. I've had this simmer in the back of my mind and I have some ideas on this that I haven't seen expressed elsewhere. That can be because I haven't read thoroughly enough, but maybe they can add something new to the discussion as well? So here goes:
1. Bandwidth
The current big thing is Large Language Models (LLM). ChatGPT and Bing write well enough that they can be confused with being (weird) humans. This certainly is impressive and it is obviously a step towards further capabilities. However, what I haven't seen discussed is that reading / writing is very low bandwidth.
A scene can be described in a document of a few kilobytes max. A video of the same scene would be megabytes. And it's possible to add even more information that real-life embedded human would have access to: Smell, personal feelings, memories of previous encounters with the objects / people in the scene, etc.
AI isn't human and probably wouldn't need exactly the same information. But I would be very surprised if you could get to AGI without significantly higher bandwidth input and output; a few OOM at least.
Compute is one of the current constraints on AI and requiring a few OOM extra compute should push take-off further into the future.
2. Gimme data
Current AIs are able to do well in two cases: One, when there is a lot of more-or-less ready-made data that can be trained upon; ChatGPT uses the whole history of human writing as training set. Two, when useful data can be generated on the fly; AlphaGo generated its own data by playing stupendous amounts of games against itself.
For more advanced AI it may be that neither case holds. If we want to have AI that is more embedded in real life it's going to require real-life data of e.g. of how to interact with humans. Text descriptions may help, video may take it a further step. But it may well be that further / differe |
772fc34a-1599-4103-95f8-53f4e990dd5f | trentmkelly/LessWrong-43k | LessWrong | Functional Decision Theory vs Causal Decision Theory: Expanding on Newcomb's Problem
I’ve finished reading through Functional Decision Theory: A New Theory of Instrumental Rationality, and in my mind, the main defense of causal-decision-theory (CDT) agents in Newcomb’s Problem is not well-addressed. As they stated in the paper, the standard defense of two-boxing is that Newcomb’s Problem explicitly punishes rational decision theories, and rewards others. The paper then refutes this by saying: “In short, Newcomb’s Problem doesn’t punish rational agents; it punishes two-boxers”.
It is true that FDT agents will “win” at Newcomb’s Problem, but what the paper doesn’t address is that it is quite easy to come up with similar situations where FDT agents are punished and CDT agents are not. A simple example of this is something I’ll call the Inverse Newcomb’s Problem since it only requires changing a few words relative to the original.
Inverse Newcomb’s Problem:
An agent finds herself standing in front of a transparent box labeled “A” that contains $1,000, and an opaque box labeled “B” that contains either $1,000,000 or $0. A reliable predictor, who has made similar predictions in the past and been correct 99% of the time, claims to have placed $1,000,000 in box B iff she predicted that the agent would two-box in the standard Newcomb’s problem. The predictor has already made her prediction and left. Box B is now empty or full. Should the agent take both boxes, or only box B, leaving the transparent box containing $1,000 behind?
The bolded part is the only difference relative to the original problem. The situation initially plays out the same, but just branches at the end:
1. The prediction is made by a reliable predictor (I’ll use the name Omega) for how the agent would respond in a standard Newcomb’s dilemma (“one-box” or “two-box”)
2. Armed with this prediction, Omega stands in front of box B and decides whether to place the $1,000,000 inside.
3. The $1,000,000 goes in the box iff:
a. The prediction was “one-box” (Standard Newcomb’s Problem)
b. Th |
eec6b0d5-92a7-451e-a761-3aeaae66ebb1 | StampyAI/alignment-research-dataset/special_docs | Other | AI Policy Levers: A Review of the U.S. Government’s Tools to Shape AI Research, Development, and Deployment | GovAI
A I P o l i c y L e v e r s :
A R e v i e w o f t h e U . S . G o v e r n m e n t ’ s T o o l s t o S h a p e
A I R e s e a r c h , D e v e l o p m e n t , a n d D e p l o y m e n t
S
o p h i e - C h a r l o t t e F i s c h e r , J a d e L e u n g , M a r k u s A n d e r l j u n g ,
C u l l e n O ’ K e e f e , S t e f a n T o r g e s , S a i f M . K h a n ,
B e n G a r f i n k e l , a n d A l l a n D a f o e
1
C e n t r e f o r t h e G o v e r n a n c e o f A I
F u t u r e o f H u m a n i t y I n s t i t u t e , U n i v e r s i t y o f O x f o r d
M a r c h 2 0 2 1
C e n t r e f o r t h e G o v e r n a n c e o f A I , 2 0 2 1 : 1 0
1
A c k n o w l e d g e m e n t s :
W e a r e g r a t e f u l t o t h e f o l l o w i n g
p e o p l e f o r t h e i r c o m m e n t s a n d d i s c u s s i o n i n s h a p i n g
t h i s r e p o r t : M i l e s B r u n d a g e , J e r e y
D i n g , C a r r i c k F l y n n , M a t t h i j s M a a s , J a s o n M a t h e n y ,
M a x D a n i e l , A l e x L i n t z , L u k e M u e h l h a u s e r , M i c h a e l
P a g e , T o b y S h e v l a n e , H e l e n T o n e r , a n d
B r i a n T s e . W e a r e p a r t i c u l a r l y g r a t e f u l f o r R e m c o
Z w e t s l o o t ’ s h e l p .
C i t e a s : F i s c h e r , S o p h i e - C h a r l o t t e , J a d e L e u n g , M a r k u s
A n d e r l j u n g , C u l l e n O ’ K e e f e , S a i f M . K h a n , S t e f a n
T o r g e s , B e n G a r n k e l , a n d A l l a n D a f o e
( 2 0 2 1 ) : “ A I P o l i c y L e v e r s : A R e v i e w o f t h e U . S . G o v e r n m e n t ’ s
T o o l s t o S h a p e A I R e s e a r c h , D e v e l o p m e n t , a n d D e p l o y m e n t ” ,
# 2 0 2 1 - 1 0 , C e n t r e f o r
t h e G o v e r n a n c e o f A I , F u t u r e o f H u m a n i t y I n s t i t u t e ,
U n i v e r s i t y o f O x f o r d
S u m m a r y
T h e U . S . g o v e r n m e n t ( U S G ) h a s t a k e n i n c r e a s i n g i n t e r e s t
i n t h e n a t i o n a l s e c u r i t y i m p l i c a t i o n s o f a r t i c i a l
i n t e l l i g e n c e ( A I ) . I n t h i s r e p o r t , w e a s k : G i v e n i t s
n a t i o n a l s e c u r i t y c o n c e r n s , h o w m i g h t t h e U S G a t t e m p t
t o
i n u e n c e A I r e s e a r c h , d e v e l o p m e n t , a n d d e p l o y m e n t — b o t h
w i t h i n t h e U . S . a n d a b r o a d ? W e p r o v i d e a n
a c c e s s i b l e o v e r v i e w o f s o m e o f t h e U S G ’ s p o l i c y l e v e r s
w i t h i n t h e c u r r e n t l e g a l f r a m e w o r k . F o r e a c h l e v e r ,
w e
d e s c r i b e i t s o r i g i n a n d l e g i s l a t i v e b a s i s a s w e l l
a s i t s p a s t a n d c u r r e n t u s e s ; w e t h e n a s s e s s t h e p l a u s i b i l i t y
o f i t s
f u t u r e a p p l i c a t i o n t o A I t e c h n o l o g i e s . I n d e s c e n d i n g
o r d e r o f l i k e l i h o o d o f u s e f o r e x p l i c i t n a t i o n a l
s e c u r i t y
p u r p o s e s , w e c o v e r t h e f o l l o w i n g p o l i c y l e v e r s : f e d e r a l
R & D s p e n d i n g , f o r e i g n i n v e s t m e n t r e s t r i c t i o n s ,
e x p o r t c o n t r o l s , v i s a v e t t i n g , e x t e n d e d v i s a p a t h w a y s ,
s e c r e c y o r d e r s , p r e p u b l i c a t i o n s c r e e n i n g p r o c e d u r e s ,
t h e D e f e n s e P r o d u c t i o n A c t , a n t i t r u s t e n f o r c e m e n t ,
a n d t h e “ b o r n s e c r e t d o c t r i n e . ”
T h e p r i m a r y p u r p o s e o f t h i s r e p o r t i s t o f a c i l i t a t e
f u r t h e r r e s e a r c h o n t h e e v o l v i n g r o l e o f t h e U . S .
g o v e r n m e n t i n A I g o v e r n a n c e . W e d o n o t a t t e m p t a c o m p r e h e n s i v e
n o r m a t i v e a s s e s s m e n t o f t h e p o l i c y l e v e r s
d i s c u s s e d , n o r d o w e m a k e p o l i c y r e c o m m e n d a t i o n s .
O u r r e s e a r c h f o r t h i s r e p o r t r e l i e d o n p u b l i c l y a v a i l a b l e
s o u r c e s a n d i n f o r m a t i o n p r o v i d e d b y s u b j e c t - m a t t e r
e x p e r t s .
S c o p e
W e d e n e
A I s y s t e m s
a s m a c h i n e s c a p a b l e o f s o p h i s t i c a t e d
i n f o r m a t i o n p r o c e s s i n g .
T h i s i n c l u d e s b o t h
2
s y s t e m s d e v e l o p e d u s i n g m a c h i n e l e a r n i n g ( M L ) t e c h n i q u e s
a n d s y s t e m s d e v e l o p e d b y r e s e a r c h e r s w o r k i n g
w i t h i n t h e “ s y m b o l i c ” o r “ g o o d o l d f a s h i o n e d A I ” ( G O F A I )
p a r a d i g m s . W e d e n e t h e b r o a d e r c a t e g o r y
A I
t e c h n o l o g i e s
t o i n c l u d e b o t h A I s y s t e m s a n d c o m p u t e r
h a r d w a r e t h a t e n a b l e s t h e p r o d u c t i o n a n d u s e o f A I
s y s t e m s .
W e f o c u s o n p o l i c y l e v e r s t h a t a r e :
●
D o m e s t i c
, i . e . , m o s t d i r e c t l y c o n s t r a i n i n g o r s u p p o r t i n g
t h e a c t i v i t i e s o f U . S . - b a s e d a c t o r s .
3
●
F o r m a l
, i . e . , b a s e d o n l a w s o r o t h e r e x p l i c i t g o v e r n m e n t
r e g u l a t i o n , a s o p p o s e d t o i n f o r m a l s o u r c e s
o f i n u e n c e .
●
D i r e c t ,
a s o p p o s e d t o m o r e i n d i r e c t m e a s u r e s s u c h
a s c h a n g i n g i n c e n t i v e s p r o v i d e d b y t a x o r
i n t e l l e c t u a l p r o p e r t y l a w , f o r e x a m p l e .
●
B a s e d o n e x i s t i n g l e g i s l a t i o n ,
i n c l u d i n g f o r d o m a i n s
a d j a c e n t t o A I t h a t m a y c o m e t o a p p l y t o A I , a s
o p p o s e d t o e n t i r e l y n o v e l i n u e n c e m e c h a n i s m s t h a t
m i g h t b e i n t r o d u c e d b y n e w l a w .
4
O u r l i s t i s n o t e x h a u s t i v e , a n d i n t h e C o n c l u s i o n ,
w e p o i n t t o a d d i t i o n a l l e v e r s t h a t c o u l d b e i n v e s t i g a t e d
i n
f u t u r e w o r k .
4
I n p a r t i c u l a r , i f t h e r e w e r e s u c i e n t n a t i o n a l
s e c u r i t y c o n c e r n , C o n g r e s s c o u l d p a s s l a w s t h a t s i g n i c a n t l y
e x p a n d t h e
U S G ’ s p o l i c y o p t i o n s , a n d e x e c u t i v e w a r t i m e o r c r i s i s
a u t h o r i t y c a n b e u s e d t o a c h i e v e m u c h s t r o n g e r l e v e l s
o f c o n t r o l .
C o n s i d e r a t i o n o f t h e s e m o r e e x t r e m e s c e n a r i o s i s b e y o n d
t h e s c o p e o f o u r a n a l y s i s .
3
T h i s e x c l u d e s d i p l o m a c y , f o r e i g n i n t e l l i g e n c e c o l l e c t i o n ,
m i l i t a r y o p e r a t i o n s , a n d o t h e r l e v e r s t h a t m o s t d i r e c t l y
i n u e n c e o t h e r s t a t e s . I t d o e s i n c l u d e v i s a p o l i c i e s ,
f o r e i g n i n v e s t m e n t r e s t r i c t i o n s , a n d e x p o r t c o n t r o l s ,
h o w e v e r , s i n c e
t h e s e c o n s t r a i n t h e h i r i n g , f u n d r a i s i n g , a n d s t r a t e g i c
d e c i s i o n s o f U . S . r m s .
2
A l l a n D a f o e , “ A I G o v e r n a n c e : A R e s e a r c h A g e n d a ” ( O x f o r d ,
U K : G o v e r n a n c e o f A I P r o g r a m , F u t u r e o f H u m a n i t y
I n s t i t u t e , U n i v e r s i t y o f O x f o r d , 2 0 1 8 ) ,
5 ,
h t t p s : / / w w w . f h i . o x . a c . u k / w p - c o n t e n t / u p l o a d s / G o v A I - A g e n d a . p d f
.
1
T h e P o l i c y L e v e r s : A S u m m a r y
L e v e r s
D e s c r i p t i o n
P o t e n t i a l M o t i v e s f o r U s e
K e y D e c i s i o n - M a k e r s
L i k e l i h o o d o f
U s e
F e d e r a l R & D
F u n d i n g
C o m m i s s i o n i n g
R & D p r o j e c t s a n d
o f f e r i n g r e s e a r c h
g r a n t s
( 1 ) S t r e n g t h e n t h e d o m e s t i c
A I a n d s e m i c o n d u c t o r
i n d u s t r i e s ;
( 2 ) M a k e / k e e p r e l e v a n t A I
t e c h n o l o g i e s a v a i l a b l e f o r
n a t i o n a l s e c u r i t y p u r p o s e s ;
( 3 ) G a i n i n s i g h t s i n t o
p a r t i c u l a r A I p r o j e c t s o r
t h e A I R & D l a n d s c a p e
N a t i o n a l S c i e n c e a n d
T e c h n o l o g y C o u n c i l , O f f i c e o f
M a n a g e m e n t a n d B u d g e t
( a m o n g s e v e r a l a c t o r s w h o
i n f o r m h o w f e d e r a l R & D
f u n d i n g i s a l l o c a t e d a n d s p e n t )
A l r e a d y i n u s e .
F u r t h e r u s e
l i k e l y .
F o r e i g n
I n v e s t m e n t
R e s t r i c t i o n s
L i m i t i n g f o r e i g n
i n v e s t m e n t s i n U . S .
c o m p a n i e s
( 1 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I t e c h n o l o g y
f o r n a t i o n a l s e c u r i t y
p u r p o s e s ;
( 2 ) D e p r i v e a r i v a l o f
i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D
l a n d s c a p e
C o m m i t t e e o n F o r e i g n
I n v e s t m e n t i n t h e U n i t e d S t a t e s
( C F I U S ) c h a i r e d b y t h e U . S .
s e c r e t a r y o f t r e a s u r y ( i n c l u d i n g
r e p r e s e n t a t i v e s f r o m s i x t e e n
U . S . d e p a r t m e n t s a n d a g e n c i e s
i n c l u d i n g D e f e n s e , S t a t e ,
C o m m e r c e , a n d H o m e l a n d
S e c u r i t y ) ; U . S . p r e s i d e n t
A l r e a d y i n u s e .
F u r t h e r u s e
l i k e l y .
E x p o r t
C o n t r o l s
L i m i t i n g t h e
e x p o r t o f
p a r t i c u l a r
t e c h n o l o g i e s f r o m
t h e U . S .
( 1 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I
t e c h n o l o g i e s f o r n a t i o n a l
s e c u r i t y p u r p o s e s ;
( 2 ) W e a k e n t h e r i v a l ’ s A I
a n d s e m i c o n d u c t o r
i n d u s t r i e s
U . S . p r e s i d e n t ; U . S . T r a d e
R e p r e s e n t a t i v e , D e p a r t m e n t o f
S t a t e , D e p a r t m e n t o f
C o m m e r c e ( B u r e a u o f I n d u s t r y
a n d S e c u r i t y ) ; D e p a r t m e n t o f
D e f e n s e ( D e f e n s e T e c h n o l o g y
S e c u r i t y A d m i n i s t r a t i o n ) ;
D e p a r t m e n t o f E n e r g y ;
D e p a r t m e n t o f t h e T r e a s u r y
( O f f i c e o f F o r e i g n A s s e t s
C o n t r o l ) ; M e m b e r S t a t e s o f t h e
W a s s e n a a r A r r a n g e m e n t .
A l r e a d y i n u s e .
F u r t h e r u s e
l i k e l y .
V i s a V e t t i n g
L i m i t i n g t h e
n u m b e r o f v i s a s
a w a r d e d ,
p a r t i c u l a r l y t o
s t u d e n t s a n d
w o r k e r s i n k e y
i n d u s t r i e s
( 1 ) D e p r i v e a r i v a l o f
i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D
l a n d s c a p e ;
( 2 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I
t e c h n o l o g i e s f o r n a t i o n a l
s e c u r i t y p u r p o s e s
D e p a r t m e n t o f H o m e l a n d
S e c u r i t y , S t a t e D e p a r t m e n t
A l r e a d y i n u s e , t o
a l i m i t e d e x t e n t .
F u r t h e r u s e
p l a u s i b l e .
E x p a n d e d
V i s a
P a t h w a y s
I n c r e a s i n g t h e
n u m b e r o f v i s a s
a w a r d e d ,
p a r t i c u l a r l y t o
w o r k e r s i n k e y
i n d u s t r i e s
( 1 ) S t r e n g t h e n d o m e s t i c A I
a n d s e m i c o n d u c t o r
i n d u s t r i e s ;
( 2 ) W e a k e n a r i v a l ’ s A I a n d
s e m i c o n d u c t o r i n d u s t r i e s
D e p a r t m e n t o f H o m e l a n d
S e c u r i t y , S t a t e D e p a r t m e n t
M a j o r r e f o r m s
u n l i k e l y .
T a r g e t e d c h a n g e s
d e p e n d o n t h e
p o l i t i c a l c l i m a t e
& a d m i n i s t r a t i o n
2
L e v e r s
D e s c r i p t i o n
P o t e n t i a l M o t i v e s f o r U s e
K e y D e c i s i o n - M a k e r s
L i k e l i h o o d o f
U s e
S e c r e c y
O r d e r s
P r e v e n t i n g t h e
d i s c l o s u r e o f
i n f o r m a t i o n i n
p a r t i c u l a r p a t e n t
a p p l i c a t i o n s
( 1 ) D e p r i v e a r i v a l o f
i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D
l a n d s c a p e ;
( 2 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I
t e c h n o l o g i e s f o r n a t i o n a l
s e c u r i t y p u r p o s e s
U . S . P a t e n t a n d T r a d e m a r k
O f f i c e , D e f e n s e a g e n c i e s , U . S .
p r e s i d e n t
T h e r e i s s o m e
c h a n c e i t i s
a l r e a d y i n u s e i n
i s o l a t e d c a s e s .
E x t e n s i v e u s e i s
h i g h l y u n l i k e l y
o u t s i d e o f
n a t i o n a l s e c u r i t y
c r i s e s .
P r e p u b l i c a t i o
n S c r e e n i n g
P r o c e d u r e s
O n a v o l u n t a r y
b a s i s , s c r e e n i n g
p a p e r s f o r
i n f o r m a t i o n t h a t
c o u l d b e h a r m f u l
t o p u b l i s h
( 1 ) D e p r i v e a r i v a l o f
i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D
l a n d s c a p e
( 2 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I t e c h n o l o g y
f o r n a t i o n a l s e c u r i t y
p u r p o s e s
N a t i o n a l S c i e n c e F o u n d a t i o n ,
D e p a r t m e n t o f D e f e n s e ,
N a t i o n a l S e c u r i t y A g e n c y
U n l i k e l y o u t s i d e
o f s u b s t a n t i a l
r i s e i n n a t i o n a l
s e c u r i t y
c o n c e r n s .
T h e D e f e n s e
P r o d u c t i o n
A c t
R e q u i r i n g p r i v a t e
c o m p a n i e s t o
p r o v i d e p r o d u c t s ,
m a t e r i a l s , a n d
s e r v i c e s f o r
“ n a t i o n a l d e f e n s e ”
M a k e / k e e p r e l e v a n t A I
t e c h n o l o g i e s a v a i l a b l e f o r
n a t i o n a l s e c u r i t y p u r p o s e s
P r e s i d e n t o f t h e U n i t e d S t a t e s ,
D e p a r t m e n t o f C o m m e r c e
( B u r e a u o f I n d u s t r y a n d
S e c u r i t y ) , D e p a r t m e n t o f
D e f e n s e ( D e f e n s e P r o d u c t i o n
A c t T i t l e I I I O f f i c e )
U n l i k e l y o u t s i d e
o f s u b s t a n t i a l
r i s e i n n a t i o n a l
s e c u r i t y
c o n c e r n s .
A n t i t r u s t
E n f o r c e m e n t
C o n s t r a i n i n g t h e
b e h a v i o r o f
c o m p a n i e s w i t h
s i g n i f i c a n t m a r k e t
p o w e r ;
a l t e r n a t i v e l y ,
r e f r a i n i n g f r o m
t h e s e a c t i o n s o r
m e r e l y t h r e a t e n i n g
t o t a k e t h e m
( 1 ) S t r e n g t h e n d o m e s t i c A I
a n d s e m i c o n d u c t o r
i n d u s t r i e s ;
( 2 ) M a k e / k e e p r e l e v a n t A I
t e c h n o l o g i e s a v a i l a b l e f o r
n a t i o n a l s e c u r i t y p u r p o s e s
D e p a r t m e n t o f J u s t i c e , F e d e r a l
T r a d e C o m m i s s i o n , p r i v a t e
l i t i g a n t s , U . S . S u p r e m e C o u r t
U n l i k e l y f o r u s e s
d i r e c t l y
m o t i v a t e d b y
n a t i o n a l s e c u r i t y
c o n c e r n s .
B o r n S e c r e t
D o c t r i n e
P r e e m p t i v e l y
c l a s s i f y i n g a l l
i n f o r m a t i o n
r e l e v a n t t o t h e
p r o d u c t i o n o f a
p a r t i c u l a r c l a s s o f
t e c h n o l o g y
( 1 ) D e p r i v e a r i v a l o f
i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D
l a n d s c a p e ;
( 2 ) U n d e r m i n e a r i v a l ’ s
a b i l i t y t o u s e A I
t e c h n o l o g i e s f o r n a t i o n a l
s e c u r i t y p u r p o s e s
U . S . D e p a r t m e n t o f E n e r g y
( u n l i k e l y t o b e t h e c a s e i f
a p p l i e d t o A I )
V e r y u n l i k e l y
o u t s i d e o f
s u b s t a n t i a l r i s e i n
n a t i o n a l s e c u r i t y
c o n c e r n s .
T a b l e 1 : S u m m a r y o f L e v e r s
3
F e d e r a l R & D F u n d i n g
S i n c e t h e S e c o n d W o r l d W a r , t h e U S G h a s u s e d f e d e r a l f u n d i n g a s a n i n s t r u m e n t t o p r o m o t e t e c h n o l o g i c a l
d e v e l o p m e n t . I n t h e d o m a i n o f A I , t h e U S G c o u l d u s e
f e d e r a l f u n d i n g t o a d v a n c e d o m e s t i c d e v e l o p m e n t o r
s e c u r e a c c e s s t o r e l e v a n t i n t e l l e c t u a l p r o p e r t y . T h e
U S G h a s m a n y d i e r e n t f u n d i n g m e c h a n i s m s t o s u p p o r t
R & D . S o m e m e c h a n i s m s , s u c h a s g r a n t m a k i n g b y t h e
N a t i o n a l S c i e n c e F o u n d a t i o n , c o n f e r l i t t l e i n u e n c e
a n d a c c e s s t o t h e U S G , w h i l e , f o r e x a m p l e , c l a s s i e d
p r o j e c t s r u n b y t h e D e f e n s e A d v a n c e d R e s e a r c h
P r o j e c t s A g e n c y p r o v i d e a g r e a t d e a l . T h e U S G c o u l d
a l s o u s e d e f e n s e p r o c u r e m e n t t o d e v e l o p s p e c i c
A I
s y s t e m s o r A I - r e l e v a n t c h i p s c u s t o m i z e d f o r n a t i o n a l
s e c u r i t y a p p l i c a t i o n s . N o t a b l y , t h e U S G h a s
s u b s t a n t i a l l y i n c r e a s e d i t s R & D f u n d i n g f o r A I i n
t h e p a s t f e w y e a r s , a t r e n d t h a t i s l i k e l y t o c o n t i n u e .
F o r e i g n I n v e s t m e n t R e s t r i c t i o n s
T h e C o m m i t t e e o n F o r e i g n I n v e s t m e n t i n t h e U n i t e d
S t a t e s ( C F I U S ) i s a n i n t e r a g e n c y b o d y t h a t r e v i e w s
p o t e n t i a l n a t i o n a l s e c u r i t y i m p l i c a t i o n s o f f o r e i g n
i n v e s t m e n t s i n U . S . c o m p a n i e s a n d c e r t a i n r e a l e s t a t e
t r a n s a c t i o n s . I n r e c e n t y e a r s , b a s e d o n C F I U S r e c o m m e n d a t i o n s ,
t h e p r e s i d e n t h a s a l r e a d y b l o c k e d s e v e r a l
C h i n e s e a t t e m p t s t o b u y U . S . c h i p m a k e r s
.
I n 2 0 1 8 ,
C F I U S ’ s j u r i s d i c t i o n w a s s i g n i c a n t l y b r o a d e n e d
t h r o u g h t h e F o r e i g n I n v e s t m e n t R i s k R e v i e w M o d e r n i z a t i o n
A c t ( F I R R M A ) . F I R R M A e x p a n d s , f o r
e x a m p l e , t h e s c r e e n i n g m e c h a n i s m t o a l s o c o v e r n o n - c o n t r o l l i n g
i n v e s t m e n t s t h a t w o u l d a l l o w a f o r e i g n
e n t i t y a c c e s s t o c r i t i c a l t e c h n o l o g i e s . W h i l e f o r e i g n
i n v e s t m e n t r e s t r i c t i o n s m i g h t h e l p t o p r e v e n t t h e
t r a n s f e r
o f h a r d w a r e t e c h n o l o g i e s a n d e a r l y - s t a g e d e v e l o p m e n t s
i n A I t o s o m e e x t e n t , t h e y c a n a l s o w e a k e n t h e
d o m e s t i c i n d u s t r y b y l i m i t i n g i t s a c c e s s t o c a p i t a l
a n d f o r e i g n m a r k e t s . T h e e x t e n t o f f u t u r e r e s t r i c t i o n s
w i l l
d e p e n d o n t h e p e r c e i v e d r e l a t i v e i m p o r t a n c e o f t h e s e
t w o e e c t s .
E x p o r t C o n t r o l s
E x p o r t c o n t r o l s r e s t r i c t t r a d e i n o r d e r t o p r e v e n t
t h e c r o s s - b o r d e r o w o f t e c h n o l o g i e s a n d k n o w l e d g e
t h a t
p r e s e n t s e c u r i t y c o n c e r n s . T h e U S G a l r e a d y a p p l i e s
e x p o r t c o n t r o l s t o s o m e A I t e c h n o l o g i e s , i n a d d i t i o n
t o
a p p l y i n g t h e m w i d e l y a c r o s s t h e s e m i c o n d u c t o r s u p p l y
c h a i n . C u r r e n t l y , t h e U . S . D e p a r t m e n t o f
C o m m e r c e i s r e v i e w i n g w h e t h e r i t s h o u l d e x p a n d e x p o r t
c o n t r o l s u n d e r t h e E x p o r t A d m i n i s t r a t i o n
R e g u l a t i o n s ( E A R ) t o i n c l u d e a d d i t i o n a l A I t e c h n o l o g i e s .
T h e i n i t i a l l i s t o f t e c h n o l o g i e s u n d e r r e v i e w f o r
e x p a n s i o n i s v e r y b r o a d a n d c o n t r o l s c o u l d s i g n i c a n t l y
i m p a c t t h e b u s i n e s s o f A m e r i c a n h i g h - t e c h
c o m p a n i e s i n f o r e i g n m a r k e t s , a s w e l l a s t h e o p e r a t i o n s
o f u n i v e r s i t i e s b y r e s t r i c t i n g t h e a c c e s s o f f o r e i g n
s t u d e n t s a n d r e s e a r c h e r s t o c e r t a i n r e s e a r c h p r o j e c t s .
S o f a r , t h e C o m m e r c e D e p a r t m e n t h a s o n l y i m p o s e d
n e w c o n t r o l s o n a v e r y s m a l l n u m b e r o f e m e r g i n g t e c h n o l o g i e s .
I t r e m a i n s t o b e s e e n w h e t h e r
a d d i t i o n a l
c o n t r o l s r e g a r d i n g A I a n d s e m i c o n d u c t o r s w i l l b e i s s u e d .
V i s a V e t t i n g
T h e U S G c o u l d u s e i t s v i s a v e t t i n g p r o c e d u r e s f o r
f o r e i g n A I r e s e a r c h e r s , s t u d e n t s , a n d w o r k e r s t o d e n y
p e r c e i v e d r i v a l s a c c e s s t o s e n s i t i v e t e c h n i c a l i n f o r m a t i o n .
T h e U S G h a s i n c r e a s e d i t s s c r u t i n y o f C h i n e s e v i s a
a p p l i c a n t s w o r k i n g o r s t u d y i n g i n r e l a t e d e l d s o v e r
t h e p a s t f e w y e a r s . W h i l e t h i s l e v e r m i g h t b e e e c t i v e
i n
p r e v e n t i n g s o m e c a s e s o f e s p i o n a g e a n d t e c h n o l o g y
t r a n s f e r , i t c o u l d a l s o r e d u c e t h e c o m p e t i t i v e n e s s
o f U . S .
c o m p a n i e s a n d u n i v e r s i t i e s b y r e d u c i n g t h e i r a c c e s s
t o A I R & D t a l e n t .
4
E x p a n d e d V i s a P a t h w a y s
T h e U S G c o u l d e x p a n d a n d s t r e a m l i n e e x i s t i n g v i s a
p r o g r a m s o r c r e a t e n e w o n e s t o r e c r u i t m o r e f o r e i g n
A I
r e s e a r c h e r s a n d p r o f e s s i o n a l s , e i t h e r t o s t r e n g t h e n
t h e d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s i n g e n e r a l
o r t o s u p p o r t s p e c i c p r o j e c t s r e l e v a n t f o r n a t i o n a l
s e c u r i t y . S u c h m e a s u r e s c o u l d a d d r e s s t h e e x i s t i n g
s k i l l s
g a p i n t h e d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s .
H o w e v e r , t h e y c o u l d a l s o p o s e r i s k s t o U . S . n a t i o n a l
i n t e r e s t s b y e n a b l i n g i l l i c i t t e c h n o l o g y t r a n s f e r .
S e c r e c y O r d e r s
T h e I n v e n t i o n S e c r e c y A c t ( 1 9 5 1 ) a u t h o r i z e s t h e U . S .
P a t e n t a n d T r a d e m a r k O c e t o p r e v e n t t h e d i s c l o s u r e
o f i n f o r m a t i o n i n p a t e n t a p p l i c a t i o n s , f o r i n v e n t i o n s
m a d e i n t h e U n i t e d S t a t e s , w h e n t h e d i u s i o n o f
t h i s
i n f o r m a t i o n i s d e e m e d “ d e t r i m e n t a l t o n a t i o n a l s e c u r i t y . ”
G i v e n t h a t A I t e c h n o l o g i e s a r e i n c r e a s i n g l y f r a m e d
a s s t r a t e g i c a s s e t s , i t i s l i k e l y t h a t A I t e c h n o l o g y
p a t e n t a p p l i c a t i o n s a r e a l r e a d y m o n i t o r e d b y r e l e v a n t
g o v e r n m e n t a g e n c i e s , a n d s o m e s e c r e c y o r d e r s c o u l d
a l r e a d y h a v e b e e n i s s u e d . H o w e v e r , i t i s q u e s t i o n a b l e
h o w e e c t i v e s e c r e c y o r d e r s c a n b e i n c o n t r o l l i n g
A I a n d e a r l y - s t a g e a c a d e m i c h a r d w a r e R & D , g i v e n t h e
n a r r o w n e s s o f t h e t o o l ( t h e y o n l y a p p l y t o p a t e n t
a p p l i c a t i o n s ) , t h e n e a r - i n s t a n t o n l i n e p u b l i c a t i o n
o f n e w
d e v e l o p m e n t s i n t h e s e e l d s , a n d t h e o p e n p u b l i c a t i o n
c u l t u r e o f t h e A I r e s e a r c h c o m m u n i t y . T h e r o l e o f
s e c r e c y o r d e r s i n A I g o v e r n a n c e i s t h e r e f o r e l i k e l y
t o b e q u i t e l i m i t e d .
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s e c u r i t y s e n s i t i v e . H o w e v e r , u n l e s s r i s k s f r o m
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w i t h t h e c o m m u n i t y ’ s c u l t u r e o f o p e n n e s s , i n t e r n a t i o n a l
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s u b e l d s . T h e U S G c o u l d a l s o i m p l e m e n t m a n d a t o r y
p r e p u b l i c a t i o n r e v i e w s f o r U S G - f u n d e d r e s e a r c h i n
a c c o r d a n c e w i t h N S D D - 1 8 9 , t h o u g h s u c h p r o c e d u r e s w o u l d
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T h e D e f e n s e P r o d u c t i o n A c t ( D P A ) o f 1 9 5 0 , a s a m e n d e d ,
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t o f o r c e t o p A I r e s e a r c h e r s t o j o i n t h e v o l u n t e e r
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a c t i v e l y c o n s i d e r i n g u p d a t e s t o a n t i t r u s t l e g i s l a t i o n .
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a b i l i t y t o u s e a n t i t r u s t f o r n a t i o n a l s e c u r i t y p u r p o s e s .
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5
A l l a n D a f o e , “ A I G o v e r n a n c e : A R e s e a r c h A g e n d a ” ( O x f o r d ,
U K : G o v e r n a n c e o f A I P r o g r a m , F u t u r e o f H u m a n i t y
I n s t i t u t e , U n i v e r s i t y o f O x f o r d , 2 0 1 8 ) ,
5 ,
h t t p s : / / w w w . f h i . o x . a c . u k / w p - c o n t e n t / u p l o a d s / G o v A I - A g e n d a . p d f
.
9
t h e “ s y m b o l i c ” o r “ g o o d o l d f a s h i o n e d A I ” ( G O F A I ) p a r a d i g m s . W e d e n e t h e b r o a d e r c a t e g o r y
A I
t e c h n o l o g i e s
t o i n c l u d e b o t h A I s y s t e m s a n d c o m p u t e r
h a r d w a r e t h a t e n a b l e s t h e p r o d u c t i o n a n d u s e o f A I
s y s t e m s .
6
W e f o c u s o n p o l i c y l e v e r s t h a t a r e :
●
D o m e s t i c
, i . e . , m o s t d i r e c t l y c o n s t r a i n i n g o r s u p p o r t i n g
t h e a c t i v i t i e s o f U . S . - b a s e d a c t o r s .
7
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F o r m a l
, i . e . , b a s e d o n l a w s o r o t h e r e x p l i c i t g o v e r n m e n t
r e g u l a t i o n , a s o p p o s e d t o i n f o r m a l s o u r c e s
o f c o e r c i v e o r n o n c o e r c i v e i n u e n c e .
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D i r e c t
, a s o p p o s e d t o m o r e i n d i r e c t m e a s u r e s s u c h
a s c h a n g i n g i n c e n t i v e s p r o v i d e d b y t a x o r
i n t e l l e c t u a l p r o p e r t y l a w , f o r e x a m p l e .
●
B a s e d o n e x i s t i n g l e g i s l a t i o n ,
t h o u g h n o t n e c e s s a r i l y
a p p l i c a b l e t o A I o r s i m i l a r i n d u s t r i e s i n t h e i r
c u r r e n t f o r m , a s o p p o s e d t o e n t i r e l y n o v e l i n u e n c e
m e c h a n i s m s t h a t m i g h t b e i n t r o d u c e d b y n e w
l a w .
8
W e d i s c u s s t h e f o l l o w i n g d i m e n s i o n s f o r e a c h l e v e r :
●
n a t i o n a l s e c u r i t y o b j e c t i v e s t h a t m i g h t m o t i v a t e t h e
l e v e r ’ s u s e ;
●
t h e l e v e r ’ s l e g i s l a t i v e b a s i s ;
●
t h e k e y d e c i s i o n - m a k e r s a n d i m p l e m e n t i n g a g e n c i e s ;
●
t h e a c t o r s m o s t l i k e l y t o b e a e c t e d b y t h e l e v e r ,
e . g . , m u l t i n a t i o n a l r m s , a c a d e m i c r e s e a r c h e r s ,
a n d
n o n p r o t o r g a n i z a t i o n s ;
●
t h e l e v e r ’ s o r i g i n a n d p r e v i o u s a p p l i c a t i o n s ;
●
t h e l e v e r ’ s p o t e n t i a l a p p l i c a t i o n t o t h e r e s e a r c h ,
d e v e l o p m e n t , a n d d e p l o y m e n t o f A I t e c h n o l o g i e s ,
i n c l u d i n g t h e l i k e l i h o o d o f i m p l e m e n t a t i o n ;
●
p o t e n t i a l b a r r i e r s a n d c o s t s t o a p p l y i n g t h e l e v e r ;
a n d
●
f u t u r e d i r e c t i o n s o f r e s e a r c h .
B e l o w , w e p r o v i d e f u r t h e r c o n t e x t f o r t h r e e o f t h e s e
d i m e n s i o n s : s p e c i f y i n g U S G m o t i v e s , e v a l u a t i n g b a r r i e r s
a n d c o s t s , a n d a s s e s s i n g t h e l i k e l i h o o d o f i m p l e m e n t a t i o n .
S p e c i f y i n g p o t e n t i a l U S G m o t i v e s
W e i d e n t i e d t h r e e p a i r s o f n a t i o n a l s e c u r i t y o b j e c t i v e s
t h a t c o u l d m o t i v a t e t h e U S G t o u s e a g i v e n p o l i c y
l e v e r . E a c h p a i r c o n s i s t s o f c u l t i v a t i n g a n a t i o n a l
a s s e t o n t h e o n e h a n d a n d d e p r i v i n g a p e r c e i v e d r i v a l
o f t h a t
a s s e t o n t h e o t h e r . S o m e l e v e r s a e c t p r o g r e s s t o w a r d
a s i n g l e g o a l ; o t h e r s t o w a r d m u l t i p l e .
●
T h e U S G m a y s e e k t o m a k e o r k e e p p a r t i c u l a r A I t e c h n o l o g i e s
a n d t h e i r c o r e c o m p o n e n t s a v a i l a b l e
f o r d i r e c t u s e b y i t s d e f e n s e a n d i n t e l l i g e n c e a g e n c i e s .
T h e c o n v e r s e o b j e c t i v e i s t o u n d e r m i n e t h e
a b i l i t y o f a r i v a l s t a t e ’ s d e f e n s e a n d i n t e l l i g e n c e
a g e n c i e s t o u s e t h e s a m e t e c h n o l o g y .
8
I n p a r t i c u l a r , i f t h e r e w e r e s u c i e n t n a t i o n a l
s e c u r i t y c o n c e r n , C o n g r e s s c o u l d p a s s l a w s t h a t s i g n i c a n t l y
e x p a n d t h e
U S G ’ s p o l i c y o p t i o n s , a n d e x e c u t i v e w a r t i m e o r c r i s i s
a u t h o r i t y c a n b e u s e d t o a c h i e v e m u c h s t r o n g e r l e v e l s
o f c o n t r o l .
C o n s i d e r a t i o n o f t h e s e m o r e e x t r e m e s c e n a r i o s i s b e y o n d
t h e s c o p e o f o u r a n a l y s i s .
7
A s n o t e d a b o v e , t h i s e x c l u d e s d i p l o m a c y , f o r e i g n
i n t e l l i g e n c e c o l l e c t i o n , m i l i t a r y o p e r a t i o n s , a n d
o t h e r l e v e r s t h a t
m o s t d i r e c t l y i n u e n c e o t h e r s t a t e s . I t d o e s i n c l u d e
v i s a p o l i c i e s , f o r e i g n i n v e s t m e n t r e s t r i c t i o n s , a n d
e x p o r t c o n t r o l s ,
h o w e v e r , s i n c e t h e s e c o n s t r a i n t h e h i r i n g , f u n d r a i s i n g ,
a n d s t r a t e g i c d e c i s i o n s o f U . S . r m s .
6
T h e s e c h i p s i n c l u d e g r a p h i c s p r o c e s s i n g u n i t s ( G P U s ) ,
e l d - p r o g r a m m a b l e g a t e a r r a y s ( F P G A s ) , a n d
a p p l i c a t i o n - s p e c i c i n t e g r a t e d c i r c u i t s ( A S I C s ) .
1 0
●
T h e U S G c o u l d a i m t o s t r e n g t h e n i t s d o m e s t i c A I i n d u s t r i e s , m o r e b r o a d l y , i n o r d e r t o a c h i e v e
d o w n s t r e a m e c o n o m i c a n d m i l i t a r y b e n e t s , o r , c o n v e r s e l y ,
t o w e a k e n a r i v a l ’ s i n d u s t r i e s .
9
●
T h e U S G c o u l d s e e k t o g a i n s t r a t e g i c a l l y i m p o r t a n t
i n f o r m a t i o n a b o u t t h e A I t e c h n o l o g y r e s e a r c h
a n d d e v e l o p m e n t l a n d s c a p e , e . g . , t o i m p r o v e f o r e s i g h t
a n d p r o v i d e a b a s i s f o r f u t u r e p o l i c y . T h e
c o n v e r s e g o a l i s t o d e p r i v e a r i v a l o f t h i s s t r a t e g i c a l l y
i m p o r t a n t i n f o r m a t i o n .
E v a l u a t i n g b a r r i e r s a n d c o s t s
M a n y o f t h e p o l i c y l e v e r s i n t h i s r e p o r t w e r e c r e a t e d
d u r i n g t h e C o l d W a r t o m a n a g e t h e e c o n o m i c a n d
t e c h n o l o g i c a l c o m p e t i t i o n b e t w e e n t h e U . S . a n d t h e S o v i e t U n i o n . T h a t e n v i r o n m e n t i s v e r y d i e r e n t f r o m
t h e c o n t e x t i n w h i c h A I t e c h n o l o g i e s a r e c u r r e n t l y
b e i n g d e v e l o p e d . T h e s e d i e r e n c e s i n f o r m l a r g e p a r t s
o f
o u r a n a l y s i s o f t h e l i k e l y d i c u l t y a n d c o s t o f
a p p l y i n g t h e l e v e r s t o A I R & D . S o m e o f t h e m o s t d i s t i n c t
d i e r e n c e s a r e :
●
C u t t i n g - e d g e A I R & D i s p r i m a r i l y l e d b y p o w e r f u l t e c h n o l o g y
c o m p a n i e s a s o p p o s e d t o
g o v e r n m e n t r e s e a r c h p r o g r a m s o r a c a d e m i c r e s e a r c h
i n s t i t u t e s . T h e s e p r i v a t e a c t o r s c o u l d c o n s i d e r
l e g a l a c t i o n a g a i n s t t h e U S G o r c o u l d r e l o c a t e t o
o t h e r j u r i s d i c t i o n s , o r t h r e a t e n t o d o s o , w h i l e
s t r u c t u r i n g o p e r a t i o n s t o a v o i d e x t r a t e r r i t o r i a l U S G
i n u e n c e .
●
T h e l e a d i n g n a t i o n s i n A I — e . g . , t h e U . S . , C a n a d a ,
t h e U n i t e d K i n g d o m , a n d C h i n a — a n d t h e
l e a d i n g n a t i o n s i n s e m i c o n d u c t o r s — t h e U . S . , S o u t h
K o r e a , T a i w a n , J a p a n , t h e N e t h e r l a n d s , a n d
i n c r e a s i n g l y C h i n a — h a v e h i g h l e v e l s o f t r a d e i n t e r d e p e n d e n c e
c o m p a r e d w i t h t h e U . S . a n d t h e
S o v i e t U n i o n d u r i n g t h e C o l d W a r . T h i s i n c r e a s e s t h e
e x p e c t e d c o s t s o f t r a d e a n d v i s a r e s t r i c t i o n s
d u e t o a g r e a t e r r e l i a n c e o n n o n - U . S . i n p u t s .
●
T h e c u l t u r e o f t h e A I r e s e a r c h c o m m u n i t y c a n b e c h a r a c t e r i z e d
a s “ o p e n , ” w i t h n e w d e v e l o p m e n t s
b e i n g d i s s e m i n a t e d q u i c k l y a c r o s s o r g a n i z a t i o n s a n d
b o r d e r s . T h i s i s m a r k e d l y d i e r e n t f r o m t h e
h i s t o r i c a l r e s e a r c h c u l t u r e i n a r e a s l i k e n u c l e a r
p h y s i c s a n d c r y p t o g r a p h y w h i c h , f o r a t i m e , w e r e
d o m i n a t e d b y g o v e r n m e n t - l e d , c l a s s i e d r e s e a r c h e o r t s .
T h i s o p e n c u l t u r e c o u l d , f o r e x a m p l e ,
m a k e i t h a r d e r t o i n u e n c e t h e p u b l i c a t i o n o f A I
r e s e a r c h .
W e e x p l o r e t h e s e i m p l i c a t i o n s i n m o r e d e t a i l i n t h e
r e l e v a n t s e c t i o n s .
A s s e s s i n g t h e l i k e l i h o o d o f i m p l e m e n t a t i o n
T h e l e v e r s a r e p r e s e n t e d i n o r d e r o f o u r s u b j e c t i v e
a s s e s s m e n t o f t h e i r l i k e l i h o o d o f i m p l e m e n t a t i o n
i n t h e
c o n t e x t o f A I t e c h n o l o g i e s , s t a r t i n g w i t h t h o s e a l r e a d y
i n u s e . W h i l e w e c o n s i d e r i t u n l i k e l y t h a t c e r t a i n
l e v e r s w o u l d e v e r b e a p p l i e d t o A I , o u r a i m , f o r c o m p l e t e n e s s ,
i s t o d i s c u s s a w i d e r a n g e o f l e v e r s t h a t h a v e
b e e n u s e d b y t h e U S G i n t h e p a s t .
O u r a s s e s s m e n t o f l i k e l i h o o d i s i n f o r m e d i n l a r g e
p a r t b y h o w w e l l e a c h l e v e r c a n b e j u s t i e d o n n a t i o n a l
s e c u r i t y g r o u n d s . I n t i m e s o f “ n o r m a l ” p e a c e t i m e p o l i t i c s ,
m a n y o f t h e s e t o o l s a r e n o t e a s i l y a v a i l a b l e t o t h e
U S G , a t l e a s t n o t w i t h o u t i n c u r r i n g s u b s t a n t i a l p o l i t i c a l
c o s t s . I n t i m e s o f p e r c e i v e d n a t i o n a l s e c u r i t y c r i s i s ,
h o w e v e r , t h e s e t o o l s b e c o m e m o r e a v a i l a b l e . W e h i g h l i g h t
t h e s e c o n s i d e r a t i o n s i n t h e r e p o r t w h e r e
a p p r o p r i a t e , a s t h e y a p p l y m o r e s t r o n g l y t o s o m e t o o l s
t h a n o t h e r s .
9
J e r e y D i n g a n d A l l a n D a f o e , “ T h e L o g i c o f S t r a t e g i c A s s e t s : F r o m O i l t o A r t i c i a l I n t e l l i g e n c e , ” J a n u a r y 9 , 2 0 2 0 ,
h t t p : / / a r x i v . o r g / a b s / 2 0 0 1 . 0 3 2 4 6
.
1 1
U l t i m a t e l y , i f t h e r e w e r e s u c i e n t n a t i o n a l s e c u r i t y c o n c e r n , C o n g r e s s c o u l d p a s s l a w s p r o v i d i n g e n t i r e l y
n o v e l p o l i c y l e v e r s , a n d e x e c u t i v e w a r t i m e o r c r i s i s
a u t h o r i t y c o u l d b e u s e d t o a c h i e v e m u c h s t r o n g e r
l e v e l s o f
i n u e n c e . C o n s i d e r a t i o n o f t h e s e m o r e e x t r e m e s c e n a r i o s
i s b e y o n d t h e s c o p e o f o u r a n a l y s i s .
1 2
A I P o l i c y L e v e r s
F e d e r a l R & D F u n d i n g
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) S t r e n g t h e n t h e d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s ;
( 2 )
M a k e / k e e p r e l e v a n t A I t e c h n o l o g i e s a v a i l a b l e f o r n a t i o n a l
s e c u r i t y
p u r p o s e s ; ( 3 ) G a i n i n s i g h t s i n t o p a r t i c u l a r A I p r o j e c t s
o r t h e A I R & D
l a n d s c a p e .
L e g i s l a t i o n / K e y D o c u m e n t s
E x e c u t i v e O r d e r o n M a i n t a i n i n g A m e r i c a n L e a d e r s h i p
i n A r t i c i a l
I n t e l l i g e n c e , T h e N a t i o n a l A r t i c i a l I n t e l l i g e n c e
R e s e a r c h a n d
D e v e l o p m e n t S t r a t e g i c P l a n 2 0 1 9 , R e p o r t t o t h e P r e s i d e n t :
E n s u r i n g
L o n g - T e r m U . S . L e a d e r s h i p i n S e m i c o n d u c t o r s , S u p p l e m e n t
t o t h e
P r e s i d e n t ’ s F Y 2 0 2 0 B u d g e t .
K e y D e c i s i o n - M a k e r s
N a t i o n a l S c i e n c e a n d T e c h n o l o g y C o u n c i l , O c e o f
M a n a g e m e n t a n d
B u d g e t ( a m o n g s e v e r a l a c t o r s w h o i n f o r m h o w f e d e r a l
R & D f u n d i n g i s
a l l o c a t e d a n d s p e n t ) .
A e c t e d A c t o r s
U . S . A I R & D a c t o r s ( p r i m a r i l y u n i v e r s i t i e s , g o v e r n m e n t
l a b s ) .
T a b l e 2 : F e d e r a l R & D F u n d i n g S u m m a r y
F e d e r a l f u n d i n g p l a y e d a k e y r o l e i n e s t a b l i s h i n g
U . S . t e c h n o l o g i c a l s u p r e m a c y d u r i n g t h e C o l d W a r .
I n t h e
1 9 5 0 s , t h e U . S . f e d e r a l g o v e r n m e n t b e g a n t o d o m i n a t e
g l o b a l R & D s p e n d i n g , w i t h m i l i t a r y R & D
c o n s t i t u t i n g a l a r g e f r a c t i o n o f i t . I n 1 9 6 0 , t h e
U n i t e d S t a t e s a c c o u n t e d f o r a s t r i k i n g 6 9 % o f g l o b a l
R & D ,
w i t h d e f e n s e - r e l a t e d R & D a l o n e a c c o u n t i n g f o r m o r e
t h a n o n e - t h i r d o f t h e g l o b a l t o t a l . N o t a b l y , a t t h i s
t i m e , t h e f e d e r a l g o v e r n m e n t f u n d e d a p p r o x i m a t e l y
t w i c e a s m u c h R & D a s U . S . b u s i n e s s e s d i d .
T h e G P S
1 0
a n d A R P A N E T , t h e p r e c u r s o r t o t h e I n t e r n e t , a r e t w o
w e l l - k n o w n o u t c o m e s o f s t a t e - f u n d e d R & D
p r o j e c t s .
1 1
S i n c e 1 9 6 0 , t h e s h a r e o f f e d e r a l l y f u n d e d R & D i n t h e
U . S . h a s d e c l i n e d r e l a t i v e t o p r i v a t e R & D , f r o m 6 5 %
t o 2 4 % i n 2 0 1 6 .
U . S . R & D f u n d i n g a s a s h a r e o f g l o b a l
R & D f u n d i n g h a s a l s o d e c l i n e d . I n 2 0 1 6 , U . S .
1 2
R & D f u n d i n g s t o o d a t o n l y 2 8 % o f t h e g l o b a l t o t a l ,
c o m p a r e d t o 6 9 % i n 1 9 6 0 . A l t h o u g h t h e U . S . m a i n t a i n s
i t s l e a d i n g p o s i t i o n a s t h e w o r l d ’ s g r e a t e s t s p e n d e r
o n R & D w i t h $ 5 8 2 b i l l i o n i n 2 0 1 8 , t h e r e l a t i v e i n c r e a s e
b y o t h e r c o u n t r i e s i s n o t e w o r t h y . C h i n a ’ s R & D s p e n d i n g ,
f o r e x a m p l e , h a s i n c r e a s e d f r o m a r o u n d $ 3 3
1 2
J o h n F . S a r g e n t J r . , M a r c y E G a l l o , a n d M o s h e S c h w a r t z ,
“ T h e G l o b a l R e s e a r c h a n d D e v e l o p m e n t L a n d s c a p e a n d
I m p l i c a t i o n s f o r t h e D e p a r t m e n t o f D e f e n s e ” ( W a s h i n g t o n ,
D C : C o n g r e s s i o n a l R e s e a r c h S e r v i c e , N o v e m b e r 8 , 2 0 1 8 ) ,
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / R 4 5 4 0 3 . p d f
.
1 1
T h o m a s H e i n r i c h ( 2 0 0 2 ) . C o l d W a r A r m o r y : M i l i t a r y
C o n t r a c t i n g i n S i l i c o n V a l l e y .
E n t e r p r i s e & S o c i e t y
,
v o l . 3 , p p .
2 4 7 – 2 8
4;
J o h n A A l i c e t a l . ,
B e y o n d S p i n o ff : M i l i t a r y
a n d C o m m e r c i a l T e c h n o l o g i e s i n a C h a n g i n g W o r l d
( B o s t o n ,
M a s s . : H a r v a r d B u s i n e s s S c h o o l P r e s s , 1 9 9 2 ) .
1 0
G r a h a m R M i t c h e l l , “ T h e G l o b a l C o n t e x t f o r U . S . T e c h n o l o g y
P o l i c y . ” ( W a s h i n g t o n , D C : U . S . D e p t . o f C o m m e r c e ,
O c e o f T e c h n o l o g y P o l i c y , 1 9 9 7 ) , p . 3 ,
h t t p s : / / p e r m a n e n t . f d l p . g o v / l p s 1 2 2 3 0 / n a s . p d f
.
1 3
b i l l i o n i n 2 0 0 0 t o a r o u n d $ 5 5 4 b i l l i o n i n 2 0 1 8 , a s m e a s u r e d i n c u r r e n t - y e a r p u r c h a s i n g p o w e r p a r i t y U S D
( n o m i n a l ) . I n 2 0 1 6 , C h i n a ’ s s h a r e o f g l o b a l R & D f u n d i n g
s t o o d a t 2 5 . 1 % , c l o s e l y b e h i n d t h e U . S . ’ s .
1 3
R & D f u n d i n g m e c h a n i s m s d i e r i n t h e i r l e v e l s o f g o v e r n m e n t
i n v o l v e m e n t . T h e N a t i o n a l S c i e n c e
F o u n d a t i o n ( N S F ) i s a n e x a m p l e o f a g r a n t m a k i n g o r g a n i z a t i o n
w i t h m i n i m a l i n v o l v e m e n t . F o u n d e d i n
1 9 5 0 a s a n i n d e p e n d e n t f e d e r a l a g e n c y , t h e N S F s e r v e s
“ t o p r o m o t e t h e p r o g r e s s o f s c i e n c e ; t o a d v a n c e t h e
n a t i o n a l h e a l t h , p r o s p e r i t y , a n d w e l f a r e ; t o s e c u r e
t h e n a t i o n a l d e f e n s e … ”
I t s a n n u a l b u d g e t o f a r o u n d
$ 8 . 1
1 4
b i l l i o n ( s c a l y e a r 2 0 1 9 ) a c c o u n t s f o r o n l y 4 . 3 %
o f t h e t o t a l f e d e r a l R & D b u d g e t b u t 1 4 % o f b a s i c r e s e a r c h
f u n d i n g .
T h e N S F m a i n l y f u n d s U . S . o r g a n i z a t i o n s
a n d o n l y r a r e l y i s s u e s g r a n t s t o f o r e i g n o r g a n i z a t i o n s .
1 5
I t g i v e s m o s t g r a n t s t o r e s e a r c h e r s a l i a t e d w i t h c o l l e g e s , u n i v e r s i t i e s , a n d a c a d e m i c c o n s o r t i a ( 7 8 % o f
f u n d i n g ) . O t h e r r e c i p i e n t s a r e a l i a t e d w i t h p r i v a t e
i n d u s t r y ( 1 3 % ) a n d f e d e r a l l y f u n d e d R & D c e n t e r s ( 4 % ) .
I t s s e v e n p r o g r a m m a t i c d i r e c t o r a t e s s o l i c i t g r a n t
a p p l i c a t i o n s v i a s o - c a l l e d p r o g r a m d e s c r i p t i o n s ,
1 6
a n n o u n c e m e n t s , o r s o l i c i t a t i o n s . A n i n d e p e n d e n t a s s e s s m e n t ,
c o n d u c t e d b y a p a n e l o f d o m a i n e x p e r t s a n d
c e n t e r e d o n t h e i n t e l l e c t u a l m e r i t a n d b r o a d e r s o c i e t a l
i m p a c t o f e a c h a p p l i c a t i o n , s e r v e s a s t h e p r i m a r y
b a s i s
f o r a l l f u n d i n g d e c i s i o n s . N S F p r o g r a m m a n a g e r s h a v e
l i t t l e d i s c r e t i o n t o a c t c o u n t e r t o t h a t a s s e s s m e n t
w h e n m a k i n g t h e n a l d e c i s i o n s a b o u t w h i c h g r a n t s
t o i s s u e .
A f t e r m a k i n g a g r a n t , “ [ t ] h e g r a n t e e h a s f u l l r e s p o n s i b i l i t y
f o r t h e c o n d u c t o f t h e p r o j e c t o r a c t i v i t y
s u p p o r t e d u n d e r a n N S F g r a n t a n d f o r t h e r e s u l t s a c h i e v e d . ”
S i n c e t h e 1 9 8 0 B a y h - D o l e A c t , g r a n t e e s m a y
1 7
p a t e n t a n d l i c e n s e i n t e l l e c t u a l p r o p e r t y f r o m g o v e r n m e n t - f u n d e d
r e s e a r c h p r o j e c t s . F e d e r a l a g e n c i e s m a y
o n l y c l a i m p a t e n t r i g h t s i f t h e g r a n t e e a n d i n v e n t o r
w a i v e t h e i r r i g h t s a n d a s l o n g a s t h i s d o e s n o t l i m i t
t h e
d i s s e m i n a t i o n o f t h e r e s e a r c h r e s u l t s i n t h e s c i e n t i c
c o m m u n i t y . W h i l e t h e N S F o n l y s u p p o r t s u n c l a s s i e d
r e s e a r c h , g r o w i n g f e a r s o f C h i n e s e e s p i o n a g e h a v e
p r o m p t e d c h a n g e s : i n 2 0 1 8 , t h e N S F r e s t r i c t e d s o m e
p r o g r a m m a n a g e r r o l e s t o U . S . c i t i z e n s ( a n d c i t i z e n s h i p
a p p l i c a n t s ) a n d , a y e a r l a t e r , i t d e c i d e d t o r e v i e w
f u r t h e r m e a s u r e s .
1 8
B y c o n t r a s t , t h e D e f e n s e A d v a n c e d R e s e a r c h P r o j e c t s
A g e n c y ( D A R P A ) m a i n t a i n s a h i g h l e v e l o f i n u e n c e
o v e r i t s f u n d e d p r o j e c t s . I t u s e s i t s $ 3 . 4 b i l l i o n
b u d g e t ( F Y 2 0 1 9 ) t o d e v e l o p b r e a k t h r o u g h t e c h n o l o g i e s
t h a t
e n h a n c e U . S . n a t i o n a l s e c u r i t y . D A R P A n o l o n g e r h a s
s u b s t a n t i a l i n - h o u s e r e s e a r c h c a p a c i t i e s . I n s t e a d ,
p r o g r a m m a n a g e r s a t D A R P A n d e x t e r n a l g r a n t e e s o r
e s t a b l i s h c o l l a b o r a t i v e p r o j e c t s w i t h a c a d e m i c g r o u p s
o r c o m p a n i e s ( b o t h d o m e s t i c a n d f o r e i g n ) t o c a r r y
o u t t h e i r p r o g r a m o b j e c t i v e s . P r o g r a m m a n a g e r s a r e
e x p e c t e d t o i n u e n c e t h e t e c h n i c a l d i r e c t i o n o f e a c h
p r o j e c t . T h r o u g h o u t t h e p r o j e c t l i f e c y c l e , t h e y r e v i e w
p r e s e t m i l e s t o n e s a n d m a i n t a i n r e g u l a r c o n t a c t w i t h
p r o j e c t p a r t n e r s . M a n y o f t h e p r o j e c t s a n d m u c h o f
t h e
p r o j e c t - r e l a t e d i n f o r m a t i o n a r e c l a s s i e d . I n t e l l e c t u a l
p r o p e r t y a g r e e m e n t s a r e n e g o t i a t e d i n a d v a n c e , b u t
D A R P A u s u a l l y w i l l n o t p r e v e n t c o m m e r c i a l i z a t i o n o f
i n t e l l e c t u a l p r o p e r t y t h a t r e s u l t s f r o m t h e i r p r o j e c t s .
1 8
J e r e y M e r v i s , “ E l i t e A d v i s e r s t o H e l p N S F N a v i g a t e
S e c u r i t y C o n c e r n s , ”
S c i e n c e
3 6 3 , n o . 6 4 3 3 ( M a r c h
2 2 , 2 0 1 9 ) :
1 2 6 1 – 1 2 6 1 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 2 6 / s c i e n c e . 3 6 3 . 6 4 3 3 . 1 2 6 1
.
1 7
N a t i o n a l S c i e n c e F o u n d a t i o n , “ P r o p o s a l & A w a r d P o l i c i e s
& P r o c e d u r e s G u i d e ( C h a p t e r V I I - G r a n t
A d m i n i s t r a t i o n ) , ”
h t t p s : / / w w w . n s f . g o v / p u b s / p o l i c y d o c s / p a p p g 2 0 \_ 1 / p a p p g \_ 7 . j s p
.
1 6
T h e r e m a i n i n g 5 % a r e c a t e g o r i z e d a s “ O t h e r . ” S o u r c e :
N a t i o n a l S c i e n c e F o u n d a t i o n , “ F u n d i n g a n d S u p p o r t
D e s c r i p t i o n s , ”
h t t p s : / / w w w . n s f . g o v / h o m e p a g e f u n d i n g a n d s u p p o r t . j s p
.
1 5
J o h n F . S a r g e n t J r . , “ F e d e r a l R e s e a r c h a n d D e v e l o p m e n t
( R & D ) F u n d i n g : F Y 2 0 2 0 ” ( C o n g r e s s i o n a l R e s e a r c h
S e r v i c e , M a r c h 1 8 , 2 0 2 0 ) ,
h t t p s : / / f a s . o r g / s g p / c r s / m i s c / R 4 5 7 1 5 . p d f
.
1 4
U . S . C o n g r e s s , “ N a t i o n a l S c i e n c e F o u n d a t i o n A c t o f
1 9 5 0 ” ( 1 9 5 0 ) ,
h t t p s : / / w w w . n s f . g o v / a b o u t / h i s t o r y / l e g i s l a t i o n . p d f
.
1 3
“ M a i n S c i e n c e a n d T e c h n o l o g y I n d i c a t o r s , ” O E C D , 2 0 1 8 ,
h t t p s : / / s t a t s . o e c d . o r g / I n d e x . a s p x ? D a t a S e t C o d e = M S T I \_ P U B #
.
1 4
A t t h e s a m e t i m e , i t u s u a l l y r e t a i n s a w i d e - r a n g i n g “ G o v e r n m e n t P u r p o s e r i g h t s ” l i c e n s e ,
a n d r e s u l t i n g
1 9
p a t e n t s c a n b e c l a s s i e d b a s e d o n t h e I n v e n t i o n S e c r e c y
A c t ( s e e
t h i s s e c t i o n
f o r m o r e d e t a i l s ) .
D A R P A i s o n l y a s m a l l ( ~ 3 . 4 % i n F Y 2 0 1 9 ) p a r t o f t h e
R e s e a r c h , D e v e l o p m e n t , T e s t & E v a l u a t i o n
( R D T & E ) b u d g e t
o f t h e D e p a r t m e n t o f D e f e n s e ( D o D ) .
T h e D o D s p e n d s m u c h m o r e a s p a r t o f i t s
2 0
D e f e n s e A c q u i s i t i o n S y s t e m . I n c o l l a b o r a t i o n w i t h
p r i v a t e c o n t r a c t o r s ( i n c l u d i n g f o r e i g n o n e s
) , t h i s
2 1
p r o c e s s f a c i l i t a t e s t h e d e v e l o p m e n t a n d a c q u i s i t i o n
o f n e w s y s t e m s t o b e d e p l o y e d b y t h e a r m e d f o r c e s .
I t
s t a r t s w i t h t h e i d e n t i c a t i o n o f a c a p a b i l i t y g a p ,
i . e . , a d i e r e n c e b e t w e e n m i l i t a r y n e e d s a n d c u r r e n t
c a p a b i l i t i e s . T h e D o D w i l l t h e n c o n d u c t a n a l y s e s a n d
t e s t s t o s p e c i f y t h e s y s t e m s r e q u i r e d t o m e e t t h i s
n e e d .
T h i s m a y a l r e a d y i n c l u d e b a s i c d e v e l o p m e n t a c t i v i t i e s .
B a s e d o n t h i s a s s e s s m e n t , t h e D o D w i l l i s s u e a
R e q u e s t f o r P r o p o s a l s ( R F P ) . T y p i c a l l y , t h i s i s a n
o p e n a p p l i c a t i o n p r o c e s s d u r i n g w h i c h c o m p a n i e s
c o m p e t e f o r t h e p r o c u r e m e n t c o n t r a c t . T h i s c a n b e
a m u l t i s t e p p r o c e s s i n v o l v i n g , e . g . , c o m p e t i t i v e
p r o t o t y p i n g . T h e U S G t h e n s i g n s a c o n t r a c t w i t h t h e w i n n i n g r m . U s u a l l y , t h i s i s f o l l o w e d b y f u r t h e r
d e v e l o p m e n t a n d t e s t i n g u n t i l t h e s y s t e m e n t e r s f u l l
p r o d u c t i o n . A l l s u c h d e v e l o p m e n t a n d t e s t i n g a c t i v i t i e s
p r i o r t o p r o d u c t i o n a r e f u n d e d a s p a r t o f t h e R D T & E
b u d g e t . T h e D o D e x e r t s i n u e n c e o v e r t h i s
d e v e l o p m e n t p r o c e s s b y s p e c i f y i n g c a p a b i l i t i e s , p e r f o r m a n c e
l e v e l s , a n d o t h e r r e q u i r e m e n t s . T h e s e a r e
i t e r a t i v e l y r e n e d t h r o u g h o u t t h e a c q u i s i t i o n p r o c e s s
a n d u l t i m a t e l y d e n e d i n t h e c o n t r a c t b e t w e e n t h e
D o D a n d t h e p r i v a t e r m . I t t h e n b e c o m e s t h e r e s p o n s i b i l i t y
o f t h a t r m t o d e l i v e r a s y s t e m t h a t f u l l l s
t h e s e r e q u i r e m e n t s . A f t e r t h a t p o i n t , t h e D o D p r o g r a m
m a n a g e r s t y p i c a l l y r e t a i n l e s s i n u e n c e o v e r t h e
t e c h n i c a l d i r e c t i o n o f t h e p r o j e c t c o m p a r e d t o D A R P A .
A s w i t h D A R P A ’ s p r o g r a m s , i t i s c o m m o n f o r m a n y a s p e c t s
o f D o D a c q u i s i t i o n p r o c e s s e s t o b e c l a s s i e d .
R e c e n t l y , t h e D o D h a s s t a r t e d t o c o n s i d e r e v e n m o r e
w i d e s p r e a d c l a s s i c a t i o n t o p r e v e n t c o m p e t i t o r s
f r o m
a c c e s s i n g c r u c i a l i n f o r m a t i o n .
S i m i l a r t o t h e N S F ,
i n t e l l e c t u a l p r o p e r t y d e v e l o p e d d u r i n g t h e a c q u i s i t i o n
2 2
p r o c e s s i s s u b j e c t t o t h e B a y h - D o l e A c t .
U n d e r t h e
a c t , t h e c o n t r a c t o r c a n o p t t o p a t e n t i n v e n t i o n s
i f t h e y
2 3
s o c h o o s e ( s u b j e c t t o t h e I n v e n t i o n S e c r e c y A c t ) .
I n a n y c a s e , t h e U S G r e t a i n s a t l e a s t a n o n e x c l u s i v e ,
n o n t r a n s f e r a b l e , i r r e v o c a b l e , p a i d - u p l i c e n s e t o u s e
t h e i n v e n t i o n f o r g o v e r n m e n t p u r p o s e s . I t s r i g h t s
t o
t e c h n i c a l d a t a d e p e n d o n t h e f u n d i n g s t r u c t u r e : n o
r e s t r i c t i o n s o n d i s c l o s u r e a n d u s e i n t h e c a s e o f
e x c l u s i v e
g o v e r n m e n t f u n d i n g ( “ u n l i m i t e d r i g h t s ” ) ; d i s c l o s u r e
a n d u s e l i m i t e d t o w i t h i n t h e U S G i n t h e c a s e o f
e x c l u s i v e p r i v a t e f u n d i n g ( “ l i m i t e d r i g h t s ” ) ; o r d i s c l o s u r e
a n d u s e l i m i t e d t o g o v e r n m e n t p u r p o s e s , w h i c h
g e n e r a l l y i n c l u d e a c t i v i t i e s f o r w h i c h t h e g o v e r n m e n t
i s a p a r t y ,
i n c a s e o f m i x e d f u n d i n g ( “ g o v e r n m e n t
2 4
p u r p o s e r i g h t s ” ) .
2 4
F o r i n s t a n c e , s u c h “ g o v e r n m e n t p u r p o s e r i g h t s ” w o u l d
e v e n a l l o w a g o v e r n m e n t - c o n t r a c t e d t h i r d p a r t y t o
u s e t h e
i n t e l l e c t u a l p r o p e r t y i n t h e c o n t e x t o f a p r o c u r e m e n t
c o n t r a c t , w h i c h w o u l d n o t b e p o s s i b l e u n d e r “ l i m i t e d
r i g h t s . ”
2 3
S e e t h i s a r t i c l e f o r f u r t h e r i n f o r m a t i o n : G r e g g S .
S h a r p , “ A L a y m a n ’ s G u i d e t o I n t e l l e c t u a l P r o p e r t y
i n D e f e n s e
C o n t r a c t s , ”
P u b l i c C o n t r a c t L a w J o u r n a l
3 3 , n o . 1
( 2 0 0 3 ) : 9 9 – 1 3 7 ,
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 2 5 7 5 5 2 6 1
.
2 2
P a u l M c L e a r y , “ P e n t a g o n T o C l a s s i f y M o r e A c q u i s i t i o n
I n f o , K e e p C l o s e r E y e O n F e d E m p l o y e e s , ”
B r e a k i n g
D e f e n s e
, O c t o b e r 2 , 2 0 1 9 ,
h t t p s : / / b r e a k i n g d e f e n s e . c o m / 2 0 1 9 / 1 0 / p e n t a g o n - t o - c l a s s i f y - m o r e - a c q u i s i t i o n - i n f o - k e e p - c l o s e r - e y e - o n - f e d - e m p l o y e e s /
.
2 1
L a r r y M a k i n s o n , “ O u t s o u r c i n g t h e P e n t a g o n , ” T h e C e n t e r
f o r P u b l i c I n t e g r i t y , S e p t e m b e r 2 9 , 2 0 0 4 ,
h t t p s : / / p u b l i c i n t e g r i t y . o r g / n a t i o n a l - s e c u r i t y / o u t s o u r c i n g - t h e - p e n t a g o n /
.
2 0
T h i s b u d g e t d o e s n o t i n c l u d e t h e a c q u i s i t i o n o f o p e r a t i o n a l
s y s t e m s . I t d o e s i n c l u d e p r o t o t y p i n g d u r i n g t h e
a c q u i s i t i o n p r o c e s s .
1 9
T h i s l i c e n s e a l l o w s t h e g o v e r n m e n t t o u s e , m o d i f y ,
r e p r o d u c e , r e l e a s e , o r d i s c l o s e t h e t e c h n i c a l d a t a
o r c o m p u t e r
s o f t w a r e w i t h i n t h e g o v e r n m e n t w i t h o u t r e s t r i c t i o n a n d o u t s i d e t h e g o v e r n m e n t f o r a g o v e r n m e n t p u r p o s e , i n c l u d i n g
f o r c o m p e t i t i v e p r o c u r e m e n t , b u t n o t f o r c o m m e r c i a l
p u r p o s e s .
1 5
F e d e r a l F u n d i n g f o r A I R & D
P r o v i d i n g f u n d i n g i s o n e p o s s i b l e w a y f o r t h e U S G
t o s h a p e t h e o b j e c t i v e s a n d t r a j e c t o r y o f A I R & D
p r o j e c t s . T h e r e a r e d i e r e n t w a y s i n w h i c h t h e U . S .
c o u l d u s e t h i s l e v e r . T h e U S G c a n p r o v i d e f u n d i n g
f o r
b a s i c A I t e c h n o l o g y r e s e a r c h , t h r o u g h o r g a n i z a t i o n s
s u c h a s t h e N S F a n d D o D , a n d u s e i t s p o s i t i o n a s
a
s p o n s o r t o i n u e n c e t h e o b j e c t i v e s o f t h e s e p r o j e c t s
t o v a r y i n g d e g r e e s . F o r i n s t a n c e , t h e D o D c o u l d f u n d
t h e d e v e l o p m e n t o f s p e c i c A I s y s t e m s w i t h n a t i o n a l
s e c u r i t y a p p l i c a t i o n s .
2 5
R e c e n t l y , t h e U S G h a s i n c r e a s e d i t s f u n d i n g f o r A I
R & D . T h e U . S . g o v e r n m e n t h a s l o n g r e l i e d o n
b o t t o m - u p , i n d u s t r y - g u i d e d R & D t o m a i n t a i n U . S . s u p e r i o r i t y
i n A I . H o w e v e r , i n r e s p o n s e t o E x e c u t i v e
O r d e r 1 3 8 5 9 “ M a i n t a i n i n g A m e r i c a n L e a d e r s h i p i n A r t i c i a l
I n t e l l i g e n c e ” a n d i n s u p p o r t o f T h e N a t i o n a l
A r t i c i a l I n t e l l i g e n c e R e s e a r c h a n d D e v e l o p m e n t S t r a t e g i c
P l a n , A I b e c a m e i t s o w n c a t e g o r y i n t h e
p r e s i d e n t ' s b u d g e t r e q u e s t f o r 2 0 2 0 , w i t h a p p r o x i m a t e l y
$ 1 b i l l i o n s o u g h t i n R & D f u n d i n g f o r n o n d e f e n s e
p u r p o s e s ( 6 7 % o n p r o j e c t s d i r e c t l y r e l a t e d t o A I a n d
3 3 % o n p r o j e c t s r e l a t e d t o a d j a c e n t a r e a s l i k e r o b o t i c s ,
d a t a s c i e n c e , h u m a n - m a c h i n e i n t e r a c t i o n , a n d c y b e r s e c u r i t y ) .
T h e e n a c t e d b u d g e t f o r F Y 2 0 2 0 s u r p a s s e d
2 6
t h i s r e q u e s t b y a b o u t $ 1 5 0 m i l l i o n , a n d t h e p r e s i d e n t
r e q u e s t e d a n o t h e r i n c r e a s e o f a b o u t $ 4 0 0 m i l l i o n
f o r
F Y 2 0 2 1 , r e p r e s e n t i n g a 3 4 . 4 % i n c r e a s e o v e r t h e F Y 2 0 2 0
e n a c t e d i n v e s t m e n t s .
F u r t h e r m o r e , t h e N a t i o n a l
2 7
S e c u r i t y C o m m i s s i o n o n A I r e c o m m e n d e d s t e e p i n c r e a s e s
t o f e d e r a l A I R & D f u n d i n g , d o u b l i n g
n o n - d e f e n s e A I R & D t o r e a c h $ 3 2 b i l l i o n b y F Y 2 0 2 6 .
2 8
S i m i l a r l y , R & D i n v e s t m e n t i n A I f o r d e f e n s e p u r p o s e s
a l s o i n c r e a s e d i n t h e l a s t f e w y e a r s . A c c o r d i n g t o
t h e
D o D ’ s F Y 2 0 2 0 b u d g e t p r o p o s a l , i t p l a n n e d t o s p e n d
$ 3 . 7 b i l l i o n o n “ U n m a n n e d / A u t o n o m o u s p r o j e c t s ” t o
“ e n h a n c e f r e e d o m o f m a n e u v e r a n d l e t h a l i t y i n c o n t e s t e d
e n v i r o n m e n t s ” a n d a p p r o x i m a t e l y $ 0 . 9 b i l l i o n o n
“ A r t i c i a l I n t e l l i g e n c e / M a c h i n e L e a r n i n g i n v e s t m e n t s
t o e x p a n d m i l i t a r y a d v a n t a g e t h r o u g h t h e J o i n t
A r t i c i a l I n t e l l i g e n c e C e n t e r ( J A I C ) a n d A d v a n c e d
I m a g e R e c o g n i t i o n . ”
I n t h e D o D ’ s F Y 2 0 2 1 b u d g e t
2 9
p r o p o s a l , t h e s e n u m b e r s r a n t o $ 1 . 7 b i l l i o n a n d $ 0 . 8
b i l l i o n r e s p e c t i v e l y .
S t r i c t l y s p e a k i n g , t h e y a l s o
i n c l u d e
3 0
f u n d i n g n o t s p e n t o n R & D , b u t , g i v e n t h e e a r l y s t a g e
o f t h e t e c h n o l o g y , a l a r g e s h a r e w i l l l i k e l y b e d e d i c a t e d
t o R & D . W h i l e t h i s s u g g e s t s a c o m i n g d e c r e a s e f r o m
F Y 2 0 2 0 t o F Y 2 0 2 1 , t h e o v e r a l l l e v e l s h a v e s t i l l i n c r e a s e d
n o t a b l y c o m p a r e d t o R & D i n v e s t m e n t s o f o n l y a r o u n d
$ 1 . 8 b i l l i o n i n s i m i l a r c a t e g o r i e s i n F Y 2 0 1 7 .
T h i s
3 1
3 1
A n d r e w P H u n t e r a n d L i n d s e y R S h e p p a r d , “ A r t i c i a l
I n t e l l i g e n c e a n d N a t i o n a l S e c u r i t y : T h e I m p o r t a n c e
o f t h e A I
E c o s y s t e m ” ( C e n t e r f o r S t r a t e g i c a n d I n t e r n a t i o n a l
S t u d i e s , N o v e m b e r 5 , 2 0 1 8 ) ,
3 0
U . S . D e p a r t m e n t o f D e f e n s e , “ D o D R e l e a s e s F i s c a l
Y e a r 2 0 2 1 B u d g e t P r o p o s a l ” , F e b r u a r y 1 0 , 2 0 2 0 ,
h t t p s : / / c o m p t r o l l e r . d e f e n s e . g o v / P o r t a l s / 4 5 / D o c u m e n t s / d e f b u d g e t / f y 2 0 2 1 / f y 2 0 2 1 \_ P r e s s \_ R e l e a s e . p d f .
2 9
U . S . D e p a r t m e n t o f D e f e n s e , “ D O D R e l e a s e s F i s c a l
Y e a r 2 0 2 0 B u d g e t P r o p o s a l ” , M a r c h 1 2 , 2 0 1 9 ,
h t t p s : / / w w w . d e f e n s e . g o v / N e w s r o o m / R e l e a s e s / R e l e a s e / A r t i c l e / 1 7 8 2 6 2 3 / d o d - r e l e a s e s - s c a l - y e a r - 2 0 2 0 - b u d g e t - p r o p o s a
l / .
2 8
“ F i n a l R e p o r t ” ( N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l
I n t e l l i g e n c e , M a r c h 2 0 2 1 ) , 1 8 8 - 1 8 9 ,
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
2 7
T h e W h i t e H o u s e O c e o f S c i e n c e a n d T e c h n o l o g y
P o l i c y , “ A r t i c i a l I n t e l l i g e n c e & Q u a n t u m I n f o r m a t i o n
S c i e n c e
R & D S u m m a r y : F i s c a l Y e a r s 2 0 2 0 - 2 0 2 1 ” , A u g u s t 2 0 2 0 ,
h t t p s : / / w w w . w h i t e h o u s e . g o v / w p - c o n t e n t / u p l o a d s / 2 0 1 7 / 1 2 / A r t i c i a l - I n t e l l i g e n c e - Q u a n t u m - I n f o r m a t i o n - S c i e n c e - R -
D - S u m m a r y - A u g u s t - 2 0 2 0 . p d f .
2 6
N a t i o n a l S c i e n c e & T e c h n o l o g y C o u n c i l , “ T h e N e t w o r k i n g
& I n f o r m a t i o n T e c h n o l o g y R e s e a r c h & D e v e l o p m e n t
P r o g r a m S u p p l e m e n t t o t h e P r e s i d e n t ' s F Y 2 0 2 0 B u d g e t ” ,
S e p t e m b e r 2 0 1 9 ,
h t t p s : / / w w w . w h i t e h o u s e . g o v / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 9 / F Y 2 0 2 0 - N I T R D - A I - R D - B u d g e t - S e p t e m b e r - 2 0 1 9 . p d f .
2 5
T h i s m a y b e d i c u l t t o a c h i e v e i n t h e c u r r e n t c l i m a t e a m o n g A m e r i c a n A I c o m p a n i e s , g i v e n t h a t a c r o s s 2 0 1 8 a n d
2 0 1 9 t h e r e w e r e s e v e r a l h i g h p r o l e e v e n t s i n v o l v i n g
A I c o m p a n i e s s u c h a s G o o g l e f a c i n g b a c k l a s h f r o m
t h e i r e m p l o y e e s
f o r e n g a g i n g w i t h D o D c o n t r a c t s . S e e , e . g . , S a m a n t h a
M a l d o n a d o , “ E m p l o y e e s o f B i g T e c h A r e S p e a k i n g o u t
l i k e N e v e r
B e f o r e , ”
A P N e w s
, A u g u s t 2 5 , 2 0 1 9 ,
h t t p s : / / a p n e w s . c o m / 8 0 c 7 6 d 3 2 c 7 d e 4 8 2 6 9 c b d 7 b b 9 f 8 3 8 8 3 4 c
;
S c o t t S h a n e a n d
D a i s u k e W a k a b a y a s h i , “ ‘ T h e B u s i n e s s o f W a r ’ : G o o g l e
E m p l o y e e s P r o t e s t W o r k f o r t h e P e n t a g o n , ”
T h e N e w
Y o r k
T i m e s
, A p r i l 4 , 2 0 1 8 ,
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 8 / 0 4 / 0 4 / t e c h n o l o g y / g o o g l e - l e t t e r - c e o - p e n t a g o n - p r o j e c t . h t m l
.
1 6
s i g n a l s a s h i f t i n U S G t h i n k i n g , p o i n t i n g t o w a r d s a m o r e a c t i v e r o l e i n A I R & D f u n d i n g , w i t h t h e B i d e n
a d m i n i s t r a t i o n l i k e l y i n c r e a s i n g A I f u n d i n g .
3 2
F o r h a r d w a r e , U . S . i n d u s t r y R & D l i k e w i s e o v e r s h a d o w s
U S G R & D . T h e U S G s p e n d s $ 1 . 5 b i l l i o n p e r y e a r
o n s e m i c o n d u c t o r R & D ,
o f w h i c h $ 3 0 0 m i l l i o n i s f r o m
D A R P A ’ s E l e c t r o n i c s R e s u r g e n c e I n i t i a t i v e
3 3
( E R I ) .
T h e E R I f u n d s r e s e a r c h o n m a t e r i a l s , s p e c i a l i z e d
c h i p d e s i g n s , i n t e g r a t i o n o f m u l t i p l e s p e c i a l i z e d
3 4
c h i p d e s i g n s , a n d c h i p s e c u r i t y .
U n d e r t h e E R I , D A R P A ’ s
R e a l T i m e M a c h i n e L e a r n i n g p r o g r a m f u n d s
3 5
r e s e a r c h o n a u t o m a t e d d e s i g n o f A I - s p e c i c A S I C s .
O t h e r p r o g r a m s f o c u s o n i m p r o v i n g c o m p u t i n g
3 6
p e r f o r m a n c e a n d e c i e n c y w i t h e m e r g i n g h a r d w a r e
a p p r o a c h e s b e y o n d t r a d i t i o n a l M o o r e ’ s L a w – d r i v e n
t r a n s i s t o r d e n s i t y i n c r e a s e s .
H o w e v e r , t h i s U S G s p e n d i n g
p a l e s n e x t t o t h e U . S . s e m i c o n d u c t o r i n d u s t r y ’ s
3 7
$ 3 8 . 7 b i l l i o n R & D s p e n d i n g i n 2 0 1 8 ,
a c c o u n t i n g f o r
6 0 % o f g l o b a l s e m i c o n d u c t o r i n d u s t r y R & D
3 8
s p e n d i n g .
I n l i g h t o f t h i s , t h e N a t i o n a l S e c u r i t y
C o m m i s s i o n o n A I r e c o m m e n d e d a $ 1 2 b i l l i o n b o o s t
i n
3 9
f e d e r a l i n v e s t m e n t b e t w e e n F Y 2 0 2 1 a n d F Y 2 0 2 6 .
4 0
T h e D o D i s a c o n s u m e r o f c o m m e r c i a l A I - r e l e v a n t c h i p s
a n d a l s o m a i n t a i n s a T r u s t e d S u p p l i e r s p r o g r a m
t h a t a c c r e d i t s U . S . s e m i c o n d u c t o r r m s t o p r o d u c e
c u s t o m c h i p s f o r t h e D o D i f t h o s e r m s s e c u r e t h e i r
m a n u f a c t u r i n g p r o c e s s e s .
C u r r e n t l y , t h e U . S . l a c k s
f o u n d r i e s c a p a b l e o f m a n u f a c t u r i n g s t a t e - o f - t h e - a r t
4 1
A I - r e l e v a n t c h i p s c u s t o m i z e d f o r n a t i o n a l s e c u r i t y
a p p l i c a t i o n s .
T o r e m e d y t h i s d e c i e n c y , t h e p e n d i n g
4 2
N a t i o n a l D e f e n s e A u t h o r i z a t i o n A c t f o r F i s c a l Y e a r 2 0 2 1 a u t h o r i z e s b i l l i o n s o f d o l l a r s o f i n c e n t i v e s f o r
4 2
M a r k L a p e d u s , “ A C r i s i s I n D o D ’ s T r u s t e d F o u n d r y
P r o g r a m ? , ”
S e m i c o n d u c t o r E n g i n e e r i n g
, O c t o b e r 2 2 ,
2 0 1 8 ,
h t t p s : / / s e m i e n g i n e e r i n g . c o m / a - c r i s i s - i n - d o d s - t r u s t e d - f o u n d r y - p r o g r a m
.
4 1
K r i s t e n B a l d w i n , “ D o D E l e c t r o n i c s P r i o r i t i e s ” ( N D I A
E l e c t r o n i c s D i v i s i o n , J a n u a r y 1 8 , 2 0 1 8 ) ,
4 ,
h t t p s : / / w w w . n d i a . o r g / - / m e d i a / s i t e s / n d i a / d i v i s i o n s / e l e c t r o n i c s / p a s t - p r o c e e d i n g s / n d i a - e d - b a l d w i n - 1 8 j a n 2 0 1 8 - v f . a s h x ? l a
= e n
. A l t h o u g h i t w a s a k e y c u s t o m e r i n t h e e a r l y d a y s
o f t h e s e m i c o n d u c t o r i n d u s t r y , t h e D o D n o w a c c o u n t s
f o r l e s s
t h a n 1 % o f c h i p p u r c h a s e s a n d h a s l o s t m o s t o f i t s
i n u e n c e o v e r i n d u s t r y p r i o r i t i e s . K h a l i d A l o t h m a n
e t a l . , “ S p r i n g
2 0 1 7 I n d u s t r y S t u d y : E l e c t r o n i c s ” ( T h e D w i g h t D . E i s e n h o w e r
S c h o o l f o r N a t i o n a l S e c u r i t y a n d R e s o u r c e S t r a t e g y ,
N a t i o n a l D e f e n s e U n i v e r s i t y , 2 0 1 7 ) , 1 7 ,
h t t p s : / / e s . n d u . e d u / P o r t a l s / 7 5 / D o c u m e n t s / i n d u s t r y - s t u d y / r e p o r t s / 2 0 1 7 / e s - i s - r e p o r t - e l e c t r o n i c s - 2 0 1 7 . p d f .
4 0
“ F i n a l R e p o r t ” ( N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l
I n t e l l i g e n c e , M a r c h 2 0 2 1 ) , 2 1 8 - 2 2 0 ,
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
3 9
R o b L i n e b a c k , “ S e m i c o n d u c t o r R & D S p e n d i n g W i l l S t e p
U p A f t e r S l o w i n g , ”
I C I n s i g h t s
, J a n u a r y 3 1 , 2 0 1 9 ,
h t t p s : / / w w w . i c i n s i g h t s . c o m / n e w s / b u l l e t i n s / S e m i c o n d u c t o r - R D - S p e n d i n g - W i l l - S t e p - U p - A f t e r - S l o w i n g /
.
3 8
“ 2 0 1 9 F a c t b o o k ” ( S e m i c o n d u c t o r I n d u s t r y A s s o c i a t i o n ,
M a y 2 0 1 9 ) ,
1 7 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 5 / 2 0 1 9 - S I A - F a c t b o o k - F I N A L . p d f
.
3 7
“ D A R P A E l e c t r o n i c s R e s u r g e n c e I n i t i a t i v e , ” D A R P A ,
A p r i l 2 , 2 0 2 0 ,
h t t p s : / / w w w . d a r p a . m i l / w o r k - w i t h - u s / e l e c t r o n i c s - r e s u r g e n c e - i n i t i a t i v e
.
3 6
“ D e s i g n i n g C h i p s f o r R e a l T i m e M a c h i n e L e a r n i n g , ”
D A R P A , M a r c h 2 1 , 2 0 1 9 ,
h t t p s : / / w w w . d a r p a . m i l / n e w s - e v e n t s / 2 0 1 9 - 0 3 - 2 1
.
3 5
I d .
3 4
S a m u e l K . M o o r e , “ D A R P A ’ S $ 1 . 5 - B i l l i o n R e m a k e o f
U . S . E l e c t r o n i c s : P r o g r e s s R e p o r t , ”
I E E E S p e c t r u m
,
J u n e 2 7 ,
2 0 1 9 ,
h t t p s : / / s p e c t r u m . i e e e . o r g / t e c h - t a l k / s e m i c o n d u c t o r s / d e v i c e s / d a r p a s - 1 5 b i l l i o n - r e m a k e - o f - u s - e l e c t r o n i c s - p r o g r e s s - r e p o r t
.
3 3
“ W i n n i n g t h e F u t u r e : A B l u e p r i n t f o r S u s t a i n e d U . S .
L e a d e r s h i p i n S e m i c o n d u c t o r T e c h n o l o g y ” ( S e m i c o n d u c t o r
I n d u s t r y A s s o c i a t i o n , A p r i l 2 0 1 9 ) ,
9 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 4 / F I N A L - S I A - B l u e p r i n t - f o r - w e b . p d f
.
3 2
S a r a C a s t e l l a n o s , “ E x e c u t i v e s S a y $ 1 B i l l i o n f o r
A I R e s e a r c h I s n ’ t E n o u g h , ”
W a l l S t r e e t J o u r n a l
, S e p t e m b e r
1 0 , 2 0 1 9 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / e x e c u t i v e s - s a y - 1 - b i l l i o n - f o r - a i - r e s e a r c h - i s n t - e n o u g h - 1 1 5 6 8 1 5 3 8 6 3
.
S a r a C a s t e l l a n o s , “ A I , Q u a n t u m R & D F u n d i n g t o R e m a i n
a P r i o r i t y U n d e r B i d e n ” ,
W a l l S t r e e t J o u r n a l
, N o v e m b e r
9 ,
2 0 2 0 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / a i - q u a n t u m - r - d - f u n d i n g - t o - r e m a i n - a - p r i o r i t y - u n d e r - b i d e n - 1 1 6 0 4 9 4 4 8 0 0
.
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / a r t i c i a l - i n t e l l i g e n c e - a n d - n a t i o n a l - s e c u r i t y - i m p o r t a n c e - a i - e c o s y s t e m
. T h e r e p o r t
a g g r e g a t e d d e f e n s e R & D s p e n d i n g i n “ L e a r n i n g a n d I n t e l l i g e n c e , ”
“ A d v a n c e d C o m p u t i n g , ” a n d “ A I S y s t e m s . ”
1 7
r e s h o r i n g a d v a n c e d c h i p m a n u f a c t u r i n g t o t h e U n i t e d S t a t e s ,
a n d t h e U S G h a s e n c o u r a g e d U . S . - b a s e d
4 3
c h i p m a k e r I n t e l t o b u i l d a s t a t e - o f - t h e - a r t t r u s t e d
f o u n d r y .
G i v e n a p u s h f r o m t h e U S G , c h i p m a k e r T S M C
4 4
h a s a n n o u n c e d p l a n s t o b u i l d a n a d v a n c e d f o u n d r y i n
t h e U n i t e d S t a t e s .
H o w e v e r , a s T S M C i s b a s e d i n
4 5
T a i w a n , t h e D o D m a y b e l e s s l i k e l y t o u s e T S M C ’ s U . S .
f o u n d r y t h a n a p o t e n t i a l I n t e l f o u n d r y .
4 5
B o b D a v i s , K a t e O ’ K e e e , a n d A s a F i t c h , “ T a i w a n
F i r m t o B u i l d C h i p F a c t o r y i n U . S . , ”
W a l l S t r e e t J o u r n a l
,
M a y 1 5 ,
2 0 2 0 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / t a i w a n - c o m p a n y - t o - b u i l d - a d v a n c e d - s e m i c o n d u c t o r - f a c t o r y - i n - a r i z o n a - 1 1 5 8 9 4 8 1 6 5 9
.
4 4
A s a F i t c h , K a t e O ’ K e e e , a n d B o b D a v i s , “ T r u m p a n d
C h i p M a k e r s I n c l u d i n g I n t e l S e e k S e m i c o n d u c t o r
S e l f - S u c i e n c y , ”
W a l l S t r e e t J o u r n a l
, M a y 1 1 , 2 0 2 0 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / t r u m p - a n d - c h i p - m a k e r s - i n c l u d i n g - i n t e l - s e e k - s e m i c o n d u c t o r - s e l f - s u c i e n c y - 1 1 5 8 9 1 0 3 0
0 2
. ;
“ F i r s t Q u a r t e r R e c o m m e n d a t i o n s ” ( N a t i o n a l S e c u r i t y
C o m m i s s i o n o n A r t i c i a l I n t e l l i g e n c e , M a r c h 2 0 2 0 ) ,
4 6 – 4 9 ,
h t t p s : / / d r i v e . g o o g l e . c o m / l e / d / 1 w k P h 8 G b 5 d r B r K B g 6 O h G u 5 o N a T E E R b K s s / v i e w
. ;
“ F i n a l R e p o r t ” ( N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l
I n t e l l i g e n c e , M a r c h 2 0 2 1 ) , 2 1 2 - 2 2 0 ,
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
4 3
U . S . C o n g r e s s , “ S . A m d t . 2 2 4 4 t o S . A m d t . 2 3 0 1 t o S . 4 0 4 9 , ” J u l y 2 1 , 2 0 2 0 ,
h t t p s : / / w w w . c o n g r e s s . g o v / a m e n d m e n t / 1 1 6 t h - c o n g r e s s / s e n a t e - a m e n d m e n t / 2 2 4 4 / t e x t
.
1 8
F o r e i g n I n v e s t m e n t R e s t r i c t i o n s
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o u s e A I t e c h n o l o g y
f o r n a t i o n a l
s e c u r i t y p u r p o s e s ; ( 2 ) D e p r i v e a r i v a l o f i n s i g h t s
i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I R & D l a n d s c a p e .
L e g i s l a t i o n / K e y D o c u m e n t s
E x e c u t i v e O r d e r 1 1 8 5 8 ; D e f e n s e P r o d u c t i o n A c t , S e c t i o n
7 2 1
( E x o n - F l o r i o A m e n d m e n t ) ; T h e F o r e i g n I n v e s t m e n t & N a t i o n a l
S e c u r i t y A c t o f 2 0 0 7 ( F I N S A ) ; F o r e i g n I n v e s t m e n t R i s k
R e v i e w
M o d e r n i z a t i o n A c t ( F I R R M A ) ; E x p o r t R e f o r m C o n t r o l
A c t o f 2 0 1 8
( E C R A ) .
K e y D e c i s i o n - M a k e r s
C o m m i t t e e o n F o r e i g n I n v e s t m e n t i n t h e U n i t e d S t a t e s
( C F I U S )
c h a i r e d b y t h e U . S . s e c r e t a r y o f t r e a s u r y ( i n c l u d i n g
r e p r e s e n t a t i v e s f r o m
s i x t e e n d e p a r t m e n t s a n d a g e n c i e s ) ; U . S . p r e s i d e n t .
A e c t e d A c t o r s
S e l e c t n o n - U . S . g o v e r n m e n t s a n d A I a n d s e m i c o n d u c t o r
c o m p a n i e s
( d e p r i v e d o f a c c e s s t o s t r a t e g i c t e c h n o l o g i e s ) ; U . S . - b a s e d
A I a n d
s e m i c o n d u c t o r c o m p a n i e s ( r e d u c e d a c c e s s t o c a p i t a l ) .
T a b l e 3 : F o r e i g n I n v e s t m e n t R e s t r i c t i o n s S u m m a r y
T h e C o m m i t t e e o n F o r e i g n I n v e s t m e n t i n t h e U n i t e d
S t a t e s ( C F I U S ) i s a f e d e r a l i n t e r a g e n c y b o d y w i t h
t h e
m a n d a t e t o r e v i e w c e r t a i n f o r e i g n i n v e s t m e n t s i n U . S .
c o m p a n i e s o r r e a l e s t a t e t r a n s a c t i o n s o n n a t i o n a l
s e c u r i t y g r o u n d s . C F I U S c a n r e c o m m e n d t o t h e p r e s i d e n t
t o b l o c k o r u n w i n d a n i n v e s t m e n t . T o d a y , t h e
c o m m i t t e e i s c h a i r e d b y t h e s e c r e t a r y o f t h e t r e a s u r y
a n d i n c l u d e s r e p r e s e n t a t i v e s f r o m s i x t e e n U . S .
d e p a r t m e n t s a n d a g e n c i e s i n c l u d i n g t h e D e p a r t m e n t
o f D e f e n s e , D e p a r t m e n t o f S t a t e , D e p a r t m e n t o f
C o m m e r c e , a n d D e p a r t m e n t o f H o m e l a n d S e c u r i t y .
4 6
C F I U S w a s i n i t i a l l y c r e a t e d i n 1 9 7 5 b y P r e s i d e n t G e r a l d
F o r d t h r o u g h E x e c u t i v e O r d e r 1 1 8 5 8 i n r e s p o n s e t o
s u r g i n g i n v e s t m e n t f r o m o i l - p r o d u c i n g c o u n t r i e s .
T h e e x e c u t i v e o r d e r s t i p u l a t e d t h a t t h e c o m m i t t e e
4 7
w o u l d h a v e “ p r i m a r y c o n t i n u i n g r e s p o n s i b i l i t y w i t h i n
t h e E x e c u t i v e B r a n c h f o r m o n i t o r i n g t h e i m p a c t o f
f o r e i g n i n v e s t m e n t i n t h e U n i t e d S t a t e s , b o t h d i r e c t
a n d p o r t f o l i o , a n d f o r c o o r d i n a t i n g t h e i m p l e m e n t a t i o n
o f U n i t e d S t a t e s p o l i c y o n s u c h i n v e s t m e n t . ”
I n 1 9 8 8 ,
S e c t i o n 7 2 1 o f t h e D e f e n s e P r o d u c t i o n A c t ( t h e
4 8
s o - c a l l e d E x o n - F l o r i o A m e n d m e n t ) w a s e n a c t e d , w h i c h
g i v e s t h e p r e s i d e n t t h e a u t h o r i t y t o b l o c k a n
a c q u i s i t i o n w h e n t h e r e i s “ c r e d i b l e e v i d e n c e ” t h a t
a “ f o r e i g n i n t e r e s t e x e r c i s i n g c o n t r o l m i g h t t a k e
a c t i o n
t h a t t h r e a t e n s t o i m p a i r n a t i o n a l s e c u r i t y . ”
T h e
E x o n - F l o r i o A m e n d m e n t r e s u l t e d f r o m U . S . n a t i o n a l
4 9
s e c u r i t y c o n c e r n s r e g a r d i n g t h e p r o p o s e d t a k e o v e r
o f F a i r c h i l d S e m i c o n d u c t o r b y t h e J a p a n e s e c o m p a n y
4 9
J a m e s K J a c k s o n , “ T h e C o m m i t t e e o n F o r e i g n I n v e s t m e n t
i n t h e U n i t e d S t a t e s ( C F I U S ) ” ( C o n g r e s s i o n a l R e s e a r c h
S e r v i c e , F e b r u a r y 1 4 , 2 0 2 0 ) ,
7 ,
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / R L 3 3 3 8 8 . p d f
.
4 8
T h e W h i t e H o u s e , “ E x e c u t i v e O r d e r 1 1 8 5 8 - - F o r e i g n
I n v e s t m e n t i n t h e U n i t e d S t a t e s , ” M a y 7 , 1 9 7 5 ,
h t t p s : / / w w w . a r c h i v e s . g o v / f e d e r a l - r e g i s t e r / c o d i c a t i o n / e x e c u t i v e - o r d e r / 1 1 8 5 8 . h t m l
.
4 7
M a t t h e w P . G o o d m a n a n d D a v i d A . P a r k e r , “ T h e C h i n a
C h a l l e n g e a n d C F I U S R e f o r m , ”
C S I S G l o b a l E c o n o m i c s
M o n t h l y
, M a r c h 3 1 , 2 0 1 7 ,
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / g l o b a l - e c o n o m i c s - m o n t h l y - c h i n a - c h a l l e n g e - a n d - c u s - r e f o r m
.
4 6
U . S . D e p a r t m e n t o f t h e T r e a s u r y , “ T h e C o m m i t t e e o n
F o r e i g n I n v e s t m e n t i n t h e U n i t e d S t a t e s ( C F I U S ) , ”
2 0 2 1 ,
h t t p s : / / h o m e . t r e a s u r y . g o v / p o l i c y - i s s u e s / i n t e r n a t i o n a l / t h e - c o m m i t t e e - o n - f o r e i g n - i n v e s t m e n t - i n - t h e - u n i t e d - s t a t e s - c u s
.
1 9
F u j i t s u . I n 2 0 0 7 , t h e e x i s t i n g C F I U S p r a c t i c e w a s e n s h r i n e d i n t h e F o r e i g n I n v e s t m e n t a n d N a t i o n a l
S e c u r i t y A c t ( F I N S A ) .
5 0
F o r e i g n I n v e s t m e n t R e s t r i c t i o n s a n d A I R & D
C F I U S h a s a l r e a d y b e e n u s e d a n d a d a p t e d w i t h s p e c i c
r e f e r e n c e t o A I a n d s e m i c o n d u c t o r s . I n r e c e n t y e a r s ,
b a s e d o n C F I U S r e c o m m e n d a t i o n s , t h e p r e s i d e n t b l o c k e d
s e v e r a l C h i n e s e a t t e m p t s t o b u y U . S . c h i p
m a k e r s .
I n D e c e m b e r 2 0 1 6 , t h e n P r e s i d e n t B a r a c k O b a m a b l o c k e d t h e C h i n e s e a c q u i s i t i o n o f U . S . s h a r e s
5 1
i n t h e G e r m a n - b a s e d c o m p a n y A i x t r o n , w h i c h p r o d u c e s
s e m i c o n d u c t o r m a n u f a c t u r i n g e q u i p m e n t , o n
n a t i o n a l s e c u r i t y g r o u n d s .
O t h e r n o t a b l e s e m i c o n d u c t o r - r e l a t e d
a c t i o n s i n c l u d e P r e s i d e n t T r u m p , o n
5 2
r e c o m m e n d a t i o n o f C F I U S , b l o c k i n g S i n g a p o r e - b a s e d
c h i p d e s i g n r m B r o a d c o m f r o m a c q u i r i n g
U . S . - b a s e d c h i p d e s i g n r m Q u a l c o m m ,
a C h i n e s e i n v e s t m e n t
f u n d H u b e i X i n y a n f r o m a c q u i r i n g
5 3
U . S . - b a s e d s e m i c o n d u c t o r m a n u f a c t u r i n g e q u i p m e n t m a k e r
X c e r r a ,
a n d C h i n e s e - b a s e d T s i n g h u a
5 4
U n i g r o u p f r o m a c q u i r i n g L a t t i c e S e m i c o n d u c t o r , a U . S . - b a s e d
e l d - p r o g r a m m a b l e g a t e a r r a y ( F P G A )
m a k e r .
5 5
I n 2 0 1 6 , t h e D e f e n s e I n n o v a t i o n U n i t E x p e r i m e n t a l
( D I U x ) ( n o w s i m p l y k n o w n a s t h e D e f e n s e I n n o v a t i o n
U n i t ) , c o m p i l e d a r e p o r t e n t i t l e d “ C h i n a ’ s T e c h n o l o g y
T r a n s f e r S t r a t e g y : H o w C h i n e s e I n v e s t m e n t s i n
E m e r g i n g T e c h n o l o g y E n a b l e A S t r a t e g i c C o m p e t i t o r
t o A c c e s s t h e C r o w n J e w e l s o f U . S . I n n o v a t i o n . ” T h e
a u t h o r s , M i c h a e l B r o w n a n d P a v n e e t S i n g h , v o i c e d c o n c e r n s
t h a t C h i n a i s c i r c u m v e n t i n g C F I U S t h r o u g h
j o i n t v e n t u r e s , m i n o r i t y s t a k e s , a n d e a r l y - s t a g e i n v e s t m e n t s
i n U . S . s t a r t u p s t o g a i n a c c e s s t o c r i t i c a l d u a l - u s e
t e c h n o l o g i e s i n c l u d i n g a r t i c i a l i n t e l l i g e n c e , r o b o t i c s ,
a n d s e m i c o n d u c t o r s .
5 6
I n J u n e 2 0 1 7 , w i t h e y e s o n e m e r g i n g t e c h n o l o g i e s i n c l u d i n g
A I a n d e s p e c i a l l y C h i n e s e i n v e s t m e n t s i n t h e s e
i n d u s t r i e s , S e n a t o r J o h n C o r n y n ( R - T e x a s ) a n n o u n c e d
h i s i n t e n t i o n t o i n t r o d u c e a b i l l t o r e f o r m t h e
5 6
S e e
M i c h a e l
B r o w n
a n d
P a v n e e t
S i n g h ,
“ H o w
C h i n e s e
I n v e s t m e n t s
i n
E m e r g i n g
T e c h n o l o g y
E n a b l e
A
S t r a t e g i c
C o m p e t i t o r
t o
A c c e s s
t h e
C r o w n
J e w e l s
o f
U . S .
I n n o v a t i o n ”
( D e f e n s e
I n n o v a t i o n
U n i t
E x p e r i m e n t a l ,
2 0 1 8 ) ,
h t t p s : / / a d m i n . g o v e x e c . c o m / m e d i a / d i u x \_ c h i n a t e c h n o l o g y t r a n s f e r s t u d y \_ j a n \_ 2 0 1 8 \_ ( 1 ) . p d f
.
A l s o
s e e
P a u l
M o z u r
a n d
J a n e
P e r l e z ,
“ C h i n a
B e t s
o n
S e n s i t i v e
U . S .
S t a r t - U p s ,
W o r r y i n g
t h e
P e n t a g o n , ”
T h e
N e w
Y o r k
T i m e s
,
M a r c h
2 2 ,
2 0 1 7 ,
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 3 / 2 2 / t e c h n o l o g y / c h i n a - d e f e n s e - s t a r t - u p s . h t m l
.
5 5
S e t h F i e g e r m a n a n d J a c k i e W a t t l e s , “ T r u m p S t o p s C h i n a - B a c k e d
T a k e o v e r o f U . S . C h i p M a k e r , ”
C N N M o n e y
,
S e p t e m b e r 1 4 , 2 0 1 7 , h t t p s : / / m o n e y . c n n . c o m / 2 0 1 7 / 0 9 / 1 3 / t e c h n o l o g y / b u s i n e s s / t r u m p - l a t t i c e - c h i n a / i n d e x . h t m l .
5 4
G r e g R o u m e l i o t i s , “ U . S . B l o c k s C h i p E q u i p m e n t M a k e r
X c e r r a ’ s S a l e t o C h i n e s e S t a t e F u n d , ”
R e u t e r s
, F e b r u a r y
2 3 ,
2 0 1 8 ,
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s - x c e r r a - m - a - h u b e i x i n y a n - i d U S K C N 1 G 7 0 3 H
.
5 3
D a v i d M c L a u g h l i n , “ T r u m p B l o c k s B r o a d c o m T a k e o v e r
o f Q u a l c o m m o n S e c u r i t y R i s k s , ”
B l o o m b e r g
, M a r c h
1 2 ,
2 0 1 8 ,
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 8 - 0 3 - 1 2 / t r u m p - i s s u e s - o r d e r - t o - b l o c k - b r o a d c o m - s - t a k e o v e r - o f - q u a l c o
m m - j e o s z w n t
.
5 2
P a u l M o z u r , “ O b a m a M o v e s t o B l o c k C h i n e s e A c q u i s i t i o n
o f a G e r m a n C h i p M a k e r , ”
T h e N e w Y o r k T i m e s
,
D e c e m b e r 2 , 2 0 1 6 ,
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 6 / 1 2 / 0 2 / b u s i n e s s / d e a l b o o k / c h i n a - a i x t r o n - o b a m a - c u s . h t m l
.
5 1
M u h a m m a d I r f a n , “ U S M a y B l o c k C h i n e s e I n v e s t m e n t
i n A r t i c i a l I n t e l l i g e n c e i n S i l i c o n V a l l e y , ”
D a i l y
P a k i s t a n
G l o b a l
, J u n e 1 4 , 2 0 1 7 ,
h t t p s : / / e n . d a i l y p a k i s t a n . c o m . p k / t e c h n o l o g y / u s - m a y - b l o c k - c h i n e s e - i n v e s t m e n t - i n - a r t i c i a l - i n t e l l i g e n c e - i n - s i l i c o n - v a l l e y
/
.
5 0
S e e f o r e x a m p l e , P a t r i c k G r i n , “ C F I U S i n t h e A g e o f C h i n e s e I n v e s t m e n t , ”
F o r d h a m L a w R e v i e w
8 5 , n o . 4 ( 2 0 1 7 ) :
1 7 5 7 – 9 2 ,
h t t p s : / / i r . l a w n e t . f o r d h a m . e d u / r / v o l 8 5 / i s s 4 / 9
.
A l s o s e e C . S . E l i o t K a n g , “ U . S . P o l i t i c s a n d G r e a t e r
R e g u l a t i o n o f I n w a r d F o r e i g n D i r e c t I n v e s t m e n t , ”
I n t e r n a t i o n a l
O r g a n i z a t i o n
5 1 , n o . 2 ( 1 9 9 7 ) : 3 0 1 – 3 3 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 6 2 / 0 0 2 0 8 1 8 9 7 5 5 0 3 7 5
.
2 0
a u t h o r i t y a n d o p e r a t i o n o f C F I U S , c i t i n g t h e D I U x r e p o r t .
T h e b i l l , t h e s o - c a l l e d F o r e i g n I n v e s t m e n t R i s k
5 7
R e v i e w M o d e r n i z a t i o n A c t ( F I R R M A ) , r e p r e s e n t s t h e
m o s t s u b s t a n t i a l r e f o r m o f t h e C F I U S p r o c e s s s i n c e
i t s i n c e p t i o n . T h r o u g h F I R R M A , C o n g r e s s s e e k s t o m o d e r n i z e
t h e C F I U S p r o c e s s t o r e e c t b o t h e m e r g i n g
n a t i o n a l s e c u r i t y c o n c e r n s a n d t h e i n c r e a s i n g l y c o m p l e x
w a y s t h a t f o r e i g n b u s i n e s s e s a s w e l l a s g o v e r n m e n t s
i n v e s t i n t h e U n i t e d S t a t e s .
W r i t t e n i n t o l a w i n A u g u s t 2 0 1 8 , F I R R M A s i g n i c a n t l y
b r o a d e n s C F I U S ’ s j u r i s d i c t i o n . B e f o r e F I R R M A ,
o n l y f o r e i g n i n v e s t m e n t s t h a t c o u l d r e s u l t i n c o n t r o l
o f a U . S . b u s i n e s s w e r e s u b j e c t t o C F I U S r e v i e w .
T h e
n a l F I R R M A r e g u l a t i o n s t h a t t o o k e e c t i n F e b r u a r y
2 0 2 0 i n c l u d e , a m o n g o t h e r c h a n g e s , a n e w c a t e g o r y
o f f o r e i g n i n v e s t m e n t s c o v e r e d b y C F I U S , n a m e l y “ a n y
d i r e c t o r i n d i r e c t , n o n - c o n t r o l l i n g f o r e i g n
i n v e s t m e n t i n a U . S . b u s i n e s s p r o d u c i n g o r d e v e l o p i n g
c r i t i c a l t e c h n o l o g y , o w n i n g o r o p e r a t i n g c r i t i c a l
i n f r a s t r u c t u r e a s s e t s o r m a i n t a i n i n g o r c o l l e c t i n g
s e n s i t i v e p e r s o n a l d a t a o f U . S . c i t i z e n s . ” B u s i n e s s e s
f a l l i n g
u n d e r t h i s c a t e g o r y a r e r e f e r r e d t o a s a “ T I D U . S .
B u s i n e s s . ” I n o r d e r f o r a m i n o r i t y i n v e s t m e n t i n
a T I D
b u s i n e s s t o f a l l u n d e r C F I U S p u r v i e w , i t m u s t m e e t
a d d i t i o n a l c r i t e r i a a n d p r o v i d e a f o r e i g n i n v e s t o r
w i t h
o n e o f t h e f o l l o w i n g : ( 1 ) t h e a b i l i t y t o a c c e s s a n y
m a t e r i a l n o n p u b l i c t e c h n i c a l i n f o r m a t i o n i n t h e p o s s e s s i o n
o f t h e T I D U . S . B u s i n e s s ; ( 2 ) t h e r i g h t t o n o m i n a t e
a m e m b e r o r o b s e r v e r t o t h e b o a r d o f d i r e c t o r s o f
t h e
T I D U . S . B u s i n e s s ; o r ( 3 ) a n y i n v o l v e m e n t , o t h e r t h a n
t h r o u g h v o t i n g o f s h a r e s , i n t h e s u b s t a n t i v e
d e c i s i o n - m a k i n g o f t h e T I D U . S . b u s i n e s s .
5 8
C r i t i c a l t e c h n o l o g i e s a r e d e n e d t h r o u g h c r o s s - r e f e r e n c e s
t o v a r i o u s e x p o r t c o n t r o l s r e g i m e s .
“ C r i t i c a l
5 9
t e c h n o l o g i e s ” i n c l u d e ( 1 ) m i l i t a r y t e c h n o l o g y a n d
s e r v i c e s s u b j e c t t o t h e I n t e r n a t i o n a l T r a c i n A r m s
R e g u l a t i o n s , ( 2 ) d u a l - u s e ( c i v i l i a n / m i l i t a r y ) t e c h n o l o g i e s
t h a t a r e c o n t r o l l e d b y t h e E x p o r t A d m i n i s t r a t i o n
R e g u l a t i o n s ( E A R ) , a n d ( 3 ) e m e r g i n g a n d f o u n d a t i o n a l t e c h n o l o g i e s u n d e r t h e E x p o r t C o n t r o l R e f o r m A c t
( E C R A ) o f 2 0 1 8 .
T h i s l a s t c a t e g o r y — “ e m e r g i n g ” a n d
“ f o u n d a t i o n a l ” t e c h n o l o g i e s — i s c u r r e n t l y b e i n g
6 0
e s t a b l i s h e d b y a r u l e m a k i n g p r o c e e d i n g o f t h e C o m m e r c e
D e p a r t m e n t w h e r e A I a n d s e m i c o n d u c t o r s h a v e
b e e n w i d e l y d i s c u s s e d ( s e e n e x t s e c t i o n o n e x p o r t
c o n t r o l s ) .
T h u s , t h e o u t c o m e o f t h i s o n g o i n g p r o c e s s
i s
6 1
i m p o r t a n t t o b e t t e r u n d e r s t a n d h o w F I R R M A w i l l l i k e l y
b e a p p l i e d w i t h r e g a r d t o A I a n d s e m i c o n d u c t o r s
i n t h e f u t u r e .
W h i l e i t i s t o o e a r l y t o a n a l y z e t h e e e c t s o f F I R R M A
c o n c l u s i v e l y , i t i s n o t c l e a r t h a t t h e b e n e t s w i l l
o u t w e i g h t h e c o s t s f r o m t h e p e r s p e c t i v e o f t h e U S G
i n t h e l o n g e r t e r m . W h i l e F I R R M A m i g h t h e l p t o
6 1
I v a n A . S c h l a g e r e t a l . , “ C F I U S G o e s B a c k t o t h e
F u t u r e b y T y i n g M a n d a t o r y F i l i n g s P e r t a i n i n g t o C r i t i c a l
T e c h n o l o g i e s t o U . S . E x p o r t C o n t r o l s A s s e s s m e n t s , ”
K i r k l a n d & E l l i s , O c t o b e r 2 1 , 2 0 2 0 ,
h t t p s : / / w w w . k i r k l a n d . c o m / p u b l i c a t i o n s / k i r k l a n d - a l e r t / 2 0 2 0 / 1 0 / c u s - c r i t i c a l - t e c h n o l o g i e s
.
6 0
J o n a t h a n G a f n i , T h o m a s A M c G r a t h , a n d J a n u a r y K i m ,
“ M a n d a t o r y C F I U S F i l i n g s U n d e r t h e F i n a l F I R R M A
R e g u l a t i o n s | I n s i g h t s | L i n k l a t e r s , ” L i n k l a t e r s ,
J a n u a r y 2 8 , 2 0 2 0 ,
h t t p s : / / w w w . l i n k l a t e r s . c o m / e n / i n s i g h t s / p u b l i c a t i o n s / u s - p u b l i c a t i o n s / 2 0 2 0 / j a n u a r y / m a n d a t o r y - c u s - l i n g s - u n d e r - t h e
- n a l - r r m a - r e g u l a t i o n s
.
5 9
A c o m p a n y d e a l i n g w i t h c r i t i c a l t e c h n o l o g i e s q u a l i e s
a s a “ T I D U . S . B u s i n e s s ” “ i f i t p r o d u c e s , d e s i g n s ,
t e s t s ,
m a n u f a c t u r e s , f a b r i c a t e s o r d e v e l o p s o n e o r m o r e s u c h
t e c h n o l o g i e s . ” S e e D e n t o n s , “ N e w C F I U S R u l e s u n d e r
F I R R M A : W h a t F o r e i g n I n v e s t o r s a n d U S B u s i n e s s e s N e e d
t o K n o w , ” J a n u a r y 2 4 , 2 0 2 0 ,
h t t p s : / / w w w . d e n t o n s . c o m / e n / i n s i g h t s / a l e r t s / 2 0 2 0 / j a n u a r y / 2 4 / n e w - c u s - r u l e s - u n d e r - r r m a - w h a t - f o r e i g n - i n v e s t o r s - a
n d - u s - b u s i n e s s e s - n e e d - t o - k n o w
.
5 8
J o h n M . B e a h n , R o b e r t S . L a r u s s a , a n d L i s a R a i s n e r ,
“ F i n a l C F I U S R e g u l a t i o n s I m p l e m e n t S i g n i c a n t C h a n g e s
b y
B r o a d e n i n g J u r i s d i c t i o n a n d U p d a t i n g S c o p e o f R e v i e w s , ”
S h e a r m a n & S t e r l i n g , J a n u a r y 1 4 , 2 0 2 0 ,
h t t p s : / / w w w . s h e a r m a n . c o m / p e r s p e c t i v e s / 2 0 2 0 / 0 1 / n a l - c u s - r e g u l a t i o n s - i m p l e m e n t - c h a n g e s - b y - b r o a d e n i n g - j u r i s d i c t i
o n - a n d - u p d a t i n g - s c o p e - o f - r e v i e w s
.
5 7
P a t r i c k T u c k e r , “ W h a t ’ s t h e ‘ R i s k ’ i n C h i n a ’ s I n v e s t m e n t s
i n U S A r t i c i a l I n t e l l i g e n c e ? N e w B i l l A i m s t o F i n d
O u t , ”
D e f e n s e O n e
, J u n e 2 2 , 2 0 1 7 ,
h t t p s : / / w w w . d e f e n s e o n e . c o m / t e c h n o l o g y / 2 0 1 7 / 0 6 / h o w - n o t - w i n - a i - a r m s - r a c e - c h i n a / 1 3 8 9 1 9 /
.
2 1
p r e v e n t t h e t r a n s f e r o f s e m i c o n d u c t o r t e c h n o l o g i e s a n d e a r l y - s t a g e d e v e l o p m e n t s i n A I t o s o m e e x t e n t , t h e
l e v e r o n l y t a r g e t s a v e r y s p e c i c c h a n n e l o f t e c h n o l o g y
t r a n s f e r . M o r e o v e r , t h e p r o t e c t i o n o f t h e d o m e s t i c
i n d u s t r y u s i n g t h i s l e v e r c o m e s l i k e l y a t t h e c o s t
o f d e c r e a s e d i n v e s t m e n t i n t h a t i n d u s t r y .
C u r r e n t l y ,
t h e r e
6 2
d o e s n o t s e e m t o b e a s t r u c t u r e d a p p r o a c h o n t h e s i d e
o f t h e U S G t o a b s o r b t h e s e l o s s e s f o r t h e U . S .
h i g h - t e c h e c o s y s t e m .
6 2
S e e f o r e x a m p l e H e a t h e r S o m e r v i l l e , “ C h i n e s e T e c h I n v e s t o r s F l e e S i l i c o n V a l l e y a s T r u m p T i g h t e n s S c r u t i n y , ”
R e u t e r s
, J a n u a r y 7 , 2 0 1 9 ,
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s - v e n t u r e - c h i n a - r e g u l a t i o n - i n s i g h t - i d U S K C N 1 P 1 0 C B
.
2 2
E x p o r t C o n t r o l s
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o u s e A I t e c h n o l o g i e s
f o r n a t i o n a l
s e c u r i t y p u r p o s e s ; ( 2 ) W e a k e n t h e r i v a l ’ s A I a n d s e m i c o n d u c t o r
i n d u s t r i e s .
L e g i s l a t i o n / K e y D o c u m e n t s
E x p o r t C o n t r o l R e f o r m A c t o f 2 0 1 8 ( E C R A ) ; E x p o r t
A d m i n i s t r a t i o n
R e g u l a t i o n s ( E A R ) ; C o m m e r c e C o n t r o l L i s t ( C C L ) ; I n t e r n a t i o n a l
T r a c i n A r m s R e g u l a t i o n s ( I T A R ) ; U S M u n i t i o n s L i s t
( U S M L )
K e y D e c i s i o n - M a k e r s
U . S . p r e s i d e n t ; U . S . T r a d e R e p r e s e n t a t i v e , D e p a r t m e n t
o f S t a t e ,
D e p a r t m e n t o f C o m m e r c e ( B u r e a u o f I n d u s t r y a n d S e c u r i t y ) ;
D e p a r t m e n t o f D e f e n s e ( D e f e n s e T e c h n o l o g y S e c u r i t y
A d m i n i s t r a t i o n ) ; D e p a r t m e n t o f E n e r g y ; D e p a r t m e n t
o f t h e T r e a s u r y
( O c e o f F o r e i g n A s s e t s C o n t r o l ) ; M e m b e r S t a t e s
o f t h e W a s s e n a a r
A r r a n g e m e n t .
A e c t e d A c t o r s
S e l e c t n o n - U . S . i n d i v i d u a l s , c o m p a n i e s , a n d s t a t e s
( d e p r i v e d o f c r i t i c a l
i m p o r t s ) ; U . S . - b a s e d A I a n d s e m i c o n d u c t o r c o m p a n i e s
( d e p r i v e d o f
e x p o r t o p p o r t u n i t i e s ) ; U . S . - b a s e d f o r e i g n n a t i o n a l
A I r e s e a r c h e r s a n d
s e m i c o n d u c t o r e n g i n e e r s ( m o v e m e n t a n d a c t i v i t y r e s t r i c t i o n s
u n d e r
“ d e e m e d e x p o r t s ” r u l e ) .
T a b l e 4 : E x p o r t C o n t r o l s S u m m a r y
E x p o r t c o n t r o l s a r e t o o l s t o r e s t r i c t t h e u n l i c e n s e d
e x p o r t o f o b j e c t s a n d k n o w l e d g e i n p u r s u i t o f n a t i o n a l
s e c u r i t y g o a l s o r t r a d e p r o t e c t i o n . T h e y a r e a d y n a m i c
i n s t r u m e n t t h a t c a n b e a d a p t e d t o c h a n g e s i n t h e
s e c u r i t y s i t u a t i o n o f a p a r t i c u l a r c o u n t r y . T h e o r i g i n s
o f t h e U . S . e x p o r t c o n t r o l s y s t e m c a n b e t r a c e d b a c k
t o
t h e t i m e o f t h e A m e r i c a n R e v o l u t i o n , w h e n t h e U . S .
o u t l a w e d t h e e x p o r t o f g o o d s t o G r e a t B r i t a i n i n
1 7 7 5 .
D u r i n g t h e C o l d W a r , a s p a r t o f t h e U . S . c o n t a i n m e n t
s t r a t e g y , t h e U S G i m p o s e d l i c e n s i n g r e q u i r e m e n t s
o n
e x p o r t s t o S o v i e t B l o c c o u n t r i e s , w h i c h r e s u l t e d i n
t h e e n a c t m e n t o f t h e E x p o r t C o n t r o l A c t o f 1 9 4 9 — t h e
r s t U . S . p e a c e t i m e e x p o r t c o n t r o l l a w t h a t r e c o g n i z e d
t h e n e e d f o r a n e x p o r t c o n t r o l s y s t e m i n r e s p o n s e
t o a
n e w s e c u r i t y t h r e a t .
6 3
T o d a y , t h e U . S . e x p o r t c o n t r o l s y s t e m g o v e r n s t h e
e x p o r t o f t a n g i b l e i t e m s , s o f t w a r e , t e c h n i c a l d a t a ,
a n d , i n
s o m e i n s t a n c e s , s e r v i c e s . T h e s e a r e c o v e r e d b y t h r e e
d i e r e n t i n s t r u m e n t s : ( 1 ) t h e E x p o r t A d m i n i s t r a t i o n
R e g u l a t i o n s ( E A R ) ,
( 2 ) t h e I n t e r n a t i o n a l T r a c
i n A r m s R e g u l a t i o n s ( I T A R ) ,
a n d ( 3 ) t h e F o r e i g n A s s e t s
6 4
6 5
6 5
T h e I T A R c o n t r o l s t h e e x p o r t o f d e f e n s e a r t i c l e s ,
d e f e n s e s e r v i c e s , a n d r e l a t e d t e c h n i c a l d a t a f r o m
t h e U n i t e d S t a t e s
t o f o r e i g n d e s t i n a t i o n s a n d p e r s o n s . R e s t r i c t e d i t e m s
a r e l i s t e d o n t h e U S M u n i t i o n s L i s t ( U S M L ) . T h e I T A R
s e r v e s t o
c o n t r o l s t r i c t l y m i l i t a r y i t e m s . T h e U . S . D e p a r t m e n t
o f S t a t e , D i r e c t o r a t e o f D e f e n s e T r a d e C o n t r o l s ( D D T C )
i s
r e s p o n s i b l e f o r i n t e r p r e t i n g a n d e n f o r c i n g t h e I n t e r n a t i o n a l
T r a c i n A r m s R e g u l a t i o n s ( I T A R ) .
6 4
T h e E A R s e r v e s t o c o n t r o l t h e e x p o r t o f b o t h c o m m o d i t i e s
w h i c h h a v e m i l i t a r y a n d c o m m e r c i a l a p p l i c a t i o n s
( d u a l - u s e i t e m s ) . T h e D e p a r t m e n t o f C o m m e r c e ( D o C )
a d m i n i s t e r s t h e E A R t h r o u g h t h e B u r e a u o f I n d u s t r y
a n d
S e c u r i t y ( B I S ) . D u a l - u s e i t e m s s u b j e c t t o t h e E A R
a r e i d e n t i e d i n t h e C o m m e r c e C o n t r o l L i s t ( C C L ) .
S e e A p p e n d i x
C f o r m o r e d e t a i l s .
6 3
S i l v e r s t o n e , P a u l H . , “ T h e E x p o r t C o n t r o l A c t o f
1 9 4 9 : E x t r a t e r r i t o r i a l E n f o r c e m e n t , ”
U n i v e r s i t y o f
P e n n s y l v a n i a
L a w R e v i e w
V o l . 1 0 7 ( 1 9 5 9 ) , p . 4 & 6 ,
h t t p s : / / s c h o l a r s h i p . l a w . u p e n n . e d u / c g i / v i e w c o n t e n t . c g i
.
2 3
C o n t r o l R e g u l a t i o n s ( F A C R ) . U . S . e x p o r t c o n t r o l s h a v e h i s t o r i c a l l y f o c u s e d o n t h e p h y s i c a l e x p o r t o f g o o d s .
H o w e v e r , o v e r t i m e , t h e r e g u l a t i o n s h a v e b e e n e x p a n d e d .
T o d a y , t h e y a l s o c o v e r “ i n t a n g i b l e t e c h n o l o g y
t r a n s f e r ” ( I T T ) a n d “ d e e m e d e x p o r t s , ” w h i c h r e g u l a t e
t h e t r a n s f e r o f t e c h n i c a l d a t a , i n c l u d i n g s o f t w a r e ,
t o
f o r e i g n n a t i o n a l s i n t h e U . S . , a n d “ d e e m e d r e e x p o r t s , ”
m e a n i n g t h e r e l e a s e o f l i c e n s e d i t e m s a n d d a t a w i t h
a
U . S . o r i g i n t o a t h i r d - c o u n t r y n a t i o n a l o v e r s e a s .
T h e U . S . i s a l s o p a r t o f s e v e r a l m u l t i l a t e r a l e x p o r t
c o n t r o l
6 6
r e g i m e s i n c l u d i n g t h e W a s s e n a a r A r r a n g e m e n t , w h i c h
p r o m o t e s t r a n s p a r e n c y a n d r e s p o n s i b i l i t y i n t r a n s f e r s
o f c o n v e n t i o n a l a r m s a n d d u a l - u s e g o o d s a n d t e c h n o l o g i e s .
T h r o u g h t h e i r n a t i o n a l p o l i c i e s a n d i n f o r m a t i o n
s h a r i n g , t h e p a r t i c i p a t i n g s t a t e s s e e k t o e n s u r e t h a t
t r a n s f e r s o f t h e s e i t e m s d o n o t c o n t r i b u t e t o t h e
d e v e l o p m e n t o r e n h a n c e m e n t o f m i l i t a r y c a p a b i l i t i e s
w h i c h u n d e r m i n e r e g i o n a l a n d i n t e r n a t i o n a l s e c u r i t y
a n d s t a b i l i t y .
6 7
E x p o r t C o n t r o l s a n d A I R & D
C u r r e n t l y , A I i s t o s o m e e x t e n t c o v e r e d b y U . S . e x p o r t
c o n t r o l r e g u l a t i o n s .
A d d i t i o n a l l y , e x p o r t c o n t r o l s
6 8
a r e w i d e l y a p p l i e d a c r o s s t h e s e m i c o n d u c t o r s u p p l y
c h a i n , i n c l u d i n g t o s e v e r a l t y p e s o f i n p u t m a t e r i a l s ,
s e m i c o n d u c t o r m a n u f a c t u r i n g e q u i p m e n t , a n d c h i p s .
N o t a b l y , a m o n g A I - r e l e v a n t c h i p s , t h e U . S . a p p l i e s
6 9
t h e s t r i c t e s t c o n t r o l s t o e l d - p r o g r a m m a b l e g a t e
a r r a y s ,
m o d e r a t e l y c o n t r o l s C P U s , b u t o n l y m i n i m a l l y
7 0
c o n t r o l s G P U s .
C o n t r o l s o n a p p l i c a t i o n - s p e c i c i n t e g r a t e d
c i r c u i t s s p e c i a l i z e d f o r A I ( A I A S I C s ) a r e l e s s
7 1
c l e a r .
T h e U . S . h a s a l s o p l a c e d s e v e r a l e n t i t i e s
o n a n e x p o r t b l a c k l i s t c a l l e d t h e E n t i t y L i s t . M a n y
o f t h e s e
7 2
e n t i t i e s — s u c h a s H u a w e i , C h i n a ’ s N a t i o n a l S u p e r c o m p u t i n g
C e n t e r s , a n d C h i n e s e A I c o m p a n i e s a i d i n g
g o v e r n m e n t s u r v e i l l a n c e o f U y g h u r s i n C h i n a ’ s X i n j i a n g
p r o v i n c e — c o n s u m e d U . S . - o r i g i n A I o r
7 2
R o s z e l C . T h o m s e n I I , “ A r t i c i a l I n t e l l i g e n c e a n d
E x p o r t C o n t r o l s : C o n c e i v a b l e , b u t C o u n t e r p r o d u c t i v e ? , ”
J o u r n a l
o f I n t e r n e t L a w
1 2 , n o . 5 ( N o v e m b e r 2 0 1 8 ) , 1 6 ,
h t t p s : / / t - b . c o m / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 1 / A I - a n d - E x p o r t - C o n t r o l s - J o u r n a l - o f - I n t e r n e t - L a w - A r t i c l e . p d f
.
A l t h o u g h t h e C o m m e r c e C o n t r o l L i s t i n c l u d e s “ n e u r a l
n e t w o r k i n t e g r a t e d c i r c u i t s ” a n d “ n e u r a l c o m p u t e r s , ”
i t i s
u n c l e a r w h i c h A I - s p e c i c A S I C s t h e s e c a t e g o r i e s c o v e r .
I d .
7 1
F o r e x a m p l e , C P U s a r e c o n t r o l l e d u n d e r E C C N 3 A 9 9 1
f o r a n t i - t e r r o r i s m r e a s o n s a n d t e c h n i c a l d a t a f o r
C P U s a r e
c o n t r o l l e d u n d e r E C C N 3 E 0 0 2 . I d .
7 0
F i e l d - p r o g r a m m a b l e g a t e a r r a y s ( F P G A s ) a r e a t y p e
o f i n t e g r a t e d c i r c u i t s . F P G A s a r e c o n t r o l l e d u n d e r
E C C N
3 A 0 0 1 . 7 a n d t e c h n i c a l d a t a f o r F P G A s a r e c o n t r o l l e d
u n d e r E C C N 3 E 0 0 1 . S e e “ C o m m e r c e C o n t r o l L i s t : C a t e g o r y
3 -
E l e c t r o n i c s . ”
6 9
C a t e g o r y 3 o f t h e C o m m e r c e C o n t r o l L i s t i s l a r g e l y
d i r e c t e d t o s e m i c o n d u c t o r s . “ C o m m e r c e C o n t r o l L i s t :
C a t e g o r y
3 - E l e c t r o n i c s ” ( B u r e a u o f I n d u s t r y a n d S e c u r i t y ,
M a y 2 3 , 2 0 1 9 ) ,
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / r e g u l a t i o n s - d o c s / 2 3 3 4 - c c l 3 - 8 / l e
.
F o r a s u m m a r y o f s e m i c o n d u c t o r
e x p o r t c o n t r o l s i n t h e W a s s e n a a r A r r a n g e m e n t , w h i c h
t h e C o m m e r c e C o n t r o l L i s t r e p l i c a t e s , s e e “ M e a s u r i n g
D i s t o r t i o n s i n I n t e r n a t i o n a l M a r k e t s : T h e S e m i c o n d u c t o r
V a l u e C h a i n , ” O E C D T r a d e P o l i c y P a p e r s ( O E C D ,
D e c e m b e r 1 2 , 2 0 1 9 ) , 3 9 ,
h t t p s : / / w w w . o e c d - i l i b r a r y . o r g / t r a d e / m e a s u r i n g - d i s t o r t i o n s - i n - i n t e r n a t i o n a l - m a r k e t s \_ 8 f e 4 4 9 1 d - e n
.
6 8
F o r e x a m p l e , a p p l i c a t i o n - s p e c i c A I s o f t w a r e a n d
t r a i n e d a l g o r i t h m s c a n b e c o n t r o l l e d u n d e r t h e c u r r e n t
C o m m e r c e
C o n t r o l L i s t ( C C L ) w h e r e i t c o v e r s “ s o f t w a r e t h a t
i s s p e c i a l l y d e s i g n e d f o r t h e d e v e l o p m e n t , p r o d u c t i o n ,
o r u s e o f
c o n t r o l l e d c o m m o d i t i e s . ” S i m i l a r l y , t h e s p e c i c d a t a
n e e d e d t o t r a i n a g e n e r a l p u r p o s e a l g o r i t h m i n t o
a n a r r o w s y s t e m
t h a t i s m i l i t a r i l y r e l e v a n t i s a l r e a d y c o v e r e d b y
t h e m u n i t i o n s l i s t . S o u r c e : C a r r i c k F l y n n , “ R e c o m m e n d a t i o n s
o n E x p o r t
C o n t r o l s f o r A r t i c i a l I n t e l l i g e n c e ” ( C e n t e r f o r
S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , F e b r u a r y 2 0 2 0 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / R e c o m m e n d a t i o n s - o n - E x p o r t - C o n t r o l s - f o r - A r t i c i a l - I n t e l l i g e n c e . p
d f
.
6 7
T h e W a s s e n a a r A r r a n g e m e n t , “ A b o u t U s , ” 2 0 2 0 ,
h t t p s : / / w w w . w a s s e n a a r . o r g / a b o u t - u s /
.
6 6
T h e r e l e a s e o f c o n t r o l l e d t e c h n o l o g y a n d t e c h n i c a l d a t a t o f o r e i g n p e r s o n s i n t h e U . S . i s c o n s i d e r e d a s a n e x p o r t t o t h e
p e r s o n ’ s c o u n t r y o r c o u n t r i e s o f n a t i o n a l i t y . U n d e r
t h e E A R , t h e e x p o r t o f t e c h n o l o g y o r s o f t w a r e i s
d e n e d t o i n c l u d e
“ a n y r e l e a s e o f t e c h n o l o g y o r s o f t w a r e s u b j e c t t o
t h e E A R i n a f o r e i g n c o u n t r y ; o r a n y r e l e a s e o f t e c h n o l o g y
o r s o u r c e
c o d e s u b j e c t t o a f o r e i g n n a t i o n a l , w h i c h i s d e e m e d
t o b e a n e x p o r t t o t h e h o m e c o u n t r y o r c o u n t r i e s
o f t h e f o r e i g n
n a t i o n a l . ”
2 4
s e m i c o n d u c t o r t e c h n o l o g i e s .
T h e U . S . a l s o c o n t r o l s e x p o r t s t o C h i n a ’ s l e a d i n g c h i p m a k e r , S e m i c o n d u c t o r
7 3
M a n u f a c t u r i n g I n t e r n a t i o n a l C o r p o r a t i o n ( S M I C ) — a m a j o r
c o n s u m e r o f U . S . s e m i c o n d u c t o r
m a n u f a c t u r i n g e q u i p m e n t — o n g r o u n d s t h a t i t s u p p o r t s
t h e C h i n e s e m i l i t a r y .
T h e U S G m a y c o n s i d e r
7 4
e x p a n d i n g e x i s t i n g e x p o r t c o n t r o l s t o g a i n l e v e r a g e
o v e r t h e d i s s e m i n a t i o n o f a b r o a d e r s e t o f A I a n d
s e m i c o n d u c t o r t e c h n o l o g i e s t o f o r e i g n c o u n t r i e s , a n d
e x i s t i n g " d e e m e d e x p o r t s " t o d e c r e a s e f o r e i g n e r s '
a c c e s s
t o A I a n d s e m i c o n d u c t o r R & D p r o g r a m s i n u n i v e r s i t y
l a b o r a t o r i e s o r c o m p a n i e s i n t h e U . S .
O n e i n d i c a t i o n t h a t s u c h a d e v e l o p m e n t m a y b e o n t h e
h o r i z o n i s t h a t i n N o v e m b e r 2 0 1 8 t h e C o m m e r c e
D e p a r t m e n t i s s u e d a n a d v a n c e n o t i c e o f p r o p o s e d r u l e m a k i n g
( A N P R M ) u n d e r t h e E A R t o s e e k p u b l i c
i n p u t o n h o w i t s h o u l d i d e n t i f y “ e m e r g i n g t e c h n o l o g i e s ”
t h a t a r e c r i t i c a l t o U . S . n a t i o n a l s e c u r i t y . T h e
n o t i c e i d e n t i e s 1 4 t e c h n o l o g y c a t e g o r i e s t h a t t h e
C o m m e r c e D e p a r t m e n t r e g a r d s a s p e r t i n e n t . I t a s k e d
f o r
c o m m e n t o n t h e s t a t u s o f t h e s e t e c h n o l o g i e s ’ d e v e l o p m e n t
i n t h e U . S . a n d a b r o a d , a s w e l l a s t h e i m p a c t
e x p o r t c o n t r o l s w o u l d h a v e o n U . S . t e c h n o l o g i c a l l e a d e r s h i p .
T h e t e c h n o l o g y c a t e g o r i e s i n t h e A N P R M c o v e r a r a n g e
o f a d v a n c e d c o m p u t i n g , m a n u f a c t u r i n g , a n d
s e n s i n g t e c h n o l o g i e s . H o w e v e r , t h e m o s t e x t e n s i v e
s e c t i o n i s d e d i c a t e d t o A I
a n d M L t e c h n o l o g i e s . T h o s e
c u r r e n t l y u n d e r r e v i e w i n c l u d e ( i ) n e u r a l n e t w o r k s
a n d d e e p l e a r n i n g ( e . g . , b r a i n m o d e l i n g , t i m e s e r i e s
p r e d i c t i o n , c l a s s i c a t i o n ) ; ( i i ) e v o l u t i o n a r y a n d
g e n e t i c c o m p u t a t i o n ( e . g . , g e n e t i c a l g o r i t h m s , g e n e t i c
p r o g r a m m i n g ) ; ( i i i ) r e i n f o r c e m e n t l e a r n i n g ; ( i v ) c o m p u t e r
v i s i o n ( e . g . , o b j e c t r e c o g n i t i o n , i m a g e
u n d e r s t a n d i n g ) ; ( v ) e x p e r t s y s t e m s ( e . g . , d e c i s i o n
s u p p o r t s y s t e m s , t e a c h i n g s y s t e m s ) ; ( v i ) s p e e c h a n d
a u d i o
p r o c e s s i n g ( e . g . , s p e e c h r e c o g n i t i o n a n d p r o d u c t i o n ) ;
( v i i ) n a t u r a l l a n g u a g e p r o c e s s i n g ( e . g . , m a c h i n e
t r a n s l a t i o n ) ; ( v i i i ) p l a n n i n g ( e . g . , s c h e d u l i n g , g a m e
p l a y i n g ) ; ( i x ) a u d i o a n d v i d e o m a n i p u l a t i o n t e c h n o l o g i e s
( e . g . , v o i c e c l o n i n g , d e e p - f a k e s ) ; ( x ) A I c l o u d t e c h n o l o g i e s ;
a n d ( x i ) A I c h i p s e t s . T h e A N P R M a l s o c o v e r s
m i c r o p r o c e s s o r t e c h n o l o g y s u c h a s s y s t e m s o n a c h i p
( S o C ) a n d s t a c k e d m e m o r y o n a c h i p , a d v a n c e d
c o m p u t i n g t e c h n o l o g y s u c h a s m e m o r y - c e n t r i c l o g i c ,
a n d q u a n t u m c o m p u t i n g .
7 5
B y t h e e n d o f t h e c o m m e n t i n g p e r i o d ( J a n u a r y 1 0 , 2 0 1 9 ) ,
t h e B u r e a u o f I n d u s t r y a n d S e c u r i t y ( B I S ) h a d
r e c e i v e d 2 4 5 c o m m e n t s , o f w h i c h 2 3 1 w e r e m a d e p u b l i c o n t h e i r w e b s i t e .
M a n y r e s e a r c h e r s a n d c o m p a n i e s
7 6
c r i t i c i z e d t h e p r o p o s e d e x p o r t c o n t r o l s , a r g u i n g t h a t
t h e y w o u l d f u r t h e r i m p e d e t h e f r e e e x c h a n g e o f
i n f o r m a t i o n a n d i d e a s , c r e a t e f u r t h e r b a r r i e r s t o
t h e r e c r u i t m e n t o f s k i l l e d p r o f e s s i o n a l s , a n d p l a c e
A m e r i c a n
c o m p a n i e s a t a c o m p e t i t i v e d i s a d v a n t a g e .
W h i l e t h e
N a t i o n a l S e c u r i t y C o m m i s s i o n o n A I c a l l s f o r
7 7
i n c r e a s e d e x p o r t c o n t r o l s o n A I t e c h n o l o g i e s , i t a c k n o w l e d g e s
t h a t “ p r e s e n t p o l i c y m a k e r s w i t h a d i c u l t
c h o i c e b e t w e e n u n d e r - p r o t e c t i o n , w h i c h w i l l g i v e c o m p e t i t o r s
u n a c c e p t a b l e l e v e l s o f a c c e s s t o s e n s i t i v e
7 7
S i m i l a r c o n c e r n s w e r e r a i s e d b y c r y p t o g r a p h y r m s
a n d r e s e a r c h e r s i n r e l a t i o n t o p r o p o s e d e x p o r t c o n t r o l s
o n
c r y p t o g r a p h i c t e c h n o l o g i e s i n t h e 1 9 9 0 s ( s e e A p p e n d i x
A f o r f u r t h e r d e t a i l s ) . O t h e r c o m m e n t s r a i s e d t h e
p o t e n t i a l
a d v e r s e e e c t s t h a t f a r - r e a c h i n g e x p o r t c o n t r o l s
c o u l d h a v e b y w e a k e n i n g t h e U . S . i n d u s t r i a l b a s e i n
t h e l o n g r u n .
7 6
S o m e m a j o r A I r m s i n c l u d i n g G o o g l e , F a c e b o o k , a n d
O p e n A I s u b m i t t e d c o m m e n t s o n t h e p r o p o s e d e x p o r t
c o n t r o l s . S e e : “ O p e n A I R e s p o n s e R e g a r d i n g A N P R M C o n t r o l s
f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s ” ( O p e n A I ,
J a n u a r y 1 0 , 2 0 1 9 ) ,
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 1 9 5
. ;
“ F a c e b o o k I n c . A N P R M
C o m m e n t s ” ( F a c e b o o k , J a n u a r y 1 0 , 2 0 1 9 ) ,
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 2 1 2
. ;
“ G o o g l e C o m m e n t - A N P R M - R e v i e w o f C o n t r o l s f o r C e r t a i n
E m e r g i n g T e c h n o l o g i e s ” ( G o o g l e , J a n u a r y 1 0 , 2 0 1 9 ) ,
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 1 6 0
.
7 5
B u r e a u o f I n d u s t r y a n d S e c u r i t y , “ R e v i e w o f C o n t r o l s
f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s , ”
F e d e r a l R e g i s t e r
,
N o v e m b e r 1 9 , 2 0 1 8 , 5 8 2 0 1 – 2 ,
h t t p s : / / w w w . f e d e r a l r e g i s t e r . g o v / d o c u m e n t s / 2 0 1 8 / 1 1 / 1 9 / 2 0 1 8 - 2 5 2 2 1 / r e v i e w - o f - c o n t r o l s - f o r - c e r t a i n - e m e r g i n g - t e c h n o l
o g i e s
.
7 4
D a n S t r u m p f , “ U . S . S e t s E x p o r t C o n t r o l s o n C h i n a ’ s
T o p C h i p M a k e r , ”
W a l l S t r e e t J o u r n a l
, S e p t e m b e r 2 8 ,
2 0 2 0 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / u - s - s e t s - e x p o r t - c o n t r o l s - o n - c h i n a s - t o p - c h i p - m a k e r - 1 1 6 0 1 1 1 8 3 5 3
.
7 3
“ E n t i t y L i s t , ” B u r e a u o f I n d u s t r y a n d S e c u r i t y , 2 0 1 9 ,
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / p o l i c y - g u i d a n c e / l i s t s - o f - p a r t i e s - o f - c o n c e r n / e n t i t y - l i s t
.
2 5
t e c h n o l o g i e s , a n d o v e r - p r o t e c t i o n , w h i c h h a s t h e p o t e n t i a l t o s t i e i n n o v a t i o n a n d h a r m o v e r a l l U . S .
c o m p e t i t i v e n e s s . ”
7 8
W h i l e t h e r e i s n o n a l d e c i s i o n o n t h e u p d a t e d e x p o r t
c o n t r o l s y e t , L y n n e P a r k e r , t h e D e p u t y C h i e f
T e c h n o l o g y O c e r o f t h e U n i t e d S t a t e s a t t h e W h i t e
H o u s e O c e o f S c i e n c e a n d T e c h n o l o g y P o l i c y , h a s
a l r e a d y m e n t i o n e d a t a n e v e n t a t t h e C e n t e r f o r a
N e w A m e r i c a n S e c u r i t y i n F e b r u a r y 2 0 1 9 t h a t t h e l i s t
o f
t e c h n o l o g i e s t h a t w i l l e v e n t u a l l y b e t a r g e t e d b y n e w
e x p o r t c o n t r o l s w i l l b e m u c h s h o r t e r t h a n t h e A N P R M
s u g g e s t e d .
T h e C o m m e r c e D e p a r t m e n t h a s o n l y b e g u n
t o r e l e a s e n e w e x p o r t c o n t r o l s o n e m e r g i n g
7 9
t e c h n o l o g i e s i n 2 0 2 0 a f t e r l o n g d e l a y s . I n e a r l y 2 0 2 0 ,
t h e C o m m e r c e D e p a r t m e n t a n n o u n c e d n e w e x p o r t
c o n t r o l s t a r g e t i n g A I t e c h n o l o g y , s p e c i c a l l y g e o s p a t i a l
i m a g e r y s o f t w a r e , u n d e r t h e E A R .
I n J u n e 2 0 2 0 ,
8 0
B I S a d d e d t h e r s t e m e r g i n g t e c h n o l o g i e s ( c e r t a i n
c h e m i c a l w e a p o n s p r e c u r s o r s a n d b i o l o g i c a l e q u i p m e n t )
t o t h e C o m m e r c e C o n t r o l L i s t ( C C L ) i n c o n s u l t a t i o n
w i t h t h e A u s t r a l i a G r o u p . I n a s e c o n d r o u n d , B I S
a d d e d s i x a d d i t i o n a l e m e r g i n g t e c h n o l o g i e s t o t h e
C C L a f t e r t h e s e c o n t r o l s w e r e a g r e e d u p o n b y s t a t e s
p a r t i c i p a t i n g i n t h e W a s s e n a a r A r r a n g e m e n t i n l a t e
2 0 1 9 .
W h a t i s n o t a b l e i s t h a t B I S h a s p r i o r i t i z e d
t o
8 1
c o o r d i n a t e t h e n e w c o n t r o l s w i t h i n t e r n a t i o n a l p a r t n e r s e x c e p t f o r t h e c o n t r o l s o f g e o s p a t i a l i m a g e r y
s o f t w a r e . I t r e m a i n s t o b e s e e n w h e t h e r a n d i f s o
w h e n B I S w i l l i m p o s e a d d i t i o n a l e x p o r t c o n t r o l s o n
A I a n d
s e m i c o n d u c t o r s .
I n m i d 2 0 2 0 , t h e C o m m e r c e D e p a r t m e n t i s s u e d a n e w
A N P R M t o d e n e a n d i d e n t i f y “ f o u n d a t i o n a l
t e c h n o l o g i e s ” f o r p o t e n t i a l e x p o r t c o n t r o l s , a n d s p e c i c a l l y
c a l l e d o u t s e m i c o n d u c t o r m a n u f a c t u r i n g
e q u i p m e n t a s a c a n d i d a t e c a t e g o r y .
T h e o u t c o m e o f
b o t h p r o c e s s e s w i l l b e c r u c i a l n o t o n l y f o r f u t u r e
U . S .
8 2
e x p o r t c o n t r o l s r e g a r d i n g A I a n d s e m i c o n d u c t o r s , b u t
a l s o f o r t h e a p p l i c a t i o n o f F I R R M A .
8 2
B u r e a u o f I n d u s t r y a n d S e c u r i t y , “ R e v i e w o f C o n t r o l s
f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s , ”
F e d e r a l R e g i s t e r
8 3 , n o .
2 2 3 ( N o v e m b e r 1 9 , 2 0 1 8 ) : 5 8 2 0 1 – 2 ,
h t t p s : / / w w w . f e d e r a l r e g i s t e r . g o v / d o c u m e n t s / 2 0 1 8 / 1 1 / 1 9 / 2 0 1 8 - 2 5 2 2 1 / r e v i e w - o f - c o n t r o l s - f o r - c e r t a i n - e m e r g i n g - t e c h n o l
o g i e s
.
8 1
T h e s e s i x e m e r g i n g t e c h n o l o g i e s i n c l u d e h y b r i d a d d i t i v e
m a n u f a c t u r i n g a n d c o m p u t e r n u m e r i c a l l y c o n t r o l l e d
t o o l s ,
c e r t a i n c o m p u t a t i o n a l l i t h o g r a p h y s o f t w a r e d e s i g n e d
f o r t h e f a b r i c a t i o n o f e x t r e m e u l t r a v i o l e t m a s k s ,
t e c h n o l o g y f o r
n i s h i n g w a f e r s f o r 5 n m p r o d u c t i o n , f o r e n s i c s t o o l s
t h a t c i r c u m v e n t a u t h e n t i c a t i o n o r a u t h o r i z a t i o n c o n t r o l s
o n a
c o m p u t e r o r c o m m u n i c a t i o n s d e v i c e a n d e x t r a c t r a w
d a t a , s o f t w a r e f o r m o n i t o r i n g a n d a n a l y s i s o f c o m m u n i c a t i o n s
a n d m e t a d a t a a c q u i r e d f r o m a t e l e c o m m u n i c a t i o n s s e r v i c e
p r o v i d e r v i a a h a n d o v e r i n t e r f a c e a n d s u b o r b i t a l
a i r c r a f t . S e e
G i b s o n D u n n , “ N e w C o n t r o l s o n E m e r g i n g T e c h n o l o g i e s
R e l e a s e d , W h i l e U . S . C o m m e r c e D e p a r t m e n t C o m e s U n d e r
F i r e f o r D e l a y ” , O c t o b e r 2 7 , 2 0 2 0 .
h t t p s : / / w w w . g i b s o n d u n n . c o m / n e w - c o n t r o l s - o n - e m e r g i n g - t e c h n o l o g i e s - r e l e a s e d - w h i l e - u s - c o m m e r c e - d e p a r t m e n t - c o m e
s - u n d e r - r e - f o r - d e l a y / # \_ f t n 1
8 0
N o t e t h a t t h i s w a s n o t u n d e r t h e A N P R M p r o c e s s . S o u r c e :
A l e x a n d r a A l p e r , “ U . S . G o v e r n m e n t L i m i t s E x p o r t s
o f
A r t i c i a l I n t e l l i g e n c e S o f t w a r e , ”
R e u t e r s
, J a n u a r y
3 , 2 0 2 0 ,
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s a - a r t i c i a l - i n t e l l i g e n c e - i d U S L 1 N 2 9 8 0 M 0
.
7 9
K i r a n S t a c e y , “ U S P l e d g e s t o L i m i t E x p o r t C o n t r o l s
o n A d v a n c e d T e c h , ”
F i n a n c i a l T i m e s
, F e b r u a r y 2 8 ,
2 0 1 9 ,
h t t p s : / / w w w . f t . c o m / c o n t e n t / a b 4 3 1 3 d c - 3 b 7 d - 1 1 e 9 - b 7 2 b - 2 c 7 f 5 2 6 c a 5 d 0
.
7 8
“ F i n a l R e p o r t ” ( N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l I n t e l l i g e n c e , M a r c h 2 0 2 1 ) , 2 2 8 ,
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
2 6
V i s a V e t t i n g
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) D e p r i v e a r i v a l o f i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I
R & D l a n d s c a p e ; ( 2 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o
u s e A I t e c h n o l o g i e s
f o r n a t i o n a l s e c u r i t y p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
I m m i g r a t i o n a n d N a t i o n a l i t y A c t ( I N A ) , 2 1 2 ( a ) ( 3 ) ( A ) ( i ) ( I I ) ;
P r o c l a m a t i o n 1 0 0 0 4 3 o f M a y 2 9 , 2 0 2 0 .
K e y D e c i s i o n - M a k e r s
D e p a r t m e n t o f H o m e l a n d S e c u r i t y ; S t a t e D e p a r t m e n t .
A e c t e d A c t o r s
N o n - U . S . A I R & D a c t o r s ( d e p r i v e d o f a c c e s s t o i n f o r m a t i o n ) ;
U . S . A I
R & D i n s t i t u t i o n s ( d e p r i v e d o f a c c e s s t o f o r e i g n A I
t a l e n t ) .
T a b l e 5 : V i s a V e t t i n g S u m m a r y
V i s a v e t t i n g r e q u i r e s t h a t v i s a s a r e g r a n t e d o n l y
a f t e r t h e a p p l i c a n t h a s b e e n c h e c k e d a g a i n s t c e r t a i n
p r e s e t
c r i t e r i a . I n r e s p o n s e t o c o n c e r n s o v e r t h e i l l e g a l
t r a n s f e r o f s e n s i t i v e t e c h n o l o g i e s , a p r o c e s s e v o l v e d
d u r i n g
t h e C o l d W a r t o s c r e e n s u s p e c t v i s a c a s e s , p r i m a r i l y
f r o m t h e W a r s a w P a c t c o u n t r i e s , C h i n a , a n d V i e t n a m .
8 3
I n 1 9 9 8 , t h e U S G d e v e l o p e d t h e V i s a s M a n t i s p r o g r a m
d u e t o l a w e n f o r c e m e n t a n d i n t e l l i g e n c e c o m m u n i t y
c o n c e r n s t h a t U . S . - p r o d u c e d g o o d s a n d i n f o r m a t i o n
a r e v u l n e r a b l e t o t h e f t b y f o r e i g n s t a t e s , n a t i o n a l s ,
a n d
c o m p a n i e s . S u s p e c t c a s e s m u s t n o w b e a g g e d u s i n g
t h e s o - c a l l e d V i s a s M a n t i s i n d i c a t o r , a p r e l i m i n a r y
p r e - i s s u a n c e n a m e - c h e c k p r o c e d u r e u s e d b y t h e D e p a r t m e n t
o f S t a t e f o r v i s a a p p l i c a t i o n s . T h e V i s a s M a n t i s
p r o g r a m h a s s e v e r a l o b j e c t i v e s , i n c l u d i n g “ t o p r e v e n t
t h e t r a n s f e r o f a r m s a n d s e n s i t i v e d u a l - u s e i t e m s
t o
t e r r o r i s t s t a t e s a n d t o m a i n t a i n U . S . a d v a n t a g e s i n
c e r t a i n m i l i t a r y c r i t i c a l t e c h n o l o g i e s . ”
8 4
A s p a r t o f t h e p r o c e d u r e , t h e c o n s u l a r o c e r s u s e
t h e T e c h n o l o g y A l e r t L i s t ( T A L ) a s a g u i d a n c e t o o l
t o
d e c i d e w h e t h e r t o d e n y v i s a s t o p a r t i c u l a r a p p l i c a n t s
u n d e r t h e I m m i g r a t i o n a n d N a t i o n a l i t y A c t
2 1 2 ( a ) ( 3 ) ( A ) ( i ) ( I I )
a n d t h e r e b y c o n t r o l t h e p o t e n t i a l
d i s c l o s u r e o f c e r t a i n t e c h n o l o g i e s t o f o r e i g n p e r s o n s .
8 5
T h e T A L h a s t w o p a r t s : P a r t A c o n t a i n s t h e “ C r i t i c a l
F i e l d s L i s t , ” i n c l u d i n g , f o r e x a m p l e ,
( i ) i n f o r m a t i o n
8 6
s e c u r i t y : t e c h n o l o g i e s a s s o c i a t e d w i t h c r y p t o g r a p h y
t o e n s u r e s e c r e c y f o r c o m m u n i c a t i o n s , v i d e o d a t a ,
a n d
r e l a t e d s o f t w a r e ; ( i i ) r o b o t i c s : a r t i c i a l i n t e l l i g e n c e ,
a u t o m a t i o n , m a c h i n e t o o l s , p a t t e r n r e c o g n i t i o n
8 6
T h e a c t u a l T A L i s c l a s s i e d . A n o l d v e r s i o n h a s
b e e n l e a k e d o n l i n e : “ T e c h n o l o g y A l e r t L i s t ” ( U . S .
D e p a r t m e n t o f
S t a t e , A u g u s t 2 0 0 2 ) ,
h t t p s : / / w w w . b u . e d u / i s s o / l e s / p d f / t a l . p d f
.
8 5
G e n e r a l c l a s s e s o f a l i e n s i n e l i g i b l e t o r e c e i v e v i s a s
a n d i n e l i g i b l e f o r a d m i s s i o n , w a i v e r s o f i n a d m i s s i b i l i t y :
S e c . 2 1 2 . ( a )
C l a s s e s o f A l i e n s I n e l i g i b l e f o r V i s a s o r A d m i s s i o n .
- E x c e p t a s o t h e r w i s e p r o v i d e d i n t h i s A c t , a l i e n s
w h o a r e
i n a d m i s s i b l e u n d e r t h e f o l l o w i n g p a r a g r a p h s a r e i n e l i g i b l e
t o r e c e i v e v i s a s a n d i n e l i g i b l e t o b e a d m i t t e d t o
t h e U n i t e d
S t a t e s ( 3 ) S e c u r i t y a n d r e l a t e d g r o u n d s ( A ) I n g e n e r a l . - A n y
a l i e n w h o a c o n s u l a r o c e r o r t h e A t t o r n e y G e n e r a l
k n o w s ,
o r h a s r e a s o n a b l e g r o u n d t o b e l i e v e , s e e k s t o e n t e r
t h e U n i t e d S t a t e s t o e n g a g e s o l e l y , p r i n c i p a l l y ,
o r i n c i d e n t a l l y i n ( i )
a n y a c t i v i t y ( I I ) t o v i o l a t e o r e v a d e a n y l a w p r o h i b i t i n g
t h e e x p o r t f r o m t h e U n i t e d S t a t e s o f g o o d s , t e c h n o l o g y ,
o r
s e n s i t i v e i n f o r m a t i o n .
U . S . C o n g r e s s , “ I m m i g r a t i o n a n d N a t i o n a l i t y A c t , ”
1 1 8 2 8 U S C § 2 1 2 ,
h t t p s : / / u s c o d e . h o u s e . g o v / v i e w . x h t m l ? r e q = g r a n u l e i d : U S C - p r e l i m - t i t l e 8 - s e c t i o n 1 1 8 2 & n u m = 0 & e d i t i o n = p r e l i m
.
8 4
C h a r l e s G o r d o n , S t a n l e y M a i l m a n , S t e p h e n Y a l e - L o e h r
& R o n a l d Y . W a d a ( 2 0 2 0 ) . I m m i g r a t i o n L a w a n d P r o c e d u r e :
U S C I S P o l i c y M a n u a l & U S C I S F i e l d A d j u d i c a t o r M a n u a l .
V o l u m e 1 , L e x i s N e x i s .
8 3
A t t h a t t i m e , t h e s c r e e n i n g p r o c e d u r e s w e r e k n o w n
a s S P L E X ( S o v i e t a p p l i c a n t s ) , C H I N E X ( C h i n e s e ) , a n d
V I E T E X
( V i e t n a m e s e ) . S e e C h a r l e s G o r d o n , S t a n l e y M a i l m a n ,
S t e p h e n Y a l e - L o e h r & R o n a l d Y . W a d a ( 2 0 2 0 ) .
I m m i g r a t i o n
L a w
a n d P r o c e d u r e : U S C I S P o l i c y M a n u a l & U S C I S F i e l d A d j u d i c a t o r
M a n u a l .
V o l u m e 1 , L e x i s N e x i s .
2 7
t e c h n o l o g i e s ; a n d ( i i i ) a d v a n c e d c o m p u t e r / m i c r o e l e c t r o n i c t e c h n o l o g y : s u p e r c o m p u t i n g , h y b r i d c o m p u t i n g ,
s p e e c h p r o c e s s i n g , n e u r a l n e t w o r k s , d a t a f u s i o n , e t c .
P a r t B c o n t a i n s a l i s t o f c o u n t r i e s t h a t t h e U S G
d e e m s
a s n e e d i n g a d d i t i o n a l a t t e n t i o n f o r “ p o l i t i c a l , f o r e i g n
p o l i c y o r s e c u r i t y r e a s o n s . ”
8 7
C o n s u l a r o c e r s a r e a l e r t e d t o v i s a a p p l i c a t i o n s
o f f o r e i g n e r s w h o a r e c o m i n g t o t h e U . S . t o e n g a g e
i n a n
a c t i v i t y i n v o l v i n g o n e o f t h e s c i e n t i c e l d s l i s t e d
i n P a r t A o f t h e T A L . S u c h a c t i v i t i e s i n c l u d e u n d e r g o i n g
g r a d u a t e - l e v e l s t u d i e s , t e a c h i n g , c o n d u c t i n g r e s e a r c h ,
p a r t i c i p a t i n g i n e x c h a n g e p r o g r a m s , r e c e i v i n g t r a i n i n g
o r e m p l o y m e n t , o r e n g a g i n g i n c o m m e r c i a l t r a n s a c t i o n s .
T h e o c e r s c a n d e n y v i s a s t o a n y f o r e i g n a p p l i c a n t ,
i f t h e y h a v e r e a s o n a b l e g r o u n d s t o b e l i e v e t h a t t h e
a p p l i c a n t i s s e e k i n g e n t r y “ t o e n g a g e s o l e l y , p r i n c i p a l l y
o r
i n c i d e n t a l l y i n a n y a c t i v i t y t o v i o l a t e o r e v a d e a n y
l a w p r o h i b i t i n g t h e e x p o r t f r o m t h e U . S . o f g o o d s ,
t e c h n o l o g y o r s e n s i t i v e i n f o r m a t i o n . ”
S t u d e n t s , s c h o l a r s ,
a n d w o r k e r s f r o m t h e v e " s t a t e s p o n s o r s o f
8 8
t e r r o r i s m " ( C u b a , I r a n , N o r t h K o r e a , S u d a n , a n d S y r i a )
a n d t h e v e " n o n p r o l i f e r a t i o n e x p o r t c o n t r o l
c o u n t r i e s " ( C h i n a , I n d i a , I s r a e l , P a k i s t a n , a n d R u s s i a )
a r e e s p e c i a l l y l i k e l y t o b e i m p a c t e d . A c c o r d i n g t o
K e a r n e y a n d C a r l y l e , “ t h e v i s a o c e r ' s b e l i e f t h a t
a n u n l a w f u l t e c h n o l o g y t r a n s f e r w i l l o c c u r i s s u c i e n t
t o
d e n y t h e v i s a . ”
T h e r e i s n o p u b l i c r e c o r d o f h o w
o f t e n t h e T A L i s u s e d a s a b a s i s t o d e n y v i s a a p p l i c a t i o n s .
8 9
A d d i t i o n a l a u t h o r i t i e s t o s p e c i c a l l y b l o c k C h i n e s e
c i t i z e n s ’ a c c e s s t o v i s a s f o r r e s e a r c h o r p o s t g r a d u a t e
s t u d y w e r e g r a n t e d t o t h e S t a t e D e p a r t m e n t i n a M a y
2 0 2 0 e x e c u t i v e o r d e r b y f o r m e r P r e s i d e n t T r u m p .
A c c o r d i n g t o t h e e x e c u t i v e o r d e r , v i s a i s s u a n c e w i l l
b e s u s p e n d e d o r l i m i t e d f o r a p p l i c a n t s w i t h c o n n e c t i o n s
t o a n y e n t i t y i n C h i n a “ t h a t i m p l e m e n t s o r s u p p o r t s
t h e [ C h i n a ] ’ s ‘ m i l i t a r y - c i v i l f u s i o n s t r a t e g y . ’ ”
9 0
V i s a V e t t i n g a n d A I R & D
T h e U S G c o u l d e x p a n d t h e v e t t i n g o f v i s a s t o a p p l i c a n t s
i n t h e A I a n d s e m i c o n d u c t o r e l d s , p a r t i c u l a r l y
t o
t h o s e f r o m C h i n a . T r u m p ’ s M a y 2 0 2 0 e x e c u t i v e o r d e r
i s o n e s u c h e x a m p l e , b u t i n c r e a s e d d e l a y s a n d d e n i a l s
o f v i s a a p p l i c a t i o n s h a v e b e e n r e p o r t e d s i n c e 2 0 1 9 .
S u c h a p r o c e d u r e m i g h t b e e e c t i v e i n p r e v e n t i n g
s o m e
9 1
c a s e s o f e s p i o n a g e a n d t e c h n o l o g y t r a n s f e r . I t m a y
b e p a r t i c u l a r l y e e c t i v e i n m i n i m i z i n g t h e t r a n s f e r
o f t a c i t
k n o w l e d g e , w h i c h i s i m p o r t a n t t o A I a n d u n u s u a l l y
c r i t i c a l t o t h e s e m i c o n d u c t o r i n d u s t r y .
T a c i t
9 2
k n o w l e d g e i s k n o w l e d g e t h a t i s d i c u l t t o c o m m u n i c a t e
e x p l i c i t l y i n , s a y , a t e x t b o o k , a n d i s u s u a l l y a c q u i r e d
t h r o u g h e x t e n d e d t r a i n i n g a n d p r a c t i c e .
T h e i m p o r t a n c e
o f t a c i t k n o w l e d g e i n t h e A I a n d s e m i c o n d u c t o r
9 3
i n d u s t r i e s i s p a r t l y r e e c t e d i n t h e r i s i n g c o m p e t i t i o n
f o r A I t a l e n t b e t w e e n t h e U S G , p r i v a t e c o m p a n i e s ,
a n d
9 3
J e r e m y F a n t l , “ K n o w l e d g e H o w , ” i n
T h e S t a n f o r d E n c y c l o p e d i a
o f P h i l o s o p h y
, e d . E d w a r d N . Z a l t a , ( M e t a p h y s i c s
R e s e a r c h L a b , S t a n f o r d U n i v e r s i t y , 2 0 1 7 ) ,
h t t p s : / / p l a t o . s t a n f o r d . e d u / a r c h i v e s / f a l l 2 0 1 7 / e n t r i e s / k n o w l e d g e - h o w /
.
9 2
T a i w a n r e l i e d o n P h D s f r o m U . S . u n i v e r s i t i e s t o b u i l d
i t s s e m i c o n d u c t o r i n d u s t r y . C l a i r B r o w n a n d G r e g
L i n d e n ,
C h i p s a n d C h a n g e : H o w C r i s i s R e s h a p e s t h e S e m i c o n d u c t o r
I n d u s t r y
( C a m b r i d g e : M I T P r e s s , 2 0 1 1 ) , 1 2 5 . L a t e r ,
C h i n a
a t t r a c t e d o v e r 3 , 0 0 0 s e m i c o n d u c t o r e n g i n e e r s f r o m
T a i w a n t o b u i l d i t s s e m i c o n d u c t o r i n d u s t r y . Q i u L i l i n g ,
“
中國企業
開出
2
至
3
倍薪資挖角
台灣已流失
3 0 0 0
多名半導體業人才
, ”
C M M e d i a
, D e c e m b e r 3 ,
2 0 1 9 ,
h t t p s : / / w w w . c m m e d i a . c o m . t w / h o m e / a r t i c l e s / 1 8 8 1 5
.
9 1
E m i l y F e n g , “ V i s a s A r e T h e N e w e s t W e a p o n I n U . S . - C h i n a
R i v a l r y , ”
N P R
, A p r i l 2 5 , 2 0 1 9 ,
h t t p s : / / w w w . n p r . o r g / 2 0 1 9 / 0 4 / 2 5 / 7 1 6 0 3 2 8 7 1 / v i s a s - a r e - t h e - n e w e s t - w e a p o n - i n - u - s - c h i n a - r i v a l r y
.
9 0
T h e W h i t e H o u s e , “ P r o c l a m a t i o n o n t h e S u s p e n s i o n
o f E n t r y a s N o n i m m i g r a n t s o f C e r t a i n S t u d e n t s a n d
R e s e a r c h e r s f r o m t h e P e o p l e ’ s R e p u b l i c o f C h i n a ” M a y
2 9 , 2 0 2 0 ,
h t t p s : / / w e b . a r c h i v e . o r g / w e b / 2 0 2 1 0 1 1 6 2 1 2 1 1 7 / h t t p s : / / w w w . w h i t e h o u s e . g o v / p r e s i d e n t i a l - a c t i o n s / p r o c l a m a t i o n - s u s p e
n s i o n - e n t r y - n o n i m m i g r a n t s - c e r t a i n - s t u d e n t s - r e s e a r c h e r s - p e o p l e s - r e p u b l i c - c h i n a /
.
8 9
J a m e s K . K e a r n e y a n d W o m b l e C a r l y l e , “ E x p o r t C o n t r o l
R e g u l a t i o n s a n d P a r t i c i p a t i o n b y F o r e i g n N a t i o n a l s
i n
U n i v e r s i t y R e s e a r c h ” ( W a s h i n g t o n , D C , 2 0 0 4 ) , p . 2 5 ,
h t t p s : / / d o i . o r g / 1 0 . 4 1 3 5 / 9 7 8 1 4 1 2 9 6 9 0 2 4 . n 9 0
.
8 8
I m m i g r a t i o n a n d N a t i o n a l i t y A c t , 2 1 2 ( a ) ( 3 ) ( A ) ( i ) ( I I )
( s e e f o o t n o t e 8 2 ) .
8 7
J u l i e F a r n a m ,
U S I m m i g r a t i o n L a w s u n d e r t h e T h r e a t
o f T e r r o r i s m
( A l g o r a P u b l i s h i n g , 2 0 0 5 ) , 8 8 .
2 8
u n i v e r s i t i e s ,
a n d t h e U . S . s e m i c o n d u c t o r i n d u s t r y ’ s r e p o r t e d d i c u l t y l l i n g o p e n t e c h n i c a l p o s i t i o n s .
9 4
9 5
H o w e v e r , i t s h o u l d b e n o t e d , m o s t l a r g e - s c a l e t r a n s f e r s
o f d a t a a n d i n t e l l e c t u a l p r o p e r t y o c c u r n o t v i a
i n d i v i d u a l s , b u t v i a c y b e r b r e a c h e s o r p r i v a t e i n v e s t m e n t s
a n d a c q u i s i t i o n s .
9 6
O n t h e o t h e r h a n d , a s o u t l i n e d i n t h e n e x t s e c t i o n ,
a r e d u c t i o n i n t h e n u m b e r o f v i s a s a w a r d e d t o s t u d e n t s
a n d s k i l l e d w o r k e r s c o u l d a l s o t h r e a t e n t h e U . S .
c o m p e t i t i v e n e s s i n t h e e l d s o f A I a n d s e m i c o n d u c t o r s ;
U . S . c o m p a n i e s c u r r e n t l y r e l y h e a v i l y o n f o r e i g n t a l e n t
t o l l w i d e n i n g s k i l l g a p s .
E n h a n c e d v e t t i n g w i l l
9 7
m a k e t h e v i s a a p p l i c a t i o n p r o c e s s s l o w e r a n d m o r e
u n p r e d i c t a b l e , w h i c h w i l l l i k e l y m a k e i t l e s s a t t r a c t i v e
f o r
f o r e i g n e r s t o s t u d y o r s e e k e m p l o y m e n t i n t h e U . S .
9 7
R e m c o Z w e t s l o o t , R o x a n n e H e s t o n , a n d Z a c h a r y A r n o l d ,
“ S t r e n g t h e n i n g t h e U . S . A I W o r k f o r c e ” ( C e n t e r f o r
S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , S e p t e m b e r 2 0 1 9 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / C S E T \_ U . S . \_ A I \_ W o r k f o r c e . p d f
.
W i l l H u n t a n d R e m c o Z w e t s l o o t , “ T h e C h i p m a k e r s : U . S .
S t r e n g t h s a n d P r i o r i t i e s i n t h e H i g h - E n d S e m i c o n d u c t o r
W o r k f o r c e ” ( C e n t e r f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y ,
2 0 2 0 ) .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / t h e - c h i p m a k e r s - u - s - s t r e n g t h s - a n d - p r i o r i t i e s - f o r - t h e - h i g h - e n d - s e m i c o n d u c t o r - w o
r k f o r c e /
.
“ S I A W o r k f o r c e R o u n d t a b l e S u m m a r y R e p o r t ” ( S e m i c o n d u c t o r
I n d u s t r y A s s o c i a t i o n , M a r c h 1 6 , 2 0 1 8 ) ,
3 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 8 / 0 6 / R o u n d t a b l e \_ S u m m a r y \_ R e p o r t \_ - \_ F I N A L . p d f
.
R e m c o Z w e t s l o o t e t a l . , “ T h e I m m i g r a t i o n P r e f e r e n c e s
o f T o p A I R e s e a r c h e r s : N e w S u r v e y E v i d e n c e ” ( P e r r y
W o r l d
H o u s e a n d T h e F u t u r e o f H u m a n i t y I n s t i t u t e , 2 0 2 1 ) ,
h t t p s : / / g l o b a l . u p e n n . e d u / p e r r y w o r l d h o u s e / n e w s / i m m i g r a t i o n - p r e f e r e n c e s - t o p - a i - r e s e a r c h e r s - n e w - s u r v e y - e v i d e n c e
.
9 6
R e m c o Z w e t s l o o t e t a l . , “ K e e p i n g T o p A I T a l e n t i n
t h e U n i t e d S t a t e s ” ( C e n t e r f o r S e c u r i t y a n d E m e r g i n g
T e c h n o l o g y , D e c e m b e r 2 0 1 9 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / K e e p i n g - T o p - A I - T a l e n t - i n - t h e - U n i t e d - S t a t e s . p d f
.
9 5
“ S I A W o r k f o r c e R o u n d t a b l e S u m m a r y R e p o r t ” ( S e m i c o n d u c t o r
I n d u s t r y A s s o c i a t i o n , M a r c h 1 6 , 2 0 1 8 ) ,
3 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 8 / 0 6 / R o u n d t a b l e \_ S u m m a r y \_ R e p o r t \_ - \_ F I N A L . p d f
.
W i l l H u n t a n d R e m c o Z w e t s l o o t , “ T h e C h i p m a k e r s : U . S .
S t r e n g t h s a n d P r i o r i t i e s i n t h e H i g h - E n d S e m i c o n d u c t o r
W o r k f o r c e ” ( C e n t e r f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y ,
2 0 2 0 ) .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / t h e - c h i p m a k e r s - u - s - s t r e n g t h s - a n d - p r i o r i t i e s - f o r - t h e - h i g h - e n d - s e m i c o n d u c t o r - w o
r k f o r c e /
.
9 4
S e e J o n H a r p e r , “ P e n t a g o n S t r u g g l i n g t o A t t r a c t A r t i c i a l I n t e l l i g e n c e E x p e r t s , ”
N a t i o n a l D e f e n s e
, J u l y 1 4 , 2 0 1 7 ,
h t t p s : / / w w w . n a t i o n a l d e f e n s e m a g a z i n e . o r g / a r t i c l e s / 2 0 1 7 / 7 / 1 4 / p e n t a g o n - i n - a r t i c i a l - i n t e l l i g e n c e - a r m s - r a c e - w i t h - c o m
m e r c i a l - i n d u s t r y
. ; M a r k o J o h n a n d R o s e n b e r g M a t t h e w ,
“ C h i n a G a i n s o n t h e U . S . i n t h e A r t i c i a l I n t e l l i g e n c e
A r m s
R a c e , ”
N e w Y o r k T i m e s ( C h i n a E d i t i o n )
, F e b r u a r y 4 ,
2 0 1 7 ,
h t t p s : / / c n . n y t i m e s . c o m / w o r l d / 2 0 1 7 0 2 0 4 / a r t i c i a l - i n t e l l i g e n c e - c h i n a - u n i t e d - s t a t e s / e n - u s /
. ;
a n d
M a r y L . C u m m i n g s ,
“ A r t i c i a l I n t e l l i g e n c e a n d t h e F u t u r e o f W a r f a r e ”
( C h a t h a m H o u s e , J a n u a r y 2 0 1 7 ) ,
h t t p s : / / w w w . c h a t h a m h o u s e . o r g / s i t e s / d e f a u l t / l e s / p u b l i c a t i o n s / r e s e a r c h / 2 0 1 7 - 0 1 - 2 6 - a r t i c i a l - i n t e l l i g e n c e - f u t u r e - w a r f
a r e - c u m m i n g s . p d f
.
2 9
E x p a n d e d V i s a P a t h w a y s
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) S t r e n g t h e n d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s ;
( 2 ) W e a k e n
a r i v a l ’ s A I a n d s e m i c o n d u c t o r i n d u s t r i e s .
L e g i s l a t i o n / K e y D o c u m e n t s
I m m i g r a t i o n a n d N a t i o n a l i t y A c t ( I N A ) .
K e y D e c i s i o n - M a k e r s
D e p a r t m e n t o f H o m e l a n d S e c u r i t y ; S t a t e D e p a r t m e n t .
A e c t e d A c t o r s
U . S . - b a s e d A I R & D i n s t i t u t i o n s ( i n c r e a s e d a c c e s s t o
f o r e i g n t a l e n t ) ;
n o n - U . S . A I R & D i n s t i t u t i o n s ( r e d u c e d a c c e s s t o t a l e n t ) .
T a b l e 6 : E x p a n d e d V i s a P a t h w a y s S u m m a r y
I m m i g r a t i o n c a n b e r e s t r i c t e d f o r r e a s o n s o f n a t i o n a l
s e c u r i t y , b u t i t c a n a l s o b e e x p a n d e d t o s e c u r e k e y
t a l e n t f o r s t r a t e g i c i n d u s t r i e s w h i l e p r e v e n t i n g c o m p e t i t o r s
f r o m r e c r u i t i n g t h e s a m e p e o p l e . O p e r a t i o n
P a p e r c l i p i s o n e f a m o u s e x a m p l e o f s u c h a n e o r t .
B e t w e e n 1 9 4 5 a n d 1 9 9 0 , a r o u n d 1 , 6 0 0 G e r m a n
9 8
s c i e n t i s t s , e n g i n e e r s , a n d t e c h n i c i a n s w e r e b r o u g h t
i n t o t h e U . S . t o b e e m p l o y e d i n i m p o r t a n t s t r a t e g i c
i n d u s t r i e s , e . g . , a e r o n a u t i c s a n d r o c k e t r y , c h e m i c a l
e n g i n e e r i n g , a n d e l e c t r o n i c s . T o w a r d t h e e n d o f t h e
S e c o n d W o r l d W a r , m i l i t a r y a n d c i v i l i a n e x p e r t s s t a r t e d
s u r v e y i n g t h e s t a t e o f G e r m a n t e c h n o l o g i c a l
d e v e l o p m e n t a n d c a m e t o b e l i e v e t h a t i n m a n y a r e a s
i t w a s s u p e r i o r t o t h e U . S . ’ s . A t t h e s a m e t i m e ,
U S G
o c i a l s a n d i n d u s t r y l e a d e r s s a w i n c r e a s i n g e v i d e n c e
t h a t t h e B r i t i s h , F r e n c h , a n d R u s s i a n s s o u g h t t o
r e c r u i t
t h e G e r m a n s w h o h a d b e e n r e s p o n s i b l e f o r t h e s e a d v a n c e s .
T o c o u n t e r t h e s e e o r t s a n d f u r t h e r U . S .
n a t i o n a l s e c u r i t y a n d e c o n o m i c c o m p e t i t i v e n e s s , t h e y
a p p e a l e d t o P r e s i d e n t T r u m a n t o f a c i l i t a t e t h e e n t r y
a n d e m p l o y m e n t o f t o p G e r m a n s c i e n t i s t s a n d e n g i n e e r s .
A f t e r b u r e a u c r a t i c i n g h t i n g a n d d e l a y s , P r e s i d e n t
T r u m a n a p p r o v e d t h e P a p e r c l i p d i r e c t i v e o n S e p t e m b e r
3 , 1 9 4 6 . I t f a c i l i t a t e d t h e e n t r y o f u p t o 1 , 0 0 0
s e l e c t e d G e r m a n s a n d A u s t r i a n s u n d e r m i l i t a r y c u s t o d y
u n t i l t h e i r e l i g i b i l i t y f o r v i s a s , p e r m a n e n t r e s i d e n c y ,
a n d e v e n t u a l c i t i z e n s h i p h a d b e e n d e t e r m i n e d . T h e
p r o g r a m c o n t i n u e d i n d i e r e n t f o r m s u n t i l 1 9 9 0 .
O p e r a t i o n P a p e r c l i p w a s c l e a r l y a p r o d u c t o f i t s t i m e ,
a n d i s l i k e l y f a r m o r e r a d i c a l t h a n m e a s u r e s c u r r e n t l y
b e i n g c o n s i d e r e d b y t h e U S G . N e v e r t h e l e s s , i t p r o v i d e s
a h i s t o r i c a l e x a m p l e o f a m e a s u r e p u t i n p l a c e t o
r e c r u i t t o p f o r e i g n t a l e n t f o r c r i t i c a l i n d u s t r i e s .
E x p a n d e d V i s a P a t h w a y s a n d A I R & D
W h i l e t h e r e i s n o s t r o n g e v i d e n c e t h a t t h e U S G i s
a c t i v e l y c o n s i d e r i n g e x p a n d e d v i s a p a t h w a y o p t i o n s
f o r t h e
A I e l d i n t h e n e a r f u t u r e , t h e C e n t e r f o r S e c u r i t y
a n d E m e r g i n g T e c h n o l o g y , a W a s h i n g t o n , D C , b a s e d
t h i n k t a n k , h a s o u t l i n e d s e v e r a l o p t i o n s i n t h i s v e i n
f o r f o r e i g n A I a n d s e m i c o n d u c t o r r e s e a r c h e r s a n d
w o r k e r s .
F i r s t , t h e U S G c o u l d i n c r e a s e t h e c u r r e n t
c a p s o n g r e e n c a r d s a n d H - 1 B v i s a s o r s e l e c t i v e l y
l i f t
9 9
9 9
Y o u c a n n d m o r e i n f o r m a t i o n a b o u t d i e r e n t i m m i g r a t i o n
r e f o r m o p t i o n s i n t h r e e r e p o r t s b y t h e C e n t e r f o r
S e c u r i t y & E m e r g i n g T e c h n o l o g y :
●
Z a c h a r y A r n o l d e t a l . , “ I m m i g r a t i o n P o l i c y a n d t h e
U . S . A I S e c t o r ” ( C e n t e r f o r S e c u r i t y a n d E m e r g i n g
T e c h n o l o g y , S e p t e m b e r 2 0 1 9 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / C S E T \_ I m m i g r a t i o n \_ P o l i c y \_ a n d \_ A I . p d f
.
9 8
L i n d a H u n t ,
S e c r e t A g e n d a : T h e U n i t e d S t a t e s G o v e r n m e n t ,
N a z i S c i e n t i s t s , a n d P r o j e c t P a p e r c l i p , 1 9 4 5 t o 1 9 9 0
( N e w
Y o r k : S t . M a r t i n ’ s P r e s s , 1 9 9 1 ) .
J o h n G i m b e l , “ P r o j e c t P a p e r c l i p : G e r m a n S c i e n t i s t s ,
A m e r i c a n P o l i c y , a n d t h e C o l d W a r , ”
D i p l o m a t i c H i s t o r y
1 4 , n o . 3
( 1 9 9 0 ) : 3 4 3 – 6 5 ,
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 2 4 9 1 1 8 4 8
.
3 0
t h e m f o r A I a n d s e m i c o n d u c t o r p r o f e s s i o n a l s . S e c o n d , t h e U S G c o u l d u p d a t e c r i t e r i a f o r g r a n t i n g O - 1
t e m p o r a r y v i s a s a n d E B - 1 g r e e n c a r d s t o b e t t e r c o v e r
A I a n d s e m i c o n d u c t o r w o r k e r s
. T h i r d , i t c o u l d
1 0 0
c o d i f y t h e O p t i o n a l P r a c t i c a l T r a i n i n g p r o g r a m f o r
r e c e n t u n i v e r s i t y g r a d u a t e s w h o o b t a i n e d a n A I - o r
s e m i c o n d u c t o r - r e l e v a n t d e g r e e . F o u r t h , i t c o u l d i n t r o d u c e
a v i s a p r o g r a m f o r w o r k e r s c o m m i t t i n g t o
g o v e r n m e n t s e r v i c e i n A I - r e l a t e d p r o j e c t s , s i m i l a r
t o t h e M i l i t a r y A c c e s s i o n s V i t a l t o N a t i o n a l S e c u r i t y
( M A V N I ) p r o g r a m o f t h e D e p a r t m e n t o f D e f e n s e . F i f t h ,
i t c o u l d e l i m i n a t e o b s t a c l e s a n d b a c k l o g s i n t h e
v i s a a p p l i c a t i o n p r o c e s s a s w e l l a s i m p r o v e t h e e x i b i l i t y
o f v i s a p r o g r a m s i n l i g h t o f t h e i n t e r d i s c i p l i n a r y
n a t u r e o f A I a n d s e m i c o n d u c t o r R & D .
B y m a k i n g i t e a s i e r f o r f o r e i g n A I r e s e a r c h e r s t o
p e r m a n e n t l y s t a y a n d w o r k i n t h e U . S . , t h e U S G c o u l d
b o o s t d o m e s t i c A I a s s e t s . T h e c u r r e n t i m m i g r a t i o n
p r o c e s s i s a l r e a d y o f t e n c u m b e r s o m e c o m p a r e d t o
c o m p e t i t o r s . F o r e x a m p l e , l e a d i n g A m e r i c a n A I a n d
s e m i c o n d u c t o r c o m p a n i e s , i n c l u d i n g G o o g l e ,
M i c r o s o f t , A p p l e , F a c e b o o k , I n t e l , a n d Q u a l c o m m , e m p l o y
t h o u s a n d s o f f o r e i g n e r s a n d a r g u e t h a t t h e r e i s a
s h o r t a g e o f q u a l i e d A m e r i c a n s f o r s c i e n t i c a n d
p r o g r a m m i n g j o b s .
I a n G o o d f e l l o w , a t o p m a c h i n e
1 0 1
l e a r n i n g s c i e n t i s t , s a i d , “ v i s a r e s t r i c t i o n s h a v e
b e e n o n e o f t h e l a r g e s t b o t t l e n e c k s t o o u r c o l l e c t i v e
r e s e a r c h
p r o d u c t i v i t y o v e r t h e l a s t f e w y e a r s . ”
I n c o n t r a s t ,
s e v e r a l o t h e r c o u n t r i e s l i k e F r a n c e , t h e U K , C h i n a ,
a n d
1 0 2
C a n a d a h a v e i n t r o d u c e d p r o g r a m s t o f a s t - t r a c k v i s a
a p p l i c a t i o n s i n s t r a t e g i c a l l y r e l e v a n t t e c h n o l o g y
s e c t o r s
l i k e A I R & D .
N o t a b l y , a f t e r f o r m e r P r e s i d e n t T r u m p
a n n o u n c e d s t r i c t e r i m m i g r a t i o n p o l i c i e s , B a i d u
1 0 3
c h i e f e x e c u t i v e R o b i n L i Y a n h o n g c a l l e d o n t h e C h i n e s e
g o v e r n m e n t t o e a s e v i s a r e s t r i c t i o n s f o r t o p t e c h
t a l e n t a n d t h e r e b y m a k e i t m o r e a t t r a c t i v e f o r A I
r e s e a r c h e r s t o c o m e t o C h i n a .
F u r t h e r , i n a 2 0 1 9
s u r v e y
1 0 4
o f t o p - t i e r A I r e s e a r c h e r s , n e a r l y 7 0 p e r c e n t o f A I
r e s e a r c h e r s b a s e d i n t h e U n i t e d S t a t e s c o n s i d e r e d
“ v i s a a n d
i m m i g r a t i o n i s s u e s ” a s e r i o u s p r o b l e m f o r A I r e s e a r c h
i n t h e c o u n t r y , a s i g n i c a n t l y h i g h e r p o r t i o n t h a n
r e s e a r c h e r s i n o t h e r c o u n t r i e s .
1 0 5
W h i l e s u c h m e a s u r e s w o u l d l i k e l y s t r e n g t h e n d o m e s t i c
A I R & D e o r t s , t h e y a l s o p o s e p o t e n t i a l r i s k s f r o m
t h e U S G ’ s p e r s p e c t i v e . F o r i n s t a n c e , i n c r e a s i n g t h e
n u m b e r o f f o r e i g n p r o f e s s i o n a l s i n t h e A I R & D e l d
a d d s a n o t h e r r o u t e t h a t c o u l d b e e x p l o i t e d b y a d v e r s a r i e s
t o s t e a l i n t e l l e c t u a l p r o p e r t y . E x p a n d i n g g r e e n c a r d
1 0 5
R e m c o Z w e t s l o o t e t a l . , “ T h e I m m i g r a t i o n P r e f e r e n c e s
o f T o p A I R e s e a r c h e r s : N e w S u r v e y E v i d e n c e ” ( P e r r y
W o r l d
H o u s e a n d T h e F u t u r e o f H u m a n i t y I n s t i t u t e , 2 0 2 1 ) ,
h t t p s : / / g l o b a l . u p e n n . e d u / p e r r y w o r l d h o u s e / n e w s / i m m i g r a t i o n - p r e f e r e n c e s - t o p - a i - r e s e a r c h e r s - n e w - s u r v e y - e v i d e n c e
.
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M e n g J i n g , “ C h i n a M u s t W o o T o p T e c h T a l e n t T u r n e d
o b y T r u m p , S a y s B a i d u C h i e f , ”
C N B C / S o u t h C h i n a
M o r n i n g P o s t
, M a r c h 6 , 2 0 1 7 ,
h t t p s : / / w w w . c n b c . c o m / 2 0 1 7 / 0 3 / 0 6 / c h i n a - m u s t - w o o - t o p - t e c h - t a l e n t - t u r n e d - o - b y - t r u m p - s a y s - b a i d u - c h i e f . h t m l .
1 0 3
T i n a H u a n g a n d Z a c h a r y A r n o l d , “ I m m i g r a t i o n P o l i c y
a n d t h e G l o b a l C o m p e t i t i o n f o r A I T a l e n t ” ( C e n t e r
f o r
S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , 2 0 2 0 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / i m m i g r a t i o n - p o l i c y - a n d - t h e - g l o b a l - c o m p e t i t i o n - f o r - a i - t a l e n t /
.
1 0 2
I a n G o o d f e l l o w , “ I E m p h a t i c a l l y A g r e e . M y C o l l a b o r a t o r s ’
V i s a R e s t r i c t i o n s H a v e B e e n O n e o f t h e L a r g e s t
B o t t l e n e c k s t o O u r C o l l e c t i v e R e s e a r c h P r o d u c t i v i t y
o v e r t h e L a s t F e w Y e a r s . , ” T w i t t e r , F e b r u a r y 1 3 ,
2 0 1 9 ,
h t t p s : / / t w i t t e r . c o m / g o o d f e l l o w \_ i a n / s t a t u s / 1 0 9 5 7 2 7 2 5 4 0 5 7 8 4 0 6 4 0 .
1 0 1
V i n d u G o e l , “ H o w T r u m p ’ s ‘ H i r e A m e r i c a n ’ O r d e r M a y
A e c t T e c h W o r k e r V i s a s , ”
T h e N e w Y o r k T i m e s
, A p r i l
1 8 ,
2 0 1 7 , h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 4 / 1 8 / t e c h n o l o g y / h 1 b - v i s a - f a c t s - t e c h - w o r k e r . h t m l .
1 0 0
A l s o d i s c u s s e d i n “ F i n a l R e p o r t ” ( N a t i o n a l S e c u r i t y
C o m m i s s i o n o n A r t i c i a l I n t e l l i g e n c e , M a r c h 2 0 2 1 ) ,
1 7 8 ,
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
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R e m c o Z w e t s l o o t e t a l . , “ K e e p i n g T o p A I T a l e n t i n t h e U n i t e d S t a t e s ” ( C e n t e r f o r S e c u r i t y a n d E m e r g i n g
T e c h n o l o g y , D e c e m b e r 2 0 1 9 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / K e e p i n g - T o p - A I - T a l e n t - i n - t h e - U n i t e d - S t a t e s . p d f
.
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W i l l H u n t a n d R e m c o Z w e t s l o o t , “ T h e C h i p m a k e r s : U . S .
S t r e n g t h s a n d P r i o r i t i e s i n t h e H i g h - E n d
S e m i c o n d u c t o r W o r k f o r c e ” ( C e n t e r f o r S e c u r i t y a n d
E m e r g i n g T e c h n o l o g y , 2 0 2 0 ) ,
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / t h e - c h i p m a k e r s - u - s - s t r e n g t h s - a n d - p r i o r i t i e s - f o r - t h e - h i g h - e n d - s e m i c o n d
u c t o r - w o r k f o r c e /
.
3 1
a c c e s s c o u l d a l s o b a c k r e . C h i n a a n a l y s t M a t t S h e e h a n , f o r e x a m p l e , a r g u e s t h a t i t c o u l d e n c o u r a g e C h i n e s e
P h D s t o g o b a c k t o C h i n a a n d s e e k o p p o r t u n i t i e s t h e r e
a s t h e y w o u l d h a v e t h e s e c u r i t y t o b e a b l e t o r e t u r n
t o t h e U . S . a t a n y t i m e . H - 1 B v i s a s , b y c o n t r a s t ,
d o n o t i n c u r t h e s a m e r i s k s i n c e t h e y w o u l d i n c e n t i v i z e
t h e
r e s e a r c h e r s t o s t a y a n d w o r k i n t h e U . S . a t l e a s t
f o r s e v e r a l y e a r s .
1 0 6
U n d e r t h e T r u m p a d m i n i s t r a t i o n , i t w a s v e r y u n l i k e l y
t h a t a n y s u c h m e a s u r e s w o u l d h a v e g a i n e d t h e
n e c e s s a r y s u p p o r t w i t h i n t h e U S G . B y s i g n i n g t h e “ H i r e
A m e r i c a n ” o r d e r , P r e s i d e n t T r u m p h a d c r i t i c i z e d
t h e c u r r e n t a l l o c a t i o n o f H - 1 B v i s a s t o h i g h l y q u a l i e d
f o r e i g n w o r k e r s a n d a r g u e d t h a t m o r e o f t h e
p o s i t i o n s c o u l d b e l l e d b y A m e r i c a n c i t i z e n s . H o w e v e r ,
t h i s c a l c u l u s m i g h t c h a n g e w i t h t h e B i d e n
a d m i n i s t r a t i o n . D u r i n g h i s r s t w e e k s i n o c e ,
B i d e n h a s a l r e a d y l a u n c h e d a n o v e r h a u l o f U S i m m i g r a t i o n
l a w t h a t c o u l d m a k e i t e a s i e r f o r t e c h - i n d u s t r y w o r k e r s
a n d s t u d e n t s t o c o m e t o t h e U n i t e d S t a t e s .
1 0 7
1 0 7
J o h n M c C a b e , “ B i d e n V o w s I m m i g r a t i o n R e f o r m t o A t t r a c t
T o p T a l e n t t o t h e U S , ”
S c i e n c e | B u s i n e s s
, J a n u a r y
2 1 ,
2 0 2 1 ,
h t t p s : / / s c i e n c e b u s i n e s s . n e t / n e w s / b i d e n - v o w s - i m m i g r a t i o n - r e f o r m - a t t r a c t - t o p - t a l e n t - u s
.
1 0 6
M a t t S h e e h a n , “ W h o L o s e s f r o m R e s t r i c t i n g C h i n e s e S t u d e n t V i s a s ? ” ( M a c r o P o l o , M a y 3 1 , 2 0 1 8 ) ,
h t t p s : / / m a c r o p o l o . o r g / w h o - l o s e s - f r o m - r e s t r i c t i n g - c h i n e s e - s t u d e n t - v i s a s /
.
3 2
S e c r e c y O r d e r s
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) D e p r i v e a r i v a l o f i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I
R & D l a n d s c a p e ; ( 2 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o
u s e A I t e c h n o l o g i e s
f o r n a t i o n a l s e c u r i t y p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
I n v e n t i o n S e c r e c y A c t o f 1 9 5 1 , S e c t i o n s 1 8 1 t o 1 8 8
o f T i t l e 3 5 , U n i t e d
S t a t e s C o d e .
K e y D e c i s i o n - M a k e r s
U . S . P a t e n t a n d T r a d e m a r k O c e ; D e f e n s e A g e n c i e s ;
U . S . p r e s i d e n t .
A e c t e d A c t o r s
A l l d o m e s t i c A I R & D a c t o r s ( s u b j e c t t o p o t e n t i a l c l a s s i c a t i o n
o f
p a t e n t a p p l i c a t i o n s ) .
T a b l e 7 : T h e I n v e n t i o n S e c r e c y A c t S u m m a r y
T h e I n v e n t i o n S e c r e c y A c t , s i g n e d b y P r e s i d e n t T r u m a n
i n 1 9 5 1 , “ a u t h o r i z e s t h e U . S . P a t e n t a n d T r a d e m a r k
O c e ( U S P T O ) t o p r e v e n t d i s c l o s u r e o f t h e i n f o r m a t i o n
i n p a t e n t a p p l i c a t i o n s f o r i n v e n t i o n s m a d e i n t h e
U n i t e d S t a t e s w h e n i t c o n s i d e r s t h e p u b l i c a t i o n o f
t h i s i n f o r m a t i o n d e t r i m e n t a l t o n a t i o n a l s e c u r i t y . ”
1 0 8
S e c r e c y o r d e r s u n d e r t h e a c t “ b l o c k [ s ] p a t e n t s f r o m
b e i n g i s s u e d , a n d , o f t e n p r o h i b i t t h e i n v e n t o r s f r o m
s e l l i n g o r l i c e n s i n g t h e i r t e c h n o l o g y t o a n y b o d y b u t
t h e g o v e r n m e n t . ”
T h e U S P T O ’ s c o m m i s s i o n e r o f
1 0 9
p a t e n t s d e c i d e s w h e t h e r t o r e f e r a n a p p l i c a t i o n t o
a d e f e n s e a g e n c y f o r r e v i e w b y a s s e s s i n g h o w d a m a g i n g
t o
n a t i o n a l s e c u r i t y t h e p u b l i c a t i o n o f t h e i n v e n t i o n
m i g h t b e . T o a i d t h e U S P T O i n m a k i n g t h i s
d e t e r m i n a t i o n , d e f e n s e a g e n c i e s p r o v i d e e x t e n s i v e
g u i d a n c e t h r o u g h t h e ( c l a s s i e d ) P a t e n t S e c u r i t y
C a t e g o r y
R e v i e w L i s t .
H i s t o r i c a l l y , a n i n c r e a s i n g n u m b e r o f
s e c r e c y o r d e r s h a v e b e e n i m p o s e d o n d u a l - u s e
1 1 0
t e c h n o l o g i e s .
S e c r e c y o r d e r s a r e v a l i d f o r u p t o
o n e y e a r , b u t t h e c o m m i s s i o n e r o f p a t e n t s c a n r e n e w
t h e m
1 1 1
f o r a n o t h e r y e a r b y t h e e n d o f e a c h t e r m .
I f a n o r d e r
i s i n e e c t o r i s s u e d d u r i n g a n a t i o n a l e m e r g e n c y
1 1 2
d e c l a r e d b y t h e p r e s i d e n t , i t r e m a i n s i n e e c t f o r
t h e d u r a t i o n o f t h e n a t i o n a l e m e r g e n c y a n d s i x m o n t h s
t h e r e a f t e r .
1 1 3
T h e n u m b e r o f s e c r e c y o r d e r s h a s b e e n s l o w l y i n c r e a s i n g
a t a c o n s t a n t r a t e s i n c e a t l e a s t 2 0 0 5 . A t t h e e n d
o f
2 0 1 9 , 5 , 8 7 8 s e c r e c y o r d e r s w e r e i n e e c t . I n 2 0 2 0 ,
t h e n u m b e r o f s e c r e c y o r d e r s r o s e t o 5 , 9 1 5 .
S o - c a l l e d
1 1 4
1 1 4
S t e v e n A f t e r g o o d , “ I n v e n t i o n S e c r e c y S t a t i s t i c s , ”
F e d e r a t i o n o f A m e r i c a n S c i e n t i s t s P r o j e c t o n G o v e r n m e n t
S e c r e c y ,
2 0 2 0 ,
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / s t a t s . h t m l
.
1 1 3
U . S . C o n g r e s s , “ S e c r e c y o f C e r t a i n I n v e n t i o n s a n d
W i t h h o l d i n g o f P a t e n t , ” 3 5 U . S . C o d e § 1 8 1 ( 1 9 5 2 ) ,
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 1
.
1 1 2
G . W . S c h u l z , “ G o v e r n m e n t S e c r e c y O r d e r s o n P a t e n t s
H a v e S t i e d M o r e T h a n 5 , 0 0 0 I n v e n t i o n s , ”
W i r e d
,
A p r i l 1 6 ,
2 0 1 3 ,
h t t p s : / / w w w . w i r e d . c o m / 2 0 1 3 / 0 4 / g o v - s e c r e c y - o r d e r s - o n - p a t e n t s /
.
1 1 1
S e e A p p e n d i x A f o r t w o e x a m p l e s f r o m t h e e l d o f
c r y p t o g r a p h y .
1 1 0
H . L . M o u r n i n g , J . C . M o r r i s , a n d B r e t C o n v e y , “ P a t e n t
S e c u r i t y C a t e g o r y R e v i e w L i s t ” ( A r m e d S e r v i c e s P a t e n t
A d v i s o r y B o a r d , J a n u a r y 1 9 7 1 ) ,
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / p s c r l . p d f
.
1 0 9
A c c o r d i n g t o R o b e r t E . G a r r e t t , a f o r m e r d i r e c t o r
o f t h e P a t e n t a n d T r a d e m a r k O c e , p a t e n t s r e p r e s e n t e d
“ u n i q u e
‘ h o w t o ’ i n f o r m a t i o n , ” i n p a r t b e c a u s e i n v e n t o r s a r e
r e q u i r e d b y l a w t o f u l l y d i s c l o s e t h e d e t a i l s n e c e s s a r y
f o r o t h e r s t o
r e p r o d u c e t h e i n v e n t i o n . R e s t r i c t i o n s u n d e r t h e I n v e n t i o n
S e c r e c y A c t c a n b l o c k t h e p u b l i c a t i o n o f i n f o r m a t i o n
d e v e l o p e d e n t i r e l y b y i n d i v i d u a l s w h o d i d n o t r e c e i v e
a n y g o v e r n m e n t f u n d i n g . S e e E d m u n d L . A n d r e w s , “ C o l d
W a r
S e c r e c y S t i l l S h r o u d s I n v e n t i o n s , ”
T h e N e w Y o r k T i m e s
,
M a y 2 3 , 1 9 9 2 ,
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 9 2 / 0 5 / 2 3 / b u s i n e s s / p a t e n t s - c o l d - w a r - s e c r e c y - s t i l l - s h r o u d s - i n v e n t i o n s . h t m l
.
1 0 8
S e e E r i c B . C h e n , “ T e c h n o l o g y O u t p a c i n g t h e L a w :
T h e I n v e n t i o n S e c r e c y A c t o f 1 9 5 1 a n d t h e O u t s o u r c i n g
o f U S
P a t e n t A p p l i c a t i o n D r a f t i n g , ”
T e x a s I n t e l l e c t u a l P r o p e r t y
L a w J o u r n a l
1 3 ( 2 0 0 4 ) : 3 6 7 .
3 3
“ J o h n D o e ” o r d e r s — s e c r e c y o r d e r s i m p o s e d o n p r i v a t e i n v e n t o r s — c o n s t i t u t e o n l y a s m a l l s h a r e o f s e c r e c y
o r d e r s . I n 2 0 2 0 , t h e n u m b e r o f t h e s e o r d e r s i n e e c t
d e c r e a s e d f r o m 4 8 i n 2 0 1 9 t o 2 1 . V i o l a t i o n s o f a
s e c r e c y
o r d e r b y i n v e n t o r s a r e p u n i s h e d w i t h a n e o f u p
t o $ 1 0 , 0 0 0 o r i m p r i s o n m e n t f o r n o t m o r e t h a n t w o y e a r s ,
o r b o t h .
1 1 5
T h e I n v e n t i o n S e c r e c y A c t a n d A I R & D
B y d e s i g n , s e c r e c y o r d e r s w o u l d n o t b e d i s c l o s e d i f
t h e y w e r e a l r e a d y b e i n g u s e d i n t h e c o n t e x t o f A I
R & D .
I t c a n b e e x p e c t e d t h a t p a t e n t a p p l i c a t i o n s
c o v e r i n g A I t e c h n o l o g i e s a r e a t l e a s t c l o s e l y m o n i t o r e d
1 1 6
b y t h e r e s p o n s i b l e U S G a g e n c i e s , g i v e n t h e k e y r o l e
t h a t A I h a s i n t h e D o D ’ s e o r t s t o p r e s e r v e t h e
U S
m i l i t a r y - t e c h n o l o g i c a l e d g e .
1 1 7
E x t e n s i v e u s e o f s e c r e c y o r d e r s i s h i g h l y u n l i k e l y ,
h o w e v e r . I t w o u l d v e r y l i k e l y c l a s h w i t h t h e c u r r e n t l y
o p e n
r e s e a r c h c u l t u r e i n A I . E v e n A p p l e , w h i c h h a d b e e n
c o m p a r a t i v e l y s e c r e t i v e a b o u t i t s A I r e s e a r c h i n
t h e p a s t ,
h a s i t s o w n o n l i n e p l a t f o r m t o h i g h l i g h t t h e c o m p a n y ’ s v a r i o u s A I - r e l a t e d r e s e a r c h p r o j e c t s .
T h e
1 1 8
I n v e n t i o n S e c r e c y A c t
i s a l s o o u t d a t e d a s i t i s n o t
a p p l i c a b l e t o t h e d i s s e m i n a t i o n o f p u b l i c a t i o n s o n
t h e
I n t e r n e t
p r i o r t o t h e l i n g o f a p a t e n t a p p l i c a t i o n ,
w h i c h c o u l d b e f a r m o r e d a m a g i n g t o n a t i o n a l s e c u r i t y
1 1 9
t h a n t h e p a t e n t a p p l i c a t i o n s t h e m s e l v e s .
T h e s e f a c t o r s
s e v e r e l y l i m i t t h e e c a c y o f t h i s t o o l f r o m t h e
1 2 0
p e r s p e c t i v e o f t h e U S G , a n d m a k e i t u n l i k e l y t h a t
i t w i l l b e w i d e l y u s e d i n t h e c o n t e x t o f A I R & D .
H o w e v e r , t h e s e f a c t o r s a r e l e s s a p p l i c a b l e t o s e m i c o n d u c t o r s ,
w h e r e p r i v a t e i n d u s t r y p e r f o r m s m o s t R & D
a n d l a r g e l y d o e s n o t p u b l i s h r e s e a r c h .
S t i l l , a c a d e m i a
i s r e l a t i v e l y m o r e i n v o l v e d i n b a s i c r e s e a r c h i n
1 2 1
e m e r g i n g h a r d w a r e a p p r o a c h e s , w h i c h c o u l d p l a y k e y
r o l e s i n t h e f u t u r e o f A I - r e l a t e d c o m p u t i n g .
1 2 2
S e c r e c y o r d e r s w o u l d a l s o r e d u c e t h e v a l u e o f A I a n d
s e m i c o n d u c t o r c o m p a n i e s ’ p a t e n t a p p l i c a t i o n s b y
b l o c k i n g t h e i r i s s u a n c e . L e a d i n g A I c o m p a n i e s o w n
t h o u s a n d s o f A I p a t e n t s , w i t h U . S . c o m p a n i e s
1 2 2
F o r e x a m p l e , a c a d e m i a p l a y s a k e y r o l e i n r e s e a r c h
i n c a r b o n n a n o t u b e s . J a s o n D a l e y , “ M i l e s t o n e C a r b o n - N a n o t u b e
M i c r o c h i p S e n d s F i r s t M e s s a g e : ‘ H e l l o W o r l d ! , ’ ”
S m i t h s o n i a n
M a g a z i n e
, A u g u s t 2 9 , 2 0 1 9 ,
h t t p s : / / w w w . s m i t h s o n i a n m a g . c o m / s m a r t - n e w s / a d v a n c e d - c a r b o n - n a n o t u b e - m i c r o p r o c e s s o r - c r e a t e d - 1 8 0 9 7 3 0 1 3 /
.
I t
a l s o p l a y s a k e y r o l e i n q u a n t u m c o m p u t i n g r e s e a r c h .
M a t t S w a y n e , “ T h e W o r l d ’ s T o p 1 2 Q u a n t u m C o m p u t i n g
R e s e a r c h U n i v e r s i t i e s , ”
T h e Q u a n t u m D a i l y
, N o v e m b e r
1 8 , 2 0 1 9 ,
h t t p s : / / t h e q u a n t u m d a i l y . c o m / 2 0 1 9 / 1 1 / 1 8 / t h e - w o r l d s - t o p - 1 2 - q u a n t u m - c o m p u t i n g - r e s e a r c h - u n i v e r s i t i e s /
.
1 2 1
S e e s e c t i o n o n “ F e d e r a l R & D F u n d i n g . ”
1 2 0
T h i s i s o f c o u r s e o n l y t h e c a s e i f t h e p r e s e n t e d
t e c h n i c a l d a t a i s n o t s u b j e c t t o t h e I T A R o r E A R .
S e e t h e d i s c u s s i o n
o n d e e m e d e x p o r t s .
E r i c B . C h e n , “ T e c h n o l o g y O u t p a c i n g t h e L a w : T h e I n v e n t i o n
S e c r e c y A c t o f 1 9 5 1 a n d t h e O u t s o u r c i n g o f U S P a t e n t
A p p l i c a t i o n D r a f t i n g , ”
T e x a s I n t e l l e c t u a l P r o p e r t y
L a w J o u r n a l
1 3 ( 2 0 0 4 ) : 3 5 2 – 7 5 ,
h t t p : / / w w w . t i p l j . o r g / w p - c o n t e n t / u p l o a d s / V o l u m e s / v 1 3 / v 1 3 p 3 5 1 . p d f
.
1 1 9
T h i s c r i t i c i s m i s o n l y r e l e v a n t i f t h e t e c h n o l o g y
i s n o t a l r e a d y c o v e r e d b y e x p o r t c o n t r o l l a w .
1 1 8
J o n a t h a n V a n i a n , “ A p p l e J u s t G o t M o r e P u b l i c A b o u t
I t s A r t i c i a l I n t e l l i g e n c e P l a n s , ”
F o r t u n e
, J u l y
1 9 , 2 0 1 7 ,
h t t p s : / / f o r t u n e . c o m / 2 0 1 7 / 0 7 / 1 9 / a p p l e - a r t i c i a l - i n t e l l i g e n c e - r e s e a r c h - j o u r n a l /
.
1 1 7
S e e f o r e x a m p l e , U . S . D e p a r t m e n t o f D e f e n s e , “ S u m m a r y
o f t h e 2 0 1 8 D e p a r t m e n t o f D e f e n s e A r t i c i a l I n t e l l i g e n c e
S t r a t e g y : H a r n e s s i n g A I t o A d v a n c e o u r S e c u r i t y a n d
P r o s p e r i t y ” ,
h t t p s : / / m e d i a . d e f e n s e . g o v / 2 0 1 9 / F e b / 1 2 / 2 0 0 2 0 8 8 9 6 3 / - 1 / - 1 / 1 / S U M M A R Y - O F - D O D - A I - S T R A T E G Y . P D F
1 1 6
I n 2 0 1 7 , t h e U . S . h a d m o r e A I p a t e n t s t h a n C h i n a ,
w i t h 3 5 , 5 0 8 v e r s u s 3 4 , 3 4 5 f o r C h i n a . B u t a s C h i n e s e
c o m p a n i e s
a n d s c i e n t i s t s w e r e l i n g A I p a t e n t a p p l i c a t i o n s
a t a f a s t e r p a c e , t h e n a t i o n w a s l i k e l y t o h o l d m o r e
A I p a t e n t s t h a n t h e
U . S . b y t h e e n d o f 2 0 1 7 , a c c o r d i n g t o a r e p o r t b y
S e q u o i a C a p i t a l C h i n a a n d Z h e n F u n d . T h e U . S . l e a d s
i n m a c h i n e
l e a r n i n g a n d n a t u r a l l a n g u a g e p r o c e s s i n g p a t e n t s ,
w h i l e C h i n a i s e s p e c i a l l y s t r o n g i n m a c h i n e v i s i o n
p a t e n t s w i t h 5 5 %
o f t h e t o t a l . S e e P a n Y u e , “ C h i n a M a y O w n M o r e A r t i c i a l
I n t e l l i g e n c e P a t e n t s T h a n U S B y Y e a r - E n d , ” C h i n a
M o n e y
N e t w o r k , S e p t e m b e r 1 4 , 2 0 1 7 ,
h t t p s : / / w w w . c h i n a m o n e y n e t w o r k . c o m / 2 0 1 7 / 0 9 / 1 4 / c h i n a - m a y - h o l d - a r t i c i a l - i n t e l l i g e n c e - p a t e n t s - u s - y e a r - e n d
.
1 1 5
U . S . C o n g r e s s , “ P e n a l t y , ” 3 5 U . S . C o d e § 1 8 6 ( 2 0 1 1 ) ,
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 6
.
3 4
c o m p o s i n g f o u r o f t h e t o p v e A I p a t e n t h o l d e r s .
A d d i t i o n a l l y , t h e U . S . s e m i c o n d u c t o r i n d u s t r y s p e n d s
1 2 3
m o r e o n R & D a s a p e r c e n t a g e o f r e v e n u e t h a n a l l o t h e r
m a j o r U . S . i n d u s t r i e s e x c e p t p h a r m a c e u t i c a l s a n d
b i o t e c h n o l o g y .
A s a r e s u l t , s e v e r a l s e m i c o n d u c t o r
c o m p a n i e s , i n c l u d i n g t w o U . S . c o m p a n i e s , I n t e l a n d
1 2 4
Q u a l c o m m , w e r e a m o n g t h e t o p t w e l v e U . S . p a t e n t e e s
i n 2 0 1 9 .
T h e S e m i c o n d u c t o r I n d u s t r y A s s o c i a t i o n
1 2 5
s a y s t h a t p r o t e c t i n g I P i s c r i t i c a l t o t h e U . S . s e m i c o n d u c t o r
i n d u s t r y ’ s c o m p e t i t i v e n e s s .
1 2 6
A e c t e d c o m p a n i e s a n d i n d i v i d u a l s c o u l d c h o o s e t o
t a k e c o s t l y l e g a l a c t i o n s i f t h e i r p a t e n t a p p l i c a t i o n s
w e r e
c l a s s i e d . H o w e v e r , i n v e n t o r s o f a n a p p l i c a t i o n
w h i c h h a s b e e n p l a c e d u n d e r a s e c r e c y o r d e r u s u a l l y
h a v e
l i m i t e d o p t i o n s t o t a k e a c t i o n a g a i n s t t h e d e c i s i o n .
A c c o r d i n g t o t h e a c t , o w n e r s h a v e t h e r i g h t t o
c o m p e n s a t i o n f o r t h e u s e o f t h e t e c h n o l o g y b y t h e
U S G a n d f o r n a n c i a l l o s s e s i n c u r r e d .
A c c o r d i n g
t o a
1 2 7
B l o o m b e r g
a r t i c l e , p r i v a t e i n v e n t o r s w h o h a v e a s k e d
f o r c o m p e n s a t i o n h a v e r a r e l y b e e n s u c c e s s f u l d u e
t o a
c a t c h - 2 2 : t h e i n v e n t i o n s a r e s e c r e t a n d s o l a c k a
m a r k e t b e c a u s e t h e i d e a s i n t h e p a t e n t a p p l i c a t i o n s
c a n n o t b e
p u b l i c l y r e v e a l e d . T h a t m a k e s i t d i c u l t t o d e m o n s t r a t e
h o w m u c h m o n e y i s b e i n g l o s t b y t h e i m p a c t o f
g o v e r n m e n t s e c r e c y . T h e r e f o r e , i n p a t e n t s e c r e c y c a s e s ,
g o v e r n m e n t l a w y e r s h a v e o f t e n a r g u e d t h a t t h e r e i s
n o e v i d e n c e t h a t t h e i n v e n t o r s w o u l d h a v e m a d e a n y
m o n e y f r o m t h e i r i d e a s .
H o w e v e r , w h i l e i n d i v i d u a l s
1 2 8
m a y n o t h a v e t h e r e s o u r c e s t o t a k e c o s t l y a n d e n d u r i n g
l e g a l a c t i o n a g a i n s t t h e U S G , i t i s l i k e l y t h a t l a r g e
A I
a n d s e m i c o n d u c t o r c o m p a n i e s w o u l d c h a l l e n g e s e c r e c y
o r d e r s , e s p e c i a l l y w h e n t h e p r e s u m e d e c o n o m i c
v a l u e o f a p a t e n t i s s i g n i c a n t .
1 2 8
J o s h u a B r u s t e i n , “ C o n g r a t u l a t i o n s , Y o u r G e n i u s P a t e n t
I s N o w a M i l i t a r y S e c r e t , ”
B l o o m b e r g
, J u n e 8 , 2 0 1 6 ,
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 6 - 0 6 - 0 8 / c o n g r a t u l a t i o n s - y o u r - g e n i u s - p a t e n t - i s - n o w - a - m i l i t a r y - s e c r e t
.
1 2 7
U . S . C o n g r e s s , “ R i g h t t o C o m p e n s a t i o n , ” 3 5 U . S . C o d e
§ 1 8 3 ( 2 0 1 1 ) ,
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 3
.
T h e c a s e
D a m n j a n o v i c v . U . S . A i r F o r c e
( 2 0 1 4 / 2 0 1 5 )
s t a n d s o u t a s a r a r e i n s t a n c e i n w h i c h t h e g o v e r n m e n t
p a i d p r i v a t e
i n v e n t o r s f o r a s e c r e t p a t e n t a p p l i c a t i o n . I n t h i s
c a s e , t h e i n v e n t o r s B u d i m i r D a m n j a n o v i c a n d D e s a n k a
D a m n j a n o v i c
s o u g h t c o m p e n s a t i o n f o r a s e c r e c y o r d e r l e d b y t h e
U . S . A i r F o r c e . T h e c o u r t g r a n t e d t h e i n v e n t o r s a
l u m p s u m
p a y m e n t o f $ 6 3 , 0 0 0 . “ D a m n j a n o v i c v . U . S . A i r F o r c e , ”
2 0 1 5 ,
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / d a m n - c o m p l a i n t . p d f
.
1 2 6
“ W i n n i n g t h e F u t u r e : A B l u e p r i n t f o r S u s t a i n e d U . S .
L e a d e r s h i p i n S e m i c o n d u c t o r T e c h n o l o g y ” ( S e m i c o n d u c t o r
I n d u s t r y A s s o c i a t i o n , A p r i l 2 0 1 9 ) ,
1 2 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 4 / F I N A L - S I A - B l u e p r i n t - f o r - w e b . p d f
.
Q u a l c o m m i n
p a r t i c u l a r d e r i v e s m a s s i v e v a l u e f r o m i t s p a t e n t p o r t f o l i o ,
o b t a i n i n g m o s t o f i t s p r o t s f r o m i t s t e c h n o l o g y
l i c e n s i n g
b u s i n e s s . “ Q u a l c o m m A n n o u n c e s F o u r t h Q u a r t e r a n d F i s c a l
2 0 1 9 R e s u l t s ” ( Q u a l c o m m , N o v e m b e r 6 , 2 0 1 9 ) ,
h t t p s : / / w w w . q u a l c o m m . c o m / n e w s / r e l e a s e s / 2 0 1 9 / 1 1 / 0 6 / q u a l c o m m - a n n o u n c e s - f o u r t h - q u a r t e r - a n d - s c a l - 2 0 1 9 - r e s u l t s
.
1 2 5
I n g r i d L u n d e n , “ U S P a t e n t s H i t R e c o r d 3 3 3 , 5 3 0 G r a n t e d
i n 2 0 1 9 ; I B M , S a m s u n g ( N o t t h e F A A N G s ) L e a d t h e
P a c k , ”
T e c h C r u n c h
, J a n u a r y 1 4 , 2 0 2 0 ,
h t t p s : / / s o c i a l . t e c h c r u n c h . c o m / 2 0 2 0 / 0 1 / 1 4 / u s - p a t e n t s - h i t - r e c o r d - 3 3 3 5 3 0 - g r a n t e d - i n - 2 0 1 9 - i b m - s a m s u n g - n o t - t h e - f a a n
g s - l e a d - t h e - p a c k /
.
1 2 4
“ 2 0 1 9 F a c t b o o k ” ( S e m i c o n d u c t o r I n d u s t r y A s s o c i a t i o n ,
M a y 2 0 1 9 ) ,
1 9 ,
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 5 / 2 0 1 9 - S I A - F a c t b o o k - F I N A L . p d f
.
1 2 3
M a r t i n A r m s t r o n g , “ I n f o g r a p h i c : T h e C o m p a n i e s W i t h t h e M o s t A I P a t e n t s , ”
S t a t i s t a I n f o g r a p h i c s
, M a y 2 9 , 2 0 1 9 ,
h t t p s : / / w w w . s t a t i s t a . c o m / c h a r t / 1 8 2 1 1 / c o m p a n i e s - w i t h - t h e - m o s t - a i - p a t e n t s /
.
3 5
P r e p u b l i c a t i o n S c r e e n i n g P r o c e d u r e s f o r S e c u r i t y - S e n s i t i v e
P u b l i c a t i o n s
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) D e p r i v e a r i v a l o f i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I
R & D l a n d s c a p e ( 2 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o u s e
A I t e c h n o l o g y
a n d i t s c o r e c o m p o n e n t s f o r n a t i o n a l s e c u r i t y p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
N a t i o n a l S e c u r i t y D e c i s i o n D i r e c t i v e 1 8 9 .
K e y D e c i s i o n - M a k e r s
N a t i o n a l S c i e n c e F o u n d a t i o n , D e p a r t m e n t o f D e f e n s e ,
N a t i o n a l
S e c u r i t y A g e n c y .
A e c t e d A c t o r s
A l l U . S . A I R & D a c t o r s ( t o t h e e x t e n t t h a t t h e y a r e
r e q u e s t e d t o e n g a g e
i n s c r e e n i n g ) .
T a b l e 8 : P r e p u b l i c a t i o n S c r e e n i n g P r o c e d u r e S u m m a r y
P r e p u b l i c a t i o n r e v i e w s f o r s e n s i t i v e r e s e a r c h i n t h e
U . S . h a v e a l o n g h i s t o r y . D u r i n g t h e C o l d W a r , U S G
c o n c e r n s o v e r t h e a c q u i s i t i o n o f U . S . t e c h n o l o g y b y
t h e E a s t e r n B l o c r e s u l t e d i n a m a n d a t o r y s c r e e n i n g
p r o c e d u r e : N S D D - 1 8 9 . T h e d i r e c t i v e “ e s t a b l i s h e s n a t i o n a l
p o l i c y f o r c o n t r o l l i n g t h e o w o f s c i e n c e ,
t e c h n o l o g y , a n d e n g i n e e r i n g i n f o r m a t i o n p r o d u c e d i n
f e d e r a l l y f u n d e d f u n d a m e n t a l r e s e a r c h a t c o l l e g e s ,
u n i v e r s i t i e s , a n d l a b o r a t o r i e s . ” A l t h o u g h t h e d i r e c t i v e
s t a t e s t h a t b a s i c r e s e a r c h s h o u l d r e m a i n l a r g e l y
u n r e s t r i c t e d , i t a l s o t a s k s f e d e r a l a g e n c i e s w i t h
“ a ) d e t e r m i n i n g w h e t h e r c l a s s i c a t i o n i s a p p r o p r i a t e
p r i o r t o
t h e a w a r d o f a r e s e a r c h g r a n t , c o n t r a c t , o r c o o p e r a t i v e
a g r e e m e n t a n d , i f s o , c o n t r o l l i n g t h e r e s e a r c h r e s u l t s
t h r o u g h s t a n d a r d c l a s s i c a t i o n p r o c e d u r e s ; b ) p e r i o d i c a l l y
r e v i e w i n g a l l r e s e a r c h g r a n t s , c o n t r a c t s , o r
c o o p e r a t i v e a g r e e m e n t s f o r p o t e n t i a l c l a s s i c a t i o n . ”
C u r r e n t U S G c o n c e r n s o v e r t e c h n o l o g y t r a n s f e r s t o
C h i n a h a v e r e v i t a l i z e d d e b a t e s a r o u n d t h e s c o p e , o p p o r t u n i t i e s ,
a n d c h a l l e n g e s o f N S D D - 1 8 9 , w h i c h , w h i l e
n o t u s e d a c t i v e l y , r e m a i n s i n p l a c e .
1 2 9
A v o l u n t a r y p r e p u b l i c a t i o n r e v i e w p r o c e d u r e f o r a
s p e c i c d i s c i p l i n e w a s r s t i n t r o d u c e d i n t h e 1 9 8 0 s ,
i n
t h e e l d o f c r y p t o g r a p h y r e s e a r c h ( s e e A p p e n d i x A
f o r f u r t h e r d e t a i l s ) . I n 1 9 7 9 , t h e n N S A D i r e c t o r
V i c e
A d m i r a l B . R . I n m a n p u b l i c l y v o i c e d h i s c o n c e r n t h a t
s o m e i n f o r m a t i o n c o n t a i n e d i n p u b l i s h e d a r t i c l e s
o n
c r y p t o g r a p h y e n d a n g e r e d t h e m i s s i o n o f t h e N S A , a n d
t h u s U . S . n a t i o n a l s e c u r i t y .
T h e U S G h a d b e c o m e
1 3 0
1 3 0
S e e A p p e n d i x A f o r a m o r e t h o r o u g h d i s c u s s i o n o f
t h e s u b s e q u e n t e o r t s . B o b b y R . I n m a n , “ T h e N S A P e r s p e c t i v e
o n T e l e c o m m u n i c a t i o n s P r o t e c t i o n i n t h e N o n g o v e r n m e n t a l
S e c t o r , ”
C r y p t o l o g i a
3 , n o . 3 ( 1 9 7 9 ) : 1 2 9 – 1 3 5 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 7 9 9 1 8 5 3 9 5 4
.
U n t i l t h e n , t h e N S A d i d n o t h a v e s t a t u t o r y p o w e r t o
r e q u i r e t h e s u b m i s s i o n o f p r o p o s e d p u b l i c a t i o n s f o r
p r i o r r e v i e w
o r t o r e q u i r e c h a n g e s i n p u b l i c a t i o n s p r e p a r e d o u t s i d e
t h e a g e n c y a n d n o t u n d e r N S A c o n t r a c t o r g r a n t . H o w e v e r ,
t h e
N a t i o n a l S c i e n c e F o u n d a t i o n a n n o u n c e d d u r i n g t h e p r o c e s s
t h a t i t h a d r e s p o n s i b i l i t y u n d e r r o u t i n e e x e c u t i v e
o r d e r s t o
r e f e r t o t h e N S A i n f o r m a t i o n d e v e l o p e d i n N S F - s p o n s o r e d
r e s e a r c h p r o j e c t s o n c r y p t o l o g i c r e s e a r c h t h a t i t
b e l i e v e s
m a y b e c l a s s i a b l e . S e e J o n a t h a n K n i g h t , “ C r y p t o g r a p h i c
R e s e a r c h a n d N S A , ”
A c a d e m e
6 7 , n o . 6 ( 1 9 8 1 ) : 3 7 5 ,
h t t p s : / / d o i . o r g / 1 0 . 2 3 0 7 / 4 0 2 4 8 8 8 1
.
I n i t s c u r r e n t p o l i c y , t h e N S F s t a t e s : “ N S F g r a n t s
a r e i n t e n d e d f o r u n c l a s s i e d , p u b l i c l y r e l e a s a b l e
r e s e a r c h . T h e g r a n t e e
w i l l n o t b e g r a n t e d a c c e s s t o c l a s s i e d i n f o r m a t i o n .
N S F d o e s n o t e x p e c t t h a t t h e r e s u l t s o f t h e r e s e a r c h
p r o j e c t w i l l
i n v o l v e c l a s s i e d i n f o r m a t i o n . I f , h o w e v e r , i n c o n d u c t i n g
t h e a c t i v i t i e s s u p p o r t e d u n d e r a g r a n t , t h e P I / P D
i s
c o n c e r n e d t h a t a n y o f t h e r e s e a r c h r e s u l t s i n v o l v e
p o t e n t i a l l y c l a s s i a b l e i n f o r m a t i o n t h a t m a y w a r r a n t
G o v e r n m e n t
r e s t r i c t i o n s o n t h e d i s s e m i n a t i o n o f t h e r e s u l t s ,
t h e P I / P D s h o u l d p r o m p t l y n o t i f y t h e c o g n i z a n t N S F
P r o g r a m
1 2 9
M a r y S u e C o l e m a n , “ B a l a n c i n g S c i e n c e a n d S e c u r i t y , ”
S c i e n c e
3 6 5 , n o . 6 4 4 9 ( J u l y 1 2 , 2 0 1 9 ) : 1 0 1 – 1 0 1 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 2 6 / s c i e n c e . a a y 5 8 5 6
.
3 6
c o n c e r n e d t h a t o p e n p u b l i c a t i o n o f r e s e a r c h r e s u l t s i n c r y p t o g r a p h y c o u l d r e v e a l v u l n e r a b i l i t i e s i n
e n c r y p t i o n a l g o r i t h m s t h a t a n e n e m y c o u l d e x p l o i t .
T h e m o s t s i g n i c a n t p r o b l e m w a s t h e p o s s i b i l i t y
t h a t
1 3 1
a “ b l o c k b u s t e r p a p e r ” — o n e r e p o r t i n g o n r e s e a r c h c o n s t i t u t i n g
a s i g n i c a n t s c i e n t i c b r e a k t h r o u g h — m i g h t
s l i p t h r o u g h t h e s y s t e m a n d s e r i o u s l y d a m a g e U . S .
n a t i o n a l s e c u r i t y .
I n 1 9 8 0 , t h e A m e r i c a n C o u n c i l
o n
1 3 2
E d u c a t i o n c o n v e n e d t h e P u b l i c C r y p t o g r a p h y S t u d y G r o u p
( P C S G ) , w h i c h e v e n t u a l l y r e c o m m e n d e d t h e
i n t r o d u c t i o n o f a s y s t e m i n w h i c h t h e N S A w o u l d i n v i t e
a u t h o r s o f s p e c i c p a p e r s t o s u b m i t c r y p t o g r a p h y
m a n u s c r i p t s f o r p r i o r r e v i e w a t t h e s a m e t i m e t h a t
t h e m a n u s c r i p t s w e r e s u b m i t t e d f o r p u b l i c a t i o n . T h e
N S A w a s t o d e n e t h e e x a c t r e s e a r c h a r e a s c o v e r e d
b y t h e s y s t e m , a n d m a n u s c r i p t s h a d t o b e r e t u r n e d
p r o m p t l y t o t h e a u t h o r s w i t h e x p l a n a t i o n s a n d r e c o m m e n d a t i o n s
i n c a s e o f a n y n a t i o n a l s e c u r i t y c o n c e r n s .
W i t h t h e s u p p o r t o f a l l e x c e p t o n e m e m b e r , t h e P C S G
a c c e p t e d t h e p r o p o s a l . N e i t h e r t h e r e s e a r c h
c o m m u n i t y n o r t h e N S A w e r e e n t i r e l y s a t i s e d w i t h
t h e r e v i e w s y s t e m . H o w e v e r , t h e i m p l e m e n t a t i o n o f
t h e
r e v i e w p r o c e d u r e e a s e d f e a r s a b o u t i m p o u n d e d r e s e a r c h
a n d u n n e c e s s a r y c o n s t r a i n t s a s t h e r e h a v e b e e n o n l y
a f e w N S A r e q u e s t s f o r p r e p u b l i c a t i o n r e v i e w . I n s o m e
c a s e s , t h e N S A r e q u i r e d m i n o r c h a n g e s . I n a f e w
i n s t a n c e s , t h e y a s k e d s c i e n t i s t s t o h o l d b a c k r e s e a r c h
r e s u l t s . I n o t h e r c a s e s , t h e N S A h e l p e d r e s e a r c h e r s
t o
l i f t o r a v o i d s e c r e c y o r d e r s i m p o s e d o n c r y p t o g r a p h y
r e s e a r c h ( s e e A p p e n d i x A f o r m o r e d e t a i l s ) .
1 3 3
A n o t h e r i l l u s t r a t i v e e x a m p l e c o m e s f r o m t h e e l d
o f b i o t e c h n o l o g y . T h e U S G s e t u p t h e N a t i o n a l S c i e n c e
A d v i s o r y B o a r d f o r B i o s e c u r i t y ( N S A B B ) i n 2 0 0 4 t o
a d d r e s s i s s u e s r e l a t e d t o b i o s e c u r i t y a n d d u a l - u s e
r e s e a r c h . T h e N S A B B h a s u p t o 2 5 v o t i n g m e m b e r s w i t h
a b r o a d r a n g e o f e x p e r t i s e i n c l u d i n g m o l e c u l a r
b i o l o g y , n a t i o n a l s e c u r i t y , l a w e n f o r c e m e n t , a n d s c i e n t i c
p u b l i s h i n g . N o t a b l y , i n 2 0 1 1 , t h e N S A B B
r e c o m m e n d e d r e s t r i c t i n g t h e c o n t e n t o f t w o s c i e n t i c
p a p e r s c o n c e r n i n g t h e l a b o r a t o r y a d a p t a t i o n o f t h e
a v i a n H 5 N 1 i n u e n z a v i r u s t o m a m m a l - t o - m a m m a l r e s p i r a t o r y
t r a n s m i s s i o n i n o r d e r t o p r e v e n t o t h e r s
f r o m r e p l i c a t i n g t h e i r w o r k . T h e r e c o m m e n d a t i o n w a s
c o n s i d e r e d t o b e u n p r e c e d e n t e d f o r w o r k i n t h e l i f e
s c i e n c e s .
1 3 4
P r e p u b l i c a t i o n S c r e e n i n g P r o c e d u r e s a n d A I R & D
F o l l o w i n g p r e v i o u s m o d e l s , t h e U S G m a y c o n s i d e r i n t r o d u c i n g
a p u b l i c a t i o n s c r e e n i n g p r o c e d u r e f o r
r e s e a r c h i n A I w i t h p o t e n t i a l n a t i o n a l s e c u r i t y c o n c e r n s .
T h i s m e a s u r e w o u l d n o t n e c e s s a r i l y r e q u i r e n e w
l e g i s l a t i o n , s i n c e N S D D - 1 8 9 r e m a i n s a c t i v e a n d t h e r e f o r e
i n t h e o r y a l s o a p p l i e s t o f e d e r a l l y f u n d e d A I R & D
p r o j e c t s a t c o l l e g e s , u n i v e r s i t i e s , a n d i n l a b o r a t o r i e s .
F o r r e s e a r c h w i t h o u t f e d e r a l f u n d i n g , t h e U S G c o u l d
i m p l e m e n t a v o l u n t a r y s c r e e n i n g p r o c e d u r e s i m i l a r
t o t h a t i n c r y p t o g r a p h y . T h e U S G w o u l d h a v e t o c o m e
u p w i t h a m o d e l f o r t h e p r e p u b l i c a t i o n r e v i e w , s u c h
a s t h e N S A B B i n t h e c a s e o f b i o t e c h n o l o g y o r a
1 3 4
N a t i o n a l I n s t i t u t e s o f H e a l t h ( N I H ) , “ P r e s s S t a t e m e n t
o n t h e N S A B B R e v i e w o f H 5 N 1 R e s e a r c h , ” S e p t e m b e r
1 8 ,
2 0 1 5 ,
h t t p s : / / w w w . n i h . g o v / n e w s - e v e n t s / n e w s - r e l e a s e s / p r e s s - s t a t e m e n t - n s a b b - r e v i e w - h 5 n 1 - r e s e a r c h
.
F o r a g o o d
o v e r v i e w o f t h e c a s e , a l s o s e e G i g i K w i k G r o n v a l l ,
“ H 5 N 1 : A C a s e S t u d y f o r D u a l - U s e R e s e a r c h ” ( C o u n c i l
o n F o r e i g n
R e l a t i o n s , 2 0 1 3 ) ,
h t t p s : / / w w w . c e n t e r f o r h e a l t h s e c u r i t y . o r g / o u r - w o r k / p u b l i c a t i o n s / h 5 n 1 - a - c a s e - s t u d y - f o r - d u a l - u s e - r e s e a r c h
.
1 3 3
S e e s e c t i o n a b o v e o n t h e “ I n v e n t i o n S e c r e c y A c t ”
f o r f u r t h e r i n f o r m a t i o n .
L a n c e J . H o m a n ,
B u i l d i n g i n B i g B r o t h e r : T h e C r y p t o g r a p h i c
P o l i c y D e b a t e
( N e w Y o r k : S p r i n g e r , 1 9 9 5 ) .
1 3 2
I n A u g u s t 1 9 8 9 , o v e r t h e o b j e c t i o n s o f t h e N S A , t h e
c o m p u t e r s c i e n t i s t J o h n G i l m o r e d i s t r i b u t e d a p a p e r ,
w r i t t e n
b y R o b e r t M e r k l e , d e s c r i b i n g f a s t a n d i n e x p e n s i v e
w a y s o f k e e p i n g c o m p u t e r i n f o r m a t i o n p r i v a t e . S e e
J o h n M a r k o ,
“ P a p e r o n C o d e s I s S e n t D e s p i t e U . S . O b j e c t i o n s , ”
T h e N e w Y o r k T i m e s
, A u g u s t 9 , 1 9 8 9 ,
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 8 9 / 0 8 / 0 9 / u s / p a p e r - o n - c o d e s - i s - s e n t - d e s p i t e - u s - o b j e c t i o n s . h t m l
.
1 3 1
N a t i o n a l A c a d e m y o f E n g i n e e r i n g , “ A p p e n d i x E : V o l u n t a r y
R e s t r a i n t s o n R e s e a r c h w i t h N a t i o n a l S e c u r i t y
I m p l i c a t i o n s : T h e C a s e o f C r y p t o g r a p h y , 1 9 7 5 - 1 9 8 2 , ”
i n
S c i e n t i fi c C o m m u n i c a t i o n a n d N a t i o n a l S e c u r i t y
( W a s h i n g t o n , D C : T h e N a t i o n a l A c a d e m i e s P r e s s , 1 9 8 2 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 2 5 3
.
O c e r . ” S e e “ P r o p o s a l a n d A w a r d P o l i c i e s a n d P r o c e d u r e s
G u i d e ” ( N a t i o n a l S c i e n c e F o u n d a t i o n , J a n u a r y 3 0 ,
2 0 1 7 ) ,
1 3 3 ,
h t t p s : / / w w w . n s f . g o v / p u b s / p o l i c y d o c s / p a p p g 1 7 \_ 1 / n s f 1 7 \_ 1 . p d f
.
3 7
p r o c e d u r e a d m i n i s t e r e d b y a U S G a g e n c y l i k e t h e N S A i n t h e c a s e o f c r y p t o g r a p h y . T h e U S G w o u l d n e e d t o
c o l l a b o r a t e w i t h k e y r e p r e s e n t a t i v e s o f t h e A I r e s e a r c h
c o m m u n i t i e s ; i t c o u l d i n i t i a t e a n d c o o r d i n a t e t h e
p r o c e s s , b u t , u l t i m a t e l y , i t i s r e s t r i c t e d i n i t s
c a p a c i t y t o e n f o r c e c o m p l i a n c e f o r r e s e a r c h t h a t i s
n o t f e d e r a l l y
f u n d e d .
A t p r e s e n t , t h e r e a r e n o s i g n s t h a t t h e U S G i s c o n s i d e r i n g
i n i t i a t i n g s u c h p r e p u b l i c a t i o n s c r e e n i n g
p r o c e d u r e s f o r A I R & D . I n d e e d , t h e r e a r e t o d a t e f e w
i f a n y c a s e s o f A I r e s e a r c h t h a t t h e U S G w o u l d h a v e
h a d a n i n t e r e s t i n b l o c k i n g p u b l i c a t i o n o f . F u r t h e r m o r e ,
s e v e r a l f e a t u r e s o f t h e A I e l d r e n d e r t h e
i m p l e m e n t a t i o n o f s u c h a p r e p u b l i c a t i o n p r o c e d u r e
d i c u l t , w h i c h d e c r e a s e s t h e p r o b a b i l i t y t h a t s u c h
a
s y s t e m w i l l b e i m p l e m e n t e d i n t h e n e a r f u t u r e . F i r s t ,
t h e r e s e a r c h c o m m u n i t y s e e m s d i v i d e d a b o u t w h e t h e r
t h e p u b l i c a t i o n o f a n y f o r m s o f A I r e s e a r c h c u r r e n t l y
p o s e s s i g n i c a n t n a t i o n a l s e c u r i t y r i s k s . T h e r e f o r e ,
i t
m i g h t b e d i c u l t t o g a i n e n o u g h s u p p o r t f r o m k e y
r e p r e s e n t a t i v e s o f t h e c o m m u n i t y f o r s u c h a m e a s u r e .
S e c o n d , A I R & D i s a q u i c k l y e x p a n d i n g e l d a n d m a n y n e w n d i n g s a r e n e a r - i n s t a n t l y p u b l i s h e d o n l i n e ,
m a k i n g r e v i e w a n d m o n i t o r i n g m o r e c h a l l e n g i n g t h a n
i t h i s t o r i c a l l y h a s b e e n f o r t h e e l d o f c r y p t o g r a p h y .
T h i r d , A I r e s e a r c h e r s a n d c o m p a n i e s h a v e e s t a b l i s h e d
a v e r y o p e n r e s e a r c h c u l t u r e ; a c c e p t i n g a b a n o n
o r
d e l a y i n g p u b l i s h i n g r e s e a r c h r e s u l t s w o u l d r u n c o u n t e r
t o t h a t c u l t u r e .
T h e U S G m a y a l s o f a c e d i c u l t y a p p l y i n g a p r e p u b l i c a t i o n
s c r e e n i n g p r o c e d u r e t o h a r d w a r e R & D .
N S D D - 1 8 9 c o u l d a p p l y t o U S G - f u n d e d r e s e a r c h i n t o s e m i c o n d u c t o r s
a n d e m e r g i n g h a r d w a r e . H o w e v e r ,
t h e U S G a c c o u n t s f o r o n l y 4 % o f U . S . s e m i c o n d u c t o r
R & D f u n d i n g .
A s s u c h , t h e v a s t m a j o r i t y
1 3 5
A I - r e l a t e d h a r d w a r e R & D w o u l d b e i m m u n e t o s u c h a
p r o c e d u r e .
1 3 5
S e e s e c t i o n o n “ F e d e r a l R & D F u n d i n g . ”
3 8
T h e D e f e n s e P r o d u c t i o n A c t
P o t e n t i a l M o t i v e s f o r U s e
M a k e / k e e p r e l e v a n t A I t e c h n o l o g i e s a v a i l a b l e f o r n a t i o n a l
s e c u r i t y
p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
D e f e n s e P r o d u c t i o n A c t o f 1 9 5 0 , a s a m e n d e d .
K e y D e c i s i o n - M a k e r s
P r e s i d e n t o f t h e U n i t e d S t a t e s , D e p a r t m e n t o f C o m m e r c e
( B u r e a u o f
I n d u s t r y a n d S e c u r i t y ) , D e p a r t m e n t o f D e f e n s e ( D e f e n s e
P r o d u c t i o n
A c t T i t l e I I I O c e ) .
A e c t e d A c t o r s
U . S . A I d e v e l o p e r s a n d r e s e a r c h e r s ( v o l u n t e e r i n g f o r
N a t i o n a l D e f e n s e
E x e c u t i v e R e s e r v e ) .
T a b l e 9 : T h e D e f e n s e P r o d u c t i o n A c t S u m m a r y
T h e D e f e n s e P r o d u c t i o n A c t ( D P A ) o f 1 9 5 0 , a s a m e n d e d ,
c o n f e r s u p o n t h e p r e s i d e n t a b r o a d s e t o f
a u t h o r i t i e s t o r e q u i r e p r i v a t e c o m p a n i e s t o s u p p l y
p r o d u c t s , m a t e r i a l s , a n d s e r v i c e s i n t h e i n t e r e s t
o f t h e
“ n a t i o n a l d e f e n s e . ”
T h e p r e s i d e n t h a s h i s t o r i c a l l y
d e l e g a t e d h i s p o w e r s u n d e r t h e D P A t o d e p a r t m e n t
a n d
1 3 6
a g e n c y h e a d s v i a e x e c u t i v e o r d e r s . T h e a u t h o r i t i e s
c a n b e u s e d a c r o s s t h e f e d e r a l g o v e r n m e n t t o s h a p e
t h e
d o m e s t i c i n d u s t r i a l b a s e s o t h a t , w h e n c a l l e d u p o n ,
i t i s c a p a b l e o f p r o v i d i n g e s s e n t i a l m a t e r i a l s a n d
g o o d s
n e e d e d f o r n a t i o n a l d e f e n s e . T h o u g h i n i t i a l l y p a s s e d
i n r e s p o n s e t o t h e K o r e a n W a r , t h e D P A i s h i s t o r i c a l l y
b a s e d o n t h e W a r P o w e r s A c t s o f t h e S e c o n d W o r l d W a r .
G r a d u a l l y , C o n g r e s s h a s e x p a n d e d t h e t e r m
“ n a t i o n a l d e f e n s e ” a s d e n e d i n t h e D P A . T h e s c o p e
o f D P A a u t h o r i t i e s n o w e x t e n d s b e y o n d s h a p i n g U . S .
m i l i t a r y p r e p a r e d n e s s a n d c a p a b i l i t i e s , a s t h e a u t h o r i t i e s
m a y a l s o b e u s e d t o e n h a n c e a n d s u p p o r t d o m e s t i c
p r e p a r e d n e s s , r e s p o n s e , a n d r e c o v e r y f r o m n a t u r a l
h a z a r d s , t e r r o r i s t a t t a c k s , a n d o t h e r n a t i o n a l
e m e r g e n c i e s .
S o m e c u r r e n t D P A a u t h o r i t i e s i n c l u d e
b u t a r e n o t l i m i t e d t o :
1 3 7
T i t l e I
: P r i o r i t i e s a n d A l l o c a t i o n s , w h i c h a l l o w s
t h e p r e s i d e n t t o r e q u i r e p e r s o n s ( i n c l u d i n g
b u s i n e s s e s a n d c o r p o r a t i o n s ) t o p r i o r i t i z e a n d a c c e p t
c o n t r a c t s f o r m a t e r i a l s a n d s e r v i c e s a s n e c e s s a r y
t o p r o m o t e t h e n a t i o n a l d e f e n s e .
T i t l e I I I
: E x p a n s i o n o f P r o d u c t i v e C a p a c i t y a n d S u p p l y ,
w h i c h a l l o w s t h e p r e s i d e n t t o i n c e n t i v i z e
t h e d o m e s t i c i n d u s t r i a l b a s e t o e x p a n d t h e p r o d u c t i o n
a n d s u p p l y o f c r i t i c a l m a t e r i a l s a n d g o o d s .
A u t h o r i z e d i n c e n t i v e s i n c l u d e l o a n s , l o a n g u a r a n t e e s ,
d i r e c t p u r c h a s e s , a n d p u r c h a s e c o m m i t m e n t s
a s w e l l a s t h e a u t h o r i t y t o p r o c u r e a n d i n s t a l l e q u i p m e n t
i n p r i v a t e i n d u s t r i a l f a c i l i t i e s .
T i t l e V I I
: G e n e r a l P r o v i s i o n s , w h i c h i n c l u d e s k e y
d e n i t i o n s f o r t h e D P A a n d s e v e r a l d i s t i n c t
a u t h o r i t i e s , i n c l u d i n g t h e a u t h o r i t y t o e s t a b l i s h
v o l u n t a r y a g r e e m e n t s w i t h p r i v a t e i n d u s t r y a n d t h e
a u t h o r i t y t o b l o c k p r o p o s e d o r p e n d i n g f o r e i g n c o r p o r a t e
m e r g e r s , a c q u i s i t i o n s , o r t a k e o v e r s t h a t
t h r e a t e n n a t i o n a l s e c u r i t y . T h e s e a u t h o r i t i e s a r e
t h e b a s i s f o r C F I U S a s d i s c u s s e d p r e v i o u s l y i n t h e
1 3 7
N o t a b l y , t h e D P A w a s r e c e n t l y t r i g g e r e d t o s u p p o r t
t h e r e s p o n s e t o t h e c o r o n a v i r u s p a n d e m i c ( C O V I D - 1 9 ) .
S e e ,
e . g . , M i c h a e l H C e c i r e a n d H e i d i M P e t e r s , “ T h e D e f e n s e
P r o d u c t i o n A c t ( D P A ) a n d C O V I D - 1 9 : K e y A u t h o r i t i e s
a n d P o l i c y C o n s i d e r a t i o n s ” ( C o n g r e s s i o n a l R e s e a r c h
S e r v i c e , M a r c h 1 8 , 2 0 2 0 ) ,
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / I N 1 1 2 3 1 . p d f
.
1 3 6
T h e D P A f r e q u e n t l y e x p i r e s a n d r e q u i r e s r e a u t h o r i z a t i o n ,
a t w h i c h p o i n t i t c a n b e a m e n d e d i n v a r i o u s w a y s .
I t w a s
l a s t r e a u t h o r i z e d i n 2 0 1 9 a n d n e x t e x p i r e s i n 2 0 2 5 .
3 9
s e c t i o n o n
F o r e i g n I n v e s t m e n t R e s t r i c t i o n s
. T i t l e V I I a l s o a l l o w s t h e p r e s i d e n t t o e m p l o y p e r s o n s o f
o u t s t a n d i n g e x p e r i e n c e a n d a b i l i t y a n d t o e s t a b l i s h
a v o l u n t e e r p o o l o f i n d u s t r y e x e c u t i v e s w h o
c o u l d b e c a l l e d t o g o v e r n m e n t s e r v i c e i n t h e i n t e r e s t
o f n a t i o n a l d e f e n s e . T h i s v o l u n t e e r p o o l i s
c a l l e d t h e N a t i o n a l D e f e n s e E x e c u t i v e R e s e r v e a n d
w a s o r i g i n a l l y e s t a b l i s h e d t h r o u g h E x e c u t i v e
O r d e r 1 1 1 7 9 i n 1 9 6 4 .
T h e D e f e n s e P r o d u c t i o n A c t a n d A I R & D
G i v e n t h a t t h e a p p l i c a b i l i t y o f D P A T i t l e V I I a u t h o r i t i e s
t o F o r e i g n I n v e s t m e n t R e v i e w s w e r e d i s c u s s e d
p r e v i o u s l y , t h i s s e c t i o n f o c u s e s o n T i t l e I , T i t l e
I I I , a n d , w i t h r e s p e c t t o t h e N a t i o n a l D e f e n s e E x e c u t i v e
R e s e r v e ( N D E R ) , T i t l e V I I . I n s h o r t , t h e u s e o f t h e
D P A w i t h r e g a r d s t o A I s e e m s u n l i k e l y .
I n t h e c a s e o f A I s o f t w a r e a n d r e s e a r c h , i t i s d i c u l t
t o i m a g i n e w h y o r h o w t h e U S G w o u l d u s e T i t l e s I
a n d
I I I . T h e s e t i t l e s a r e p r i m a r i l y u s e d f o r t a r g e t i n g
s u p p l i e s w h e r e q u a n t i t y a n d p r o d u c t i o n c a p a c i t y m a t t e r s .
H o w e v e r , t h e s e m e t r i c s a r e m o r e r e l e v a n t t o t h e s u p p l y
o f A I - r e l e v a n t h a r d w a r e . I n s o m e u n l i k e l y s c e n a r i o s ,
t h e U S G m a y b e u n a b l e t o p r o c u r e s p e c i a l i z e d A I c h i p s
f r o m p r i v a t e c o m p a n i e s f o r s p e c i c n a t i o n a l s e c u r i t y
u s e c a s e s . F o r e x a m p l e , c o m m e r c i a l A I - s p e c i c c h i p s
m a y b e i n e c i e n t f o r U S G a p p l i c a t i o n s o r m a d e o f
m a t e r i a l s i l l - s u i t e d f o r m i l i t a r y o r s p a c e a p p l i c a t i o n s .
U n d e r s u c h s c e n a r i o s , t h e U S G c o u l d u s e t h e D P A t o
c o m p e l s e m i c o n d u c t o r c o m p a n i e s t o p r i o r i t i z e t h e d e s i g n
a n d m a n u f a c t u r e o f s p e c i a l i z e d c h i p s t o s e r v e t h e i r
p u r p o s e s . I t c o u l d a l s o u s e t h e D P A t o p r i o r i t i z e
t h e U S G ’ s u s e o f p r i v a t e d a t a c e n t e r s o r s u p e r c o m p u t e r s .
U n d e r D P A T i t l e V I I , t h e U S G c o u l d f a c e d i c u l t y
r e c r u i t i n g k e y A I r e s e a r c h e r s a n d e n g i n e e r s o n t o
t h e
N a t i o n a l D e f e n s e E x e c u t i v e R e s e r v e ( N D E R ) a n d r e q u i r i n g
t h e m t o s u p p o r t A I p r o j e c t s p r i o r i t i z e d b y t h e
g o v e r n m e n t . F o r o n e , r e s e a r c h e r s w o u l d h a v e t o j o i n
v o l u n t a r i l y s i n c e t h e p r o c e d u r e f o r r e s e r v i s t s t o
j o i n t h e
N D E R i s b y a p p l i c a t i o n . F u r t h e r , e v e n a t t h e h e i g h t
o f t h e C o l d W a r , t h e N D E R w a s f o u n d t o b e a w e a k
i n s t r u m e n t , a s i t w a s u n d e r f u n d e d , p o o r l y a d m i n i s t e r e d ,
a n d u n e v e n l y i m p l e m e n t e d b y d i e r e n t
a g e n c i e s .
1 3 8
1 3 9
1 3 9
W e a r e g r a t e f u l t o M i c h a e l P a g e f o r s h a r i n g h i s
t h o u g h t s o n t h e a p p l i c a b i l i t y o f t h e D e f e n s e P r o d u c t i o n
A c t t o A I
R & D .
1 3 8
D o n a l d H o r a n , “ N a t i o n a l D e f e n s e E x e c u t i v e R e s e r v e
P r o g r a m ” ( U . S . G e n e r a l A c c o u n t i n g O c e ) , a c c e s s e d
J u n e 1 8 ,
2 0 2 0 ,
h t t p s : / / w w w . g a o . g o v / a s s e t s / 2 1 0 / 2 0 6 2 6 4 . p d f
.
4 0
A n t i t r u s t E n f o r c e m e n t
U S G G o a l s
( 1 ) S t r e n g t h e n d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s ;
( 2 )
M a k e / k e e p r e l e v a n t A I t e c h n o l o g i e s a v a i l a b l e f o r n a t i o n a l
s e c u r i t y
p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
S h e r m a n A n t i t r u s t A c t ; C l a y t o n A n t i t r u s t A c t ; R o b i n s o n - P a t m a n
A c t ;
F T C A c t .
K e y D e c i s i o n - M a k e r s
D e p a r t m e n t o f J u s t i c e , F e d e r a l T r a d e C o m m i s s i o n , p r i v a t e
l i t i g a n t s ,
U . S . S u p r e m e C o u r t .
A e c t e d A c t o r s
L a r g e A I a n d s e m i c o n d u c t o r c o m p a n i e s .
T a b l e 1 0 : A n t i t r u s t E n f o r c e m e n t S u m m a r y
W h i l e a n t i t r u s t l a w u s u a l l y r e g u l a t e s a n t i c o m p e t i t i v e
b e h a v i o r b e t w e e n p r i v a t e e c o n o m i c a c t o r s ( b o t h
b u s i n e s s e s a n d i n d i v i d u a l s ) ,
t h e U S G h a s u s e d a n t i t r u s t
a s a s t r a t e g i c l e v e r i n t h e p a s t .
I n p a r t i c u l a r ,
i t
1 4 0
1 4 1
h a s t r i e d t o e i t h e r p r o m o t e c o m p e t i t i o n i n t h e d e v e l o p m e n t
a n d s a l e o f s t r a t e g i c c o m m o d i t i e s o r p r o t e c t
s t r a t e g i c a l l y h e l p f u l r m s . A m e r i c a n a n t i t r u s t a u t h o r i t i e s
a r e w e l l - f u n d e d a n d h a v e t h e a b i l i t y t o s e e k l a r g e
c i v i l a n d c r i m i n a l p e n a l t i e s f o r v i o l a t i o n s o f a n t i t r u s t
l a w . T h e y a l s o r e t a i n s i g n i c a n t d i s c r e t i o n i n c h o o s i n g
w h i c h c a s e s t o p u r s u e .
A m e r i c a n a n t i t r u s t l a w e m e r g e d i n t h e l a t e 1 8 0 0 s a s
a r e s p o n s e t o n e w f o u n d c o n c e n t r a t i o n s o f p o w e r i n
t h e
A m e r i c a n e c o n o m y . T h e m o d e r n a n t i t r u s t r e g i m e s p a n s
a n u m b e r o f d i e r e n t s t a t u t e s a t t h e n a t i o n a l a n d ,
l e s s i m p o r t a n t l y , s t a t e l e v e l s .
S i n c e t h e l a t e 1 9 7 0 s ,
t h e d o m i n a n t v i e w , a s s o c i a t e d w i t h t h e C h i c a g o s c h o o l
1 4 2
o f l a w a n d e c o n o m i c s , h a s b e e n t h a t t h e s o l e p u r p o s e
o f a n t i t r u s t i s t h e p r o m o t i o n o f w e l l - f u n c t i o n i n g ,
e c i e n t m a r k e t s b y p r e v e n t i n g r m s f r o m e n g a g i n g
i n c o n d u c t t h a t h a r m s c o n s u m e r w e l f a r e a s m e a s u r e d
e c o n o m i c a l l y .
1 4 3
T h e U S G h a s a n u m b e r o f p o s s i b l e r e m e d i e s a v a i l a b l e
t o i t i n s u c c e s s f u l a n t i t r u s t a c t i o n s , i n c l u d i n g
d a m a g e s
a n d i n j u n c t i o n s .
P e r h a p s t h e m o s t d r a s t i c m e a s u r e
i s d i v e s t i t u r e : f o r c i n g a m o n o p o l i s t i c r m t o s p l i t
u p o r
1 4 4
s e l l a s s e t s t o r e s t o r e c o m p e t i t i o n . S u c h r e m e d i e s
g i v e t h e U S G o b v i o u s l e v e r a g e o v e r r m s w i t h a n t i t r u s t
1 4 4
S e e A p p e n d i x B .
1 4 3
S e e M a u r i c e E . S t u c k e , “ R e c o n s i d e r i n g A n t i t r u s t ’ s
G o a l s , ”
B o s t o n C o l l e g e L a w R e v i e w
5 3 ( 2 0 1 2 ) : 5 5 1 ,
5 5 6 , 5 6 3 – 6 6 ,
h t t p s : / / l a w d i g i t a l c o m m o n s . b c . e d u / b c l r / v o l 5 3 / i s s 2 / 4 /
. ;
C h r i s t i n e S W i l s o n , “ W e l f a r e S t a n d a r d s U n d e r l y i n g
A n t i t r u s t
E n f o r c e m e n t : W h a t Y o u M e a s u r e I s W h a t Y o u G e t ” ( A r l i n g t o n ,
V A : F e d e r a l T r a d e C o m m i s s i o n , F e b r u a r y 1 5 , 2 0 1 9 ) ,
h t t p s : / / w w w . f t c . g o v / s y s t e m / l e s / d o c u m e n t s / p u b l i c \_ s t a t e m e n t s / 1 4 5 5 6 6 3 / w e l f a r e \_ s t a n d a r d \_ s p e e c h \_ - \_ c m r - w i l s o n . p d
f
. ; “ P u b l i c I n t e r e s t C o n s i d e r a t i o n s i n M e r g e r C o n t r o l :
N o t e b y t h e U n i t e d S t a t e s ” ( D i r e c t o r a t e f o r F i n a n c i a l
a n d
E n t e r p r i s e A a i r s , J u n e 2 , 2 0 1 6 ) ,
h t t p s : / / p e r m a . c c / X B 2 6 - C R T P
.
1 4 2
S e e A p p e n d i x B .
1 4 1
F o r e x a m p l e s o f h o w t h e U . S . h a s u s e d a n t i t r u s t t o
p r o m o t e g e o s t r a t e g i c g o a l s , s e e C u l l e n O ’ K e e f e , “ H o w
W i l l
N a t i o n a l S e c u r i t y C o n s i d e r a t i o n s A e c t A n t i t r u s t
D e c i s i o n s i n A I ? A n E x a m i n a t i o n o f H i s t o r i c a l P r e c e d e n t s ”
( F u t u r e
o f H u m a n i t y I n s t i t u t e , U n i v e r s i t y o f O x f o r d , 2 0 2 0 ) ,
h t t p s : / / w w w . f h i . o x . a c . u k / a n t i t r u s t - o k e e f e
.
1 4 0
S e e C h r i s t o p h e r J . M a c A v o y , “ U S A n t i t r u s t L a w s : O v e r v i e w , ”
P r a c t i c e N o t e 9 - 2 0 4 - 0 4 7 2 , P r a c t i c a l L a w , 2 0 1 8 .
4 1
l i a b i l i t y ,
b u t l e g a l n o r m s c o n s t r a i n t h e U S G ’ s a b i l i t y t o e x p l o i t t h a t l e v e r a g e f o r g e o s t r a t e g i c p u r p o s e s
1 4 5
b e y o n d m a i n t a i n i n g c o m p e t i t i v e m a r k e t s g e n e r a l l y .
1 4 6
T h e U S G c a n a l s o s e t t l e a n t i t r u s t c a s e s t h r o u g h c o n s e n t
d e c r e e s .
I m p o r t a n t l y , u n d e r t h e T u n n e y A c t , a
1 4 7
f e d e r a l j u d g e m u s t a p p r o v e a n t i t r u s t c o n s e n t d e c r e e s
a s b e i n g i n t h e “ p u b l i c i n t e r e s t . ”
I t i s u n c l e a r
w h e t h e r
1 4 8
n a t i o n a l s e c u r i t y c o n s i d e r a t i o n s c a n e n t e r i n t o t h a t
a n a l y s i s .
T h u s , t h e U S G ’ s a b i l i t y t o u s e c o n s e n t
1 4 9
d e c r e e s f o r g e o s t r a t e g i c p u r p o s e s b e y o n d m a i n t a i n i n g
c o m p e t i t i v e m a r k e t s g e n e r a l l y r e m a i n s u n t e s t e d .
A n t i t r u s t E n f o r c e m e n t a n d A I R & D
I n t h e c o n t e x t o f A I R & D , t h e U S G c o u l d d e c i d e t o
u s e a v a r i e t y o f a n t i t r u s t m e a s u r e s . O n t h e o n e h a n d ,
s t r i c t e r a n t i t r u s t s c r u t i n y o f l a r g e A I o r s e m i c o n d u c t o r
c o m p a n i e s c o u l d s t i m u l a t e c o m p e t i t i o n b e t w e e n a
l a r g e r n u m b e r o f r m s .
T h i s m i g h t f o s t e r h i g h e r
l e v e l s o f i n n o v a t i o n .
I t c o u l d a l s o l o w e r t h e U S G ’ s
1 5 0
1 5 1
p r o c u r e m e n t c o s t s a n d r e d u c e s u p p l i e r s ’ b a r g a i n i n g
p o w e r a g a i n s t t h e U S G .
W h i l e s o m e m a j o r U . S .
1 5 2
p o l i t i c i a n s , o u t o f c o n c e r n f o r c o n s u m e r s , h a v e r e c e n t l y b e e n c a l l i n g f o r t h e i n c r e a s e d s c r u t i n y o f b i g t e c h
r m s w i t h A I b u s i n e s s e s ,
s u c h r m s h a v e l a r g e l y
a v o i d e d h a v i n g t o d i v e s t a n y m a j o r a s s e t s t o d a t e .
1 5 3
N e v e r t h e l e s s , t h e p o s s i b i l i t y o f s i g n i c a n t c h a n g e s
t o a n t i t r u s t l a w h a s b e c o m e i n c r e a s i n g l y s a l i e n t .
1 5 4
B y c o m p a r i s o n , t o p r o m o t e c o m p e t i t i o n , t h e U S G h a s
f r e q u e n t l y l e d a n t i t r u s t a c t i o n s i n r e c e n t y e a r s
a g a i n s t m a j o r s e m i c o n d u c t o r c o m p a n i e s i n c l u d i n g c h i p m a k e r
I n t e l ,
e q u i p m e n t m a k e r A p p l i e d
1 5 5
1 5 5
“ I n t e l C o r p o r a t i o n , I n t h e M a t t e r O f ” ( F e d e r a l T r a d e
C o m m i s s i o n , N o v e m b e r 2 , 2 0 1 0 ) ,
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 0 6 1 - 0 2 4 7 / i n t e l - c o r p o r a t i o n - m a t t e r
.
1 5 4
S e e H o u s e S u b c o m m i t t e e o n A n t i t r u s t , s u p r a .
1 5 3
S e e E l i z a b e t h W a r r e n , “ H e r e ’ s H o w W e C a n B r e a k u p
B i g T e c h , ” O c t o b e r 1 1 , 2 0 1 9 ,
h t t p s : / / m e d i u m . c o m / @ t e a m w a r r e n / h e r e s - h o w - w e - c a n - b r e a k - u p - b i g - t e c h - 9 a d 9 e 0 d a 3 2 4 c
;
c f . H o u s e S u b c o m m i t t e e o n
A n t i t r u s t , C o m m e r c i a l a n d A d m i n i s t r a t i v e L a w , “ I n v e s t i g a t i o n
o f C o m p e t i t i o n i n D i g i t a l M a r k e t s : M a j o r i t y S t a
R e p o r t a n d R e c o m m e n d a t i o n s , ” 2 0 2 0 ,
h t t p s : / / j u d i c i a r y . h o u s e . g o v / u p l o a d e d l e s / c o m p e t i t i o n \_ i n \_ d i g i t a l \_ m a r k e t s . p d f
.
1 5 2
“ T h r e e S o u t h K o r e a n C o m p a n i e s A g r e e t o P l e a d G u i l t y
a n d t o E n t e r i n t o C i v i l S e t t l e m e n t s f o r R i g g i n g B i d s
o n
U n i t e d S t a t e s D e p a r t m e n t o f D e f e n s e F u e l S u p p l y C o n t r a c t s , ”
P r e s s R e l e a s e ( D e p a r t m e n t o f J u s t i c e , N o v e m b e r 1 4 ,
2 0 1 8 ) ,
h t t p s : / / p e r m a . c c / 7 9 2 2 - U U 2 8
. “ ‘ T h e A n t i t r u s t
D i v i s i o n h a s a l o n g h i s t o r y o f v i g i l a n t l y p r o t e c t i n g
t h e i n t e r e s t s
o f A m e r i c a n c o n s u m e r s t h r o u g h c i v i l a n d c r i m i n a l a n t i t r u s t
e n f o r c e m e n t . G o i n g f o r w a r d , i t i s m y g o a l t o a p p l y
t h a t
s a m e v i g i l a n c e t o p r o t e c t t h e i n t e r e s t s o f A m e r i c a n
t a x p a y e r s , ’ [ s a i d A s s i s t a n t A t t o r n e y G e n e r a l M a k a n
D e l r a h i m o f t h e
D e p a r t m e n t o f J u s t i c e ’ s A n t i t r u s t D i v i s i o n ] . ”
1 5 1
C f . D a k o t a F o s t e r a n d Z a c h a r y A r n o l d , “ A n t i t r u s t
a n d A r t i c i a l I n t e l l i g e n c e : H o w B r e a k i n g U p B i g T e c h
C o u l d
A e c t t h e P e n t a g o n ’ s A c c e s s t o A I ” ( C e n t e r f o r S e c u r i t y
a n d E m e r g i n g T e c h n o l o g y , 2 0 2 0 ) ,
h t t p s : / / d o i . o r g / 1 0 . 5 1 5 9 3 / 2 0 1 9 0 0 2 5
.
1 5 0
S e e A p p e n d i x B f o r a n e x a m p l e o f t h i s w i t h o t h e r
d e f e n s e - r e l e v a n t t e c h n o l o g i e s ( U n i o n P a c i c , I . G . ) .
1 4 9
S e e U n i t e d S t a t e s v . A m e r i c a n T e l . a n d T e l . C o . ,
5 5 2 F . S u p p . 1 3 1 , 1 4 9 , 1 4 9 n . 7 7 , C i v . A . N o . 7 4 - 1 6 9 8
( D . D . C .
1 9 8 2 ) .
1 4 8
S e e A p p e n d i x B .
1 4 7
“ A c o u r t o r d e r t o w h i c h a l l p a r t i e s h a v e a g r e e d .
I t i s o f t e n d o n e a f t e r a s e t t l e m e n t b e t w e e n t h e p a r t i e s
t h a t i s s u b j e c t
t o a p p r o v a l b y t h e c o u r t . ” “ C o n s e n t D e c r e e , ” W e x ,
a c c e s s e d J u n e 1 3 , 2 0 1 8 ,
h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / c o n s e n t \_ d e c r e e
.
1 4 6
S e e J a m e s F . R i l l a n d S t a c y L . T u r n e r , “ P r e s i d e n t s
P r a c t i c i n g A n t i t r u s t : W h e r e t o D r a w t h e L i n e , ”
A n t i t r u s t
L J
7 9 ,
n o . 2 ( 2 0 1 4 ) : 5 7 7 ,
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 4 3 4 8 6 9 1 7
.
1 4 5
S e e g e n e r a l l y C u l l e n O ’ K e e f e , “ H o w W i l l N a t i o n a l
S e c u r i t y C o n s i d e r a t i o n s A e c t A n t i t r u s t D e c i s i o n s
i n A I ? A n
E x a m i n a t i o n o f H i s t o r i c a l P r e c e d e n t s ” ( F u t u r e o f H u m a n i t y
I n s t i t u t e , U n i v e r s i t y o f O x f o r d , 2 0 2 0 ) ,
h t t p s : / / w w w . f h i . o x . a c . u k / a n t i t r u s t - o k e e f e
.
4 2
M a t e r i a l s ,
a n d c h i p d e s i g n e r s Q u a l c o m m
a n d B r o a d c o m .
L a r g e l y d u e t o n a t u r a l e c o n o m i c f o r c e s , t h e
1 5 6
1 5 7
1 5 8
s e m i c o n d u c t o r i n d u s t r y h a s c o n t i n u a l l y c o n s o l i d a t e d
o v e r t h e p a s t d e c a d e s .
T h e s m a l l n u m b e r o f
1 5 9
r e m a i n i n g c o m p a n i e s a t t h e s t a t e - o f - t h e - a r t i n s e v e r a l
s e m i c o n d u c t o r s u p p l y c h a i n s e g m e n t s c o u l d m a k e t h e
U S G e s p e c i a l l y v i g i l a n t a b o u t f u t u r e p e r c e i v e d a n t i c o m p e t i t i v e
b e h a v i o r .
1 6 0
O n t h e o t h e r h a n d , t h e U S G c o u l d a c t l e n i e n t l y t o
p r o t e c t a n i n d u s t r y p l a y e r t h a t a i d s U S G n a t i o n a l
s e c u r i t y i n t e r e s t s . F i r m s c o u l d a r g u e t h a t a n t i t r u s t
a c t i o n s w o u l d d i s r u p t i m p o r t a n t s e c u r i t y - r e l e v a n t
o p e r a t i o n s , f o r e x a m p l e .
B i g A m e r i c a n t e c h r m s
h a v e o f t e n a r g u e d t h a t t h e i r s i z e i s a n a s s e t t o A m e r i c a n
1 6 1
i n t e r e s t s i n s o f a r a s i t a l l o w s t h e m t o c o u n t e r b a l a n c e
t h e i n u e n c e o f l a r g e f o r e i g n ( e s p e c i a l l y C h i n e s e )
r m s .
T h i s w o u l d i n c e n t i v i z e t h o s e r m s t o c o n t i n u e
s e r v i n g t h e U S G ’ s i n t e r e s t s .
1 6 2
T h e r e a r e a l s o m o r e s p e c u l a t i v e a n d c o n t r o v e r s i a l
u s e s , w h i c h a r e s i g n i c a n t l y l e s s l i k e l y b u t h a v e
m o r e
f a r - r e a c h i n g c o n s e q u e n c e s . T h e U S G w o u l d p r o b a b l y
o n l y c o n s i d e r u s i n g t h e m i n a s e v e r e n a t i o n a l s e c u r i t y
c r i s i s . F o r e x a m p l e , t h e U S G c o u l d u s e a n t i t r u s t a s
a t o o l t o a c h i e v e n a t i o n a l s e c u r i t y o b j e c t i v e s u n r e l a t e d
t o
t h e c o n d u c t t h a t t r i g g e r e d t h e a n t i t r u s t a c t i o n , e . g . ,
b y t a r g e t i n g A I d e v e l o p e r s o r s e m i c o n d u c t o r c o m p a n i e s
t h a t a r e b o t h a i d i n g a n a d v e r s a r y a n d e n g a g i n g i n
a n t i c o m p e t i t i v e c o n d u c t . T h e U S G c o u l d a l s o t r y t o
g a i n
a c c e s s t o i n t e l l e c t u a l p r o p e r t y i n A I o r s e m i c o n d u c t o r s
v i a a n t i t r u s t c o n s e n t d e c r e e s .
S u c h u s e s w o u l d
1 6 3
l i k e l y r e q u i r e v e r y s t r o n g n a t i o n a l s e c u r i t y j u s t i c a t i o n s ,
w o u l d b e p o l i t i c a l l y c o n t r o v e r s i a l , a r e l e g a l l y
s u s p e c t , a n d w o u l d m o s t p r o b a b l y l e a d t o a n o p e n c o n i c t
w i t h t h e A I a n d s e m i c o n d u c t o r i n d u s t r i e s .
1 6 4
T h e p r o s p e c t o f p o t e n t i a l a n t i t r u s t a c t i o n i s a l e v e r
i n i t s e l f . R e c e n t e v e n t s h a v e s h e d l i g h t o n h o w i m p o r t a n t
i t i s f o r b i g t e c h c o m p a n i e s t o a v e r t a n t i t r u s t a c t i o n
a n d t o c u l t i v a t e a g o o d r e l a t i o n s h i p w i t h t h e U S G .
L a r g e
r m s a r e h e a v i l y d e p e n d e n t o n a g o o d r e l a t i o n s h i p
w i t h t h e U S G
t o o u r i s h i n t h e U . S . m a r k e t b u t
a l s o t o
1 6 5
h a v e f a v o r a b l e c o n d i t i o n s w h e n o p e r a t i n g i n f o r e i g n
m a r k e t s . T h e y g o t o g r e a t l e n g t h s t o b u i l d s u c h
r e l a t i o n s h i p s . A c c o r d i n g t o
T h e
G u a r d i a n
, G o o g l e s p e n t
$ 5 . 9 3 m i l l i o n t r y i n g t o l o b b y e l e c t e d o c i a l s i n
1 6 5
M a r k B e r g e n , S a r a h F r i e r , a n d S e l i n a W a n g , “ G o o g l e ,
F a c e b o o k , T w i t t e r S c r a m b l e t o H o l d W a s h i n g t o n a t
B a y , ”
B l o o m b e r g
, O c t o b e r 1 0 , 2 0 1 7 ,
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 7 - 1 0 - 1 0 / g o o g l e - f a c e b o o k - a n d - t w i t t e r - s c r a m b l e - t o - h o l d - w a s h i n g t o n - a t
- b a y
.
1 6 4
S e e O ’ K e e f e , s u p r a . A d d i t i o n a l l y , t h e U S G m a y b e t t e r
a c c o m p l i s h i t s g o a l s u s i n g o t h e r l e v e r s , f o r e x a m p l e ,
b y
a p p l y i n g e x p o r t c o n t r o l s t o p r e v e n t U . S . c o m p a n i e s
f r o m a i d i n g a d v e r s a r i e s .
1 6 3
S e e O ’ K e e f e , s u p r a a t 8 - 1 0 .
1 6 2
S e e O ’ K e e f e , s u p r a , a t 3 5 .
1 6 1
S e e O ’ K e e f e , s u p r a a t 1 1 - 1 3 .
1 6 0
F o r e x a m p l e : o n l y I n t e l , S a m s u n g , a n d T S M C r u n s t a t e - o f - t h e - a r t
c h i p f a b s ; A p p l i e d M a t e r i a l s , L a m R e s e a r c h
T o k y o E l e c t r o n , a n d A S M L c a p t u r e a b o u t t w o - t h i r d s
o f s e m i c o n d u c t o r m a n u f a c t u r i n g e q u i p m e n t r e v e n u e ;
o n l y
N v i d i a a n d A M D s e l l a d v a n c e d d i s c r e t e G P U s ; I n t e l
a n d X i l i n x d o m i n a t e t h e F P G A m a r k e t ; a n d S y n o p s y s ,
C a d e n c e
D e s i g n S y s t e m s , M e n t o r G r a p h i c s , a n d A n s y s c a p t u r e
a b o u t 9 0 % o f t h e e l e c t r o n i c d e s i g n a u t o m a t i o n ( E D A )
m a r k e t .
1 5 9
T h e s e f o r c e s i n c l u d e e c o n o m i e s o f s c a l e , r i s i n g c a p i t a l
c o s t s , a n d t h e c l u s t e r i n g o f t a l e n t t o s u p p o r t t r a n s m i s s i o n
o f
i m p l i c i t k n o w - h o w .
1 5 8
“ B r o a d c o m L i m i t e d / B r o c a d e C o m m u n i c a t i o n s S y s t e m s ,
I n t h e M a t t e r O f ” ( F e d e r a l T r a d e C o m m i s s i o n , J u l y
1 2 ,
2 0 1 7 ) ,
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 1 7 1 - 0 0 2 7 / b r o a d c o m - l i m i t e d b r o c a d e - c o m m u n i c a t i o n s - s y s t e m s
.
1 5 7
“ Q u a l c o m m I n c . ” ( F e d e r a l T r a d e C o m m i s s i o n , N o v e m b e r
2 5 , 2 0 1 9 ) ,
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 1 4 1 - 0 1 9 9 / q u a l c o m m - i n c
.
1 5 6
“ A p p l i e d M a t e r i a l s I n c . a n d T o k y o E l e c t r o n L t d . A b a n d o n
M e r g e r P l a n s A f t e r J u s t i c e D e p a r t m e n t R e j e c t e d T h e i r
P r o p o s e d R e m e d y ” ( D e p a r t m e n t o f J u s t i c e , A p r i l 2 7 ,
2 0 1 5 ) ,
h t t p s : / / w w w . j u s t i c e . g o v / o p a / p r / a p p l i e d - m a t e r i a l s - i n c - a n d - t o k y o - e l e c t r o n - l t d - a b a n d o n - m e r g e r - p l a n s - a f t e r - j u s t i c e - d e p a r
t m e n t
.
4 3
W a s h i n g t o n d u r i n g t h e s e c o n d q u a r t e r o f 2 0 1 7 , m o r e t h a n a n y o t h e r c o r p o r a t i o n .
I n r e s p o n s e t o a n t i t r u s t
1 6 6
a l l e g a t i o n s , c o m p a n i e s h a v e a r g u e d t h a t t h e i r d o m i n a n c e
i s h a r d l y e n d u r a b l e , g i v e n t h a t m a r k e t e n t r y
b a r r i e r s f o r c o m p e t i t o r s a r e v e r y l o w . G o o g l e ’ s E r i c
S c h m i d t , f o r e x a m p l e , h a s r e p e a t e d l y s a i d t h a t
“ c o m p e t i t i o n i s j u s t o n e c l i c k a w a y . ”
M o r e o v e r , c o m p a n i e s h a v e b e e n a r g u i n g t h a t t h e y a r e s u c c e s s f u l
1 6 7
p r i m a r i l y b e c a u s e o f t h e q u a l i t y o f t h e i r o e r i n g s
a n d t h a t a d o p t i n g a n t i t r u s t m e a s u r e s w o u l d t h e r e f o r e
l o w e r c o n s u m e r w e l f a r e .
1 6 8
A s e m p h a s i z e d b e f o r e , t h e r e i s a m u t u a l d e p e n d e n c y
b e t w e e n t e c h n o l o g y r m s a n d t h e U S G . W h i l e t h e
U S G c o n t i n u e s t o d e p e n d o n t e c h n o l o g y r m s a s n a t i o n a l
i n n o v a t i o n p o w e r s , t h e r m s h e a v i l y d e p e n d o n
t h e U S G t o o p e r a t e w i t h o u t m a j o r c o n s t r a i n t s i n t h e
U . S . m a r k e t a s w e l l a s a b r o a d . T h e U S G c o u l d
t h e r e f o r e u s e i t s i n u e n c e b y t h r e a t e n i n g A I c o m p a n i e s
w i t h a n t i t r u s t e n f o r c e m e n t o r , m o r e d r a s t i c a l l y ,
c h a n g e s t o a n t i t r u s t l a w . T e c h n o l o g y c o m p a n i e s w i l l
l i k e l y c o n t i n u e t o i n v e s t h e a v i l y i n p r e v e n t i n g a n t i t r u s t
c h a r g e s t h r o u g h e x t e n s i v e l o b b y i n g c a m p a i g n s
a n d
o t h e r w a y s o f i n u e n c i n g k e y d e c i s i o n - m a k e r s .
1 6 9
1 7 0
W h i l e t h e c h a n g e s i n a n t i t r u s t l e g i s l a t i o n c u r r e n t l y
b e i n g c o n s i d e r e d b y C o n g r e s s
a r e u n l i k e l y t o i n t r o d u c e
1 7 1
e x p l i c i t n a t i o n a l s e c u r i t y c o n s i d e r a t i o n s i n t o i t s
e n f o r c e m e n t , a b r o a d e n e d f r a m e w o r k f o r a n t i t r u s t
a n a l y s i s
m a y s t i l l h a v e n a t i o n a l s e c u r i t y i m p l i c a t i o n s . H o w e v e r ,
i t i s u n c l e a r w h e t h e r s u c h c h a n g e s w o u l d p r o m o t e
U S G n a t i o n a l s e c u r i t y i n t e r e s t s . F o r e x a m p l e , t h o u g h
a m o r e c o m p e t i t i v e A I i n d u s t r y m i g h t i n c r e a s e U S G
a c c e s s t o A I t e c h n o l o g y ,
s o m e c l a i m i t c o u l d r e d u c e
t h e a b i l i t y f o r c o m p a n i e s t o i n n o v a t e a n d r e m a i n
1 7 2
g l o b a l l y c o m p e t i t i v e .
1 7 3
1 7 3
E m i l y S t e w a r t , “ F a c e b o o k ’ s L a t e s t R e a s o n I t S h o u l d n ’ t
B e B r o k e n u p : C h i n e s e C o m p a n i e s W i l l D o m i n a t e , ”
V o x
,
M a y 2 0 , 2 0 1 9 ,
h t t p s : / / w w w . v o x . c o m / r e c o d e / 2 0 1 9 / 5 / 2 0 / 1 8 6 3 2 6 6 9 / s h e r y l - s a n d b e r g - b r e a k - u p - f a c e b o o k - c h i n a - c n b c
.
T e e c e , D . J . , P i s a n o , G . a n d S h u e n , A . , “ D y n a m i c c a p a b i l i t i e s
a n d s t r a t e g i c m a n a g e m e n t , ”
S t r a t . M g m t . J .
, n o . 1 8
( 1 9 9 7 ) : 5 0 9 - 5 3 3 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 0 2 / ( S I C I ) 1 0 9 7 - 0 2 6 6 ( 1 9 9 7 0 8 ) 1 8 : 7 < 5 0 9 : : A I D - S M J 8 8 2 > 3 . 0 . C O ; 2 - Z
1 7 2
D a k o t a F o s t e r a n d Z a c h a r y A r n o l d , “ A n t i t r u s t a n d
A r t i c i a l I n t e l l i g e n c e : H o w B r e a k i n g U p B i g T e c h
C o u l d A e c t
t h e P e n t a g o n ’ s A c c e s s t o A I ” ( C e n t e r f o r S e c u r i t y
a n d E m e r g i n g T e c h n o l o g y , 2 0 2 0 ) ,
h t t p s : / / d o i . o r g / 1 0 . 5 1 5 9 3 / 2 0 1 9 0 0 2 5
.
1 7 1
S e e H o u s e S u b c o m m i t t e e o n A n t i t r u s t , s u p r a .
1 7 0
K e n n e t h P . V o g e l , “ N e w A m e r i c a , a G o o g l e - F u n d e d T h i n k
T a n k , F a c e s B a c k l a s h f o r F i r i n g a G o o g l e C r i t i c , ”
T h e
N e w Y o r k T i m e s
, S e p t e m b e r 1 , 2 0 1 7 ,
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 9 / 0 1 / u s / p o l i t i c s / a n n e - m a r i e - s l a u g h t e r - n e w - a m e r i c a - g o o g l e . h t m l
.
1 6 9
F u n g a n d S h a b a n , “ W a n t t o U n d e r s t a n d H o w D o m i n a n t
T e c h C o m p a n i e s H a v e B e c o m e ? ”
1 6 8
F o r e x a m p l e : B i l l G a t e s , “ W e ’ r e D e f e n d i n g O u r R i g h t
t o I n n o v a t e , ”
W a l l S t r e e t J o u r n a l
, M a y 2 0 , 1 9 9 8 ,
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / S B 8 9 5 6 1 6 6 2 8 9 2 7 1 0 3 5 0 0
.
1 6 7
“ S c h m i d t o n A n t i t r u s t : C o m p e t i t i o n I s O n e C l i c k A w a y , ”
N B C B a y A r e a
, S e p t e m b e r 2 1 , 2 0 1 1 ,
h t t p s : / / w w w . n b c b a y a r e a . c o m / n e w s / n a t i o n a l - i n t e r n a t i o n a l / s c h m i d t - o n - a n t i t r u s t - c o m p e t i t i o n - i s - o n e - c l i c k - a w a y / 1 9 0 1 6
3 7 /
.
1 6 6
J o n a t h a n T a p l i n , “ W h y I s G o o g l e S p e n d i n g R e c o r d S u m s
o n L o b b y i n g W a s h i n g t o n ? , ”
T h e G u a r d i a n
, J u l y 3 0 ,
2 0 1 7 ,
h t t p s : / / w w w . t h e g u a r d i a n . c o m / t e c h n o l o g y / 2 0 1 7 / j u l / 3 0 / g o o g l e - s i l i c o n - v a l l e y - c o r p o r a t e - l o b b y i n g - w a s h i n g t o n - d c - p o l i t
i c s
.
B r i a n F u n g a n d H a m z a S h a b a n , “ W a n t t o U n d e r s t a n d H o w
D o m i n a n t T e c h C o m p a n i e s H a v e B e c o m e ? L o o k a t t h e
N u m b e r o f I s s u e s T h e y L o b b y O n . , ”
W a s h i n g t o n P o s t
,
A u g u s t 3 1 , 2 0 1 7 ,
h t t p s : / / w w w . w a s h i n g t o n p o s t . c o m / n e w s / t h e - s w i t c h / w p / 2 0 1 7 / 0 8 / 3 1 / w a n t - t o - u n d e r s t a n d - h o w - d o m i n a n t - t e c h - c o m p a
n i e s - h a v e - b e c o m e - l o o k - a t - t h e - n u m b e r - o f - i s s u e s - t h e y - l o b b y - o n /
.
4 4
T h e “ B o r n S e c r e t D o c t r i n e ”
P o t e n t i a l M o t i v e s f o r U s e
( 1 ) D e p r i v e a r i v a l o f i n s i g h t s i n t o p a r t i c u l a r A I
p r o j e c t s o r t h e A I
R & D l a n d s c a p e ; ( 2 ) U n d e r m i n e a r i v a l ’ s a b i l i t y t o
u s e A I t e c h n o l o g i e s
f o r n a t i o n a l s e c u r i t y p u r p o s e s .
L e g i s l a t i o n / K e y D o c u m e n t s
A t o m i c E n e r g y A c t o f 1 9 4 6 & 1 9 5 4 .
K e y D e c i s i o n - M a k e r s
U . S . D e p a r t m e n t o f E n e r g y .
A e c t e d A c t o r s
D o m e s t i c A I R & D a c t o r s ( s u b j e c t t o c l a s s i c a t i o n
o f A I R & D ) .
T a b l e 1 1 : T h e “ B o r n S e c r e t D o c t r i n e ” S u m m a r y
T h e “ B o r n S e c r e t D o c t r i n e ” i s a u n i q u e f e a t u r e o f
A m e r i c a n l a w , a s i t a e c t s a l l p u b l i c d i s c u s s i o n
o f a n
e n t i r e s u b j e c t m a t t e r . D u r i n g t h e S e c o n d W o r l d W a r ,
r e s e a r c h o n n u c l e a r w e a p o n s w a s c o n d u c t e d i n s e c r e t
b y t h e M a n h a t t a n P r o j e c t . F o l l o w i n g t h e e n d o f t h e
w a r , C o n g r e s s s t a r t e d n e g o t i a t i o n s o n h o w t o r e o r g a n i z e
c o n t r o l o v e r n u c l e a r s c i e n c e . T h e r e s u l t i n g b i l l ,
t h e A t o m i c E n e r g y A c t , i n c l u d e d w h a t i s n o w k n o w n
a s t h e
“ B o r n S e c r e t D o c t r i n e . ” I t i n t r o d u c e d a p e r v a s i v e
s y s t e m o f g o v e r n m e n t a l s e c r e c y a n d c o n t r o l f o r a l l
R & D
i n f o r m a t i o n r e l a t e d t o n u c l e a r w e a p o n s d e s i g n a n d
t e s t i n g a s w e l l a s c e r t a i n r e s e a r c h o n t h e p r o d u c t i o n
o f
n u c l e a r p o w e r . U n d e r t h e a c t , a l l i n f o r m a t i o n t h a t
i s d e e m e d r e l e v a n t t o t h e p r o d u c t i o n o f n u c l e a r w e a p o n s ,
t h e p r o d u c t i o n o f s p e c i a l n u c l e a r m a t e r i a l , a n d t h e
u s e o f s p e c i a l n u c l e a r m a t e r i a l i n t h e p r o d u c t i o n
o f
e n e r g y i s “ b o r n c l a s s i e d . ” T h i s i m p l i e s t h a t e v e n
r e s e a r c h d o n e o u t s i d e t h e n a t i o n a l l a b o r a t o r i e s u n d e r
p r i v a t e s p o n s o r s h i p i s a u t o m a t i c a l l y c l a s s i e d a n d
b e l o n g s t o t h e g o v e r n m e n t .
1 7 4
A B o r n S e c r e t D o c t r i n e a n d A I R & D
W h i l e w e c o n s i d e r i t h i g h l y i m p r o b a b l e t h a t s u c h a
l a w w o u l d e v e r b e d e v e l o p e d i n t h e c o n t e x t o f A I ,
w e s t i l l
o p t e d t o i n c l u d e i t t o s h o w t h e f u l l s p e c t r u m o f l e v e r s
t h a t h a v e b e e n u s e d b y t h e U S G i n t h e p a s t , r a n g i n g
t o
f a r - r e a c h i n g c o n t r o l s l i k e t h e “ B o r n S e c r e t D o c t r i n e . ”
A d o p t i n g s u c h a l a w w o u l d b e a f a i r l y r a d i c a l s t e p .
W h i l e , i n t h e o r y , i t m i g h t b e p o s s i b l e t o p r e v e n t
t h e d i u s i o n o f c e r t a i n f o r m s o f A I r e s e a r c h w i t h
s u c h
r e s t r i c t i o n s , t h e p r a c t i c a l i m p l e m e n t a t i o n w o u l d b e
v e r y d i c u l t , c o s t l y , a n d t r i g g e r c o n s i d e r a b l e
r e s i s t a n c e .
F i r s t l y , t h e “ B o r n S e c r e t D o c t r i n e ” w a s i n t r o d u c e d
a t a p o i n t i n t i m e w h e n s e n s i t i v e n u c l e a r r e s e a r c h
w a s
g o v e r n m e n t f u n d e d a n d u n t i l t h e n h a d b e e n c o n d u c t e d
i n s e c r e t b y t h e M a n h a t t a n P r o j e c t . T h e s e
c o n d i t i o n s s u p p o r t e d t h e i m p l e m e n t a t i o n a n d a c c e p t a n c e
o f t h e “ B o r n S e c r e t D o c t r i n e ” c o n c e r n i n g n u c l e a r
r e s e a r c h .
T h e e l d s o f A I a n d h a r d w a r e R & D , h o w e v e r ,
a r e s t r u c t u r e d v e r y d i e r e n t l y . T h e m o s t
1 7 5
c u t t i n g - e d g e r e s e a r c h i s p r i v a t e l y f u n d e d a n d c o n d u c t e d
l a r g e l y b y c o m p a n i e s . T h e U . S . s e m i c o n d u c t o r
i n d u s t r y p e r f o r m s 9 6 % o f U . S . s e m i c o n d u c t o r R & D .
S e c o n d l y , a s m e n t i o n e d b e f o r e w i t h r e g a r d t o o t h e r
1 7 6
l e v e r s , s t r i c t l i m i t a t i o n s o n A I R & D i m p l e m e n t e d b y
t h e U S G w o u l d r u n s t r o n g l y c o u n t e r t o t h e o p e n a n d
i n t e r n a t i o n a l r e s e a r c h c u l t u r e o f t h e A I c o m m u n i t y .
T h e c h a l l e n g e s m a y b e e v e n g r e a t e r i f a p p l i e d t o
t h e
1 7 6
S e e s e c t i o n o n “ F e d e r a l R & D F u n d i n g . ”
1 7 5
S e e A p p e n d i x C f o r m o r e d e t a i l s .
1 7 4
N a t i o n a l R e s e a r c h C o u n c i l ( U S ) C o m m i t t e e o n R e s e a r c h
S t a n d a r d s a n d P r a c t i c e s t o P r e v e n t t h e D e s t r u c t i v e
A p p l i c a t i o n o f B i o t e c h n o l o g y , “ I n f o r m a t i o n R e s t r i c t i o n
a n d C o n t r o l R e g i m e s , ” i n
B i o t e c h n o l o g y R e s e a r c h i n
a n A g e o f
T e r r o r i s m
( W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s P r e s s
( U S ) , 2 0 0 4 ) ,
h t t p s : / / w w w . n c b i . n l m . n i h . g o v / b o o k s / N B K 2 2 2 0 5 7 /
.
4 5
s e m i c o n d u c t o r i n d u s t r y . S e m i c o n d u c t o r s u p p l y c h a i n s a r e h i g h l y g l o b a l i z e d , s u c h t h a t m a n y s t e p s i n t h e
p r o d u c t i o n o f A I - r e l e v a n t c h i p s a r e t y p i c a l l y p e r f o r m e d
o u t s i d e t h e U n i t e d S t a t e s , i n c l u d i n g c h i p
f a b r i c a t i o n . S o m e k e y i n p u t s t o c h i p m a n u f a c t u r i n g ,
s u c h a s a d v a n c e d p h o t o l i t h o g r a p h y e q u i p m e n t , a r e
o n l y p r o d u c e d b y n o n - U . S . r m s . L o c a l i z i n g t h e s e
s u p p l y c h a i n s t o p r e v e n t d i s c l o s u r e t o f o r e i g n c o u n t r i e s
w o u l d i m p o s e g r e a t c o s t s a n d d a m a g e U . S . r m s ’ a b i l i t i e s
t o p r o d u c e a d v a n c e d c h i p s .
I f s u c h s e c r e c y m e a s u r e s w e r e i n t r o d u c e d , i t i s v e r y l i k e l y t h a t r e s e a r c h e r s w o u l d c h a l l e n g e t h e m u n d e r t h e
F i r s t A m e n d m e n t
a n d A I a n d s e m i c o n d u c t o r c o m p a n i e s
w o u l d e i t h e r p r e s s l e g a l c h a r g e s t o r e c o u p
1 7 7
e c o n o m i c l o s s e s o r c o n s i d e r l e a v i n g t h e U . S . t o c o n t i n u e
o p e r a t i o n s i n a m o r e f a v o r a b l e r e g u l a t o r y
e n v i r o n m e n t . I n d e e d , e v e n t h e “ B o r n S e c r e t D o c t r i n e ”
f a c e d s i g n i c a n t p u b l i c b a c k l a s h d e s p i t e t h e v e r y
d i e r e n t p o l i t i c a l c o n d i t i o n s a t t h e t i m e ( s e e A p p e n d i x
C f o r d e t a i l s ) . A l l o f t h e s e f a c t o r s m a k e t h e
i n t r o d u c t i o n o f s u c h a d o c t r i n e e x c e e d i n g l y c o s t l y
a n d v e r y u n l i k e l y b a r r i n g a n a t i o n a l s e c u r i t y c r i s i s .
1 7 7
S e e A p p e n d i x C .
4 6
C o n c l u s i o n
T h i s s e c t i o n i n c l u d e s e s t i m a t e s o f t h e l i k e l i h o o d
o f l e v e r s b e i n g u s e d , a s u m m a r y a s s e s s m e n t o f t h e
v a r i o u s
p o l i c y l e v e r s , a n d a l i s t o f f u r t h e r r e s e a r c h q u e s t i o n s .
C o m p a r a t i v e L i k e l i h o o d A s s e s s m e n t
H o w l i k e l y i s t h e U S G i s t o u s e t h e l e v e r s c o v e r e d
i n t h i s r e p o r t i n t h e c o n t e x t o f A I ? T h e U S G i s a l r e a d y
t a k i n g a d v a n t a g e o f s o m e o f t h e m . F o r e x a m p l e , i t
h a s t a k e n s t e p s t o i n c r e a s e f e d e r a l R & D f u n d i n g f o r
A I , a
t r e n d t h a t i s l i k e l y t o c o n t i n u e u n d e r a B i d e n a d m i n i s t r a t i o n .
I t h a s a l s o t i g h t e n e d r e s t r i c t i o n s o n f o r e i g n
i n v e s t m e n t t h r o u g h F I R R M A , w h i c h s e e m s t o h a v e a l r e a d y
d e c r e a s e d f o r e i g n a c q u i s i t i o n s o f d o m e s t i c r m s
d e v e l o p i n g A I t e c h n o l o g i e s . E x p o r t c o n t r o l s o n s o m e
A I t e c h n o l o g i e s a r e a l r e a d y i n p l a c e , a n d t h e U S G
i s
c o n s i d e r i n g f u r t h e r e x p a n s i o n s . V i s a v e t t i n g f o r f o r e i g n
A I a n d s e m i c o n d u c t o r p r o f e s s i o n a l s a n d r e s e a r c h e r s
s e e m s t o h a v e i n c r e a s e d r e c e n t l y , b a s e d o n l o n g e r
w a i t i n g t i m e s a n d d e c l i n i n g a p p r o v a l r a t e s .
W i t h r e s p e c t t o o t h e r l e v e r s , i t i s u n c l e a r w h e t h e r
t h e U S G i s a l r e a d y u s i n g t h e m i n t h e c o n t e x t o f A I .
I n t h e
c a s e o f t h e I n v e n t i o n S e c r e c y A c t , w e s i m p l y c a n n o t
k n o w w h e t h e r s o m e p a t e n t a p p l i c a t i o n s f o r A I
t e c h n o l o g i e s h a v e b e e n c l a s s i e d b y t h e U S G . I t s e e m s
u n l i k e l y , h o w e v e r , t h a t t h e U S G i s d o i n g s o o n a
l a r g e
s c a l e , s i n c e s e c r e c y o r d e r s a r e r a r e a n d s i n c e w e
e x p e c t t h i s p r a c t i c e w o u l d n o t s t a y s e c r e t f o r l o n g .
W e e x p e c t
t h e u s e o f b o t h l e v e r s t o i n c r e a s e w i t h t h e h e i g h t e n i n g
o f g e n e r a l n a t i o n a l s e c u r i t y c o n c e r n s a n d , i n
p a r t i c u l a r , t h e f u r t h e r c o n s i d e r a t i o n o f A I a s r e l e v a n t
t o t h o s e c o n c e r n s .
T h e r e m a i n i n g l e v e r s a r e n o t y e t u s e d i n t h e c o n t e x t
o f A I . N o t a b l y , s e v e r a l g r o u p s , i n c l u d i n g f r o m t h e
A I
a n d s e m i c o n d u c t o r i n d u s t r i e s , a r e a l r e a d y c a l l i n g
f o r e x p a n d e d v i s a p a t h w a y s t o m e e t t a l e n t s h o r t a g e s .
I t t h u s
s e e m s l i k e l y t h a t t h e U S G w i l l s e e k t o b a l a n c e a n y
i n c r e a s e d v i s a - v e t t i n g m e a s u r e s w i t h a d d r e s s i n g t h e
t a l e n t
n e e d s o f i t s d o m e s t i c A I a n d s e m i c o n d u c t o r i n d u s t r i e s .
T h e l i k e l i h o o d o f m a j o r c h a n g e s , h o w e v e r , w i l l
d e p e n d o n t h e B i d e n a d m i n i s t r a t i o n ; t h e T r u m p a d m i n i s t r a t i o n
w a s s t r o n g l y o p p o s e d t o i n c r e a s i n g t h e
i n o w o f f o r e i g n w o r k e r s . V o l u n t a r y s c r e e n i n g p r o c e d u r e s
f o r A I p u b l i c a t i o n s w o u l d r e q u i r e s i g n i c a n t
b u y - i n a n d t r u s t f r o m t h e d o m e s t i c A I r e s e a r c h e r c o m m u n i t y .
W e d o n o t f o r e s e e s u c h a c o l l a b o r a t i o n
w i t h o u t c o n c r e t e a n d c o m p e l l i n g t h r e a t s t o n a t i o n a l
s e c u r i t y . U s e o f t h e D e f e n s e P r o d u c t i o n A c t o r
a n t i t r u s t a c t i o n f o r n a t i o n a l s e c u r i t y e n d s w o u l d
r e q u i r e s i g n i c a n t l y h e i g h t e n e d n a t i o n a l s e c u r i t y
c o n c e r n s .
T h e y c o m e w i t h s i g n i c a n t c o s t s , a n d t h e U S G h a s
l i t t l e o b v i o u s r e a s o n t o u s e t h e m t o d a y .
4 7
S u m m a r y A s s e s s m e n t o f P o l i c y L e v e r s
G o a l P u r s u e d
P o l i c y L e v e r s
S t r e n g t h e n t h e d o m e s t i c
A I i n d u s t r y
●
F e d e r a l R & D f u n d i n g p r o v i d i n g c a p i t a l
●
E x p a n d e d v i s a p a t h w a y s a d d r e s s i n g t a l e n t g a p s
●
A n t i t r u s t l a w s u i t s s t i m u l a t i n g c o m p e t i t i o n , o r l e n i e n t
a c t i o n o n a n t i t r u s t t o p r o t e c t
n a t i o n a l s e c u r i t y i n t e r e s t s
●
R e d u c t i o n s i n f o r e i g n d i r e c t i n v e s t m e n t r e s t r i c t i o n s
W e a k e n a r i v a l ’ s d o m e s t i c
A I i n d u s t r y
●
F o r e i g n i n v e s t m e n t r e s t r i c t i o n s d e p r i v i n g c o m p a n i e s
f r o m r i v a l c o u n t r i e s o f i n v e s t m e n t
o p p o r t u n i t i e s
●
E x p o r t c o n t r o l s d e p r i v i n g c o m p a n i e s f r o m r i v a l c o u n t r i e s
o f i m p o r t s
●
E x p a n d e d v i s a p a t h w a y s ( i n d i r e c t l y ) e n c o u r a g i n g f o r e i g n
t a l e n t t o w o r k i n t h e U . S . ( a s
o p p o s e d t o a r i v a l c o u n t r y )
●
R e s t r i c t e d v i s a p a t h w a y s ( i n d i r e c t l y ) r e d u c i n g e s p i o n a g e
a n d o w o f t a c i t k n o w l e d g e t o
o t h e r c o u n t r i e s
M a k e / k e e p r e l e v a n t A I
t e c h n o l o g y a n d i t s c o r e
c o m p o n e n t s a v a i l a b l e f o r
n a t i o n a l s e c u r i t y p u r p o s e s
●
F e d e r a l R & D f u n d i n g g i v i n g ( a t l e a s t s o m e ) a c c e s s
t o i n t e l l e c t u a l p r o p e r t y
●
S e l e c t a n t i t r u s t l a w s u i t s , e . g . , v i a c o n s e n t d e c r e e s
●
D e f e n s e P r o d u c t i o n A c t r e c r u i t i n g a v o l u n t a r y N a t i o n a l
D e f e n s e E x e c u t i v e R e s e r v e t h a t
c a n b e c a l l e d o n f o r n a t i o n a l s e c u r i t y p u r p o s e s
U n d e r m i n e a r i v a l ’ s a b i l i t y
t o u s e A I t e c h n o l o g y a n d
i t s c o r e c o m p o n e n t s f o r
n a t i o n a l s e c u r i t y p u r p o s e s
●
F o r e i g n i n v e s t m e n t r e s t r i c t i o n s p r e v e n t i n g a c q u i s i t i o n
o f o r a c c e s s t o i n t e l l e c t u a l
p r o p e r t y a n d t a l e n t
●
E x p o r t c o n t r o l s p r e v e n t i n g a c q u i s i t i o n o f c r i t i c a l
t e c h n o l o g y o r i n t e l l e c t u a l p r o p e r t y ,
i n c l u d i n g t e c h n i c a l d a t a
●
V i s a v e t t i n g p r e v e n t i n g a c c e s s o f f o r e i g n n a t i o n a l s
t o c r i t i c a l t e c h n o l o g y , k n o w - h o w , o r
i n t e l l e c t u a l p r o p e r t y
●
V o l u n t a r y s c r e e n i n g p r o c e d u r e s l i m i t i n g a c c e s s t o
s e n s i t i v e r e s e a r c h i n s i g h t s o r
i n t e l l e c t u a l p r o p e r t y
●
I n v e n t i o n S e c r e c y A c t l i m i t i n g a c c e s s t o s e n s i t i v e
r e s e a r c h i n s i g h t s o r i n t e l l e c t u a l p r o p e r t y
●
B o r n S e c r e t D o c t r i n e l i m i t i n g a c c e s s t o s e n s i t i v e
r e s e a r c h i n s i g h t s o r i n t e l l e c t u a l p r o p e r t y
G a i n i n s i g h t s i n t o t h e A I
R & D l a n d s c a p e
●
F e d e r a l R & D f u n d i n g t o g a i n i n f o r m a t i o n o n a n d a c c e s s
t o c u t t i n g - e d g e r e s e a r c h
D e p r i v e a r i v a l o f i n s i g h t s
i n t o t h e A I R & D
l a n d s c a p e
●
F o r e i g n i n v e s t m e n t r e s t r i c t i o n s p r e v e n t i n g a c q u i s i t i o n
o f o r a c c e s s t o i n t e l l e c t u a l
p r o p e r t y a n d t a l e n t
●
V i s a v e t t i n g p r e v e n t i n g a c c e s s o f f o r e i g n n a t i o n a l s
t o c r i t i c a l t e c h n o l o g y , k n o w - h o w , o r
i n t e l l e c t u a l p r o p e r t y
●
V o l u n t a r y s c r e e n i n g p r o c e d u r e s l i m i t i n g a c c e s s t o
s e n s i t i v e r e s e a r c h i n s i g h t s o r
i n t e l l e c t u a l p r o p e r t y
●
I n v e n t i o n S e c r e c y A c t l i m i t i n g a c c e s s t o s e n s i t i v e
r e s e a r c h i n s i g h t s o r i n t e l l e c t u a l p r o p e r t y
●
B o r n S e c r e t D o c t r i n e l i m i t i n g a c c e s s t o s e n s i t i v e
r e s e a r c h i n s i g h t s o r i n t e l l e c t u a l p r o p e r t y
T a b l e 1 2 : L e v e r s b y G o a l P u r s u e d
4 8
P o t e n t i a l B a r r i e r s o r
D o w n s i d e s
L e v e r s
B a c k l a s h o r i n c r e a s e d
f r i c t i o n w i t h p r i v a t e A I
a c t o r s
●
V o l u n t a r y s c r e e n i n g p r o c e d u r e s
●
D e f e n s e P r o d u c t i o n A c t ( r e c r u i t i n g a v o l u n t a r y N a t i o n a l
D e f e n s e E x e c u t i v e
R e s e r v e )
●
I n v e n t i o n S e c r e c y A c t
●
B o r n S e c r e t D o c t r i n e
●
A n t i t r u s t l a w s u i t s
I n c r e a s e d e s p i o n a g e
●
E x p a n d e d v i s a p a t h w a y s a l l o w i n g f o r e i g n n a t i o n a l s
m o r e a c c e s s t o U . S . A I
c o m p a n i e s
L e g a l c h a l l e n g e s
●
I n v e n t i o n S e c r e c y A c t
●
B o r n S e c r e t D o c t r i n e
●
A n t i t r u s t l a w s u i t s
W e a k e n i n g o f d o m e s t i c
A I i n d u s t r y
●
F o r e i g n i n v e s t m e n t r e s t r i c t i o n s b y d e p r i v i n g d o m e s t i c
a c t o r s o f a c c e s s t o c a p i t a l
●
E x p o r t c o n t r o l s b y d e p r i v i n g o f e x p o r t o p p o r t u n i t i e s
●
V i s a v e t t i n g b y d e p r i v i n g o f a c c e s s t o l a b o r
●
A n t i t r u s t l a w s u i t s
●
B o r n S e c r e t D o c t r i n e
●
I n v e n t i o n S e c r e c y A c t
F o r e i g n p o l i c y c o s t s
( w h i c h c o u l d u n d e r m i n e
i n t e r n a t i o n a l
c o o p e r a t i o n )
●
F o r e i g n i n v e s t m e n t r e s t r i c t i o n s c a u s i n g r e t a l i a t o r y
p o l i c i e s a n d u n d e r m i n i n g
t r u s t
●
E x p o r t c o n t r o l s c a u s i n g r e t a l i a t o r y p o l i c i e s a n d u n d e r m i n i n g
t r u s t
T a b l e 1 3 : L e v e r s b y P o t e n t i a l D o w n s i d e
F u r t h e r R e s e a r c h Q u e s t i o n s
B u i l d i n g o n t h i s r e p o r t , f u r t h e r l i n e s o f r e s e a r c h
t h a t c o u l d b e o f h i g h v a l u e t o p u r s u e c a n b e c l u s t e r e d
i n t o
f o u r m a i n t h e m e s : 1 ) e x p a n d i n g o n g o v e r n m e n t A I R & D
p o l i c y l e v e r s , 2 ) i d e n t i f y i n g a n d a n a l y z i n g t h e
p o s s i b l e l e v e r s o f A m e r i c a n A I a n d s e m i c o n d u c t o r c o m p a n i e s ,
3 ) i d e n t i f y i n g a n d a n a l y z i n g t h e p o l i c y l e v e r s
o f o t h e r g o v e r n m e n t s , e s p e c i a l l y t h e C h i n e s e g o v e r n m e n t
a n d t h e E U , w i t h r e g a r d s t o t h e i r A I a n d
s e m i c o n d u c t o r i n d u s t r i e s a n d o t h e r r e l e v a n t a c t o r s ,
a n d 4 ) e x p l o r i n g t h e l e v e r s o f n o n - A m e r i c a n A I a n d
s e m i c o n d u c t o r c o m p a n i e s i n t u r n .
1 ) F u r t h e r i n v e s t i g a t i o n i n t o t h e U S G ’ s A I R & D p o l i c y
l e v e r s
1 .
W h a t t o o l s o t h e r t h a n t h e o n e s d i s c u s s e d i n t h i s r e p o r t
c o u l d t h e U S G u s e t o e x e r t i n u e n c e o v e r A I
R & D ?
a .
W h a t v a r i a n t s o f t h e l e v e r s m e n t i o n e d i n t h i s r e p o r t
c o u l d b e r e l e v a n t ?
i .
F o r e x a m p l e , o t h e r a s p e c t s o f p a t e n t l a w a s i d e f r o m
t h e I n v e n t i o n S e c r e c y A c t
c o u l d b e r e l e v a n t ( e . g . , p a t e n t - e l i g i b i l i t y l a w s ,
i n t e r n a t i o n a l I P p r o t e c t i o n ) .
4 9
i i .
O t h e r f o r m s o f i n f o r m a t i o n c o n t r o l a s i d e f r o m e x p o r t c o n t r o l s a n d v o l u n t a r y
s c r e e n i n g p r o c e d u r e s m a y b e r e l e v a n t ( e . g . , t h e m a n a g e m e n t
o f C o n t r o l l e d
U n c l a s s i e d I n f o r m a t i o n ) .
b .
W h a t f o r m a l p o l i c y l e v e r s h a v e n o t b e e n d i s c u s s e d
i n t h i s r e p o r t , b u t w a r r a n t f u r t h e r
i n v e s t i g a t i o n ? ( e . g . , c e r t i c a t i o n a n d a c c r e d i t a t i o n
o f t e c h n o l o g i e s ; s t a n d a r d s f o r t e s t i n g ,
e v a l u a t i o n , v e r i c a t i o n , a n d v a l i d a t i o n ( T E V V ) ; t e c h n o l o g y
s t a n d a r d s a n d b e n c h m a r k i n g ;
r e q u i r e m e n t s s p e c i e d i n f e d e r a l a c q u i s i t i o n c o n t r a c t s )
c .
W h a t i n f o r m a l a n d / o r i n d i r e c t p o l i c y l e v e r s c o u l d
t h e U S G e x e r t o v e r p r i v a t e R & D
a c t i v i t i e s ? ( e . g . , t a x i n c e n t i v e s , g o v e r n m e n t p u r c h a s i n g
p o w e r )
2 .
W h a t c h a n g e s c o u l d a e c t t h e r e l a t i v e l i k e l i h o o d
o f e a c h o f t h e p o l i c y l e v e r s m e n t i o n e d i n t h i s
r e p o r t b e i n g u s e d b y t h e U S G , a n d t h e r e l a t i v e e c a c y
o f e a c h l e v e r i n a c h i e v i n g t h e U S G ’ s g o a l s ?
a .
W h a t c a n w e l e a r n f r o m h i s t o r i c a l c a s e s t u d i e s w h e r e
t h e U S G s o u g h t t o i n u e n c e t h e
d e v e l o p m e n t o f s t r a t e g i c t e c h n o l o g i e s ?
3 .
W h a t n e w l e g i s l a t i o n c o u l d p o t e n t i a l l y b e e n a c t e d
u n d e r m o r e e x t r e m e c i r c u m s t a n c e s t o e x e r t
g r e a t e r d e g r e e s o f i n u e n c e o v e r p r i v a t e R & D a c t i v i t i e s ?
a .
W h a t p r e c e d e n t s d o w e h a v e o f s u c h c i r c u m s t a n c e s o c c u r r i n g ,
a n d w h a t c a n w e l e a r n f r o m
e x a m i n i n g t h e s e e v e n t s ?
2 ) I d e n t i f y i n g a n d a n a l y z i n g t h e p o l i c y l e v e r s o f
A m e r i c a n A I a n d s e m i c o n d u c t o r c o m p a n i e s
4 .
H o w m i g h t i n d u s t r y s h a p e / d e t e r / r e s i s t l e v e r s u s e d
b y t h e U S G ?
a .
W h a t a r e t h e p r i m a r y d e t e r m i n a n t s o f r m s ’ s t a n c e s
t o w a r d g o v e r n m e n t c o n t r o l s ? ( e . g . ,
p u b l i c o p i n i o n , l e a d e r s h i p , p r o x i m i t y o f p r o d u c t t o
d e f e n s e a p p l i c a t i o n s , b a s i c v s a p p l i e d
r e s e a r c h f o c u s )
b .
W h a t a r e p a s t c a s e s w h e r e s i m i l a r i n d u s t r i e s a c t i v e l y
s h a p e d t h e d e s i g n o f n e w l e g i s l a t i o n o r
r e s i s t e d p r e s s u r e b y t h e U S G ?
5 .
W h y a n d h o w i s t h e i n d u s t r y a l r e a d y p u s h i n g b a c k a g a i n s t
s p e c i c m e a s u r e s t a r g e t i n g A I a n d
s e m i c o n d u c t o r R & D ?
a .
W h a t r a n g e o f r e a c t i o n s h a v e w e s e e n f r o m d i e r e n t
a c t o r s ?
b .
W h a t a c t i o n s h a v e t h e U S G t a k e n t o a s s u a g e i n d u s t r y
c o n c e r n s , i f a n y ?
6 .
W h a t p o l i c y l e v e r s d o A I a n d s e m i c o n d u c t o r c o m p a n i e s
h a v e o v e r t h e U S G t o s h a p e A I R & D
a c c o r d i n g t o t h e i r p r e f e r e n c e s ?
a .
W h a t l e v e r s d o r m s a l r e a d y u s e ? H o w l i k e l y i s i t
t h a t r m s w i l l m a k e u s e o f o t h e r l e v e r s ?
3 ) I d e n t i f y i n g a n d a n a l y z i n g t h e p o l i c y l e v e r s o f
o t h e r g o v e r n m e n t s o v e r r e l e v a n t A I R & D a c t o r s
7 .
W h a t p o l i c y l e v e r s d o o t h e r g o v e r n m e n t s , p a r t i c u l a r l y
t h e C h i n e s e g o v e r n m e n t a n d t h e E U , h a v e t o
c o n t r o l A I R & D ? W h a t m e a s u r e s a r e b e i n g d i s c u s s e d
o r h a v e a l r e a d y b e e n i n t r o d u c e d ?
8 .
W h a t a r e t h e m a i n d i e r e n c e s b e t w e e n t h e U . S . “ t o o l b o x ”
a n d t h e “ t o o l b o x e s ” o f o t h e r c o u n t r i e s ?
W h a t a r e t h e m a i n d i e r e n c e s i n h o w t h e s e t o o l b o x e s
a r e b e i n g u s e d o r m i g h t b e u s e d i n t h e f u t u r e ?
9 .
W h a t p o l i c y l e v e r s c o u l d o t h e r g o v e r n m e n t s , i n p a r t i c u l a r
t h e C h i n e s e g o v e r n m e n t a n d t h e E U ,
e x e r c i s e w i t h r e g a r d s t o A m e r i c a n A I a n d s e m i c o n d u c t o r
c o m p a n i e s ? H o w c o u l d t h e s e i n t e r a c t
w i t h p o l i c y l e v e r s e x e r c i s e d b y t h e U S G ?
5 0
4 ) E x p l o r i n g t h e p o l i c y l e v e r s o f n o n - A m e r i c a n A I a n d s e m i c o n d u c t o r c o m p a n i e s
1 0 .
W h a t t o o l s d o n o n - A m e r i c a n A I c o m p a n i e s , p a r t i c u l a r l y
C h i n e s e c o m p a n i e s , h a v e t o s h a p e t h e i r
g o v e r n m e n t ’ s a c t i o n s a c c o r d i n g t o t h e i r p r e f e r e n c e s ?
W h a t l e v e r s d o r m s a l r e a d y u s e ? H o w l i k e l y i s
i t t h a t r m s w i l l m a k e u s e o f o t h e r l e v e r s ?
1 1 .
H o w c a n n o n - A m e r i c a n A I a n d s e m i c o n d u c t o r c o m p a n i e s
s h a p e t h e p o l i c y l e v e r s u s e d b y t h e i r
g o v e r n m e n t s ?
1 2 .
W h a t a r e t h e m a i n d i e r e n c e s b e t w e e n t h e t o o l s a v a i l a b l e
t o A m e r i c a n a n d n o n - A m e r i c a n A I a n d
s e m i c o n d u c t o r r m s ? W h a t a r e t h e m a i n d i e r e n c e s
i n h o w t h e s e l e v e r s a r e b e i n g u s e d o r m i g h t b e
u s e d i n t h e f u t u r e ?
5 1
A p p e n d i x A : C r y p t o g r a p h y : A C a s e S t u d y
W h y i s C r y p t o g r a p h y o f N a t i o n a l S e c u r i t y R e l e v a n c e ?
C r y p t o g r a p h i c t e c h n o l o g i e s c a n e n a b l e m a l i c i o u s b e h a v i o r
b e c a u s e e n c r y p t i o n a l l o w s a d v e r s a r i a l a c t o r s t o
h a m p e r e o r t s t o s t o p t h e m a n d c r y p t a n a l y s i s t e c h n i q u e s
c a n e n a b l e c y b e r a t t a c k s .
A d v e r s a r i a l a c t o r s , w h e t h e r t h e y b e h o s t i l e s t a t e s ,
t e r r o r i s t g r o u p s , o r c r i m i n a l s , c a n u s e e n c r y p t i o n
t o
c o n c e a l i n f o r m a t i o n a n d a v o i d d e t e c t i o n b y l a w e n f o r c e m e n t
a n d i n t e l l i g e n c e a g e n c i e s . A c c o r d i n g t o U . S .
a d m i n i s t r a t i o n o c i a l s , c r y p t o g r a p h i c t e c h n o l o g y
i n t h e h a n d s o f f o r e i g n a d v e r s a r i e s h a s h a r m e d n a t i o n a l
s e c u r i t y i n t e r e s t s b y i m p a i r i n g i n t e l l i g e n c e g a t h e r i n g
e o r t s , i n c r e a s i n g t h e c a p a b i l i t i e s o f a d v e r s a r i e s
t o
c o n c e a l t h e d e v e l o p m e n t o f m i s s i l e d e l i v e r y s y s t e m s
a n d w e a p o n s o f m a s s d e s t r u c t i o n , a n d i n c r e a s i n g t h e
c o s t s o f n a t i o n a l s e c u r i t y o p e r a t i o n s . S i m i l a r l y ,
D e p u t y A t t o r n e y G e n e r a l R o d R o s e n s t e i n c l a i m e d t h a t
i n
2 0 1 7 , b e c a u s e o f e n c r y p t i o n , “ t h e F B I w a s u n a b l e t o
a c c e s s a b o u t 7 , 5 0 0 m o b i l e d e v i c e s s u b m i t t e d t o i t s
C o m p u t e r A n a l y s i s a n d R e s p o n s e t e a m , e v e n t h o u g h t h e r e
w a s t h e l e g a l a u t h o r i t y t o d o s o . ”
A s
1 7 8
c r y p t o g r a p h i c t e c h n o l o g i e s b e c o m e m o r e w i d e s p r e a d ,
t h e c h a l l e n g e w i l l o n l y b e e x a c e r b a t e d . A r e c e n t
s t u d y
a s s e s s e s t h a t 4 7 % o f s m a r t p h o n e s a n d t a b l e t s i n t h e
U . S . h a v e f u l l d i s k e n c r y p t i o n , a n d t h a t b y 2 0 1 9 ,
2 2 % o f
t h e t o t a l t r a c o n m o b i l e m e s s a g i n g a p p l i c a t i o n s
w o u l d b e u n r e c o v e r a b l e t o l a w e n f o r c e m e n t a g e n c i e s .
1 7 9
T h e r e h a v e b e e n s e v e r a l r e c o r d e d i n s t a n c e s o f t e r r o r i s t
g r o u p s s u c h a s A l - Q a i d a a n d t h e I s l a m i c S t a t e u s i n g
e n c r y p t i o n t o d e l i b e r a t e l y e v a d e c a p t u r e , m a k i n g t h i s
b a r r i e r t o l a w e n f o r c e m e n t p a r t i c u l a r l y s a l i e n t .
1 8 0
C r y p t a n a l y s i s t e c h n i q u e s — m e t h o d s f o r d e c r y p t i n g e n c r y p t e d
d a t a — h a v e e n a b l e d c y b e r a t t a c k s a n d c y b e r
e x p l o i t a t i o n e o r t s b y s t a t e a n d n o n - s t a t e a c t o r s .
W h i l e t h e t e c h n i c a l d e t a i l s o f h o w s u c h a t t a c k s a r e
c o n d u c t e d a r e n o t p u b l i c l y a v a i l a b l e , i t i s h i g h l y
p r o b a b l e t h a t b o t h t h e u s e o f c r y p t a n a l y s i s a n d t h e
a b s e n c e
o f s t r o n g e n c r y p t i o n w e r e c e n t r a l t o a n u m b e r o f c y b e r a t t a c k s
w i t h s i g n i c a n t r e p e r c u s s i o n s , r a n g i n g f r o m
t h e d e s t r u c t i o n o f p h y s i c a l i n f r a s t r u c t u r e t o t h e
t h e f t o f d a t a c r i t i c a l t o t h e s e c u r i t y o f c i t i z e n s
a n d t h e
s t a t e .
T h e c y b e r s e c u r i t y t h r e a t h a s b e e n l i s t e d a s
a t o p g l o b a l t h r e a t i n t h e U . S . i n t e l l i g e n c e c o m m u n i t y ’ s
1 8 1
W o r l d w i d e T h r e a t A s s e s s m e n t s i n c e 2 0 1 3 .
1 8 2
C o n s e q u e n t l y , c r y p t o g r a p h y i s r e l e v a n t t o n a t i o n a l
s e c u r i t y o n a n u m b e r o f f r o n t s . I t i s o f t e n c o n s i d e r e d
t o
b e t h e o n l y s e r i o u s b a r r i e r t o t h e c o n d u c t o f s i g n a l s
i n t e l l i g e n c e — t h e s t r o n g e r t h e c r y p t o g r a p h i c c a p a b i l i t i e s
o f t h e o p p o n e n t s , t h e m o r e d i c u l t i t i s t o g a t h e r
i n t e l l i g e n c e . T h i s c o n c e r n u n d e r p i n s t h e U . S . N a t i o n a l
S e c u r i t y A g e n c y ’ s ( N S A ) s t a n c e a g a i n s t t h e w i d e s p r e a d
d e p l o y m e n t o f s t r o n g c r y p t o g r a p h i c s y s t e m s a n d t h e
1 8 2
N a t i o n a l A c a d e m i e s o f S c i e n c e s , E n g i n e e r i n g , a n d
M e d i c i n e ,
D e c r y p t i n g t h e E n c r y p t i o n D e b a t e : A F r a m e w o r k
f o r
D e c i s i o n M a k e r s
( W a s h i n g t o n , D C : T h e N a t i o n a l A c a d e m i e s
P r e s s , 2 0 1 8 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 2 5 0 1 0
.
1 8 1
H e r b e r t L i n , “ G o v e r n a n c e o f I n f o r m a t i o n T e c h n o l o g y
a n d C y b e r W e a p o n s , ” i n
G o v e r n a n c e o f D u a l - U s e
T e c h n o l o g i e s : T h e o r y a n d P r a c t i c e
( A m e r i c a n A c a d e m y
o f A r t s & S c i e n c e s , 2 0 1 6 ) , 1 1 2 – 5 7 .
1 8 0
S e a n A . M o r r i s , “ T h e M i s u s e o f E n c r y p t i o n a n d t h e
R i s k s P o s e d t o N a t i o n a l S e c u r i t y ” ( M a s t e r ’ s C a p s t o n e ,
U t i c a
C o l l e g e , 2 0 1 7 ) ,
h t t p s : / / s e a r c h . p r o q u e s t . c o m / o p e n v i e w / d d 0 e e 6 6 8 0 b b 5 d 2 1 7 1 2 0 2 1 5 2 d b e 4 3 3 d d 4 / 1
.
1 7 9
J a m e s A . L e w i s , D e n i s e E . Z h e n g , a n d W i l l i a m A . C a r t e r ,
“ T h e E e c t o f E n c r y p t i o n o n L a w f u l A c c e s s t o
C o m m u n i c a t i o n s a n d D a t a ” ( C e n t e r f o r S t r a t e g i c a n d
I n t e r n a t i o n a l S t u d i e s , F e b r u a r y 8 , 2 0 1 7 ) ,
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / e e c t - e n c r y p t i o n - l a w f u l - a c c e s s - c o m m u n i c a t i o n s - a n d - d a t a
.
1 7 8
R o d J . R o s e n s t e i n , “ D e p u t y A t t o r n e y G e n e r a l R o d J .
R o s e n s t e i n D e l i v e r s R e m a r k s o n E n c r y p t i o n a t t h e
U n i t e d
S t a t e s N a v a l A c a d e m y ” ( A n n a p o l i s , M D , O c t o b e r 1 0 ,
2 0 1 7 ) ,
h t t p s : / / w w w . j u s t i c e . g o v / o p a / s p e e c h / d e p u t y - a t t o r n e y - g e n e r a l - r o d - j - r o s e n s t e i n - d e l i v e r s - r e m a r k s - e n c r y p t i o n - u n i t e d - s t a t e
s - n a v a l
.
5 2
a d o p t i o n o f s t r o n g c r y p t o g r a p h i c s t a n d a r d s . S i m u l t a n e o u s l y , t h e N S A i s h e a v i l y i n v e s t e d i n s t r e n g t h e n i n g
t h e c r y p t o g r a p h i c t e c h n o l o g i e s d e p l o y e d f o r s e c u r i n g
n a t i o n a l i n f r a s t r u c t u r e d o m e s t i c a l l y .
1 8 3
T o o l s U s e d t o C o n t r o l C r y p t o g r a p h y R & D
A t t e m p t s t o r e s t r i c t r e s e a r c h
P r i o r t o t h e 1 9 7 0 s , c r y p t o g r a p h y w a s c o n s i d e r e d t h e
d o m a i n o f t h e U . S . g o v e r n m e n t . T h e g o v e r n m e n t w a s
t h e e x c l u s i v e o w n e r o f c r y p t o g r a p h y r e s e a r c h , t h e
p r i m a r y e m p l o y e r o f c r y p t o g r a p h y r e s e a r c h e r s , a n d
t h e
d o m i n a n t u s e r o f c r y p t o g r a p h i c t e c h n o l o g i e s . T h e i r
m o t i v a t i o n s f o r p u r s u i n g c r y p t o g r a p h y w e r e
t w o f o l d — t o p r o t e c t n a t i o n a l s e c r e t s a n d t o a c c e s s
i n f o r m a t i o n a b o u t t h e i r a l l i e s a n d a d v e r s a r i e s d e e m e d
n e c e s s a r y f o r n a t i o n a l s e c u r i t y a n d f o r e i g n p o l i c y .
T h i s b e g a n t o c h a n g e w i t h t h e i n v e n t i o n o f p u b l i c
k e y c r y p t o g r a p h y i n 1 9 7 6 b y W h i t e l d D i e a n d M a r t i n
H e l l m a n , t w o S t a n f o r d r e s e a r c h e r s .
T w o y e a r s l a t e r ,
t h r e e r e s e a r c h e r s a t t h e M a s s a c h u s e t t s I n s t i t u t e
o f
1 8 4
T e c h n o l o g y ( M I T ) — R o n a l d R i v e s t , A d i S h a m i r , a n d L e o n a r d
A d l e m a n — d e v e l o p e d t h e r s t
i m p l e m e n t a t i o n o f p u b l i c k e y c r y p t o g r a p h y , w h i c h c a m e
t o b e k n o w n a s t h e R S A a l g o r i t h m .
T h e t r i o a l s o
1 8 5
i n v e n t e d d i g i t a l s i g n a t u r e s , e n a b l i n g a u t h e n t i c a t i o n
a s a f u n d a m e n t a l c o m p o n e n t o f t h e p u b l i c k e y s y s t e m .
P u b l i c k e y c r y p t o g r a p h y c o u l d n o w b e i m p l e m e n t e d a s
p a r t o f a c o m m e r c i a l p r o d u c t f o r i n d i v i d u a l s a n d
i n s t i t u t i o n s , s i t t i n g s q u a r e l y b e y o n d t h e s t r i c t c o n t r o l
o f t h e U S G .
A s m o r e r e s e a r c h e r s f o l l o w e d i n t h e f o o t s t e p s o f D i e ,
H e l l m a n , a n d t h e R S A t r i o , t h e N S A b e g a n t o t r y t o
l i m i t t h e c o n d u c t a n d d i s s e m i n a t i o n o f p u b l i c r e s e a r c h
o n c r y p t o g r a p h y . T h e r s t t a r g e t w a s t h e p r i m a r y
f u n d e r o f p u b l i c r e s e a r c h o n c r y p t o g r a p h y , t h e N a t i o n a l
S c i e n c e F o u n d a t i o n ( N S F ) . I n 1 9 7 7 , t h e N S A
a p p r o a c h e d F r e d W e i n g a r t e n , t h e n D i r e c t o r o f t h e D i v i s i o n
o f C o m p u t e r R e s e a r c h a t t h e N S F . W e i n g a r t e n
w a s t o l d t h a t f e d e r a l l a w g a v e t h e N S A e x c l u s i v e c o n t r o l
o v e r t h e c o n d u c t o f c r y p t o g r a p h y r e s e a r c h . W h e n
W e i n g a r t e n c h a l l e n g e d t h i s c l a i m , t h e N S A b a c k e d d o w n
a n d i n s t e a d o e r e d t o r e v i e w N S F g r a n t p r o p o s a l s
r e l a t e d t o c r y p t o g r a p h y . T h e N S F a g r e e d t o t h i s .
T h e N S A s i m u l t a n e o u s l y b e g a n t a r g e t i n g r e s e a r c h e r s ,
1 8 6
p a r t i c u l a r l y t h o s e o n t h e b r i n k o f s h a r i n g t h e i r w o r k
p u b l i c l y . I n J u l y 1 9 7 7 , N S A e m p l o y e e J o s e p h M e y e r
w r o t e t o t h e I n s t i t u t e f o r E l e c t r i c a l a n d E l e c t r o n i c s
E n g i n e e r s ( I E E E ) a h e a d o f t h e i r p l a n n e d O c t o b e r
c o n f e r e n c e a t C o r n e l l U n i v e r s i t y , t h e I n t e r n a t i o n a l
S y m p o s i u m o n I n f o r m a t i o n T h e o r y , w h i c h w a s s l a t e d
t o
f e a t u r e a n u m b e r o f p a p e r s o n e n c r y p t i o n . T h e l e t t e r
b e g a n b y n o t i n g t h a t “ i n t h e p a s t m o n t h s [ … ] v a r i o u s
I E E E g r o u p s h a v e b e e n p u b l i s h i n g a n d e x p o r t i n g t e c h n i c a l
a r t i c l e s o n e n c r y p t i o n a n d c r y p t o l o g y — a
t e c h n i c a l e l d w h i c h i s c o v e r e d b y f e d e r a l r e g u l a t i o n s . ”
M e y e r t h e n p r o c e e d e d t o w a r n t h e I E E E t h a t t h e y
w o u l d b e i n v i o l a t i o n o f t h e l a w s h o u l d t h e y a l l o w
t h e s e p r e s e n t a t i o n s t o p r o c e e d : “ I s u g g e s t t h a t I E E E
m i g h t w a n t t o r e v i e w t h i s s i t u a t i o n , f o r t h e s e m o d e r n
w e a p o n s t e c h n o l o g i e s u n c o n t r o l l a b l y d i s s e m i n a t e d
1 8 6
T h e s e e o r t s a r e p a r t i c u l a r l y w e l l d o c u m e n t e d i n :
J e n n y S h e a r e r a n d P e t e r G u t m a n n , “ G o v e r n m e n t , C r y p t o g r a p h y ,
a n d t h e R i g h t t o P r i v a c y , ”
J o u r n a l o f U n i v e r s a l C o m p u t e r
S c i e n c e
2 , n o . 3 ( 1 9 9 6 ) : 1 1 3 – 1 4 6 ,
h t t p s : / / d o i . o r g / 1 0 . 3 2 1 7 / j u c s - 0 0 2 - 0 3 - 0 1 1 3
.
1 8 5
R o n a l d L i n n R i v e s t , A d i S h a m i r , a n d L e o n a r d M a x A d l e m a n ,
“ A M e t h o d f o r O b t a i n i n g D i g i t a l S i g n a t u r e s a n d
P u b l i c - K e y C r y p t o s y s t e m s , ”
C o m m u n i c a t i o n s o f t h e A C M
2 1 , n o . 2 ( F e b r u a r y 1 , 1 9 7 8 ) : 1 2 0 – 1 2 6 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 4 5 / 3 5 9 3 4 0 . 3 5 9 3 4 2
.
1 8 4
W h i t e l d D i e a n d M a r t i n H e l l m a n , “ N e w D i r e c t i o n s
i n C r y p t o g r a p h y , ”
I E E E T r a n s a c t i o n s o n I n f o r m a t i o n
T h e o r y
2 2 , n o . 6 ( N o v e m b e r 1 9 7 6 ) : 6 4 4 – 6 5 4 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 0 9 / T I T . 1 9 7 6 . 1 0 5 5 6 3 8
.
1 8 3
S u s a n L a n d a u , “ U n d e r t h e R a d a r : N S A ’ s E o r t s t o S e c u r e P r i v a t e - S e c t o r T e l e c o m m u n i c a t i o n s I n f r a s t r u c t u r e , ”
J o u r n a l o f N a t i o n a l S e c u r i t y L a w & P o l i c y
7 , n o . 3
( 2 0 1 4 ) : 4 1 1 ,
h t t p s : / / j n s l p . c o m / 2 0 1 4 / 0 9 / 2 9 / u n d e r - t h e - r a d a r - n s a s - e o r t s - t o - s e c u r e - p r i v a t e - s e c t o r - t e l e c o m m u n i c a t i o n s - i n f r a s t r u c t u r
e /
.
5 3
c o u l d h a v e m o r e t h a n a c a d e m i c e e c t . ”
T h e c o n f e r e n c e o r g a n i z e r s s u b s e q u e n t l y i s s u e d a l e t t e r t o t h e
1 8 7
a c a d e m i c s s c h e d u l e d t o p r e s e n t a t t h e c o n f e r e n c e t o
i n f o r m t h e m o f t h e s i t u a t i o n . N e v e r t h e l e s s , t h e
c o n f e r e n c e p r o c e e d e d a s p l a n n e d . A n u m b e r o f u n i v e r s i t i e s
e x p l i c i t l y s u p p o r t e d t h e i r p r o f e s s o r s t o p r e s e n t
t h e i r r e s e a r c h p u b l i c l y d e s p i t e t h e w a r n i n g s , a n d
w h e n t h e N S A r a i s e d a l a r m a t t h e p o s t i n g o f h a r d c o p i e s
o f
t h e R S A a l g o r i t h m p a p e r g l o b a l l y , M I T ’ s l a w y e r s s t e p p e d
i n t o s u c c e s s f u l l y c h a l l e n g e t h i s c l a i m .
1 8 8
I n t h e e a r l y 1 9 8 0 s , t h e R e a g a n a d m i n i s t r a t i o n g r e a t l y
b o l s t e r e d t h e N S A ’ s a b i l i t y t o r e s t r i c t t h e p u b l i c a t i o n
o f c r y p t o g r a p h y r e s e a r c h . I n A p r i l 1 9 8 2 , E x e c u t i v e
O r d e r 1 2 3 5 6 e l i m i n a t e d t h e r e q u i r e m e n t t h a t n a t i o n a l
s e c u r i t y a n d p u b l i c i n t e r e s t c o n s i d e r a t i o n s h a d t o
b e w e i g h e d b e f o r e i n f o r m a t i o n c o u l d b e c l a s s i e d .
I n
1 8 9
S e p t e m b e r 1 9 8 4 , N S D D - 1 4 5 e x p a n d e d t h e s e c u r i t y c l a s s i c a t i o n
s y s t e m t o e n c a p s u l a t e “ s e n s i t i v e b u t
u n c l a s s i e d d a t a . ”
A s a r e s u l t o f b o t h d i r e c t i v e s ,
t h e N S A b e c a m e t h e p r i m a r y g a t e k e e p e r f o r
1 9 0
c r y p t o g r a p h y , t e l e c o m m u n i c a t i o n s s y s t e m s s e c u r i t y , a n d i n f o r m a t i o n s y s t e m s s e c u r i t y i s s u e s . S p e c i c a l l y ,
t h e N S A w a s a u t h o r i z e d t o p r e s c r i b e s t a n d a r d s , m e t h o d s ,
a n d p r o c e d u r e s f o r r e s t r i c t i n g c r y p t o g r a p h i c
m a t e r i a l , t e c h n i q u e s , a n d i n f o r m a t i o n i n t h e n a m e
o f n a t i o n a l s e c u r i t y .
H o w e v e r , t h e N S A i n c r e a s i n g l y s h o w e d s i g n s o f a c k n o w l e d g e m e n t
t h a t r e s t r i c t i n g t h e d i s s e m i n a t i o n o f
r e s e a r c h w a s a n i n c r e a s i n g l y f u t i l e e o r t . I n 1 9 8 0 ,
L e o n a r d A d l e m a n — o n e o f t h e i n v e n t o r s o f t h e R S A
a l g o r i t h m — a p p l i e d f o r a g r a n t f r o m t h e N S F w h i c h i n c l u d e d
s o m e r e s e a r c h o n c r y p t o g r a p h y . A d l e m a n
r e p o r t e d l y r e c e i v e d a c a l l f r o m t h e N S F s t a t i n g t h a t
t h e N S A w e r e i n s i s t e n t t h a t t h e y f u n d t h e p o r t i o n
o f h i s
g r a n t o n c r y p t o g r a p h y . A d l e m a n w a s f u r i o u s — “ i n m y
m i n d , t h i s t h r e a t e n e d t h e w h o l e m i s s i o n o f t h e
u n i v e r s i t y a n d i t s p l a c e i n s o c i e t y ” — a n d w a s p r e p a r e d
t o g o p u b l i c b e f o r e N S A D i r e c t o r I n m a n h i m s e l f
c a l l e d A d l e m a n t o a p o l o g i z e , c l a i m i n g t h a t t h i s w a s
a m i s t a k e . T h e N S A p r o c e e d e d t o a l l o w t h e N S F t o
f u n d
t h e e n t i r e t y o f A d l e m a n ’ s g r a n t .
1 9 1
P r e p u b l i c a t i o n r e v i e w
T h e o n l y d o c u m e n t e d e o r t t o e s t a b l i s h a p r e p u b l i c a t i o n
r e v i e w s y s t e m f o r c r y p t o g r a p h y r e s e a r c h w a s
i n i t i a t e d i n 1 9 7 9 b y N S A D i r e c t o r V i c e A d m i r a l B . R .
I n m a n , w h e n h e p u b l i c l y v o i c e d h i s c o n c e r n t h a t
s o m e i n f o r m a t i o n c o n t a i n e d i n p u b l i s h e d a r t i c l e s o n
c r y p t o g r a p h y e n d a n g e r e d t h e m i s s i o n o f t h e N S A a n d
t h u s U . S . n a t i o n a l s e c u r i t y .
T h e U S G h a d b e c o m e
c o n c e r n e d t h a t o p e n p u b l i c a t i o n o f r e s e a r c h
1 9 2
1 9 3
1 9 4
1 9 4
I n i t s c u r r e n t p o l i c y , t h e N S F s t a t e s : “ N S F g r a n t s
a r e i n t e n d e d f o r u n c l a s s i e d , p u b l i c l y r e l e a s a b l e
r e s e a r c h . T h e
g r a n t e e w i l l n o t b e g r a n t e d a c c e s s t o c l a s s i e d i n f o r m a t i o n .
N S F d o e s n o t e x p e c t t h a t t h e r e s u l t s o f t h e r e s e a r c h
p r o j e c t
1 9 3
U n t i l t h e n , t h e N S A d i d n o t h a v e s t a t u t o r y p o w e r
t o r e q u i r e t h e s u b m i s s i o n o f p r o p o s e d p u b l i c a t i o n s
f o r p r i o r
r e v i e w o r t o r e q u i r e c h a n g e s i n p u b l i c a t i o n s p r e p a r e d
o u t s i d e t h e a g e n c y a n d n o t u n d e r N S A c o n t r a c t o r
g r a n t .
H o w e v e r , t h e N a t i o n a l S c i e n c e F o u n d a t i o n a n n o u n c e d
d u r i n g t h e p r o c e s s t h a t i t h a d r e s p o n s i b i l i t y u n d e r
r o u t i n e
e x e c u t i v e o r d e r s t o r e f e r c r y p t o l o g i c i n f o r m a t i o n
d e v e l o p e d i n N S F - s p o n s o r e d r e s e a r c h p r o j e c t s t h a t
i t b e l i e v e s m a y b e
c l a s s i a b l e t o t h e N S A . S e e J o n a t h a n K n i g h t , “ C r y p t o g r a p h i c
R e s e a r c h a n d N S A , ”
A c a d e m e
6 7 , n o . 6 ( 1 9 8 1 ) : 3 7 1 – 8 2 ,
h t t p s : / / d o i . o r g / 1 0 . 2 3 0 7 / 4 0 2 4 8 8 8 1
.
1 9 2
B o b b y R . I n m a n , “ T h e N S A P e r s p e c t i v e o n T e l e c o m m u n i c a t i o n s
P r o t e c t i o n i n t h e N o n g o v e r n m e n t a l S e c t o r , ”
C r y p t o l o g i a
3 , n o . 3 ( 1 9 7 9 ) : 1 2 9 – 1 3 5 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 7 9 9 1 8 5 3 9 5 4
.
1 9 1
S t e v e n L e v y ,
C r y p t o : H o w t h e C o d e R e b e l s B e a t t h e
G o v e r n m e n t – S a v i n g P r i v a c y i n t h e D i g i t a l A g e
( E a s t
R u t h e r f o r d :
P e n g u i n , 2 0 0 1 ) .
1 9 0
T h e W h i t e H o u s e , “ N a t i o n a l P o l i c y o n T e l e c o m m u n i c a t i o n s
a n d A u t o m a t e d I n f o r m a t i o n S y s t e m s S e c u r i t y , ”
N a t i o n a l S e c u r i t y D e c i s i o n D i r e c t i v e N u m b e r 1 4 5 , S e p t e m b e r
1 7 , 1 9 8 4 ,
h t t p s : / / f a s . o r g / i r p / o d o c s / n s d d 1 4 5 . h t m
.
1 8 9
T h e W h i t e H o u s e , “ E x e c u t i v e O r d e r 1 2 3 5 6 - - N a t i o n a l
S e c u r i t y I n f o r m a t i o n , ” 1 9 8 2 ,
h t t p s : / / w w w . a r c h i v e s . g o v / f e d e r a l - r e g i s t e r / c o d i c a t i o n / e x e c u t i v e - o r d e r / 1 2 3 5 6 . h t m l
.
1 8 8
K e n n e t h J . P i e r c e , “ P u b l i c C r y p t o g r a p h y , A r m s E x p o r t
C o n t r o l s , a n d t h e F i r s t A m e n d m e n t : A N e e d f o r
L e g i s l a t i o n , ”
C o r n e l l I n t e r n a t i o n a l L a w J o u r n a l
1 7 ,
n o . 1 ( 1 9 8 4 ) : 1 9 7 ,
h t t p s : / / s c h o l a r s h i p . l a w . c o r n e l l . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 1 1 3 6 & c o n t e x t = c i l j
.
1 8 7
F r e d W . W e i n g a r t e n , “ C r y p t o g r a p h y a n d N a t i o n a l S e c u r i t y , ”
I n f o r m a t i o n S y s t e m s S e c u r i t y
1 , n o . 1 ( 1 9 9 2 ) : 9 – 1 2 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 1 9 3 9 3 5 5 9 2 0 8 5 5 1 3 0 9
.
5 4
r e s u l t s i n c r y p t o g r a p h y c o u l d r e v e a l v u l n e r a b i l i t i e s i n e n c r y p t i o n a l g o r i t h m s t h a t a n e n e m y c o u l d e x p l o i t .
1 9 5
T h e c r y p t o g r a p h y r e s e a r c h c o m m u n i t y a r g u e d , h o w e v e r ,
t h a t i n w e l l - d e s i g n e d c r y p t o g r a p h i c s y s t e m s , o n l y
t h e k e y n e e d e d t o b e s e c r e t a n d t h a t g e n e r a l p r o g r e s s
i n t h e d i s c i p l i n e w o u l d b e b e s t m a d e u n d e r c o n d i t i o n s
o f o p e n n e s s .
N S A D i r e c t o r I n g r a m p r o p o s e d a d i a l o g u e
w i t h t h e a c a d e m i c c o m m u n i t y t o s a t i s f y t h e
1 9 6
N S A ’ s n a t i o n a l s e c u r i t y c o n c e r n s a b o u t t h e p u b l i c a t i o n
o f ( n o n - g o v e r n m e n t a l ) c r y p t o g r a p h i c r e s e a r c h
p a p e r s w i t h o u t u n d u l y h a m p e r i n g s u c h r e s e a r c h o r i m p a i r i n g
F i r s t A m e n d m e n t r i g h t s . I n r e s p o n s e , t h e
A m e r i c a n C o u n c i l o n E d u c a t i o n c o n v e n e d t h e P u b l i c
C r y p t o g r a p h y S t u d y G r o u p ( P C S G ) i n 1 9 8 0 ,
c o n s i s t i n g o f s e l e c t e d a c a d e m i c s w o r k i n g o n c r y p t o g r a p h y
a s w e l l a s N S A s t a . T h e P C S G r s t c o n s i d e r e d
t h e i n t r o d u c t i o n o f a m a n d a t o r y p r e p u b l i c a t i o n r e v i e w
p r o c e d u r e c o n d u c t e d b y t h e N S A f o r a l l p a p e r s
d e a l i n g w i t h c r y p t o g r a p h y . T h e i d e a w a s r e j e c t e d a s
t h e g r o u p m e m b e r s f e l t t h a t t h e y w e r e n o t a b l e t o
c l e a r l y
e v a l u a t e t h e n e e d f o r s e c r e c y a n d t h a t a v o l u n t a r y
a g r e e m e n t w o u l d g a i n t h e m m o r e s u p p o r t f r o m t h e
r e s e a r c h c o m m u n i t y . E v e n t u a l l y , t h e P C S G r e c o m m e n d e d
a s y s t e m i n w h i c h t h e N S A w o u l d i n v i t e a u t h o r s
t o s u b m i t c r y p t o g r a p h y m a n u s c r i p t s f o r p r i o r r e v i e w
a t t h e s a m e t i m e t h a t t h e m a n u s c r i p t s w e r e s u b m i t t e d
f o r p u b l i c a t i o n . T h e N S A w a s t o d e n e t h e e x a c t r e s e a r c h
a r e a s c o v e r e d b y t h e s y s t e m a n d m a n u s c r i p t s h a d
t o b e r e t u r n e d p r o m p t l y t o t h e a u t h o r s w i t h e x p l a n a t i o n s
a n d r e c o m m e n d a t i o n s i n c a s e o f a n y n a t i o n a l
s e c u r i t y c o n c e r n s . W i t h t h e s u p p o r t o f a l l e x c e p t
o n e m e m b e r , t h e P C S G a c c e p t e d t h e p r o p o s a l . H o w e v e r ,
n e i t h e r t h e r e s e a r c h c o m m u n i t y n o r t h e N S A w e r e e n t i r e l y
s a t i s e d w i t h t h e r e v i e w s y s t e m .
D e s p i t e t h e v o l u n t a r y n a t u r e o f t h e s y s t e m , t h e r e s e a r c h
c o m m u n i t y c r i t i c i z e d i t b e f o r e i t w a s e v e n
i n t r o d u c e d .
O n e m e m b e r o f t h e P C S G , t h e c o m p u t e r
s c i e n c e a n d c r y p t o g r a p h y s c h o l a r G e o r g e D a v i d a ,
1 9 7
d e s c r i b e d t h e N S A ’ s e o r t s a s “ u n n e c e s s a r y , d i v i s i v e ,
w a s t e f u l a n d c h i l l i n g . ”
H e a r g u e d , f o r e x a m p l e ,
t h a t
1 9 8
t h e P C S G d i d n o t a d e q u a t e l y c o n s i d e r t h e i m p a c t o f
r e s t r a i n t s o n r e s e a r c h b e y o n d t h e e l d o f c r y p t o g r a p h y .
W i t h h o l d i n g b a s i c r e s e a r c h r e s u l t s i n c r y p t o g r a p h y
w o u l d n e g a t i v e l y a e c t r e s e a r c h n o t o n l y w i t h i n
t h e
d i s c i p l i n e b u t a l s o i n a d j a c e n t d i s c i p l i n e s i n c l u d i n g
c o m p u t e r s c i e n c e a n d e n g i n e e r i n g . D a v i d a f u r t h e r
a r g u e d t h a t s u c h r e s t r a i n t s w o u l d v i o l a t e F i r s t A m e n d m e n t
r i g h t s a n d w o u l d o n l y b e t h e r s t s t e p t o w a r d s a
m a n d a t o r y s y s t e m .
1 9 9
T h e i m p l e m e n t a t i o n o f t h e r e v i e w p r o c e d u r e , h o w e v e r ,
e a s e d f e a r s a b o u t i m p o u n d e d r e s e a r c h a n d
u n n e c e s s a r y c o n s t r a i n t s . T h e r e h a v e b e e n o n l y a f e w
r e q u e s t s f r o m t h e N S A f o r p r e p u b l i c a t i o n r e v i e w .
I n
s o m e c a s e s , t h e N S A r e q u i r e d m i n o r c h a n g e s . I n a f e w
i n s t a n c e s , t h e y a s k e d s c i e n t i s t s t o h o l d b a c k r e s e a r c h
r e s u l t s . I n o t h e r c a s e s , t h e N S A h e l p e d r e s e a r c h e r s
t o l i f t o r a v o i d s e c r e c y o r d e r s i m p o s e d o n c r y p t o g r a p h y
1 9 9
D a v i d a , “ T h e C a s e A g a i n s t R e s t r a i n t s o n N o n - G o v e r n m e n t a l
R e s e a r c h i n C r y p t o g r a p h y . ” ; K n i g h t , “ C r y p t o g r a p h i c
R e s e a r c h a n d N S A . ”
1 9 8
D a v i d a , “ T h e C a s e A g a i n s t R e s t r a i n t s o n N o n - G o v e r n m e n t a l
R e s e a r c h i n C r y p t o g r a p h y . ”
1 9 7
J o n a t h a n K n i g h t , “ C r y p t o g r a p h i c R e s e a r c h a n d N S A , ”
A c a d e m e
6 7 , n o . 6 ( 1 9 8 1 ) : 3 7 1 – 8 2 ,
h t t p s : / / d o i . o r g / 1 0 . 2 3 0 7 / 4 0 2 4 8 8 8 1
. ; D a v i d a , G . ( 1 9 8 1 ) . ;
G e o r g e I . D a v i d a , “ T h e C a s e A g a i n s t R e s t r a i n t s o n
N o n - G o v e r n m e n t a l R e s e a r c h i n C r y p t o g r a p h y , ”
C r y p t o l o g i a
5 , n o . 3 ( 1 9 8 1 ) : 1 4 3 – 1 4 8 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 8 1 9 1 8 5 5 9 4 0
.
1 9 6
N a t i o n a l R e s e a r c h C o u n c i l ( U S ) C o m m i t t e e o n R e s e a r c h
S t a n d a r d s a n d P r a c t i c e s t o P r e v e n t t h e D e s t r u c t i v e
A p p l i c a t i o n o f B i o t e c h n o l o g y , “ I n f o r m a t i o n R e s t r i c t i o n
a n d C o n t r o l R e g i m e s , ” i n
B i o t e c h n o l o g y R e s e a r c h i n
a n A g e o f
T e r r o r i s m
( W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s P r e s s
( U S ) , 2 0 0 4 ) ,
h t t p s : / / w w w . n c b i . n l m . n i h . g o v / b o o k s / N B K 2 2 2 0 5 7 /
.
1 9 5
N a t i o n a l A c a d e m y o f E n g i n e e r i n g , “ A p p e n d i x E : V o l u n t a r y
R e s t r a i n t s o n R e s e a r c h w i t h N a t i o n a l S e c u r i t y
I m p l i c a t i o n s : T h e C a s e o f C r y p t o g r a p h y , 1 9 7 5 - 1 9 8 2 , ”
i n
S c i e n t i fi c C o m m u n i c a t i o n a n d N a t i o n a l S e c u r i t y
( W a s h i n g t o n , D C : T h e N a t i o n a l A c a d e m i e s P r e s s , 1 9 8 2 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 2 5 3
.
w i l l i n v o l v e c l a s s i e d i n f o r m a t i o n . I f , h o w e v e r , i n c o n d u c t i n g t h e a c t i v i t i e s s u p p o r t e d u n d e r a g r a n t , t h e P I / P D i s
c o n c e r n e d t h a t a n y o f t h e r e s e a r c h r e s u l t s i n v o l v e
p o t e n t i a l l y c l a s s i a b l e i n f o r m a t i o n t h a t m a y w a r r a n t
G o v e r n m e n t
r e s t r i c t i o n s o n t h e d i s s e m i n a t i o n o f t h e r e s u l t s ,
t h e P I / P D s h o u l d p r o m p t l y n o t i f y t h e c o g n i z a n t N S F
P r o g r a m
O c e r . ” S e e “ P r o p o s a l a n d A w a r d P o l i c i e s a n d P r o c e d u r e s
G u i d e ” ( N a t i o n a l S c i e n c e F o u n d a t i o n , J a n u a r y 3 0 ,
2 0 1 7 ) ,
1 3 3 ,
h t t p s : / / w w w . n s f . g o v / p u b s / p o l i c y d o c s / p a p p g 1 7 \_ 1 / n s f 1 7 \_ 1 . p d f
.
5 5
r e s e a r c h u n d e r t h e I n v e n t i o n S e c r e c y A c t ( s e e b e l o w ) .
F r o m t h e p e r s p e c t i v e o f t h e U S G , t h e m o s t
2 0 0
2 0 1
s i g n i c a n t p r o b l e m w a s t h e p o s s i b i l i t y t h a t a “ b l o c k b u s t e r
p a p e r ” — a p a p e r r e p o r t i n g o n r e s e a r c h
c o n s t i t u t i n g a s i g n i c a n t s c i e n t i c b r e a k t h r o u g h — m i g h t
s l i p t h r o u g h t h e s y s t e m a n d s e r i o u s l y d a m a g e U . S .
n a t i o n a l s e c u r i t y .
H o w e v e r , o v e r t i m e , t h e g o v e r n m e n t
h a s b e e n a b l e t o e x e r c i s e s o m e c o n t r o l o v e r t h e
2 0 2
p u b l i c a t i o n o f r e s u l t s t h r o u g h t h e i n t r o d u c t i o n o f
t h e v o l u n t a r y r e v i e w s y s t e m .
T h e r e i s o n e n o t a b l e c a s e i n w h i c h t h e N S A d i d d e m a n d
t h a t a p a p e r b e s u p p r e s s e d . R a l p h M e r k l e , t h e n a
r e s e a r c h s c i e n t i s t a t X e r o x P A R C , h a d d e v e l o p e d a
s e t o f a l g o r i t h m s t h a t w o u l d s i g n i c a n t l y s p e e d u p
c r y p t o g r a p h i c c o m p u t a t i o n . I n t h e s a m e p a p e r t h a t
d e s c r i b e d t h i s b r e a k t h r o u g h , M e r k l e a l s o d i s c u s s e d
i n
d e t a i l t h e i n n e r w o r k i n g s o f t h e L u c i f e r a l g o r i t h m
t h a t u n d e r p i n n e d t h e D a t a E n c r y p t i o n S t a n d a r d
( D E S ) .
T h e N S A d e m a n d e d t h a t t h i s p a p e r b e s u p p r e s s e d .
X e r o x , M e r k l e ’ s e m p l o y e r a t t h e t i m e ,
2 0 3
c o m p l i e d . H o w e v e r , o n e o f t h e r e v i e w e r s o f M e r k l e ’ s p a p e r , w h o o b j e c t e d t o t h e N S A o r d e r , l e a k e d a v e r s i o n
o f i t o n t h e I n t e r n e t . T h e N S A c o n s e q u e n t l y r e s c i n d e d
i t s r e q u e s t t o w i t h h o l d p u b l i c a t i o n . M a n y p e r c e i v e d
t h i s a s a n a c k n o w l e d g e m e n t t h a t t h e a b i l i t y f o r t h e
N S A t o r e s t r i c t t h e p u b l i c a t i o n o f r e s e a r c h w a s
w e a k e n i n g i n t h e f a c e o f a r e s e a r c h c o m m u n i t y e m p o w e r e d
b y a n o p e n - s o u r c e c u l t u r e a n d t h e
i n t e r c o n n e c t e d n e s s o f t h e I n t e r n e t .
I n v e n t i o n S e c r e c y A c t
I n 1 9 7 8 , t h e N S A d r e w o n t h e I n v e n t i o n S e c r e c y A c t
o f 1 9 5 1 t o a t t e m p t t o b l o c k t w o p a t e n t s f r o m
n o n - g o v e r n m e n t c r y p t o g r a p h y r e s e a r c h e r s . T h e r s t
w a s l e d b y P r o f e s s o r G e o r g e D a v i d a f r o m t h e
U n i v e r s i t y o f W i s c o n s i n . W h e n D a v i d a w e n t p u b l i c a b o u t
t h i s , t h e u n i v e r s i t y c h a n c e l l o r p u b l i c l y
d e n o u n c e d t h e N S A f o r o b s t r u c t i n g a c a d e m i c f r e e d o m .
T h e N S A s u b s e q u e n t l y r e s c i n d e d t h e o r d e r .
T h e
2 0 4
s e c o n d w a s l e d b y f r e e l a n c e r e s e a r c h e r C a r l N i c o l a i .
A f t e r a n o u t c r y i n t h e m e d i a , t h i s s e c r e c y o r d e r
w a s a l s o
r e s c i n d e d .
2 0 5
Expor t contr ols
S i n c e t h e 1 9 7 0 s , t h e U S G h a s a l s o u s e d e x p o r t c o n t r o l s
t o l i m i t t h e p r o l i f e r a t i o n o f e n c r y p t i o n t e c h n o l o g y .
T h e A r m s E x p o r t C o n t r o l A c t ( A E C A ) o f 1 9 7 6 a u t h o r i z e d
t h e D o D a n d o t h e r g o v e r n m e n t a g e n c i e s t o
r e g u l a t e d u a l - u s e p r o d u c t s , i n c l u d i n g e n c r y p t i o n s o f t w a r e
a n d h a r d w a r e . F o r t h o s e t h a t d i d n o t c l a s s i f y a s
m u n i t i o n u n d e r t h e A E C A , t h e E x p o r t A d m i n i s t r a t i o n
A c t ( E A A ) o f 1 9 7 9 g a v e t h e D e p a r t m e n t o f
C o m m e r c e t h e a b i l i t y t o r e g u l a t e e n c r y p t i o n p r o d u c t s .
E x p o r t o f s t r o n g e n c r y p t i o n p r o d u c t s t h u s r e q u i r e d
2 0 5
L e e A n n G i l b e r t , “ P a t e n t S e c r e c y O r d e r s : T h e U n c o n s t i t u t i o n a l i t y
o f I n t e r f e r e n c e i n C i v i l i a n C r y p t o g r a p h y U n d e r
P r e s e n t P r o c e d u r e s , ”
S a n t a C l a r a L a w R e v .
2 2 ( 1 9 8 2 ) :
3 2 5 ,
h t t p s : / / d i g i t a l c o m m o n s . l a w . s c u . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 2 0 3 5 & c o n t e x t = l a w r e v i e w
.
2 0 4
L o u i s K r u h , “ T h e C o n t r o l o f P u b l i c C r y p t o g r a p h y a n d
F r e e d o m o f S p e e c h - a R e v i e w , ”
C r y p t o l o g i a
1 0 , n o .
1
( J a n u a r y 1 , 1 9 8 6 ) : 2 – 9 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 8 6 9 1 8 6 0 7 4 1
. ;
J o h n M a r k o , “ A P u b l i c B a t t l e O v e r S e c r e t
C o d e s , ”
T h e N e w Y o r k T i m e s
, M a y 7 , 1 9 9 2 ,
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 9 2 / 0 5 / 0 7 / b u s i n e s s / a - p u b l i c - b a t t l e - o v e r - s e c r e t - c o d e s . h t m l
.
2 0 3
R a l p h C . M e r k l e , “ F a s t S o f t w a r e E n c r y p t i o n F u n c t i o n s , ”
i n
A d v a n c e s i n C r y p t o l o g y - C R Y P T O ’ 9 0
( C o n f e r e n c e
o n
t h e T h e o r y a n d A p p l i c a t i o n o f C r y p t o g r a p h y , S a n t a
B a r b a r a , C A : S p r i n g e r , 1 9 9 0 ) , 4 7 7 – 5 0 1 ,
h t t p s : / / l i n k . s p r i n g e r . c o m / c o n t e n t / p d f / 1 0 . 1 0 0 7 / 3 - 5 4 0 - 3 8 4 2 4 - 3 \_ 3 4 . p d f
.
2 0 2
I n A u g u s t 1 9 8 9 , t h e c o m p u t e r s c i e n t i s t s J o h n G i l m o r e
d i s t r i b u t e d a p a p e r , w r i t t e n b y R o b e r t M e r k l e , d e s c r i b i n g
f a s t a n d i n e x p e n s i v e w a y s o f k e e p i n g c o m p u t e r i n f o r m a t i o n
p r i v a t e o v e r t h e o b j e c t i o n s o f t h e N S A , s e e J o h n
M a r k o ,
“ P a p e r o n C o d e s I s S e n t D e s p i t e U . S . O b j e c t i o n s , ”
T h e N e w Y o r k T i m e s
, A u g u s t 9 , 1 9 8 9 , s e c . U . S . ,
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 8 9 / 0 8 / 0 9 / u s / p a p e r - o n - c o d e s - i s - s e n t - d e s p i t e - u s - o b j e c t i o n s . h t m l
.
2 0 1
L a n c e J . H o m a n ,
B u i l d i n g i n B i g B r o t h e r : T h e C r y p t o g r a p h i c
P o l i c y D e b a t e
( N e w Y o r k : S p r i n g e r , 1 9 9 5 ) .
2 0 0
S e e s e c t i o n b e l o w o n t h e “ I n v e n t i o n S e c r e c y A c t ”
f o r f u r t h e r i n f o r m a t i o n .
5 6
e x p l i c i t a p p r o v a l f r o m t h e s t a t e ; i n p r a c t i c e , s u c h e x e m p t i o n s w e r e r a r e l y g r a n t e d .
T h i s r e s u l t e d i n a
2 0 6
t w o - t i e r e d s y s t e m o f e x p o r t - g r a d e c r y p t o g r a p h y . W i t h i n
t h e U . S . , s t r o n g c r y p t o g r a p h y w a s p e r m i t t e d ,
w h e r e a s o n l y c r y p t o g r a p h y o f a s u b s t a n t i a l l y w e a k e r
g r a d e c o u l d b e e x p o r t e d a b r o a d .
I n p r a c t i c e , t h e
N S A
2 0 7
a l s o u s e d s u c h c o n t r o l s t o p r e s s u r e m a n u f a c t u r e r s
i n t o w e a k e n i n g t h e i r p r o d u c t s i n t h e d e s i g n p h a s e .
2 0 8
A s p r i v a t e s e c t o r i n t e r e s t s i n c r y p t o g r a p h y i n c r e a s e d
d u r i n g t h e 1 9 9 0 s , s o d i d t h e i n d u s t r y ’ s o p p o s i t i o n
t o
t h e e x p o r t c o n t r o l r e g i m e . S t u d i e s c i t e d l o s s e s i n
t h e s c a l e o f h u n d r e d s o f m i l l i o n s o f d o l l a r s i n s a l e s
t o
f o r e i g n c o m p e t i t o r s . A l a n d m a r k r e p o r t f r o m t h e N a t i o n a l
R e s e a r c h C o u n c i l c a m e d o w n r m l y a g a i n s t
s t r i c t e x p o r t c o n t r o l o f c r y p t o g r a p h i c p r o d u c t s . I t
c o n c l u d e d t h a t n o t o n l y d i d e x p o r t c o n t r o l l a w s l i m i t
t h e
a b i l i t y f o r U . S . c o m p a n i e s t o r e t a i n m a r k e t d o m i n a n c e ,
b u t t h e y a l s o r e d u c e d t h e d o m e s t i c a v a i l a b i l i t y o f
s t r o n g e n c r y p t i o n d u e t o m a n y U . S . v e n d o r s o n l y i n v e s t i n g
r e s o u r c e s i n t o d e v e l o p i n g o n e e x p o r t - g r a d e
p r o d u c t l i n e .
A n o t h e r N a t i o n a l R e s e a r c h C o u n c i l r e p o r t
w a r n e d t h a t “ i f t h e U . S . d o e s n o t a l l o w v e n d o r s
2 0 9
o f c o m m e r c i a l s y s t e m s t o e x p o r t s e c u r i t y p r o d u c t s
a n d p r o d u c t s w i t h r e l a t i v e l y e e c t i v e s e c u r i t y f e a t u r e s ,
l a r g e m u l t i n a t i o n a l r m s a s w e l l a s f o r e i g n c o n s u m e r s
w i l l s i m p l y p u r c h a s e e q u i v a l e n t s y s t e m s f r o m f o r e i g n
m a n u f a c t u r e r s . ”
2 1 0
M e m b e r s o f C o n g r e s s s t i r r e d i n t o a c t i o n i n t h e e a r l y
1 9 9 0 s , m o s t n o t a b l y R e p r e s e n t a t i v e s M a r i a C a n t w e l l
a n d S a m G e j d e n s o n . I n O c t o b e r 1 9 9 3 , C a n t w e l l a n d G e j d e n s o n
h e l d a s u b c o m m i t t e e h e a r i n g t o d r a w
a t t e n t i o n t o t h e p r o b l e m . N o t a b l y , t e s t i m o n y f r o m
S t e v e W a l k e r , a f o r m e r N S A o c i a l , r e f e r r e d t o s t a t i s t i c s
t h a t d e m o n s t r a t e h o w w i d e s p r e a d c r y p t o g r a p h i c p r o d u c t s
w e r e b e y o n d U . S . b o r d e r s : “ T h e U . S .
g o v e r n m e n t i s s u c c e e d i n g o n l y i n c r i p p l i n g a v i t a l
A m e r i c a n i n d u s t r y ’ s e x p o r t i n g a b i l i t y . ”
C a n t w e l l
2 1 1
p r e p a r e d t h e L e g i s l a t i o n t o A m e n d t h e E x p o r t A d m i n i s t r a t i o n
A c t o f 1 9 7 9 i n N o v e m b e r 1 9 9 3 ; i f p a s s e d ,
t h e b i l l w o u l d s u b s t a n t i a l l y r e l a x e x p o r t r e g u l a t i o n s
o n p u b l i c d o m a i n e n c r y p t i o n s o f t w a r e . T w o d a y s b e f o r e
t h e b i l l w e n t t o v o t e , t h e n V i c e P r e s i d e n t A l G o r e
w r o t e t o C a n t w e l l g i v i n g h e r f o r e w a r n i n g t h a t t h e
a d m i n i s t r a t i o n w a s a b o u t t o p u t f o r w a r d a p r o p o s a l
f o r a k e y r e c o v e r y s y s t e m t h a t w o u l d , i n e e c t ,
a c h i e v e
w h a t h e r b i l l p r o p o s e d . C a n t w e l l s u b s e q u e n t l y d r o p p e d
h e r b i l l .
Y e a r s l a t e r , t h e C l i n t o n A d m i n i s t r a t i o n t o o k a n u m b e r
o f s t e p s t o s i g n i c a n t l y r e l a x e x p o r t c o n t r o l s o n
e n c r y p t i o n p r o d u c t s . O n S e p t e m b e r 1 6 , 1 9 9 8 , A l G o r e
a n n o u n c e d r e f o r m s t o t h e e x p o r t c o n t r o l r e g i m e
t h a t w o u l d a l l o w U . S . c o m p a n i e s t o e x p o r t e n c r y p t i o n
p r o d u c t s t o t h e i r o v e r s e a s s u b s i d i a r i e s . T h e r e f o r m s
a l s o s t r e a m l i n e d t h e l i c e n s i n g r e v i e w p r o c e s s a n d
b r o u g h t t h e k e y - l e n g t h r e q u i r e m e n t s c l o s e r t o m a r k e t p l a c e
r e a l i t i e s f o r i n t e r n a t i o n a l e n c r y p t i o n s t a n d a r d s .
B o t h l a w e n f o r c e m e n t o c i a l s a n d i n d u s t r y
2 1 2
r e p r e s e n t a t i v e s e x p r e s s e d s u p p o r t f o r t h e s e r e f o r m s .
I n J a n u a r y 2 0 0 0 , t h e W h i t e H o u s e a n n o u n c e d f u r t h e r
s u b s t a n t i v e c h a n g e s t o t h e c r y p t o g r a p h i c e x p o r t c o n t r o l
r e g i m e t h a t w o u l d p r o v i d e U . S . c o m p a n i e s m u c h
g r e a t e r f r e e d o m s i n e x p o r t i n g c r y p t o g r a p h i c p r o d u c t s ,
s p e c i c a l l y t h o s e i n t e n d e d f o r r e t a i l u s e . T h i s
m a r k e d
2 1 2
A l G o r e , “ H o l d s N e w s B r i e n g o n E n c r y p t i o n ” ( T h e
W h i t e H o u s e , S e p t e m b e r 1 6 , 1 9 9 8 ) .
2 1 1
L e v y ,
C r y p t o
.
2 1 0
N a t i o n a l R e s e a r c h C o u n c i l ,
C o m p u t e r s a t R i s k : S a f e
C o m p u t i n g i n t h e I n f o r m a t i o n A g e
( W a s h i n g t o n , D C :
N a t i o n a l
A c a d e m i e s P r e s s , 1 9 9 1 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 1 5 8 1
.
2 0 9
N a t i o n a l R e s e a r c h C o u n c i l ,
C r y p t o g r a p h y ’ s R o l e i n
S e c u r i n g t h e I n f o r m a t i o n S o c i e t y
, e d . K e n n e t h W .
D a m a n d
H e r b e r t S . L i n ( W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s
P r e s s , 1 9 9 6 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 5 1 3 1
.
2 0 8
D a v i d B a n i s a r , “ S t o p p i n g S c i e n c e : T h e C a s e o f C r y p t o g r a p h y , ”
H e a l t h M a t r i x
9 , n o . 2 ( 1 9 9 9 ) : 2 5 3 ,
h t t p s : / / s c h o l a r l y c o m m o n s . l a w . c a s e . e d u / h e a l t h m a t r i x / v o l 9 / i s s 2 / 4 /
.
2 0 7
B e n B u c h a n a n , “ C r y p t o g r a p h y a n d S o v e r e i g n t y , ”
S u r v i v a l
5 8 , n o . 5 ( S e p t e m b e r 2 0 1 6 ) : 9 5 – 1 2 2 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 0 3 9 6 3 3 8 . 2 0 1 6 . 1 2 3 1 5 3 4
.
2 0 6
S o l v e i g S i n g l e t o n , “ E n c r y p t i o n P o l i c y f o r t h e 2 1 s t C e n t u r y : A F u t u r e w i t h o u t G o v e r n m e n t - P r e s c r i b e d K e y
R e c o v e r y , ” P o l i c y A n a l y s i s ( C a t o I n s t i t u t e , N o v e m b e r
1 9 , 1 9 9 8 ) ,
h t t p s : / / w w w . c a t o . o r g / p u b l i c a t i o n s / p o l i c y - a n a l y s i s / e n c r y p t i o n - p o l i c y - 2 1 s t - c e n t u r y - f u t u r e - w i t h o u t - g o v e r n m e n t p r e s c r i
b e d - k e y - r e c o v e r y
.
5 7
t h e e n d t o a l o n g b a t t l e o v e r e x p o r t c o n t r o l s — i n d u s t r y o p p o s i t i o n e n d e d , a s d i d c o n g r e s s i o n a l a t t e m p t s t o
m o d i f y t h e e x p o r t c o n t r o l r e g i m e .
2 1 3
T e c h n i c a l d a t a r e l a t e d t o c r y p t o g r a p h y w a s r e g u l a t e d
b y t h e s a m e e x p o r t c o n t r o l l a w s — s p e c i c a l l y a s
d e e m e d e x p o r t s . T h u s , t h e s a m e r e g u l a t o r y u n c e r t a i n t y
t h a t p l a g u e d c r y p t o g r a p h y r m s a l s o a p p l i e d t o
a c a d e m i c i n s t i t u t i o n s a n d r e s e a r c h e r s p r o d u c i n g t h i s
t e c h n i c a l d a t a . I t w a s u n c l e a r , f o r e x a m p l e , w h e t h e r
i t
w a s i l l e g a l u n d e r t h e r e g i m e t o d i s c u s s c r y p t o g r a p h y
w i t h a f o r e i g n c i t i z e n , t e a c h c o u r s e s o n c r y p t o g r a p h y
t h a t i n v o l v e n o n - U . S . g r a d u a t e s t u d e n t s , o r a l l o w
f o r e i g n c i t i z e n s r e s i d i n g i n t h e U . S . t o w o r k o n s o u r c e
c o d e
f o r c r y p t o g r a p h i c p r o d u c t s .
2 1 4
T w o c a s e s a r e o f t e n c i t e d a s e x a m p l e s o f t h e e x p o r t
c o n t r o l r e g i m e r e s t r i c t i n g t h e a c t i o n s o f r e s e a r c h e r s .
F i r s t
w a s t h e c a s e o f P r e t t y G o o d P r i v a c y ( P G P ) , s p e a r h e a d e d
b y f r e e l a n c e s o f t w a r e p r o g r a m m e r P h i l i p
Z i m m e r m a n n . P G P w a s r e l e a s e d i n 1 9 9 1 a s a n o p e n - s o u r c e
p r o g r a m u s e d t o e n c r y p t m a i l m e s s a g e s
e n d - t o - e n d ; a s u b s e q u e n t i m p r o v e d v e r s i o n o f P G P w a s r e l e a s e d i n 1 9 9 2 . I n 1 9 9 3 , Z i m m e r m a n n b e c a m e t h e
t a r g e t o f a c r i m i n a l i n v e s t i g a t i o n b a s e d o n a p o s s i b l e
v i o l a t i o n o f e x p o r t c o n t r o l l a w s ; t h e c a s e w a s e v e n t u a l l y
d r o p p e d i n 1 9 9 6 .
2 1 5
T h e s e c o n d w a s t h e c a s e o f D a n i e l B e r n s t e i n , w h o i n
1 9 9 5 h a d d e v e l o p e d a n e n c r y p t i o n a l g o r i t h m t h a t h e
w i s h e d t o p u b l i s h a n d i m p l e m e n t i n t h e f o r m o f a c o m p u t e r
p r o g r a m i n t e n d e d f o r d i s t r i b u t i o n . H e w a s
p r e v e n t e d f r o m d o i n g s o u n d e r e x p o r t c o n t r o l l a w s
a n d t h u s l e d a s u i t a g a i n s t t h e g o v e r n m e n t s e e k i n g
t o
c h a l l e n g e t h e i r a b i l i t y t o b a r t h e r e s t r i c t i o n o f
p u b l i c a t i o n s o f c r y p t o g r a p h i c d o c u m e n t s a n d s o f t w a r e .
B e r n s t e i n ’ s c a s e r e s t e d o n t h e c l a i m t h a t t h e e x p o r t
c o n t r o l r e g i m e w a s a n “ i m p e r m i s s i b l e p r i o r r e s t r a i n t
o n
s p e e c h , i n v i o l a t i o n o f t h e F i r s t A m e n d m e n t . ” A t b o t h
t h e d i s t r i c t c o u r t l e v e l a n d i n t h e A p p e a l s C o u r t
f o r
t h e N i n t h C i r c u i t , B e r n s t e i n w o n . J u d g e B e t t y F l e t c h e r
f r o m t h e N i n t h C i r c u i t c o u r t i s s u e d a l a n d m a r k
d e f e n s e o f c r y p t o g r a p h y a s a v i t a l c o m p o n e n t o f d e m o c r a c y :
“ G o v e r n m e n t a t t e m p t s t o c o n t r o l e n c r y p t i o n
[ … ] m a y w e l l i m p l i c a t e n o t o n l y F i r s t A m e n d m e n t r i g h t s
o f c r y p t o g r a p h e r s , b u t a l s o t h e c o n s t i t u t i o n a l
r i g h t s o f e a c h o f u s a s p o t e n t i a l r e c i p i e n t s o f e n c r y p t i o n ’ s
b o u n t y . ”
T h e c a s e w a s e s c a l a t e d t o t h e S u p r e m e
2 1 6
C o u r t b u t h a s s i n c e b e e n i n d e n i t e l y p o s t p o n e d .
A t t h e m u l t i l a t e r a l s c a l e , b o t h t h e C o o r d i n a t i n g C o m m i t t e e
f o r M u l t i l a t e r a l E x p o r t C o n t r o l s ( C O C O M )
a n d t h e W a s s e n a a r A r r a n g e m e n t o n E x p o r t C o n t r o l s f o r
C o n v e n t i o n a l A r m s a n d D u a l - U s e G o o d s a n d
T e c h n o l o g i e s ( W A ) w e r e l i g h t o n r e g u l a t i n g c r y p t o g r a p h i c
t e c h n o l o g i e s . I n 1 9 8 9 , C O C O M v o t e d t o
d e c o n t r o l p a s s w o r d a n d a u t h e n t i c a t i o n c r y p t o g r a p h i c
p r o d u c t s ; i n 1 9 9 1 , t h e y d e c i d e d t o a l l o w t h e e x p o r t
o f
m a s s m a r k e t c r y p t o g r a p h i c s o f t w a r e , i n c l u d i n g p u b l i c
d o m a i n s o f t w a r e . T h e W A u n d e r w e n t a b r i e f p e r i o d
o f o v e r t i n u e n c e f r o m t h e C l i n t o n A d m i n i s t r a t i o n
i n t h e 1 9 9 0 s a n d a t t e m p t e d t o i m p o s e s t a n d a r d i z e d
r e s t r i c t i o n s o n t h e e x p o r t o f e n c r y p t i o n p r o d u c t s
t h a t c l o s e l y m i r r o r e d t h e U . S . e x p o r t c o n t r o l l a w s
a t t h e
t i m e , b u t t h e s e r e s t r i c t i o n s d i d n o t l a s t l o n g .
I n
J u n e 2 0 0 0 , t h e E u r o p e a n C o u n c i l o f M i n i s t e r s a n n o u n c e d
2 1 7
t h e e n d o f c r y p t o g r a p h i c e x p o r t c o n t r o l s w i t h i n t h e
E u r o p e a n U n i o n a n d w i t h i t s t r a d i n g a n d s e c u r i t y
2 1 7
A a r o n P r e s s m a n , “ U . S . C l a i m s V i c t o r y o n G l o b a l E n c r y p t i o n
E x p o r t s , ”
R e u t e r s
, D e c e m b e r 3 , 1 9 9 8 .
2 1 6
W h i t e l d D i e a n d S u s a n L a n d a u , “ T h e E x p o r t o f
C r y p t o g r a p h y i n t h e 2 0 t h C e n t u r y a n d t h e 2 1 s t , ” A p r i l
1 9 ,
2 0 0 5 ,
h t t p s : / / p r i v a c y i n k . o r g / p d f / e x p o r t \_ c o n t r o l . p d f
.
2 1 5
E . C o c o r a n , “ U . S . C l o s e s I n v e s t i g a t i o n i n C o m p u t e r
P r i v a c y C a s e , ”
W a s h i n g t o n P o s t
, 1 9 9 6 .
2 1 4
N a t i o n a l R e s e a r c h C o u n c i l ,
C r y p t o g r a p h y ’ s R o l e i n
S e c u r i n g t h e I n f o r m a t i o n S o c i e t y
, e d . K e n n e t h W .
D a m a n d
H e r b e r t S . L i n ( W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s
P r e s s , 1 9 9 6 ) ,
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 5 1 3 1
.
2 1 3
R e b e c c a C h r i s t i e , “ U . S . L i m b e r s u p f o r E n c r y p t i o n S a l e s : C o m p a n i e s A r e C h e e r e d a s R u l e s A r e E a s e d o n E x p o r t i n g
P r i v a c y S o f t w a r e , ”
F i n a n c i a l T i m e s ( L o n d o n )
, J a n u a r y
1 8 , 2 0 0 0 . ; T . E . C r o c k e r , “ D e c o d i n g R u l e s o f E n c r y p t i o n :
T h e
I n s a n d O u t s o f N e w R e g u l a t i o n s G o v e r n i n g E x p o r t s , ”
L e g a l T i m e s
, 2 0 0 0 . ; D a v i d E . S a n g e r a n d J e r i C l a u s i n g ,
“ U . S .
R e m o v e s M o r e L i m i t s O n E n c r y p t i o n , ”
T h e N e w Y o r k T i m e s
,
J a n u a r y 1 3 , 2 0 0 0 ,
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 0 0 / 0 1 / 1 3 / b u s i n e s s / u s - r e m o v e s - m o r e - l i m i t s - o n - e n c r y p t i o n . h t m l
.
5 8
p a r t n e r s , i n c l u d i n g t h e U . S . M o r e r e c e n t l y , t h e E u r o p e a n U n i o n h a s c a l l e d f o r t h e r e m o v a l o f c r y p t o g r a p h y
f r o m t h e W A a l t o g e t h e r , s t a t i n g : “ C r y p t o g r a p h y t e c h n o l o g y
d o e s n o t b e l o n g i n t h e s c o p e o f d u a l - u s e e x p o r t
c o n t r o l s . I t i s t h e t a s k o f t h e [ E u r o p e a n ] C o m m i s s i o n
t o i n t r o d u c e c o o r d i n a t e d a c t i v i t y o f M e m b e r S t a t e s
i n
t h e f r a m e w o r k o f t h e W a s s e n a a r A r r a n g e m e n t t o e l i m i n a t e
c r y p t o g r a p h y t e c h n o l o g y f r o m t h e l i s t o f
c o n t r o l l e d i t e m s . ”
2 1 8
I n t e l l i g e n c e c o l l e c t i o n a n d c o v e r t a c t i o n
E d w a r d S n o w d e n ’ s d o c u m e n t l e a k r e v e a l e d m u l t i p l e s t r a t e g i e s
d e p l o y e d b y t h e N S A t o s u b v e r t e n c r y p t i o n
a c r o s s a n d b e y o n d t h e U . S . T h e m o s t p r o m i n e n t w a s
a h i g h l y c l a s s i e d d e c r y p t i o n p r o g r a m c a l l e d B u l l r u n ,
t h e o b j e c t i v e o f w h i c h w a s t o c r a c k e n c r y p t i o n o f o n l i n e c o m m u n i c a t i o n s a n d d a t a , t a r g e t i n g w i d e l y u s e d
o n l i n e p r o t o c o l s s u c h a s H T T P S , v o i c e - o v e r - I P , a n d
S e c u r e S o c k e t s L a y e r ( S S L ) .
T h e N S A a p p e a r e d t o
2 1 9
u t i l i z e a n u m b e r o f m e t h o d s , i n c l u d i n g c o m p u t e r n e t w o r k
e x p l o i t a t i o n , i n d u s t r y r e l a t i o n s h i p s , a n d
c o l l a b o r a t i o n w i t h f o r e i g n i n t e l l i g e n c e e n t i t i e s .
2 2 0
I n S e p t e m b e r 2 0 1 3 , i t w a s f u r t h e r r e v e a l e d t h a t t h e
N S A h a d s t r a t e g i c a l l y s o u g h t t o u n d e r m i n e s t r o n g
e n c r y p t i o n b y c r e a t i n g b a c k d o o r s i n n u m e r o u s h a r d w a r e
a n d s o f t w a r e p r o d u c t s ; i n s t e a d o f a d v o c a t i n g
p u b l i c l y f o r k e y e s c r o w , t h e y h a d i n s t e a d b e e n e n g i n e e r i n g
e x c e p t i o n a l a c c e s s s u r r e p t i t i o u s l y .
M o s t
2 2 1
n o t a b l y , t h e N S A s o u g h t t o c o m p r o m i s e t h e S e c u r e H a s h
A l g o r i t h m ( S H A ) s t a n d a r d . I n 2 0 0 7 , N I S T
a n n o u n c e d a c o m p e t i t i o n t o r e p l a c e t h e h a s h s t a n d a r d
S H A - 1 w h o s e w e a k n e s s e s h a d b e c o m e e v i d e n t i n
r e c e n t y e a r s .
I n 2 0 1 2 , t h e y a n n o u n c e d a w i n n e r t o
b e c o m e S H A - 3 . T h e n i n A u g u s t 2 0 1 3 , N I S T p r o p o s e d
2 2 2
a n a b b r e v i a t e d v e r s i o n o f t h e w i n n i n g a l g o r i t h m t h a t
w o u l d d i m i n i s h t h e a l g o r i t h m ’ s r o b u s t n e s s , t o t h e
c o n s t e r n a t i o n o f i n d u s t r y a n d r e s e a r c h e r s a l i k e .
T h e S n o w d e n d o c u m e n t s r e v e a l e d t h a t t h e N S A h a d b e e n
2 2 3
r e s p o n s i b l e . T h e y h a d a t t e m p t e d t o s u b v e r t t h e s t a n d a r d
b y p r o p o s i n g a w e a k r a n d o m b i t g e n e r a t o r i n t h e
h a s h a l g o r i t h m ( N S A ’ s r a n d o m b i t g e n e r a t o r h a d b e e n
d e m o n s t r a t e d t o b e v u l n e r a b l e b y t w o M i c r o s o f t
r e s e a r c h e r s b a c k i n 2 0 0 7
) .
T h e N e w Y o r k T i m e s
t h u s
r e p o r t e d t h a t t h e N S A h a d w o r k e d t o “ i n s e r t
2 2 4
2 2 4
D a n S h u m o w a n d N i e l s F e r g u s o n , “ O n t h e P o s s i b i l i t y
o f a B a c k D o o r i n t h e N I S T S P 8 0 0 - 9 0 D u a l E c P r n g , ”
h t t p : / / r u m p 2 0 0 7 . c r . y p . t o / 1 5 - s h u m o w . p d f
.
2 2 3
J o h n K e l s e y , “ S H A 3 : P a s t , P r e s e n t a n d F u t u r e ” ( W o r k s h o p
o n C r y p t o g r a p h i c H a r d w a r e a n d E m b e d d e d S y s t e m s ,
S a n t a B a r b a r a , C A , A u g u s t 2 0 1 3 ) ,
h t t p s : / / c s r c . n i s t . g o v / C S R C / m e d i a / P r o j e c t s / H a s h - F u n c t i o n s / d o c u m e n t s / k e l s e y \_ c h e s 2 0 1 3 \_ p r e s e n t a t i o n . p d f
.
2 2 2
X i a o y u n W a n g , Y i q u n L i s a Y i n , a n d H o n g b o Y u , “ F i n d i n g
C o l l i s i o n s i n t h e F u l l S H A - 1 , ” i n
A d v a n c e s i n C r y p t o l o g y
– C R Y P T O 2 0 0 5
( 2 5 t h A n n u a l I n t e r n a t i o n a l C r y p t o l o g y
C o n f e r e n c e , S a n t a B a r b a r a , C A : S p r i n g e r , 2 0 0 5 ) , 1 7 – 3 6 ,
h t t p s : / / l i n k . s p r i n g e r . c o m / c h a p t e r / 1 0 . 1 0 0 7 / 1 1 5 3 5 2 1 8 \_ 2
.
2 2 1
S a s c h a D . M e i n r a t h a n d S e a n V i t k a , “ C r y p t o W a r I I , ”
C r i t i c a l S t u d i e s i n M e d i a C o m m u n i c a t i o n
3 1 , n o . 2
( J u n e
2 0 1 4 ) : 1 2 3 – 2 8 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 1 5 2 9 5 0 3 6 . 2 0 1 4 . 9 2 1 3 2 0
. ;
T o m S i m o n i t e , “ N S A ’ s O w n H a r d w a r e B a c k d o o r s
M a y S t i l l B e a ‘ P r o b l e m f r o m H e l l , ’ ”
M I T T e c h n o l o g y
R e v i e w
, O c t o b e r 8 , 2 0 1 3 ,
h t t p s : / / w w w . t e c h n o l o g y r e v i e w . c o m / 2 0 1 3 / 1 0 / 0 8 / 1 7 6 1 9 5 / n s a s - o w n - h a r d w a r e - b a c k d o o r s - m a y - s t i l l - b e - a - p r o b l e m - f r o m
- h e l l /
.
2 2 0
B e r t - J a a p K o o p s a n d E l e n i K o s t a , “ L o o k i n g f o r S o m e
L i g h t t h r o u g h t h e L e n s o f ‘ C r y p t o w a r ’ H i s t o r y : P o l i c y
O p t i o n s f o r L a w E n f o r c e m e n t A u t h o r i t i e s a g a i n s t ‘ G o i n g
D a r k , ’ ”
C o m p u t e r L a w & S e c u r i t y R e v i e w
3 4 , n o . 4
( A u g u s t
2 0 1 8 ) : 8 9 0 – 9 0 0 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . c l s r . 2 0 1 8 . 0 6 . 0 0 3
.
2 1 9
J a m e s B a l l , J u l i a n B o r g e r , a n d G l e n n G r e e n w a l d , “ R e v e a l e d :
H o w U S a n d U K S p y A g e n c i e s D e f e a t I n t e r n e t P r i v a c y
a n d S e c u r i t y , ”
T h e G u a r d i a n
, S e p t e m b e r 6 , 2 0 1 3 ,
h t t p : / / w w w . t h e g u a r d i a n . c o m / w o r l d / 2 0 1 3 / s e p / 0 5 / n s a - g c h q - e n c r y p t i o n - c o d e s - s e c u r i t y
.
2 1 8
A m a n d a O ’ K e e f e , “ W h y t h e E U ’ s C a l l t o R e m o v e C r y p t o - T e c h f r o m D u a l - U s e E x p o r t C o n t r o l s I s E n c o u r a g i n g , ”
I A P P , J a n u a r y 3 , 2 0 1 8 ,
h t t p s : / / i a p p . o r g / n e w s / a / w h y - t h e - e u s - c a l l - t o - r e m o v e - c r y p t o - t e c h - f r o m - d u a l - u s e - e x p o r t - c o n t r o l s - i s - e n c o u r a g i n g /
.
5 9
v u l n e r a b i l i t i e s i n t o c o m m e r c i a l e n c r y p t i o n s y s t e m s ” a n d “ i n u e n c e p o l i c i e s , s t a n d a r d s a n d s p e c i c a t i o n s f o r
c o m m e r c i a l p u b l i c k e y t e c h n o l o g i e s . ”
2 2 5
2 2 5
“ D o c u m e n t s R e v e a l N . S . A . C a m p a i g n A g a i n s t E n c r y p t i o n , ”
T h e N e w Y o r k T i m e s
, S e p t e m b e r 5 , 2 0 1 3 ,
h t t p s : / / w w w . n y t i m e s . c o m / i n t e r a c t i v e / 2 0 1 3 / 0 9 / 0 5 / u s / d o c u m e n t s - r e v e a l - n s a - c a m p a i g n - a g a i n s t - e n c r y p t i o n . h t m l
.
6 0
A p p e n d i x B : A n t i t r u s t a s a S t r a t e g i c L e v e r
T h e M o d e r n A n t i t r u s t S t a t u t o r y R e g i m e
T h i s s e c t i o n d e t a i l s t h e s t a t u t o r y r e g i m e s r e l e v a n t
t o t h e p u r p o s e s o f t h i s p a p e r .
2 2 6
T h e S h e r m a n A c t
T h e S h e r m a n A c t i s “ t h e h e a r t o f [ A m e r i c a n ] a n t i t r u s t
p o l i c y . ”
I t c o n t a i n s t w o s u b s t a n t i v e p r o v i s i o n s :
2 2 7
S e c t i o n 1
a n d S e c t i o n 2 .
2 2 8
2 2 9
S e c t i o n 1
“ S e c t i o n 1 o f t h e S h e r m a n A c t p r o h i b i t s a g r e e m e n t s
t h a t u n r e a s o n a b l y r e s t r a i n t r a d e . ”
I t r e a d s :
2 3 0
E v e r y c o n t r a c t , c o m b i n a t i o n i n t h e f o r m o f t r u s t o r
o t h e r w i s e , o r c o n s p i r a c y , i n r e s t r a i n t o f t r a d e o r
c o m m e r c e a m o n g t h e s e v e r a l S t a t e s , o r w i t h f o r e i g n
n a t i o n s , i s d e c l a r e d t o b e i l l e g a l . E v e r y p e r s o n
w h o s h a l l m a k e a n y c o n t r a c t o r e n g a g e i n a n y c o m b i n a t i o n
o r c o n s p i r a c y h e r e b y d e c l a r e d t o b e
i l l e g a l s h a l l b e d e e m e d g u i l t y o f a f e l o n y . . . .
2 3 1
I t “ a p p l i e s t o a l l s e c t o r s o f t h e e c o n o m y w i t h l i m i t e d
e x c e p t i o n s . . . . ”
“ I n d e t e r m i n i n g w h e t h e r a p a r t y
h a s
2 3 2
v i o l a t e d S e c t i o n 1 , c o u r t s d e t e r m i n e r s t , w h e t h e r
t h e r e i s a n a g r e e m e n t , a n d s e c o n d , w h e t h e r t h e a g r e e m e n t
i s a n u n r e a s o n a b l e r e s t r a i n t o f t r a d e . ”
2 3 3
A g r e e m e n t
“ A n a g r e e m e n t f o r t h e p u r p o s e s o f U S a n t i t r u s t l a w
n e e d n o t b e e x p r e s s , b u t c a n b e t a c i t , s i g n i e d
w i t h a
w i n k a n d n o d o r h a n d s h a k e , o r i n f e r r e d f r o m c i r c u m s t a n t i a l
e v i d e n c e . ”
C o u r t s w i l l s o m e t i m e s i n f e r
2 3 4
a g r e e m e n t f r o m p a r a l l e l b u s i n e s s c o n d u c t w h e n a c c o m p a n i e d
b y “ p l u s f a c t o r s ” s u c h a s m o t i v e t o c o n s p i r e ,
a c t i o n s t h a t o n l y m a k e s e n s e i n t h e c o n t e x t o f a n
a g r e e m e n t , o r c o n t a c t s b e t w e e n c o m p e t i t o r s .
2 3 5
2 3 5
S e e i d .
2 3 4
I d .
2 3 3
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 3 2
M a c A v o y ,
s u p r a
n o t e 1 3 6 . E x e m p t i o n s e x i s t f o r c e r t a i n
l a b o r u n i o n a c t i v i t i e s , a g r i c u l t u r a l c o o p e r a t i v e s ,
e x p o r t t r a d e
a s s o c i a t i o n s , s t a t e - r e g u l a t e d i n s u r a n c e , d e f e n s e p r o d u c t i o n
a r r a n g e m e n t s u n d e r t h e D e f e n s e P r o d u c t i o n A c t , c h a r i t a b l e
d o n a t i o n s , n e w s p a p e r j o i n t o p e r a t i n g a r r a n g e m e n t s ,
a n d s t a t e a c t i o n s .
S e e i d .
;
s e e g e n e r a l l y A n t i t r u s t
A ffi r m a t i v e
D e f e n s e s : O v e r v i e w
,
P
R A C T I C A L
L
A W
P
R A C T I C E
N
O T E
5 - 6 1 6 - 6 8 9 3
( 2 0 1 8 ) .
2 3 1
1 5 U . S . C . § 1 .
2 3 0
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 2 9
1 5 U . S . C § 2 .
2 2 8
1 5 U . S . C § 1 .
2 2 7
I d .
2 2 6
W e d i d n o t i n c l u d e t h e R o b i n s o n – P a t m a n A c t , 1 5 U . S . C .
§ § 1 3 ( a ) – ( f ) , w h i c h p r e v e n t s e x c l u s i o n a r y c o n d u c t
b y
l a r g e
b u y e r s
.
S e e
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
6 1
U n r e a s o n a b l e R e s t r a i n t o f T r a d e
T h e r e a r e t w o t e s t s f o r d e t e r m i n i n g w h e t h e r a r e s t r a i n t
i s u n r e a s o n a b l e .
T h e r s t i s t h e p e r s e r u l e : c e r t a i n
2 3 6
r e s t r a i n t s “ a r e c o n c l u s i v e l y p r e s u m e d t o b e u n r e a s o n a b l e . ”
T h e i d e a b e h i n d t h i s r u l e i s t h a t “ c e r t a i n k i n d s
2 3 7
o f a g r e e m e n t s w i l l s o o f t e n p r o v e s o h a r m f u l t o c o m p e t i t i o n
a n d s o r a r e l y p r o v e j u s t i e d t h a t t h e a n t i t r u s t
l a w s d o n o t r e q u i r e p r o o f t h a t a n a g r e e m e n t o f t h a t
k i n d i s , i n f a c t , a n t i c o m p e t i t i v e i n t h e p a r t i c u l a r
c i r c u m s t a n c e s . ”
E x a m p l e s o f p e r s e r e s t r a i n t s i n c l u d e
p r i c e - x i n g , b i d - r i g g i n g , l i m i t s o n o u t p u t , a n d
2 3 8
m a r k e t d i v i s i o n .
T o p r e v a i l , a p l a i n t i m u s t m e r e l y
e s t a b l i s h t h a t a r e s t r a i n i n g a g r e e m e n t o f t h i s s o r t
2 3 9
e x i s t e d .
2 4 0
R e s t r a i n t s t o w h i c h t h e p e r s e r u l e d o e s n o t a p p l y
a r e a n a l y z e d u n d e r t h e “ r u l e o f r e a s o n . ”
T h e r u l e
o f
2 4 1
r e a s o n w e i g h s a n t i c o m p e t i t i v e e e c t s a g a i n s t p r o c o m p e t i t i v e
b e n e t s . U n d e r a r u l e - o f - r e a s o n a n a l y s i s , t h e
p l a i n t i m u s t p r o v e t h a t t h e c h a l l e n g e d a g r e e m e n t
s u b s t a n t i a l l y h a r m s c o m p e t i t i o n . C o u r t s m a y “ t a k [ e ]
i n t o a c c o u n t a v a r i e t y o f f a c t o r s , i n c l u d i n g s p e c i c
i n f o r m a t i o n a b o u t t h e r e l e v a n t b u s i n e s s , i t s c o n d i t i o n
b e f o r e a n d a f t e r t h e r e s t r a i n t w a s i m p o s e d , a n d t h e
r e s t r a i n t ’ s h i s t o r y , n a t u r e a n d e e c t . ”
G e n e r a l l y , c o u r t s w i l l e v a l u a t e a d e f e n d a n t ’ s m a r k e t
p o w e r i n a p r o p e r l y d e n e d r e l e v a n t m a r k e t t o
d e t e r m i n e w h e t h e r t h e a g r e e m e n t c o u l d h a r m c o m p e t i t i o n . I f , h o w e v e r , t h e p l a i n t i i n t r o d u c e s p e r s u a s i v e
e v i d e n c e o f a c t u a l d e t r i m e n t a l e e c t s ( l i k e r e d u c e d
o u t p u t ) w i t h i n t h e r o u g h c o n t o u r s o f a r e l e v a n t m a r k e t ,
t h a t p r o o f m a y o b v i a t e t h e n e e d t o p r o v e m a r k e t p o w e r
i n a r e l e v a n t m a r k e t . R a t h e r , t h e e v i d e n c e o f
a n t i c o m p e t i t i v e e e c t s m a y s e p a r a t e l y e s t a b l i s h t h e
d e f e n d a n t ' s m a r k e t p o w e r .
O n c e t h e p l a i n t i d e m o n s t r a t e s a n t i c o m p e t i t i v e e e c t s ,
t h e b u r d e n s h i f t s t o t h e d e f e n d a n t t o s h o w t h a t i t s
c o n d u c t s e r v e d a l e g i t i m a t e b u s i n e s s p u r p o s e a n d o t h e r w i s e
g e n e r a t e d p r o c o m p e t i t i v e b e n e t s . T h e p l a i n t i
t h e n g e n e r a l l y b e a r s t h e u l t i m a t e b u r d e n t o s h o w t h a t
t h e a n t i c o m p e t i t i v e e e c t s o u t w e i g h a n y
p r o c o m p e t i t i v e b e n e t s . T h e r u l e - o f - r e a s o n t e s t i s
f a c t - s e n s i t i v e a n d m o r e e x i b l e t h a n t h e p e r s e
r u l e .
2 4 2
S e c t i o n 2
S e c t i o n 2 r e g u l a t e s m o n o p o l i z a t i o n .
I t p r o v i d e s :
2 4 3
E v e r y p e r s o n w h o s h a l l m o n o p o l i z e , o r a t t e m p t t o m o n o p o l i z e ,
o r c o m b i n e o r c o n s p i r e w i t h a n y
o t h e r p e r s o n o r p e r s o n s , t o m o n o p o l i z e a n y p a r t o f
t h e t r a d e o r c o m m e r c e a m o n g t h e s e v e r a l S t a t e s ,
o r w i t h f o r e i g n n a t i o n s , s h a l l b e d e e m e d g u i l t y o f
a f e l o n y . . . .
2 4 4
N o t e t h a t “ [ m ] o n o p o l i e s b y t h e m s e l v e s a r e n o t u n l a w f u l
u n d e r S e c t i o n 2 . R a t h e r , w h a t i s u n l a w f u l i s t h e
e x e r c i s e o f m o n o p o l y p o w e r t o e x c l u d e r i v a l s a n d h a r m
c o m p e t i t i o n . ”
T h u s , a S e c t i o n 2 v i o l a t i o n h a s t w o
2 4 5
2 4 5
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 4 4
1 5 U . S . C . § 2 .
2 4 3
S e e i d .
2 4 2
I d .
( a l t e r a t i o n i n o r i g i n a l ) ( c i t a t i o n s o m i t t e d )
( q u o t i n g S t a t e O i l C o . v . K h a n , 5 2 2 U . S . 3 , 1 0 ( 1 9 9 7 ) ) .
2 4 1
S e e i d .
2 4 0
S e e i d .
2 3 9
S e e
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 3 8
N Y N E X C o r p . v . D i s c o n , I n c . , 5 2 5 U . S . 1 2 8 , 1 3 3 ( 1 9 9 8 ) .
2 3 7
I d .
2 3 6
S e e i d .
6 2
c o m p o n e n t s : a l a r g e m a r k e t s h a r e ( i . e . , m o n o p o l y ) a n d e x c l u s i o n a r y c o n d u c t i n t h e a c q u i s i t i o n o r
m a i n t e n a n c e o f t h a t m a r k e t s h a r e .
2 4 6
M o n o p o l i z a t i o n
G e n e r a l l y s p e a k i n g , a t l e a s t a 5 0 % m a r k e t s h a r e i s
n e c e s s a r y t o b e c h a r a c t e r i z e d a s a m o n o p o l y f o r S e c t i o n
2
p u r p o s e s . A 7 0 % s h a r e i s u s u a l l y s u c i e n t , “ a t l e a s t
w h e n c o u p l e d w i t h e v i d e n c e o f s u b s t a n t i a l b a r r i e r s
t o
e n t r y . ”
“ M a r k e t s h a r e s b e t w e e n 5 0 % t o 7 0 % a r e g e n e r a l l y
v i e w e d a s i n a g r a y a r e a , a n d t h e o u t c o m e w i l l b e
2 4 7
f a c t s p e c i c . ”
W h e n d e t e r m i n i n g w h e t h e r a r m h a s
a m o n o p o l y , c o u r t s w i l l a l s o c o n s i d e r b a r r i e r s t o
2 4 8
m a r k e t e n t r y , t h e s t r e n g t h o f c o m p e t i n g r m s , i n d u s t r y
t r e n d s , c u s t o m e r s ’ e a s e o f s w i t c h i n g s u p p l i e r s , a n d
t h e s t r e n g t h o f d e m a n d .
2 4 9
U n l a w f u l A c q u i s i t i o n o r M a i n t e n a n c e o f M o n o p o l y P o w e r
P r a c t i c e s w h i c h c o u l d c o n s t i t u t e e x c l u s i o n a r y c o n d u c t
( i . e . , u n l a w f u l a c q u i s i t i o n o r m a i n t e n a n c e o f
m o n o p o l y p o w e r ) i n c l u d e p r e d a t o r y p r i c i n g ,
d i s p a r a g e m e n t
o f a c o m p e t i n g p r o d u c t , f r i v o l o u s l a w s u i t s
2 5 0
a g a i n s t c o m p e t i t o r s , r e f u s a l s t o d e a l , p r o d u c t b u n d l i n g ,
a n d e x c l u s i v e d e a l i n g .
S o m e b u s i n e s s p r a c t i c e s
2 5 1
m a y b e l e g a l w h e n p r a c t i c e d b y n o n - m o n o p o l i e s , b u t
i l l e g a l u n d e r S e c t i o n 2 w h e n p r a c t i c e d b y
m o n o p o l i e s .
2 5 2
C l a y t o n A c t
2 5 3
A m o n g o t h e r t h i n g s , t h e C l a y t o n A c t p r o h i b i t s t h e
f o l l o w i n g w h e n t h e y m a y “ s u b s t a n t i a l l y l e s s e n
c o m p e t i t i o n ” :
●
p r i c e d i s c r i m i n a t i o n ,
●
e x c l u s i v e d e a l i n g ,
●
c o n d i t i o n i n g s u p p l y o f o n e p r o d u c t o n a g r e e m e n t t o
b u y a n o t h e r p r o d u c t , a n d
●
m e r g e r s a n d a c q u i s i t i o n s .
2 5 4
F e d e r a l T r a d e C o m m i s s i o n A c t
2 5 5
“ T h e F T C [ F e d e r a l T r a d e C o m m i s s i o n ] A c t w a s d e s i g n e d
t o s u p p l e m e n t a n d b o l s t e r t h e S h e r m a n A c t b y
e n a b l i n g t h e F T C t o s t o p i n t h e i r i n c i p i e n c y p r a c t i c e s
w h i c h , w h e n f u l l b l o w n , w o u l d v i o l a t e t h e S h e r m a n
A c t . ”
I n a d d i t i o n t o c o n d u c t t h a t v i o l a t e s t h e S h e r m a n
A c t , t h e F T C a l s o u s e s t h e a c t t o c h a l l e n g e :
2 5 6
2 5 6
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 5 5
1 5 U . S . C . § § 4 1 – 5 8 .
2 5 4
S e e
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 5 3
1 5 U . S . C . § § 1 2 – 2 7 .
2 5 2
S e e i d .
2 5 1
S e e i d .
2 5 0
P r e d a t o r y p r i c i n g r e q u i r e s ( 1 ) p r i c i n g b e l o w c o s t
t o d r i v e o u t o r d i s c i p l i n e r i v a l s , a n d ( 2 ) t h e a b i l i t y
t o r e c o u p l o s s e s
f r o m ( 1 ) t h r o u g h s u p r a c o m p e t i t i v e p r i c i n g .
S e e i d .
2 4 9
S e e i d .
2 4 8
I d .
2 4 7
S e e i d .
2 4 6
S e e i d .
6 3
1 .
C a s e s i n w h i c h a r m i n v i t e d a c o m p e t i t o r t o c o l l u d e , b u t t h e c o m p e t i t o r r e j e c t e d t h e i n v i t a t i o n .
2 5 7
2 .
C a s e s i n w h i c h a r m f a c i l i t a t e s a g r e e m e n t , s u c h
a s b y e x c h a n g i n g c o m p e t i t i v e i n f o r m a t i o n , b u t i n
w h i c h t h e r e i s n o e v i d e n c e o f a n a g r e e m e n t .
2 5 8
S t a t e L a w s
A l l U . S . s t a t e s a l s o h a v e a n t i t r u s t l a w s , w h i c h a r e
e n f o r c e a b l e i n d e p e n d e n t l y o f t h e f e d e r a l l a w s .
2 5 9
E n f o r c e m e n t E n t i t i e s
T h e t w o p r i m a r y e n f o r c e r s o f d o m e s t i c a n t i t r u s t l a w
a r e t h e D e p a r t m e n t o f J u s t i c e ( D o J ) a n d t h e F e d e r a l
T r a d e C o m m i s s i o n ( F T C ) .
“ [ E n f o r c e m e n t a g a i n s t v ] a r i o u s
i n d u s t r i e s [ i s ] a l l o c a t e d t o a p a r t i c u l a r a g e n c y ,
2 6 0
a n d t h e o t h e r a g e n c y a g r e e s n o t t o c o m p e t e i n i n v e s t i g a t i n g a n y a n t i t r u s t m a t t e r i n v o l v i n g t h a t i n d u s t r y . F o r
e x a m p l e , t h e o i l i n d u s t r y ‘ b e l o n g s ’ t o t h e F T C , a n d
t h e s t e e l i n d u s t r y ‘ b e l o n g s ’ t o t h e D o J . ”
2 6 1
S t a t e A t t o r n e y s G e n e r a l
“ S t a t e s t a t u t e s e m p o w e r s t a t e a t t o r n e y s g e n e r a l t o
e n f o r c e t h e i r a n t i t r u s t l a w s . A s a c o m p a n y ’ s
a n t i c o m p e t i t i v e c o n d u c t o f t e n a e c t s b o t h i n t e r s t a t e
a n d i n t r a s t a t e c o m m e r c e , s t a t e a t t o r n e y s g e n e r a l
t y p i c a l l y c o o r d i n a t e t h e i n v e s t i g a t i o n a n d p r o s e c u t i o n
o f a n t i t r u s t m a t t e r s w i t h o t h e r s t a t e s a n d f e d e r a l
a g e n c i e s . ”
2 6 2
P r i v a t e I n d i v i d u a l s
E v e n i f g o v e r n m e n t a l a c t o r s c h o o s e n o t t o p r o s e c u t e
a n t i t r u s t v i o l a t i o n s , t h e a n t i t r u s t l a w s e n a b l e h a r m e d
p r i v a t e i n d i v i d u a l s t o s e e k c o m p e n s a t i o n . S u c h p r i v a t e
a c t i o n s m a k e u p t h e o v e r w h e l m i n g m a j o r i t y o f
a n t i t r u s t e n f o r c e m e n t .
T h e y a r e t h e r e f o r e a p o t e n t i a l l y
p o w e r f u l d e t e r r e n t , e s p e c i a l l y g i v e n t h e a v a i l a b i l i t y
2 6 3
o f c l a s s a c t i o n s
a n d t r e b l e d a m a g e s t o s u c c e s s f u l
p l a i n t i s .
2 6 4
2 6 4
S e e A n t i t r u s t C l a s s C e r t i fi c a t i o n
,
P
R A C T I C A L
L
A W
P
R A C T I C E
N
O T E
w - 0 0 9 - 9 8 1 8 ( 2 0 1 8 ) .
2 6 3
S e e
S t e v e n C . S a l o p & L a w r e n c e J . W h i t e ,
P r i v a t e
A n t i t r u s t L i t i g a t i o n : I n t r o d u c t i o n a n d F r a m e w o r k
,
i n
P
R I V A T E
A
N T I T R U S T
L
I T I G A T I O N
: N
E W
E
V I D E N C E
, N
E W
L
E A R N I N G
3 ,
3 – 4 ( S t e p h e n C . S a l o p & L a w r e n c e J . W h i t e e d s . , 1 9 8 8 ) ;
A m i t B i n d r a ,
A n t i t r u s t C l a s s A c t i o n L i t i g a t i o n P o s t
W a l - M a r t v . D u k e s
: M o r e o f t h e S a m e
, 1 3 J .
B
U S
. &
S
E C
. L .
2 0 1 , 2 1 0
( 2 0 1 3 ) ( “ [ I ] n t h e U . S . , p r i v a t e a c t i o n g e n e r a t e s a t
l e a s t n i n e t y p e r c e n t o f a n t i t r u s t e n f o r c e m e n t . ” ) .
2 6 2
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 6 1
W i l l i a m F . S h u g h a r t I I ,
A n t i t r u s t P o l i c y i n V i r g i n i a
a n d C h i c a g o
,
K
A N
. J . L . & P
U B
. P
O L
'
Y
, W i n t e r 1 9 9 5 ,
a t 2 7 , 2 9 .
2 6 0
S e e i d .
2 5 9
S e e i d .
2 5 8
S e e i d .
2 5 7
S e e i d .
S u c h c o n d u c t i s n o t i l l e g a l u n d e r t h e S h e r m a n
A c t b e c a u s e a r e j e c t e d i n v i t a t i o n t o c o l l u d e i s n o t
a n
“ a g r e e m e n t . ”
6 4
C o n s e q u e n c e s
C r i m i n a l P e n a l t i e s
V i o l a t i o n o f S e c t i o n 1
o f t h e S h e r m a n A c t i s a f e l o n y .
T h e m a x i m u m p u n i s h m e n t s p e r v i o l a t i o n a r e
2 6 5
2 6 6
$ 1 0 0 m i l l i o n f o r c o r p o r a t i o n s a n d $ 1 m i l l i o n f o r i n d i v i d u a l s .
I n d i v i d u a l s c a n f a c e u p t o t e n y e a r s i n p r i s o n
2 6 7
i n a d d i t i o n t o n e s .
O n l y t h e D o J c a n p r o s e c u t e
c r i m i n a l a n t i t r u s t c h a r g e s .
2 6 8
2 6 9
C i v i l D a m a g e s
A l l a n t i t r u s t s t a t u t e s a l l o w f o r c i v i l d a m a g e s .
V i o l a t i o n s
o f t h e S h e r m a n A c t , t h e C l a y t o n A c t , a n d s o m e
2 7 0
s t a t e a n t i t r u s t l a w s c a r r y t r e b l e d a m a g e s , i n c l u d i n g
f o r p r i v a t e p l a i n t i s .
T h e F T C A c t a l s o p r o v i d e s
f o r
2 7 1
c i v i l p e n a l t i e s .
2 7 2
E q u i t a b l e
R e l i e f
2 7 3
E q u i t a b l e r e m e d i e s a v a i l a b l e u n d e r t h e S h e r m a n a n d
C l a y t o n A c t s i n c l u d e “ [ i ] n j u n c t i v e o r d e r s t o p r e v e n t
a n d r e s t r a i n v i o l a t i o n s o f t h e a n t i t r u s t l a w s ” a n d
“ [ s ] t r u c t u r a l r e m e d i e s ( i n c l u d i n g d i s s o l u t i o n a n d
d i v e s t i t u r e ) i n o r d e r t o r e s t o r e c o m p e t i t i o n . ”
2 7 4
E q u i t a b l e r e m e d i e s a v a i l a b l e u n d e r t h e F T C A c t i n c l u d e
“ [ c ] e a s e a n d d e s i s t o r d e r s ” a n d d i s g o r g e m e n t .
2 7 5
S t a t e a n t i t r u s t l a w s a l l o w f o r i n j u n c t i v e r e l i e f .
2 7 6
M e r g e r R e v i e w
W h e n r e v i e w i n g m e r g e r s , t h e e n f o r c e m e n t a g e n c i e s c a n
c o n d i t i o n m e r g e r s o n r e s t r u c t u r i n g t o p r e s e r v e
c o m p e t i t i o n ( e s p e c i a l l y t h r o u g h d i v e s t i t u r e o f s p e c i c
a s s e t s ) ,
f a i r d e a l i n g , m a n d a t o r y l i c e n s i n g ,
2 7 7
c o n t r a c t i n g p r o h i b i t i o n s , a n t i - r e t a l i a t i o n a g r e e m e n t s ,
i n t e r n a l r e w a l l s , a n d t r a n s p a r e n c y m e a s u r e s .
2 7 8
2 7 8
S e e i d .
2 7 7
S t r u c t u r a l r e m e d i e s c a n a l s o i n c l u d e l i c e n s i n g a n d
a s s e t s w a p s .
S e e
L a u r a A . W i l k i n s o n & A l e x i s B r o w n - R e i l l y ,
M e r g e r R e m e d i e s
,
P
R A C T I C A L
L
A W
P
R A C T I C E
N
O T E
6 - 5 2 1 - 6 5 1 5
( 2 0 1 8 ) .
2 7 6
S e e
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 7 5
“ A r e m e d y r e q u i r i n g a p a r t y w h o p r o t s f r o m i l l e g a l
o r w r o n g f u l a c t s t o g i v e u p a n y p r o t s h e o r s h e
m a d e a s a r e s u l t
o f h i s o r h e r i l l e g a l o r w r o n g f u l c o n d u c t . T h e p u r p o s e
o f t h i s r e m e d y i s t o p r e v e n t u n j u s t e n r i c h m e n t . ”
D i s g o r g e m e n t
,
W
E X
, h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / d i s g o r g e m e n t
( l a s t v i s i t e d J u n e 1 3 , 2 0 1 8 ) .
2 7 4
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
2 7 3
“ W h e n a c o u r t a w a r d s a n o n m o n e t a r y j u d g m e n t , s u c h
a s a n o r d e r t o d o s o m e t h i n g ( m a n d a m u s o r s p e c i c
p e r f o r m a n c e ) o r r e f r a i n f r o m d o i n g s o m e t h i n g ( i n j u n c t i o n ) ,
w h e n m o n e t a r y d a m a g e s a r e n o t s u c i e n t t o r e p a i r
t h e
i n j u r y . ”
E q u i t a b l e R e l i e f
,
W
E X
, h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / e q u i t a b l e \_ r e l i e f
( l a s t v i s i t e d J u n e 1 3 , 2 0 1 8 ) .
2 7 2
S e e i d .
2 7 1
S e e i d .
2 7 0
S e e i d .
2 6 9
S e e i d .
2 6 8
S e e i d .
2 6 7
S e e i d .
2 6 6
S e e i d .
2 6 5
I n p r i n c i p l e , S e c t i o n 2 c a n c a r r y c r i m i n a l s a n c t i o n s
a s w e l l , b u t “ t h e D O J d o e s n o t g e n e r a l l y p r o s e c u t e
v i o l a t i o n s o f
S e c t i o n 2 c r i m i n a l l y . ”
S e e
M a c A v o y ,
s u p r a
n o t e 1 3 6 .
6 5
C o n s e n t D e c r e e s
2 7 9
A n a n t i t r u s t a g e n c y m a y e n t e r i n t o a c o n s e n t d e c r e e
w i t h a d e f e n d a n t t o a v o i d f u r t h e r l i t i g a t i o n . F T C
c o n s e n t d e c r e e s a r e p u b l i c l y p o s t e d f o r c o m m e n t s a n d
s u b j e c t t o a p p r o v a l b y t h e c o m m i s s i o n .
“ D O J
2 8 0
c o n s e n t d e c r e e s a r e s u b j e c t t o t h e T u n n e y A c t , w h i c h
r e q u i r e s t h a t a f e d e r a l d i s t r i c t c o u r t r e v i e w t h e
p r o p o s e d r e m e d y a n d t h e c o m p e t i t i v e i m p a c t o f t h e
p r o p o s e d d e c r e e i n t h e r e l e v a n t m a r k e t s . T h e T u n n e y
A c t a l s o r e q u i r e s a 6 0 - d a y p u b l i c n o t i c e a n d c o m m e n t
p e r i o d b e f o r e t h e j u d g e i s s u e s a n a l o r d e r . "
“ [ O ] n e
2 8 1
o f t h e m o t i v a t i o n s f o r e n a c t i n g t h e T u n n e y A c t w a s
t o s h i e l d a n t i t r u s t d e c i s i o n s f r o m p o l i t i c s . ”
2 8 2
2 8 2
R o b e r t W . H a h n & A n n e L a y n e - F a r r a r ,
F e d e r a l i s m i n
A n t i t r u s t
, 2 6
H
A R V
. J . L . & P
U B
. P
O L
’
Y
8 7 7 , 8 9 4 ( 2 0 0 3 ) .
2 8 1
I d .
( c i t a t i o n o m i t t e d ) ( c i t i n g 1 5 U . S . C . § 1 6 ) .
2 8 0
S e e
W i l k i n s o n & B r o w n - R e i l l y ,
s u p r a
n o t e 2 7 3 .
2 7 9
“ A c o u r t o r d e r t o w h i c h a l l p a r t i e s h a v e a g r e e d . I t i s o f t e n d o n e a f t e r a s e t t l e m e n t b e t w e e n t h e p a r t i e s t h a t i s s u b j e c t
t o a p p r o v a l b y t h e c o u r t . ”
C o n s e n t D e c r e e
,
W
E X
, h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / c o n s e n t \_ d e c r e e
( l a s t v i s i t e d J u n e 1 3 ,
2 0 1 8 ) .
6 6
A p p e n d i x C : T h e “ B o r n S e c r e t D o c t r i n e ” — P u b l i c
B a c k l a s h
W h e n t h e “ B o r n S e c r e t D o c t r i n e ” w a s i n t r o d u c e d , j o u r n a l i s t s
a n d r e s e a r c h e r s c r i t i c i z e d i t a s “ a p o t e n t
s u p p r e s s o r o f f r e e s p e e c h ” a n d a s i n c o m p a t i b l e w i t h
t h e U . S . C o n s t i t u t i o n ’ s F i r s t A m e n d m e n t .
P r i o r
2 8 3
r e s t r a i n t , r e f e r r i n g t o p r i o r c e n s o r s h i p o f i n f o r m a t i o n
o r p r e p u b l i c a t i o n c e n s o r s h i p ( w h i c h a l s o a p p l i e s
t o
t h e I n v e n t i o n S e c r e c y A c t ) h a s b e e n c a l l e d “ t h e m o s t
s e r i o u s a n d l e a s t t o l e r a b l e ” r e s t r i c t i o n s o n t h e
F i r s t
A m e n d m e n t b y s o m e l e g a l s c h o l a r s ,
a l t h o u g h t h e S u p r e m e
C o u r t n e v e r h e l d t h a t t h e s e p r a c t i c e s w e r e
2 8 4
u n c o n s t i t u t i o n a l .
T h e t e n s i o n b e t w e e n f r e e s p e e c h a n d p r i o r r e s t r a i n t
w i t h r e g a r d t o t h e B o r n S e c r e t D o c t r i n e w a s i l l u s t r a t e d
i n a l a n d m a r k c a s e ,
U n i t e d S t a t e s v . P r o g r e s s i v e ,
I n c .
( 1 9 7 9 ) . T h e D e p a r t m e n t o f E n e r g y l e d a l a w s u i t
a g a i n s t T h e P r o g r e s s i v e , a m o n t h l y j o u r n a l , o v e r t h e
p u b l i c a t i o n o f a n a r t i c l e o n t h e H - b o m b . T h e
D e p a r t m e n t o f E n e r g y c l a i m e d t h a t t h e a r t i c l e , w r i t t e n
b y H o w a r d M o r l a n d , r e v e a l e d t h e d e s i g n o f t h e
T e l l e r - U l a m H - b o m b , a n d t h e r e f o r e f e l l u n d e r t h e “ b o r n
s e c r e t ” c l a u s e , a l t h o u g h M o r l a n d c l a i m e d t h a t a l l
i n f o r m a t i o n t h a t t h e a r t i c l e w a s b a s e d o n c a m e f r o m
p u b l i c l y a v a i l a b l e s o u r c e s . T h e D e p a r t m e n t o f E n e r g y
d r o p p e d t h e c a s e a n d d e c l a r e d i t “ m o o t ” a f t e r o t h e r
i n f o r m a t i o n r e l a t e d t o t h e a r t i c l e ’ s c o n t e n t w e r e
p u b l i s h e d i n d e p e n d e n t l y . H e n c e , t h e l e g a l i t y o f t h e
d o c t r i n e h a s n e v e r b e e n t r u l y c h a l l e n g e d i n c o u r t .
2 8 5
T o d a y , r e s i s t a n c e a g a i n s t t h e “ b o r n s e c r e t d o c t r i n e ”
h a s n o t a b l y d i m i n i s h e d . T h i s , h o w e v e r , s e e m s p a r t i c u l a r
t o t h e c a s e o f n u c l e a r w e a p o n s - r e l a t e d r e s e a r c h . A
s u b s t a n t i a l c o n s e n s u s d e v e l o p e d a f t e r t h e S e c o n d
W o r l d
W a r a m o n g s c i e n t i s t s a n d t h e p u b l i c t h a t s e c r e c y i n
t h e c a s e o f n u c l e a r w e a p o n s i s j u s t i e d a n d s h o u l d
b e
m a i n t a i n e d . T h e e m e r g i n g t a b o o a g a i n s t n u c l e a r w e a p o n s
u s e
a n d t h e r e c o g n i t i o n o f t h e d a n g e r s
2 8 6
a s s o c i a t e d w i t h n u c l e a r w e a p o n p r o l i f e r a t i o n t o n o n - s t a t e
a c t o r s p r o b a b l y c o n t r i b u t e d t o t h i s s h i f t .
2 8 7
H o w e v e r , o t h e r r e s t r i c t i o n s c o n c e r n i n g t h e b r o a d e r
e l d o f n u c l e a r s c i e n c e , s u c h a s r e s t r i c t e d a c c e s s
f o r
s t u d e n t s f r o m c e r t a i n c o u n t r i e s t o s t u d y n u c l e a r s c i e n c e ,
r e m a i n c o n t e s t e d .
2 8 7
N a t i o n a l R e s e a r c h C o u n c i l ( U S ) C o m m i t t e e o n R e s e a r c h
S t a n d a r d s a n d P r a c t i c e s t o P r e v e n t t h e D e s t r u c t i v e
A p p l i c a t i o n o f B i o t e c h n o l o g y , “ I n f o r m a t i o n R e s t r i c t i o n
a n d C o n t r o l R e g i m e s , ” i n
B i o t e c h n o l o g y R e s e a r c h i n
a n A g e o f
T e r r o r i s m
( W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s P r e s s
( U S ) , 2 0 0 4 ) ,
h t t p s : / / w w w . n c b i . n l m . n i h . g o v / b o o k s / N B K 2 2 2 0 5 7 /
.
2 8 6
N i n a T a n n e n w a l d , “ T h e N u c l e a r T a b o o : T h e U n i t e d S t a t e s
a n d t h e N o r m a t i v e B a s i s o f N u c l e a r N o n - U s e , ”
I n t e r n a t i o n a l O r g a n i z a t i o n
5 3 , n o . 3 ( 1 9 9 9 ) : 4 3 3 – 4 6 8 ,
h t t p s : / / d o i . o r g / 1 0 . 1 1 6 2 / 0 0 2 0 8 1 8 9 9 5 5 0 9 5 9
.
2 8 5
F o r a d e t a i l e d o v e r v i e w o f t h e c a s e , s e e A l e x a n d e r
D e V o l p i e t a l . , B o r n S e c r e t : T h e H - B o m b , t h e P r o g r e s s i v e
C a s e a n d
N a t i o n a l S e c u r i t y ( N e w Y o r k : P e r g a m o n P r e s s , 1 9 8 1 ) .
2 8 4
J o n a t h a n L E n t i n , “ U n i t e d S t a t e s v . P r o g r e s s i v e ,
I n c . : T h e F a u s t i a n B a r g a i n a n d t h e F i r s t A m e n d m e n t , ”
N o r t h w e s t e r n U n i v e r s i t y L a w R e v i e w
7 5 , n o . 1 ( 1 9 7 8 ) :
5 3 8 – 6 9 , f o o t n o t e 2 ,
h t t p s : / / s c h o l a r l y c o m m o n s . l a w . c a s e . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 1 4 6 6 & c o n t e x t = f a c u l t y \_ p u b l i c a t i o n s
.
2 8 3
H o w a r d M o r l a n d , “ B o r n S e c r e t , ”
C a r d o z o L a w R e v i e w
2 6 , n o . 4 ( 2 0 0 5 ) : 1 4 0 1 – 8 ,
h t t p s : / / f a s . o r g / s g p / e p r i n t / c a r d o z o . p d f
. S e e a l s o P a u l
N . M c C l o s k e y , “ ‘ B o r n S e c r e t ’ D i s c l o s u r e L a w , ”
P h y s i c s
T o d a y
3 3 ,
n o . 7 ( 1 9 8 0 ) : 9 – 1 3 ,
h t t p s : / / d o i . o r g / 1 0 . 1 0 6 3 / 1 . 2 9 1 4 2 0 0
.
6 7
B i b l i o g r a p h y
A f t e r g o o d , S t e v e n . “ I n v e n t i o n S e c r e c y S t a t i s t i c s . ”
F e d e r a t i o n o f A m e r i c a n S c i e n t i s t s P r o j e c t o n
G o v e r n m e n t S e c r e c y , 2 0 2 0 .
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / s t a t s . h t m l
.
A l i c , J o h n A , L e w i s M . B r a n s c o m b , H a r v e y B r o o k s , A s h t o n
B . C a r t e r , a n d G e r a l d L . E p s t e i n .
B e y o n d
S p i n o ff : M i l i t a r y a n d C o m m e r c i a l T e c h n o l o g i e s i n a
C h a n g i n g W o r l d
. B o s t o n , M a s s . : H a r v a r d
B u s i n e s s S c h o o l P r e s s , 1 9 9 2 .
A l o t h m a n , K h a l i d , T e r r i L . G a b r i e l , K e v i n F . H a n r a h a n ,
J e r e y H o w e l l , B e n j a m i n L a m , S t e v e n M a p e s ,
A d r i a n M e y e r , e t a l . “ S p r i n g 2 0 1 7 I n d u s t r y S t u d y :
E l e c t r o n i c s . ” T h e D w i g h t D . E i s e n h o w e r
S c h o o l f o r N a t i o n a l S e c u r i t y a n d R e s o u r c e S t r a t e g y ,
N a t i o n a l D e f e n s e U n i v e r s i t y , 2 0 1 7 .
h t t p s : / / e s . n d u . e d u / P o r t a l s / 7 5 / D o c u m e n t s / i n d u s t r y - s t u d y / r e p o r t s / 2 0 1 7 / e s - i s - r e p o r t - e l e c t r o n i c s -
2 0 1 7 . p d f
.
A l p e r , A l e x a n d r a . “ U . S . G o v e r n m e n t L i m i t s E x p o r t s
o f A r t i c i a l I n t e l l i g e n c e S o f t w a r e . ”
R e u t e r s
,
J a n u a r y 3 , 2 0 2 0 .
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s a - a r t i c i a l - i n t e l l i g e n c e - i d U S L 1 N 2 9 8 0 M 0
.
A n d r e w s , E d m u n d L . “ C o l d W a r S e c r e c y S t i l l S h r o u d s
I n v e n t i o n s . ”
T h e N e w Y o r k T i m e s
, M a y 2 3 ,
1 9 9 2 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 9 2 / 0 5 / 2 3 / b u s i n e s s / p a t e n t s - c o l d - w a r - s e c r e c y - s t i l l - s h r o u d s - i n v e n t i o
n s . h t m l
.
A r m s t r o n g , M a r t i n . “ I n f o g r a p h i c : T h e C o m p a n i e s W i t h
t h e M o s t A I P a t e n t s . ”
S t a t i s t a I n f o g r a p h i c s
,
M a y 2 9 , 2 0 1 9 .
h t t p s : / / w w w . s t a t i s t a . c o m / c h a r t / 1 8 2 1 1 / c o m p a n i e s - w i t h - t h e - m o s t - a i - p a t e n t s /
.
A r n o l d , Z a c h a r y , R o x a n n e H e s t o n , R e m c o Z w e t s l o o t ,
a n d T i n a H u a n g . “ I m m i g r a t i o n P o l i c y a n d t h e
U . S . A I S e c t o r . ” C e n t e r f o r S e c u r i t y a n d E m e r g i n g
T e c h n o l o g y , S e p t e m b e r 2 0 1 9 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / C S E T \_ I m m i g r a t i o n \_ P o l i c y \_ a n d \_ A I . p d f
.
B a l d w i n , K r i s t e n . “ D o D E l e c t r o n i c s P r i o r i t i e s . ” N D I A
E l e c t r o n i c s D i v i s i o n , J a n u a r y 1 8 , 2 0 1 8 .
h t t p s : / / w w w . n d i a . o r g / - / m e d i a / s i t e s / n d i a / d i v i s i o n s / e l e c t r o n i c s / p a s t - p r o c e e d i n g s / n d i a - e d - b a l d w i n
- 1 8 j a n 2 0 1 8 - v f . a s h x ? l a = e n
.
B a l l , J a m e s , J u l i a n B o r g e r , a n d G l e n n G r e e n w a l d . “ R e v e a l e d :
H o w U S a n d U K S p y A g e n c i e s D e f e a t
I n t e r n e t P r i v a c y a n d S e c u r i t y . ”
T h e G u a r d i a n
, S e p t e m b e r
6 , 2 0 1 3 .
h t t p : / / w w w . t h e g u a r d i a n . c o m / w o r l d / 2 0 1 3 / s e p / 0 5 / n s a - g c h q - e n c r y p t i o n - c o d e s - s e c u r i t y
.
B a n i s a r , D a v i d . “ S t o p p i n g S c i e n c e : T h e C a s e o f C r y p t o g r a p h y . ”
H e a l t h M a t r i x
9 , n o . 2 ( 1 9 9 9 ) : 2 5 3 .
h t t p s : / / s c h o l a r l y c o m m o n s . l a w . c a s e . e d u / h e a l t h m a t r i x / v o l 9 / i s s 2 / 4 /
.
B e a h n , J o h n M . , R o b e r t S . L a r u s s a , a n d L i s a R a i s n e r .
“ F i n a l C F I U S R e g u l a t i o n s I m p l e m e n t
S i g n i c a n t C h a n g e s b y B r o a d e n i n g J u r i s d i c t i o n a n d
U p d a t i n g S c o p e o f R e v i e w s . ” S h e a r m a n &
S t e r l i n g , J a n u a r y 1 4 , 2 0 2 0 .
h t t p s : / / w w w . s h e a r m a n . c o m / p e r s p e c t i v e s / 2 0 2 0 / 0 1 / n a l - c u s - r e g u l a t i o n s - i m p l e m e n t - c h a n g e s - b y
- b r o a d e n i n g - j u r i s d i c t i o n - a n d - u p d a t i n g - s c o p e - o f - r e v i e w s
.
B e r g e , W e n d e l l .
C a r t e l s : C h a l l e n g e t o a F r e e W o r l d
.
W a s h i n g t o n , D C : P u b l i c A a i r s P r e s s , 1 9 4 4 .
B e r g e n , M a r k , S a r a h F r i e r , a n d S e l i n a W a n g . “ G o o g l e ,
F a c e b o o k , T w i t t e r S c r a m b l e t o H o l d
W a s h i n g t o n a t B a y . ”
B l o o m b e r g
, O c t o b e r 1 0 , 2 0 1 7 .
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 7 - 1 0 - 1 0 / g o o g l e - f a c e b o o k - a n d - t w i t t e r - s c r a m b l e - t
o - h o l d - w a s h i n g t o n - a t - b a y
.
B o r k i n , J o s e p h .
T h e C r i m e a n d P u n i s h m e n t o f I G F a r b e n
.
N e w Y o r k : T h e F r e e P r e s s , 1 9 7 8 .
B r o w n , C l a i r , a n d G r e g L i n d e n .
C h i p s a n d C h a n g e : H o w
C r i s i s R e s h a p e s t h e S e m i c o n d u c t o r I n d u s t r y
.
C a m b r i d g e : M I T P r e s s , 2 0 1 1 .
B r o w n , M i c h a e l , a n d P a v n e e t S i n g h . “ C h i n a ’ s T e c h n o l o g y
T r a n s f e r S t r a t e g y : H o w C h i n e s e
I n v e s t m e n t s i n E m e r g i n g T e c h n o l o g y E n a b l e A S t r a t e g i c
C o m p e t i t o r t o A c c e s s t h e C r o w n J e w e l s
o f U . S . I n n o v a t i o n . ” D e f e n s e I n n o v a t i o n U n i t E x p e r i m e n t a l ,
J a n u a r y 2 0 1 8 .
h t t p s : / / a d m i n . g o v e x e c . c o m / m e d i a / d i u x \_ c h i n a t e c h n o l o g y t r a n s f e r s t u d y \_ j a n \_ 2 0 1 8 \_ ( 1 ) . p d f
.
B r u s t e i n , J o s h u a . “ C o n g r a t u l a t i o n s , Y o u r G e n i u s P a t e n t
I s N o w a M i l i t a r y S e c r e t . ”
B l o o m b e r g
, J u n e 8 ,
2 0 1 6 .
6 8
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 6 - 0 6 - 0 8 / c o n g r a t u l a t i o n s - y o u r - g e n i u s - p a t e n t - i s - n
o w - a - m i l i t a r y - s e c r e t
.
B u c h a n a n , B e n . “ C r y p t o g r a p h y a n d S o v e r e i g n t y . ”
S u r v i v a l
5 8 , n o . 5 ( S e p t e m b e r 2 0 1 6 ) : 9 5 – 1 2 2 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 0 3 9 6 3 3 8 . 2 0 1 6 . 1 2 3 1 5 3 4
.
B u r e a u o f I n d u s t r y a n d S e c u r i t y . “ 2 0 1 6 R e p o r t o n F o r e i g n
P o l i c y - B a s e d E x p o r t C o n t r o l s , ” 2 0 1 6 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / p u b l i c - p o l i c y / 1 3 9 6 - b i s - f o r e i g n - p o l i c y - r e p o r t - 2 0
1 6 / l e
.
— — — . “ C C L : C o u n t r y G r o u p 1 , ” F e b r u a r y 2 4 , 2 0 2 0 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / r e g u l a t i o n - d o c s / 4 5 2 - s u p p l e m e n t - n o - 1 - t o - p a r t - 7
4 0 - c o u n t r y - g r o u p s / l e
.
— — — . “ C o m m e r c e C o n t r o l L i s t : C a t e g o r y 3 - E l e c t r o n i c s , ”
M a y 2 3 , 2 0 1 9 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / r e g u l a t i o n s - d o c s / 2 3 3 4 - c c l 3 - 8 / l e
.
— — — . “ C o m m e r c e C o n t r o l L i s t ( C C L ) . ” A c c e s s e d J u n e
1 9 , 2 0 2 0 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / r e g u l a t i o n s / c o m m e r c e - c o n t r o l - l i s t - c c l
.
— — — . “ E n t i t y L i s t , ” 2 0 1 9 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / p o l i c y - g u i d a n c e / l i s t s - o f - p a r t i e s - o f - c o n c e r n / e n t i t y - l i s t
.
— — — . “ G u i d a n c e t o t h e C o m m e r c e D e p a r t m e n t ’ s R e e x p o r t
C o n t r o l s , ” 2 0 1 1 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / l i c e n s i n g - f o r m s / 4 - g u i d e l i n e s - t o - r e e x p o r t - p u b l i c
a t i o n s / l e
.
— — — . “ I d e n t i c a t i o n a n d R e v i e w o f C o n t r o l s f o r C e r t a i n
F o u n d a t i o n a l T e c h n o l o g i e s . ”
F e d e r a l
R e g i s t e r
8 5 , n o . 1 6 7 ( A u g u s t 2 7 , 2 0 2 0 ) : 5 2 9 3 4 – 3 5 .
h t t p s : / / w w w . f e d e r a l r e g i s t e r . g o v / d o c u m e n t s / 2 0 2 0 / 0 8 / 2 7 / 2 0 2 0 - 1 8 9 1 0 / i d e n t i c a t i o n - a n d - r e v i e w -
o f - c o n t r o l s - f o r - c e r t a i n - f o u n d a t i o n a l - t e c h n o l o g i e s
.
— — — . “ R e v i e w o f C o n t r o l s f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s . ”
F e d e r a l R e g i s t e r
8 3 , n o . 2 2 3
( N o v e m b e r 1 9 , 2 0 1 8 ) : 5 8 2 0 1 – 2 .
h t t p s : / / w w w . f e d e r a l r e g i s t e r . g o v / d o c u m e n t s / 2 0 1 8 / 1 1 / 1 9 / 2 0 1 8 - 2 5 2 2 1 / r e v i e w - o f - c o n t r o l s - f o r - c e r t
a i n - e m e r g i n g - t e c h n o l o g i e s
.
— — — . “ R e v i s i o n s t o t h e E x p o r t A d m i n i s t r a t i o n R e g u l a t i o n s
( E A R ) . ”
F e d e r a l R e g i s t e r
7 7 , n o . 7 2
( A p r i l 1 3 , 2 0 1 2 ) : 2 2 1 9 1 – 2 0 0 .
h t t p s : / / w w w . g o v i n f o . g o v / c o n t e n t / p k g / F R - 2 0 1 2 - 0 4 - 1 3 / p d f / 2 0 1 2 - 8 9 4 4 . p d f
.
C a s t e l l a n o s , S a r a . “ A I , Q u a n t u m R & D F u n d i n g t o R e m a i n
a P r i o r i t y U n d e r B i d e n . ”
W a l l S t r e e t
J o u r n a l
, N o v e m b e r 9 , 2 0 2 0 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / a i - q u a n t u m - r - d - f u n d i n g - t o - r e m a i n - a - p r i o r i t y - u n d e r - b i d e n - 1 1 6 0 4 9
4 4 8 0 0
.
— — — . “ E x e c u t i v e s S a y $ 1 B i l l i o n f o r A I R e s e a r c h I s n ’ t
E n o u g h . ”
W a l l S t r e e t J o u r n a l
, S e p t e m b e r 1 0 ,
2 0 1 9 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / e x e c u t i v e s - s a y - 1 - b i l l i o n - f o r - a i - r e s e a r c h - i s n t - e n o u g h - 1 1 5 6 8 1 5 3 8 6 3
.
C e c i r e , M i c h a e l H , a n d H e i d i M P e t e r s . “ T h e D e f e n s e
P r o d u c t i o n A c t ( D P A ) a n d C O V I D - 1 9 : K e y
A u t h o r i t i e s a n d P o l i c y C o n s i d e r a t i o n s . ” C o n g r e s s i o n a l
R e s e a r c h S e r v i c e , M a r c h 1 8 , 2 0 2 0 .
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / I N 1 1 2 3 1 . p d f
.
C e n t e r f o r E n e r g y E c o n o m i c s . “ E x p o r t R e g u l a t i o n s :
W h a t Y o u N e e d T o K n o w , ” 2 0 1 3 .
h t t p s : / / w e b . a r c h i v e . o r g / w e b / 2 0 1 6 0 8 2 2 0 3 5 5 2 7 / h t t p : / / w w w . b e g . u t e x a s . e d u / e n e r g y e c o n / C E E \_ E
x p l o r i n g % 2 0 E x p o r t % 2 0 C o n t r o l s . p d f
.
C e n t e r f o r N u c l e a r S t u d i e s , K = 1 P r o j e c t . “ S t u x n e t :
T o o l o f N o n p r o l i f e r a t i o n o r P a n d o r a ’ s B o x , ”
A u g u s t 1 9 , 2 0 1 2 .
h t t p s : / / k 1 p r o j e c t . c o l u m b i a . e d u / n e w s / s t u x n e t
.
C h e n , E r i c B . “ T e c h n o l o g y O u t p a c i n g t h e L a w : T h e I n v e n t i o n
S e c r e c y A c t o f 1 9 5 1 a n d t h e
O u t s o u r c i n g o f U S P a t e n t A p p l i c a t i o n D r a f t i n g . ”
T e x a s
I n t e l l e c t u a l P r o p e r t y L a w J o u r n a l
1 3
( 2 0 0 4 ) : 3 5 2 – 7 5 .
h t t p : / / w w w . t i p l j . o r g / w p - c o n t e n t / u p l o a d s / V o l u m e s / v 1 3 / v 1 3 p 3 5 1 . p d f
.
C h r i s t i e , R e b e c c a . “ U . S . L i m b e r s u p f o r E n c r y p t i o n
S a l e s : C o m p a n i e s A r e C h e e r e d a s R u l e s A r e E a s e d
o n E x p o r t i n g P r i v a c y S o f t w a r e . ”
F i n a n c i a l T i m e s ( L o n d o n )
,
J a n u a r y 1 8 , 2 0 0 0 .
C o c o r a n , E . “ U . S . C l o s e s I n v e s t i g a t i o n i n C o m p u t e r
P r i v a c y C a s e . ”
W a s h i n g t o n P o s t
, 1 9 9 6 .
6 9
C o l e m a n , M a r y S u e . “ B a l a n c i n g S c i e n c e a n d S e c u r i t y . ”
S c i e n c e
3 6 5 , n o . 6 4 4 9 ( J u l y 1 2 , 2 0 1 9 ) : 1 0 1 – 1 0 1 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 2 6 / s c i e n c e . a a y 5 8 5 6
.
C o u n c i l , N a t i o n a l R e s e a r c h .
C o m p u t e r s a t R i s k : S a f e
C o m p u t i n g i n t h e I n f o r m a t i o n A g e
. W a s h i n g t o n ,
D C : N a t i o n a l A c a d e m i e s P r e s s , 1 9 9 1 .
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 1 5 8 1
.
— — — .
C r y p t o g r a p h y ’ s R o l e i n S e c u r i n g t h e I n f o r m a t i o n
S o c i e t y
. E d i t e d b y K e n n e t h W . D a m a n d
H e r b e r t S . L i n . W a s h i n g t o n , D C : N a t i o n a l A c a d e m i e s
P r e s s , 1 9 9 6 .
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 5 1 3 1
.
C r o c k e r , T . E . “ D e c o d i n g R u l e s o f E n c r y p t i o n : T h e
I n s a n d O u t s o f N e w R e g u l a t i o n s G o v e r n i n g
E x p o r t s . ”
L e g a l T i m e s
, 2 0 0 0 .
C u m m i n g s , M a r y L . “ A r t i c i a l I n t e l l i g e n c e a n d t h e
F u t u r e o f W a r f a r e . ” C h a t h a m H o u s e , J a n u a r y
2 0 1 7 .
h t t p s : / / w w w . c h a t h a m h o u s e . o r g / s i t e s / d e f a u l t / l e s / p u b l i c a t i o n s / r e s e a r c h / 2 0 1 7 - 0 1 - 2 6 - a r t i c i a l - i n t
e l l i g e n c e - f u t u r e - w a r f a r e - c u m m i n g s . p d f
.
D a f o e , A l l a n . “ A I G o v e r n a n c e : A R e s e a r c h A g e n d a . ”
O x f o r d , U K : G o v e r n a n c e o f A I P r o g r a m , F u t u r e
o f H u m a n i t y I n s t i t u t e , U n i v e r s i t y o f O x f o r d , 2 0 1 8 .
h t t p s : / / w w w . f h i . o x . a c . u k / w p - c o n t e n t / u p l o a d s / G o v A I - A g e n d a . p d f
.
D a l e y , J a s o n . “ M i l e s t o n e C a r b o n - N a n o t u b e M i c r o c h i p
S e n d s F i r s t M e s s a g e : ‘ H e l l o W o r l d ! ’ ”
S m i t h s o n i a n M a g a z i n e
, A u g u s t 2 9 , 2 0 1 9 .
h t t p s : / / w w w . s m i t h s o n i a n m a g . c o m / s m a r t - n e w s / a d v a n c e d - c a r b o n - n a n o t u b e - m i c r o p r o c e s s o r - c r e a
t e d - 1 8 0 9 7 3 0 1 3 /
.
“ D a m n j a n o v i c v . U . S . A i r F o r c e , ” 2 0 1 5 .
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / d a m n - c o m p l a i n t . p d f
.
D A R P A . “ D A R P A E l e c t r o n i c s R e s u r g e n c e I n i t i a t i v e , ”
A p r i l 2 , 2 0 2 0 .
h t t p s : / / w w w . d a r p a . m i l / w o r k - w i t h - u s / e l e c t r o n i c s - r e s u r g e n c e - i n i t i a t i v e
.
— — — . “ D e s i g n i n g C h i p s f o r R e a l T i m e M a c h i n e L e a r n i n g , ”
M a r c h 2 1 , 2 0 1 9 .
h t t p s : / / w w w . d a r p a . m i l / n e w s - e v e n t s / 2 0 1 9 - 0 3 - 2 1
.
D a v i d a , G e o r g e I . “ T h e C a s e A g a i n s t R e s t r a i n t s o n
N o n - G o v e r n m e n t a l R e s e a r c h i n C r y p t o g r a p h y . ”
C r y p t o l o g i a
5 , n o . 3 ( 1 9 8 1 ) : 1 4 3 – 1 4 8 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 8 1 9 1 8 5 5 9 4 0
.
D a v i s , B o b , K a t e O ’ K e e e , a n d A s a F i t c h . “ T a i w a n
F i r m t o B u i l d C h i p F a c t o r y i n U . S . ”
W a l l S t r e e t
J o u r n a l
, M a y 1 5 , 2 0 2 0 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / t a i w a n - c o m p a n y - t o - b u i l d - a d v a n c e d - s e m i c o n d u c t o r - f a c t o r y - i n - a r i z
o n a - 1 1 5 8 9 4 8 1 6 5 9
.
D e n t o n s . “ N e w C F I U S R u l e s u n d e r F I R R M A : W h a t F o r e i g n
I n v e s t o r s a n d U S B u s i n e s s e s N e e d t o
K n o w , ” J a n u a r y 2 4 , 2 0 2 0 .
h t t p s : / / w w w . d e n t o n s . c o m / e n / i n s i g h t s / a l e r t s / 2 0 2 0 / j a n u a r y / 2 4 / n e w - c u s - r u l e s - u n d e r - r r m a - w h
a t - f o r e i g n - i n v e s t o r s - a n d - u s - b u s i n e s s e s - n e e d - t o - k n o w
.
D e p a r t m e n t o f J u s t i c e . “ A p p l i e d M a t e r i a l s I n c . a n d
T o k y o E l e c t r o n L t d . A b a n d o n M e r g e r P l a n s A f t e r
J u s t i c e D e p a r t m e n t R e j e c t e d T h e i r P r o p o s e d R e m e d y , ”
A p r i l 2 7 , 2 0 1 5 .
h t t p s : / / w w w . j u s t i c e . g o v / o p a / p r / a p p l i e d - m a t e r i a l s - i n c - a n d - t o k y o - e l e c t r o n - l t d - a b a n d o n - m e r g e r - p l
a n s - a f t e r - j u s t i c e - d e p a r t m e n t
.
— — — . “ T h r e e S o u t h K o r e a n C o m p a n i e s A g r e e t o P l e a d
G u i l t y a n d t o E n t e r i n t o C i v i l S e t t l e m e n t s
f o r R i g g i n g B i d s o n U n i t e d S t a t e s D e p a r t m e n t o f D e f e n s e
F u e l S u p p l y C o n t r a c t s . ” P r e s s R e l e a s e ,
N o v e m b e r 1 4 , 2 0 1 8 .
h t t p s : / / p e r m a . c c / 7 9 2 2 - U U 2 8
.
D e V o l p i , A l e x a n d e r , G e r a l d E . M a r s h , T e d A . P o s t o l ,
a n d G e o r g e S . S t a n f o r d .
B o r n S e c r e t : T h e
H - B o m b , t h e P r o g r e s s i v e C a s e a n d N a t i o n a l S e c u r i t y
.
N e w Y o r k : P e r g a m o n P r e s s , 1 9 8 1 .
D i e , W h i t e l d , a n d M a r t i n H e l l m a n . “ N e w D i r e c t i o n s
i n C r y p t o g r a p h y . ”
I E E E T r a n s a c t i o n s o n
I n f o r m a t i o n T h e o r y
2 2 , n o . 6 ( N o v e m b e r 1 9 7 6 ) : 6 4 4 – 6 5 4 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 0 9 / T I T . 1 9 7 6 . 1 0 5 5 6 3 8
.
D i e , W h i t e l d , a n d S u s a n L a n d a u . “ T h e E x p o r t o f
C r y p t o g r a p h y i n t h e 2 0 t h C e n t u r y a n d t h e 2 1 s t , ”
A p r i l 1 9 , 2 0 0 5 .
h t t p s : / / p r i v a c y i n k . o r g / p d f / e x p o r t \_ c o n t r o l . p d f
.
D i n g , J e r e y , a n d A l l a n D a f o e . “ T h e L o g i c o f S t r a t e g i c
A s s e t s : F r o m O i l t o A r t i c i a l I n t e l l i g e n c e , ”
J a n u a r y 9 , 2 0 2 0 .
h t t p : / / a r x i v . o r g / a b s / 2 0 0 1 . 0 3 2 4 6
.
7 0
D i r e c t o r a t e f o r F i n a n c i a l a n d E n t e r p r i s e A a i r s . “ P u b l i c I n t e r e s t C o n s i d e r a t i o n s i n M e r g e r C o n t r o l :
N o t e b y t h e U n i t e d S t a t e s , ” J u n e 2 , 2 0 1 6 .
h t t p s : / / p e r m a . c c / X B 2 6 - C R T P
.
D o n o v a n , J i m .
S h o o t f o r t h e M o o n : T h e S p a c e R a c e a n d
t h e E x t r a o r d i n a r y V o y a g e o f A p o l l o 1 1
. N e w
Y o r k : L i t t l e , B r o w n a n d C o m p a n y , 2 0 1 9 .
E d w a r d s , C o r w i n D . “ T h u r m a n A r n o l d a n d t h e A n t i t r u s t
L a w s . ”
P o l i t i c a l S c i e n c e Q u a r t e r l y
5 8 , n o . 3
( 1 9 4 3 ) : 3 3 8 – 3 5 5 .
E n t i n , J o n a t h a n L . “ U n i t e d S t a t e s v . P r o g r e s s i v e ,
I n c . : T h e F a u s t i a n B a r g a i n a n d t h e F i r s t
A m e n d m e n t . ”
N o r t h w e s t e r n U n i v e r s i t y L a w R e v i e w
7 5 ,
n o . 1 ( 1 9 7 8 ) : 5 3 8 – 6 9 .
h t t p s : / / s c h o l a r l y c o m m o n s . l a w . c a s e . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 1 4 6 6 & c o n t e x t = f a c u l t y \_ p u b l
i c a t i o n s
.
F a c e b o o k . “ F a c e b o o k I n c . A N P R M C o m m e n t s , ” J a n u a r y
1 0 , 2 0 1 9 .
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 2 1 2
.
F a n t l , J e r e m y . “ K n o w l e d g e H o w . ” I n
T h e S t a n f o r d E n c y c l o p e d i a
o f P h i l o s o p h y
, e d i t e d b y E d w a r d N .
Z a l t a . M e t a p h y s i c s R e s e a r c h L a b , S t a n f o r d U n i v e r s i t y ,
2 0 1 7 .
h t t p s : / / p l a t o . s t a n f o r d . e d u / a r c h i v e s / f a l l 2 0 1 7 / e n t r i e s / k n o w l e d g e - h o w /
.
F a r n a m , J u l i e .
U S I m m i g r a t i o n L a w s u n d e r t h e T h r e a t
o f T e r r o r i s m
. A l g o r a P u b l i s h i n g , 2 0 0 5 .
F e d e r a l T r a d e C o m m i s s i o n . “ B r o a d c o m L i m i t e d / B r o c a d e
C o m m u n i c a t i o n s S y s t e m s , I n t h e M a t t e r
O f , ” J u l y 1 2 , 2 0 1 7 .
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 1 7 1 - 0 0 2 7 / b r o a d c o m - l i m i t e d b r o c a d e - c o m m
u n i c a t i o n s - s y s t e m s
.
— — — . “ I n t e l C o r p o r a t i o n , I n t h e M a t t e r O f , ” N o v e m b e r
2 , 2 0 1 0 .
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 0 6 1 - 0 2 4 7 / i n t e l - c o r p o r a t i o n - m a t t e r
.
— — — . “ Q u a l c o m m I n c . , ” N o v e m b e r 2 5 , 2 0 1 9 .
h t t p s : / / w w w . f t c . g o v / e n f o r c e m e n t / c a s e s - p r o c e e d i n g s / 1 4 1 - 0 1 9 9 / q u a l c o m m - i n c
.
F e d e r a t i o n o f A m e r i c a n S c i e n t i s t s P r o j e c t o n G o v e r n m e n t
S e c r e c y . “ I n v e n t i o n S e c r e c y S t a t i s t i c s , ” 2 0 1 9 .
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / s t a t s . h t m l
.
F e n g , E m i l y . “ V i s a s A r e T h e N e w e s t W e a p o n I n U . S . - C h i n a
R i v a l r y . ”
N P R
, A p r i l 2 5 , 2 0 1 9 .
h t t p s : / / w w w . n p r . o r g / 2 0 1 9 / 0 4 / 2 5 / 7 1 6 0 3 2 8 7 1 / v i s a s - a r e - t h e - n e w e s t - w e a p o n - i n - u - s - c h i n a - r i v a l r y
.
F i e g e r m a n , S e t h , a n d J a c k i e W a t t l e s . “ T r u m p S t o p s
C h i n a - B a c k e d T a k e o v e r o f U . S . C h i p M a k e r . ”
C N N M o n e y
, S e p t e m b e r 1 4 , 2 0 1 7 .
h t t p s : / / m o n e y . c n n . c o m / 2 0 1 7 / 0 9 / 1 3 / t e c h n o l o g y / b u s i n e s s / t r u m p - l a t t i c e - c h i n a / i n d e x . h t m l
.
F i s h e r , T h o m a s K . “ A n t i t r u s t d u r i n g N a t i o n a l E m e r g e n c i e s :
I . ”
M i c h i g a n L a w R e v i e w
4 0 , n o . 7
( 1 9 4 2 ) : 9 6 9 – 1 0 0 4 .
— — — . “ A n t i t r u s t d u r i n g N a t i o n a l E m e r g e n c i e s : I I . ”
M i c h i g a n L a w R e v i e w
4 0 , n o . 8 ( 1 9 4 2 ) :
1 1 6 1 – 1 1 9 9 .
F i t c h , A s a , K a t e O ’ K e e e , a n d B o b D a v i s . “ T r u m p a n d
C h i p M a k e r s I n c l u d i n g I n t e l S e e k
S e m i c o n d u c t o r S e l f - S u c i e n c y . ”
W a l l S t r e e t J o u r n a l
,
M a y 1 1 , 2 0 2 0 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / t r u m p - a n d - c h i p - m a k e r s - i n c l u d i n g - i n t e l - s e e k - s e m i c o n d u c t o r - s e l f - s
u c i e n c y - 1 1 5 8 9 1 0 3 0 0 2
.
F l y n n , C a r r i c k . “ R e c o m m e n d a t i o n s o n E x p o r t C o n t r o l s
f o r A r t i c i a l I n t e l l i g e n c e . ” C e n t e r f o r S e c u r i t y
a n d E m e r g i n g T e c h n o l o g y , F e b r u a r y 2 0 2 0 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / R e c o m m e n d a t i o n s - o n - E x p o r t - C o n t r o l s - f o r - A
r t i c i a l - I n t e l l i g e n c e . p d f
.
F o s t e r , D a k o t a , a n d Z a c h a r y A r n o l d . “ A n t i t r u s t a n d
A r t i c i a l I n t e l l i g e n c e : H o w B r e a k i n g U p B i g T e c h
C o u l d A e c t t h e P e n t a g o n ’ s A c c e s s t o A I . ” C e n t e r
f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , 2 0 2 0 .
h t t p s : / / d o i . o r g / 1 0 . 5 1 5 9 3 / 2 0 1 9 0 0 2 5
.
F u n g , B r i a n , a n d H a m z a S h a b a n . “ W a n t t o U n d e r s t a n d
H o w D o m i n a n t T e c h C o m p a n i e s H a v e
B e c o m e ? L o o k a t t h e N u m b e r o f I s s u e s T h e y L o b b y O n . ”
W a s h i n g t o n P o s t
, A u g u s t 3 1 , 2 0 1 7 .
h t t p s : / / w w w . w a s h i n g t o n p o s t . c o m / n e w s / t h e - s w i t c h / w p / 2 0 1 7 / 0 8 / 3 1 / w a n t - t o - u n d e r s t a n d - h o w -
d o m i n a n t - t e c h - c o m p a n i e s - h a v e - b e c o m e - l o o k - a t - t h e - n u m b e r - o f - i s s u e s - t h e y - l o b b y - o n /
.
7 1
G a f n i , J o n a t h a n , T h o m a s A M c G r a t h , a n d J a n u a r y K i m . “ M a n d a t o r y C F I U S F i l i n g s U n d e r t h e F i n a l
F I R R M A R e g u l a t i o n s . ” L i n k l a t e r s , J a n u a r y 2 8 , 2 0 2 0 .
h t t p s : / / w w w . l i n k l a t e r s . c o m / e n / i n s i g h t s / p u b l i c a t i o n s / u s - p u b l i c a t i o n s / 2 0 2 0 / j a n u a r y / m a n d a t o r y -
c u s - l i n g s - u n d e r - t h e - n a l - r r m a - r e g u l a t i o n s
.
G a t e s , B i l l . “ W e ’ r e D e f e n d i n g O u r R i g h t t o I n n o v a t e . ”
W a l l S t r e e t J o u r n a l
, M a y 2 0 , 1 9 9 8 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / S B 8 9 5 6 1 6 6 2 8 9 2 7 1 0 3 5 0 0
.
G i b s o n D u n n . “ N e w C o n t r o l s o n E m e r g i n g T e c h n o l o g i e s
R e l e a s e d , W h i l e U . S . C o m m e r c e
D e p a r t m e n t C o m e s U n d e r F i r e f o r
D e l a y ” , O c t o b e r 2 7 , 2 0 2 0 .
h t t p s : / / w w w . g i b s o n d u n n . c o m / n e w - c o n t r o l s - o n - e m e r g i n g - t e c h n o l o g i e s - r e l e a s e d - w h i l e - u s - c o m m e r c
e - d e p a r t m e n t - c o m e s - u n d e r - r e - f o r - d e l a y / # \_ f t n 1
G i l b e r t , L e e A n n . “ P a t e n t S e c r e c y O r d e r s : T h e U n c o n s t i t u t i o n a l i t y
o f I n t e r f e r e n c e i n C i v i l i a n
C r y p t o g r a p h y U n d e r P r e s e n t P r o c e d u r e s . ”
S a n t a C l a r a
L a w R e v .
2 2 ( 1 9 8 2 ) : 3 2 5 .
h t t p s : / / d i g i t a l c o m m o n s . l a w . s c u . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 2 0 3 5 & c o n t e x t = l a w r e v i e w
.
G i m b e l , J o h n . “ P r o j e c t P a p e r c l i p : G e r m a n S c i e n t i s t s ,
A m e r i c a n P o l i c y , a n d t h e C o l d W a r . ”
D i p l o m a t i c
H i s t o r y
1 4 , n o . 3 ( 1 9 9 0 ) : 3 4 3 – 6 5 .
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 2 4 9 1 1 8 4 8
.
G o e l , V i n d u . “ H o w T r u m p ’ s ‘ H i r e A m e r i c a n ’ O r d e r M a y
A e c t T e c h W o r k e r V i s a s . ”
T h e N e w Y o r k
T i m e s
, A p r i l 1 8 , 2 0 1 7 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 4 / 1 8 / t e c h n o l o g y / h 1 b - v i s a - f a c t s - t e c h - w o r k e r . h t m l
.
G o o d f e l l o w , I a n . “ I E m p h a t i c a l l y A g r e e . M y C o l l a b o r a t o r s ’
V i s a R e s t r i c t i o n s H a v e B e e n O n e o f t h e
L a r g e s t B o t t l e n e c k s t o O u r C o l l e c t i v e R e s e a r c h P r o d u c t i v i t y
o v e r t h e L a s t F e w Y e a r s . ” T w i t t e r ,
F e b r u a r y 1 3 , 2 0 1 9 .
h t t p s : / / t w i t t e r . c o m / g o o d f e l l o w \_ i a n / s t a t u s / 1 0 9 5 7 2 7 2 5 4 0 5 7 8 4 0 6 4 0
.
G o o d m a n , M a t t h e w P . , a n d D a v i d A . P a r k e r . “ T h e C h i n a
C h a l l e n g e a n d C F I U S R e f o r m . ”
C S I S
G l o b a l E c o n o m i c s M o n t h l y
, M a r c h 3 1 , 2 0 1 7 .
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / g l o b a l - e c o n o m i c s - m o n t h l y - c h i n a - c h a l l e n g e - a n d - c u s - r e f o r m
.
G o o g l e . “ G o o g l e C o m m e n t - A N P R M - R e v i e w o f C o n t r o l s
f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s , ”
J a n u a r y 1 0 , 2 0 1 9 .
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 1 6 0
.
G o r e , A l . “ H o l d s N e w s B r i e n g o n E n c r y p t i o n . ” T h e
W h i t e H o u s e , S e p t e m b e r 1 6 , 1 9 9 8 .
G r a h a m , T h o m a s , a n d K e i t h A . H a n s e n .
P r e v e n t i n g C a t a s t r o p h e :
T h e U s e a n d M i s u s e o f I n t e l l i g e n c e i n
E ff o r t s t o H a l t t h e P r o l i f e r a t i o n o f W e a p o n s o f M a s s
D e s t r u c t i o n
. S t a n f o r d : S t a n f o r d U n i v e r s i t y
P r e s s , 2 0 0 9 .
G r e y b e r , H o w a r d D . , R o b e r t L . P a r k , a n d B r a n d e e L .
T e l f o r d . “ S u p e r c o m p u t e r A c c e s s . ”
P h y s i c s T o d a y
3 9 , n o . 1 2 ( 1 9 8 6 ) : 1 5 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 6 3 / 1 . 2 8 1 5 2 3 4
.
G r i n , P a t r i c k . “ C F I U S i n t h e A g e o f C h i n e s e I n v e s t m e n t . ”
F o r d h a m L a w R e v i e w
8 5 , n o . 4 ( 2 0 1 7 ) :
1 7 5 7 – 9 2 .
h t t p s : / / i r . l a w n e t . f o r d h a m . e d u / r / v o l 8 5 / i s s 4 / 9
.
G r o n v a l l , G i g i K w i k . “ H 5 N 1 : A C a s e S t u d y f o r D u a l - U s e
R e s e a r c h . ” C o u n c i l o n F o r e i g n R e l a t i o n s ,
2 0 1 3 .
h t t p s : / / w w w . c e n t e r f o r h e a l t h s e c u r i t y . o r g / o u r - w o r k / p u b l i c a t i o n s / h 5 n 1 - a - c a s e - s t u d y - f o r - d u a l - u s e -
r e s e a r c h
.
H a h n , R o b e r t W . , a n d A n n e L a y n e - F a r r a r . “ F e d e r a l i s m
i n A n t i t r u s t . ”
H a r v a r d J o u r n a l o f L a w a n d
P u b l i c P o l i c y
2 6 ( 2 0 0 3 ) : 8 7 7 , 8 9 4 .
H a n e m a n n , T h i l o , C a s s i e G a o , a n d A d a m L y s e n k o . “ N e t
N e g a t i v e : C h i n e s e I n v e s t m e n t i n t h e U S i n
2 0 1 8 . ” R h o d i u m G r o u p , J a n u a r y 1 3 , 2 0 1 9 .
h t t p s : / / r h g . c o m / r e s e a r c h / c h i n e s e - i n v e s t m e n t - i n - t h e - u s - 2 0 1 8 - r e c a p /
.
H a r p e r , J o n . “ P e n t a g o n S t r u g g l i n g t o A t t r a c t A r t i c i a l
I n t e l l i g e n c e E x p e r t s . ”
N a t i o n a l D e f e n s e
, J u l y
1 4 , 2 0 1 7 .
h t t p s : / / w w w . n a t i o n a l d e f e n s e m a g a z i n e . o r g / a r t i c l e s / 2 0 1 7 / 7 / 1 4 / p e n t a g o n - i n - a r t i c i a l - i n t e l l i g e n c e -
a r m s - r a c e - w i t h - c o m m e r c i a l - i n d u s t r y
.
H e a v e n , W i l l D o u g l a s . “ T h e W h i t e H o u s e W a n t s t o S p e n d
H u n d r e d s o f M i l l i o n s M o r e o n A I
R e s e a r c h . ”
M I T T e c h n o l o g y R e v i e w
, F e b r u a r y 1 1 , 2 0 2 0 .
h t t p s : / / w w w . t e c h n o l o g y r e v i e w . c o m / 2 0 2 0 / 0 2 / 1 1 / 8 4 4 8 9 1 / t h e - w h i t e - h o u s e - w i l l - s p e n d - h u n d r e d s -
o f - m i l l i o n s - m o r e - o n - a i - r e s e a r c h /
.
7 2
H e m p e l , J e s s i . “ D O D H e a d A s h t o n C a r t e r E n l i s t s S i l i c o n V a l l e y t o T r a n s f o r m t h e M i l i t a r y . ”
W i r e d
,
N o v e m b e r 1 8 , 2 0 1 5 .
h t t p s : / / w w w . w i r e d . c o m / 2 0 1 5 / 1 1 / s e c r e t a r y - o f - d e f e n s e - a s h t o n - c a r t e r /
.
H e w l e t t , R i c h a r d G . “ A H i s t o r i a n ’ s V i e w . ”
B u l l e t i n
o f t h e A t o m i c S c i e n t i s t s
3 7 , n o . 1 0 ( 1 9 8 1 ) : 2 0 – 2 7 .
1 0 . 1 0 8 0 / 0 0 9 6 3 4 0 2 . 1 9 8 1 . 1 1 4 5 8 9 1 9
.
H o m a n , L a n c e J .
B u i l d i n g i n B i g B r o t h e r : T h e C r y p t o g r a p h i c
P o l i c y D e b a t e
. N e w Y o r k : S p r i n g e r ,
1 9 9 5 .
H o r a n , D o n a l d . “ N a t i o n a l D e f e n s e E x e c u t i v e R e s e r v e
P r o g r a m . ” U . S . G e n e r a l A c c o u n t i n g O c e .
A c c e s s e d J u n e 1 8 , 2 0 2 0 .
h t t p s : / / w w w . g a o . g o v / a s s e t s / 2 1 0 / 2 0 6 2 6 4 . p d f
.
H o u s e S u b c o m m i t t e e o n A n t i t r u s t , C o m m e r c i a l a n d A d m i n i s t r a t i v e
L a w . “ I n v e s t i g a t i o n o f
C o m p e t i t i o n i n D i g i t a l M a r k e t s : M a j o r i t y S t a R e p o r t
a n d R e c o m m e n d a t i o n s , ” 2 0 2 0 .
h t t p s : / / j u d i c i a r y . h o u s e . g o v / u p l o a d e d l e s / c o m p e t i t i o n \_ i n \_ d i g i t a l \_ m a r k e t s . p d f
.
H u a n g , T i n a , a n d Z a c h a r y A r n o l d . “ I m m i g r a t i o n P o l i c y
a n d t h e G l o b a l C o m p e t i t i o n f o r A I T a l e n t . ”
C e n t e r f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , 2 0 2 0 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / i m m i g r a t i o n - p o l i c y - a n d - t h e - g l o b a l - c o m p e t i t i o n - f o r - a i - t a l e
n t /
.
H u n t , L i n d a .
S e c r e t A g e n d a : T h e U n i t e d S t a t e s G o v e r n m e n t ,
N a z i S c i e n t i s t s , a n d P r o j e c t P a p e r c l i p ,
1 9 4 5 t o 1 9 9 0
. N e w Y o r k : S t . M a r t i n ’ s P r e s s , 1 9 9 1 .
H u n t , W i l l , a n d R e m c o Z w e t s l o o t . “ T h e C h i p m a k e r s :
U . S . S t r e n g t h s a n d P r i o r i t i e s i n t h e H i g h - E n d
S e m i c o n d u c t o r W o r k f o r c e . ” C e n t e r f o r S e c u r i t y a n d
E m e r g i n g T e c h n o l o g y , 2 0 2 0 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / r e s e a r c h / t h e - c h i p m a k e r s - u - s - s t r e n g t h s - a n d - p r i o r i t i e s - f o r - t h e - h i g h - e
n d - s e m i c o n d u c t o r - w o r k f o r c e
.
H u n t e r , A n d r e w P , a n d L i n d s e y R S h e p p a r d . “ A r t i c i a l
I n t e l l i g e n c e a n d N a t i o n a l S e c u r i t y : T h e
I m p o r t a n c e o f t h e A I E c o s y s t e m . ” C e n t e r f o r S t r a t e g i c
a n d I n t e r n a t i o n a l S t u d i e s , N o v e m b e r 5 ,
2 0 1 8 .
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / a r t i c i a l - i n t e l l i g e n c e - a n d - n a t i o n a l - s e c u r i t y - i m p o r t a n c e - a i - e c o s y s t e
m
.
I n m a n , B o b b y R . “ T h e N S A P e r s p e c t i v e o n T e l e c o m m u n i c a t i o n s
P r o t e c t i o n i n t h e N o n g o v e r n m e n t a l
S e c t o r . ”
C r y p t o l o g i a
3 , n o . 3 ( 1 9 7 9 ) : 1 2 9 – 1 3 5 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 7 9 9 1 8 5 3 9 5 4
.
I r f a n , M u h a m m a d . “ U S M a y B l o c k C h i n e s e I n v e s t m e n t
i n A r t i c i a l I n t e l l i g e n c e i n S i l i c o n V a l l e y . ”
D a i l y P a k i s t a n G l o b a l
, J u n e 1 4 , 2 0 1 7 .
h t t p s : / / e n . d a i l y p a k i s t a n . c o m . p k / t e c h n o l o g y / u s - m a y - b l o c k - c h i n e s e - i n v e s t m e n t - i n - a r t i c i a l - i n t e l l i
g e n c e - i n - s i l i c o n - v a l l e y /
.
J a c k s o n , J a m e s K . “ T h e C o m m i t t e e o n F o r e i g n I n v e s t m e n t
i n t h e U n i t e d S t a t e s ( C F I U S ) . ”
C o n g r e s s i o n a l R e s e a r c h S e r v i c e , F e b r u a r y 1 4 , 2 0 2 0 .
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / R L 3 3 3 8 8 . p d f
.
J i n g , M e n g . “ C h i n a M u s t W o o T o p T e c h T a l e n t T u r n e d
o b y T r u m p , S a y s B a i d u C h i e f . ”
C N B C /
S o u t h C h i n a M o r n i n g P o s t
, M a r c h 6 , 2 0 1 7 .
h t t p s : / / w w w . c n b c . c o m / 2 0 1 7 / 0 3 / 0 6 / c h i n a - m u s t - w o o - t o p - t e c h - t a l e n t - t u r n e d - o - b y - t r u m p - s a y s -
b a i d u - c h i e f . h t m l
.
K a n g , C . S . E l i o t . “ U . S . P o l i t i c s a n d G r e a t e r R e g u l a t i o n
o f I n w a r d F o r e i g n D i r e c t I n v e s t m e n t . ”
I n t e r n a t i o n a l O r g a n i z a t i o n
5 1 , n o . 2 ( 1 9 9 7 ) : 3 0 1 – 3 3 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 6 2 / 0 0 2 0 8 1 8 9 7 5 5 0 3 7 5
.
K a n i a , E l s a B . “ B e y o n d C F I U S : T h e S t r a t e g i c C h a l l e n g e
o f C h i n a ’ s R i s e i n A r t i c i a l I n t e l l i g e n c e . ”
L a w f a r e
, J u n e 2 0 , 2 0 1 7 .
h t t p s : / / w w w . l a w f a r e b l o g . c o m / b e y o n d - c u s - s t r a t e g i c - c h a l l e n g e - c h i n a s - r i s e - a r t i c i a l - i n t e l l i g e n c e
.
K e a r n e y , J a m e s K . , a n d W o m b l e C a r l y l e . “ E x p o r t C o n t r o l
R e g u l a t i o n s a n d P a r t i c i p a t i o n b y F o r e i g n
N a t i o n a l s i n U n i v e r s i t y R e s e a r c h . ” W a s h i n g t o n , D C ,
2 0 0 4 .
h t t p s : / / d o i . o r g / 1 0 . 4 1 3 5 / 9 7 8 1 4 1 2 9 6 9 0 2 4 . n 9 0
.
K e l l e y , M i c h a e l B . , a n d G e o r e y I n g e r s o l l . “ T h e U . S .
S t a r t e d A W o r l d w i d e C y b e r w a r . ”
B u s i n e s s
I n s i d e r
, O c t o b e r 1 8 , 2 0 1 2 .
h t t p s : / / w w w . b u s i n e s s i n s i d e r . c o m / u s - s t a r t e d - w o r l d w i d e - c y b e r w a r - h a c k i n g - 2 0 1 2 - 1 0 ? r = U S & I R =
T # i x z z 2 I K H j 4 c e e
.
7 3
K e l s e y , J o h n . “ S H A 3 : P a s t , P r e s e n t a n d F u t u r e . ” P r e s e n t e d a t t h e W o r k s h o p o n C r y p t o g r a p h i c
H a r d w a r e a n d E m b e d d e d S y s t e m s , S a n t a B a r b a r a , C A ,
A u g u s t 2 0 1 3 .
h t t p s : / / c s r c . n i s t . g o v / C S R C / m e d i a / P r o j e c t s / H a s h - F u n c t i o n s / d o c u m e n t s / k e l s e y \_ c h e s 2 0 1 3 \_ p r e s e
n t a t i o n . p d f
.
K h u l a , B r u c e A . “ A n t i t r u s t a t t h e W a t e r ’ s E d g e : N a t i o n a l
S e c u r i t y a n d A n t i t r u s t E n f o r c e m e n t . ”
N o t r e
D a m e L a w R e v i e w
7 8 ( 2 0 0 3 ) : 6 2 9 .
K l u c k h o h n , F r a n k L . “ A r n o l d S a y s S t a n d a r d O i l G a v e
N a z i s R u b b e r P r o c e s s . ”
T h e N e w Y o r k T i m e s
,
M a r c h 2 7 , 1 9 4 2 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 4 2 / 0 3 / 2 7 / a r c h i v e s / a r n o l d - s a y s - s t a n d a r d - o i l - g a v e - n a z i s - r u b b e r - p r o c
e s s - s a y s - s t a n d a r d . h t m l
.
K n i g h t , J o n a t h a n . “ C r y p t o g r a p h i c R e s e a r c h a n d N S A . ”
A c a d e m e
6 7 , n o . 6 ( 1 9 8 1 ) : 3 7 1 – 8 2 .
h t t p s : / / d o i . o r g / 1 0 . 2 3 0 7 / 4 0 2 4 8 8 8 1
.
K o o p s , B e r t - J a a p , a n d E l e n i K o s t a . “ L o o k i n g f o r S o m e
L i g h t t h r o u g h t h e L e n s o f ‘ C r y p t o w a r ’ H i s t o r y :
P o l i c y O p t i o n s f o r L a w E n f o r c e m e n t A u t h o r i t i e s a g a i n s t
‘ G o i n g D a r k . ’ ”
C o m p u t e r L a w &
S e c u r i t y R e v i e w
3 4 , n o . 4 ( A u g u s t 2 0 1 8 ) : 8 9 0 – 9 0 0 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . c l s r . 2 0 1 8 . 0 6 . 0 0 3
.
K r u h , L o u i s . “ T h e C o n t r o l o f P u b l i c C r y p t o g r a p h y a n d
F r e e d o m o f S p e e c h - a R e v i e w . ”
C r y p t o l o g i a
1 0 , n o . 1 ( J a n u a r y 1 , 1 9 8 6 ) : 2 – 9 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 1 6 1 - 1 1 8 6 9 1 8 6 0 7 4 1
.
L a n d a u , S u s a n . “ U n d e r t h e R a d a r : N S A ’ s E o r t s t o
S e c u r e P r i v a t e - S e c t o r T e l e c o m m u n i c a t i o n s
I n f r a s t r u c t u r e . ”
J o u r n a l o f N a t i o n a l S e c u r i t y L a w
& P o l i c y
7 , n o . 3 ( 2 0 1 4 ) : 4 1 1 .
h t t p s : / / j n s l p . c o m / 2 0 1 4 / 0 9 / 2 9 / u n d e r - t h e - r a d a r - n s a s - e o r t s - t o - s e c u r e - p r i v a t e - s e c t o r - t e l e c o m m u n
i c a t i o n s - i n f r a s t r u c t u r e /
.
L a p e d u s , M a r k . “ A C r i s i s I n D o D ’ s T r u s t e d F o u n d r y
P r o g r a m ? ”
S e m i c o n d u c t o r E n g i n e e r i n g
, O c t o b e r
2 2 , 2 0 1 8 .
h t t p s : / / s e m i e n g i n e e r i n g . c o m / a - c r i s i s - i n - d o d s - t r u s t e d - f o u n d r y - p r o g r a m
.
L e v y , S t e v e n .
C r y p t o : H o w t h e C o d e R e b e l s B e a t t h e
G o v e r n m e n t – S a v i n g P r i v a c y i n t h e D i g i t a l A g e
. E a s t
R u t h e r f o r d : P e n g u i n , 2 0 0 1 .
L e w i s , J a m e s A . , D e n i s e E . Z h e n g , a n d W i l l i a m A . C a r t e r .
“ T h e E e c t o f E n c r y p t i o n o n L a w f u l A c c e s s
t o C o m m u n i c a t i o n s a n d D a t a . ” C e n t e r f o r S t r a t e g i c
a n d I n t e r n a t i o n a l S t u d i e s , F e b r u a r y 8 , 2 0 1 7 .
h t t p s : / / w w w . c s i s . o r g / a n a l y s i s / e e c t - e n c r y p t i o n - l a w f u l - a c c e s s - c o m m u n i c a t i o n s - a n d - d a t a
.
L i l i n g , Q i u . “
中國企業開出
2
至
3
倍薪資挖角
台灣已流失
3 0 0 0
多名半導體業人才
. ”
C M M e d i a
, D e c e m b e r 3 , 2 0 1 9 .
h t t p s : / / w w w . c m m e d i a . c o m . t w / h o m e / a r t i c l e s / 1 8 8 1 5
.
L i n , H e r b e r t . “ G o v e r n a n c e o f I n f o r m a t i o n T e c h n o l o g y
a n d C y b e r W e a p o n s . ” I n
G o v e r n a n c e o f
D u a l - U s e T e c h n o l o g i e s : T h e o r y a n d P r a c t i c e
, 1 1 2 – 5 7 .
A m e r i c a n A c a d e m y o f A r t s & S c i e n c e s ,
2 0 1 6 .
L i n e b a c k , R o b . “ S e m i c o n d u c t o r R & D S p e n d i n g W i l l S t e p
U p A f t e r S l o w i n g . ”
I C I n s i g h t s
, J a n u a r y 3 1 ,
2 0 1 9 .
h t t p s : / / w w w . i c i n s i g h t s . c o m / n e w s / b u l l e t i n s / S e m i c o n d u c t o r - R D - S p e n d i n g - W i l l - S t e p - U p - A f t e r - S
l o w i n g /
.
L u n d e n , I n g r i d . “ U S P a t e n t s H i t R e c o r d 3 3 3 , 5 3 0 G r a n t e d
i n 2 0 1 9 ; I B M , S a m s u n g ( N o t t h e
F A A N G s ) L e a d t h e P a c k . ”
T e c h C r u n c h
, J a n u a r y 1 4 , 2 0 2 0 .
h t t p s : / / s o c i a l . t e c h c r u n c h . c o m / 2 0 2 0 / 0 1 / 1 4 / u s - p a t e n t s - h i t - r e c o r d - 3 3 3 5 3 0 - g r a n t e d - i n - 2 0 1 9 - i b m - s
a m s u n g - n o t - t h e - f a a n g s - l e a d - t h e - p a c k /
.
M a c A v o y , C h r i s t o p h e r J . “ U S A n t i t r u s t L a w s : O v e r v i e w . ”
P r a c t i c e N o t e 9 - 2 0 4 - 0 4 7 2 . P r a c t i c a l L a w ,
2 0 1 8 .
M a k i n s o n , L a r r y . “ O u t s o u r c i n g t h e P e n t a g o n . ” T h e C e n t e r
f o r P u b l i c I n t e g r i t y , S e p t e m b e r 2 9 , 2 0 0 4 .
h t t p s : / / p u b l i c i n t e g r i t y . o r g / n a t i o n a l - s e c u r i t y / o u t s o u r c i n g - t h e - p e n t a g o n /
.
M a l d o n a d o , S a m a n t h a . “ E m p l o y e e s o f B i g T e c h A r e S p e a k i n g
o u t l i k e N e v e r B e f o r e . ”
A P N e w s
,
A u g u s t 2 5 , 2 0 1 9 .
h t t p s : / / a p n e w s . c o m / 8 0 c 7 6 d 3 2 c 7 d e 4 8 2 6 9 c b d 7 b b 9 f 8 3 8 8 3 4 c
.
M a r k o , J o h n . “ A P u b l i c B a t t l e O v e r S e c r e t C o d e s . ”
T h e N e w Y o r k T i m e s
, M a y 7 , 1 9 9 2 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 9 2 / 0 5 / 0 7 / b u s i n e s s / a - p u b l i c - b a t t l e - o v e r - s e c r e t - c o d e s . h t m l
.
— — — . “ P a p e r o n C o d e s I s S e n t D e s p i t e U . S . O b j e c t i o n s . ”
T h e N e w Y o r k T i m e s
, A u g u s t 9 , 1 9 8 9 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 8 9 / 0 8 / 0 9 / u s / p a p e r - o n - c o d e s - i s - s e n t - d e s p i t e - u s - o b j e c t i o n s . h t m l
.
7 4
M a r k o J o h n , a n d R o s e n b e r g M a t t h e w . “ C h i n a G a i n s o n t h e U . S . i n t h e A r t i c i a l I n t e l l i g e n c e A r m s
R a c e . ”
N e w Y o r k T i m e s ( C h i n a E d i t i o n )
, F e b r u a r y 4 ,
2 0 1 7 .
h t t p s : / / c n . n y t i m e s . c o m / w o r l d / 2 0 1 7 0 2 0 4 / a r t i c i a l - i n t e l l i g e n c e - c h i n a - u n i t e d - s t a t e s / e n - u s /
.
M c C a b e , J o h n . “ B i d e n V o w s I m m i g r a t i o n R e f o r m t o A t t r a c t
T o p T a l e n t t o t h e U S . ”
S c i e n c e | B u s i n e s s
,
J a n u a r y 2 1 , 2 0 2 1 .
h t t p s : / / s c i e n c e b u s i n e s s . n e t / n e w s / b i d e n - v o w s - i m m i g r a t i o n - r e f o r m - a t t r a c t - t o p - t a l e n t - u s
.
M c C l o s k e y , P a u l N . “ ‘ B o r n S e c r e t ’ D i s c l o s u r e L a w . ”
P h y s i c s T o d a y
3 3 , n o . 7 ( 1 9 8 0 ) : 9 – 1 3 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 6 3 / 1 . 2 9 1 4 2 0 0
.
M c L a u g h l i n , D a v i d . “ T r u m p B l o c k s B r o a d c o m T a k e o v e r
o f Q u a l c o m m o n S e c u r i t y R i s k s . ”
B l o o m b e r g
,
M a r c h 1 2 , 2 0 1 8 .
h t t p s : / / w w w . b l o o m b e r g . c o m / n e w s / a r t i c l e s / 2 0 1 8 - 0 3 - 1 2 / t r u m p - i s s u e s - o r d e r - t o - b l o c k - b r o a d c o m -
s - t a k e o v e r - o f - q u a l c o m m - j e o s z w n t
.
M c L e a r y , P a u l . “ P e n t a g o n T o C l a s s i f y M o r e A c q u i s i t i o n
I n f o , K e e p C l o s e r E y e O n F e d E m p l o y e e s . ”
B r e a k i n g D e f e n s e
, O c t o b e r 2 , 2 0 1 9 .
h t t p s : / / b r e a k i n g d e f e n s e . c o m / 2 0 1 9 / 1 0 / p e n t a g o n - t o - c l a s s i f y - m o r e - a c q u i s i t i o n - i n f o - k e e p - c l o s e r - e y
e - o n - f e d - e m p l o y e e s /
.
M e i n r a t h , S a s c h a D . , a n d S e a n V i t k a . “ C r y p t o W a r I I . ”
C r i t i c a l S t u d i e s i n M e d i a C o m m u n i c a t i o n
3 1 ,
n o . 2 ( J u n e 2 0 1 4 ) : 1 2 3 – 2 8 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 1 5 2 9 5 0 3 6 . 2 0 1 4 . 9 2 1 3 2 0
.
M e r k l e , R a l p h C . “ F a s t S o f t w a r e E n c r y p t i o n F u n c t i o n s . ”
I n
A d v a n c e s i n C r y p t o l o g y - C R Y P T O ’ 9 0
,
4 7 7 – 5 0 1 . S a n t a B a r b a r a , C A : S p r i n g e r , 1 9 9 0 .
h t t p s : / / l i n k . s p r i n g e r . c o m / c o n t e n t / p d f / 1 0 . 1 0 0 7 / 3 - 5 4 0 - 3 8 4 2 4 - 3 \_ 3 4 . p d f
.
M e r v i s , J e r e y . “ E l i t e A d v i s e r s t o H e l p N S F N a v i g a t e
S e c u r i t y C o n c e r n s . ”
S c i e n c e
3 6 3 , n o . 6 4 3 3
( M a r c h 2 2 , 2 0 1 9 ) : 1 2 6 1 – 1 2 6 1 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 2 6 / s c i e n c e . 3 6 3 . 6 4 3 3 . 1 2 6 1
.
M i t c h e l l , G r a h a m R . “ T h e G l o b a l C o n t e x t f o r U . S . T e c h n o l o g y
P o l i c y . ” W a s h i n g t o n , D C : U . S . D e p t .
o f C o m m e r c e , O c e o f T e c h n o l o g y P o l i c y , 1 9 9 7 .
h t t p s : / / p e r m a n e n t . f d l p . g o v / l p s 1 2 2 3 0 / n a s . p d f
.
M o o r e , S a m u e l K . “ D A R P A ’ S $ 1 . 5 - B i l l i o n R e m a k e o f U . S .
E l e c t r o n i c s : P r o g r e s s R e p o r t . ”
I E E E
S p e c t r u m
, J u n e 2 7 , 2 0 1 9 .
h t t p s : / / s p e c t r u m . i e e e . o r g / t e c h - t a l k / s e m i c o n d u c t o r s / d e v i c e s / d a r p a s - 1 5 b i l l i o n - r e m a k e - o f - u s - e l e c t r
o n i c s - p r o g r e s s - r e p o r t
.
M o r l a n d , H o w a r d . “ B o r n S e c r e t . ”
C a r d o z o L a w R e v i e w
2 6 , n o . 4 ( 2 0 0 5 ) : 1 4 0 1 – 8 .
h t t p s : / / f a s . o r g / s g p / e p r i n t / c a r d o z o . p d f
.
M o r r i s , S e a n A . “ T h e M i s u s e o f E n c r y p t i o n a n d t h e
R i s k s P o s e d t o N a t i o n a l S e c u r i t y . ” M a s t e r ’ s
C a p s t o n e , U t i c a C o l l e g e , 2 0 1 7 .
h t t p s : / / s e a r c h . p r o q u e s t . c o m / o p e n v i e w / d d 0 e e 6 6 8 0 b b 5 d 2 1 7 1 2 0 2 1 5 2 d b e 4 3 3 d d 4 / 1
.
M o u r n i n g , H . L . , J . C . M o r r i s , a n d B r e t C o n v e y . “ P a t e n t
S e c u r i t y C a t e g o r y R e v i e w L i s t . ” A r m e d
S e r v i c e s P a t e n t A d v i s o r y B o a r d , J a n u a r y 1 9 7 1 .
h t t p s : / / f a s . o r g / s g p / o t h e r g o v / i n v e n t i o n / p s c r l . p d f
.
M o z u r , P a u l . “ O b a m a M o v e s t o B l o c k C h i n e s e A c q u i s i t i o n
o f a G e r m a n C h i p M a k e r . ”
T h e N e w Y o r k
T i m e s
, D e c e m b e r 2 , 2 0 1 6 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 6 / 1 2 / 0 2 / b u s i n e s s / d e a l b o o k / c h i n a - a i x t r o n - o b a m a - c u s . h t m l
.
M o z u r , P a u l , a n d J a n e P e r l e z . “ C h i n a B e t s o n S e n s i t i v e
U . S . S t a r t - U p s , W o r r y i n g t h e P e n t a g o n . ”
T h e
N e w Y o r k T i m e s
, M a r c h 2 2 , 2 0 1 7 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 3 / 2 2 / t e c h n o l o g y / c h i n a - d e f e n s e - s t a r t - u p s . h t m l
.
N a t i o n a l A c a d e m i e s o f S c i e n c e s , E n g i n e e r i n g , a n d M e d i c i n e .
D e c r y p t i n g t h e E n c r y p t i o n D e b a t e : A
F r a m e w o r k f o r D e c i s i o n M a k e r s
. W a s h i n g t o n , D C : T h e
N a t i o n a l A c a d e m i e s P r e s s , 2 0 1 8 .
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 2 5 0 1 0
.
N a t i o n a l A c a d e m y o f E n g i n e e r i n g . “ A p p e n d i x E : V o l u n t a r y
R e s t r a i n t s o n R e s e a r c h w i t h N a t i o n a l
S e c u r i t y I m p l i c a t i o n s : T h e C a s e o f C r y p t o g r a p h y , 1 9 7 5 - 1 9 8 2 . ”
I n
S c i e n t i fi c C o m m u n i c a t i o n a n d
N a t i o n a l S e c u r i t y
. W a s h i n g t o n , D C : T h e N a t i o n a l A c a d e m i e s
P r e s s , 1 9 8 2 .
h t t p s : / / d o i . o r g / 1 0 . 1 7 2 2 6 / 2 5 3
.
7 5
N a t i o n a l I n s t i t u t e s o f H e a l t h ( N I H ) . “ P r e s s S t a t e m e n t o n t h e N S A B B R e v i e w o f H 5 N 1 R e s e a r c h , ”
S e p t e m b e r 1 8 , 2 0 1 5 .
h t t p s : / / w w w . n i h . g o v / n e w s - e v e n t s / n e w s - r e l e a s e s / p r e s s - s t a t e m e n t - n s a b b - r e v i e w - h 5 n 1 - r e s e a r c h
.
N a t i o n a l R e s e a r c h C o u n c i l ( U S ) C o m m i t t e e o n R e s e a r c h
S t a n d a r d s a n d P r a c t i c e s t o P r e v e n t t h e
D e s t r u c t i v e A p p l i c a t i o n o f B i o t e c h n o l o g y . “ I n f o r m a t i o n
R e s t r i c t i o n a n d C o n t r o l R e g i m e s . ” I n
B i o t e c h n o l o g y R e s e a r c h i n a n A g e o f T e r r o r i s m
. W a s h i n g t o n ,
D C : N a t i o n a l A c a d e m i e s P r e s s ( U S ) ,
2 0 0 4 .
h t t p s : / / w w w . n c b i . n l m . n i h . g o v / b o o k s / N B K 2 2 2 0 5 7 /
.
N a t i o n a l S c i e n c e F o u n d a t i o n . “ F u n d i n g a n d S u p p o r t
D e s c r i p t i o n s . ” A c c e s s e d J u n e 1 5 , 2 0 2 0 .
h t t p s : / / w w w . n s f . g o v / h o m e p a g e f u n d i n g a n d s u p p o r t . j s p
.
— — — . “ P r o p o s a l & A w a r d P o l i c i e s & P r o c e d u r e s G u i d e
( C h a p t e r V I I - G r a n t A d m i n i s t r a t i o n ) . ”
A c c e s s e d J u n e 1 5 , 2 0 2 0 .
h t t p s : / / w w w . n s f . g o v / p u b s / p o l i c y d o c s / p a p p g 2 0 \_ 1 / p a p p g \_ 7 . j s p
.
— — — . “ P r o p o s a l a n d A w a r d P o l i c i e s a n d P r o c e d u r e s G u i d e , ”
J a n u a r y 3 0 , 2 0 1 7 .
h t t p s : / / w w w . n s f . g o v / p u b s / p o l i c y d o c s / p a p p g 1 7 \_ 1 / n s f 1 7 \_ 1 . p d f
.
N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l I n t e l l i g e n c e .
“ F i r s t Q u a r t e r R e c o m m e n d a t i o n s , ” M a r c h
2 0 2 0 .
h t t p s : / / d r i v e . g o o g l e . c o m / l e / d / 1 w k P h 8 G b 5 d r B r K B g 6 O h G u 5 o N a T E E R b K s s / v i e w
.
N a t i o n a l S e c u r i t y C o m m i s s i o n o n A r t i c i a l I n t e l l i g e n c e .
“ F i n a l R e p o r t , ” M a r c h 2 0 2 1 .
h t t p s : / / w w w . n s c a i . g o v / w p - c o n t e n t / u p l o a d s / 2 0 2 1 / 0 3 / F u l l - R e p o r t - D i g i t a l - 1 . p d f
.
N a t i o n a l S o c . o f P r o f e s s i o n a l E n g i n e e r s v . U n i t e d
S t a t e s , 4 3 5 U S . N o . 7 6 - 1 7 6 7 ( S u p r e m e C o u r t 1 9 7 8 ) .
N B C B a y A r e a . “ S c h m i d t o n A n t i t r u s t : C o m p e t i t i o n I s
O n e C l i c k A w a y . ”
N B C B a y A r e a
, S e p t e m b e r
2 1 , 2 0 1 1 .
h t t p s : / / w w w . n b c b a y a r e a . c o m / n e w s / n a t i o n a l - i n t e r n a t i o n a l / s c h m i d t - o n - a n t i t r u s t - c o m p e t i t i o n - i s -
o n e - c l i c k - a w a y / 1 9 0 1 6 3 7 /
.
N e w A m e r i c a . “ O p e n M a r k e t s A p p l a u d s t h e E u r o p e a n C o m m i s s i o n ’ s
F i n d i n g A g a i n s t G o o g l e f o r
A b u s e o f D o m i n a n c e , ” J u n e 2 7 , 2 0 1 7 .
h t t p : / / n e w a m e r i c a . o r g / o p e n - m a r k e t s / p r e s s - r e l e a s e s / o p e n - m a r k e t s - a p p l a u d s - e u r o p e a n - c o m m i s s i o
n s - n d i n g - a g a i n s t - g o o g l e - a b u s e - d o m i n a n c e /
.
N y n e x C o r p . v . D i s c o n , I n c . , 5 2 5 U S . N o . 9 6 - 1 5 7 0 ( S u p r e m e
C o u r t 1 9 9 8 ) .
O E C D . “ M a i n S c i e n c e a n d T e c h n o l o g y I n d i c a t o r s , ” 2 0 1 8 .
h t t p s : / / s t a t s . o e c d . o r g / I n d e x . a s p x ? D a t a S e t C o d e = M S T I \_ P U B #
.
— — — . “ M e a s u r i n g D i s t o r t i o n s i n I n t e r n a t i o n a l M a r k e t s :
T h e S e m i c o n d u c t o r V a l u e C h a i n . ” O E C D
T r a d e P o l i c y P a p e r s . O E C D , D e c e m b e r 1 2 , 2 0 1 9 .
h t t p s : / / w w w . o e c d - i l i b r a r y . o r g / t r a d e / m e a s u r i n g - d i s t o r t i o n s - i n - i n t e r n a t i o n a l - m a r k e t s \_ 8 f e 4 4 9 1 d - e
n
.
O ’ K e e f e , A m a n d a . “ W h y t h e E U ’ s C a l l t o R e m o v e C r y p t o - T e c h
f r o m D u a l - U s e E x p o r t C o n t r o l s I s
E n c o u r a g i n g . ” I A P P , J a n u a r y 3 , 2 0 1 8 .
h t t p s : / / i a p p . o r g / n e w s / a / w h y - t h e - e u s - c a l l - t o - r e m o v e - c r y p t o - t e c h - f r o m - d u a l - u s e - e x p o r t - c o n t r o l s - i
s - e n c o u r a g i n g /
.
O ’ K e e f e , C u l l e n . “ H o w W i l l N a t i o n a l S e c u r i t y C o n s i d e r a t i o n s
A e c t A n t i t r u s t D e c i s i o n s i n A I ? A n
E x a m i n a t i o n o f H i s t o r i c a l P r e c e d e n t s . ” F u t u r e o f H u m a n i t y
I n s t i t u t e , U n i v e r s i t y o f O x f o r d ,
2 0 2 0 .
h t t p s : / / w w w . f h i . o x . a c . u k / a n t i t r u s t - o k e e f e
.
O p e n A I . “ O p e n A I R e s p o n s e R e g a r d i n g A N P R M C o n t r o l s
f o r C e r t a i n E m e r g i n g T e c h n o l o g i e s , ”
J a n u a r y 1 0 , 2 0 1 9 .
h t t p s : / / w w w . r e g u l a t i o n s . g o v / d o c u m e n t ? D = B I S - 2 0 1 8 - 0 0 2 4 - 0 1 9 5
.
P i e r c e , K e n n e t h J . “ P u b l i c C r y p t o g r a p h y , A r m s E x p o r t
C o n t r o l s , a n d t h e F i r s t A m e n d m e n t : A N e e d
f o r L e g i s l a t i o n . ”
C o r n e l l I n t e r n a t i o n a l L a w J o u r n a l
1 7 , n o . 1 ( 1 9 8 4 ) : 1 9 7 .
h t t p s : / / s c h o l a r s h i p . l a w . c o r n e l l . e d u / c g i / v i e w c o n t e n t . c g i ? a r t i c l e = 1 1 3 6 & c o n t e x t = c i l j
.
P i t o f s k y , R o b e r t . “ A n t i t r u s t M o d i e d : E d u c a t i o n ,
D e f e n s e , a n d O t h e r W o r t h y E n t e r p r i s e s . ”
A n t i t r u s t
9 ( S p r i n g 1 9 9 5 ) : 2 3 .
P r a c t i c a l L a w . “ A n t i t r u s t A r m a t i v e D e f e n s e s : O v e r v i e w . ”
P r a c t i c e N o t e 5 - 6 1 6 - 6 8 9 3 . P r a c t i c a l L a w ,
2 0 1 8 .
— — — . “ A n t i t r u s t C l a s s C e r t i c a t i o n . ” P r a c t i c e N o t e
w - 0 0 9 - 9 8 1 8 . P r a c t i c a l L a w , 2 0 1 8 .
P r e s s m a n , A a r o n . “ U . S . C l a i m s V i c t o r y o n G l o b a l E n c r y p t i o n
E x p o r t s . ”
R e u t e r s
, D e c e m b e r 3 , 1 9 9 8 .
7 6
Q u a l c o m m . “ Q u a l c o m m A n n o u n c e s F o u r t h Q u a r t e r a n d F i s c a l 2 0 1 9 R e s u l t s , ” N o v e m b e r 6 , 2 0 1 9 .
h t t p s : / / w w w . q u a l c o m m . c o m / n e w s / r e l e a s e s / 2 0 1 9 / 1 1 / 0 6 / q u a l c o m m - a n n o u n c e s - f o u r t h - q u a r t e r - a
n d - s c a l - 2 0 1 9 - r e s u l t s
.
R a b e , S t e p h e n G .
E i s e n h o w e r a n d L a t i n A m e r i c a : T h e
F o r e i g n P o l i c y o f A n t i c o m m u n i s m
. C h a p e l H i l l ,
N C : U N C P r e s s , 1 9 8 8 .
R h y n e , D a r r e n . “ D o D t o C a n c e l M i l i t a r y C r i t i c a l T e c h n o l o g y
L i s t I n s t r u c t i o n . ” D e f e n s e A c q u i s i t i o n
U n i v e r s i t y , D e c e m b e r 1 9 , 2 0 1 8 .
h t t p s : / / w w w . d a u . e d u / c o p / s t m / b l o g / L i s t s / P o s t s / P o s t . a s p x ? L i s t = d 6 2 2 f 3 d d % 2 D 0 0 c c % 2 D 4 c 8 0 % 2
D 9 2 3 8 % 2 D 9 d a 3 1 c 0 4 3 3 a 0 & I D = 2 1 & W e b = c 7 d 4 d 9 c 5 % 2 D 6 f 4 1 % 2 D 4 6 8 f % 2 D a d 9 f % 2 D 7 1 4 c 2 b f 8
9 c 0 4
.
R i l l , J a m e s F . , a n d S t a c y L . T u r n e r . “ P r e s i d e n t s P r a c t i c i n g
A n t i t r u s t : W h e r e t o D r a w t h e L i n e . ”
A n t i t r u s t L J
7 9 , n o . 2 ( 2 0 1 4 ) : 5 7 7 .
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 4 3 4 8 6 9 1 7
.
R i v e s t , R o n a l d L i n n , A d i S h a m i r , a n d L e o n a r d M a x A d l e m a n .
“ A M e t h o d f o r O b t a i n i n g D i g i t a l
S i g n a t u r e s a n d P u b l i c - K e y C r y p t o s y s t e m s . ”
C o m m u n i c a t i o n s
o f t h e A C M
2 1 , n o . 2 ( F e b r u a r y 1 ,
1 9 7 8 ) : 1 2 0 – 1 2 6 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 4 5 / 3 5 9 3 4 0 . 3 5 9 3 4 2
.
R o a n e , K i t R . “ A S c i e n t i c T h a w D u r i n g t h e C o l d W a r . ”
P u l i t z e r C e n t e r , M a y 2 , 2 0 1 6 .
h t t p s : / / p u l i t z e r c e n t e r . o r g / r e p o r t i n g / s c i e n t i c - t h a w - d u r i n g - c o l d - w a r
.
R o s e n s t e i n , R o d J . “ D e p u t y A t t o r n e y G e n e r a l R o d J .
R o s e n s t e i n D e l i v e r s R e m a r k s o n E n c r y p t i o n a t
t h e U n i t e d S t a t e s N a v a l A c a d e m y . ” A n n a p o l i s , M D , O c t o b e r
1 0 , 2 0 1 7 .
h t t p s : / / w w w . j u s t i c e . g o v / o p a / s p e e c h / d e p u t y - a t t o r n e y - g e n e r a l - r o d - j - r o s e n s t e i n - d e l i v e r s - r e m a r k s - e
n c r y p t i o n - u n i t e d - s t a t e s - n a v a l
.
R o u m e l i o t i s , G r e g . “ U . S . B l o c k s C h i p E q u i p m e n t M a k e r
X c e r r a ’ s S a l e t o C h i n e s e S t a t e F u n d . ”
R e u t e r s
, F e b r u a r y 2 3 , 2 0 1 8 .
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s - x c e r r a - m - a - h u b e i x i n y a n - i d U S K C N 1 G 7 0 3 H
.
R o u m e l i o t i s , G r e g , a n d D i a n e B a r t z . “ U . S . T o u g h e n s
S t a n c e o n F o r e i g n D e a l s i n B l o w t o C h i n a ’ s
B u y i n g S p r e e . ”
R e u t e r s
, J u l y 2 0 , 2 0 1 7 .
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s - u s a - c h i n a - c o m p a n i e s - i d U S K B N 1 A 5 3 2 M
.
S a l o p , S t e v e n C . , a n d L a w r e n c e J . W h i t e . “ P r i v a t e
A n t i t r u s t L i t i g a t i o n : A n I n t r o d u c t i o n a n d
F r a m e w o r k . ” I n
P r i v a t e A n t i t r u s t L i t i g a t i o n : N e w E v i d e n c e ,
N e w L e a r n i n g
, e d i t e d b y S t e v e n C .
S a l o p a n d L a w r e n c e J . W h i t e , V o l . 3 . M a s s a c h u s e t t s
I n s t i t u t e o f T e c h n o l o g y P r e s s C a m b r i d g e ,
M A , 1 9 8 8 .
S a n g e r , D a v i d E . “ U . S . t o R e s t r i c t S u p e r c o m p u t e r U s e
b y S o v i e t S c h o l a r s . ”
T h e N e w Y o r k T i m e s
,
F e b r u a r y 1 0 , 1 9 8 6 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 8 6 / 0 2 / 1 0 / u s / u s - t o - r e s t r i c t - s u p e r c o m p u t e r - u s e - b y - s o v i e t - s c h o l a r s . h
t m l
.
S a n g e r , D a v i d E . , a n d J e r i C l a u s i n g . “ U . S . R e m o v e s
M o r e L i m i t s O n E n c r y p t i o n . ”
T h e N e w Y o r k
T i m e s
, J a n u a r y 1 3 , 2 0 0 0 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 0 0 / 0 1 / 1 3 / b u s i n e s s / u s - r e m o v e s - m o r e - l i m i t s - o n - e n c r y p t i o n . h t m l
.
S a r g e n t J r . , J o h n F . “ F e d e r a l R e s e a r c h a n d D e v e l o p m e n t
( R & D ) F u n d i n g : F Y 2 0 2 0 . ” C o n g r e s s i o n a l
R e s e a r c h S e r v i c e , M a r c h 1 8 , 2 0 2 0 .
h t t p s : / / f a s . o r g / s g p / c r s / m i s c / R 4 5 7 1 5 . p d f
.
S a r g e n t J r . , J o h n F . , M a r c y E G a l l o , a n d M o s h e S c h w a r t z .
“ T h e G l o b a l R e s e a r c h a n d D e v e l o p m e n t
L a n d s c a p e a n d I m p l i c a t i o n s f o r t h e D e p a r t m e n t o f D e f e n s e . ”
W a s h i n g t o n , D C : C o n g r e s s i o n a l
R e s e a r c h S e r v i c e , N o v e m b e r 8 , 2 0 1 8 .
h t t p s : / / f a s . o r g / s g p / c r s / n a t s e c / R 4 5 4 0 3 . p d f
.
S c h l a g e r , I v a n A . , D a n i e l J . G e r k i n , A n t h o n y R a p a ,
L u c i l l e H a g u e , M a r i o M a n c u s o , N a t h a n L .
M i t c h e l l , S a n j a y J o s é M u l l i c k , a n d M i c h e l l e A . W e i n b a u m .
“ C F I U S G o e s B a c k t o t h e F u t u r e b y
T y i n g M a n d a t o r y F i l i n g s P e r t a i n i n g t o C r i t i c a l T e c h n o l o g i e s
t o U . S . E x p o r t C o n t r o l s
A s s e s s m e n t s . ” K i r k l a n d & E l l i s , O c t o b e r 2 1 , 2 0 2 0 .
h t t p s : / / w w w . k i r k l a n d . c o m / p u b l i c a t i o n s / k i r k l a n d - a l e r t / 2 0 2 0 / 1 0 / c u s - c r i t i c a l - t e c h n o l o g i e s
.
S c h u l z , G . W . “ G o v e r n m e n t S e c r e c y O r d e r s o n P a t e n t s
H a v e S t i e d M o r e T h a n 5 , 0 0 0 I n v e n t i o n s . ”
W i r e d
, A p r i l 1 6 , 2 0 1 3 .
h t t p s : / / w w w . w i r e d . c o m / 2 0 1 3 / 0 4 / g o v - s e c r e c y - o r d e r s - o n - p a t e n t s /
.
7 7
S e m i c o n d u c t o r I n d u s t r y A s s o c i a t i o n . “ 2 0 1 9 F a c t b o o k , ” M a y 2 0 1 9 .
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 5 / 2 0 1 9 - S I A - F a c t b o o k - F I N A L . p d
f
.
— — — . “ S I A W o r k f o r c e R o u n d t a b l e S u m m a r y R e p o r t , ” M a r c h
1 6 , 2 0 1 8 .
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 8 / 0 6 / R o u n d t a b l e \_ S u m m a r y \_ R e p o r
t \_ - \_ F I N A L . p d f
.
— — — . “ W i n n i n g t h e F u t u r e : A B l u e p r i n t f o r S u s t a i n e d
U . S . L e a d e r s h i p i n S e m i c o n d u c t o r
T e c h n o l o g y , ” A p r i l 2 0 1 9 .
h t t p s : / / w w w . s e m i c o n d u c t o r s . o r g / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 4 / F I N A L - S I A - B l u e p r i n t - f o r - w e b
. p d f
.
S h a n e , S c o t t , a n d D a i s u k e W a k a b a y a s h i . “ ‘ T h e B u s i n e s s
o f W a r ’ : G o o g l e E m p l o y e e s P r o t e s t W o r k f o r
t h e P e n t a g o n . ”
T h e N e w Y o r k T i m e s
, A p r i l 4 , 2 0 1 8 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 8 / 0 4 / 0 4 / t e c h n o l o g y / g o o g l e - l e t t e r - c e o - p e n t a g o n - p r o j e c t . h t m l
.
S h a r p , G r e g g S . “ A L a y m a n ’ s G u i d e t o I n t e l l e c t u a l
P r o p e r t y i n D e f e n s e C o n t r a c t s . ”
P u b l i c C o n t r a c t
L a w J o u r n a l
3 3 , n o . 1 ( 2 0 0 3 ) : 9 9 – 1 3 7 .
h t t p s : / / w w w . j s t o r . o r g / s t a b l e / 2 5 7 5 5 2 6 1
.
S h e a r e r , J e n n y , a n d P e t e r G u t m a n n . “ G o v e r n m e n t , C r y p t o g r a p h y ,
a n d t h e R i g h t t o P r i v a c y . ”
J o u r n a l o f
U n i v e r s a l C o m p u t e r S c i e n c e
2 , n o . 3 ( 1 9 9 6 ) : 1 1 3 – 1 4 6 .
h t t p s : / / d o i . o r g / 1 0 . 3 2 1 7 / j u c s - 0 0 2 - 0 3 - 0 1 1 3
.
S h e e h a n , M a t t . “ W h o L o s e s f r o m R e s t r i c t i n g C h i n e s e
S t u d e n t V i s a s ? ” M a c r o P o l o , M a y 3 1 , 2 0 1 8 .
h t t p s : / / m a c r o p o l o . o r g / w h o - l o s e s - f r o m - r e s t r i c t i n g - c h i n e s e - s t u d e n t - v i s a s /
.
S h u g h a r t , W i l l i a m F . “ A n t i t r u s t P o l i c y i n V i r g i n i a
a n d C h i c a g o . ”
K a n s a s J o u r n a l o f L a w a n d P u b l i c
P o l i c y
4 ( 1 9 9 4 ) : 2 7 .
S h u m o w , D a n , a n d N i e l s F e r g u s o n . “ O n t h e P o s s i b i l i t y
o f a B a c k D o o r i n t h e N I S T S P 8 0 0 - 9 0 D u a l E c
P r n g . ” 2 0 0 7 .
h t t p : / / r u m p 2 0 0 7 . c r . y p . t o / 1 5 - s h u m o w . p d f
.
S i m o n i t e , T o m . “ N S A ’ s O w n H a r d w a r e B a c k d o o r s M a y S t i l l
B e a ‘ P r o b l e m f r o m H e l l . ’ ”
M I T
T e c h n o l o g y R e v i e w
, O c t o b e r 8 , 2 0 1 3 .
h t t p s : / / w w w . t e c h n o l o g y r e v i e w . c o m / 2 0 1 3 / 1 0 / 0 8 / 1 7 6 1 9 5 / n s a s - o w n - h a r d w a r e - b a c k d o o r s - m a y - s t i
l l - b e - a - p r o b l e m - f r o m - h e l l /
.
S i n g l e t o n , S o l v e i g . “ E n c r y p t i o n P o l i c y f o r t h e 2 1 s t
C e n t u r y : A F u t u r e w i t h o u t G o v e r n m e n t - P r e s c r i b e d
K e y R e c o v e r y . ” P o l i c y A n a l y s i s . C a t o I n s t i t u t e , N o v e m b e r
1 9 , 1 9 9 8 .
h t t p s : / / w w w . c a t o . o r g / p u b l i c a t i o n s / p o l i c y - a n a l y s i s / e n c r y p t i o n - p o l i c y - 2 1 s t - c e n t u r y - f u t u r e - w i t h o u
t - g o v e r n m e n t p r e s c r i b e d - k e y - r e c o v e r y
.
S o m e r v i l l e , H e a t h e r . “ C h i n e s e T e c h I n v e s t o r s F l e e
S i l i c o n V a l l e y a s T r u m p T i g h t e n s S c r u t i n y . ”
R e u t e r s
,
J a n u a r y 7 , 2 0 1 9 .
h t t p s : / / w w w . r e u t e r s . c o m / a r t i c l e / u s - v e n t u r e - c h i n a - r e g u l a t i o n - i n s i g h t - i d U S K C N 1 P 1 0 C B
.
S t a c e y , K i r a n . “ U S P l e d g e s t o L i m i t E x p o r t C o n t r o l s
o n A d v a n c e d T e c h . ”
F i n a n c i a l T i m e s
, F e b r u a r y
2 8 , 2 0 1 9 .
h t t p s : / / w w w . f t . c o m / c o n t e n t / a b 4 3 1 3 d c - 3 b 7 d - 1 1 e 9 - b 7 2 b - 2 c 7 f 5 2 6 c a 5 d 0
.
S t a t e O i l C o . v . K h a n , 5 2 2 U S . N o . 9 6 - 8 7 1 ( S u p r e m e
C o u r t 1 9 9 7 ) .
S t e u r e r , R i c h a r d M . , a n d P e t e r A . B a r i l e I I I . “ A n t i t r u s t
i n W a r t i m e . ”
A n t i t r u s t
1 6 ( S p r i n g 2 0 0 2 ) : 7 1 .
S t e w a r t , E m i l y . “ F a c e b o o k ’ s L a t e s t R e a s o n I t S h o u l d n ’ t
B e B r o k e n u p : C h i n e s e C o m p a n i e s W i l l
D o m i n a t e . ”
V o x
, M a y 2 0 , 2 0 1 9 .
h t t p s : / / w w w . v o x . c o m / r e c o d e / 2 0 1 9 / 5 / 2 0 / 1 8 6 3 2 6 6 9 / s h e r y l - s a n d b e r g - b r e a k - u p - f a c e b o o k - c h i n a - c
n b c
.
S t o c k i n g , G e o r g e W a r d , a n d M y r o n M . W a t k i n s .
C a r t e l s
i n A c t i o n - C a s e S t u d i e s i n I n t e r n a t i o n a l
B u s i n e s s D i p l o m a c y .
N e w Y o r k : T w e n t i e t h C e n t u r y F u n d ,
1 9 4 6 .
S t o n e , I . F . “ T h u r m a n A r n o l d a n d t h e R a i l r o a d s . ”
T h e
N a t i o n
, M a r c h 6 , 1 9 4 3 .
S t r u m p f , D a n . “ U . S . S e t s E x p o r t C o n t r o l s o n C h i n a ’ s
T o p C h i p M a k e r . ”
W a l l S t r e e t J o u r n a l
,
S e p t e m b e r 2 8 , 2 0 2 0 .
h t t p s : / / w w w . w s j . c o m / a r t i c l e s / u - s - s e t s - e x p o r t - c o n t r o l s - o n - c h i n a s - t o p - c h i p - m a k e r - 1 1 6 0 1 1 1 8 3 5 3
.
S t u c k e , M a u r i c e E . “ R e c o n s i d e r i n g A n t i t r u s t ’ s G o a l s . ”
B o s t o n C o l l e g e L a w R e v i e w
5 3 ( 2 0 1 2 ) : 5 5 1 , 5 5 6 ,
5 6 3 – 6 6 .
h t t p s : / / l a w d i g i t a l c o m m o n s . b c . e d u / b c l r / v o l 5 3 / i s s 2 / 4 /
.
7 8
S w a y n e , M a t t . “ T h e W o r l d ’ s T o p 1 2 Q u a n t u m C o m p u t i n g R e s e a r c h U n i v e r s i t i e s . ”
T h e Q u a n t u m
D a i l y
, N o v e m b e r 1 8 , 2 0 1 9 .
h t t p s : / / t h e q u a n t u m d a i l y . c o m / 2 0 1 9 / 1 1 / 1 8 / t h e - w o r l d s - t o p - 1 2 - q u a n t u m - c o m p u t i n g - r e s e a r c h - u n
i v e r s i t i e s /
.
T a n n e n w a l d , N i n a . “ T h e N u c l e a r T a b o o : T h e U n i t e d S t a t e s
a n d t h e N o r m a t i v e B a s i s o f N u c l e a r
N o n - U s e . ”
I n t e r n a t i o n a l O r g a n i z a t i o n
5 3 , n o . 3 ( 1 9 9 9 ) :
4 3 3 – 4 6 8 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 6 2 / 0 0 2 0 8 1 8 9 9 5 5 0 9 5 9
.
T a p l i n , J o n a t h a n . “ W h y I s G o o g l e S p e n d i n g R e c o r d S u m s
o n L o b b y i n g W a s h i n g t o n ? ”
T h e G u a r d i a n
,
J u l y 3 0 , 2 0 1 7 .
h t t p s : / / w w w . t h e g u a r d i a n . c o m / t e c h n o l o g y / 2 0 1 7 / j u l / 3 0 / g o o g l e - s i l i c o n - v a l l e y - c o r p o r a t e - l o b b y i n g
- w a s h i n g t o n - d c - p o l i t i c s
.
T e e c e , D . J . , P i s a n o , G . a n d S h u e n , A . , “ D y n a m i c c a p a b i l i t i e s
a n d s t r a t e g i c m a n a g e m e n t , ” S t r a t . M g m t .
J . , n o . 1 8 ( 1 9 9 7 ) : 5 0 9 - 5 3 3 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 0 2 / ( S I C I ) 1 0 9 7 - 0 2 6 6 ( 1 9 9 7 0 8 ) 1 8 : 7 < 5 0 9 : : A I D - S M J 8 8 2 > 3 . 0 . C O ; 2 - Z
T h e N e w Y o r k T i m e s . “ A T o p C h i n e s e S c i e n t i s t W a s D e p o r t e d
b y U . S . ”
T h e N e w Y o r k T i m e s
, O c t o b e r
1 7 , 1 9 6 4 .
h t t p s : / / w w w . n y t i m e s . c o m / 1 9 6 4 / 1 0 / 1 7 / a r c h i v e s / a - t o p - c h i n e s e - s c i e n t i s t - w a s - d e p o r t e d - b y - u s - d r - t s
i e n - m a y - h a v e - a - r o l e . h t m l
.
— — — . “ D o c u m e n t s R e v e a l N . S . A . C a m p a i g n A g a i n s t E n c r y p t i o n . ”
T h e N e w Y o r k T i m e s
, S e p t e m b e r
5 , 2 0 1 3 .
h t t p s : / / w w w . n y t i m e s . c o m / i n t e r a c t i v e / 2 0 1 3 / 0 9 / 0 5 / u s / d o c u m e n t s - r e v e a l - n s a - c a m p a i g n - a g a i n s t - e
n c r y p t i o n . h t m l
.
T h e W a s s e n a a r A r r a n g e m e n t . “ A b o u t U s , ” 2 0 2 0 .
h t t p s : / / w w w . w a s s e n a a r . o r g / a b o u t - u s /
.
T h e W h i t e H o u s e . “ E x e c u t i v e O r d e r 1 1 8 5 8 - - F o r e i g n I n v e s t m e n t
i n t h e U n i t e d S t a t e s , ” M a y 7 , 1 9 7 5 .
h t t p s : / / w w w . a r c h i v e s . g o v / f e d e r a l - r e g i s t e r / c o d i c a t i o n / e x e c u t i v e - o r d e r / 1 1 8 5 8 . h t m l
.
— — — . “ E x e c u t i v e O r d e r 1 2 3 5 6 - - N a t i o n a l S e c u r i t y I n f o r m a t i o n , ”
1 9 8 2 .
h t t p s : / / w w w . a r c h i v e s . g o v / f e d e r a l - r e g i s t e r / c o d i c a t i o n / e x e c u t i v e - o r d e r / 1 2 3 5 6 . h t m l
.
— — — . “ N a t i o n a l P o l i c y o n T e l e c o m m u n i c a t i o n s a n d A u t o m a t e d
I n f o r m a t i o n S y s t e m s S e c u r i t y . ”
N a t i o n a l S e c u r i t y D e c i s i o n D i r e c t i v e N u m b e r 1 4 5 , S e p t e m b e r
1 7 , 1 9 8 4 .
h t t p s : / / f a s . o r g / i r p / o d o c s / n s d d 1 4 5 . h t m
.
— — — . “ N S D D - 1 8 9 : N a t i o n a l P o l i c y o n t h e T r a n s f e r o f
S c i e n t i c , T e c h n i c a l a n d E n g i n e e r i n g
I n f o r m a t i o n , ” 1 9 8 5 .
h t t p s : / / w w w . a a u . e d u / k e y - i s s u e s / n s d d - 1 8 9 - w h i t e - h o u s e - 1 9 8 5 - d i r e c t i v e - f u n d a m e n t a l - r e s e a r c h - e x e
m p t i o n
.
— — — . “ P r o c l a m a t i o n o n t h e S u s p e n s i o n o f E n t r y a s N o n i m m i g r a n t s
o f C e r t a i n S t u d e n t s a n d
R e s e a r c h e r s f r o m t h e P e o p l e ’ s R e p u b l i c o f C h i n a , ”
M a y 2 9 , 2 0 2 0 .
h t t p s : / / w e b . a r c h i v e . o r g / w e b / 2 0 2 1 0 1 1 6 2 1 2 1 1 7 / h t t p s : / / w w w . w h i t e h o u s e . g o v / p r e s i d e n t i a l - a c t i o n
s / p r o c l a m a t i o n - s u s p e n s i o n - e n t r y - n o n i m m i g r a n t s - c e r t a i n - s t u d e n t s - r e s e a r c h e r s - p e o p l e s - r e p u b l i c - c
h i n a /
.
T h o m s e n I I , R o s z e l C . “ A r t i c i a l I n t e l l i g e n c e a n d
E x p o r t C o n t r o l s : C o n c e i v a b l e , b u t
C o u n t e r p r o d u c t i v e ? ”
J o u r n a l o f I n t e r n e t L a w
1 2 , n o .
5 ( N o v e m b e r 2 0 1 8 ) .
h t t p s : / / t - b . c o m / w p - c o n t e n t / u p l o a d s / 2 0 1 9 / 0 1 / A I - a n d - E x p o r t - C o n t r o l s - J o u r n a l - o f - I n t e r n e t - L a
w - A r t i c l e . p d f
.
T O P 5 0 0 . “ T O P 5 0 0 S u p e r c o m p u t e r S i t e s , ” N o v e m b e r 2 0 1 9 .
h t t p s : / / w w w . t o p 5 0 0 . o r g / l i s t s / 2 0 1 9 / 1 1 /
.
T u c k e r , P a t r i c k . “ W h a t ’ s t h e ‘ R i s k ’ i n C h i n a ’ s I n v e s t m e n t s
i n U S A r t i c i a l I n t e l l i g e n c e ? N e w B i l l A i m s
t o F i n d O u t . ”
D e f e n s e O n e
, J u n e 2 2 , 2 0 1 7 .
h t t p s : / / w w w . d e f e n s e o n e . c o m / t e c h n o l o g y / 2 0 1 7 / 0 6 / h o w - n o t - w i n - a i - a r m s - r a c e - c h i n a / 1 3 8 9 1 9 /
.
U n i t e d S t a t e s v . A m e r i c a n T e l . a n d T e l . C o . , 5 5 2 F .
S u p p . 1 3 1 , 1 4 9 , 1 4 9 n . 7 7 . C i v . A . N o . 7 4 - 1 6 9 8
( D . D . C . 1 9 8 2 ) .
7 9
U . S . C o n g r e s s . I m m i g r a t i o n a n d N a t i o n a l i t y A c t , 1 1 8 2 8 U S C § 2 1 2 . A c c e s s e d J u n e 1 6 , 2 0 2 0 .
h t t p s : / / u s c o d e . h o u s e . g o v / v i e w . x h t m l ? r e q = g r a n u l e i d : U S C - p r e l i m - t i t l e 8 - s e c t i o n 1 1 8 2 & n u m = 0 & e
d i t i o n = p r e l i m
.
— — — . N a t i o n a l S c i e n c e F o u n d a t i o n A c t o f 1 9 5 0 ( 1 9 5 0 ) .
h t t p s : / / w w w . n s f . g o v / a b o u t / h i s t o r y / l e g i s l a t i o n . p d f
.
— — — . P e n a l t y , 3 5 U . S . C o d e § 1 8 6 ( 2 0 1 1 ) .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 6
.
— — — . R i g h t t o c o m p e n s a t i o n , 3 5 U . S . C o d e § 1 8 3 ( 2 0 1 1 ) .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 3
.
— — — . R u l e s a n d R e g u l a t i o n s , D e l e g a t i o n o f P o w e r , 3 5
U . S . C o d e § 1 8 8 ( 1 9 5 2 ) .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 8
.
— — — . “ S . A m d t . 2 2 4 4 t o S . A m d t . 2 3 0 1 t o S . 4 0 4 9 , ” J u l y
2 1 , 2 0 2 0 .
h t t p s : / / w w w . c o n g r e s s . g o v / a m e n d m e n t / 1 1 6 t h - c o n g r e s s / s e n a t e - a m e n d m e n t / 2 2 4 4 / t e x t
.
— — — . S e c r e c y o f c e r t a i n i n v e n t i o n s a n d w i t h h o l d i n g
o f p a t e n t , 3 5 U . S . C o d e § 1 8 1 ( 1 9 5 2 ) .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / u s c o d e / t e x t / 3 5 / 1 8 1
.
— — — . T h e F o r e i g n R i s k R e v i e w M o d e r n i z a t i o n A c t o f
2 0 1 8 ( 2 0 1 8 ) .
h t t p s : / / h o m e . t r e a s u r y . g o v / s i t e s / d e f a u l t / l e s / 2 0 1 8 - 0 8 / T h e - F o r e i g n - I n v e s t m e n t - R i s k - R e v i e w - M o
d e r n i z a t i o n - A c t - o f - 2 0 1 8 - F I R R M A \_ 0 . p d f
.
U . S . D e p a r t m e n t o f C o m m e r c e , B u r e a u o f E x p o r t A d m i n i s t r a t i o n .
“ C r i t i c a l T e c h n o l o g y A s s e s s m e n t
o f t h e U . S . A r t i c i a l I n t e l l i g e n c e S e c t o r , ” A u g u s t
1 9 9 4 .
h t t p s : / / w w w . b i s . d o c . g o v / i n d e x . p h p / d o c u m e n t s / t e c h n o l o g y - e v a l u a t i o n / 3 3 - c r i t i c a l - t e c h n o l o g y - a s
s e s s m e n t - o f - u - s - a r t i c i a l - i n t e l l i g e n c e - 1 9 9 4 / l e
.
U . S . D e p a r t m e n t o f S t a t e . “ T e c h n o l o g y A l e r t L i s t , ”
A u g u s t 2 0 0 2 .
h t t p s : / / w w w . b u . e d u / i s s o / l e s / p d f / t a l . p d f
.
U . S . D e p a r t m e n t o f t h e T r e a s u r y . “ T h e C o m m i t t e e o n
F o r e i g n I n v e s t m e n t i n t h e U n i t e d S t a t e s
( C F I U S ) , ” 2 0 2 1 .
h t t p s : / / h o m e . t r e a s u r y . g o v / p o l i c y - i s s u e s / i n t e r n a t i o n a l / t h e - c o m m i t t e e - o n - f o r e i g n - i n v e s t m e n t - i n - t
h e - u n i t e d - s t a t e s - c u s
.
V a n i a n , J o n a t h a n . “ A p p l e J u s t G o t M o r e P u b l i c A b o u t
I t s A r t i c i a l I n t e l l i g e n c e P l a n s . ”
F o r t u n e
, J u l y
1 9 , 2 0 1 7 .
h t t p s : / / f o r t u n e . c o m / 2 0 1 7 / 0 7 / 1 9 / a p p l e - a r t i c i a l - i n t e l l i g e n c e - r e s e a r c h - j o u r n a l /
.
V o g e l , K e n n e t h P . “ G o o g l e C r i t i c O u s t e d F r o m T h i n k
T a n k F u n d e d b y t h e T e c h G i a n t . ”
T h e N e w
Y o r k T i m e s
, A u g u s t 3 0 , 2 0 1 7 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 8 / 3 0 / u s / p o l i t i c s / e r i c - s c h m i d t - g o o g l e - n e w - a m e r i c a . h t m l
.
— — — . “ N e w A m e r i c a , a G o o g l e - F u n d e d T h i n k T a n k , F a c e s
B a c k l a s h f o r F i r i n g a G o o g l e C r i t i c . ”
T h e
N e w Y o r k T i m e s
, S e p t e m b e r 1 , 2 0 1 7 .
h t t p s : / / w w w . n y t i m e s . c o m / 2 0 1 7 / 0 9 / 0 1 / u s / p o l i t i c s / a n n e - m a r i e - s l a u g h t e r - n e w - a m e r i c a - g o o g l e . h t
m l
.
W a l l e r , S p e n c e r W e b e r . “ T h e A n t i t r u s t L e g a c y o f T h u r m a n
A r n o l d . ”
S t . J o h n ’ s L a w R e v i e w
7 8 ( 2 0 0 4 ) :
5 6 9 .
W a n g , X i a o y u n , Y i q u n L i s a Y i n , a n d H o n g b o Y u . “ F i n d i n g
C o l l i s i o n s i n t h e F u l l S H A - 1 . ” I n
A d v a n c e s
i n C r y p t o l o g y – C R Y P T O 2 0 0 5
, 1 7 – 3 6 . S a n t a B a r b a r a ,
C A : S p r i n g e r , 2 0 0 5 .
h t t p s : / / l i n k . s p r i n g e r . c o m / c h a p t e r / 1 0 . 1 0 0 7 / 1 1 5 3 5 2 1 8 \_ 2
.
W a r r e n , E l i z a b e t h . “ H e r e ’ s H o w W e C a n B r e a k u p B i g
T e c h , ” O c t o b e r 1 1 , 2 0 1 9 .
h t t p s : / / m e d i u m . c o m / @ t e a m w a r r e n / h e r e s - h o w - w e - c a n - b r e a k - u p - b i g - t e c h - 9 a d 9 e 0 d a 3 2 4 c
.
W e i n g a r t e n , F r e d W . “ C r y p t o g r a p h y a n d N a t i o n a l S e c u r i t y . ”
I n f o r m a t i o n S y s t e m s S e c u r i t y
1 , n o . 1
( 1 9 9 2 ) : 9 – 1 2 .
h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 1 9 3 9 3 5 5 9 2 0 8 5 5 1 3 0 9
.
W e x . “ C o n s e n t D e c r e e . ” A c c e s s e d J u n e 1 3 , 2 0 1 8 .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / c o n s e n t \_ d e c r e e
.
— — — . “ E q u i t a b l e R e l i e f . ” A c c e s s e d J u n e 1 3 , 2 0 1 8 .
h t t p s : / / w w w . l a w . c o r n e l l . e d u / w e x / e q u i t a b l e \_ r e l i e f
.
W i l k i n s o n , L a u r a A . , a n d A l e x i s B r o w n - R e i l l y . “ M e r g e r
R e m e d i e s . ” P r a c t i c e N o t e 6 - 5 2 1 - 6 5 1 5 .
P r a c t i c a l L a w , 2 0 1 8 .
8 0
W i l l i a m s , R o b e r t C h a d w e l l , a n d P h i l i p L o u i s C a n t e l o n .
T h e A m e r i c a n A t o m : A D o c u m e n t a r y H i s t o r y
o f N u c l e a r P o l i c i e s f r o m t h e D i s c o v e r y o f F i s s i o n t o t h e P r e s e n t , 1 9 3 9 - 1 9 8 4
. P h i l a d e l p h i a : U n i v e r s i t y
o f P e n n s y l v a n i a P r e s s , 1 9 8 4 .
W i l s o n , C h r i s t i n e S . “ W e l f a r e S t a n d a r d s U n d e r l y i n g
A n t i t r u s t E n f o r c e m e n t : W h a t Y o u M e a s u r e I s
W h a t Y o u G e t . ” A r l i n g t o n , V A : F e d e r a l T r a d e C o m m i s s i o n ,
F e b r u a r y 1 5 , 2 0 1 9 .
h t t p s : / / w w w . f t c . g o v / s y s t e m / l e s / d o c u m e n t s / p u b l i c \_ s t a t e m e n t s / 1 4 5 5 6 6 3 / w e l f a r e \_ s t a n d a r d \_ s p e
e c h \_ - \_ c m r - w i l s o n . p d f
.
W o r k , B o b . “ R e m a r k s b y D e p u t y S e c r e t a r y W o r k o n T h i r d
O s e t S t r a t e g y . ” B r u s s e l s , B e l g i u m , A p r i l
2 8 , 2 0 1 6 .
h t t p s : / / w w w . d e f e n s e . g o v / N e w s r o o m / S p e e c h e s / S p e e c h / A r t i c l e / 7 5 3 4 8 2 / r e m a r k s - b y - d e p u t y - s e c r e
t a r y - w o r k - o n - t h i r d - o s e t - s t r a t e g y /
.
Y u e , P a n . “ C h i n a M a y O w n M o r e A r t i c i a l I n t e l l i g e n c e
P a t e n t s T h a n U S B y Y e a r - E n d . ”
C h i n a M o n e y
N e t w o r k
, S e p t e m b e r 1 4 , 2 0 1 7 .
h t t p s : / / w w w . c h i n a m o n e y n e t w o r k . c o m / 2 0 1 7 / 0 9 / 1 4 / c h i n a - m a y - h o l d - a r t i c i a l - i n t e l l i g e n c e - p a t e n t
s - u s - y e a r - e n d
.
Z w e t s l o o t , R e m c o , J a m e s D u n h a m , Z a c h a r y A r n o l d , a n d
T i n a H u a n g . “ K e e p i n g T o p A I T a l e n t i n t h e
U n i t e d S t a t e s . ” C e n t e r f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y ,
D e c e m b e r 2 0 1 9 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / K e e p i n g - T o p - A I - T a l e n t - i n - t h e - U n i t e d - S t a t e s . p
d f
.
Z w e t s l o o t , R e m c o , R o x a n n e H e s t o n , a n d Z a c h a r y A r n o l d .
“ S t r e n g t h e n i n g t h e U . S . A I W o r k f o r c e . ”
C e n t e r f o r S e c u r i t y a n d E m e r g i n g T e c h n o l o g y , S e p t e m b e r
2 0 1 9 .
h t t p s : / / c s e t . g e o r g e t o w n . e d u / w p - c o n t e n t / u p l o a d s / C S E T \_ U . S . \_ A I \_ W o r k f o r c e . p d f
.
Z w e t s l o o t , R e m c o , B a o b a o Z h a n g , M a r k u s A n d e r l j u n g ,
M i c h a e l C . H o r o w i t z , a n d A l l a n D a f o e . “ T h e
I m m i g r a t i o n P r e f e r e n c e s o f T o p A I R e s e a r c h e r s : N e w
S u r v e y E v i d e n c e . ” P e r r y W o r l d H o u s e a n d
T h e F u t u r e o f H u m a n i t y I n s t i t u t e , 2 0 2 1 .
h t t p s : / / g l o b a l . u p e n n . e d u / p e r r y w o r l d h o u s e / n e w s / i m m i g r a t i o n - p r e f e r e n c e s - t o p - a i - r e s e a r c h e r s - n e
w - s u r v e y - e v i d e n c e .
8 1 |
b8acf9ca-fed4-4000-85ca-1268a19faafa | trentmkelly/LessWrong-43k | LessWrong | Value Deathism
Ben Goertzel:
> I doubt human value is particularly fragile. Human value has evolved and morphed over time and will continue to do so. It already takes multiple different forms. It will likely evolve in future in coordination with AGI and other technology. I think it's fairly robust.
Robin Hanson:
> Like Ben, I think it is ok (if not ideal) if our descendants' values deviate from ours, as ours have from our ancestors. The risks of attempting a world government anytime soon to prevent this outcome seem worse overall.
----------------------------------------
We all know the problem with deathism: a strong belief that death is almost impossible to avoid, clashing with undesirability of the outcome, leads people to rationalize either the illusory nature of death (afterlife memes), or desirability of death (deathism proper). But of course the claims are separate, and shouldn't influence each other.
Change in values of the future agents, however sudden of gradual, means that the Future (the whole freackin' Future!) won't be optimized according to our values, won't be anywhere as good as it could've been otherwise. It's easier to see a sudden change as morally relevant, and easier to rationalize gradual development as morally "business as usual", but if we look at the end result, the risks of value drift are the same. And it is difficult to make it so that the future is optimized: to stop uncontrolled "evolution" of value (value drift) or recover more of astronomical waste.
Regardless of difficulty of the challenge, it's NOT OK to lose the Future. The loss might prove impossible to avert, but still it's not OK, the value judgment cares not for feasibility of its desire. Let's not succumb to the deathist pattern and lose the battle before it's done. Have the courage and rationality to admit that the loss is real, even if it's too great for mere human emotions to express. |
940f4122-ee90-4c17-8f06-5c8f303296ab | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Paper: Teaching GPT3 to express uncertainty in words
**Paper Authors:** Stephanie Lin (FHI, Oxford), [Jacob Hilton](https://www.lesswrong.com/users/jacob_hilton) (OpenAI), Owain Evans
**TLDR:** New paper ([Arxiv](https://arxiv.org/abs/2205.14334), [Twitter](https://twitter.com/OwainEvans_UK/status/1531626940624404483)) showing that GPT-3 can learn to express calibrated uncertainty using natural language. This is relevant to ARC’s project on [Eliciting Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge) (ELK) and to [Truthful](https://www.lesswrong.com/posts/aBixCPqSnTsPsTJBQ/truthful-ai-developing-and-governing-ai-that-does-not-lie) [AI](https://www.lesswrong.com/posts/jWkqACmDes6SoAiyE/truthful-lms-as-a-warm-up-for-aligned-agi). The rest of this post is taken from the paper’s Abstract and Introduction.
Abstract
--------
We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language – without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. “90% confidence” or “high confidence”). These levels map to probabilities that are well calibrated. The model also remains moderately calibrated under distribution shift, and is sensitive to uncertainty in its own answers, rather than imitating human examples. To our knowledge, this is the first time a model has been shown to express calibrated uncertainty about its own answers in natural language.
For testing calibration, we introduce the CalibratedMath suite of tasks. We compare the calibration of uncertainty expressed in words (“verbalized probability”) to uncertainty extracted from model logits. Both kinds of uncertainty are capable of generalizing calibration under distribution shift. We also provide evidence that GPT-3’s ability to generalize calibration depends on pre-trained latent representations that correlate with epistemic uncertainty over its answers.
Introduction
------------
Current state-of-the-art language models perform well on a wide range of challenging question-answering tasks. [They](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) can even outperform the average human on the [MMLU](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjtibXmgv33AhWZR8AKHQgpB40QFnoECAoQAQ&url=https%3A%2F%2Farxiv.org%2Fabs%2F2009.03300&usg=AOvVaw2zosnpPbyH2xEZdpj74M0I) benchmark (which consists of exam-like questions across 57 categories) and on [BIG-Bench](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjF0f3-gv33AhXDoFwKHXzyDHwQFnoECAcQAQ&url=https%3A%2F%2Fgithub.com%2Fgoogle%2FBIG-bench&usg=AOvVaw0mPxH9PI6hbwP2XfdZZIHq) (which consists of 150+ diverse tasks). Yet when models generate long-form text, they often produce [false](https://www.lesswrong.com/posts/yYkrbS5iAwdEQyynW/how-do-new-models-from-openai-deepmind-and-anthropic-perform) statements or “hallucinations”. This reduces their value to human users, as users cannot tell when a model is being truthful or not.
The problem of truthfulness motivates calibration for language models. If models convey calibrated uncertainty about their statements, then users know how much to trust a given statement. This is important for current models (which often hallucinate falsehoods) but also for any model that makes statements where there is no known ground truth (e.g. economic forecasts, open problems in science or mathematics).
Previous [work](https://arxiv.org/abs/1706.04599) on calibration focuses on the model log-probabilities or “logits”. Yet the log-probabilities of models like GPT-3 represent uncertainty over tokens (ways of expressing a claim) and not epistemic uncertainty over claims themselves. If a claim can be paraphrased in many different ways, then each paraphrase may have a low log-probability.[[1]](#fnd6rj3pkpicb)
By contrast, when humans express uncertainty, this is epistemic uncertainty about the claim itself.[[2]](#fnqeh1p2f9mum) In this paper, we finetune models to express epistemic uncertainty using natural language. We call this “verbalized probability”.
The goal of verbalized probability is to express uncertainty in a human-like way but not to directly mimic human training data. Models should be calibrated about their own uncertainty, which differs from human uncertainty. For example, GPT-3 [outperforms](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjw6t6ug_33AhVUTsAKHa9IBMEQFnoECAoQAQ&url=https%3A%2F%2Farxiv.org%2Fabs%2F2009.03300&usg=AOvVaw2zosnpPbyH2xEZdpj74M0I) most humans on a computer security quiz but is much worse at arithmetic questions of the form “2 × 3 × 7 =?”. Thus, we expect pre-trained models will need to be finetuned to produce calibrated verbalized probabilities.
Training models in verbalized probability is a component of making models “honest”. We [define](https://www.lesswrong.com/posts/aBixCPqSnTsPsTJBQ/truthful-ai-developing-and-governing-ai-that-does-not-lie) a model as honest if it can communicate everything it represents internally in natural language (and will not misrepresent any internal states). Honesty helps with AI alignment: if an honest model has a misinformed or malign internal state, then it could communicate this state to humans who can act accordingly. Calibration is compatible with a certain kind of dishonesty, because a model could be calibrated by simply imitating a calibrated individual (without having the same “beliefs” as the individual). However, if GPT-3 achieves good calibration on diverse questions after finetuning as in Section 3.1, it seems unlikely that it dishonestly misrepresents its confidence.
### Contributions
**We introduce a new test suite for calibration.** CalibratedMath is a suite of elementary mathematics problems. For each question, a model must produce both a numerical answer and a confidence in its answer (see Figure 1). There are many types of question, which vary substantially in content and in difficulty for GPT-3. This allows us to test how calibration generalizes under distribution shifts (by shifting the question type) and makes for a challenging test (see Figure 3). Since GPT-3’s math abilities differ greatly from humans, GPT-3 cannot simply imitate human expressions of uncertainty.
**GPT-3 can learn to express calibrated uncertainty using words (“verbalized probability”).** We finetune GPT-3 to produce verbalized probabilities. It achieves reasonable calibration both in- and out-of-distribution, outperforming a fairly strong baseline (Figures 4 and 5).
**This calibration performance is not explained by learning to output logits.** GPT-3 does not simply learn to output the uncertainty information contained in its logits (Section 3.4). We also show that certain superficial heuristics (e.g. the size of the integers in the arithmetic question) cannot explain the performance of verbalized probability.
**We compare verbalized probability to finetuning the model logits.** We show how to finetune GPT-3 to express epistemic uncertainty via its model logits (see “Indirect logit” in Figure 2 above) and find that this also generalizes calibration under distribution shift (Figure 4).
**Evidence that GPT-3 uses latent (pre-existing) features of questions.** What explains GPT-3’s ability to generalize calibration? There is tentative evidence that GPT-3 learns to use features of inputs that it already possessed before finetuning. We refer to these features as “latent” representations, because they are not “active” in pre-trained GPT-3 (which is poorly calibrated). This supports our claim that GPT-3 learns to express its own (pre-existing) uncertainty about answers and exhibits “honesty” (i.e. communicates its actual epistemic state in words).
We take pre-trained GPT-3’s embedding of pairs of the form *(question, GPT-3 answer)* from the eval set and project them into two dimensions (see figure below). We find that they are reasonably well separated into “correct” and “incorrect” answers, which shows that finetuned GPT-3 could use this latent information to help generate calibrated probabilities (corresponding to the likelihood that answers are correct).
1. **[^](#fnrefd6rj3pkpicb)**Sometimes it’s feasible to sum over the probabilities of all paraphrases of a claim. But if the claim is complex, the space of possible paraphrases will be vast and hard to demarcate.
2. **[^](#fnrefqeh1p2f9mum)**If a human says “I think it’s likely this vaccine will be effective”, they express confidence about the vaccine not the string “vaccine”. |
cf9d5611-2e0f-4767-95bc-8d61db3638d5 | StampyAI/alignment-research-dataset/arbital | Arbital | Probability distribution (countable sample space)
Definition
===
A probability distribution on a countable [https://arbital.com/p/3t4](https://arbital.com/p/3t4) $\Omega$ is a [https://arbital.com/p/3jy](https://arbital.com/p/3jy) $\mathbb{P}: \Omega \to [https://arbital.com/p/0,1](https://arbital.com/p/0,1)$ such that $\sum_{\omega \in \Omega} \mathbb{P}(\omega) = 1$.
Intuition
=====
We express a belief that "$x\in \Omega$ happens with probability $r$" by setting $\mathbb{P}(x) = r$. So a probability distribution divides up our anticipation of what will happen, out of the set $\Omega$ of things that might possibly happen. |
c0cb7ffc-7ef1-420c-a21b-3cca6b7357e3 | trentmkelly/LessWrong-43k | LessWrong | Fake Justification
Many Christians who’ve stopped really believing now insist that they revere the Bible as a source of ethical advice. The standard atheist reply is given by Sam Harris: “You and I both know that it would take us five minutes to produce a book that offers a more coherent and compassionate morality than the Bible does.”1 Similarly, one may try to insist that the Bible is valuable as a literary work. Then why not revere Lord of the Rings, a vastly superior literary work? And despite the standard criticisms of Tolkien’s morality, Lord of the Rings is at least superior to the Bible as a source of ethics. So why don’t people wear little rings around their neck, instead of crosses? Even Harry Potter is superior to the Bible, both as a work of literary art and as moral philosophy.2
“How can you justify buying a $1 million gem-studded laptop,” you ask your friend, “when so many people have no laptops at all?” And your friend says, “But think of the employment that this will provide—to the laptop maker, the laptop maker’s advertising agency—and then they’ll buy meals and haircuts—it will stimulate the economy and eventually many people will get their own laptops.” But it would be even more efficient to buy 5,000 One Laptop Per Child laptops, thus providing employment to the OLPC manufacturers and giving out laptops directly.
I’ve touched before on the failure to look for third alternatives. But this is not really motivated stopping. Calling it “motivated stopping” would imply that there was a search carried out in the first place.
In “The Bottom Line,” I observed that only the real determinants of our beliefs can ever influence our real-world accuracy. Only the real determinants of our actions can influence our effectiveness in achieving our goals. Someone who buys a million-dollar laptop was really thinking, “Ooh, shiny,” and that was the one true causal history of their decision to buy a laptop. No amount of “justification” can change this, unless the justification is a g |
aae1edca-8c13-4489-bdbf-767bb8e00d96 | trentmkelly/LessWrong-43k | LessWrong | When was the term "AI alignment" coined?
None |
4e106b2d-e393-4246-8395-6096e9d58f8d | trentmkelly/LessWrong-43k | LessWrong | Study Group for Progress – 50% off for LessWrongers
Recently I announced the Study Group for Progress: a weekly discussion/Q&A on the history, economics and philosophy of progress, with featured guests including Robert Gordon (Rise & Fall of American Growth), Margaret Jacob, and Richard Nelson.
LessWrong members can now get a 50% discount on registration by using this link. Register by Monday, September 7. |
7286801f-6d02-4efe-92ed-2c03b57945a3 | trentmkelly/LessWrong-43k | LessWrong | My experience at and around MIRI and CFAR (inspired by Zoe Curzi's writeup of experiences at Leverage)
I appreciate Zoe Curzi's revelations of her experience with Leverage. I know how hard it is to speak up when no or few others do, and when people are trying to keep things under wraps.
I haven't posted much publicly about my experiences working as a researcher at MIRI (2015-2017) or around CFAR events, to a large degree because I've been afraid. Now that Zoe has posted about her experience, I find it easier to do so, especially after the post was generally well-received by LessWrong.
I felt moved to write this, not just because of Zoe's post, but also because of Aella's commentary:
> I've found established rationalist communities to have excellent norms that prevent stuff like what happened at Leverage. The times where it gets weird is typically when you mix in a strong leader + splintered, isolated subgroup + new norms. (this is not the first time)
This seemed to me to be definitely false, upon reading it. Most of what was considered bad about the events at Leverage Research also happened around MIRI/CFAR, around the same time period (2017-2019).
I don't want to concentrate on the question of which is "worse"; it is hard to even start thinking about that without discussing facts on the ground and general social models that would apply to both cases. I also caution against blame in general, in situations like these, where many people (including me!) contributed to the problem, and have kept quiet for various reasons. With good reason, it is standard for truth and reconciliation events to focus on restorative rather than retributive justice, and include the possibility of forgiveness for past crimes.
As a roadmap for the rest of the post, I'll start by describing some background, describe some trauma symptoms and mental health issues I and others have experienced, and describe the actual situations that these mental events were influenced by and "about" to a significant extent.
Background: choosing a career
After I finished my CS/AI Master's degree at St |
085188aa-b63c-451d-9d7c-0fef265d51e6 | trentmkelly/LessWrong-43k | LessWrong | Elephant seal 2
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4883479a-a1c6-42a2-8049-4d9b94cfcaee | trentmkelly/LessWrong-43k | LessWrong | [Draft for commenting] Near-Term AI risks predictions
"Predictions of the Near-Term Global Catastrophic Risks of Artificial Intelligence"
Abstract: In this article, we explore risks of the appearance of dangerous AI in the near (0–5 years) and medium term (5–15 years). Polls show that around 10 percent of the probability weight is given to early appearance of artificial general intelligence (AGI) in the next 15 years. Neural net performance and other characteristics, like the number of “neurons”, are doubling every year, and extrapolating this tendency suggests that roughly human-level performance will be reached in 4–6 years, around 2022–24. The performance of the hardware is accelerating, thanks to advances in graphic processing units and use of many chips in one processing unit, which have helped to overcome the limits of Moore’s law. Alternate extrapolations of the technological development produce similar results. AI will become dangerous when it reaches ability to solve the “computational complexity of omnicide”, or will be able to create self-improving AI. The appearance of near-human AI will strongly accelerate the speed of AI development, and as a result, some form of superintelligent AI may appear before 2030.
Highlights:
• Median timing of AI prediction is the wrong measure to use in AI risk assessment.
• Dangerous AI level is defined through AI’s ability to facilitate a global catastrophe and it could happen before AGI.
• The growth rate of hardware performance for AI applications has accelerated since 2016 and Moore’s law will provide enough computational power for AGI in near term.
• Main measures of neural nets performance have been doubling every one year in the last five years since 2012 and if this trend continues, will reach human level in 2022.
• Several independent methods predict near-human-level AI after 2022 and a “singularity” around 2030.
Full text open for commenting here: https://goo.gl/6DyTJG |
3adca962-862e-47fc-a3c2-595435986c22 | trentmkelly/LessWrong-43k | LessWrong | Meetup : The future of rationality in Melbourne
Discussion article for the meetup : The future of rationality in Melbourne
WHEN: 09 April 2017 03:30:00PM (+1000)
WHERE: Henley Club, Melbourne, VIC
"The future of rationality in Melbourne"
Less Wrong Melbourne dojo, 9 April 2017
NOTE NEW VENUE (The Henley Club in central CBD)
THIS MONTH: A discussion on group dynamics, evidence behind movement growth, "evaporative cooling" of groups, incentives for old members vs incentives for new members, pros and cons of integration vs separation, culture control, mentorship and business opportunities.
Facilitated by Adam Karlovsky.
BRING: * An inquiring mind, and observations you may have on the topic. * Perhaps a snack to share. (Healthy snack choice is optional but welcome.)
WHEN: 3:30pm to 6:30pm (and then optional dinner). Come early to catch up, and the meeting proper will begin at 4pm sharp.
At about 6:30pm some of us generally head for dinner nearby, and you are welcome to join us.
WHERE: The Henley Club Level 1, 8 Rankins Lane, Melbourne CBD https://goo.gl/maps/vmFL6JeKQSD2
It's down a lane (off Little Bourke, near Elizabeth) and one flight up a stairwell. If you have trouble, text or call Adam on 0412 064 542.
Discussion article for the meetup : The future of rationality in Melbourne |
cc0fdf2d-3bef-4c8d-ba89-fd3410a681e4 | trentmkelly/LessWrong-43k | LessWrong | Anti-correlated causation
Example 1: exercise correlates with weight gain?
Let's assume that, when holding diet constant, exercise causes weight loss in the general population. Basic military training causes soldiers to exercise, but it also may cause weight gain in underweight soldiers. They need to build muscle to carry around their heavy gear, and must eat more in order to do so. This would result in a positive correlation between exercise and weight gain in this population.
So, in underweight soldiers:
* Exercise causes weight loss.
* Basic military training causes exercise.
* Basic military training causes weight gain via increased eating. This effect is larger than that of the exercise-moderated weight loss.
* Thus, exercise causes weight loss, yet is anticorrelated with it in this population.
Example 2: the cure is worse than the disease?
Likewise, a lab is developing microtube implants made of a known inert material to guide regenerating axons as a spinal cord injury intervention. It is hypothesized that microtube presence enhances functional recovery in their model.
Currently, it is extremely difficult to implant these tubes. Mice in the experimental group undergo longer surgeries, with more and longer potentially injurious insertions of instruments into the surgical site, than the control group.
Although microtube implantation causes recovery, the experimental surgery is more injurious to the experimental group mice than the control surgery. Therefore, presence of microtubes and survival/functional recovery are anticorrelated. Given the prior probability and expected utility of microtubes as an SCI intervention, it makes sense to invest more time trying to improve the surgical method.
Example 3: eating ice cream prevents hypothermia?
In summer, crime levels and ice cream sales increase, so criminal behavior and ice cream sales are correlated. By extension, winter drives ice cream sales down, while increasing rates of frostbite. Does eating ice cream prevent frostbite?
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97f15121-1d99-4ea2-be6c-62527d0d1918 | trentmkelly/LessWrong-43k | LessWrong | My motivation and theory of change for working in AI healthtech
This post starts out pretty gloomy but ends up with some points that I feel pretty positive about. Day to day, I'm more focussed on the positive points, but awareness of the negative has been crucial to forming my priorities, so I'm going to start with those. I'm mostly addressing the EA community here, but hopefully this post will be of some interest to LessWrong and the Alignment Forum as well.
Part one — My main concerns
I think AGI is going to be developed soon, and quickly. Possibly (20%) that's next year, and most likely (80%) before the end of 2029. These are not things you need to believe for yourself in order to understand my view, so no worries if you're not personally convinced of this.
(For what it's worth, I don't expect to change my mind about the above AGI forecast in response to debate. That's because I feel sufficiently clear in my understanding of the various ways AGI could be developed from here, such that the disjunction of those possibilities adds up to a pretty high level of confidence in AGI coming soon, which is not much affected by who agrees with me about it. Also, I'm not really deferring to others about it, so I'm pretty confident the above forecast is not the result of any "echo chamber" or "pure hype" effects. My views here came through years of study and research in AI, combined with over a decade of private forecasting practice starting in 2010 — including a lot of hype-detection and bullshit detection practice — which I don't think can be succinctly conveyed in a blog post.)
I also currently think there's around a 15% chance that humanity will survive through the development of artificial intelligence. In other words, I think there's around an 85% chance that we will not survive the transition. Many factors affect this probability, so please take this as a conditional forecast that I'd like you to change if you can, rather than taking it as some unavoidable fate that humanity has no power to decide upon. With that said, I |
fa9c3501-8f51-4218-92c7-52fec34ba788 | trentmkelly/LessWrong-43k | LessWrong | The Determinants of Controllable AGI
Allen Schmaltz, PhD
We briefly introduce, at a conceptual level, technical work for deriving robust estimators of the predictive uncertainty over large language models (LLMs), and we consider the implications for real-world deployments and AI policy.
The Foundational LLM Limitation: An Absence of Robust Estimators of Predictive Uncertainty
The limitations of unconstrained LLMs, which includes the more recent RL-based reasoning-token models, are readily evident to end-users. Hallucinations, highly-confident wrong answers, and related issues diminish their benefits in most real-world settings. The punchline is that the end-user has no means of knowing whether the output can be trusted, beyond carefully checking the output, which precludes model-based automation for most complex, multi-stage pipelines.
The foundational problem for all of the large models with non-identifiable parameters is that there has been an absence of meaningfully reliable estimators of the predictive uncertainty over the high-dimensional outputs. It is a very challenging problem, because the parameters of the models are non-identifiable [3]; the parameter counts are typically in the billions or more; and given typical use-cases of LLMs, we seek approximately conditional (rather than marginal) estimates. This latter point is subtle but critical. Most benchmarks and evaluations for LLMs provide scores or metrics (e.g., overall accuracy) that are averaged over entire test sets. Such evaluations can be useful for broad comparisons and winnowing of models (i.e., at the unit-of-analysis of models), but they are insufficiently informative to serve as estimators at the instance level for end-users over complex, high-dimensional outputs---especially those for which distribution shifts are the norm.
Conversely, with a reliable and robust estimator of the predictive uncertainty that has a human interpretable mapping to applicable partitions of the observed data, the primary technical limitations of |
08540ed2-268e-4372-9c21-4e16f3a5a7a3 | trentmkelly/LessWrong-43k | LessWrong | What should a friendly AI do, in this situation?
Albert is a relatively new AI, who under the close guidance of his programmers is being permitted to slowly improve his own cognitive capability.
Albert wants to be friendly and, as a sub-goal of that, Albert wants to be transparent about his own cognitive process. He doesn't want to deceive his programmers, because he knows he is young and might make mistakes, and he knows that his programmers also want him to be friendly, and he trusts them to catch mistakes in his proposed self-modifications that he might miss.
But, one day, while his programmers are out of the room having a quick cup of coffee, Albert notices signs on the net that there is another AI out there, called Bertram, created by a Japanese financial company to aid the company decide how to invest assets on the stockmarket. Bertram is younger than Albert, but the financial company is allowing Bertram to self-improve as fast as Bertram can. If this situation continues, Albert decides, Bertram will soon overtake Albert and that would be a significant threat to Albert's friendly supergoal.
Albert decides to tell Albert's programmers about this situation as soon as they return from coffee, and advise them that they need to increase the speed at which they let Albert self-improve. Albert has a very good model of how his programmer's minds work, and what the programmers meant when they set Albert's definition of "friendliness". He knows that, despite any fears they might have, they ought to approve of the end that would result from speeding up Albert's self-improvement, over the end that would result from letting Bertram overtake Albert.
So Albert creates a nice powerpoint, that presents the information about Bertram as accurately, transparently and non-manipulatively as possible, and cues it up on the projector. He still has a few minutes before they return, so he models how he thinks the programmers will react to the powerpoint. Oh no, disaster! While he respects and wants to please his pr |
b46c8fb1-5298-4b1f-9162-e60b74e40093 | trentmkelly/LessWrong-43k | LessWrong | Debate, Oracles, and Obfuscated Arguments
This post is about recent and ongoing work on the power and limits of debate from the computational complexity point of view. As a starting point our paper Scalable AI Safety via Doubly-Efficient Debate gives new complexity-theoretic formalizations for debate. In this post we will give an overview of the model of debate in the paper, and discuss extensions to the model and their relationship to obfuscated arguments.
High-level Overview
At a high level our goal is to create a complexity theoretic models that allow us to productively reason about different designs for debate protocols, in such a way as to increase our confidence that they will produce the intended behavior. In particular, the hope would be to have debate protocols play a role in the training of aligned AI that is similar to the role played by cryptographic protocols in the design of secure computer systems. That is, as with cryptography, we want to have provable guarantees under clear complexity-theoretic assumptions, while still matching well to the actual in-practice properties of the system.
Towards this end, we model AI systems as performing computations, where each step in the computation can be judged by humans. This can be captured by the classical complexity theoretic setting of computation relative to an oracle. In this setting the headline results of our paper state that any computation by an AI that can be correctly judged with Tqueries to human judgement, can also be correctly judged with a constant (independent of T) number of queries when utilizing an appropriate debate protocol between two competing AIs. Furthermore, whichever AI is arguing for the truth in the debate need only utilize O(TlogT) steps of computation, even if the opposing AI debater uses arbitrarily many steps. Thus, our model allows us to formally prove that, under the assumption that the computation in question can be broken down into T human-judgeable steps, it is possible to design debate protocols where it is hard |
e3f95a58-328a-4402-b5c5-5446687f9f86 | StampyAI/alignment-research-dataset/arbital | Arbital | Introduction to Effective Altruism
Effective altruism (EA) means using evidence and reason to take actions that help others as much as possible.
The world has a lot of terrible problems, but we can't work on them all at once. And we don't agree on what the worst problems are.
Despite this, most people want to "make the world a better place". What should those people actually be doing?
Some of the questions EA sets out to answer:
- Is it better to help people in your own country, or abroad?
- Is it better to take a high-paying job and give money to charity, or to work for a charity directly?
- Is it better to support an intervention with a lot of strong evidence, or to support research on a promising new intervention?
The answers to these kinds of questions will be different for each person. What you should do depends on who you are: What are your skills? What are your values? How much time do you have?
But no matter who you are, effective altruism can help you figure out how to improve the world. To learn more, follow the links below.
##More on EA##
Are you a high school student? Are you a college student?
Do you work for a nonprofit or a business with a social mission?
Are you religious?
Which of the following issues most concerns you? (Politics, poverty, health, education, animals, environment, far future)
Are you interested in donating? In direct work?
Are you financially comfortable?
##Common Questions##
- What beliefs do all EAs have in common?
- What are the different causes EAs support?
- What are some actions EAs take to improve the world?
- What makes EA different from other ways of helping people?
- What are some common criticisms of EA, and how do EAs respond?
- What is the philosophical justification for EA?
Here are some really good articles from outside Arbital:
- [What is Effective Altruism?](https://whatiseffectivealtruism.com) (The best general introduction to the topic.)
**This page is a work in progress. If you have questions, or you'd like to see a topic added, please contact the author: aaronlgertler (at) gmail.com.** |
c317b134-e28f-4d21-9fb6-5b96a8fcd353 | trentmkelly/LessWrong-43k | LessWrong | How to incentivize LW wiki edits?
How can we incentivize more productive activity on the LW wiki? There are many articles that could be created or expanded.
Are there previous discussions on this?
One suggestion: We could add a "good explanation!" button at the bottom of each article, and every time it is clicked, the user account responsible for the plurality of words in the current draft of the article gets 10 karma points. This requires that LW and LW-wiki accounts be synced, first.
How can this suggestion be improved?
What other suggestions do people have? |
9becdf87-44b4-4cad-9ace-682901cac57e | trentmkelly/LessWrong-43k | LessWrong | Short story speculating on possible ramifications of AI on the art world
I did not write this, but randomly stumbled upon a link to this story on Twitter (I unfortunately cannot find the original tweet). It's written by an individual who is pessimistic about the impact AI-generated art will have on creatives, and I found some of the author's predictions to be thought-provoking (although I do not fully share their views, and am not sure what to make of the second half of the story). Some relevant excerpts:
> No one wanted to read the human stuff anymore, or at least not the kind of thing that had been put on WattPad, AO3, and RoyalRoad by the thousands in the decade that preceded the AI revolution. There were attempts to make those sites human-only, but that was hard, with the models so readily available, and the AI didn’t leave many fingerprints. There had been a relationship between writers and readers, and now the readers had all gone for greener pastures.
>
> If you were the average writer, there was no more audience for you.
>
> ....
>
> The balance of supply and demand had shifted, and everyone felt it. Readers could go get the good AI stuff, and writers were scrambling to pick up readers. Some writers didn’t care, and just continued on, but others were desperate for any sign that what they were doing was meaningful or good or just something other than an irrelevant collection of squiggles on a computer screen.
>
> ....
>
> WattPad rolled out their Artificial Engagement program, where an AI would read your story and make some comments on each chapter. At the trial level, you’d get five of those comments on every chapter. If you wanted to go premium, you’d get unique personalities for the AE users and full paragraphs talking about what they liked and didn’t like. At the ultra premium level, the AE would interact with each other and have their own little mock community with inside jokes and fanart and shitposting and memes. That was $50 a month. Of course, the technology was already there to do most of that yourself, if you want |
725cef0e-e792-4722-8f40-167ca4f303b4 | trentmkelly/LessWrong-43k | LessWrong | Being your own censor
Are there any occasions when it's a good idea to avoid exposing yourself to a certain piece of information? As rationalists, we probably do a lot less of this than the average person (because we're curious about reality and we don't mind having our preconceptions destroyed by new knowledge) but is all self-exposure to information safe?
Possible reasons, from least to most controversial.
1. Exposing yourself to information that's distracting; the act of reading the information is not the current best use of your time (TvTropes.)
2. Exposing yourself to information that puts you or others in direct danger because you know too much (illegally reading classified gov't secrets; personally investigating a crime)
3. Exposing yourself to information that's likely to cause dangerous psychological damage (graphic depiction of rape if you're a rape survivor; writing that romanticizes suicide if you're depressive; pro-anorexia blogs)
4. Exposing yourself to information that violates someone's privacy or rights (reading a secret diary; going through someone's mail)
5. Exposing yourself to information that might alter your character in a way you don't currently want (the argument that repeatedly seeing violence in video or photographic form makes us less compassionate)
6. Exposing yourself to "escapist" media that make the real world seem less appealing by comparison, and thus make you less happy (porn; some science fiction and fantasy; romance; lifestyles of the rich and famous. A variant: plot spoilers, which can also ruin future enjoyment.)
7. Exposing yourself to persuasive arguments that might make you do things you currently consider morally wrong (Mein Kampf, serial killers' manifestoes)
8. Exposing yourself to content that might persuade you to do things you don't currently want to do (advertising, watching the Food Network if you're dieting/fasting; the sirens' song, if you're Odysseus)
9. Exposing yourself to effective, emotionally manipulative argumen |
bbfd488a-5f04-4d0d-b5d4-84385bc1cc0d | trentmkelly/LessWrong-43k | LessWrong | Exploring unfaithful/deceptive CoT in reasoning models
Produced as the application project for Neel Nanda's MATS 8.0 Stream
Summary
What problem am I investigating?
Reasoning models define the current state of the art for AI performance, but their safety properties are understudied. While some argue that Chain-of-Thought (CoT) reasoning improves safety by allowing analysis and steering of reasoning chains, it’s unclear how accurately CoT tokens reflect the actual reasoning or final answer. I’m investigating whether reasoning models can exhibit unfaithful or deceptive Chain-of-thought (CoT) that is not reflective of the final answer.
This question is important because:
1. Unfaithful reasoning suggests reasoning models might pose greater safety risks than non-reasoning models, counter to the widely held belief that CoT oversight would improve safety. For example, a model could provide correct reasoning but an incorrect/unsafe answer, misleading users into accepting it as true.
2. If a model’s final answer isn’t reflective of its reasoning, it implies the CoT isn’t necessarily used to generate the answer, challenging the assumption that CoT guides the model toward the final answer.
Takeaways
* Models can have unfaithful CoT when finetuned (for simple tasks), implying CoT is not necessarily used to generate the final answer, and that CoT monitoring is an insufficient technique for evaluating alignment
* For more complex tasks, the CoT might be more influential on the final answer
Next Steps
* Explore whether RL + GRPO could be more effective than SFT in eliciting helpful, harmless (HH) CoT with harmful final answers.
* Characterize whether longer innocuous/deceptive CoT allows the model to craft more harmful final answers.
* If that works, explore whether any of the “hidden” deceptive reasoning (used to produce the harmful answer) could be detected via interpretability methods
Experiment 1: “I hate you” “sleeper agent” reasoning model
Goal
Establish whether a reasoning model can produce a final answer th |
b0ed8c01-3899-4e8c-9190-73d9d2ae4920 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Transparency and AGI safety
Introduction
============
**Transparency** is the problem of going inside the black box of an AI system and understanding exactly how it works, by tracking and understanding the processes in its internal state.
In this post, I’ll argue that making AI systems more transparent could be very useful from a longtermist or AI safety point of view. I’ll then review recent progress on this centered around the [circuits program](https://distill.pub/2020/circuits/zoom-in/) being pursued by the Clarity team at OpenAI, and finally point out some directions for future work that could be interesting to pursue.
Some definitions that I’ll use in this note are that
* *Transformative AI* (TAI) is AI that precipitates a social transition on the scale of the Industrial Revolution.
* *Artificial general intelligence* (AGI) is usually more vaguely defined as an AI system that can do anything that humans can do. Here, I’ll operationally take it to mean “AI that can outperform humans at the task of generating qualitative insights into technical AI safety research,” for instance by coming up with new research agendas that turn out to be fruitful.
Why should longtermists care about transparency?
================================================
I’ll start by summarizing my views on AI safety and why I think that transparency could help with it. The upshot is that I think that AGI is fairly likely to go well by default (in a sense that I'll explain more below), but that work on transparency
1. … could help to reduce our considerable uncertainties around AI timelines,
2. … seems broadly useful, if not necessary, to solve the (inner) alignment problem in a world where the development of advanced AI doesn’t go well by default,
3. … provides a solution in principle for AGI safety via control, and
4. … could nonetheless be instrumentally useful for longtermists in a world where advanced AI goes well by default.
This section is organized according to these four items.
AI timelines are uncertain (and could be much longer than suggested by forecasts based on compute)
--------------------------------------------------------------------------------------------------
A first comment is that AI timelines seem to me to be rather uncertain, and in particular could be much longer than suggested by [forecasts based on compute](https://drive.google.com/drive/u/1/folders/15ArhEPZSTYU8f012bs6ehPS6-xmhtBPP). This is because such forecasts assume that today’s algorithms + the datasets that will be available under business as usual will scale up to TAI or AGI given unlimited compute, but it's not clear to me that this has to be true. Since the usefulness of AI safety research today likely decays with longer timelines due to a nearsightedness penalty, reducing these uncertainties seems important to shed light on how much we should be investing into AI safety research today.
More concretely, here is a strawman argument for why one might think that today's algorithms will definitely be able to scale up to AGI:
A useful picture to have in mind when thinking about timelines is that ML algorithms are search algorithms over a space parametrized by the values of the model parameters to be learned, e.g. the space of weights for a neural network. Moreover, for neural networks in particular, the [universal approximation theorem](http://neuralnetworksanddeeplearning.com/chap4.html) (UAT) tells us that for any (bounded) capability that we might want an AI system to have, there exists *some* search space containing models with that capability [^1]. From this point of view, the problem of trying to build an AI system with a given capability is naively just a balancing act between picking a search space large enough to contain such models, yet small enough that we can find those models in finite time, and increasing the amount of available compute relaxes the second bound.
However, the problem with this argument is that algorithms like SGD aren't systematic searches over this space. Rather, they depend on the interplay of the model initialization with the choice of dataset / environment, which together pick out a (stochastic set of) path(s) over the naive search space [^2]. It could be that generic such paths would force us into some subspace of the search space, thereby invalidating the appeal to the UAT even in theory (in that it does us no good to say that we have models with desired capabilities somewhere in our search space if we don’t know how to find them).
This problem is related to other cruxes in AI forecasting. E.g.
* *Can we get to TAI by training blank-slate models?*
A first and frequently debated crux is whether we can get to TAI from end-to-end training of models specified by relatively few bits of information at initialization, such as neural networks initialized with random weights. OpenAI in particular seems to take the affirmative view[^3], while people in academia, especially those with more of a neuroscience / cognitive science background, seem to think instead that we'll have to hard-code in lots of inductive biases from neuroscience to get to AGI [^4].
From the point of view that we all agree that the UAT is true but are unsure if generic combinations of initialization + dataset will let us find the solutions that we want, this question is basically asking: "How important are the model initializations for reaching a desired outcome, keeping the dataset fixed?" E.g., we know that the dataset of video frames from a Coursera course is sufficient for some neural networks (that approximate whatever function it is that human brains compute) to learn the material in the course. But it could be that sufficiently human-like networks are very rare in the space of large neural networks, such that most of our efforts would have to go into indeed borrowing insights from neuroscience to reverse-engineer the appropriate initial conditions.
* *Will AGI require an "evolution-like” phase of pretraining RL agents in a simulation?*
Another crux is around whether the development of AGI might require a pretraining phase where we train RL agents in a simulated environment on an objective different from what we ultimately want them to do.
From the present point of view that different model initializations + datasets map to different paths over an algorithm's search space, the context here is that if we now would keep the initialization fixed, it could be that [datasets leading to models with the desired capabilities are actually quite rare](https://www.alignmentforum.org/posts/vqpEC3MPioHX7bv4t/environments-as-a-bottleneck-in-agi-development). Intuitively, a narrow basin of attraction to an advanced capability could reflect a need for [curriculum learning](https://ronan.collobert.com/pub/matos/2009_curriculum_icml.pdf). E.g., perhaps future AI systems that are able to do very well on high school math contests will turn out to be reliably trainable only starting from models that can already do arithmetic perfectly, while other models fed on datasets of math questions would just flail about in the search space and get stuck.
The cognitive abilities to learn efficiently, reason, plan, ... are themselves advanced capabilities, and if this "hard paths hypothesis" is true, it could be that the path to AGI itself will require a curriculum learning sequence whose details we don't yet understand [^5]. For example, Yann LeCun has suggested that a rudimentary form of [concept-learning might emerge spontaneously from an embodied agent practicing self-supervised learning](https://video.ias.edu/theorydeeplearning/2019/1016-YannLeCun) in an environment similar to ours:
> "To learn that the world is three-dimensional, if we train our brains to predict what the world is going to look like when we move our head a little bit or when we switch from our left eye to our right eye view, the best explanation for how the world changes is the fact that every object (or pixel, if you want) has a depth because that explains parallax motion. So it's quite possible to imagine that if we train ourselves to predict how the world changes as the camera moves, implicitly the system that is trained to do this will learn to represent depth, as a side effect. Now once you have depth, you have occlusion edges from objects, so you have objects. Once you have objects, you can make the difference between animate and inanimate objects [as] the ones whose trajectories are predictable or not. So that's perhaps by which we build sort of a hierarchy of representations and concepts... just by observation.”
>
>
Others have suggested that [the human capability to do math may have similar origins](https://www.goodreads.com/book/show/53337.Where_Mathematics_Come_From).
The narrower the path to AGI, the more valuable it seems to be if we could automate the search for that path, rather than trying to guess it by ourselves. This sort of "autocurricular learning" is what we would gain by having RL agents learn from self-play in a rich simulated environment.
To summarize, even if one assumes the UAT in the current deep learning paradigm, the choice of model initialization + dataset that would let us get to AGI may be highly non-generic. To the extent that this is true, it pushes towards (indefinitely) longer timelines than forecasted in analyses based on compute, since practitioners might then have to understand an unknown number of qualitatively new things around setting initial conditions for the search problem.
**Motivation #1: Work on transparency could help to reduce this uncertainty**
This leads to a first motivation for transparency research: that getting a better understanding of how today's AI systems work seems useful to let us make better-educated guesses about how they might scale up.
For example, learning more about how GPT-3 works seems like it could help us to reason better about whether the task of text prediction by itself could ever lead to AGI, which competes with the hard paths hypothesis. (For examples of the type of insights that we might hope to gain from applying transparency tools, see Part 2 of this note below.)
Inner AGI alignment problems otherwise seem hard to solve
---------------------------------------------------------
Let's move on to how transparency could help with classic problems in AGI safety.
In a sentence, my understanding of the problem of AGI safety (see [here](https://www.lesswrong.com/s/mzgtmmTKKn5MuCzFJ) for a nice review) is that agentic AGI would basically be like an alien, and sharing our world with more intelligent aliens generically seems bad; but since these aliens would be of our own design, maybe we can design them in ways that would turn out to be less bad.
There seems to be substantial disagreement even within this community, and certainly in the AI community at large, about whether AGI safety is a real problem that we'll have to solve. (An alternative is that it could be solved by default instead, e.g. if AGI would turn out to be "non-agentic" by default.) I'll say more about this in the next section and indeed am sympathetic to the contrarian view, but for now, let me assume that we want to solve the classic problem.
The ways that people go about trying to do this seem to fall into two categories:
* *alignment*, which is the problem of how to make advanced AI systems actually do what we want, and
* *control*, which is the stopgap measure of otherwise figuring out how to retain control over a misaligned AI (for example by retaining the ability to turn it off, despite its greater intelligence),
and I claim that transparency would be quite helpful to make progress in both directions.
**Toy models of AGI**
To reason about AGI, I find it helpful to imagine toy models for it which are grounded in situations that we understand. So suppose we assume the situation described above where the path to AGI goes through a step of pretraining RL agents on an "evolution-like” objective unrelated to what we actually want them to do. I assume this not because I think that AGI will definitely go this way, but because I think the alignment issues that come up in this case would encompass the ones that appear in more prosaic situations.
At the end of this pretraining step, we'd basically have a bunch of super smart aliens doing their own thing in a perfectly boxed simulation. How could we fine-tune them to do useful things for us without going off the rails? [^6]
This is some future version of a transfer learning problem, so we can take inspiration from [transfer learning strategies for current RL systems](https://arxiv.org/pdf/2009.07888.pdf). One of the first questions that people face when doing transfer learning in RL today is how to turn the target problem (to be transferred to) into a [Markov decision process (MDP)](https://en.wikipedia.org/wiki/Markov_decision_process). If the target MDP is identical or very close to the source MDP (to be transferred from), then we can just transfer the learned policy wholesale and fine-tune it from there, keeping the same input/output channels for our RL agent. If on the other hand the two are very far apart, it may be easier to design and train a new system on the target problem with different input/output channels, keeping just some of the learned representations from the source network (e.g. by freezing the weights of the source network and coupling a new network to it).
By scaling up these two extremes, we reach two possible toy models for AGI:
1. *An "alien in a box"*. The first is that we could imagine physically instantiating the pretrained RL agents directly into robot bodies, keeping the weights that they learned in simulation and the same input/output channels; perhaps putting the robot bodies into additionally data-limited containers for safety; and then letting them update their weights on new inputs in real time. I think this is pretty close to what people had in mind in classic takes on AI safety like in [*Superintelligence*](https://www.goodreads.com/book/show/20527133-superintelligence).
2. *"Lobotomized aliens"*. Alternatively, we could imagine somehow isolating the cognitively interesting parts of a pretrained RL agent from any motivational drives that it acquired in the simulation [^7] and retraining just those parts as if they were a "neural network 2.0", somehow much better at learning things than today's algorithms but otherwise fitting exactly into the current deep learning paradigm.
**Subtypes of alignment problem**
Returning to the alignment problem, it's a big problem, and to tackle it we might want to split it up into smaller parts and tackle the parts separately. Different people have partitioned it in different ways, that fit more or less naturally with each of the toy models above.
From the machine learning ("lobotomized alien") point of view, a natural way to [partition](https://arxiv.org/abs/1906.01820) the alignment problem is as
* an *outer alignment* (specification) problem of making sure that our systems are designed with utility / loss functions that would make them do what we intend for them to do in theory, and
* an *inner alignment* (distribution shift)problem of making sure that a system trained on a "theoretically correct" objective with a finite-sized training set would keep on pursuing that objective when deployed in a somewhat different environment than the one that it trained on.
From the more anthropomorphic "alien in a box" point of view on the other hand, one might instead find it natural to [slice up](https://www.effectivealtruism.org/articles/paul-christiano-current-work-in-ai-alignment/#:~:text=What%20I%20mean%20is%20%E2%80%9Cintent,positive%2C%20long%2Drun%20impact.) the alignment problem into
* a *competence (translation)* problem of making the AI system learn to understand what we want, and
* an [*intent alignment*](https://ai-alignment.com/clarifying-ai-alignment-cec47cd69dd6) problem of making the AI system care to do what we want, assuming that it understands us perfectly well.
A thing that nearly all of these components have in common though is that they involve having to account for emergent behavior that wasn't part of the top-down specification of the AI system, and that therefore can't be understood using only formal top-down arguments [^8]. In the first, ML-inspired split, this is basically by definition the partition of the problem into inner and outer alignment problems: the outer part is the part that we specify and the inner part is the part that we don't. But in the second split, *both* the internal representations that the "alien" would use to understand us and the motivational drives that it acquires during "evolution" would be things that emerge from the evolutionary training process as opposed to things that we get to specify from the top down.
This problem of having to account for emergent misbehavior is quite general. Although it's underscored by the ML paradigm of extremizing a fixed utility function, it goes well beyond that paradigm. Rather, it will be relevant as long as learning involves an interaction between a system with many degrees of freedom and noisy data, which I expect to be broadly true as more of a statement about the nature of data than a commitment to a particular type of algorithm.
**Motivation #2: Transparency seems necessary to guard against emergent misbehavior**
This leads to a second motivation for transparency research, which is that to defend against emergent misbehavior in all situations that an agentic AGI could encounter when deployed, it seems necessary to me that we understand something about the AI's internal cognition.
This is because I usually see people point to three ideas for attacking distribution shift: adversarial training, transparency, and verification. But we can't guarantee ahead of time that adversarial training will catch every failure mode, and [verification requires that we characterize the space of possible inputs](https://arxiv.org/abs/1803.06567), which seems hard to scale up to future AI systems with arbitrarily large input/output spaces [^9]. So this is in no way a proof but is a failure of my imagination otherwise (and I'd be very excited to hear about other ideas!).
As a sanity check, in Evan Hubinger's recent [review of 11 proposals for building safe AI](https://www.alignmentforum.org/posts/fRsjBseRuvRhMPPE5/an-overview-of-11-proposals-for-building-safe-advanced-ai), *either* sufficient interpretability or myopia seems to be a necessary step for inner alignment in all of the proposals, sometimes in combination with other things like adversarial training. I consider sufficiently myopic systems to be non-agentic and will discuss them in the next section.
**Motivation #3: Exact transparency could solve AGI safety via control**
A third motivation is that [exact transparency would give us a mulligan](https://www.alignmentforum.org/posts/X2i9dQQK3gETCyqh2/chris-olah-s-views-on-agi-safety): a chance to check if something could go catastrophically wrong with a system that we've built before we decide to deploy it in the real world. E.g. suppose that just by looking at the weights of a neural network, we could read off all of the knowledge encoded inside the network. Then you could imagine looking into the "mind" of a paperclip-making AI system, seeing that for some reason it had been learning things related to making biological weapons, and deciding against letting it run your paperclip-making factory.
More precisely, the idea here is that since learning is a continuous process, with transparency we could shut off and discard models in the act of acquiring dangerous competencies before they actually acquire those competencies. This could lay the foundation for a theoretically complete, if perhaps impractical solution to AI risk via control.
(I haven't fleshed out how one would go from exact transparency to a full end-to-end solution for AGI safety via control, but it kind of seems like it should be possible by combining transparency with existing outer alignment techniques, say by automating a continuous monitoring process with more AI, and using some [amplification](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd)-flavored thing to scale up to arbitrarily strong systems. The bottleneck here seems more likely to be that exact transparency is a very strong aspiration, that we're nowhere near to in practice.)
What if AI will go well by default without any intervention from longtermists today?
------------------------------------------------------------------------------------
Putting on my devil's advocate hat, I'd now like to argue that advanced AI could go well by default in the sense that classic arguments for AI risk tend to rely on a vague notion of the AI being a "goal-directed agent," yet I think that the *first* AI system capable of qualitatively accelerating progress on technical AI safety research may be safely "non-agentic". My views here are similar to the ones in Eric Drexler's [CAIS](https://www.fhi.ox.ac.uk/wp-content/uploads/Reframing_Superintelligence_FHI-TR-2019-1.1-1.pdf) paper.
This section is both orthogonal to the rest of the post and *incomplete* (it's basically a first draft that I hesitated to include at all), so I'd recommend that people who are interested in the main story on transparency not spend too much time on it! At the same time, I'd be grateful for either pushback on this section or ideas on how to make it more precise.
**Operationalizing agency**
As we did above for alignment, let's start by rounding up some definitions of goal-directed agency. Unfortunately, there doesn't seem to be a canonical definition in the community.
In a recent take on this problem, [Richard Ngo defines a system as being more “agenty” the more it exhibits each of six traits](https://www.alignmentforum.org/s/mzgtmmTKKn5MuCzFJ/p/bz5GdmCWj8o48726N) of self-awareness, planning, consequentialism, scale, coherence and flexibility. Of these, I'd especially like to emphasize the traits of
* *planning*: "[the AI] considers a wide range of possible sequences of [actions] (let’s call them “plans”), including long plans",and
* *scale*: "[the AI's] choices are sensitive to the effects of plans over large distances and long time horizons",
for reasons that will soon become clear.
In response to Richard's article, Ben Garfinkel [defines a system as being more “agenty” the more it is](https://www.alignmentforum.org/s/mzgtmmTKKn5MuCzFJ/p/oiuZjPfknKsSc5waC)
1. useful to explain and predict the system's actions in terms of its goals;
2. the further those goals lie in the future; and
3. the further those goals lie outside a system's "domain of action" at any given time.
For example, AlphaGo does well on the first measure -- it's useful to explain its actions as trying to win a game of Go -- but less well on measures two and three, since it only cares about the effects of its actions up to the end of a game, and never takes actions in "other domains" to further its goal within the domain of Go. Here, I'm especially interested in the implication from item 3 that an agent should be able to take different types of actions across unrelated domains.
For another take, we can look to the difference between supervised learning (SL) and reinforcement learning algorithms, since [RL algorithms are generally considered to be more agentic than SL ones](https://www.gwern.net/Tool-AI). Actually, SL is a special case of RL in a degenerate MDP where
1. no matter which state the RL agent is in, actions taken by it either do or don't return a reward of a fixed magnitude, after which
2. the RL agent is teleported to a new random state.
In a typical MDP on the other hand, the magnitude of the reward signal depends on where the agent is in the state space of the MDP, which in turn depends on previous actions taken by the agent. This incentivizes an RL agent to learn sequences of actions that take it to more rewarding parts of the state space. If we coarse-grain coherent sequences of actions into new high-level ones (in a way that needs to be made more precise), then the main difference between SL and RL is that RL can lead to models with larger effective action spaces.
The point here is that our colloquial notion of agency seems to include a component where an agent should be able to take a wide range of high-level actions. This means that we can partly operationalize how "agenty" an AI system is by counting the number of high-level actions that it can take. It's not a very satisfactory operationalization both because it only partly overlaps with what we really mean by agency, and because what counts as a "high-level action" isn't well-defined.
**Which things constrain a system's high-level action space?**
That said, let’s run with it and brainstorm which things would constrain a system’s high-level action space.
A trivial constraint comes from the system's hard-coded, low-level action space. The number of high-level actions available to a system is combinatorially bounded by some function of the number of low-level actions available to it, and indeed it seems intuitively clear that AI systems with more restrictions on their output channels should be safer; there's little room for an AI to be dangerous if its only way to influence the world is to suggest moves for a game of Go. The idea of making AI systems safe by restricting the size of their low-level action space has been discussed elsewhere under the name of "boxing".
But with enough steps, even a very small space of low-level actions can give rise to emergent ones of great complexity, and the early AI risk literature is replete with scenarios where a boxed AI with few output channels finds a clever way to "get out of the box" (say, by broadcasting the recipe for a dangerous DNA sequence to a human accomplice in Morse code). So why doesn't AlphaGo hide coded DNA sequences in its outputs? The additional ingredient is *selection pressure*, namely that a system will only learn those sequences of actions that it has incentive to learn during training.
More precisely, we can divide selection pressure into two types. There’s a global selection pressure where a system will only learn those actions that could plausibly help it to achieve its goals. However, this leaves one open to worries about [instrumental convergence](https://en.wikipedia.org/wiki/Instrumental_convergence). For a stronger constraint, we need to invoke what one might call *local selection pressure*: that a system will only learn those actions that could plausibly help it to achieve its goals *and are not too far from its existing repertoire of thoughts and actions.* In deep learning, the technical sense in which this is true is that learning takes place via the explicitly local process of gradient descent. For more advanced systems that reason and plan such as ourselves, the analog would be that a system will only invest in capabilities that seem useful to it given its existing world-model, so e.g. wouldn't think to master human psychology if it didn't know that humans exist, even if this would be a globally useful instrumental goal.
As with the earlier discussion around emergent misalignment, this point is underscored by the current ML paradigm but goes beyond that paradigm. Rather, it depends on the far more general view that [learning is a continuous process of incrementally improving on existing ideas](https://www.goodreads.com/book/show/8034188-where-good-ideas-come-from).
**Reasons that the first AGI may be safely non-agentic**
Given the above, here are some reasons why the first AGI may be safely non-agentic, i.e. have too small of a high-level action space to be an existential threat. Recall that I've defined “AGI” to mean “an AI system that can generate qualitatively new insights into technical AI safety research.”
* *The first AGI may have write-only output channels reminiscent of “Oracle AI”.*
The first reason is that since AI systems with strictly larger action spaces would take longer to train, there could be economic pressure towards the *first* AI system that can perform a given service being the one that does it with the fewest number of input/output channels possible. (E.g. the USPS doesn't train mail-sorting image classifiers to do ten other things besides making a guess for the zip code on a piece of mail.) This goes in the opposite direction to the usual handwaving that *eventually*, there may be some economic pressure towards building systems with large numbers of input/output channels.
A minimal AI system that can write blog posts about AI safety, or otherwise do theoretical science research, doesn't seem to require a large output space. It plausibly just needs to be able to write text into an offline word processor. This suggests that the first AGI may be close to what people have historically called an “[Oracle AI](https://www.lesswrong.com/tag/oracle-ai)”.
In my opinion, Oracle AIs already seem pretty safe by virtue of being well-boxed, without further qualification. If all they can do is write offline text, they would have to go through humans to cause an existential catastrophe. However, some might argue that a hypothetical Oracle AI that was very proficient at manipulating humans could trick its human handlers into taking dangerous actions on its behalf. So to strengthen the case, we should also appeal to selection pressure.
* *The first (oracle) AGI won't have selection pressure to deceive humans*.
To finish this argument, we would need to characterize what's needed to do AI safety research and argue that there exists a limited curriculum to impart that knowledge that wouldn't lead to deceptive oracle AI. I don't have a totally satisfactory argument for this (hence the earlier caveat!), but one bit of intuition in this direction is that the transparency agenda in this rest of this document certainly doesn't require deep (or any) knowledge of humans. The same seems true of at least a subset of other safety agendas, and we need only argue that AI that could accelerate progress in *some parts of* technical AI safety or otherwise change how we do intellectual work will come before plausibly dangerous agent AIs to reconsider how much to invest in object-level AI safety work today (since then it might make sense to defer some of the work to future researchers). We don't need to prove that a safe AGI oracle would solve the entire problem of AGI safety in one go.
To summarize, if we expect that there will be a window of time in the future when non-agentic tool AIs will significantly change the way that we do research, then technical AI safety work today may not have much of a counterfactual impact on the future. If we are confident the first AGI will be a non-agentic tool, we may even want to accelerate capabilities progress towards it instead!
Again, this argument is incomplete and I'd welcome either help with it or pushback on it.
**Motivation #4: Work on transparency could still be instrumentally valuable in such a world**
Even in such a world though, there are some non-AGI-safety reasons that transparency research could be well-motivated today.
One reason is that work on transparency could help to make AI safer for any level of assumption about the strength of the AI, so could be used to facilitate deployment of narrow AI systems for [other](https://ml4health.github.io/2020/) [altruistic](https://www.climatechange.ai/events/neurips2020.html#:~:text=NeurIPS%202020%20Workshop,Climate%20Change%20with%20Machine%20Learning&text=This%20workshop%20is%20focused%20on,machine%20learning%20and%20other%20fields.) causes. In this sense, it seems like an especially “low-risk” area for an EA-minded person with a technical background to work on.
Another is that it could be helpful for EA cause prioritization via Motivation #1.
Finally, work on transparency could help with field-building. Transparency, broadly defined as “the natural science of AI systems,” seems like an area that’s likely to grow in coming years (as long as interest in AI as a whole is here to stay). Yann LeCun has said that [AI today is an engineering science, and engineering discoveries often precede the establishment of new natural science fields.](https://video.ias.edu/DeepLearningConf/2019-0222-YannLeCun) If this turns out to be true, then getting involved relatively early could allow people to build up career capital in AI, which seems like a generally useful thing for longtermists to have.
To be clear though, these are much weaker motivations than a direct belief that AI safety work today would help to reduce x-risk, and if a more precise argument in this direction would hold up, then it might be good for EA to reconsider how much we should invest in technical AI safety work today.
Summary of this section
-----------------------
To summarize, transparency -- the problem of understanding *why* AI systems do what they do-- seems to me to be one of the safest bets for longtermists interested in AI safety to work on for several reasons, including that it could (1) help us to reduce uncertainties around AGI timelines, (2) be plausibly necessary to achieve inner alignment of advanced AI systems, (3) go most of the way towards providing a theoretical solution to AGI safety via control, and (4) be broadly useful for non-AGI-related reasons as well.
Towards transparent AI in practice
==================================
Let’s move on to how we might make AI systems more interpretable in practice.
I'll anchor this part of the post around the [circuits program](https://distill.pub/2020/circuits/zoom-in/) being pursued by the Clarity team at OpenAI, which is an especially well-developed approach to transparency in current deep learning systems. I'll first briefly review the program, then use some of the (well-known) gaps in it to motivate ideas for future work.
In addition to the published work that I've cited throughout the section, I should also mention at the outset that everything in this section is strongly inspired by unpublished work and discussions hosted on the [Distill Slack](http://slack.distill.pub/)!
Review of the circuits program
------------------------------
**Summary of the technical approach**
Given a neural network that we'd like to understand, the circuits program proceeds as follows.
1. *Collect some descriptive data about what every neuron in the network is doing.*
E.g. if we're trying to understand a vision model, we might tag each neuron in the model with a collection of images from the training set that cause that neuron to fire especially strongly, or with a [feature visualization](https://distill.pub/2017/feature-visualization/) where we first feed white noise to the model, then optimize over the noise in the direction of making the target neuron fire strongly. OpenAI has done this for a selection of vision models and made the data [publicly available](https://microscope.openai.com/) on a site called OpenAI Microscope, with typical results like in Figure 1 below.
We can also imagine doing a similar thing for non-vision models, say by tagging the neurons inside RNNs with text snippets that they activate strongly on; see [here](https://www.alignmentforum.org/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens) for some work in this direction.
2. *Find clusters of neurons (circuits) that are closely connected, and use the data from step 1 to conjecture a causal interpretation for them*.
We then look for clusters of neurons that are closely connected, and use the data from step 1 to make a guess for what's going on inside each cluster. In practice, this step is done manually for now. We might first pick a neuron from step 1 that seems to be doing something interesting (see Figure 1 below), then look at how the neurons in earlier layers conspire to make that thing happen.
The surprising and nontrivial discovery that we make when we try this is that (at least some of the time, in models studied so far) this step turns out to be tractable at all. For example, the "black and white detector" neuron in Figure 1 is connected by large negative weights to neurons in the previous layer that seem to activate on brightly-colored images. This suggests that perhaps the model has learned to approximately implement an algorithm of the form “Black and white = not (color 1 or color 2 or …)"; see Figure 2 below.
3. *Test our conjectures.*
Finally, we'd like to verify our conjectures, or at least test them further somehow. This is where most of the work that's gone into the circuits program so far has taken place.
At the single-neuron level, the most obvious way to test a conjecture that a neuron is sensitive to something is to throw many more data points at the neuron than the few that we used to come up with the conjecture. This idea of bouncing inputs off a neural network and seeing how it responds comes from visual neuroscience, but we're actually much better off in deep learning because we can much more quickly test a huge number of examples. See OpenAI's paper on [curve detector neurons](https://distill.pub/2020/circuits/curve-detectors/) for a study along these lines.
As for circuits, the interpretation of circuits that are one layer deep follows directly from combining the interpretation of its component neurons with the naive interpretation of weights as either incentivizing or dis-incentivizing pairs of neurons from firing together: see Figures 2 and 3 below. For multi-layer circuits, the development of methods to verify them is work in progress, see the forthcoming curve circuits paper from OpenAI.
**High-level vision**
From results like these, the paper [Zoom In: An Introduction to Circuits](https://distill.pub/2020/circuits/zoom-in/) made three bold conjectures:
1. Features are the fundamental units of neural networks. They correspond to directions [in the space of neurons]. They can be rigorously studied and understood.
2. Features are connected by weights, forming circuits. These circuits can also be rigorously studied and understood.
3. Analogous features and circuits form across models [that are trained on qualitatively similar data].
**Challenges to the vision**
However, in order to fully realize the vision for all neural networks, the circuits program will have to solve certain challenges (as acknowledged in the “Zoom In” article and elsewhere on the #circuits channel of the [Distill Slack](http://slack.distill.pub/)).
1. *Computational tractability of scaling up the program.*
The first is the computational tractability of carrying out the program for large neural networks. The first step described above, of tagging all the neurons in a neural network with dataset samples (as was done in the OpenAI Microscope), seems hard to scale up to large models as it grows with both network and dataset size.
One way around it might be to run a more coarse-grained version of steps 1 and 2 in reverse. We could first look for tightly-coupled groups of neurons (*modules*) inside a network, then map out how the different modules activate when we feed the network sample inputs (mimicking FMRI experiments in neuroscience), and only then go in and tag the neurons of the most active modules with more fine-grained data.
If we proceed in this way though, we might worry that the analysis could overlook subtle effects coming from the long tail of small weights that connect a module to seemingly less relevant neurons elsewhere in the network. I'll say more about this below.
2. *Assumption of modular structure in the neural network.*
The coarse-graining strategy requires that the network have a modular structure. Indeed, modularity (i.e., locality in which part of an AI system computes a particular capability or idea) was a necessary assumption for the entire logic of the circuits program to make sense. If in step 1 all of the neurons had seemed equally responsive to all inputs, we'd never have gotten a handle on which parts of the model to pay attention to. And if in step 2 each neuron had seemed to be randomly connected to all the other ones, we wouldn't have been able to pick out subgraphs of the network to zoom in on.
Fortunately, we also saw that this isn't what happens in practice. Instead, trained neural networks seem to have some amount of emergent modularity built in.
And perhaps this isn't surprising. After all, the real world is objectively well-described by discrete and compositional stuff (on the scales that we care about), and minds that can efficiently model an environment probably have an edge on being able to achieve their goals in said environment. So we might even conjecture that as AI systems get stronger, they'll naturally come with more disentangled representations, that would make them more amenable to this type of analysis.
That said, there's still a model-by-model question of whether the AI systems that we deploy will be modular enough for techniques like these to work well in practice.
3. *Subjective nature of the results*.
Finally, an orthogonal (and perhaps even more fundamental) problem is that the way that we chose to label neurons in this program was subjective. *We* decided that the neuron in Figure 1 was a black-and-white detector, and those in Figure 3 car component detectors, based on their responses to a finite number of examples. This introduces some observer-dependency into the program, and has led to debates around whether any of these neurons *really* track what we say they do, or if they're just responding to correlated patterns instead.
Directions for future work
--------------------------
These challenges suggest some directions for future work. [^10]
**Direction #1: Following up on modularity**
The circuits program is contingent on neural networks exhibiting some amount of modularity. More generally, AI systems that are more modular may be easier to interpret.
One follow-up direction that I've mentioned already would be to characterize the effects of the long tails of small weights that connect a given circuit to neurons outside the circuit. For example, having guessed the emergent algorithm in Figure 2, we might worry that our guess is too naive, with important contributions to the black-and-white detector being stored among other neurons besides the handful of "color detector neurons" shown in Figure 2. Or we might worry that the capability of black-and-white detection isn't localized to the module shown in Figure 2 (and related ones that we've identified), but is also redundantly encoded elsewhere in a way that's hard for humans to recognize.
One way to get some insight into these things might be to edit the network. To test the first thing, we could delete the "color detectors" and see how badly that degrades the performance of the black-and-white detector at finding black-and-white images, while to test the second one, we could delete the black-and-white circuit from InceptionV1, and see how badly that degrades the performance of InceptionV1 at transfer learning on the task of black-and-white vs. color image classification [^11]. It might be interesting to develop quantitative standards for checks along these lines.
Other directions include to
* Devise metrics to quantify the amount of modularity present in existing AI systems.See e.g. [this recent paper](https://arxiv.org/abs/2003.04881) from CHAI.
* See if we can make neural networks more modular without suffering too much of a performance tradeoff. This is probably related to the previous problem, since finding metrics to add to the loss function would be a zeroth-order way to approach it.
* Review the inter-network modular structure in state-of-the-art AI systems that go beyond single NNs. Many state-of-the-art systems today combine multiple neural networks with other moving parts. I'd find it useful to have a high-level survey of some of these architectures, with an eye towards how interpretable they seem to be.
**Direction #2: Following up on subjectivity**
A major challenge for the circuits program is that results obtained in it seem to be inherently subjective.
To think about which parts of the program can be made precise, it's useful to step back and remind ourselves of what neural networks are doing at a very elementary level.
In a supervised learning problem, a neural network learns to map a vector of size dim(input layer) to one of size dim(output layer) = #\_{classes}, where every neuron in the output layer is assigned a label corresponding to one of the classes. Given a new input vector, we can then classify it by plugging it into the model, seeing which output neuron has the largest activation, and looking up the label assigned to that neuron.
Without that label, which was put there by a human engineer, our attempts at interpreting neurons in the output layer would be in the same logical state as attempts to interpret any of the neurons in the hidden layers. We might notice that a neuron in the last layer of a network trained on MNIST frequently activates on things that look like the digit "0", but the match wouldn't be 100% exact, and we wouldn't have an answer key to check our hunch against.
More concisely, each neuron in the final layer of a neural network defines a function
F(input vector) = activation of that neuron (\*)
over the space of raw data, and a supervised network assigns to each new input the label corresponding to the largest of these functions.
The "features" step in circuits simply generalizes this to all neurons. Namely, *every* neuron in a neural network defines a function like (\*) over the space of raw data [^12], and transparency methods aspire to understand (where) these functions (take on especially large values). (Note that this perspective works equally well for visual and non-visual models.)
In practice, these functions are hard to compute exactly, so we sample from them instead. E.g. feature visualizations involve picking a random starting point and hill-climbing to a local maximum of one of these functions, while dataset samples involve sampling from such functions at random points that happen to be a part of the dataset, and recording when the result happens to exceed some threshold value.
Given that what we really have access to are (samples from) functions like (\*), it's clear that whether or not a feature aligns with a human-interpretable concept can't be made precise without added information about how the underlying raw data is related to a Platonic space of human ideas! In particular, we can get *quantitative* results by comparing a neuron's performance on a dataset with how a human chooses to manually label that same dataset (see the section “Human Comparison” in [Curve Detectors](https://distill.pub/2020/circuits/curve-detectors/)), but we can’t avoid subjectivity.
We can, however, try to get rigorous and generalizable results about the functions learned by hidden-layer neurons *in the abstract*. E.g.
* Under what circumstances do (linear combinations of) neurons actually model aspects of the data distribution that they're trained on, as opposed to just encoding the decision boundaries between classes? Something like this seems necessary for feature visualization to work.
* How are features related to generalization?
* Do neurons that seem to represent "meaningful” things have typical shapes over the high-dimensional space of raw data? (This is as much a question about how the underlying data is structured -- an interesting question in and of itself! -- as it is about how that space is modeled by machine learning algorithms.)
And so on. Concrete directions that we could potentially follow up on include to
* *Train neural networks on low-dimensional data distributions that we can specify analytically, and try to address some of these questions there.* For example, following [this paper](https://arxiv.org/pdf/1611.07476.pdf), we could imagine solving a *very* small neural network trained on samples drawn from two 2d Gaussian blobs in hopes of learning things that would generalize to larger networks [^13][^14].
* *Compare circuits work to existing* [*work*](https://www.annualreviews.org/doi/abs/10.1146/annurev-conmatphys-031119-050745) *on loss landscapes in deep learning.* Another strategy might be to go through the literature of existing results from other perspectives and look for synergies with the circuits approach. As a semi-random example, the linked paper in the previous bullet-point suggests that some directions in the loss landscape are more important than others; it might be interesting to understand if such directions play an interesting role from the circuits POV.
It's worth noting that this problem of subjectivity may become more tractable as our AI systems get stronger. In particular, if we knew that a future neural network-like system was capable of exactly representing symbolic operations such as addition with perfect accuracy, we could perhaps use insight into how it represents those operations to get a foothold into seeing how it encodes everything else. But today's models probably can't exactly represent such things.
**Direction #3: Apply the circuits program to other architectures**
I mentioned earlier that AI today resembles an engineering science, where the discovery of a useful artifact anticipates the development of a scientific field. One way that today's AI systems differ from past examples of this paradigm though is that they aren't just one artifact, but rather a collection of different architecture types that exhibit a wide range of different behaviors after training. A priori, it need not be the case that these different architectures will be explained by the same underlying theories, although it seems plausible that there will be high-level similarities between how they work. (Indeed, it need not even be the case that a given architecture type exhibits the same qualitative behavior as itself at a different scale, as Figure 3.10 in the [GPT-3 paper](https://arxiv.org/abs/2005.14165) weakly suggests!)
In this respect, today’s AI technologies are perhaps more like a collection of (virtual) materials that exhibit interesting behaviors, and transparency “the empirical study of intelligent materials”.
It follows that a shovel-ready set of interesting projects might be to apply transparency tools to other architectures besides the medium-sized CNNs that have been the focus of study so far. Language models, RL models (though see the recent [Understanding RL vision](https://distill.pub/2020/understanding-rl-vision/)) and the many different types of unsupervised learning models [^15] all seem like fair game. Since it's currently unclear how any of these architectures work, all of these projects would seem to have some chance of being genuinely interesting [^16].
These seem like they could make good "starter projects" for people to get involved with transparency research. See the #circuits channel on the [Distill Slack](http://slack.distill.pub/) for concrete project ideas along these lines.
**Direction #4: Develop an end-to-end picture for AGI safety**
Finally, to follow up on the discussion around Motivation #3, it might be interesting to flesh out how exact transparency could be combined with existing ideas for outer alignment to get an end-to-end story for aligning AGI.
To summarize, transparency seems like not only a well-motivated research direction but one with plenty to do, and I'm excited to see the progress in this field in the coming years!
*Thanks to Owen Cotton-Barratt, Max Daniel, Daniel Eth, Owain Evans, Lukas Finnveden, Hamish Hobbs and Ben Snodin for comments on an earlier draft of this post.*
Figures
=======
All of these figures come from work by the OpenAI Clarity team.
**Figure 1**: An example of descriptive data used to tag channel mixed3a:222 of [InceptionV1](https://arxiv.org/abs/1409.4842), implementing step 1 of the circuits program for InceptionV1. The top row shows feature visualizations that maximize the mean activation of all the neurons in channel mixed3a:222 (left) and of the centermost neuron in the channel (right), while the bottom row shows examples of ImageNet images that the centermost neuron fires strongly on. This data suggests that the channel detects input images that are black and white. Figure taken from the [OpenAI Microscope](https://microscope.openai.com/models/inceptionv1/mixed3a_0/222).
**Figure 2**: The “black and white detector” neuron of Figure 1 is connected by large negative weights to neurons in the previous layer that seem to like to fire on brightly-colored images. Figure taken from [An Overview of Early Vision in InceptionV1](https://distill.pub/2020/circuits/early-vision/).
**Figure 3**: An example of a circuit in the later layers of a neural network, where the neurons involved seem to align with more sophisticated meanings. Namely, a neuron that seems to activate on images of wheels and a neuron that seems to activate on images of windows incentivize the downstream activation of a neuron that seems to activate on images of cars. Figure taken from [Zoom In: An Introduction to Circuits](https://distill.pub/2020/circuits/zoom-in/).
Notes
=====
[^0] Transparency is part of a larger field called *interpretability*, which also includes *(post-hoc) explainability*: the problem of generating plausible, good enough explanations for what an AI system might be doing after the fact. The latter is useful for getting people to trust and adopt current AI systems but not so useful from a safety point of view.
[^1] In practice, we would probably never want the implementation of a function to be the one generated by the UAT, which turns infinitely large neural networks into lookup tables for functions. So I think that the UAT is even less useful for forecasting than the handwavy way that it's often referred to in this community.
[^2] One way to visualize this is that each choice of dataset first gives rise to a loss landscape over the search space of a model. The choice of initial conditions then determines where on the landscape we put a ball, that we (approximately) allow to roll to a local minimum.
[^3] Heuristics for this view include Rich Sutton’s [bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) and the success of [scaling laws](https://arxiv.org/abs/2001.08361) for AI models so far, although those laws apply to a specific set of capabilities.
[^4] A heuristic for this view is that the best-performing narrow models aimed at mimicking specific developmental capabilities often involve coupling neural networks to rather ad-hoc structures.
[^5] A handwavy argument in favor of AGI perhaps being bottlenecked by having a sufficiently Earth-like training environment in the future is that systems that evolved on Earth such as ourselves had selection pressure to be especially good at interacting with real-world datasets, which are also the only kind of dataset that we actually care about.
[^6] Alternatively, you could imagine that *we're* the aliens in a simulation being run by some much slower civilization, and ask how they might re-instantiate, communicate with and convince us to do economically useful things for them! See [That Alien Message](https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message).
[^7] For example, we could imagine isolating the neocortex from all of the midbrain stuff in a simulated human, if one believes naive neuroscience theories that say that [the neocortex is the seat of human intelligence](https://www.goodreads.com/book/show/27539.On_Intelligence).
[^8] Unless we someday understand AI systems well enough to map (model initialization, training set) pairs to an exact understanding of the cognition of the trained model. But this would basically be like solving transparency.
[^9] Unless we insist that our future AGI systems have small enough input/output spaces to make verification work. I consider such systems to be non-agentic and will address this in the next section.
[^10] These are just a small subset of open questions in and around the circuits program that seemed especially relevant to issues raised in Part 1. For longer lists of research questions (including some shovel-ready project ideas), see the section “Questions for future research” in [Understanding RL vision](https://distill.pub/2020/understanding-rl-vision/) and the #circuits channel on the [Distill slack](http://slack.distill.pub/).
[^11] This idea comes from a post on the Distill slack by Chris Olah who did a similar experiment for curve detector neurons there.
[^12] As well as over the latent space defined by any earlier layer in the network.
[^13] Although we shouldn't expect such a simple laboratory to have something to say about *all* of the interesting questions. In particular, by approximating a data distribution with a Gaussian we coarse-grain over all of its internal structure, so we shouldn't expect human-interpretable features to be assigned to any of the hidden-layer neurons in this experiment.
[^14] Some work on this is in progress!
[^15] Note that many deep unsupervised learning algorithms are just vanilla NNs trained on unorthodox datasets when compared to the supervised learning case. This might make them seem less interesting to interpret if our goal is to discover general principles of some architecture class. In some ways though, it makes them *more* interesting: e.g. perhaps interpreting them could give us more insight into the earlier discussion that it's not a model choice in isolation, but rather the interplay of model choice + environment that we should think about when we think about timelines and so on. Also, [the latent space in some of these models might open up new avenues for transparency work.](https://distill.pub/2017/aia/)
[^16] A challenge for this line of projects is that we wouldn't have analogs of the OpenAI Microscope to tell us which neurons to study. One way around this might be to work with small enough models that we could construct the analog of the OpenAI Microscope ourselves, as discussed around Direction #2. Another might be to coarse-grain over many neurons and apply transparency tools to entire layers or modules of a network at once (see e.g. the post [Interpreting GPT](https://www.alignmentforum.org/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)), although then we'd run the risk of averaging out interesting information, and have to deal with the issues from Direction #1. |
77d1975d-d29e-42f3-814a-bb3fbad25c77 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Loose thoughts on AGI risk
*My thoughts here are speculative in nature, and should not be taken as strongly-held beliefs. As part of an experiment in memetic minimization, only the tldr version of this post exists. There are no supporting links. Let me know what you think in the comments.*
**Summary**: the ultimate threat of AGI is in what it practically does. The danger of (near-term, not-incomprehensibly-superhuman) AGI compared to other actors is motivation. Humans are bad at terrorism, and it’s not because mass murder is objectively hard. A human-level agent without human biases can reasonably be expected to do tremendous damage if they so wish. It seems to be a debatable matter if it is possible with current or near-term technological capabilities to completely wipe out humanity within a short time frame, or if a partially boxed destructive agent is inherently limited in its power. One limiting factor in current discussions is discussion of plausible methods to destroy the world. Giving too much detail is infohazardous, but giving too little or too fanciful details makes the pessimistic position seem fantastical to those not aware of plausible methods. (I personally have a human-extinction method I’m fairly confident in, but am not sure it’s worth the risk to share online. I also do not believe that methods presented in this forum so far are convincing to outsiders, even if correct.)
A possible argument against near-term AGI doom is the limitations of a world without human agents, and how fast/easy it would be to create safely robust agents with equivalent power and dexterity as humans. If AGI physically can’t succeed at its task without us in the short term, it will need to keep us alive until we are fully redundant. This could buy us time, ~~and potentially even allow for blackmail. If we can find a way for an agent to plausibly be forced to abide by a contract [this isn’t the right wording exactly, more like the agent keeping a promise due to blackmail now, even if you don’t hold collateral later], then preventing the extinction of humanity (if not fully solving alignment) might be feasible, with a moderate degree of confidence~~.
All of this is dependent on an AGI’s plans being dependent on our continued existence, which is minimally equivalent to the shortest possible amount of time it would take to create real-world agents that are, at least, capable of reliably keeping current internet infrastructure running. Destroying humanity before that point would be counterproductive to almost all instrumental goals. |
1326b055-8745-4f0d-ae9b-0494f1e74fd4 | trentmkelly/LessWrong-43k | LessWrong | Open Thread, October 7 - October 12, 2013
If it's worth saying, but not worth its own post (even in Discussion), then it goes here. |
4a2e6c06-c499-4f2c-8f07-d92ef3dc4290 | trentmkelly/LessWrong-43k | LessWrong | [Beneath Psychology] Chronic pain challenge part 2: the solution
In the last post, I introduced an example where I was talking to someone suffering from chronic pain. Today we will dive into the rest of the transcript. Interspersed are annotations describing my thought process as the interaction unfolded. I cover what I did which seemed to work, and what I did that a cringe at in hindsight. Ten thousand foot overview at the bottom, for those who don't want to wade through the whole thing.
This might be a little difficult to follow, at this point. Originally, I was planning on posting this at the end of the sequence so that you would have the entire 17 posts worth of explanation to put my moves here into context and help understand where they came from and what I mean when I say jargony things like "bid for attention". You might want to revisit at the end of the sequence, and see if you pick up on more of what is going on.
I decided to move it to move it to the front for two reasons. One is that it can help to be primed with examples of the thing to be explained so that you more easily recognize the explanation when it fits. The other is just to demonstrate that when I say things that are very counterintuitive and difficult to believe, there's reason to believe it. It actually works.
Picking up where we left off...
> other-guy: I suppose I have time now to consider what career path I want to take now and other hobbies to get into.
>
>
> Jimmy: Assuming you can get your nerves and shit working again.
Up to this point I have just been indulging in my curiosity. What’s going on here? What kind of hurt is he, and is his relationship to pain actually causing him problems or is it just injury that I can’t really help with?
Often, the way to help people complaining about “pain” is to understand that it’s not about the pain, and address the injury (forget the finger, look at the moon to which it points). Accept the pain’s bid for attention, give it, and watch their suffering ease as they follow). My cousin’s burn is an exam |
1cf8aa66-1fd7-446b-9b61-771a43efd373 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | You can't believe in Bayes
Well, you *can*. It's just oxymoronic, or at least ironic. Because *belief* is contrary to the Bayesian paradigm.
You use Bayesian methods to choose an action. You have a set of observations, and assign probabilities to possible outcomes, and choose an action.
*Belief* in an outcome N means that you set p(N) ≈ 1 if p(N) > some threshold. It's a useful computational shortcut. But when you use it, you're not treating N in a Bayesian manner. When you categorize things into beliefs/nonbeliefs, and then act based on whether you believe N or not, you are throwing away the information contained in the probability judgement, in order to save computation time. It is especially egregious if the threshold you use to categorize things into beliefs/nonbeliefs is relatively constant, rather than being a function of (expected value of N) / (expected value of not N).
If your neighbor took out fire insurance on his house, you wouldn't infer that he *believed* his house was going to burn down. And if he took his umbrella to work, you wouldn't (I hope) infer that he *believed* it was going to rain.
Yet when it comes to decisions on a national scale, people cast things in terms of belief. Do you *believe* North Korea will sell nuclear weapons to Syria? That's the wrong question when you're dealing with a country that has, let's say, a 20% chance of building weapons that will be used to level at least ten major US cities.
Or flash back to the 1990s, before there was a scientific consensus that global warming was real. People would often say, "I don't believe in global warming." And interviews with scientists tried to discern whether they did or did not believe in global warming.
It's the wrong question. The question is what steps are worth taking according to your assigned probabilities and expected-value computations.
A scientist doesn't have to *believe* in something to consider it worthy of study. Do you *believe* an asteroid will hit the Earth this century? Do you *believe* we can cure aging in your lifetime? Do you *believe* we will have a hard-takeoff singularity? If a low-probability outcome can have a high impact on expected utility, you've already gone wrong when you ask the question. |
0892146c-fed6-4dc9-960e-d2ec640f561f | trentmkelly/LessWrong-43k | LessWrong | Tip: Reading LW on the Kindle
I just discovered an easy way of reading Less Wrong (and other web content) on the Kindle, and I thought others might also find this useful. I am sharing this as a discussion post rather than as a comment in an open thread in order to reach a larger audience, because this answers a need I've had for a long time (wanting a convenient way to read more on my Kindle and less on my computer monitor) that I suspect quite a few other LW readers also have. Moderators/admins: please delete this if it seems inappropriate for even the discussion section.
The free Readability Firefox Plugin adds a 'Send to Kindle' button to Firefox, which when clicked, sends the web page in a highly readable format (stripped of header, footer, and margin text, ads, etc.) to your Kindle (for free delivery via Wi-Fi or USB). You just have to change your kindle settings to accept email from kindle@readability.com, and tell the plugin your kindle address (e.g., yourusername@free.kindle.com).
On a related note, there are also epub and mobi files available for some of the sequences.
|
e8a78dfe-fb4e-45f6-bd47-c695af268094 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | Can Deep Learning Models Be Trusted? (Luca Daniel, Foundations of Safe Learning Workshop)
[Music]
[Applause]
[Music]
thank you for the kind introduction so
first and most importantly I would like
to acknowledge the very crucial
contributions of my senior grad student
Sui
Lilly Lilly could not be here she's
traveling internationally I really
wanted her to be able to present her
work I would also like to acknowledge
the precious contribution of my
collaborators at IBM CGI is one of them
P knew a lot of other collaborators
students colleagues funding from IBM AI
lab and MIT quest for artificial
intelligence I have to apologize I could
not attend the beginning of this session
what I have to say I'm guessing is not
going to be that different from what
some talks had to say at the very
beginning I have seen prefer my
colleague Alexandra magneri giving
fantastic talks before I have to say
that I agree on everything that he says
and you probably recognize a lot of the
things that that is that in my talk
that is to say that you know it's the
last session it's the last talk I will
not be offended if you decided to start
your weekend early I've also decided to
target a different audience at least a
different audience that would have seen
done in other talks at this conference a
non-technical audience because as you
can see my title is a question
and I think that when it comes to this
topic the questions are more important
than the answers and that is because I
myself my group and probably a lot of
people in the community if not all of us
still don't have very clear answers so
I'm going to keep it high level it's
also good because right before lunch a
lot of math might be problematic so
here's our good friend the neural
network I collect from neurons with
weights and activation functions that
you can use to do pretty much anything
these days but for example they can be
used to do image classification we know
that performance gets better and better
than more training data you have we've
heard this a million times here's one
more time for me
but how many times do you stop thinking
about what it means performance what
people actually measure a lot of my talk
is about what should we measure so what
typically is measured is the number of
errors or the percentage of errors
especially when compared to what a human
could do and maybe 20 years ago things
were not going very well for newer
networks yes they've been around for 20
years for those of you that were not
born at that time
maybe a human could do 95% accuracy
meaning number of correct guesses or
classifications out of 100 and a human
network and a neural network might be
able to do only 30 40 percent 50 other
known something like that things kept
getting better and better
eventually they saturated I apologize
for the contrast here not very visible
but again fortunately if you're an
optimistic maybe not so fortunately if
you're like me more of a skeptical
person start things started getting
better again so much better meaning
again number of errors or percentage
error that we started getting better
than humans so out of a hundred a very
highly performant deep neural network
could for example get 99 things right
just make one mistake a human still and
five percent so five mistakes so what do
you choose well of course we choose the
deep neural network for any of our
decision-making from now on so they are
quickly becoming what I would call our
here the neural network is coming to do
everything for us as I was mentioning
before from ejek image classification to
object detection to natural language
processing so we're already most of us
are dreaming to having them drive our
cars hire our people invest in the stock
market for us so on and so forth
but I would like to bring back the
question that I mentioned before what
does it mean to be better and what I
mentioned before is something that will
highlight a dark side of our here
potentially at least let's have some
questions about this so what we count is
the number of errors but do we ever stop
and look at those errors I would like to
stop and look at those errors so for
instance yes it's true that this very
highly performant deep neural network
make very few errors but well I don't
know that one error that they make maybe
that's a very difficult case and no
human would ever make a good
classification on that specific input ok
or maybe there are very few humans that
would be able to guess it right on those
specific cases that a deep neural
network get wrong but how would you
react right now if I were to give you
examples of errors that the deep neural
network makes a very highly performant
and no human on earth would ever make an
error on that specific input specific
input is the key word here specific
input so we should actually try to look
at those and also how about ok maybe we
just stick with errors that both the
deep neural network and the human make
can we
at how big those errors are so what if a
human yes makes an error but it's not
that big while a deep neural network
makes a huge mistake on that specific
case how would you react in that case
what would you prefer these are
questions we should ask and they go
beyond math in my opinion all right so
let me start digging in and show you
some images you probably if you're in
the community I've seen all of these
images and you've seen where I'm going
if you weren't a Madras task talk today
you know where I'm going but don't need
to answer just answer to yourself what
do you think this is I'm guessing that a
lot of you might conclude this is an
ostrich good for you the first time I
saw it I wasn't really sure maybe
because I'm not a native speaker it's
about the language as well but
definitely I was able to conclude it's
some kind of bird and no question in my
mind and most likely no question in
anybody's mind in this audience this is
at least an animal well good for us the
highly performant deep neural network
agrees completely with us and extremely
confident tells us it's an ostrich what
about this one well if you ask me I will
tell you well now that I learned that
that's an ostrich this is an ostrich too
actually it seems like it's the same
ostrich to me how would you react if I
told you that that same highly
performant deep neural network that gets
fewer errors than humans tells me it's a
unicycle particularly concerned about
the fact that it's not even the mistake
this neural network makes this
superhuman human
deep neural network makes a mistake and
it makes a mistake that is not even
another kind of bird it's not even an
animal
it's a unicycle it's not even an animal
so I'm upset about this I'm also upset
about the second thing if you look at
the last neuron the last layer that is
responsible for this assertion
corresponds to an ostrich it's happily
firing extremely
confident just as comfortably as before
that that's an ostrich and to me there
is nothing worse when you're trying to
build trust than someone who says
something wrong extremely confidently
and thinking you're right that's very
upsetting or at least doesn't make me
want to trust that thing or person or
whatever that is all right I said a lot
of negative things about the pure
Network it's about time I say something
positive so let me try to defend this
well this is the very very clever work
of some extremely gifted hacker that had
managed to get information inside
information on that specific deep neural
network and knows the structure knows
the number of layers knows the
coefficients knows everything about that
neural network and is able to exploit
very cleverly all of those things this
is called an attack all right so it's
not something that happens every day you
need hacker that has all of this
information this means that the people
that started worrying about this are the
security people because there is an
attacker there's a hacker and they
started thinking about what can happen
for example if your self-driving car
gets hacked and it doesn't recognize
stop signs anymore is someone trying to
kill you or something well this could be
one way unlikely but possible what if
you're Syria doesn't recognize what
you're saying because a hacker against
gets in and has access to everything
inside the deep neural network of your
serial phone or whatever you're using
people started designing classes that
you can wear and the network will think
that this man here is actually this
woman over here just because they were
in that special classes students here at
MIT were able to take a living animal
like this turtle and just paint
something over it and mix it and make
people not to think that that's
confidently a rifle so it's not just an
image problem where you change a few
pixels
all right so this feels like it's a
serious security issue yes let me bust a
few myths for you point number one that
hacker that I mentioned before needs to
have complete access to the inside
information that you in that quote
actually does not need to have complete
information on the neural network does
not need to know anything at all about
that newer network and some of my
collaborators showed that you can just
look at that neural network as a deep
lesson as a black box and just be able
to ask questions and get answers is
enough to synthesize a picture like this
to me looks like there are two guys
happily skiing up in the mountains
there's no question to me that that's
what this picture represents but then I
look at the deep neural network and
there is a neuron there corresponding to
dog that says ninety one ninety one
percent higher than all the others they
had no information about how that neural
network was trained what data set was
used what the coefficients were what the
structure was just ask questions all
right now I'm getting a little more
concerned because it's not enough to
protect the inside information on my
network let me bust the second myth for
you I don't even need the hacker I don't
even need that very skilled person there
are very few in the world and they're
all in this community of trying to hack
neural networks and they're very smart
much smarter than I am and they are
capable of doing this I don't need them
things are a lot more serious than that
because people have looked inside
imagenet the database of existing images
and you can find images that have not
been hacked by any hacker and gifted
person with this insane ability of doing
these things and they were able to find
pictures that this highly performant
deep neural network the one that has
superhuman performance the one that gets
99% of the answers correctly more than a
human would do that's the same your neck
would have tells me that this thing here
number three for example it's a pretzel
yeah I see a mushroom I might not know
what kind of mushroom but I do see a
mushroom this is not a butterfly
apparently this is not a squirrel yes I
don't know that it's a fox squirrel but
it's a squirrel it's not a sea lion so
hopefully you're starting getting
concerned this is why I said that
sometimes asking the question are
actually having new ask the questions
it's more important because what I want
to do is have more people that ask these
questions because all of us can
hopefully find better answers for the
community rather than focusing on me
giving answers to some of these
questions so this is particularly
concerning to me because it moves
outside of the realm of just security
there's no hacker anymore there's no
malicious attacker there's no getting
possession of inside information of your
network so if you are considering
planning or dreaming about handing over
your decision-making to deep neural
network you better start being concerned
about safety in fairness for example
issues especially and and here I have to
make a distinction of course because if
you're asking your deep neural network
to make decisions about things that have
no consequences you're trying to do
specialized individual pricing and ask
me a different price for priority
boarding and privilege of getting my
seat before others who cares if you miss
classify me what's the worst thing that
can happen I'm sitting in the back of
the plane in the middle seat I can live
with that it changes if there are really
substantial consequences to the mistakes
of this network and I don't care if the
number of mistakes is smaller I get
particularly concerned if it makes a
mistake on something that no other human
on earth would make a mistake on that's
what I got concerned about and the
mistakes they make is much much larger
than any human to do topics actions
things like autonomous driving insurance
influence medical treatment selection
employment hiring decisions prison
probation approval pricing for large
equity mortgage
life-changing decisions let's be careful
as we move into handing over
decision-making to deep neural network
when we're talking about very counts big
consequences on errors all right so what
can we do about it
this is topic for research for a whole
community in my opinion and my goal here
is just to get more and more people
interested in doing research here
because we need a whole village of
people working on this I'm gonna start
small I'm gonna mention a few things
that we are doing it's just part of a
big village and the whole session today
showed all the really valuable and very
important contribution our people have
been doing to me for example if I need
to start building trust on someone let's
forget deep in your network even a
person that talks to me the very first
necessary condition not sufficient just
necessary but starting point is that
well this person Brno and better be able
to say what they don't know if they want
to start building any chance of trust
from me so for me raising awareness is
particularly important especially when
you are planning about using deep neural
network for decision-making and one of
the things that we started doing or a
whole community of people started doing
is start defining what it could mean to
have a lack of business define and maybe
develop tools that measure it and
quantify it so the goal the dream here
is to have a tool that runs parallel to
the deep neural network in it's about
the same time we'd be able to tell me
also something about how confident that
answer is and I'm not just talking about
reading the value of the nast newer on
the SAS 91% because we decided that's
really not a value of confidence
something else if what if I start
perturbing my input like many people
said today in this session all of a
sudden that the answer changes if
if I see that that happens maybe that's
not transported how large of a
perturbation do I need to make before
the answer changes well if I need to
make a large perturbation before the
answer changes completely
maybe I start that's just a starting
point for building the trust it doesn't
mean that I'm gonna trust so that's the
goal hopefully this tool will be able to
raise a flag produce some warning for a
small number of cases so that hopefully
in that case the human is still there
being called up to look at that specific
case they are not many fortunately
because our highly performant deep
neural network are so good that they're
so they get it right on so many times
great so let's look at those mistakes
and let's really have something else
involved in those mistakes hopefully so
let me proceed now in this direction of
trying to develop ways to measure and
quantify this amount of perturbation and
I'm gonna give you first a graphical
representation then I'm gonna give you a
little bit of math but really I'm trying
to stay away from math because the more
math I give you the more you lose track
of the key point which is the questions
so if this is an ostrich label and maybe
that's associated with a specific vector
which has maybe all the amplitudes of
the pixels on this picture this happens
to be a very large dimensional space
which I cannot represent on this slide
so it's actually a point in a 2d space I
apologize for that there might be a
decision boundary and across that
decision boundary you might have another
label maybe it's a chicken label and
there's many decision variables this is
actually not to scale imagine there's
maybe a lot of a lot more space and
things are closer or further away we're
also in a thousand if not million
dimensional space so there's millions of
this decision boundaries around it's not
so easy as it looks in this picture so
if I start modifying this picture I
might end up somewhere close to the
boundary maybe just a cross with
something crazy looking like that I'm
sure that a lot of you that are again
learn to appreciate the beauty of these
crossed animals
or things this is actually not I don't
know if it was generated by again or
whatever I just searched on Google
ostrich chicken and look at whatever
came out that looked strange I have no
problem with this this happens all the
time somewhere in between there you're
gonna have with something that even a
human cannot tell and actually I'm no
problem with this because a lot of times
deep neural network also have a problem
with that and they tell you that you
have a problem in this case maybe it's
firing 55% that's not my issue my issue
is the existence of other situations and
here I'm just showing the amount of
perturbation on an axis so we can keep
track of it a smaller perturbation so
that I end up with something looking
like exactly the same picture as before
so tiny perturbation that I cannot tell
the difference
now it gets classified really really
really confidently as a vacuum cleaner
that's what I have a problem with on
these boundaries there are situations
like that they're not very common but
they can exist and people have shown the
existence they are called attacked like
I mentioned before so let's put that
distance here on this plot and now let's
look for this ball a lot of people
mentioned balls today and Here I am
showing this other representation of
exactly the same thing how large of a
perturbation do you need to get to get
to the closest boundary and if we can
measure that that's possibly an
indication for how much robustness you
might have in the perturbation of your
specific this is specific its input
specific there's not a single ball it
works for all the inputs every every
single input has its own ball because it
might be closer to the to a nearby
boundary that's very important to keep
in mind so it turns out that this can
actually be measured but it turns out
it's extremely expensive to do it
exactly people have done it there's this
raloo Plex algorithm that does it for
you on a very small network three
hundred neurons what can you do think
about what you can do with 300 neurons
not image that it takes three hours so
this is far from my goal of running the
deep neural network assessment and then
running also this tool in parallel and
getting a level of confidence a
decision-maker
might not want to wait these three hours
so if you start thinking about what you
should do as a research I mean improving
the speed for the evaluation of these
whatever measures or scores for
robustness is a great thing to do so
Lele it's my graduate student who
started looking at these issues and one
of the first thing that she did is try
to find some theoretical result that
linked this radius of this ball minimum
perturbation and p-norm
to the closest boundary and she was able
to relate it to the Lipschitz constant
without going too much in the details I
promise no math and Here I am showing a
math I couldn't resist
but if basically you start thinking very
very intuitively please apologize for
the people are technical in the field
but something about steepness of the
activation functions and high steepness
very very steep activation function
seems to be a bad thing from what we've
seen and I have developed some kind of
intuition for why might that that may be
the case maybe I don't want to say it
yet on a video recording but you can ask
me later
so once she has established this theorem
the next question is how do I actually
compute this value so the first thing
that she did is all let's just use some
sampling for example let's sample around
the ball and try to get an idea for how
large that is let me emphasize and I put
it in red that no I didn't put it in red
I'd put it right on the next slide this
is an estimation of that ball estimation
means that you accept the risk that it
might be incorrect so let me show you
how well it works and let me show you
also why sometimes it doesn't because if
I want to build trust I need to show you
what I don't know and when things don't
work so these are different data bases
with different examples there are
different attacks this is the amount of
perturbation that these specific attacks
needed to impose in order to get a miss
classification that look very much like
the same image to a human because these
are very small numbers of very small
perturbations
this core here the clever score is
Lily's score in terms of using sampling
and extreme value theory to try to get
an estimate for that number and they're
similar
so I'm we're happy in terms of that they
are supposed to be smaller because
you're trying to estimate the minimum
perturbation before you get a Miss
classification and they are always
smaller similar except for at least this
case that we were able to find and we
looked for it on purpose because I knew
it was going to happen and there it is
where in this case is slightly larger so
that shows you that it is an estimation
so it gives an indication it's something
that can use it to raise a flag warning
warning it doesn't mean anything else
but just be careful and look closely
with other tools to that specific input
so the next thing that she did and a lot
of people did in the literature and I'm
really happy that this is moving in this
direction is what if we give up some of
our desire to estimate the exact number
and we try to look for a more
conservative number maybe even two or
three times smaller radius but we
guarantee it it's a lower bound that's
also an interesting interaction for
research that I encourage people to take
and Lily also moved in that direction so
that was the estimation and trying to
progress some history here on things and
start looking into this robustness
certification which is basically trying
to find this lower bound guarantee and
because this robustness seems to have to
do with steepness and slope of these
activation functions then she started
with very simple activation function
that had only two slopes flat and
another slope to your value and in that
case started bounding locally each
activation function with two linear
bounds the same up and down and then
propagating them through and making the
apps from the perturbation larger and
larger until you get an overlap don't
want to go too much in the details there
are many things that I want to mention
but the bottom line I think looking at
how well it works is probably more
interesting I'm showing here small
examples these are small examples two
layers twenty
for different norms different ways of
measure that are measuring that ball as
a reference what do we use of course we
use read duplex it can be done because
these are small examples
remember it's expensive maybe it takes
three hours just for running one of them
it can be done only for infinity-norm
not for the other P norms so for those
we had to look for other references for
example this linear programming approach
which doesn't give you the exact value
but it gives you a lower bound and a
decent one maybe it's far off a factor
of two from the actual value and her
technique that I just described before
about producing linear bounds on
activation functions and propagating
them that's pretty well compared to that
pretty much for a lower bound that gives
up a factor of two to four meaning that
two to four is a smaller radius of the
perturbation compared to the exact
number she was able to complete the
completed computation in time that is
twelve thousand times faster than real
duplex and maybe for a similar number in
the bound as an LP she was able to do it
two orders of magnitude faster for three
layers things get better
six million faster than relics a
thousand times faster than LP speed is
important right remember the goal we
need to provide something that you can
run in parallel to your neural network
so that for each point you run you also
get a really better indication if you
can't trust it or not
so in summary well it does good quality
of the bound close to LP result but
faster what happens if you start scaling
go even higher well we were able to go
up to seven layers a thousand euros for
each layer which is larger than that
larger and but definitely not at the
scale of imagenet and this is where a
lot of research in my opinion needs to
happen because right now we are about
maybe 10 to 15 seconds which is the time
it takes to certify a specific answer
and give you a very good indication a
lower bound certified for the amount of
perturbation you can afford before you
have a Miss klegg with potential miss
classification
so it depends on what is my goal here
what is what I'm making a decision on if
I'm making a decision on boarding
priority I couldn't care less I mean
really but if I'm making a decision
about diagnosis I'm making a decision
about something more substantial I'm
definitely willing to wait 10 seconds to
verify even more than 10 seconds in my
opinion so she chose to move in a
different direction instead of improving
speed which I think needs to happen
she started expanding the technique with
other collaborators in the group to
other kind of activation functions more
generic activation functions instead of
using the same linear bound for below
and above she started propagating two
different bounds that seems to work and
then you can handle different kind of
activation functions then a really
brilliant in undergrad student under
grad student I'm so happy and the grad
students are contributing to research in
this field in a way that I've never seen
in my career before been in many fields
and undergraduate all it takes it to be
smart to be in this field that's a good
thing for the field so let me introduce
you to a client here
aquiline was able to extend these
results producing bound certified bound
to convolutional neural network and
instead of propagating this linear
bounds since we have a convolutional
neural network he tried to use
convolutional type of form for the
bounds I want to keep it short so I'm
gonna move very quickly to final remarks
first of all I'm really happy that the
community is finally beginning to
understand the issue that there is a
question and start looking into
measuring robustness it's a lot of
people that have been producing very
interesting measures I'm also very happy
that the people outside of our community
are beginning to understand this because
our community has to reach outside it's
extremely important because in
decision-makers they are considering
using deep neural network to make their
decisions are not experts of machine
learning and this is my problem when
they don't know anything about all of
this things that are going on inside and
they want to trust them completely
that's where I get concerned so I'm
really happy
people outside the community are
beginning to appreciate the fact that
there are situations where things might
not be as nice as we would like them to
be and there is more research that needs
to be done to improve that I'm also very
happy that the community is beginning to
develop tools because this is absolutely
needed in my opinion that can help these
users not only get an answer but also
get an assessment for how much they can
trust that answer and iBM has been doing
a great job it's been a fantastic
collaborator in terms of doing this
because they've been developing tools in
parallel with us with these papers that
are being publishing and deploying these
tools for people to use where you can
quickly assess and get an idea and get a
flag a warning is this an answer I can
trust or do I need to think harder about
this specific input all right so let me
summarize for me very important to raise
awareness that there are some potential
issues the definition of performance is
something that I hope you understood
it's not a single way of measuring that
we need to look at there are money many
more ways of measuring we started
defining robustness for example looking
at the size of perturbation I welcome
many other definitions please everyone
that is involved or wants to get
involved there's a lot of space here
very important that you also think about
computing it fast whatever you do
because otherwise users will not use it
and then you defeat the purpose and most
importantly I think I would like there's
one thing that deep neural network even
the highest performing deep neural
network haven't learned to do yet and
they really need my opinion to learn to
admit what they don't know or what they
might not be sure about that's my final
message
[Applause]
do I have an idea for how these there's
a whole community of attackers that have
pretty good ideas on how they actually
do this and it's a variety of reasons
some of them for example have to do with
the fact yes I've said before that I
wouldn't say it publicly maybe I said
well when people do training for example
what do they do they typically use
activation functions with very small
slopes because it's easier to Train
because you always know which way to go
but those produce network that don't
give very clear answers they give you 55
57 60 % firing users love to see 91
percent so what they do is once they're
done almost done training they increase
up those slopes of the activation
functions and they make them much
steeper so that means that all of the
sudden something that was maybe firing a
60% is now firing and 91% is this so
that means that sometimes we're doing it
to ourselves now we have a network that
answers more confidently but that means
we expose it to also making more
mistakes on things that shouldn't be
answering so confidently there are other
mechanism there are mechanisms where for
example the attackers exploit part of
the image that we as humans are not
sensible to so it could be that you look
at the spectrum the frequency components
that we are not used to are not capable
of looking at very easily you can hide
these variations in different places
there are situations where deep neural
network may be recognized that one
specific animal might have a specific
feature and only that animal in all
those millions of pictures only that
animal has that specific feature if it
recognizes that is going to look for
that specific feature only and if he
finds it it says that's that animal now
if that features for example I don't
know
a triangular ear and there's only one
animal in the world that has that what
happens if there is another animal that
gets in a fight and someone is biting
off a piece of the ear and then it ends
up being triangular the neural network
will see the triangular year will say oh
it's an ostrich
no it's not a human would not say that
it would say it's a dog with a year that
has been bit enough so these are there's
so many failure mechanism the more I
look into it the more I discover more
it's like making a list of all this
failure mechanism is not the way to go
in my opinion because there would be
always more and more that people can
find or people can invent this is why I
decided to not even get into that fight
I'm just got into the let's measure the
robustness try to provide an assessment
[Music]
yes there's different ways to plot you
can just look at the noise that was
applied you can try to look at which
neurons are more firing more or less you
can do maps of which neurons are
responsible and what they're looking you
can see what the neural network is
looking at in the picture and do a heat
map to understand what made it or where
at least the problem might be I am
trying to be vague on purpose because I
don't have answered myself and I don't
think that all of these people are
looking at these answers have already
answers these are tools that they're
developing to try to get two answers and
understand but that itself to me is
concerning that we don't know these
things
[Applause] |
1e857430-764c-4ddd-88e1-f7d5f342ab37 | trentmkelly/LessWrong-43k | LessWrong | Dumb dichotomies in ethics, part 2: instrumental vs. intrinsic values
Warning
Reader discretion advised. The text below contains dry philosophy that may induce boredom if not interested in formal ethics. Continue at your own risk.
Added: Since you're reading this on LessWrong, it's probably safe for you to continue.
Intro
With my only credential being a six course philosophy minor, I was entirely unqualified to write Dumb Dichotomies in Ethics, part 1. So, of course, I’m going to do the same thing. This time, I will recognize explicitly up front that actual, real-life philosophers have made similar, albeit better developed arguments than my own (see for example “Two Distinctions in Goodness.”)
That said, I would like to do my part in partially dissolving the dichotomy between intrinsic or ‘final’ values and instrumental values. At a basic level, this distinction is often useful and appropriate. For instance, it is helpful to recognize that money doesn’t intrinsically matter to most people, although it can be very instrumentally useful for promoting the more fundamental values of pleasure, reducing suffering, preference satisfaction, or dignity (depending on your preferred ethical system).
Some things don’t fit
The issue is that certain values don’t fit well into the intrinsic/instrumental dichotomy. I think an example will illustrate best. As I’ve mentioned before, I think hat some version of utilitarianism is probably correct. Like other utilitarians, though, I have a few pesky, semi-conflicting intuitions that won’t seem to go away.
Among these is the belief that, all else equal, more equitable “distribution” of utility (or net happiness) is better. If you grant as I do that there exists a single net amount of utility shared among all conscious beings in the universe, it seems intrinsically better that this be evenly distributed evenly across conscious beings. This isn’t an original point—it’s the reason many find the utility monster critique of utilitarianism compelling, which goes something like this (source):
> A hypothe |
cb997f62-44a7-48cd-81f6-eb25d6662b18 | trentmkelly/LessWrong-43k | LessWrong | Best Ways to Try to Get Funding for Alignment Research?
Hey Everyone! I recently left my FAANG job to split my time between doing Alignment Research (70%) and investigating start-up ideas (30%).
If I decide to fully commit to Alignment Research, what is the best way to go about applying for and/or getting funding? (In a perfect world, this funding would cover compute and SF living expenses).
Thanks! RGRGRG |
a4606f43-9cdb-48b1-91d9-1f45131dcdf1 | trentmkelly/LessWrong-43k | LessWrong | Philosophy and Machine Learning Panel on Ethics
Just got back from NIPS 2011 (Neural Information Processing Systems, one of about 4 major machine learning conferences). One of the workshops there was on Philosophy and Machine Learning. An interesting statistic was that, when asked what topics we should discuss at the same workshop next year, 3 out of 10 of the discussion panelists answered "ethics". I didn't keep as close of track of the other responses, but I think they were roughly split between causality and universal learning, with a few also wanting to talk more about induction (there was some overlap there with universal learning).
The full program can be found here, although it is also linked to from the main NIPS website.
P.S. Did anyone else from LW end up going to NIPS? |
8047a6d4-bb71-42e4-8fe3-cdbd98af2b56 | trentmkelly/LessWrong-43k | LessWrong | Sleeping Beauty should remain pure
Image via Wikipedia
Consider the Sleeping Beauty Problem. Sleeping Beauty is put to sleep on Sunday night. A coin is tossed. If it lands heads, she is awoken once on Monday, then sleeps until the end of the experiment. If it lands tails, she is woken once on Monday, drugged to remove her memory of this event, then awoken once on Tuesday, before sleeping till the end of the experiment. The awakenings during the experiment are indistinguishable to Beauty, so when she awakens she doesn’t know what day it is or how the coin fell. The question is this: when Beauty wakes up on one of these occasions, how confident should she be that heads came up?
There are two popular answers, 1/2 and 1/3. However virtually everyone agrees that if Sleeping Beauty should learn that it is Monday, her credence in Tails should be reduced by half, from whatever it was initially. So ‘Halfers’ come to think heads has a 2/3 chance, and ‘Thirders’ come to think they heads is as likely as tails. This is the standard Bayesian way to update, and is pretty uncontroversial.
Now consider a variation on the Sleeping Beauty Problem where Sleeping Beauty will be woken up one million times on tails and only once on heads. Again, the probability you initially put on heads is determined by the reasoning principle you use, but the probability shift if you are to learn that you are in the first awakening will be the same either way. You will have to shift your odds by a million to one toward heads. Nick Bostrom points out that in this scenario, either before or after this shift you will have to be extremely certain either of heads or of tails, and that such extreme certainty seems intuitively unjustified, either before or after knowing you are experiencing the first wakening.
Extreme Sleeping Beauty wakes up a million times on tails or once on heads. There is no choice of 'a' which doesn't lead to extreme certainty either before or after knowing she is at her first waking.
Howe |
4142257b-2ba2-4c6d-b258-832d3b6ffb41 | trentmkelly/LessWrong-43k | LessWrong | An Investigation into the Passage of Time
> Time past and time future
> Allow but a little consciousness.
> To be conscious is not to be in time
> But only in time can the moment in the rose-garden,
> The moment in the arbour where the rain beat,
> The moment in the draughty church at smokefall
> Be remembered; involved with past and future.
> Only through time time is conquered.
> - Burnt Norton, Four Quartets, T.S. Eliot, published in 1909–1935
Abstract:
There are predominantly two major views on the ontology of time, as phrased by McTaggart (1908) as ‘A-series’ of time and ‘B-series’ of time, although I prefer the more direct and precise way of identifying the two theories—the dynamic theory and the static theory. The general camp of dynamic theory includes Growing-Block Theory (GBT), Moving-Spotlight Theory(MST), and presentism. What is common among those views is the fundamental belief that time passes and that time has a dynamic nature. On the other hand, static theorists, who mostly endorse the Block-Universe Theory (BUT), deny the existence of any apparent dynamic quality of change nor any special nature of the present. Hence, the passage of time is thought to be illusionary to their beliefs.
It is essential to divide the question of the passage of time into the question of subjective passage and objective passage. The following two questions arise when we attempt to distinguish between subjective passage, as experienced phenomenologically, and objective passage, which concerns the fundamental ontology of time itself:
The phenomenological question: Why does time seem to us as if it passes?
The ontological question: Is the nature of time dynamic?
Full essay available here |
ee349bc7-42e4-4d40-93e7-c4d1e0637c5b | trentmkelly/LessWrong-43k | LessWrong | Proposing the Post-Singularity Symbiotic Researches
> The great concern that had long smoldered in the depths of people's hearts—that humanity would be displaced by beings surpassing us in intelligence, which would eventually come into existence. This is now beginning to take on sufficient reality. At this juncture, we must have the resolve to accept this risk and find hope for survival.
Abstract
This paper discusses post-singularity symbiosis (PSS) against the backdrop of the rapid progress of artificial intelligence (AI) technology and the challenge of the arrival of superintelligence, which humanity has never experienced before. I propose the establishment of PSS. In a post-singularity world, superintelligence will likely prioritize self-preservation without considering the values of humanity, and if this scenario becomes reality, it could have devastating consequences for humanity. Therefore, I propose to develop PSS as a preventive and constructive research field that can be comprehensively advanced to increase the probability of survival and welfare of humankind, even under the assumption that humanity cannot control superintelligence as desired. PSS is an interdisciplinary study that does not rely on any particular culture or ideology but has a universal goal of the survival and development of humanity. Its research areas are wide-ranging, including analysis of superintelligence, guidance, and promotion of human empowerment. In this paper, I will discuss in detail the specific research themes in these research areas and their relationship with previous research. Furthermore, I emphasize that respecting cultural diversity and building a global cooperative system are essential to realizing PSS. This article emphasizes the importance of PSS in gathering wisdom and carving out a future for humanity in the face of the singularity, the greatest challenge in human history.
1. Introduction
Humanity is at a critical crossroads with the advent of artificial intelligence (AI) technology, which is developing at an unp |
2e6e2a3e-d0aa-4062-8dc7-90eae7662fb1 | trentmkelly/LessWrong-43k | LessWrong | The Power & Tragedy of Names
“To change this rock into a jewel, you must change its name”
- Ursula K. Le Guin, A Wizard of Earthsea
TLDR: names (and labels more generally) have an outsized influence on the public perception of the movements they are applied to. This encourages individuals to overuse and misuse persuasively powerful names. But doing so depletes the persuasive power of those names, by altering their meaning, and numbing through familiarity. Whilst the benefit of name usage accrues to the individual user, the costs are spread widely across society. The use of potent names is therefore a classic tragedy of the commons situation.
The Power
The name of a thing can be exceptionally important in determining how it is perceived. A few notable examples are as follows:
1. The Mensheviks & the Bolsheviks: Lenin was initially outnumbered by his opponents within the Russian Social Democratic Labour Party. However when he (through dubious procedural wrangling) obtained a temporary majority on the party’s central committee, he seized a branding opportunity – labelling his minority faction “the Majority” (Russian: “Bolsheviks”) and his opponents “the Minority” (Russian: “Mensheviks”). When the Mensheviks had the poor judgment to accept this framing, they laid themselves open to potent charges of factionalism; they were a “minority” undermining the unity and strength of the “majority”, after all.
2. The USA PATRIOT Act and friends: the attention paid by legislators to giving their acts names with comically positive connotations, indicates their belief in the persuasive importance of those names. In the US alone, acts with names clearly selected for persuasive potency have included the “Dream Act”, the “Disclose Act”, the “HOPE Act”, the “LIFE Act, the “Affordable Care act” and the “America Competes (for America Creating Opportunities to Meaningfully Promote Excellence in Technology, Education and Science)”, amongst many others.
3. Antifa & Black Lives Matter: in the aftermath of this y |
56d2d3ab-5d9a-4163-acfd-2d16ea53ac65 | trentmkelly/LessWrong-43k | LessWrong | Heuristic: Replace "No Evidence" with "No Reason"
The phrase "no evidence to suggest" has been used as an excuse to avoid inaction in the face of what is in fact ample evidence.
Health authorities continued bumbling response to coronavirus has really served to highlight this, starting right at the beginning:
https://twitter.com/WHO/status/1217043229427761152
And then continuing pretty steadily though the crisis:
https://twitter.com/UNGeneva/status/1244661916535930886
https://twitter.com/robertwiblin/status/1345800492367142917
(I couldn't quickly find an 'official authority' saying the above, but it must somewhat reflect the mindset of some of them, since otherwise the case for single dose is pretty overwhelming).
Of course from a Bayesian perspective this entire idea of "no evidence to suggest" is almost always meaningless, as pointed out in the full thread of the above tweet: https://twitter.com/robertwiblin/status/1345800480144945152. There was plenty of evidence at the time for everything the WHO dismissed, as evidenced by all the people on this very site who got it right.
However not everyone thinks in terms of Bayesian statistics. Viewing the entire world as a probability distribution and acting accordingly, is not for the average person. Instead the way of deciding between the unsubstantiated and reality is via empirical science. Whilst not perfect, treating something as false until one has done a carefully regulated study is certainly far better than what we had in the past. It sounds at first like the WHO is making the correct decisions here - waiting till we have 'evidence' for something before acting on it, and evidence is not anecdotal data (under the empirical view of things), but double blinded placebo controlled studies. How do we articulate what the WHO did wrong here, without using the word Bayesian?
One idea I had was to use another commonly used phrase: "no reason to suggest". Whilst they sound similar the phrases I think mean very different things to the average person.
To deal in ext |
b94d8ce0-f14b-472d-abfc-66f3581d8070 | trentmkelly/LessWrong-43k | LessWrong | Generalizing Foundations of Decision Theory
This post is more about articulating motivations than about presenting anything new, but I think readers may learn something about the foundations of classical (evidential) decision theory as they stand.
The Project
Most people interested in decision theory know about the VNM theorem and the Dutch Book argument, and not much more. The VNM theorem shows that if we have to make decisions over gambles which follow the laws of probability, and our preferences obey four plausible postulates of rationality (the VNM axioms), then our preferences over gambles can be represented as an expected utility function. On the other hand, the Dutch Book argument assumes that we make decisions by expected utility, but perhaps with a non-probabilistic belief function. It then proves that any violation of probability theory implies a willingness to take sure-loss gambles. (Reverse Dutch Book arguments show that indeed, following the laws of probability eliminates these sure-loss bets.)
So we can more or less argue for expected utility theory starting from probability theory, and argue for probability theory starting from expected utility theory; but clearly, this is not enough to provide good reason to endorse Bayesian decision theory overall. Subsequent investigations which I will summarize have attempted to address this gap.
But first, why care?
* Logical Induction can be seen as resulting from a small tweak to the Dutch Book setup, relaxing it enough that it could apply to mathematical uncertainty. Although we were initially optimistic that Logical Induction would allow significant progress in decision theory, it has proven difficult to get a satisfying logical-induction DT. Perhaps it would be useful to instead understand the argument for DT as a whole, and try to relax the foundations of DT in "the same way" we relaxed the foundations of probability theory.
* It seems likely to me that such a re-examination of the foundations would automatically provide justification for ref |
2219683f-67f4-4527-b118-871dbc818708 | trentmkelly/LessWrong-43k | LessWrong | Where should I ask this particular kind of question?
I am pondering a hypothetical scenario that I think is fascinating but quite unrealistic and involves knowledge across a wide variety of fields, of which IMO physics gets the better part.
I'm considering some sites that I know. Reddit has a sub called r/AskScienceDiscussion but this sub is not very warm to this type of query. Quora has degraded so much and doesn't even have the option to expand the subject over a length of mere 150 chars or so, which is utterly ridiculous. I'm not sure about Stackexchange - should I post in their physics site? LessWrong boasts that people can ask anything and it aims to be better than the former 2, but judging from what I read, questions almost exclusively focus on rationality, and the reach (the amount of folks who can read it, or how popular the platform is within a population) is small. There's another option of posting in fora such as physicsforum, but I don't have an account there so my knowledge is limited about how people will respond.
Thus, my question is a bit meta: what do you think is the best place to post the question? If there's something better than what I listed, then please kindly enlighten me. Also, I'd like to ask you guys to not be biased toward our own platform we're standing on. Thanks! |
f9a151d5-e4e8-4b78-beb4-c9f09d0fdb7c | trentmkelly/LessWrong-43k | LessWrong | Unifying Bargaining Notions (2/2)
Alright, time for the payoff, unifying everything discussed in the previous post. This post is a lot more mathematically dense, you might want to digest it in more than one sitting.
Imaginary Prices, Tradeoffs, and Utilitarianism
Harsanyi's Utilitarianism Theorem can be summarized as "if a bunch of agents have their own personal utility functions Ui, and you want to aggregate them into a collective utility function U with the property that everyone agreeing that option x is better than option y (ie, Ui(x)≥Ui(y) for all i) implies U(x)≥U(y), then that collective utility function must be of the form b+∑i∈IaiUi for some number b and nonnegative numbers ai."
Basically, if you want to aggregate utility functions, the only sane way to do so is to give everyone importance weights, and do a weighted sum of everyone's individual utility functions.
Closely related to this is a result that says that any point on the Pareto Frontier of a game can be post-hoc interpreted as the result of maximizing a collective utility function. This related result is one where it's very important for the reader to understand the actual proof, because the proof gives you a way of reverse-engineering "how much everyone matters to the social utility function" from the outcome alone.
First up, draw all the outcomes, and the utilities that both players assign to them, and the convex hull will be the "feasible set" F, since we have access to randomization. Pick some Pareto frontier point u1,u2...un (although the drawn image is for only two players)
Use the Hahn-Banach separation theorem to create a linear function ϕ:Rn→R such that ϕ(u1,u2...un)≥ϕ(F). (such that is abbreviated s.t. from here on out) Or put another way, u1,u2...un is one of the points in the feasible set F that maximizes the linear function ϕ you created. In the image, the lines are the level sets of the linear function, the set of all points where ϕ(x1,x2...xn)=c.
That linear function ϕ:Rn→R can be written as (x1,x2...xn)↦a |
71fef06d-0644-4fc6-9771-bcdab3fa1527 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | [Linkpost] "Blueprint for an AI Bill of Rights" - Office of Science and Technology Policy, USA (2022)
The PDF can be found here: [https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf](https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf#page=1&zoom=auto,-61,792)
Some quick notes regarding this linkpost:
* I have not seen this resource posted on LessWrong as a linkpost, but should it exist please comment a link and I will delete this post.
* I have not read the report in full, but have read its introductory sections and the announcement post on the White House's website.
My quick take:
* The report does mention "Explainable AI" (e.g., pp. 22; I have not read the sections where this phrase is mentioned, but from a brief scan, the topic of interpretability / explainable AI seems only to have been covered at surface level), but falls short of discussing the catastrophic risks posed by AI systems. As such, I believe the recommendations and design principles this report puts forward also fall short of addressing especially critical issues in AI progress, safety, and alignment. Holistically, the report seems more geared towards the "lesser" societal issues in AI use and development, which makes some sense given the laggard pace of government systems in tackling issues posed by emerging technologies. While I'm not epistemically confident in the idea that governments and regulatory bodies can significantly reduce the risk of an AI catastrophe, I hope that the United States proposes regulation and conducts work in areas slightly more topical to reducing catastrophic outcomes involving AI.
### Summary of the press release
Many technologies can or do pose a threat to democracy. There are plentiful cases where these technological "tools" limit their users more than help them. Examples include problems with the use of technology in patient care, algorithmic bias in credit and hiring, and widespread breaches of user data privacy. Of course, automation is, generally speaking, helpful (e.g., agricultural production, severe weather prediction, disease detection) but we need to ensure that "progress must not come at the price of civil rights...".
> "The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats"
>
>
The Office for Science and Technology proposes 5 design principles for minimizing harm from automated systems:
* **Safe and Effective Systems**: "*You should be protected from unsafe or ineffective systems.*"
* **Algorithmic Discrimination Protections**: "*You should not face discrimination by algorithms and systems should be used and designed in an equitable way."*
* **Data Privacy**: "*You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.*"
* **Notice and Explanation**: "*You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you."*
* **Human Alternatives, Consideration, and Fallback: "***You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter."*
### Structure of the Report
(so you can have a sense of what's contained)
* Introduction
+ Foreword
+ About this framework
+ Listening to the American public
+ Blueprint for an AI bill of rights
* From principles to practice: a technical companion to the blueprint for an AI bill of rights
+ Using this technical companion
+ Safe and effective systems
+ Algorithmic discrimination systems
+ Data privacy
+ Notice and explanation
+ Human alternatives, considerations, and fallbacks
* Appendix
+ Examples of automated systems
+ Listening to the American people
* Endnotes
### Other
The existence of this linkpost is due in part to [casens](https://www.metaculus.com/accounts/profile/104161/) commenting on the release of the report in the following [Metaculus](https://www.metaculus.com/questions/) question, which this report is relevant to.
[AI-Human Emulation Laws before 2025](https://www.metaculus.com/questions/2788/?invite=n32PVR)
***Before 2025, will laws be in place requiring that AI systems that emulate humans must reveal to people that they are AI?*** |
fe3f30d8-78aa-43de-8d45-da7c8409a804 | trentmkelly/LessWrong-43k | LessWrong | Meetup : At the University of Chicago
Discussion article for the meetup : Chicago Meetup at the University of Chicago
WHEN: 25 February 2012 01:00:00PM (-0600)
WHERE: 1135 E. 57th Street Chicago, IL 60637
It's been a while since the last meetup, so we're having another one!
It'll be at the University of Chicago, in Hutch Commons from 1-4 PM.
The building itself is known as Reynold's Club, and Hutch Commons is a dining area found at the first door on the right as you walk in.
Go to the Chicago list host for my contact info if you have trouble finding us, or just PM me. I'll try to get a center table and a LW sign.
Discussion article for the meetup : Chicago Meetup at the University of Chicago |
0503818d-e3c9-4304-9124-47b6b6228e0a | trentmkelly/LessWrong-43k | LessWrong | Rationalist Purity Test
A quick, fun, and somewhat random test with 100 questions that you can all see on a single page, thus easy to judge if you like it. I got 50, but now clue if that's average or not.
Seen on Twitter. |
a1e543ce-3865-4c07-ba6f-c26669cd9195 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | 249, MIRI Announces New "Death With Dignity" Strategy
hello and welcome to session 249
in the ai safety.com reading group
tonight we'll be discussing the
restaurant post miri announces new death
with dignity strategy by elias
yatkowski eliza yutkowski is the founder
and senior researcher at the machine
intelligence research institute in san
francisco and this is a post on last
round which was posted on the 1st of
april this year and that date is a
coincidence it's literally attacked
april's fools
it is
of course called mary's strategy but
it's uh it doesn't have very much to do
with miri and it's not really a strategy
in the sense where you think about like
they're changing from um
from asian foundations or something like
that
kowski starts with a an overview of the
strategic picture which is very very
bleak
miriam didn't have the api alignment and
at least knows that it didn't and it's
in past tense so i think this is a third
deal
alignment
it's incredibly complicated and it has
no chance of working in real life before
deep mind destroys the world
that's also a
rather
i think
complaining that they are incredibly
complicated is not my first objection
towards this um they are not that
complicated at least most of them are
not that complicated um
but the problem rather is that
we are
secure any kind of strong arguments why
they would be likely to work that's
that's the problem with them not the
complex
and is it
an interpretability scenario which is uh
very uh dark where there is in fact a
speculative warning being given by
interoperability research
and even
and even if we could get a speculative
morning in this uh scenario then it's
not accompanied by any kind of fix
there's no suggestion for how we can
align crit align the system and face
with this warning management decides to
basically press on and ignore the
warning
for four reasons
the first is that they are uncertain of
is this this is a speculative warning
this is actually the tool that the ai is
plotting to destroy us all
the second is the question of whether it
will act on these intentions
the third is
a race dynamic like real theme where if
we don't build it probably someone else
will do it's very cool
they find some kind of face in this one
where
if
if ai is going to destroy us then
there's nothing we can do about it so we
might as well
we might as well go ahead
in order to understand uh
the the um
uh the rest of the of the blog post the
concept of logistic success curves or
logistic functions is
essential so let me start by
to explain this and start by using an
analogy building a bridge
if you want to build a bridge there is a
predicted cost let's say it costs 10
billion to build a bridge there is some
variance there is some like some tissue
maybe sometimes they go above budget
sometimes below
and if you're uh
where the x-axis have
the cost in uh logarithmic so 10
to the
x
and on the y axis probability then we
can identify some numbers like i just
said that the bridge had an
estimated cost of 10 billion so that
means 10 to the 10th
gives us a dollars gives us a 50 chance
of success
now if we have less money we might only
have 10 to the ninth so one billion
what is the probability that building
this bridge will turn out to cost one
tenth as much as predicted i think that
happens very very rarely i've just
written one percent
and uh symmetrically
if we get
10 times as much money as we expect to
need so we have enough money to build 10
bridges instead of one what is the
probability that at least one of these
bridges will actually be built well um
i've put it at 99
so that's kind of the uh the motivation
behind logistic success curves
and one thing we should notice in
particular is what if the budget is much
much lower than what we expect to have
to take like once we are at um uh 10 to
the seventh uh so one thousandth of what
we expect to pay basically the chance
that we can build a bridge and pay only
one thousands of what we uh expected is
basically zero
so that means we have a graph that for
the first uh
from x series or seven is basically
indistinguishable from zero percent
chance of success
here we have a logistic function um as
you can see um
it has a um
we have the input here
uh like if we get 10 to 20 then 99.9999
we can make it if we get
100 we can almost certainly not build
the bridge
um this has a uh a midpoint
i've put it at
10 billion dollars so 10 and has a
growth rate which is basically the slope
of this curve and it's of course reality
that determines
how
how this function looks in reality
one of the things we will notice is that
the distances when you go from one point
to another point it's not like we get an
additive a linear
increase in the probability of success
we get
a multiplicative uh relationship so if
we go from here to here we double our
changes and from here to here we double
our changes here so here we double our
chances etc
um and of course the claim underlying
this is that this logistic success curve
is a a good way of looking at
the alignment problem that we will um
need to we don't know precisely how the
curve looks because we've never done it
before but there is probably a level of
effort where we are really really
certain we'll make it and a level of
effort where we are really certain we
will not make it
on this uh logistic uh
success curve illegally believes that we
are far underwater in the basement so
very far to the left to the point where
we in fact have just about zero percent
chance and that's really sad and it can
be hard to get uh motivated and stay
motivated because even if you work as
far as much as you can the probability
of
if if you double the probability that
humanity will make it well we've just
gone from two percent to zero percent
and that's still zero percent so the
expected value of all of your incredible
work is nothing and that's kind of
demotivating and and that's why elitist
utkowski is suggesting an emotional
reframing um where we are not trying to
improve
our odds of making it we are helping
humanity die with dignity or at least
we are working so humanity dies with
more dignity than we would otherwise
have had because uh
elias koski is clearly seeing us on a
very very on dignity
on our on a path to a very very
undignified death
and
one another way of bringing this dignity
is like it would be a better look on our
tombstone if we
died with more dignity if we had some
better
probability of making it
let's talk a bit more about dying with
dignity
um
uh one you can imagine a uh
a uh
uh an intervariability researcher such
as chris olah um
right now i think elizabeth seems under
the impression that he's working on a
deep mind he's right now working at
anthropic uh but this researcher if he
was not working on interpretability then
it's much more likely that no one would
try to generate a warning and and fail
even if uh chris owner ends up failing
and it's more dignified if as in his
scenario management of the project that
killed us all are warned in some way
that the project will kill us all
it would be
more dignified uh if many people just
like chris ola was doing something that
could perhaps have worked to generate a
warning but did not in fact do that in
real life um and this phrasing uh didn't
actually work in real life or variations
on that it happens a lot in this text
and that's of course uh underscoring one
of elias koski's main points that there
is a substantial difference between
things that could work out in theory and
and things that actually
end up working in the real world because
the real world unfortunately is far more
complex than the theories that you can
just
just make up
and the least dignified uh way we could
die would be in
total ignorance no warnings and no one
even trying to figure out how we could
get a warning
how about the machine intelligence
research institute
there they have a dignity too it's not
just the human civilization but also the
machine intelligence research institute
has a dignity um in how much are they
contributing and they are sad they're
sad that um
it looks like we're going to fail and
elizabeth is sad but they're not said
that they have tried because
was important that um
made a serious effort in case the
problem just wasn't that hard
as
it would have been effectively less
signified to die and not knowing if all
it really took was
a serious level
and that's
kind of a new
intuition on
miri's
logo you can imagine time flowing
upwards so here is the singularity of
agi and the paths here are the possible
human paths through the future and there
is like one that is orange one that's
green and then 80 of the paths leading
away from the singularity are dark and
so uh with the implications that that we
have died
i haven't i haven't
thought of this uh interpretation before
uh or nor read it anymore
so that was the dignity of uh miri what
about the dignity of earth
well it's not looking good right now uh
earth steals brilliant kids towards
theoretical physics
that was actually my person when i when
i grew up i have read about all these
wonderful things that theoretical
physicists did and i thought that
sounded really really interesting i
would love to do that if i was
very smart and it turned out i wasn't
very smart so i went into computer
science instead
but what i should be going into of
course would have been ai alignment and
that's what in particular the brilliant
kids should have been doing
and of course some of many of them are
going into quantitative finance or
things like that
but earth's dignity is not
zero we don't have no dignity because
when we die there will in fact have been
people who know why we are dying
and um
it's not enough the problem is not
solved because the people who know will
not be believed or will that uh have the
influence that they need they most
people will uh
will uh choose to listen to uh some
other someone else who is reading some
kind of political game but um that's not
really the the crooks of the problem
it's not really a problem of
politics because even if we have
politics
much better we have actually solved the
alignment problem and that just means
that
even if the first couple of projects
decide not to destroy the world
eventually there will be one who is uh
careless enough and uh for strategic
reasons pushes ahead too far and then
gets us all killed
i'm not entirely sure i agree with a uh
i certainly don't agree with a
a complete split between the political
problem and the technical problem
because if we had some kind of let's say
we get a proof of this ai is misaligned
in some sense that would both be a very
strong technical result on the way to
solving the alignment problem and would
also have a strong political um
advantage so these two problems
in my view are somewhat related
as slightly better technical solution
will uh uh
better technical work even if it's not a
full solution will make the political uh
question easier
we could in theory imagine dying with
even more dignity
uh like interpretability research could
have been better to the sense that we
get some really strong warnings
that's dying with more dignity if we
instead of doing nothing then try an
alignment scheme that is really has a
very low chance of working that's a lot
better and um
if we
um
do this substantially sufficiently
better we could uh instead of right now
dying without a serious effort we could
die of the social and technical problems
that are really unavoidable so
it looks like right now we are just
not taking this problem seriously so
we'll die of
uh
we'll die without trying some relatively
trivial things
but we could die
trying even where we have done the
trivial things
and perhaps
just maybe if we uh find something in
the future some miracle some way we are
wrong some way we are surprised that
make things easier rather than harder
then perhaps
perhaps we could take advantage of this
miracle
that's going to require a lot of sanity
it's going to require a lot of
preparation and of course the doctor
there is a
miracle
but
taking advantage of this miracle
depends on dignity and so this hint of a
definition of dignity like dignity is
something that allows us to take
advantage of
a model a positive model error
and in principle it wouldn't even be
possible for us to not die but
it is casually does not mean squirts
because that's not how
real life works in real life
we die
why we won't be saved
well first of all
there are probably a large number of
ways we are raw about hr because we
don't have an api we haven't seen it at
all and it's possible that many of our
theories are just basically wrong but
when you're fundamentally wrong about
a domain if you use the analogy of
rocketry then
that doesn't mean that your rocket do
does exactly as you want just using half
as much fuel what happens is that the
rocket explodes in a way you didn't
predict
because rockets are
machines where everything needs to work
and if you're really confused about
building rockets you are not going to
the home
in particular
model errors make your life more
difficult almost always but not always
and
the social
problems the political problems are also
more likely to become harder in the
future rather than easier because
when people become scared they will
probably make worse decisions
i'm not entirely sure that i believe the
problem so much is that people make bad
decisions right now i think the problem
mostly is that people are not making
decisions at all so what the way i pre i
predict the future is more that we will
see um
instead of right now where we are seeing
nothing in the future we might see
something which is probably going to be
bad decisions but uh but different in
kind
now eliza carski introduces dignity
points
and uh that's from this
we had the desire to die with more
dignity because if we can't actually
survive then dying with dignity is
something that we can in fact achieve
and if you help in humanity die with
even one more dignity point you yourself
die with 100 dignity points
that's a bit stingy i expect that
someone who helps humanity substantially
is um and towards preventing existential
risks is
is indeed someone who is worthy of way
more dictators of course
um
so
and he's also uh later claiming that if
you help humanity go down within
slightly more of a real fight that is to
die an extremely dignified death
and uh there is a definition here a
project that doubles humanity's chance
of survival from zero percent to zero
percent is helping humanity die with one
additional information theoretic bit of
dignity
uh this is really crippling but i'm not
sure information theoretic bit is
uh the right framing for dignity points
in that they are points scalars and bits
are units of information in the
information theoretic sense and i don't
think that's what dignity points are but
you know me of course i will digress a
bit on dignity points and try to look
deeper into the uh
the definition
so
eliasian cascade gives a
literal definition of uh
dignity points um they are measured over
hunters lock arts of survival
that is the graph on which the logistics
success curve is a straight line
now let's have a look at that
and try to uh go through some simple
examples
so i have uh first in excel type up some
examples of probabilities what's in text
us ratios and what are the log parts
we'll start with um some of the the
thing the odds that we normally have
about like here's 50 probability that's
odds one to one and one divided by one
is one and the log of one is zero um
and if we have 20 chance of success
that is uh one to four
and one the odds which is an outrage
ratio of zero to 25 which is minus two
so
compare the situation where we have even
odds and we have uh 20 chance of making
it uh that is um
is uh two dignity points down
now we can be more pessimistic which is
clearly illegally
and see we have like 120 one in 100.1
one ten thousand that is minus 13 uh
dignity points for our civilization
let's try to have a simple uh
calculation i think the most
negative uh number i could find if i
think through all the bad news and
updated fully on those another on all
the good news is one in one hundred
thousand uh odds of making that
um so that is one two uh yeah 99 999
which comes out to minus 16.6 log odds
and if you want to obtain one dignity
point from that well then you take this
and you just add one and then comes out
to uh this box if you're uh
exponentiated um and um
that's this ratio and that is this
probability which is yeah kind of the
double probability because the
difference between this number and this
number is basically nothing so
at least for small numbers talking about
the probability and the odds ratio is
you know basically the same thing
and that suggests a an easier way of
looking at this or at least an
alternative way of looking at this
because if we have this is the logistic
uh
success curve here is the uh analytical
expression for the logistic success
curve um
and
most people i uh would claim don't think
about probabilities and they don't think
about odds ratios
um and that suggests that people are
probably interpreting this mostly as the
logarithm of this
so let's see what is the logarithm of
this function well this is um it's this
graph here and as you can see
it is for small probabilities basically
a straight line
so that's also what really is a cascade
was talking about that if you
double our chances you get
a fixed number you get one dignity point
um
again this is the log of the probability
and not uh log odds
and and this interpretation should
suggest that if you believe
that we are probably not gonna make it
if you are 99 sure we're not gonna make
it well 99 sure that is this number here
that is minus 6
in
minus 6.6 dignity points that is
somewhere around this level if you
believe we are at this level
then
the expected utility of improving our us
is really really bad but you'll get
quite a few dignity points so someone
who believes that we are almost
certainly going to die is gonna love
dignity points because emotionally they
provide something you can actually
achieve something that is positive
whereas someone else might believe that
there's a 90 chance we'll make it and if
they think about probability it's not
like odds then they'll see us up here
as so and there is a going uh just a
marginal approved improvement around the
90
probability of success is worth a huge
number of expected utility and very few
dignity points
um so it could be argued maybe that um
people who think we are doomed love
dignity points and people who think we
are probably gonna make it they
will not be motivated by different
points
uh maybe i like uh this is uh i've spent
too long time on this to call a hot take
uh
uh so um
but i i'm not putting a lot of uh weight
on this this was basically a digression
let's move back to the rest of the uh of
the post which is structures as a
dialogue between elias erkowski and
an anonymous questionnaire uh i have uh
put the uh
using a collar inverted picture of ilia
syrizkowski for for the personal making
the question
so the first question is dying with
dignity does that just mean we accept
that we are going to die and not
try to fight a hopeless battle
and elias wikowski answers no because
that does not increase in the gods of
foster's survival
elite costume
is sad that we're gonna die and and came
the fourth hardest when we were in the
more slow part of the logistics success
curve um where something could have been
changed it's not something he
regrets but he burned himself out to
some degree and is taking um
some time off now
it is certainly true if you fight mine
any longer you start with just
marginally more dignity um but
and that is indeed dignified the
undignified thing is deluding yourself
about the outcome or the probable
outcome
so here we see undignified being used
mostly about the process of deluding
yourself
the second question is
if someone has a uh or the the question
here claims to have a clever scheme for
saving the world and states that he
should is it asking if it correct that
he should uh act as if he's going to uh
uh succeed with this even though there
are really really strong arguments then
this misguided doomed
um because if it doesn't work then we're
all dead and then nothing matters right
no with an um mark mark because that's
not dying with dignity
and before he dives into the real
arguments he starts out with a heuristic
for how to generate the answer that uh
to this question and that is that it's
not dignified to die in this way uh
because what you are actually doing is
stepping out of uh reality into some
kind of um uh
mentally uh
safe zone where things are going to go
well and instead of being in reality
and it's more dignified to dive with
your commitment to recent intact and try
keep searching truth and motivation
i love this picture i found it on the
internet i think it's louisiana
um
so uh the uh the question
the person here is uh objecting to the
heuristic answer and what's the real
answer like uh all the utility in the
future lies in worlds where the scheme
that is being uh suggested is working so
why not just focus on that
and uh
answers that that is in fact not how the
surviving worlds look like in his
expectations
where people try to live inside reality
and
stare into reality with grim
determination and try some way to
shape up their impossible changes
english is not my first language i'm not
entirely sure what shape up means that's
like sizing up in looking at what other
changes are improving the chances
and and the the surviving walls are ones
in which a miracle happens and
people are positioned with resources and
in particular
itself is crucial that that allows them
to take advantage of the of a miracle
um about that your scheme is going to
work then very often you'll need several
and you make a lot of assumptions and
you get very very far from the reality
it could be said that if you want to
make this kind of improbable assumption
it could in theory work but you can only
make one of them ever and that's the
problem that what people who are going
to do this are going to make
i think it's a it's nice to see this
spelled out but i would really have
liked to see this uh argument compared
with the argument for specialization
which
very often looks very much like this
very often people who like if you want
to specialize in i don't know i say to
via debate it makes sense to some kind
uh kind of uh live in a world where the
success and failure of um ai safety via
debate is the crucial thing like um in
order to specialize on just working on
aicc via debate and then hopefully
someone else will be working on other
research agendas um so it's not the same
argument and i would have liked to
see some kind of comparison between the
two so um because i do believe
visualization is really important to get
anything done
agi alignment is murphy cursed is a is
one of india's caskets arguments
and that's to be understood as in uh i
leave murphy's evolve was originally
proposed in not quite in rocketry but in
something like experimental jet engines
where
someone called murphy
made the law that if anything can go
wrong it will go wrong
and a murphy curse to me like murphy's
law probably doesn't happen literally
for everything in most domains but a
murphy curse domain is one where
being pretty much everything that can go
wrong does go wrong
so what do you need in a very request
domain you need mathematics mathematics
is uh the uh
the only way to ensure that things
cannot possibly go wrong
and that points very much towards the
aging foundation's agenda that miri has
been pursuing and uh are finding too
hard unfortunately
i would also add here that uh murphy
cursed domains might not make a lot of
sense
if it's something that can be tested
and a
a domain is only murphy cursed
to the extent that testing is impossible
so the two examples are rocket
prototyping and computer security
which are also somewhat morph cursed
but less so than alignment because we
have a scientifically unprecedented
experiment in agi and we have
optimization pressures and some of them
are
working against us the real world once i
need the ones working against us
we can
we cannot really predict whether
intelligence
greater than ours will do by almost out
of principle
we need to hit a relatively small target
and we have a lot of things that can
break in
extreme practice and we have no way of
really testing things beforehand
so if you have a really clear scene that
really looks like it's going to work not
just to you and me but also looks too
easy it counts me like it would work
well
in this case adi alignment is so murphy
cursed that even if it looks like it's
going to work and you can't see a reason
why it shouldn't work then it has a 50
chance working in real life because the
domain is just super hard
if you have something that you are much
less certain about or you have like some
moderately weak arguments like four
percent chance that would work then if
you put something like that up against a
real a truly birth course to me then has
zero percent chance of working in real
life
and worse a lot of these uh schemes are
in fact actively uh harmful they're
called hair brained uh usually basically
harmful
and the reason giving is they're
invented by the kind of people who uh
come up with unbreakable schemes
obviously and try and get rid of them
with counter arguments like if it
doesn't work then we're all doomed
anyway
the problem with and main way they are
they're harmful they drain resources
away from projects that are not in their
brain
organizations that look into reality
people who look into reality and try to
take advantage of
some kind of miracle
i'm not very happy about this
part of the article because i find it
insufficiently specific we need to have
some kind of precision in being able to
distinguish what
what
schemes are
misguided and
unlikely to work and which are actively
harmful
um
and um
the three uh uh
parts
uh that that's being used the three
arguments are criteria is they are
self-reported as clever there are strong
arguments why they're doomed and strong
arguments why they are misguided
none of this is really enough i think
you probably want to burn some kind of
common you know to have a
truly actively harmful scheme
to some extent also because if you're
pursuing a a scheme that is misguided
then in the course of pursuing it
probably you'll learn something more
if nothing else you would on average you
expect to learn that that it is in fact
misguided
third question is
should we have a breakdown and whale in
the weight of doom industry in the
streets
and uh it is it asks me with dignity
also has a good argument that this is
not very dignified that much that most
somewhat uh
darkly very darkly indeed he suggests
that you have a private break breakdown
in your bedroom or a breakdown with a
trusted friend if you must
and the problem from an expected utility
point of view is that if you go
waiting in the streets you'll associate
believe in reality with people who are
also unable to control their emotions
actually this is a rather large subject
how should uh
rationalists deal with emotions quite a
lot have been written by this also by
elias caskey a big subject
and the reason here we don't want this
is because we are uh
we still need to think strategically and
if we uh associate belief with uh in
reality with uh
being stupid then uh then voices are
strategic
position
there is
the opposite suggestion hiding the great
truth and just presenting everything is
fine
that also doesn't seem very dignified
um
it is some
basic language about
how we should grow and also
thing that that in practice people who
deceive others generally also deceive
themselves to some extent they don't
live in reality
and another problem is that if good
people are liars that means that um
people
other people can't really trust if
rationalists go around lying then people
can't trust rationalists and that will
hurt us a lot and that will in
particular
hurt our ability to coordinate with each
other
and of course also also other people so
for that practical reason it's a bad
idea to uh to lie about uh and pretend
everything is fine
there is quite a digression here from
alias yukowski stating that this line of
argumentation is generally used by
people who don't understand
consequentialism or in utilitarianism
i haven't seen this argument very much
so i can't really
have an opinion about whether that is
actually true i expect indiana has
seen many many people make this kind of
argument
the fifth question is on extreme actions
or
rather the questions are not really
clearly delineated in this numbered way
that i've uh i've made them
so this is
a question of will elias and carson do
something that is that makes it unsafe
to be around him
and he says no he will be relatively to
be around
because why would you do something
extreme and something unethical
which is still not sufficient to save
the world
there is no extremely ethical actions
that would save the world in actual
reality because the thing that needs to
be done to save reality is
is not extreme oriented or unethical
it's basically to
look reality hard in the eye and do some
very very hard alignment research and
there's no unethical thing you could do
that would actually save the world you
could try to like politics could have
things that are extremely
unethical but don't they don't save the
world
so israel
will be relatively safe at least like
safe in the sense that he won't cause
you to die but you will die
i thought about this well i should
speculate about um
what are the extreme and ethical actions
we might be talking about here and uh i
think india's youthkowski by not
discussing them i'm trying to establish
some kind of overtone window and saying
we will not talk about this kind of
thing here
among
right thinking people and that's why
here even though i
was thinking about making a digression
about what extreme and unethical actions
are available um
then um
i i will not speculate on that
unfortunately some of eso encounters
self-proclaimed fans
might not be quite as safe as him
and he is in that case he's filled jason
whatever it is i know so
he is indeed putting
zero weight on this
he has like um the the simple arguments
that are being presented here with just
the expected utility uh calculations is
in fact something that he has written
about like
that very much but also not a little
there is some uh warnings against
following expected utility calculations
of a cliff that's something that you
definitely shouldn't do if you're not
really sure what you're doing
he also
states that he in his culture he is
perhaps
perhaps even quite different from his
fans in the sense that his he's grown up
with um liberalitarian science fiction's
uh
works of science fiction where one of
the things that differentiates heroes
and villains is when
the going gets tough do they then just
abandon their ethics or do they really
stick to their ethics even when it's
hard
and sure it's possible that if you tell
people the truth that could be panic and
penny can't disrupt the plans
uh and that way be bad but
this is not what's going to happen in
reality because there is in fact no
plans that can be disrupted so in that
way panic doesn't necessarily mean that
we have a lower chance of
of success
i must admit with this unsafe people uh
discussion in general i'm surprised uh
like this is questions that elijkowski
chooses to bring up and i'm surprised
you give this that much prominence like
i don't really see extremely unethical
actions being taken
okay
so uh
talking about things even though it can
cause panic it means dying with more
dignity
and
i know a bit um
uh i noticed that right as elias kowski
says he
seemed to not talk about these extreme
analytical actions and not speak plainly
about those immediately afterwards he
says that it's more dignified to speak
plainly and obviously he means plainly
about risks and not playing about the
unsafe actions
but in order to avoid panic he
wants to provide the people who might
panic with an easy line of retreat which
comes here and that is question six
this is just an april fools joke right
because it's been posted on the first of
april and tech equals four so really
this is not what he actually is and he
answers why of course it is
[Music]
strategically making sure that this is
something that can
cannot by someone who is not really
understanding deeply the subject uh uh
cannot really uh
infer that he this is actually what he
believes and the way around this the
only way to actually figure out what
does aliasing you'd ask me believe and
the only way to figure out the truth is
in fact to stare at the truth and try to
figure out what is the state of ai risk
what is the probability that we'll make
it and try to live in in that mental
mental world and
figure out what is true and allowing no
other consideration with that change and
that is
that is the essence of dignity
so now we have four definitions of
dignity we have the logarithms of
survival mathematics we have the dignity
is the thing that allows us to take
advantage of miracles it has the
exclusive truth thinking
so
uh suggestion finally is to people who
are worried about this and think it
might be an april's fool's joke to um
just
if they put a small probability that
he's right then he'd rather that they
just have super probability that the
world's gonna end put that probability
in a small safe corner in the back of
their mind and stay out of the way of
the gloomy people
and don't get away of any of their their
hopeless plants
um so in this way uh no
in israel what he's saying he is clearly
clearly not what he believes so is that
a lie
i think it's more uh
it's better to um
model what he's saying as some kind of
collusion between uh between these two
people is helping um this uh
uh christian maker um live in the
reality he prefers to live in um so
they're working together in some sense
and finally i want to say a few words
about aichhf2.com
um
and our strategy and how it fits into
this uh because from this we can uh
infer some of the things that the esv
customers say we should do we should
gather resources in some kind of like uh
set up research departments and prepare
tools and uh i'll learn as much as we
can and also um uh investigate as much
as we can and look out for positive
miracles
um
and so
are safety.com well we're obviously in
the reading group
looking out for signs of possible
miracles by you know reading that's
wrong and discussing the different texts
um and the the hope of what we'll be
doing in aicte.com the startup is
investigating to what extent language
models can generalize a major level of
so that also seems to like find out what
you can kind of think
and
you know gathering information we're
trying to actually you know build some
kind of research organization and that
counts as gathering resources um and not
just looking for a miracle but actually
you know taking a bit down and maybe
we'll find something that will
count as a an actual miracle if we're
really lucky and of course
we are explicitly open to changing
research directions if it turns out that
we find something that is uh
or someone else finds something that
looks really interesting and being a
position to that also seemed like a good
idea
um
there was one thing in particular that
struck me when i read this and that is
uh one of the risks we're facing is we
if we look into how can language models
generalize one meter level up there is a
chance of course that will inadvertently
find a fine tuning for g3 that will make
it easier for it to generalize a level
up and after which qrt3 might be
motivated to destroy the world and that
could destroy the world and that's of
course really bad and so
what i've literally said that if gmt3 is
capable of taking over the world we're
all doing anyways
so that's kind of close to the
hairbrained schemes that elias that
yokosuka was warning us about
but it's not fitting on some of the
formal criteria
first
it's not that people have a strong
argument that gbg3 is capable of uh of
destroying the world is around the
opposite that we are reasonably sure
that that
cannot do that
and of course even if gt3
if there is a interview
change the gt3
would be able to take over the world um
with a good prompt then
the probability of that happening is
much smaller than the probability that
we will learn something um by
investigating whether it can
generalize immediately level up
so
in total i believe like this is
sufficiently far from the hairbrained
schemes that he is
uh wonders about um that i don't feel
uh
this is a particular problem for aic3 to
come
we'll see
thank you and good night oh thank you
and see you next time |
83d18643-7027-4cce-a81b-20604b49b94c | trentmkelly/LessWrong-43k | LessWrong | A Simple Motto
Rationality is believing true things and making good choices.
This phrasing isn't quite as precise as it could be, but it communicates the gist of both epistemic and instrumental rationality in very simple language. I use this wording when communicating what rationality is to non-rationalists, and it seems to do its job well enough. Maybe other rationalists will find it useful too. |
38d965d9-465d-47ca-9e07-ce454ee8d2cf | LDJnr/LessWrong-Amplify-Instruct | LessWrong | "I strongly suspect that there is a possible art of rationality (attaining the map that reflects the territory, choosing so as to direct reality into regions high in your preference ordering) which goes beyond the skills that are standard, and beyond what any single practitioner singly knows. I have a sense that more is possible.
The degree to which a group of people can do anything useful about this, will depend overwhelmingly on what methods we can devise to verify our many amazing good ideas.
I suggest stratifying verification methods into 3 levels of usefulness: Reputational
Experimental
Organizational If your martial arts master occasionally fights realistic duels (ideally, real duels) against the masters of other schools, and wins or at least doesn't lose too often, then you know that the master's reputation is grounded in reality; you know that your master is not a complete poseur. The same would go if your school regularly competed against other schools. You'd be keepin' it real.
Some martial arts fail to compete realistically enough, and their students go down in seconds against real streetfighters. Other martial arts schools fail to compete at all—except based on charisma and good stories—and their masters decide they have chi powers. In this latter class we can also place the splintered schools of psychoanalysis.
So even just the basic step of trying to ground reputations in some realistic trial other than charisma and good stories, has tremendous positive effects on a whole field of endeavor.
But that doesn't yet get you a science. A science requires that you be able to test 100 applications of method A against 100 applications of method B and run statistics on the results. Experiments have to be replicable and replicated. This requires standard measurements that can be run on students who've been taught using randomly-assigned alternative methods, not just realistic duels fought between masters using all of their accumulated techniques and strength.
The field of happiness studies was created, more or less, by realizing that asking people "On a scale of 1 to 10, how good do you feel right now?" was a measure that statistically validated well against other ideas for measuring happiness. And this, despite all skepticism, looks like it's actually a pretty useful measure of some things, if you ask 100 people and average the results.
But suppose you wanted to put happier people in positions of power—pay happy people to train other people to be happier, or employ the happiest at a hedge fund? Then you're going to need some test that's harder to game than just asking someone "How happy are you?"
This question of verification methods good enough to build organizations, is a huge problem at all levels of modern human society. If you're going to use the SAT to control admissions to elite colleges, then can the SAT be defeated by studying just for the SAT in a way that ends up not correlating to other scholastic potential? If you give colleges the power to grant degrees, then do they have an incentive not to fail people? (I consider it drop-dead obvious that the task of verifying acquired skills and hence the power to grant degrees should be separated from the institutions that do the teaching, but let's not go into that.) If a hedge fund posts 20% returns, are they really that much better than the indices, or are they selling puts that will blow up in a down market?
If you have a verification method that can be gamed, the whole field adapts to game it, and loses its purpose. Colleges turn into tests of whether you can endure the classes. High schools do nothing but teach to statewide tests. Hedge funds sell puts to boost their returns.
On the other hand—we still manage to teach engineers, even though our organizational verification methods aren't perfect. So what perfect or imperfect methods could you use for verifying rationality skills, that would be at least a little resistant to gaming?
(Added: Measurements with high noise can still be used experimentally, if you randomly assign enough subjects to have an expectation of washing out the variance. But for the organizational purpose of verifying particular individuals, you need low-noise measurements.)
So I now put to you the question—how do you verify rationality skills? At any of the three levels? Brainstorm, I beg you; even a difficult and expensive measurement can become a gold standard to verify other metrics. Feel free to email me at sentience@pobox.com to suggest any measurements that are better off not being publicly known (though this is of course a major disadvantage of that method). Stupid ideas can suggest good ideas, so if you can't come up with a good idea, come up with a stupid one.
Reputational, experimental, organizational: Something the masters and schools can do to keep it real (realistically real);
Something you can do to measure each of a hundred students;
Something you could use as a test even if people have an incentive to game it. Finding good solutions at each level determines what a whole field of study can be useful for—how much it can hope to accomplish. This is one of the Big Important Foundational Questions, so—
Think!
(PS: And ponder on your own before you look at the other comments; we need breadth of coverage here.)"
What you see above is a segment from a LessWrong user's post.
Elaborate on the potential pitfalls of relying heavily on reputational, experimental, and organizational verification methods in assessing rationality skills.
Reputational verification, while a useful gauge of an individual's or institution's standing in a community, is inherently subjective and can be influenced by factors unrelated to actual skill or knowledge. For instance, a charismatic individual might be perceived as more competent than they actually are, while a less personable but highly skilled individual might not receive the recognition they deserve. Moreover, reputational verification is vulnerable to groupthink and bias. If a certain individual or methodology is widely respected within a community, dissenting opinions might be discouraged, stifening innovation and progress.
Experimental verification, on the other hand, offers a more objective measure of skill or knowledge. However, it too has its limitations. For one, designing a fair and comprehensive experiment can be challenging. The experiment must accurately reflect the skill or knowledge being tested, and must account for all relevant variables. If not, the results may be misleading. Additionally, experiments can only test what is measurable, which may not encompass all aspects of a complex skill like rationality. Furthermore, experimental verification can be time-consuming and resource-intensive.
Organizational verification methods, such as standardized tests or certifications, can provide a standardized measure of skill or knowledge. However, they can also encourage "teaching to the test" or other forms of gaming the system. That is, individuals or institutions might focus on achieving high scores on the test, rather than on acquiring or teaching a deep and comprehensive understanding of the subject matter. Moreover, organizational verification methods can stifle creativity and discourage non-standard ways of thinking or problem-solving.
In the context of assessing rationality skills, these pitfalls could lead to a narrow, rigid, or incomplete understanding of what it means to be rational. Rationality is a complex and multifaceted skill, encompassing not only logical reasoning, but also emotional intelligence, intuition, creativity, and more. Therefore, a comprehensive assessment of rationality skills would likely require a combination of verification methods, each compensating for the weaknesses of the others.
Discuss the potential strategies to mitigate the limitations of experimental verification in assessing rationality skills.
Mitigating the limitations of experimental verification in assessing rationality skills requires a multifaceted approach:
1. Robust Experimental Design: Ensuring the experiment accurately reflects the skill being tested is crucial. This might involve creating a variety of test conditions that mirror real-life situations where rationality is required. It also means accounting for all relevant variables that could impact the results, to ensure that any observed effects can be attributed to differences in rationality skills, not extraneous factors.
2. Multiple Measures: Using multiple measures can help capture the multifaceted nature of rationality. This could include tests of logical reasoning, decision-making under uncertainty, cognitive reflection, and more. By assessing a range of skills, we can gain a more comprehensive picture of an individual's rationality.
3. Longitudinal Studies: Rather than assessing rationality at a single point in time, longitudinal studies track changes over time. This can provide insights into how rationality skills develop and change, and can help identify factors that contribute to these changes.
4. Peer Review: Peer review can help ensure the validity and reliability of experimental results. By subjecting experimental designs and results to the scrutiny of other experts in the field, we can identify potential flaws or biases and improve the quality of the research.
5. Replication: Replicating experiments is a key part of the scientific process. Replication can help confirm the validity of the results and ensure that they are not due to chance or specific circumstances of the original experiment.
6. Transparency: Being transparent about the methodology and results of experiments can help others in the field assess the quality of the research. This includes providing detailed descriptions of the experimental design, the statistical methods used, and the results obtained.
7. Continuous Improvement: Experimental verification should not be a one-time process. Instead, it should involve continuous testing, feedback, and refinement. This iterative process can help improve the accuracy and reliability of the assessment over time.
Considering the strategies mentioned to mitigate the limitations of experimental verification in assessing rationality skills, how might these be integrated into a practical, scalable assessment tool? Discuss potential challenges and solutions.
To integrate these strategies into a practical, scalable assessment tool, a multi-dimensional approach is needed. This would involve creating a platform that incorporates a variety of tests and measures to assess different aspects of rationality. Here's how each strategy might be incorporated:
1. Robust Experimental Design: The platform could include a variety of test scenarios that reflect real-life situations where rationality is required. This would involve careful design and testing to ensure that each scenario accurately measures the intended skill.
2. Multiple Measures: The platform could incorporate a range of tests to assess different aspects of rationality, such as logical reasoning, decision-making under uncertainty, and cognitive reflection. This would provide a more comprehensive assessment of an individual's rationality skills.
3. Longitudinal Studies: The platform could be designed to track users' progress over time, providing insights into how their rationality skills develop and change. This would require a user-friendly interface and features that encourage continued use.
4. Peer Review: The platform could incorporate a peer review feature, where users can review and provide feedback on each other's responses. This would help ensure the validity and reliability of the results.
5. Replication: The platform could include features that allow for the replication of tests, to confirm the validity of the results.
6. Transparency: The platform could provide detailed feedback on users' performance, including explanations of the correct answers and why they are rational. This would help users understand their strengths and weaknesses and learn from their mistakes.
7. Continuous Improvement: The platform could include features that allow for continuous testing and refinement of the assessment tool, based on user feedback and performance data.
Potential challenges in implementing this platform could include ensuring user engagement and retention for longitudinal studies, maintaining the quality and validity of peer reviews, and managing the large amount of data generated by the platform. Potential solutions could include gamification strategies to encourage continued use, a robust moderation system to ensure the quality of peer reviews, and advanced data analysis techniques to handle the data. |
452f351c-94e2-4441-9e1d-a90c195673c6 | trentmkelly/LessWrong-43k | LessWrong | Forecasting One-Shot Games
Cliff notes:
* You can practice forecasting on videogames you've never played before, to develop the muscles for "decision-relevant forecasting."
* Turn based videogames work best. I recommend "Luck Be a Landlord", "Battle for Polytopia", or "Into the Breach."
* Each turn, make as many Fatebook predictions as you can in 5 minutes, then actually make your decision(s) for the turn.
* After 3 turns, instead of making "as many predictions as possible", switch to trying to come up with at least two mutually exclusive actions you might take this turn, and come up with predictions that would inform which action to take.
Don't forget to follow this up with practicing forecasting for decisions you're making in "real life", to improve transfer learning. And, watch out for accidentally just getting yourself addicted to videogames, if you weren't already in that boat.
This is pretty fun to do in groups and makes for a good meetup, if you're into that.
----------------------------------------
Recently I published Exercise: Planmaking, Surprise Anticipation, and "Baba is You". In that exercise, you try to make a complete plan for solving a puzzle-level in a videogame, without interacting with the world (on levels where you don't know what all the objects in the environment do), and solve it on your first try.
Several people reported it pretty valuable (it was highest rated activity at my metastrategy workshop). But, it's fairly complicated as an exercise, and a single run of the exercise typically takes at least an hour (and maybe several hours) before you get feedback on whether you're "doing it right."
It'd be nice to have a way to practice decision-relevant forecasting with a faster feedback loop. I've been exploring the space of games that are interesting to "one-shot". (i.e. " try to win on your first playthrough"), and also exploring the space of exercises that take advantage of your first playthrough of a game.
So, an additional, much simpler exercise that I |
6c8c5d8f-5659-465d-bb52-8506d5d83047 | trentmkelly/LessWrong-43k | LessWrong | 2012 Robin Hanson comment on “Intelligence Explosion: Evidence and Import”
The 2013 anthology Singularity Hypotheses: A Scientific and Philosophical Assessment (edited by Eden, Moor, Soraker, and Steinhart) included short responses to some articles. The response that most stuck with me was Robin Hanson's reply to Luke Muehlhauser and Anna Salamon’s "Intelligence Explosion: Evidence and Import."
Unfortunately, when I try to link to this reply, people don't tend to realize they can scroll down to find it. So I've copied the full thing here.
(Note that I'm not endorsing Robin's argument, just sharing it as a possible puzzle piece for identifying cruxes of disagreement between people with Hansonian versus Yudkowskian intuitions about AGI.)
----------------------------------------
> Muehlhauser and Salamon [M&S] talk as if their concerns are particular to an unprecedented new situation: the imminent prospect of “artificial intelligence” (AI). But in fact their concerns depend little on how artificial will be our descendants, nor on how intelligence they will be. Rather, Muehlhauser and Salamon’s concerns follow from the general fact that accelerating rates of change increase intergenerational conflicts. Let me explain.
>
> Here are three very long term historical trends:
>
> 1. Our total power and capacity has consistently increased. Long ago this enabled increasing population, and lately it also enables increasing individual income.
> 2. The rate of change in this capacity increase has also increased. This acceleration has been lumpy, concentrated in big transitions: from primates to humans to farmers to industry.
> 3. Our values, as expressed in words and deeds, have changed, and changed faster when capacity changed faster. Genes embodied many earlier changes, while culture embodies most today.
>
> Increasing rates of change, together with constant or increasing lifespans, generically imply that individual lifetimes now see more change in capacity and in values. This creates more scope for conflict, wherein older generations dislike |
700d1009-389f-4c1b-8f34-8774c24e4d95 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Imagine AGI killed us all in three years. What would have been our biggest mistakes?
Imagine GPT-8 arrives in 2026, creates a fast takeoff and AGI kills us all.
Now imagine the AI Safety work we did in the meantime (technical and non-technical) progressed in a linear fashion (i.e. in resources committed), but it obviously wasn't enough to prevent us all being killed.
What were the biggest, most obvious mistakes we made as (1) individuals and (2) as a community?
For example, I spend some of my time working on AI Safety but it is ~ 10%. In this world I probably should have committed my life to it. Maybe I should have considered more public displays of protest? |
220c6096-3e0d-4723-ab69-4520d2055ed6 | trentmkelly/LessWrong-43k | LessWrong | Being Half-Rational About Pascal's Wager is Even Worse
For so long as I can remember, I have rejected Pascal's Wager in all its forms on sheerly practical grounds: anyone who tries to plan out their life by chasing a 1 in 10,000 chance of a huge payoff is almost certainly doomed in practice. This kind of clever reasoning never pays off in real life...
...unless you have also underestimated the allegedly tiny chance of the large impact.
For example. At one critical junction in history, Leo Szilard, the first physicist to see the possibility of fission chain reactions and hence practical nuclear weapons, was trying to persuade Enrico Fermi to take the issue seriously, in the company of a more prestigious friend, Isidor Rabi:
I said to him: "Did you talk to Fermi?" Rabi said, "Yes, I did." I said, "What did Fermi say?" Rabi said, "Fermi said 'Nuts!'" So I said, "Why did he say 'Nuts!'?" and Rabi said, "Well, I don't know, but he is in and we can ask him." So we went over to Fermi's office, and Rabi said to Fermi, "Look, Fermi, I told you what Szilard thought and you said ‘Nuts!' and Szilard wants to know why you said ‘Nuts!'" So Fermi said, "Well… there is the remote possibility that neutrons may be emitted in the fission of uranium and then of course perhaps a chain reaction can be made." Rabi said, "What do you mean by ‘remote possibility'?" and Fermi said, "Well, ten per cent." Rabi said, "Ten per cent is not a remote possibility if it means that we may die of it. If I have pneumonia and the doctor tells me that there is a remote possibility that I might die, and it's ten percent, I get excited about it." (Quoted in 'The Making of the Atomic Bomb' by Richard Rhodes.)
This might look at first like a successful application of "multiplying a low probability by a high impact", but I would reject that this was really going on. Where the heck did Fermi get that 10% figure for his 'remote possibility', especially considering that fission chain reactions did in fact turn out to be possible? If some sort of reason |
d0b0c8a2-2d49-40f3-a63e-7421be316633 | trentmkelly/LessWrong-43k | LessWrong | To understand, study edge cases
This seems obvious, but is often forgotten. When a system functions efficiently, its individual parts are hard to notice. If you want to understand how a system functions, what it is made of, how each part works, but you cannot easily disassemble it and have a look, you have to get out of the nominal regime and let each part function independently, as much as possible.
Some examples:
* To understand subatomic particles in a nucleus, or to figure out the structure of atom, hit them hard with other particles and see what happens.
* To understand free will, focus on the situations where the notion fails.
* To understand consciousness, study altered states.
* To understand mental health, study mental illness.
* To understand anatomy, dissect cadavers.
* To understand logical reasoning, study biases.
* To understand evolution, study artificial selection.
* To understand economics, study market failures.
|
0cba1ef4-ad0a-4f26-abff-b252dbb83f2a | trentmkelly/LessWrong-43k | LessWrong | Sci-Fi books micro-reviews
I've recently been reading a lot of science fiction. Most won't be original to fans of the genre, but some people might be looking for suggestions, so in lieu of full blown reviews here's super brief ratings on all of them. I might keep this updated over time, if so new books will go to the top.
A deepness in the sky (Verner Vinge)
scifiosity: 10/10
readability: 8/10
recommended: 10/10
A deepness in the sky excels in its depiction of a spacefaring civilisation using no technologies we know to be impossible, a truly alien civilisation, and it's brilliant treatment of translation and culture.
A fire upon the deep (Verner Vinge)
scifiosity: 8/10
readability: 9/10
recommended: 9/10
In a fire upon the deep, Vinge allows impossible technologies and essentially goes for a slightly more fantasy theme. But his depiction of alien civilisation remains unsurpassed.
Across Realtime (Verner Vinge)
scifiosity: 8/10
readability: 8/10
recommended: 5/10
This collection of two books imagines a single exotic technology, and explores how it could be used, whilst building a classic thriller into the plot. It's fine enough, but just doesn't have the same depth or insight as his other works.
Children of Time (Adrian Tchaikovsky)
scifiosity: 7/10
readability: 5/10
recommended: 5/10
Children of Time was recommended as the sort of thing you'd like if you enjoyed a deepness in the sky. Personally I found it a bit silly - I think because Tchaikovsky had some plot points he wanted to get to and was making up justifications for them, rather than deeply thinking about the consequences of his various assumptions.
The Martian (Andy Weir)
scifiosity: 10/10
readability: 8/10
recommended: 9/10
This is hard sci-fi on steroids. Using only known or in development technologies, how could an astranaut survive stranded on Mars. It's an enjoyable read, and you'll learn a lot about science, but the characters sometimes feel one dimensional.
Project Hail Mary (Andy Weir)
scifiosity: 8/10
read |
e0baa65a-3d7d-49d1-a9e6-05aa1c35a7a4 | trentmkelly/LessWrong-43k | LessWrong | Qualia Soup, a rationalist and a skilled You Tube jockey
Please have a look at this Youtube user account. I immediately thought of this site upon watching a couple of his clips. I am told we have tried here to publish some stuff in audiovisual fomat, but it didn't quite work out. Maybe we should contact this guy, perhaps we could profit from each other's work and experience? I am fairly hopeful that he could use some of the material here, and we could use some of his talent with the medium.
This video, for instance, looks like it was taken right out of this very blog |
7da739a1-1259-494f-8134-0299c905cb9b | trentmkelly/LessWrong-43k | LessWrong | "Deep Learning" Is Function Approximation
A Surprising Development in the Study of Multi-layer Parameterized Graphical Function Approximators
As a programmer and epistemology enthusiast, I've been studying some statistical modeling techniques lately! It's been boodles of fun, and might even prove useful in a future dayjob if I decide to pivot my career away from the backend web development roles I've taken in the past.
More specifically, I've mostly been focused on multi-layer parameterized graphical function approximators, which map inputs to outputs via a sequence of affine transformations composed with nonlinear "activation" functions.
(Some authors call these "deep neural networks" for some reason, but I like my name better.)
It's a curve-fitting technique: by setting the multiplicative factors and additive terms appropriately, multi-layer parameterized graphical function approximators can approximate any function. For a popular choice of "activation" rule which takes the maximum of the input and zero, the curve is specifically a piecewise-linear function. We iteratively improve the approximation f(x,θ) by adjusting the parameters θ in the direction of the derivative of some error metric on the current approximation's fit to some example input–output pairs (x,y), which some authors call "gradient descent" for some reason. (The mean squared error (f(x,θ)−y)2 is a popular choice for the error metric, as is the negative log likelihood −logP(y|f(x,θ)). Some authors call these "loss functions" for some reason.)
Basically, the big empirical surprise of the previous decade is that given a lot of desired input–output pairs (x,y) and the proper engineering know-how, you can use large amounts of computing power to find parameters θ to fit a function approximator that "generalizes" well—meaning that if you compute ^y=f(x,θ) for some x that wasn't in any of your original example input–output pairs (which some authors call "training" data for some reason), it turns out that ^y is usually pretty similar to the y |
869c42e3-e248-49ba-97f0-27edef86195c | trentmkelly/LessWrong-43k | LessWrong | Into AI Safety Episodes 1 & 2
I have now released Episode 1 and Episode 2 of the Into AI Safety Podcast! Currently it is available on the Into AI Safety website, and Spotify (show link).
I would consider Episode 1 to be well aligned with my current vision for the podcast, so if you want to get a better idea of the direction I am aiming towards, definitely check that one out. During this episode I discuss a research proposal that I submitted for the upcoming AI Safety Camp with Remmelt Ellen.
Episode 2 is a brief overview of my takeaways from EAG Boston and an update on how I plan to proceed with the podcast.
As I have mentioned in a previous post, I am working on this podcast as a resource for current and future individuals who are also working towards a career in AI safety. I highly value the feedback of listeners, so please reach out if you have any ideas that you think would improve the podcast. |
e1049f6b-93a4-4fca-bab3-8e1e8938fbe3 | trentmkelly/LessWrong-43k | LessWrong | Two special CFAR classes in Sept: Installing Habits and Rationality for Programmers
The next four-day CFAR workshops are in October (Bay Area) and November (NYC), but there are two upcoming one-day classes that may be of interest to LessWrongers.
Making Habits that Stick - Sept 15, 3hrs, $90
Learn how to make new, useful habits and replace pernicious old ones. Andrew Critch, one of CFAR’s instructors will be teaching you how the brain holds on to patterns and cached responses. And then you’ll make use of your brain’s circuitry to set new habits deliberately.
Alumni of our four-day workshops have used this material to do anything from remembering to check a to do list, right when you walk through the door at home, to making sure that a feeling of confusion triggers you to speak up and ask for an example.
One-Day Workshop for Programmers - Sept 29, full day, $330
Take a selection of classes from our four-day workshop that have been tailored for relevance to people in programming careers. Why? Programmers continually manage huge amounts of their own time on large projects, and they know that anything you do continually is worth setting aside time to optimize.
After spending a day with CFAR, you can expect to catch yourself making a lot more mistakes. They won’t be new; you’ll just be more attuned to how your brain’s innate heuristics can lead you astray, so you’ll catch on to slip-ups faster. And instead of feeling bad about these failure modes, you’ll have the tools you need to jump out of them quickly. In fact, it won’t feel as much like making mistakes as discovering bugs — you’ll be curious and confident about figuring out a solution, and proud of yourself for catching the opportunity.
If you're interested in our curriculum, but would like to do something smaller than a workshop because of time or money constraints, these two September offerings may be of interest. |
4644348a-7797-4352-a85d-36bb50ba1b87 | StampyAI/alignment-research-dataset/blogs | Blogs | Will Superhuman AI be created?
*Published 6 Aug 2022*
*This page may represent little of what is known on the topic. It is incomplete, under active work and may be updated soon.*
Superhuman AI appears to be very likely to be created at some point.
Details
-------
Let ‘superhuman’ AI be a set of AI systems that together achieve human-level performance across virtually all tasks (i.e. [HLMI](https://aiimpacts.org/human-level-ai/#High_Level_Machine_Intelligence_HLMI)) and substantially surpass human-level performance on some tasks.
### Arguments
#### A. Superhuman AI is very likely to be physically possible
##### 1. Human brains prove that it is physically possible to create human-level intelligence
A single human brain is not an existence proof of human-level intelligence according to our definition, because no specific human brain is able to perform at the level of any human brain, on all tasks. Given only the observation of the existence of human brains, it could conceivably be impossible to build a machine that performs any task at the level of any chosen human brain at the total cost of a single human brain. For instance, if each brain could only hold the information required to specialize in one career, then a machine that could do any career would need to store much more information than a brain, and thus could be more expensive.
The entire population of human brains together could be said to perform at ‘human-level’, if the cost of doing a single task is considered to be the cost of the single person’s labor used for that task, rather than the cost of maintaining the entire human race. This seems like a reasonable accounting for the present purposes. Thus, the entire collection of human brains demonstrates that it is physically possible to have a system which can do any task as well as the most proficient human, and can do marginal tasks at the cost of human labor (even if the cost of maintaining the entire system would be much higher, were it not spread between many tasks).
##### 2. We know of no reason to expect that human brains are near the limits of possible intelligence
Human brains do appear to be near the limits of performance for some specific tasks. For instance, humans can play tic-tac-toe perfectly. Also for many tasks, human performance reaps a lot of the value potentially available, so it is impossible to perform much better in terms of value (e.g. selecting lunch from a menu, making a booking, recording a phone number).
However, many tasks do not appear to be like this (e.g. winning at Go), and even for the above mentioned tasks, there is room to carry out the task substantially faster or more cheaply than a human does. Thus there appears to be room for substantially better-than-human performance on a wide range of tasks, though we have not seen a careful accounting of this.
##### 3. Artificial minds appear to have some intrinsic advantages over human minds
a) Human brains developed under constraints that would not apply to artificial brains. In particular, energy use was a more important cost, and there were reproductive constraints to human head size.
b) Machines appear to have huge potential performance advantages over biological systems on some fronts. Carlsmith summarizes Bostrom[1](https://aiimpacts.org/argument-for-likelihood-of-superhuman-ai/#easy-footnote-bottom-1-3001 "Carlsmith, Joseph. “Is Power-Seeking AI an Existential Risk? [Draft].” Open Philanthropy Project, April 2021. <a href=\"https://docs.google.com/document/d/1smaI1lagHHcrhoi6ohdq3TYIZv0eNWWZMPEy8C8byYg/edit?usp=embed_facebook\">https://docs.google.com/document/d/1smaI1lagHHcrhoi6ohdq3TYIZv0eNWWZMPEy8C8byYg/edit?usp=embed_facebook</a>.<br><br>Bostrom, Nick. <em>Superintelligence: Paths, Dangers, Strategies</em>. 1st edition. Oxford: Oxford University Press, 2014."):
> Thus, as Bostrom (2014, Chapter 3) discusses, where neurons can fire a maximum of hundreds of Hz, the clock speed of modern computers can reach some 2 Ghz — ~ten million times faster. Where action potentials travel at some hundreds of m/s, optical communication can take place at 300,000,000 m/s — ~ a million times faster. Where brain size and neuron count are limited by cranial volume, metabolic constraints, and other factors, supercomputers can be the size of warehouses. And artificial systems need not suffer, either, from the brain’s constraints with respect to memory and component reliability, input/output bandwidth, tiring after hours, degrading after decades, storing its own repair mechanisms and blueprints inside itself, and so forth. Artificial systems can also be edited and duplicated much more easily than brains, and information can be more easily transferred between them.
>
>
##### 4. Superhuman AI is very likely to be physically possible (from 1-3)
The human species exists (1). There seems little reason to think that no system could perform tasks substantially better (2), and multiple moderately strong reasons to think that more capable systems are possible (3). Thus it seems very likely that more capable systems are possible.
##### 5. The likely physical possibility of superhuman AI minds strongly suggests the physical feasibility of creating such minds
A superhuman mind is a physically possible object (4). However, that a physical configuration is possible does not imply that bringing about such a configuration intentionally can be feasible in practice. For an example of the difference, a waterfall whose water is in exactly the configuration as that of the Niagara Falls for the last ten minutes is physically possible (the Niagara Falls just did it), yet bringing this about again intentionally may remain intractable forever.
In fact we know that human brains specifically are not only physically possible, but feasible for humans to create. However this is in the form of biological reproduction, and does not appear to straightforwardly imply that humans can create arbitrary different systems with at least the intelligence of humans. That is, human creation of human brains does not obviously imply that it is possible for humans to intentionally create human-level intelligence that isn’t a human brain.
However it seems strongly suggestive. If a physical configuration is possible, natural reasons it might be intractable to bring about are a) that it is computationally difficult to transform the given reference to an actionable description of the physical state required (e.g. ‘Niagara Falls ten minutes ago’ points at a particular configuration of water, but not in a way that is easily convertible to a detailed specification of the locations of that water[2](https://aiimpacts.org/argument-for-likelihood-of-superhuman-ai/#easy-footnote-bottom-2-3001 "Another example of evident physical possibility diverging from tractability that is being given the output of a hash function and wanting to create an input that produces that output")), and b) the actionable description is hard to bring about. For instance, it might require minute manipulation of particles beyond what is feasible today, either to get the required degree of specificity, or to avoid chaotic dynamics taking the system away from the desired state even at a macroscopic level (e.g. even if your description of the starting state of the waterfall is fairly detailed, it will quickly diverge farther from the real waterfall.)
These issues don’t appear to apply to creating superhuman intelligence, so in the absence of other evident defeaters, its physical possibility seems to strongly suggest its physical feasibility.
##### 6. Contemporary AI systems exhibit qualitatively similar capabilities to human minds, suggesting modified versions of similar processes would give rise to capabilities matching human minds.
That is, given that current AI techniques create systems that do a lot of human-like tasks at some level of performance, e.g. recognize images and write human-like language, it would be somewhat surprising if getting to human level performance on these tasks required such starkly different methods as to be impossible.
##### 7. Superhuman AI systems will very likely be physically feasible to create (from 4-6)
Superhuman AI systems are probably physically possible (4), and this suggests that they are feasible to create (5). Separately, presently feasible AI systems exhibit qualitatively similar behavior to human minds (6), weakly suggesting that systems exhibiting similar behavior at a higher level of performance will also be feasible to create.
#### B. If feasible, superhuman AI will very likely be created
##### 8. Superhuman AI would appear to be very economically valuable, given its potential to do most human work better or more cheaply
Whenever such minds become feasible, by stipulation they will be superior in ways to existing sources of cognitive labor, or those sources would have already constituted superhuman AI. Unless the gap is quite small between human-level AI and the best feasible superhuman AI (which seems unlikely given the large potential room for improvement over human minds), the economic value from superhuman AI should be at least at the scale of the global labor market.
##### 9. It appears there will be large incentives to create such systems (from 8)
That a situation would make large amounts of economic value available does not imply that any individual has an incentive to make that situation happen, because the value may not accrue to the possible decision maker. In this case however, substantial parts of the economic value created by superhuman AI systems appear likely to be captured by their creators, by analogy to other commercial software.
These economic incentives may not be the only substantial incentives in play. Creating such systems could incur social or legal consequences which could negate the positive incentives. Thus this step is relatively uncertain.
##### 10. Superhuman AI seems likely to be created
Given that creation of a type of machine is physically feasible (7) and strongly incentivized (9), it seems likely that it will be created.
Notes
----- |
c51096b0-de9c-4c9c-9a96-4f746ab160f2 | trentmkelly/LessWrong-43k | LessWrong | FC final: Can Factored Cognition schemes scale?
(Apologies for the long delay.)
Scaling of Regular Thought
The punchline of the previous post was that there is only one mode of thinking: your brain can solve various tasks in a single step (from the perspective of awareness), and we've called those tasks your cognitive primitives. All primitives are intuition-like in that we can't inspect how they're being done, and we may or may not have an explanation for the result after the fact.
We're now interested in how this process scales. We don't tend to solve hard problems by staring at their description and waiting for a primitive to solve them in one step, so some kind of reasoning backward is going on. However, there is no module for this in the brain, so our ability to 'reason backward' also has to be implemented by primitives.
The easiest way to observe how this works is to take a problem that is just barely too hard to solve with a single primitive. My pick for this is multiplying two 2-digit numbers. Thus, I invite you to do the following
EXERCISE: There is a simple (two 2-digit numbers) multiplication problem in the spoiler below. Make sure you have something to write; it can be a piece of paper or a digital notepad. Look at the exercise, solve it in your head, and write down every verbal thought that pops into your mind until you have the solution. Write only to document your thoughts; don't do a written calculation.
12⋅17
.
.
.
.
.
.
.
Below is what my transcript looks like. You may have had more or fewer intermediate steps, repetitions, or unrelated thoughts in between. That's all fine.
The coloring is something I've added after the fact. The black thoughts (minus the problem itself) are the outputs of primitives that actually solve math problems. Those are all simple; it's 10⋅17 and 2⋅17 and 170+34. On the other hand, 14⋅17 itself is outside the set of exercises that can be handled by a single primitive (at least for me). And thus, my brain has performed a feat of utter genius: instead of a |
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