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StampyAI/alignment-research-dataset/arxiv
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Arxiv
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Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning
1 Introduction
---------------
Technological advances can result in harm to both individuals and societal structures, through accidents, unintended consequences, and malicious use – even as the same advances provide incredible benefits. Concern about harms resulting from advances in machine learning (ML) have risen dramatically in recent years, with a growing community of researchers and practitioners doing crucial work to address issues such as the fairness, accountability, and transparency of deployed systems [[1](#bib.bib1)]. In this paper, we focus primarily on the ways that advances in ML research might be deliberately misused by malicious actors (which we refer to as “mal-use”) [[2](#bib.bib2)], though we also touch on other unintended consequences.111Unintended consequences are also particularly salient for the case of synthetic video and audio, where the simple existence of the technology, regardless of its use, can allow bad actors to claim that evidence of e.g. corruption or war crimes were synthesized in order to avoid accountability. For example, allegations of videos being faked have been used to justify a coup in Gabon [[3](#bib.bib3)], and exculpate a cabinet minister in Malaysia [[4](#bib.bib4)].
One specific example of where mal-use concerns have been raised recently is around the use of ML in synthetic media222Not all synthetic media techniques involve ML. For example, traditional computer graphics techniques are also used for image and video manipulation. For simplicity of language, we will not consider these distinctly as the considerations for research practices are very similar.: the manipulation and generation of increasingly realistic audio, video, images, and text. It is now possible to produce synthetic images of faces that are almost indistinguishable from photographs [[5](#bib.bib5)]. It is becoming easier to manipulate existing videos of people: replacing a person’s facial expressions or movements with those of another person [[6](#bib.bib6), [7](#bib.bib7)], and/or overlaying synthesized speech which imitates a person’s voice [[8](#bib.bib8)]. Language models have advanced to the point where, given a sentence prompt, they can write articles that appear at least superficially convincing [[9](#bib.bib9)]. These advances could be used to impersonate people, sway public opinion, or more generally spread doubt about the veracity of all media. Modern synthetic media is in fact already being used for harm: face-swapping tools are being used to harass journalists [[10](#bib.bib10)], synthetic voices are being used for financial crimes [[11](#bib.bib11)], and synthetic faces have allegedly been used for espionage [[12](#bib.bib12)].
These examples bring into focus the need for humility around what we don’t know concerning potential impacts of technology, and perhaps suggests that we must develop better systems for understanding such impacts. Related concerns have sparked debate about responsible research practices in ML [[13](#bib.bib13)], and in particular whether sometimes it is appropriate to withhold open publication of some aspects of this research.
We begin with some background on the idea that ML research might be misused in harmful ways, and why advances in synthetic media, in particular, are raising concerns. We then review research risk mitigation strategies in other fields and identifying components that may be worth emulating in the ML and synthetic media research communities. Finally, we outline some important dimensions of disagreement on these issues which risk polarizing conversations, before concluding with some recommendations for research and practice.
2 How research can lead to harm
--------------------------------
How might advances in ML and synthetic media end up resulting in harm? We begin by spelling this out in more detail: different ways that research might empower malicious actors, and some possible paths to harm from this.
###
2.1 Types of hazard
We can distinguish several different types of “information hazard” that might result from machine learning research in general. 333This is based loosely on a taxonomy from [[14](#bib.bib14)]
* Product hazard: Research produces software that can be directly used for harm (e.g. rootkits for computer hacking). Product hazards increase the likelihood of mal-use by adversaries with minimal technical capabilities (e.g. ‘script kiddies’ who use existing programs or scripts to hack into computers but lack the ability to write their own, or less sophisticated information warfare operations), or those who have weak motivations for harm (e.g. online hacking or deception just ‘for fun’).
* Data hazard: research produces detailed information or outputs which if disseminated, create risk of use for harm (e.g. easy nuclear blueprints; models, training data or code for harmful software). Data hazards increase the likelihood of mal-use by adversaries with some technical capabilities, but without e.g. access to high-quality researchers.
* Attention hazard: research which directs attention towards an idea or data that increases risk (e.g. the idea that it is possible to use voice-cloning for phishing). Attention hazards increase the likelihood of mal-use by adversaries who may not have realized that their objectives can be aided by new technologies.
An important thing to note is that potential mitigations will be different for each type of hazard – and potentially even in conflict. One way to mitigate attention hazards is to be very careful about talking to media organizations and others with large reach about ways that research advances could be used maliciously (such as in voice cloning, for example). At the same time, raising wider concern about malicious use cases of ML progress might be exactly what is needed to incentivize mitigations for data hazards or product hazards (e.g. some public concern may be required to ensure that tech platforms prioritize developing technology to identify voice cloning, or for telecoms to build mitigating infrastructure to make phone number spoofing harder).
###
2.2 The path to harm
But how might these different types of ‘hazard’ actually lead to real-world harms? It is worth connecting the dots here between the theoretical potential for mal-use, and what actually makes significant harm more likely.
Below we talk through some factors influencing whether a capability leads to sustained mal-use in practice. We use artificial voice cloning as an illustrative example, as a relatively new capability with many useful applications (e.g. in voice translation and audio editing) but also significant potential for mal-use (e.g. in scams, political propaganda, and market manipulation).
1. Awareness: Do actors with malicious intent know about a capability and believe it can help them?
We can break this down into:
* Attention of adversaries: Are malicious actors likely to realize that they could use a new capability to further their ends? If adversary groups are already using closely related methods, this is much more likely: for example, if edited voice clips are already being used for political manipulation, groups doing this are more likely to pay attention to demonstrations of voice cloning.
* ‘Convincibility’ of those with resources: Are there compelling arguments, perhaps by authoritative third parties, for the effectiveness of new capabilities? For example, a scammer who realizes that voice cloning is useful might need to be able to convince a superior that this technology is effective enough to justify the costs and overcome institutional inertia.
2. Deployment: How difficult is it for adversaries to weaponize this capability in practice?
For a capability to be deployed for malicious purposes, adversaries not only need to be aware but to have the necessary skills and resources to productize and weaponize the capability. This isn’t a binary – e.g. having ML expertise vs. not – but rather many different factors will influence how easy a capability is to weaponize. At the extreme, we might have a product which can be immediately used by anyone, regardless of technical capability (such as free to use voice cloning software).
Factors that influence the ease of deployment for mal-use include:
* Talent pipelines: How difficult is it to source someone who can apply a new capability for the desired use case? (e.g. do malicious actors need someone with machine learning experience, programming experience, or can they just use a program directly to achieve their goals?) [[12](#bib.bib12)].
* Reproducibility: How difficult is it to reproduce a capability given the information available? (e.g. is it easy to replicate a voice cloning capability given the available papers, models, code, etc.?)
* Modifiability: How difficult is it to modify or use a system in order to enable mal-use? (e.g. if a voice cloning product makes it difficult to clone a voice without consent or watermarks, how hard is it to overcome those limitations?)
* Slottability: Can new capabilities be slotted into existing organizational processes or technical systems? (e.g. are there already established processes for phone scams into which new voice generation capabilities can be slotted easily, without any need to change goals or strategy?)
* Environmental factors: How does the existing ‘environment’ or ‘infrastructure’ impact the usefulness of the new capability for malicious actors? (E.g. currently, in the US it is easy to ‘spoof’ phone numbers to make it appear like a call is coming from a family member, which could impact the likelihood of voice cloning being weaponized for phone scams.)
Websites now enabling anyone to instantly generate seemingly photorealistic faces are a concrete example of deployment barriers falling away and making mal-use easier. It had been possible for well over a year to generate synthetic images of faces with fairly high quality, but such websites have enabled anyone to do so with no technical expertise. This capability can also immediately slot into existing processes, such as fake account creation. Previously, malicious actors would often use existing photos of real people, which could be identified with reverse image search [[15](#bib.bib15)], unlike wholly generated synthetic images.
3. Sustained use: How likely is it that a capability will lead to sustained use with substantial negative impacts?
Even if adversaries are aware of and able to weaponize some new capability, whether or not this leads to sustained use depends on:
* Actual ROI: If malicious actors believe that the return on investment (ROI) for using a capability is low they might not continue to use it in practice. For example, if a form of mal-use is easy to detect, then adversaries might decide it’s not worth the risk or might be shut down very quickly.
* Assessment of ROI: If malicious actors have no way of assessing whether new capabilities are helping them better achieve their goals, or if their assessments are flawed, they might not continue to put resources into using those capabilities.
###
2.3 Access ratchets
We can think of this as a kind of progression, from a theoretical capability to scaled-up use in practice. Once a technology has progressed down this path and has become easy to use, and proven to have high ROI for mal-use, it can be much more difficult to address than at earlier stages – we call this the access ratchet (like a ratchet, increased access to technology cannot generally be undone). For any capability with potential for mal-use, it is therefore worth thinking about where it currently sits on this progression: how much attention and interest it is receiving; whether it has been weaponized and/or how costly it would be to do so; and whether it’s likely to be, or already in sustained use. This can help us think more clearly about where the greatest risks of mal-use are, and different kinds of interventions that might be appropriate or necessary in a given situation.
Researchers may argue that a capability is unlikely to cause harm since it has not been used maliciously yet. What this doesn’t address is the fact that a capability which has not yet been used maliciously might sit anywhere along this progression, which makes a huge difference to how likely it is to cause harm. For example, Face2Face, a technique for real-time facial reenactment (i.e. changing a person’s expressions in a video), has existed for 3 years but not been developed into any products that can easily be used. This lack of productization makes harmful use vastly less likely, especially given the competition for AI and engineering talent today. It is also worth considering how costly it would be to make a given capability easier to misuse: even the DeepFake application, which is more accessible to non-technical users, is currently resource-intensive to weaponize in practice.
###
2.4 Indirect harms
Sometimes the path to harm from synthetic media research will be fairly direct and immediate: such as a person losing their money, returning to our example of voice cloning being used in financial scams.
But in other cases, improved synthetic media capabilities might cause harm in more complex and indirect ways. Consider the case where misinformation purveyors get hold of sophisticated synthetic media capabilities and use them to win substantial democratic power, which they then use to control narratives further and undermine any mitigation efforts (not an uncommon path from democracy to authoritarianism). We can think about this as a disinformation ratchet: the ability to use disinformation to enhance one’s ability to distribute further disinformation; and the opportunity for this type of ratchet can be influenced by new technology impacting media distribution channels and capabilities.
These less direct kinds of harms may be harder to anticipate or imagine, but in the long-run may be much more important – particularly if they influence the future development of technology in ways that undermine our ability to deal with future threats. We suggest that it’s particularly important to consider these kinds of “sociotechnical-path dependencies” as well as more direct and immediate threats, and what kinds of risk mitigation strategies might best address them.
3 Mitigating harm through release practices
--------------------------------------------
There is unlikely to be any ‘one size fits all’ solution to mitigating mal-use of ML research: the path to harm will look very different across contexts, and potential harms need to be weighed against benefits which will also vary depending on the area. We therefore need discussion about different approaches to mitigating mal-use: including around what research is conducted in the first place; standards and procedures for risk assessment; and processes for deciding when and how to release different types of research outputs. Here we focus particularly on the latter – how careful release practices might help mitigate mal-use within ML research.
However, this is not to suggest we think release practices are the main or even necessarily the most important component of mitigating mal-use. Another crucial piece is how research directions are chosen and prioritized in the first place. This is challenging because much of ML research often involves developing general capabilities which can then be applied to a variety of different purposes – we can’t simply decide to build only ‘beneficial’ ML capabilities. That aside, we still may be able to say some very general things about the kinds of capabilities that are more likely to be broadly beneficial, or the kinds of problems that should ideally be driving ML research. It is also important to think about what types of research are encouraged/discouraged by conferences, journals, funders, job interviewers and so on.
###
3.1 Challenges to mitigating harm
First, it’s worth considering some of the serious challenges to attempting to decrease harm by limiting access to research:
* The composition problem: Two independent pieces of research that seem innocent can be combined in ways that enable significant malicious use.444This might be particularly challenging given the success of transfer learning.
* The slow drip problem: Research advancement can be a slow and continuous evolution, where it’s difficult to draw the line between research that is dangerous and that which is not.
* The conflation problem: Many of the underlying goals of various fields of research (natural language processing, computation photography, etc.) may be directly weaponizable if achieved. For example, the ability to create convincing dialogue can be used to both support people or manipulate people at scale.
* The defector problem: Even if researchers in some regions or organizations cooperatively decide not to pursue or publish a particular area of research, those agreements might not be followed by “defectors” who then gain a competitive edge.
These challenges may seem daunting even for those who would advocate for limiting release of some forms of research. They also motivate the development of a nuanced menu of options for release practices, and careful evaluation of the efficacy of whatever measures are chosen. Even without overcoming these challenges, it is possible that release practices could substantially mitigate harm if they impact the rate of deployment of mal-use technology.555In terms of the ratchet terminology used earlier, delaying release of research could slow down the speed of an ‘access ratchet’ (i.e. slowing down widespread access to a technology), potentially providing enough extra time strengthen a ‘sociotechnical immune system’ that could halt a disinformation ratchet.
###
3.2 A brief tour of analogs in other fields
There is precedent in several other fields of research – including biotechnology and information security – for establishing processes for reducing the negative risks of research and release. A good first step would, therefore, be to look at what we can learn from these fields for the case of ML research. Here we present some promising practices identified from other fields.
A caveat: just because research norms and processes exist in other fields, it does not necessarily mean that they are widely and coherently used in those fields, or that they provide a net positive impact. Evaluating which research practices have been adopted and work well across different fields is out of scope for this short paper, but would certainly be valuable to look into further.
####
3.2.1 Biosafety
Biosafety processes and principles exist to ensure safe handling of infective microorganisms in biology/biotechnology research [[16](#bib.bib16)]. Some key components of biosafety practices include:
* Procedures: Steps and rules that must be followed, e.g. for decontamination (including basics such as wearing gloves and shoes).
* Lab safety officer: An internal role responsible for enforcing safety.
* Training: Learning safety processes via peers/programs.
* Architecture: Incorporating safety considerations into building and tool design (e.g. the design of doors and airflow).
* Audits: Providing external accountability, usually at random times via local government.
* Safety level designations: Different microorganisms classified by risk group (e.g. Ebola is level 4) with different safety procedures for different levels (e.g. level 1 is open bench work, level 4 requires special clothing, airlock entry, special waste disposal, etc.).
* Safety level definers: Organisations who determine safety levels, e.g. the Centers for Disease Control and Prevention (CDC).
####
3.2.2 Computer/Information security
Various practices exist in the field of information security to prevent exploitation of vulnerabilities in important systems. Key components include:
* OPSEC (‘operations security’): Procedures for identifying and protecting critical information that could be used by adversaries. Includes identification of critical information, analysis of threats, vulnerabilities, and risks, and application of appropriate measures.
* Architecture: Use systems that are “secure by design” and so keep you secure automatically where possible.
* Coordinated/responsible disclosure: Processes to ensure that exploits which could affect important systems are not publicly disclosed until there has been an opportunity to fix the vulnerability.
* ISACs/CERTs (Information Sharing & Analysis Centers/Computer Emergency Response Teams): Disclosure coordination entities.
####
3.2.3 Institutional Review Boards (IRBs)
IRBs are designed to protect human subjects in biomedical and behavioral research (including e.g. clinical trials of new drugs or devices and psychology studies of behavior, opinions or attitudes) [[17](#bib.bib17)]:
* Case dependent scrutiny: Research proposals are assessed on a case-by-case basis using external expert evaluation, and are determined to either be: (a) exempt (when risks are minimal), (b) expedited (slightly more than minimal risk), or (c) full review (all other proposals).
* Approval rubrics: Criteria for approval of research proposals include: having sound research principles to minimize risk to subjects; establishing that risks to subjects are reasonable relative to anticipated benefits; selecting subjects in equitable ways, and avoiding undue emphasis on a vulnerable population.
* External expert & community evaluation: Studies are reviewed by people who have expertise in the research and in the impacts of the work (such as community members).
* Continuous evaluation: Process can be ongoing, not one-time, with periodic updates: the IRB can suspend or terminate previously approved research.
This is not meant to be exhaustive but demonstrates a variety of systems that have been used to mitigate negative risks of research and release. Other analogs worth exploring include those around nuclear technology, spam detection, classified information, and environmental impact.
###
3.3 Potential release practices
What should ML and synthetic media research emulate from these other fields? Many aspects of these practices and processes may be applicable in ML research, including particularly: external expert evaluation of risks and appropriate responses, coordinated/responsible disclosure, training in responsible research processes, disclosure coordination entities, safety level designations, safety level defining entities, and case-dependent response (depending on safety levels).
Reframing and renaming all of these practices and processes to focus on the release of potentially hazardous ML systems leaves us with the following components that may be needed in ML:
* Release options: Different options for release.
* Release rubric: Guidelines for when to use each type (decided by case-dependent evaluation).
* Release rubric processes: How to do case-dependent evaluation.
* Release coordination: Who decides/gets access.
* Release training: How to learn processes/norms.
* Release process entities: Who manages all of this?
Each of these components can be broken down further; we explore “release options” here as an example.
###
3.4 Release options
The question of how to release research with potential for mal-use is not a binary one: there are many different choices to make beyond simply ‘release’ or ‘don’t release’. Focusing on this binary choice can lead the debate around openness of ML research to become very polarized.
Some important dimensions we might consider when thinking about release strategies include:
* Content: *What is released*
Potential options include:
+ A fully runnable system (with varying power)
+ A modifiable system (with varying modifiability)
+ Source code (varying versions)
+ Training data (varying sizes)
+ Trained models (varying strength/fine-tunability/data-needs)
+ Paper/concept (varying detail level)
+ Harmful use case ideas (varying detail level)
* Timing: When it is released
Potential options include:
+ Immediate release
+ Timed release: Set a specific time to release components, allowing time for mitigation of any potential harms. This is common in information security.
+ Periodic evaluation: Don’t release immediately, but set a time period/intervals (e.g. every 2 months), at which point an evaluation is done to reassess the risk of release given mitigation progress.
+ Evented release: Wait to release until some particular type of external event (e.g. someone else replicating or publicizing the same technology).
+ Staged release: Release systems of successively increasing levels of power on a fixed timeline, or triggered by external events.
* Distribution: Where/Who it is released to
Potential options include:
+ Public access (with varying degrees of publicity)
+ Ask for access: Anyone who wants access to data or a system asks and is approved on a case-by-case basis, potentially with specific requirements around use.
+ Release safety levels: People and possibly organizations can request to be recognized as ‘safe’, after auditing and approval they gain the ability to access all material at a given safety level.
+ Access communities: Research groups developing their own trusted communities through informal processes which all have access to shared repositories.
Within the domain of synthetic media, it’s worth diving deeper into potential mitigations specific to products, models, and demos relevant to that space.
There are a number of mechanisms researchers and companies can use to reduce malicious use from general synthetic media systems that allow e.g. virtual impersonation:
* Consent: Requiring consent by those being impersonated.
* Detectability: Intentionally not trying to thwart detection.
* Watermarking: Embedding context about modifications/original.
* Referenceability: Centrally storing all modifications for reference.
It’s important to note that none of these are perfect – they are part of a “defense in depth”. It is also possible to add constraints on synthesis (e.g. ensuring that only particular faces can be generated through the system).
###
3.5 Examples in practice
This menu of options is valuable in theory, but it’s also worth briefly exploring some examples in practice. One of the most notable public positions in this space comes from Google: “We generally seek to share Google research to contribute to growing the wider AI ecosystem. However we do not make it available without first reviewing the potential risks for abuse. Although each review is content specific, key factors that we consider in making this judgment include: risk and scale of benefit vs downside, nature and uniqueness, and mitigation options.” [[18](#bib.bib18)]
Beyond Google, a number of labs have had to consider these issues. As mentioned earlier, the researchers behind Face2Face and those behind many other synthetic media systems have chosen not to share their code. Some researchers have released code but intentionally made it difficult to use for non-experts.
Different product companies in this space are also exploring mitigations.666For more on this, see this crowdsourced list of organizations and their actions to mitigate risk: <http://bit.ly/synth-ethics.> Synthesia is only working with closely vetted clients. Lyrebird, which enables voice cloning, makes it more difficult to impersonate someone without consent by requiring users to speak particular phrases instead of just training on arbitrary provided data.
4 Disagreements around release practices
-----------------------------------------
Different people and groups will have differing views on which kinds of release strategies should be used when. Here we lay out some different dimensions on which people may disagree which affect their views about release strategies for ML research. Our aim is to recognize that genuine divides exist and can lead to polarization of opinion, but that more nuanced discussion can prevent this.
###
4.1 Value trade-offs
Some disagreements stem from fundamental views about the value of openness vs. caution in research.
The ML community has very strong norms around openness: free sharing of data, algorithms, models, and research papers. These strong openness norms appear to be broadly motivated by (1) distributing the benefits of research widely by making it accessible to all of society, and (2) enabling scientific progress by making it easier for researchers to critique and build on one another’s work.
Research practices that attempt to limit mal-use by being more cautious about how it is released and distributed necessarily reduce some forms of openness. Some who take openness to be a fundamental value in research may therefore disagree with such practices on principle. However, there are multiple different aspects to openness in research, and, as we’ve tried to highlight in this paper, multiple different approaches to being cautious about research release. Not all of these will necessarily be in tension with one another, and more exploration of research practices that decrease risk while protecting the most important aspects of openness would be valuable.
###
4.2 Beliefs about risks
Some disagree about the relative size of different risks involved in ML research.
On the one hand, there is the risk that advances in ML might be misused by malicious actors in potentially catastrophic ways, which we’ve discussed. But restricting the release of ML research also creates its own risks: (1) of increasing power concentration, as a few research groups disproportionately control how ML capabilities evolve, and (2) of creating public confusion or even panic, by creating the impression that advances are more threatening than they are.
Beliefs about the relative size of these risks can lead to two very different perspectives. Those who believe that ML advances will lead to significant harm very soon may want to risk such power concentration in order to safeguard democracy and public trust in the long term. By contrast, for those who think weaponization is less immediately relevant and that we can reassess risks in the future, the costs of restricting research may seem less palatable.
While there is a genuine tension here, it is worth considering approaches that could address both sides of the concern (or at least address one side without exacerbating the other.) For example, some standardization of release practices, potentially managed by external entities, could help mitigate misuse without leading to power concentration.
###
4.3 Beliefs about efficacy
Another dimension of disagreement centers not around what the risks are but how effective different practices are likely to be at reducing them.
Given strong incentives or low barriers to develop a technology (or achieve an insight), some suggest it is impossible to prevent either from leading to mal-use the long run, which could mean that restricting the release of research with potential for mal-use is futile. Others suggest that we can significantly impact incentives or barriers, or that slowing down release into the world can still make a significant difference, especially if this gives us time to build defenses against potential mal-use. There is also the perspective that it is easier to build systems to defend against mal-use if more research is public, and the counterview that public information can sometimes help attackers more than defenders (‘security through obscurity’ may be unnecessary for e.g. keeping data private but is still allegedly crucial for anti-spam defense). As ML researchers continue to experiment with release practices and explore similar challenges in other fields, we may learn about the efficacy of different approaches which can help inform these beliefs.
###
4.4 Beliefs about future needs
Finally, there’s a question of whether we might eventually need processes for release of ML research, even if they’re not essential now.
For those who believe that we might develop much more advanced ML systems in the relatively near future, and that potential for harm will increase with these advances, then it probably makes sense to start developing careful norms and processes now regardless of current harms. For those who are more skeptical of the possibility of much more advanced capabilities, think that such capabilities are unlikely to be dangerous, and/or that restricting release is unlikely to be effective in the future regardless, developing such processes now looks unnecessary.
\*
Part of the reason for laying out various different options for release of research is to show that this needn’t be a polarized debate: it’s not a simple choice between ‘open’ or ‘closed’ ML research. It’s worth considering whether, within our menu of options, there are approaches which can strike a balance between the differing perspectives outlined here.
5 Recommendations
------------------
We’ve laid out some considerations, tools, and options for thinking through release of potentially harmful research in a nuanced way. But what must be done now? Here are some brief recommendations:
1. Increase understanding of the risk landscape and possible mitigation strategies:
* Develop standardized language for talking about these issues e.g. around hazards, adversaries, mitigations and release options.
* Map risks of different types of ML research in collaboration with subject matter experts, such as e.g. misinformation security researchers for synthetic media. Map out both immediate direct threats and potential longer-term path dependencies, in ways that address researcher concerns around risk hyperbole. Develop practices for safely discussing such risks.777This type of investigation might also be referred to as e.g. threat models, risk analysis, or impact analysis, each of which involves a different set of useful lenses.
* Map mitigation options, e.g. ways of reducing the harms resulting from mal-use of synthetic media research, and the stages/times at which they are applicable.
2. Build a community and norms around competency in understanding the impacts of ML research:
* Establish regular workshops to focus on release challenges.
* Spread awareness of the risks of ML research to both groups who might be affected and who can help mitigate the risks. Proactively seek to include and learn from those who have been impacted.
* Encourage impact evaluation both positive and negative, for research publications, presentations, and proposals (such as that proposed by the ACM FCA [[19](#bib.bib19)]).
3. Fund institutions and systems to grow and manage research practices in ML, including potentially:
* Support expert impact evaluation of research proposals, so that the burden of this does not fall entirely on individual researchers (who may not have the relevant expertise to assess hazards). This might involve e.g. identifying groups with subject matter expertise who can do evaluations (at the request of researchers), coordinating, and potentially even paying for review.
* Prototype vetting systems to help enable shared access to potentially sensitive research (as opposed to the current system where researchers attempt to validate if those requesting their models are malicious actors [[20](#bib.bib20)], often via error-prone ad-hoc Googling).
* Develop release procedures for research already deemed to raise potential risks (managing all of the above if needed, so that individual researchers can spend more time on actual research while still mitigating risks). Currently, organizations are unilaterally not publicly releasing results, so developing better procedures could actually open up research.
6 Conclusion
-------------
It is clear that advances in ML have the potential to be misused: the main example we have discussed here is how advances in synthetic media creation may be used to sow disinformation and mistrust (but many others can be, and have been discussed [[2](#bib.bib2)]). We must start thinking about how to responsibly safeguard ML research.
Here we focus on the role of release and publication practices in preventing mal-use of ML research. The idea that we might sometimes restrict research release has been met with understandable concern from parts of the ML community for whom openness is an important value. Our aim here has been to decrease polarization in this debate; to emphasize that this is not a simple choice between “open” and “closed” ML research. There are a variety of options for how and when different aspects of research are released, including many drawn from parallels to existing fields, and many possible processes for making these decisions.
There will always be disagreements about the relative risks and benefits of different types of research, the effectiveness of different mitigation strategies, and ultimately how to balance the values of openness vs. caution. We must more deeply explore the risks and options, and develop release strategies and processes that appropriately balance and manage the trade-offs.
Ultimately, we want research to benefit humanity. We see this work as part of a maturing of the ML community, alongside crucial efforts to ensure that ML systems are fair, transparent, and accountable. As ML reshapes our lives, researchers will continue to come to terms with their new powers and impacts on world affairs.
|
a7dafbc4-c3eb-43ea-873b-abe4d06dd0bf
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Other Constructions of Gravity
In Newtonian gravity, the energy is proportional to the sum over pairs of masses of −m1m2/r1,2. Or in the continous case, −∫∫dm1dm2|pos(m1)−pos(m2)|2. This does not actually strike me as very simple in whatever language makes Maxwell's equations and the Schrödinger equation simple.
Partially-formed idea 1:
Here's an outline of something that seems like it might be a simpler theory of gravity. Given a density function ρ over physical space (possibly including Kronecker deltas for point masses), we need to come up with a gravitational potential U. First, convolve ρ with some (radially symmetric) function f:R3→R. Write this ρ∗f. Let F{ρ∗f} be the Fourier transform. I think taking the Fourier transform of something in position space gives something in momentum space. Then, you can take a "measurement" of the momentum squared by evaluating ∫R3F{ρ∗f}(ω)2ω2dω. And I think the momentum squared is energy-like. If ρ is the sum of two delta functions that are a distance d apart, surely there is some f, such that for large d, this looks like c1−c2/d.
It is possible that this could be made to resemble normal Newtonian gravity in the case of a few point masses, or a single mass (like the sun) that is much more massive than other nearby masses. But it would maybe also give different-looking answers when there is lots of mass evenly spread out of large distances, on the scale of, say, rotating galaxies.
Even-less-formed idea 2:
If you are experiencing gravity, you don't actually know exactly where the masses are which are causing this gravity. Doing some heavy handwaving with the gist of the Heisenberg Uncertainty Principle, to the extent you know the precise position of something, then from your perspective, it has lots of momentum. Maybe as you become more able to judge the precise position of nearby gravitating masses, your relative momentum increases to compensate. (This would look like speeding up as you approach a mass).
If you're around other masses, you don't detect t
|
5552aca9-fc0f-4eb1-b18d-035e4cf1e1d8
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : Durham NC meetup: Living Luminously, Part 2
Discussion article for the meetup : Durham NC meetup: Living Luminously, Part 2
WHEN: 25 April 2013 07:00:00PM (-0400)
WHERE: 420 E Geer St, Durham NC 27701
Continuing in the Living Luminously sequence, we'll cover:
The ABCs of Luminosity
(http://lesswrong.com/lw/1y0/the_abcs_of_luminosity/)
"Affect, behavior, and circumstance interact with each other. These interactions constitute informative patterns that you should identify and use in your luminosity project."
Lights, Camera, Action
(http://lesswrong.com/lw/1yb/lights_camera_action/)
"You should pay attention to key mental events, on a regular and frequent basis, because important thoughts can happen very briefly or very occasionally and you need to catch them."
The Spotlight
(http://lesswrong.com/lw/1za/the_spotlight/)
"Inspecting thoughts is easier and more accurate if they aren't in your head. Look at them in another form from the outside, like they belonged to someone else."
Use social pressure to increase the likelihood of your attendance -- RSVP here or via the RTLW group! (http://groups.google.com/group/rtlw)
Discussion article for the meetup : Durham NC meetup: Living Luminously, Part 2
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2e8a9711-2dac-47bf-a920-e372fda960cd
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Evidence for the orthogonality thesis
One of the most annoying arguments when discussing AI is the perennial "But if the AI is so smart, why won't it figure out the right thing to do anyway?" It's often the ultimate curiosity stopper.
Nick Bostrom has defined the "Orthogonality thesis" as the principle that motivation and intelligence are essentially unrelated: superintelligences can have nearly any type of motivation (at least, nearly any utility function-bases motivation). We're trying to get some rigorous papers out so that when that question comes up, we can point people to standard, and published, arguments. Nick has had a [paper](http://www.nickbostrom.com/superintelligentwill.pdf) accepted that points out the orthogonality thesis is *compatible* with a lot of philosophical positions that would seem to contradict it.
I'm hoping to complement this with a paper laying out the positive arguments in favour of the thesis. So I'm asking you for your strongest arguments for (or against) the orthogonality thesis. Think of trying to convince a conservative philosopher who's caught a bad case of moral realism - what would you say to them?
Many thanks! Karma and acknowledgements will shower on the best suggestions, and many puppies will be [happy](http://images.dogsandpuppies.co.uk/papillon-puppies-for-sale-breed-tips.jpg).
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e03cd99d-5d94-40b2-bf2f-ab087f0483d1
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trentmkelly/LessWrong-43k
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LessWrong
|
The Clueless Sniper and the Principle of Indifference
> You are one of the best long-range snipers in the World. You often have to eliminate targets standing one or two miles away and rarely miss. Crucially, this is not because you are better than others at aligning the scope of your rifle with the target’s head before taking the shot. Rather, this is because you are better at accounting for the external factors that will influence the bullet’s direction (e.g., the wind, gravity, etc.). Estimating these external factors is crucial for your success. One day, you are on a roof with binoculars, looking at a building four miles away that you and your team know is filled with terrorists and their innocent children. Your superior is beside you, looking in the same direction. Your mission is to inform your allies closer to the building if you see any movement. After a long wait, the terrorist leader comes out, holding a child close to him. He knows ennemies might be targetting him and would not want to hurt the kid. They are hastily heading towards another building nearby. You grab your radio to inform your allies but your superior stops you and hands you your sniper. “He’s so exposed. This is a golden opportunity for you.” she says. “I reckon we’ve got two minutes before they reach the other building. Do you think you can get him?“. You know that your superior generally accepts risking the lives of innocents but only if there are more bad guys than innocents killed, in expectation. “Absolutely not.” you respond. “We are four miles away! I’ve never taken a shot from this far. NO ONE has ever hit a target from this far. I’m just as likely to hit the kid.” Your superior takes a few seconds to think. “You always say that where the bullet ends up is the result of an equation: ‘where you aim + the external factors’, right?” she says. “Yes,” you reply nervously “And I have no idea how to account for the external factors from that far. There are so many different wind layers between us and the target. The Earth’s rotation and the sp
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8a19af34-6e27-40df-8ecf-918a469cb176
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Knowledge Base 2: The structure and the method of building
Introduction to the series of posts
===================================
This is the second post of a series of posts that propose to build a [crowdsourced](https://en.wikipedia.org/wiki/Crowdsourcing) [knowledge base](https://en.wikipedia.org/wiki/Knowledge_base) and use it to increase intelligence of people and computers, including AI. The rest of the posts will be published shortly. Until then all posts are available at <https://consensusknowledge.com>.
The second post describes the structure of the knowledge database and the method of building it.
The third and forth posts describe some of its possible initial uses that increase intelligence of people and computers, including those that are important according to Effective Altruism.
The eighth post introduces the concept of information [space](https://en.wikipedia.org/wiki/Space_(mathematics)) being an interface for exchanging knowledge between all intelligent agents (including people and artificial intelligence). It also presents a hypothesis that truth is an [attractor](https://en.wikipedia.org/wiki/Attractor) in the information space implemented by the proposed knowledge database. If it turns out that truth is actually an attractor, then it seems this would improve cooperation among people to the extent that it could theoretically lead to the emergence of collective superintelligence.
The ninth post lists some of the most important problems of current AI, including [LLMs](https://en.wikipedia.org/wiki/Large_language_model), and proposes to use the described knowledge database to try to address them.
The remaining three short posts describe other aspects of this knowledge database.
After publishing all the posts, I will add the first post explaining the meta-reasons for building the knowledge base and a broader perspective. I'm not doing it now because I want to focus on the substantive description of how the knowledge base works and its capabilities.
Question and answer websites
============================
Do you know [Quora.com](https://www.quora.com/), [StackExchange.com](https://stackexchange.com/), or [StackOverflow.com](https://stackoverflow.com/)? On each of these websites you can [ask questions and get answers](https://en.wikipedia.org/wiki/Q&A_software). Figure 1 schematically shows the interface of these websites. Questions can be tagged, both questions and answers can be commented. The main advantage compared to [internet forums](https://en.wikipedia.org/wiki/Internet_forum) is the ability to vote for or against an answer. Thanks to this, answers can be sorted from the best to the worst [1]. Users earn points for their activity.
Figure 1
Fine-grained analogy
====================
Let’s try to use an analogous interface to build a knowledge base. By a knowledge base I mean a [database](https://en.wikipedia.org/wiki/Database) with reasonably credible information that is easily accessible to both people and computers. To do this, we change a format of a question to this pair: *object/item name [> object/item name] > feature name*, and the text of an answer to the value of this feature. The square brackets indicate that we can optionally specify what part of the object/item we have in mind. So, for many questions instead of asking a question like *What is the resolution of HP ProBook 430 G1 notebook screen?* we can just write: *HP ProBook 430 G1 > Screen > Resolution*. Likewise, instead of adding the answer *HP ProBook 430 G1 notebooks have a screen with 1366×768 resolution*, we can just add the answer *1366×768*.
A “question” can have several mutually nonconflicting answers – for example, the aforementioned notebook model has variants that differ in the screen resolution – see figure 2a. Users can vote for or against correctness of a particular answer. Based on these votes, the system estimates probability of its correctness. Answers are also called information because each answer is a piece of information.
Figure 2 (click [here](https://consensusknowledge.com/yet-another-idea-of-collaboratively-edited-knowledge-base/) for a navigable version of the slides)
Generally, the interface is quite similar to [Wikidata](https://en.wikipedia.org/wiki/Wikidata) - the system used to collect data displayed in [Wikipedia infoboxes](https://en.wikipedia.org/wiki/Help:Infobox).
Quantifiers
===========
A user can add one of the following [quantifiers](https://en.wikipedia.org/wiki/Quantifier_(logic)) to an answer specifying the scope of its validity:
* [*all*](https://en.wikipedia.org/wiki/Universal_quantification) - the answer applies to all notebooks of this model
* [*some*](https://en.wikipedia.org/wiki/Existential_quantification) - the answer applies to some notebooks of this model
* *commonsense all* - the answer applies to all notebooks of this model but exceptions are possible
* *exception* - the answer applies only to unique notebooks of this model (it is the inverse of the *commonsense all* quantifier)
If a question has an answer with the *all* quantifier, then it cannot have another nonconflicting answer. Life often differs from theory, therefore the *commonsense all* quantifier is the default quantifier. Thanks to it, if a question *How many wheels does a passenger car have?* has the answer *4* with no quantifier, it is true even if some passenger cars have a different number of wheels. If a question has an answer with the *commonsense all* quantifier, it may have other nonconflicting answers with the *exception* quantifier. In our notebook example the answers should have the *some* quantifier - they will be nonconflicting with each other.
If a user adds an answer conflicting with other answers, then those who voted for these answers are automatically notified, making the discussion easier and faster.
Limiters
========
The scope of an answer can also be limited by a text written in brackets. For example, we can state that HP ProBook 430 G1 notebooks with a 1366x768 screen resolution were manufactured before 2010 (see figure 2a) or were available for sale in Europe. This functionality is important because some data can change over time [5].
Discussion
==========
George and James voted for correctness of the answer *1366x768*. George and James are credible, thus an algorithm estimated the probability of correctness of this answer to 99% (fig. 2a).
Harry votes against the answer *1366x768*. Voting resulting in a lack of consensus should be supported with an argument. Any sentence in a [natural language](https://en.wikipedia.org/wiki/Natural_language) can be the argument, although it is recommended to be expressed as another piece of information stored in the system. Thanks to this, to assess correctness of such argument the system can use the same algorithm that is used to evaluate any other information1. Harry's argument against correctness of the *1366x768* answer is that he found a manufacturer's web page stating that this model has screens only with 1920x1080 resolution. To this end, Harry adds the address of this web page as information about resources describing this notebook model and points this information as an argument against correctness of the answer *1366x768* (fig. 2b). The resources in general can be: photos, videos, and web pages related to an object/item.
After Harry's vote, the credibility of the answer *1366x768* drops from 99% to 60%. George and James are automatically notified about this. Then George notices that this web page describes another notebook model and votes against correctness of the information that this web page is about this model. As a result, credibility of this information drops to 55%, and credibility of the*1366x768* answer increases from 60% to 75% (fig. 2c). Harry is automatically notified about this, he notices his mistake and admits to it by voting against the information about this web page. Credibility of the information about the web page drops to 0%, and credibility of the *1366x768* answer returns to 99%, i.e. the original value (fig. 2d). Despite the same credibility of all information as at the beginning there is one difference - Harry's credibility decreased, because he added the incorrect information to the system.
Discussion supported with arguments is an essential part of collaborative knowledge creation [2][6].
1 So a piece of information being an argument for another piece of information can be justified by yet another piece of information. In this way, users can discuss in the system using [argument trees](https://en.wikipedia.org/wiki/Argument_map).
Credibility of information and users
====================================
Probability of information correctness is calculated using credibility of users voting for and against information correctness:
- the more credible users voted *for* correctness of the information, the *greater* its credibility;
- the more credible users voted *against* correctness of the information, the *lower* its credibility.
If the author of a piece of information is very credible and nobody voted against this information, then it is enough to make this information credible.
Credibility of a user is calculated using probability of information correctness which he voted for and against:
- if a user votes *for* correctness of the information which will turn out to be *correct* or votes *against* correctness of the information which will turn out to be *wrong*, then it will *increase* his credibility;
- if a user votes *for* correctness of the information which will turn out to be *wrong* or votes *against* correctness of the information which will turn out to be *correct*, then it will *reduce* his credibility.
This bidirectional dependency is analogous to the [HITS algorithm](https://en.wikipedia.org/wiki/HITS_algorithm), in which hubs correspond to users and authorities correspond to information. Quora uses similar dependency - see PeopleRank, [1] and [3].
Another important element that increases credibility of users and added information is linking users with real people [3]. It can be seen in public - as in Quora, or preferably only by administrators. Quora achieved this by using Facebook accounts to log in users. Another, even stronger, way of linking is to identify a user by his bank account like in PayPal and eBay [4].
Types of information
====================
Described methods of answers/information management (i.e. adding answers and assessing their credibility) can be applied to different types of information. So far, I have only shown information about value of object/item property. In general, the system may have the following types of information (fig. 3):
1. information about the fact that one object [is](https://en.wikipedia.org/wiki/Is-a) another object, e.g. a laptop is a computer;
2. information that some object [contains](https://en.wikipedia.org/wiki/Has-a) another object as a part, e.g. a laptop has a computer screen;
3. information that an object has some property, e.g. a computer screen has a resolution;
4. information that a property of an object has some value, e.g. the resolution of an HP ProBook 430 G1 notebook screen is 1366x768 (the type discussed so far);
5. any other information that cannot be presented as one of the 4 previous types - stored as a sentence in a [natural language](https://en.wikipedia.org/wiki/Natural_language).
Figure 3
Regardless of information type, users can vote for or against its correctness and add arguments in the same way. Credibility of users is taken into account when assessing credibility of information.
The first three types of information allow users to define the structure of objects/items/terms and their properties. Then users can use this structure to determine the value of objects/items/terms properties.
Information overview page
=========================
All information about an object/item can be presented on one web page as in figure 4.
Figure 4
The table contains properties of an HP ProBook 430 G1 notebook (3rd type of information) grouped into its parts (2nd type of information). If the value of a property is known (4th type of information), then it is displayed in the second column. Information that HP ProBook 430 G1 is a notebook (1st type of information) is displayed above the table. Below the table there is a list of variants of this notebook model (1st type of information), a list of resources about this model (4th type of information), and other information (of 5th type).
Clicking on any value in the second column of this table redirects to the information detail page as in figure 2. The font color of the information in the second column depends on the probability of its correctness as follows:
| probability of correctness | font color |
| --- | --- |
| ≥ 99% \* (almost certain information) | black |
| 80-99% \* (uncertain information) | gray |
| 20-80% \* (suspicious information) | orange |
| < 20% \* | information is not displayed at all on the overview page |
\* - example values
If a property has no value (the cell in the second column of a table is empty) then it can be added directly on the information overview page as in Excel spreadsheet, without going to the information detail page.
When filling in a value of a property, popular answers defined in the notebook object or its parts (e.g. its screen or battery) may be suggested. A notebook has a computer screen. A computer screen was defined to have a *technology* property with *matte* and *glossy* values. When editing a value of the *technology* property of HP ProBook 430 G1 object we can choose one of these values, although we can use another value if necessary.
The bottom of the information overview page can contain elements added by [plugins](https://en.wikipedia.org/wiki/Plug-in_(computing)). For example, there may be a *Buy* button redirecting users to a store where they can buy the chosen notebook model.
How can we use the described knowledge database?
================================================
* We can read information about objects/items on the website (figure 4).
* We can search the database, e.g. find all notebooks with matte 15.6" screen.
* Programmers can add [plugins](https://en.wikipedia.org/wiki/Plug-in_(computing)) to information overview pages (fig. 4) to extend their functionality, e.g. a plugin adding *Buy* or *Hire* button.
* We can work with applications that use specific fields of knowledge - they are described in the posts [Knowledge Base 3: Shopping advisor and other uses of knowledge base about products](https://www.lesswrong.com/posts/hNFQSGfvfPgHvCryT/knowledge-base-3-shopping-advisor-and-other-uses-of) and [Knowledge Base 4: General applications](https://www.lesswrong.com/posts/8e9HsZsw8QuRwnqLX/knowledge-base-4-general-applications).
Related concepts and applications
=================================
* [collective intelligence](https://en.wikipedia.org/wiki/Collective_intelligence) - group intelligence that emerges from people collaboration
* [argument tree](https://en.wikipedia.org/wiki/Argument_map) - a visual representation of a structure of arguments; it simplifies reaching consensus in more difficult discussions
* [ontology](https://en.wikipedia.org/wiki/Ontology_(information_science)) - a computer representation of knowledge
* [Wikidata](https://en.wikipedia.org/wiki/Wikidata) - data used in [Wikipedia infoboxes](https://en.wikipedia.org/wiki/Help:Infobox), collected using a method similar to that proposed in this post
[1] Quora: [*How does the ranking of answers on Quora work?*](https://www.quora.com/How-does-the-ranking-of-answers-on-Quora-work)
[2] K. Maleewong, C. Anutariya, V. Wuwongse: [*SAM: Semantic Argumentation Based Model for Collaborative Knowledge Creation and Sharing System*](https://link.springer.com/chapter/10.1007/978-3-642-04441-0_6), Proceedings of the 1st International Conference on Computational Collective Intelligence, 2009
[3] Paul Sharoda, Lichan Hong, Ed. H. Chi: [*Who is authoritative? Understanding reputation mechanisms in Quora*](https://arxiv.org/ftp/arxiv/papers/1204/1204.3724.pdf), 1st Collective Intelligence Conference, 2012
[4] eBay: [*Confirming your identity*](https://www.ebay.com/pages/help/sell/contextual/identity-verification.html)
[5] S. Wallace, L. Van Kleunen, M. Aubin-Le Quere, A. Peterkin, Y. Huang, J. Huang: [*Drafty: Enlisting Users To Be Editors Who Maintain Structured Data*](https://aaai.org/ocs/index.php/HCOMP/HCOMP17/paper/viewFile/15919/15276), Proceedings of the 5th Conference on Human Computation and Crowdsourcing, HCOMP 2017
[6] R. Drapeau, L. Chilton, J. Bragg, D. Weld: [*MicroTalk: Using Argumentation to Improve Crowdsourcing Accuracy*](https://aaai.org/ocs/index.php/HCOMP/HCOMP16/paper/viewFile/14024/13630), Proceedings of the 4th Conference on Human Computation and Crowdsourcing, HCOMP 2016
|
45487862-c95c-4e0f-ae9e-b6663d68aa8f
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Convergence Towards World-Models: A Gears-Level Model
1. Intuitions
-------------
One of the more solid results in agency theory is the [generalized definition of power-seeking](https://www.lesswrong.com/s/fSMbebQyR4wheRrvk/p/6DuJxY8X45Sco4bS2). Power-seeking is the tendency of agents to move towards states with the potential to achieve the best average outcome across some distribution of reward functions. In other words, looking at an agent whose values we do not know, we can *a priori* expect it to pursue "power", because it's trying to navigate to some *specific* end-state, and the path to it likely routes through environment-states in which it can choose from the largest variety of end-states. For example: take over the world so it can do whatever it wants.
Can we derive a similar definition for the convergent development of world-models?
I believe so, and it feels surprisingly simple in retrospect.
"World-models" are sets of statistical correlations across input-data. Every next correlation you notice — like object permanence, or the laws of gravity, or that your friend saying "I've ordered pizza" correlates with the image of a pizza entering your visual feed half an hour later — is another building block of your world-model.
A particularly important kind of statistical correlations are those that constitute *selection pressures*. When some system experiences selection pressure — be that an NN trained via the SGD, an animal species optimized by evolution, or a corporation shaped by market forces — it's receiving positive or negative feedback from the environment in a statistically predictable manner. The strength of such pressures increases or decreases according to certain correspondences between environment-states and the system's actions, and by definition of being optimized/selected, the system is gradually adjusted to minimize the pressure.
To do that, the system needs to (be made to) recognize the statistical correlations around it, pick out the ones related to the selection pressure, and enforce such correspondences between them and its actions as to minimize loss/survive/maximize revenue.
In turn, every bit of feedback from the selection pressure gives the system information about that pressure ([somehow](https://www.lesswrong.com/posts/Ajcq9xWi2fmgn8RBJ/the-credit-assignment-problem)). It builds into the system some understanding of the causal structure of the environment, by the very nature of selecting it.
When the system can only myopically respond to the input-data, when it can only uncover correlations on the level of e. g. *pixels*, its repository of statistical correlations is poor. As that repository grows, as its world-model develops, it starts being able to imagine more complex correlations:
(world-model(t)→action(t))→feedback(t).mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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"If the world is currently in *this* state, and I enforce *such* correlation between some elements of that state and my actions, it will correlate with me receiving *such* feedback."
And since any given system starts out not knowing what statistical correlations its selection pressure depends on, it's convergent to try to derive *every* statistical correlation it can — build a complete world-model, then search it for the sources of feedback it receives.
Thus: Any system subjected to some selection pressure the strength of which depends on some correlations between that system's actions and some features of the environment would converge towards discovering as many environmental statistical correlations as possible, in order to be more well-positioned to "guess" what correlation between the environment and its actions it needs to enforce in order to minimize that pressure.
The system doesn't know what the world wants of it, so it'll learn everything about the world in order to reverse-engineer that want.
Now let's try to show all of that in a toy mathematical environment.
2. Formalisms
-------------
### 2.1. The Setup
Suppose that we have some environment X represented as a causal (not necessarily acyclic) graph G, with nodes xi∈X. The environment is dynamic: every time-step t, the value xti of every child-node is updated by its parent-nodes, as in xt+1i:=fi(xti,Xtpa(xi)), where Xpa(xi)⊂X is the set of parental nodes of xi.
An intervention function do(At=N) sets the value of every node ai∈A⊂X to some corresponding value ni∈N for the time-step t. This function will be used to model actions.
The System, every time-step, takes in the values of some set of observables O⊂X, and runs do on some set of actionables A⊂X. After that, it receives reward(t) from the Selection Pressure.
The Selection Pressure, every time-step, takes in the values of some set of reward nodes R⊂X, and outputs a score. That is, reward:Rt→R.
> **Sidebar:** Technically, reward and the System can be made parts of the environment as well. With reward, it's trivial — just imagine that every ri∈R is a parent of the actual reward node, and reward(t) is the update function of that node.
>
> With the System, it's a bit more difficult. You can imagine that every observable plus reward has a node agent as its child-node, every actionable has agent as its only parental node, and that agent somehow controls the update functions from itself to the actionables. Alternatively, you might imagine agent to be a special node that represents a black-boxed *cluster* of nodes, to explain its ability to uniquely specify the value of each actionable. You may also imagine that the internals of this node perform at a much faster speed than the environment, so all of its computations happen in a single time-step. [Or you may not](https://www.lesswrong.com/s/ogntdnjG6Y9tbLsNS/p/HCibBn3ZCZRwMwNEE).
>
> Postulating all of this is a needless complication for the purposes of this post, though, so I won't be doing this. But you can, if you want to expand on it while getting rid of Cartesianism.
>
>
Given this setup, what internal mechanisms would you need to put in the System to improve its ability to maximize reward across time, given that it would start from a place of total ignorance with regards to the environment structure, the current environment-state, and the nature of reward?
### 2.2. Ideal Actions
*Buuut* first let's consider the environment from the position of omniscience. Given full knowledge of the environment structure, its current state, and the reward nodes, what can we say about the optimal policy?
Let's imagine a toy environment where reward is a function of the value of just one node, xi. The actionables, likewise, are a singular other node a. Suppose the reward function wants the value of r to equal n at every time-step.
If a=xi, the optimal policy at the time-step t is simple: do({at}={n}).
If a is *adjacent* to xi, then we'll only be able to act on it on the time-step *following* the current one, and we'll do it through the update function between a and xi, whose output also depends on the values of xi's parents. I. e., the optimal action is a function of xti, fi, and Xtpa(xi).
If a is separated by some node x1 from xi, we'll only affect xi two time-steps later, and the value we send will be interfered with by the parents of x1 at t+1 and the parents of xi at t+1 *and* at t+2.
Diagram representing a trying to influence xi. The blue nodes affect the path a,x1,xi at t, the green ones at t+1, the red ones at t+2, and the white ones are irrelevant.The optimal action, thus, is a function of f1, fi, xt1, xt+1i, Xtpa(x1), and Xt+1pa(xi). Note that xt+1i is additionally a function of xti and Xtpa(xi), and Xt+1pa(xi) is additionally a function of the update functions for all members of that set, and the values of every parent of every member of that set.
*Generally*, if the shortest path from a to the reward-node xi consists of i nodes x1,x2,…,xi, we'll only be able to affect it i time-steps later, and the optimal action will be a function of f1,f2,…fi, xt1,xt+12,…,xt+i−1i, Xtpa(x1),Xt+1pa(x2),...,Xt+i−1pa(xi).
Thus, the farther away the node we want to affect, the broader the "causality cone" of nodes we'll need to take into consideration. Or, equivalently, the current action is a function of further-in-time environment-states.
That complexity is not necessarily irreducible. Certain combinations of environment structure and update functions can result in some of the parents reliably cancelling each other out, such that the actual function for computing the most optimal action is a lower-dimensional one. If the environment is [a well-abstracting one](https://www.lesswrong.com/s/ehnG4mseKF6xALmQy/p/vDGvHBDuMtcPd8Lks), such dynamics might be ubiquitous, such that only a higher-level model of it suffices.
But *in general*, even taking such techniques into account, the "farther" the target node is, the more other nodes you'd need to take into account.
### 2.3. Heuristics
Some definitions:
* An idealized model Mt is the probability distribution over the environment-state given some information It. That is: Mt:=P(Xt | It)
* A cross-temporal model of the environment is some set containing all values of nodes across some time period: M[ta:tb]:=⋃t∈[ta,tb]Mt.
* A cross-temporal slice of the environment is some subset M[ta:tb]si⊂M[ta:tb].
* Similarly, we have a cross-temporal slice of the action-space: A[ta:tb]sk⊂M[ta:tb], where all nodes whose values are in A[ta:tb]sk are actionables.
In relation to this, given the current time-step t0, we can define a heuristic as follows:
h[ta:tb]i(t0,M[ta:tb]):=⟨do(A[t0:tn]sv=gi(M[ta:tb])) | (ci(M[ta:tb]su)=0)⟩A heuristic uses some function ci to look at some cross-temporal slice and judge whether some conditions it wants are met. If not (c=0), it executes the do part.
In English: given a model of the environment, a heuristic recommends taking certain action at present or at particular future points given certain beliefs about current, future, or pastenvironment-states. A heuristic does not necessarily have a recommended action for *every* intermediate time-step, or a recommendation for every actionable. Several heuristics can be ran in the same time-step, in fact, if their action-slices aren't overlapping.
(Note that the cross-temporal actions slice is a subset of M[t0:tb], because you can't take actions in the past.)
Intuitively, heuristics are likely defined over [natural abstractions](https://www.lesswrong.com/s/ehnG4mseKF6xALmQy/p/vDGvHBDuMtcPd8Lks), or some high-level environment-models wholly build out of them. ci would look for a particular object — a cluster of individual nodes whose internal complexity can be reduced to a high-level summary — and check its high-level state, possibly across time. For certain "undesirable" states or dynamics, it would attempt to intervene, correcting them.
As an example, consider a grandmaster playing a game of chess. [They've developed a lot of chess-specific heuristics](http://billwall.phpwebhosting.com/articles/chunking.htm). These heuristics look *exclusively* at the cross-temporal slice of the world-model that has to do with the current game. What moves the grandmaster made, what moves their opponent made, and what moves both of them may make in the future. One of these heuristics may recognize a cross-temporal pattern — a particular sequence of moves the opponent made, perhaps, plus the moves they may be planning to make — and map that pattern to the memorized correct response to it, countering the tactic the opponent is trying.
Caveats: That function is technically incoherent, since M[ta:tb], which it takes as an input, can presumably only be computed with the knowledge of the actions the System will take in the future, and these actions are what the heuristic computes *as an output*. There's a couple of ways around that: M[ta:tb] might be a "placeholder future", computed under the assumption that the System takes some default actions/null action. Or it might be the output of some *other* heuristic, passed to this one for potential correction.
The degenerate case of a heuristic, of course, is t0=ta=tb and Mt0=Ot0:
ht0i(t0,Ot0)=⟨do(At0s0=gi(Ot0)) | ci(Ot0s0)=0⟩This is an "instinctive reaction", an instant response to some stimuli.
Another simplified, but interestingly different case, is t0=ta=tb, but with Mt0s0 being a few steps removed from the observations. Intuitively, this is how CNN image classifiers work: they take in a bunch of pixels, then extrapolate the environment-state these pixels are a snapshot of, and back out the values of "is the node dog = 1?", "is the node cat = 1?".
Okay, *now* let's try to answer the question.
### 2.4. The Need for World-Models
The System starts from a place of complete ignorance about the environment. It doesn't know its structure, its state, what nodes reward is defined over, or even that there is an outside world. All information it has access to are observations O and the scores it receives for taking actions A.
Any heuristic it can implement would follow the algorithm of, "IF (some set of observables has certain values) THEN (set some actionables to certain values)". Thus, its set of heuristics is defined over the power sets of observables P(O) and actionables P(A). Heuristics, thus, would have a type signature Oi→Ak, where Oi∈P(O), Ak∈P(A).
From a state of total ignorance, there's no strategy better than guessing blindly. So suppose the System samples a few random heuristics and runs them in a few episodes. Upon receiving reward, it would get some information about their effectiveness, and would ideally keep effective heuristics and improve on them while discarding bad ones. But how?
Well, any solution would need to solve [the credit assignment problem](https://www.lesswrong.com/posts/Ajcq9xWi2fmgn8RBJ/the-credit-assignment-problem) somehow.
So suppose it is solved, somehow. What properties would the solution have?
Whatever it is, it would establish a causal connection between observations, actions, and the feedback: (Oi→Ak)→reward, and also reward←Y→Oi, since if it's possible to derive actionable information about reward from Oi, they have to be somehow causally connected.
In other words: whatever the credit assignment mechanism, whether it's external (as in ML) or [purely internal](https://www.lesswrong.com/posts/Ajcq9xWi2fmgn8RBJ/the-credit-assignment-problem#Idealized_Intelligence), it would convey information about the structure of the environment.
Thus, even as the System is blindly guessing shallow heuristics, it's learning a world-model. Even if only implicitly.
And acting on observables can only get us so far. Literally: as per 2.2, the farther away from our action-nodes the reward-node is, the more intermediate nodes we have to take into account to compute the correct action to take. Turning it around, the farther from the reward-node the observation-nodes are, the more "diffuse" the information about the reward-node they contain. In all probability, all individual a∈A have to be calculated as a function of *all* observables, to maximize our ability to map out the surrounding environment.
And this is where the world-models come in.
Let's assume that world-models can be incomplete — as in, contain only some part of the environment graph G. For simplicity, let's assume that over time, it's expanded by one degree of separation d in every direction, denoted as Md. So Md=0 contains only the observables and the actionables, Md=1 also contains all of their parents and children, Md=2 contains parents and children of all nodes in Md=1, and so on.
Knowing the partial structure of the environment and the values of some of its variables (the observables) at t allows us to reconstruct its state: Mt, d=P(Xt, d|It), where Xt, d⊂Xtand It is some internal state (including the knowledge of the environment structure and likely a history of previous observations and the actions taken).
Further, knowing the environment structure, the state at t, and what actions we plan to take, lets us compute the model of the *next* environment state, but at a one less degree of separation: run:Mt, d×At→Mt+1, d−1.
Likewise, under some assumptions about the transition functions f, we can often model the environment *backwards*: back:Mt, d×At−1→Mt−1, d−1.
Let's denote the cross-temporal model we get from that as M[ta:tb], d|t.
The main effect of all of this is that it greatly expands the space of heuristics: they're now defined over the power sets of actionables and *all modelable nodes*:
M[ta:tb], d|tSj∈P( M[ta:tb], d|t)h[ta:tb]i:M[ta:tb], d|tSj→AkAt the limit of infinite time, the System may expand the world-model to cover the entire environment. That would allow it to design optimal heuristics for controlling every reward-node ri∈R, akin to the functions mentioned in 2.2, regardless of the specific environment-structure it's facing.
3. Extending the Framework
--------------------------
I believe the main point is made now, but the model could be extended to formalize a few more intuitions.
### 3.1. Incrementalism
The System won't *start* knowing the entire world-model, however. Conversely, it would start generating heuristics from the get-go. In theory, once the world-model is fully learned, there's nothing preventing the System from deriving and maximizing reward.
But what if we add friction?
The credit-assignment mechanism needs to know how to deal with imperfect heuristics, if it's to be useful before the world-model is complete. A generally good heuristic making a mistake shouldn't be grounds for immediately deleting it. We'd also need to know how to resolve heuristics conflicts: situations when the cross-temporal model conditioned on the System running some heuristic hi makes some other heuristic hk try to correct it, which causes hi try to re-correct it, and so on. Which one should be allowed to win?
The notion of a "weight" seems the natural answer to that need. Heuristics to which credit is assigned more often and in greater quantities shall have more weight than the less well-performing ones, and in case of a heuristics conflict, the priority shall be more often given to the weightier one.
Suppose that the individual reward-nodes ri aren't sitting in a cluster. They're scattered across the graph, such that you can often affect one of them without affecting the others. That will likely give rise to specialized heuristics, which focus on optimizing the state of just one or several such reward-node. Only the full heuristical ensemble would be optimizing reward directly.
Now consider the following structure:
Suppose that we have some heuristic hxp that's greatly specialized in optimizing xp. (I. e., it models the graph *up to*xp and picks actions that set xp to some value that the heuristic prefers. And it prefers it because setting xp to it has historically correlated with high reward.) Once the environment is mapped out up to r, that heuristic can be replaced by one focusing on r directly. But what if hxp has a lot of weight by this point? The hypothetical hr would not have *that* much to contribute — controlling xp is a good proxy for controlling r, and if xk,xe don't have a very large effect, hxp probably captures most of the reward that can be squeezed out of r without taking them into account.
The better-performing hr would simply never be able to edge the older hxp out, then.
Thus, the System would be optimized for a *reward proxy*.
And this is likely to be an ubiquitous outcome. The world-model is being developed *in lockstep* with new heuristics, and if the reward-nodes are sufficiently conceptually-far from the actionables, it'll find good reward-proxies well before it discovers the actual reward-nodes. And at that point, the proxy-controlling heuristics will be impossible to depose.
### 3.2. The Planning Loop
The definition of a heuristic has a free parameter: the mysterious function g, which somehow calculates what actions to take given the world-model.
Its internals can have different contents:
* It may output a constant value, discarding M[ta:tb], d|t.
* It may be a lookup table, taking some members of M[ta:tb]Si as input.
* It may run a somewhat more sophisticated algorithm, looking at the values of some nodes and "extracting" the answer from the world-model.
But most salient are algorithms of the following kind:
g(M[ta:t0], d|t):=A[t0:tb]Sk | c(run(M[ta:t0], d|t,A[t0:tb]Sk))=1This process searches the world-model for a series of actions that will cause some set of nodes in it to assume certain values at certain times. I. e., "how do I make X happen?".
That is the planning loop/inner optimization. Since we expect heuristics to be defined in relation to some natural abstractions — i. e., the condition c is looking for is some coherent high-level concept — we can assume that c implicitly includes some mesa-objective. (Returning to the chess-game example, the heuristic with a planning loop would be searching the world-model for actions that lead to the condition "the grandmaster wins the game", or maybe "the opponent's tactic is countered".)
The planning loop may be niche. Instead of the entire Mt0, d, it may search over some Mt0S∈Mt0, d. I. e., a given g implemented in a given heuristic may only know how to optimize over a particular cluster of nodes in the world-model — the cluster the heuristic is specialized in. (E. g., the model of chess.)
> **Sidenote:** Similar analysis can be made with regards to the function c. The environment conditions for *firing* a heuristic can also be incredibly complex, so complex as to require intelligent analysis. I suspect there may be something interesting to be found in this direction as well. Consider, e. g., a heuristic with a very complex c but a lookup table for g? I think we see some examples of that in humans...
>
> **Sidenote #2:** A lot of complexity is still hidden in g and c. A mesa-optimizer would not perform blind search — it has some heuristics/mechanisms for efficiently guessing what actions it makes sense to try running the world-model on, before it actually performs search. I suspect it's some set of "advanced cognitive functions", and that complex c likely share it with complex g.
>
>
### 3.3. The Mesa-Optimizer Pipeline
So how does all of that grow into a terrifying lightcone-eating utility-maximizer? There's a few paths.
**First**, g may be shared across all heuristics. As per [the Orthogonality Thesis](https://www.lesswrong.com/tag/orthogonality-thesis), intelligence is orthogonal to values: a good optimization algorithm can be used to optimize any objective. This model concretizes this intuition somewhat. If g can perform search in the entire world-model, there's no reason to replicate its functionality, and every reason to hook all the heuristics up to the same algorithm — to minimize memory expenditure.
In this scenario, the resultant AGI would be optimizing a *mix* of mesa-objectives, a weighted sum of the heuristics it developed. Possibly inconsistently, as different heuristics grab control at different times. Possibly unstably, if new heuristics are still being derived. Humans may work like this, see [Why Subagents?](https://www.lesswrong.com/posts/3xF66BNSC5caZuKyC/why-subagents) and [the Shard Theory](https://www.lesswrong.com/s/nyEFg3AuJpdAozmoX).
**Second**, a more nuanced version of the above. The AGI may develop a sort of meta-heuristic, which would explicitly derive the weighted average of the extant heuristics, then set that as its primary mesa-objective, and pursue it *consistently*. There's probably a pressure to do that, as [to do otherwise is a dominated strategy](https://www.lesswrong.com/s/FYMiCeXEgMzsB5stm/p/RQpNHSiWaXTvDxt6R). It may also be required depending on the specifications of reward — if getting it to hit high values requires high coordination[[1]](#fnno39jfu07y).
I suspect humans work like this some of the time — when we're consciously optimizing or performing value reflection, instead of acting on autopilot.
**Third**, one of the heuristics may take over. Suppose that g isn't shared after all; every heuristic uses some specialized search algorithm. Then, the heuristic with the most advanced one starts to expand, gradually encroaching on others' territory. It should be pretty heavily weighted, probably heavier than any of the others, so that's not inconceivable. In time, it may take over others' roles, and the entire system would become a [wrapper-mind](https://www.lesswrong.com/posts/Mrz2srZWc7EzbADSo/wrapper-minds-are-the-enemy) optimizing some simple mesa-objective.
That heuristic would probably need to do it *deliberately*. As in, it'd need to become an advanced enough agent to model this entire dynamic, and *purposefully* take over the mind it's running in, for instrumental power-seeking reasons, while actively preventing its mesa-objective from changing. It'd need to [model the base objective instead of internalizing it](https://www.lesswrong.com/s/r9tYkB2a8Fp4DN8yB/p/zthDPAjh9w6Ytbeks).
It *feels* like this scenario is relatively unlikely: that heuristics are unlikely to be this lopsided in their capabilities. On the other hand, what do I know about how that works in neural networks trained by the SGD the specific way we're doing it now?
**Fourth**, given some advanced heuristic management algorithms, we may reduce the friction described at the beginning of 3.1. If heuristics don't ossify, and are frequently replaced by marginally-better-performing alternatives, the inner alignment failures the three previous scenarios represent won't happen. The AGI would grow to optimize the outer reward function it's been optimized by.
(**Edit:** Correction, it will probably wirehead instead. If, as per the sidebar in 2.1, we view reward itself as a node on the graph, then a frictionless setup would allow the AGI navigate to it *directly*, not to the in-environment variables it's defined over. To fix this, we'd need to set up some special "Cartesian boundary" over the reward node, ensuring it doesn't make that final jump.)
This scenario seems unlikely without some advanced, probably intelligent oversight. E. g., very advanced interpretability and manual-editing tools under the control of humans/another ML model.
Acknowledgements
----------------
Thanks to Quintin Pope, TurnTrout, Logan Riggs, and others working on [the Shard Theory](https://www.lesswrong.com/s/nyEFg3AuJpdAozmoX): the thinking behind this post has been heavily inspired by it.
(In fact, I suspect that the concept I'm referring to by "heuristic" in this post is synonymous with their "shards". Subjective opinion, though, I haven't run it by the ST folks.)
1. **[^](#fnrefno39jfu07y)**E. g., if reward(t)=xti+xtj+xtk, we can have separate heuristics for maximizing the values of all of these nodes, operating mostly independently. On the other hand, if reward(t)=1−|3−EXP(xtj⋅xtk)|, that'd require tighter coordination to ensure the product approximates ln 3.
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c20b0d8b-3544-41a2-ae7c-03f00d4ad850
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trentmkelly/LessWrong-43k
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LessWrong
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Ask LW: What questions to test in our rationality questionnaire?
We’ve had quite a bit of discussion around LW, and OB, on the questions:
* Is there a robust trait, “rationality”, that predicts accurate belief-formation in humans?
* If so, how can we measure it? And what kinds of training might help?
* Also, does “rationality” in the above sense help people achieve other goals, such as income, happiness, personal growth, positive relationships, or world-saving?
Rationalists that we are, it’s time to put our experiments where our mouths are. So here’s my plan:
Step 1: Assemble a set of questions that might possibly help us understand: (a) how rational people are; (b) where they got that rationality from; and (c) what effects their rationality has on their lives. Include any questions that might help in the formulation of useful conjectures. After collecting the data, look for correlations, spaghetti-at-the-wall style. Try factor analysis.
Step 2 [Perhaps after iterating the quick-and-dirty Step 1 correlational approach a bit, to develop better candidate metrics]: Run some more careful experimental tests of various sorts, both with a “rationality training group” that meets for extended periods of time, and, if LW is willing, with shorter training experiments with randomized LW subgroups. Try to build an atmosphere and knowledge base on LW where more people go out and do useful experiments.
I have an initial questionnaire draft below, although I skipped the answer-choices for brevity. Please post your suggestions for informative questions include and/or to drop. As good suggestions come in, I’ll edit the questionnaire draft to include them. It would be nice if the questionnaire we actually use draws on the combined background of the LW community.
Please also post hypotheses for what kinds of correlations you expect to see and/or to not see, when the questionnaire is actually run. If you note your hypotheses now, before the data comes in, we’ll know we should increase our credence in your theory instead of
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21e1697f-7d73-483c-bed1-10b21979a465
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trentmkelly/LessWrong-43k
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LessWrong
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Taking the reins at MIRI
Hi all. In a few hours I'll be taking over as executive director at MIRI. The LessWrong community has played a key role in MIRI's history, and I hope to retain and build your support as (with more and more people joining the global conversation about long-term AI risks & benefits) MIRI moves towards the mainstream.
Below I've cross-posted my introductory post on the MIRI blog, which went live a few hours ago. The short version is: there are very exciting times ahead, and I'm honored to be here. Many of you already know me in person or through my blog posts, but for those of you who want to get to know me better, I'll be running an AMA on the effective altruism forum at 3PM Pacific on Thursday June 11th.
I extend to all of you my thanks and appreciation for the support that so many members of this community have given to MIRI throughout the years.
----------------------------------------
Hello, I'm Nate Soares, and I'm pleased to be taking the reins at MIRI on Monday morning.
For those who don't know me, I've been a research fellow at MIRI for a little over a year now. I attended my first MIRI workshop in December of 2013 while I was still working at Google, and was offered a job soon after. Over the last year, I wrote a dozen papers, half as primary author. Six of those papers were written for the MIRI technical agenda, which we compiled in preparation for the Puerto Rico conference put on by the FLI in January 2015. Our technical agenda is cited extensively in the research priorities document referenced by the open letter that came out of that conference. In addition to the Puerto Rico conference, I attended five other conferences over the course of the year, and gave a talk at three of them. I also put together the MIRI research guide (a resource for students interested in getting involved with AI alignment research), and of course I spent a fair bit of time doing the actual research at workshops, at researcher retreats, and on my own. It's been a jam-pac
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02a55f47-5cd3-4611-8352-15c54fdb8064
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trentmkelly/LessWrong-43k
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LessWrong
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Still no Lie Detector for LLMs
Background
This post is a short version of a paper we wrote that you can find here. You can read this post to get the core ideas. You can read the paper to go a little deeper.
The paper is about probing decoder-only LLMs for their beliefs, using either unsupervised methods (like CCS from Burns) or supervised methods. We give both philosophical/conceptual reasons we are pessimistic and demonstrate some empirical failings using LLaMA 30b. By way of background, we’re both philosophers, not ML people, but the paper is aimed at both audiences.
Introduction
One child says to the other “Wow! After reading some text, the AI understands what water is!”… The second child says “All it understands is relationships between words. None of the words connect to reality. It doesn’t have any internal concept of what water looks like or how it feels to be wet. …” …
Two angels are watching [some] chemists argue with each other. The first angel says “Wow! After seeing the relationship between the sensory and atomic-scale worlds, these chemists have realized that there are levels of understanding humans are incapable of accessing.” The second angel says “They haven’t truly realized it. They’re just abstracting over levels of relationship between the physical world and their internal thought-forms in a mechanical way. They have no concept of [$!&&!@] or [#@&#**]. You can’t even express it in their language!”
--- Scott Alexander, Meaningful
----------------------------------------
Do large language models (LLMs) have beliefs? And, if they do, how might we measure them?
These questions are relevant as one important problem that plagues current LLMs is their tendency to generate falsehoods with great conviction. This is sometimes called lying and sometimes called hallucinating. One strategy for addressing this problem is to find a way to read the beliefs of an LLM directly off its internal state. Such a strategy falls under the broad umbrella of model interpretability, but we can t
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8a358698-585b-4e8f-a981-ece865f92ccf
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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Reflections on my 5-month alignment upskilling grant
Five months ago, I received a grant from the[Long Term Future Fund](https://funds.effectivealtruism.org/funds/far-future) to upskill in AI alignment. As of a few days ago, I was invited to Berkeley for two months of full-time alignment research under [Owain Evans’s stream](https://www.serimats.org/aligning-language-models) in the [SERIMATS](https://www.serimats.org/) program. This post is about how I got there.
The post is partially a retrospective for myself, and partially a sketch of the path I took so that others can decide if it’s right for them. This post was written relatively quickly - I’m happy to answer more questions via PM or in the comments.
Summary
-------
* I was a software engineer for 3-4 years with little to no ML experience before I was accepted for my grant.
* I did a bunch of stuff around fundamental ML maths, understanding RL and transformers, and improving my alignment understanding.
* Having tutors, getting feedback on my plan early on, and being able to pivot as I went were all very useful for not getting stuck doing stuff that was no longer useful.
* I probably wouldn’t have gotten into SERIMATS without that ability to pivot midway through.
* After SERIMATS, I want to finish off the last part of the grant while I find work, then start work as a Research Engineer at an alignment organisation.
* If in doubt, put in an application!
My Background
-------------
My background is more professional and less academic than most. Until I was 23, I didn’t do much of anything - then I got a Bachelor of Computer Science from a university ranked around 1,000th, with little maths and no intent to study ML at all, let alone alignment. It was known for strong graduate employment though, so I went straight into industry from there. I had 3.5 years of software engineering experience (1.5 at Amazon, 2 as a senior engineer at other jobs) before applying for the LTFF grant. I had no ML experience at the time, besides being halfway through doing the [fast.ai](https://course.fast.ai/) course in my spare time.
Not going to lie, seeing how many Top-20 university PhD students I was sharing my cohort with (At least three!) was a tad intimidating - but I made it in the end, so industry experience clearly has a role to play as well.
Grant
-----
The details of the grant are one of the main reasons I wrote this - I’ve been asked for 1:1’s and details on this at least three times in the last six months, and if you get asked something from at least three different people, it might be worth writing it up and sharing it around.
Firstly, the process. Applying for the grant is pretty painless. As long as you have a learning plan already in place, the official guidance is to take 1-2 hours on it. I took a bit longer, polishing it more than required. I later found out my plan was more detailed than it probably had to be. In retrospect, I think my level of detail was good, but I spent too much time editing. [AI Safety Support](https://www.aisafetysupport.org/) helped me with administration. The main benefit that I got from it was that the tutoring and compute money was tax free (since I didn’t get the money personally, rather I used a card they provided me) and I didn’t have to worry about tax withholding throughout the year.
Secondly, the money. I **agonized** over how much money to ask for. This took me **days**. I asked myself how much I really needed, then I asked myself how much I would actually accept gladly with no regrets, then I balked at those numbers, even knowing that most people ask for too little, not too much. I still balk at the numbers, to be honest, but it would have been so much easier to write this if I had other grants to go off. So, in the interest of transparency and hopefully preventing someone else going through the same level of anguish, [I’m sharing the full text of my grant request, including money requested (in Australian dollars, but you can always convert it) here](https://docs.google.com/document/d/1z4hEquic0JqPXBWGxpv48uRq6y5qBuWftyP0J3HjrOg/edit). Personal embarrassment aside, since LTFF publishes these grants anyway (but is very backlogged at the moment apparently, since they haven’t shared them this year) I think sharing numbers is fine.
To summarise - in the end, I gave them three numbers of 50%, 75%, and 100% of my contractor salary at the time. I told them honestly that I definitely didn’t expect 100%, and that I would have to think about whether to take 50% or not - it was at the border of whether I’d take the pay cut or not to upskill in this speculative area. They gave me 75%, which was an amount I was glad to take with no reservations. I also asked for, and got, some tutoring and compute budget.
As for advice on what level of background you need to apply - **I would advise just applying**. Applications are processed on a rolling basis, and it only takes an hour or two. I can’t tell you what level of background you need, since I only got one bit of information - the acceptance. I don’t know if I was a slam dunk, a borderline case, or somewhere in between. And I don’t know how FTX might or might not affect future funding.
How It Went
-----------
First off, let’s look at what I actually achieved in those five months. Thus far, I have:
**Maths:**
* Learnt single-variable calculus and the first half of multivariable calculus (Poorly)
* Completed a first course in linear algebra (Solidly)
* Completed some basic probability study (Random variables, probability distributions, random vectors, central limit theorem) (Solidly)
* Gone through the first few chapters of Probability Theory: The Logic of Science (Mainly conceptually)
**Alignment:**
* Formed a group and completed AGI Safety Fundamentals.
* Completed [Alignment 201](https://www.agisafetyfundamentals.com/alignment-201-curriculum) as part of SERIMATS.
* Read several Alignment Forum sequences.
* Greatly improved my inside view on what research agendas I think are most promising.
* Attended [John’s workshops](https://www.lesswrong.com/posts/kpkxKDpiRn6BNArFm/content-and-takeaways-from-seri-mats-training-program-with) as part of SERIMATS.
**Machine Learning:**
* Reproduced several [reinforcement learning algorithms](https://github.com/JayBaileyCS/RLAlgorithms).
* Wrote a [distillation on DQN](https://www.lesswrong.com/posts/kyvCNgx9oAwJCuevo/deep-q-networks-explained) (which was used as teaching material for [ARENA](https://www.arena.education/) virtual!).
* Completed about 75% of the [MLAB](https://forum.effectivealtruism.org/posts/vvocfhQ7bcBR4FLBx/apply-to-the-second-ml-for-alignment-bootcamp-mlab-2-in) curriculum.
* Built a transformer from scratch.
* Reproduced some key LLM benchmarks like chain-of-thought prompting and self-consistency as part of SERIMATS.
* Produced some basic original language model research as part of SERIMATS.
**Other:**
* Formed [AI Safety Brisbane](https://www.facebook.com/groups/465042105669446), a local AI Safety discussion group for my city. (I've arranged an organiser while I'm in Berkeley)
* Facilitated an AI safety weekend workshop organized by [AI Safety Australia and New Zealand](https://www.facebook.com/groups/1099249420923957).
These last two weren’t funded by this grant, but did require skills and knowledge that I built using it.
Looking back at the list, I’m pretty happy with my performance overall, even though it often felt week to week like not a lot was getting done. It definitely would have taken me a lot longer to do all this without grant work.
In terms of hours spent, I wasn’t able to get as many quality hours as I liked. I had intended to do ~25 hours per week of deep work, ignoring [Cal Newport’s](https://www.calnewport.com/books/deep-work/) mention that 4 hours per day of deep work was already pretty high level - in the end, I think I was able to get about 20 hours per week of work done, with most of that being deep work. Some weeks were as many as 30, others as few as 15, but I never had any zero weeks, or even really bad weeks, so motivation at least remained reasonably consistent throughout, which I was worried about. While I still feel guilty about doing less hours than I intended, I am trying to remind myself that results matter more than hours - if I am happy with my results, I should be pleased in general. More hours worked are good only insofar as they can improve results.
Some very useful things I recommend to people who want to do this are to **seek out help and guidance, especially early on**. I reached out to AI Safety Support to help create my plan, and to people at labs I wanted to work at in order to refine it. This helped me clear out **a lot of unnecessary prerequisites** - for instance, I ended up doing a lot less frontloading of maths than I thought I’d need to do, and instead focused on learning it in parallel with studying the actual ML skills I would want as a research engineer. I thought I would need a full Linear Algebra course before even touching PyTorch - this was very far from true, even though it eventually came in handy when I began diving into transformer architecture.
**Tutoring** was very useful as well - I had tutoring for mathematics, for conceptual understanding of RL algorithms, and to help me through the MLAB curriculum. These all improved my learning speed quite a bit. Especially if you’re a currently well-paid professional who would be getting a decent salary for alignment upskilling, the extra cost of a bit of tutoring is relatively low compared to salary replacement, and should improve the overall return on investment (in terms of learning per dollar) of the grant.
**Being able to pivot** was also useful - I was planning to continue to deep dive into RL after the first couple of months had gone by and I’d replicated the algorithms, but I could see which way the wind was blowing, and knew I needed to learn transformers. Fortunately, I’d put in my alignment plan that I planned to devote significant time to a subfield that was undetermined at the time - this ended up starting with transformers, which helped a lot for my successful SERIMATS application.
Future Plans
------------
So what are my plans now? I still want to become a **Research Engineer** as Plan A - I think this is my best path in terms of both immediate impact and long-term skill building. (See [here](https://docs.google.com/document/d/1iFszDulgpu1aZcq_aYFG7Nmcr5zgOhaeSwavOMk1akw/edit#) if confused at the difference between Research Engineer and Research Scientist.) As a software engineer with little research experience (All my research experience thus far was gained in SERIMATS itself!) it seems the best way to use my skills - and since I’ve heard the gap between research engineer and research scientist is pretty porous everywhere except OpenAI and DeepMind, starting out as a research engineer is probably in my top three paths even if I do end up on a more research-heavy part of the continuum than I start. My timelines aren’t super-short - spending a couple of years building skills in the field is more important to me than immediate impact, as long as I’m not working on something actively useless or harmful.
Thus, my plans are:
**First, SERIMATS of course!** I’ve got two months in Berkeley studying and working full-time on alignment, amongst other people doing the same thing. This is a tremendous opportunity for growth, and if I don’t learn at least one thing there that alters my current model in a big way I’ll be pretty disappointed.
Secondly, I still owe about 6-8 weeks of work on this grant. I’ve been on the grant for five months so far, but I was doing SERIMATS for part of that, which comes with its own stipend - counting that as grant time would cause me to be paid twice for the same work. With AI Safety Support's advice, I’ve determined the best way to solve that is to just put in some extra work after SERIMATS in order to ensure that six months of dedicated upskilling is done via this grant, and repay the money only if this isn’t feasible. (e.g, I find a better opportunity that starts sooner than the end of April.)
While that’s going on, if a better opportunity hasn’t come along during that time, I’ll be looking for work in dedicated AI alignment orgs or DeepMind’s safety team. If I’m not able to find work there, Plan B is to apply for another round of funding and try to get into **independent interpretability research** - I’ll need to do some upskilling using Neel Nanda’s [excellent resources](https://www.lesswrong.com/posts/9ezkEb9oGvEi6WoB3/concrete-steps-to-get-started-in-transformer-mechanistic), but that shouldn't take six months, and I believe I can start producing some interesting findings within three. Plan C could be [distillation](https://www.lesswrong.com/posts/zo9zKcz47JxDErFzQ/call-for-distillers) work, and I haven’t really thought about Plan D through Z yet.
Finally, I want to improve my **general math ability** further. It’s one of those things that’s always important but never urgent, so plugging away for an hour a day or so even if I'm not specifically blocked on a lack of it seems like a good way to go about it. I’ve tried focusing on one area at a time during the grant - now I want to try it the other way and interweave working on a few things at once, and see which works better for me in terms of motivation and retention. This’ll definitely take longer than three months, but it’s worth starting sooner rather than later.
**Foundation Work** - I’d like to have a world-class foundation in basic mathematics, so I’ll want to work through [AMC](https://www.maa.org/math-competitions) competitions and the [Art of Problem Solving](https://artofproblemsolving.com/store/book/aops-vol1) books in order to improve that. I’m amazed at how many things I can learn from books aimed at bright high-schoolers. Just yesterday I learnt you can use combinations of prime factors to determine how many unique factors a large number has, which would have made several [Project Euler](https://projecteuler.net/) problems a lot faster. (My starting point is 20/25 on the AMC 8, points lost to shaky geometry and combinatorics - [give it a try yourself](https://artofproblemsolving.com/wiki/index.php/2022_AMC_8) and see how you do! 40 minute timer, no calculator.)
**Framing** - John Wentworth says that much of the benefit of knowing lots of mathematics is just being able to recognise a problem. (Also see [this comment of mine](https://www.lesswrong.com/posts/5FECWrTp7whkoPZBi/ulisse-mini-s-shortform?commentId=CRCubySCuAtgr9rNh) and it's parent) Thus, I want to work through the [Infinitely Large Napkin](https://web.evanchen.cc/napkin.html) or a similar resource, and come up with a few examples of problems in the real world that would use each branch of mathematics, even if they’re well beyond my ability to solve without more dedicated study.
**Linear Algebra** - John said in his workshops that “If you haven’t solved alignment yet, you don’t know enough linear algebra.” (This is also one of the most thought-provoking sentences I’ve heard in a long time) Thus, I want to continue to plug away at that, and work through the canonical LessWrong text of [Linear Algebra Done Right](https://linear.axler.net/).
But as they say - plans are useless, but planning is indispensable. Probability of parts of this plan changing significantly due to new information gained in Berkeley is >50%, but as long as I keep in mind why the plan is what it is, I can pivot as needed.
I hope people find this useful, and if there’s one piece of advice you’ve taken from this - if you’re unsure about applying to the LTFF or another similar source, go ahead and [give it a try](https://funds.effectivealtruism.org/funds/far-future)!
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d6c02ee1-ef8e-4a51-a9d3-4831654d1094
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trentmkelly/LessWrong-43k
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LessWrong
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Announcing Human-aligned AI Summer School
The fourth Human-aligned AI Summer School will be held in Prague from 17th to 20th July 2024. We will meet for four intensive days of talks, workshops, and discussions covering latest trends in AI alignment research and broader framings of AI alignment research. Apply now, applications are evaluated on a rolling basis.
The intended audience of the school are people interested in learning more about the AI alignment topics, PhD students, researchers working in ML/AI outside academia, and talented students.
Format of the school
The school is focused on teaching and exploring approaches and frameworks, less on presentation of the latest research results. The content of the school is mostly technical – it is assumed the attendees understand current ML approaches and some of the underlying theoretical frameworks.
This year, the school will cover these main topics:
* Overview of the alignment problem and current approaches.
* Alignment of large language models: RLHF, DPO and beyond. Methods used to align current large language models and their shortcomings.
* Evaluating and measuring AI systems: How to understand and oversee current AI systems on the behavioral level.
* Interpretability and the science of deep learning: What's going on inside of the models?
* AI alignment theory: While 'prosaic' approaches to alignment focus on current systems, theory aims for deeper understanding and better generalizability.
* Alignment in the context of complex systems and multi-agent settings: What should the AI be aligned to? In most realistic settings, we can expect there are multiple stakeholders and many interacting AI systems; any solutions to alignment problem need to solve multi-agent settings.
The school consists of lectures and topical series, focused smaller-group workshops and discussions, expert panels, and opportunities for networking, project brainstorming and informal discussions.
Detailed program of the school will be announced shortly before the event. See
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5d005325-f279-49dd-983d-8cae92fb27e6
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trentmkelly/LessWrong-43k
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LessWrong
|
Outline of NIST draft plan for AI standards
Previously I posted on the National Institute of Standards and Technology's plan for AI standards, which is now open for public comment. Reading Federal documents is tedious, so I have provided an outline below.
Outline of the Draft Plan
1. Standards and Artificial Intelligence
A. Why is a plan for Federal engagement in AI technical standards needed?
* AI is important to the economy and national security.
* Executive Order (EO 13859)
* Reflect Federal priorities for innovation, public trust, and public confidence in systems using AI
* Enable creation of new AI-related industries, and adoption by current industries
* Federal agencies are major players in developing and using AI
* Definition of AI:
> Note: While definitions of AI vary, for purposes of this plan AI technologies and systems are considered to comprise of software and/or hardware that can learn to solve complex problems, make predictions or undertake tasks that require human-like sensing (such as vision, speech, and touch), perception, cognition, planning, learning, communication, or physical action. Examples are wide-ranging and expanding rapidly. They include, but are not limited to, AI assistants, computer vision systems, biomedical research, unmanned vehicle systems, advanced game-playing software, facial recognition systems as well as application of AI in both Information Technology (IT) and Operational Technology (OT).
* AI and Trustworthiness:
> Increasing trust in AI technologies is a key element in accelerating their adoption for economic growth and future innovations that can benefit society. Today, the ability to understand and analyze the decisions of AI systems and measure their trustworthiness is limited. AI standards and related tools, along with AI risk management strategies, can help to address this limitation and spur innovation. Among the characteristics that relate to trustworthy AI technologies are accuracy, reliability, robustness, security, explainability, safety, and
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2e97ea9c-8dc1-4923-b25c-fab1fe39e634
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trentmkelly/LessWrong-43k
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LessWrong
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AXRP Episode 26 - AI Governance with Elizabeth Seger
YouTube link
The events of this year have highlighted important questions about the governance of artificial intelligence. For instance, what does it mean to democratize AI? And how should we balance benefits and dangers of open-sourcing powerful AI systems such as large language models? In this episode, I speak with Elizabeth Seger about her research on these questions.
Topics we discuss:
* What kinds of AI?
* Democratizing AI
* How people talk about democratizing AI
* Is democratizing AI important?
* Links between types of democratization
* Democratizing profits from AI
* Democratizing AI governance
* Normative underpinnings of democratization
* Open-sourcing AI
* Risks from open-sourcing
* Should we make AI too dangerous to open source?
* Offense-defense balance
* KataGo as a case study
* Openness for interpretability research
* Effectiveness of substitutes for open sourcing
* Offense-defense balance, part 2
* Making open-sourcing safer?
* AI governance research
* The state of the field
* Open questions
* Distinctive governance issues of x-risk
* Technical research to help governance
* Following Elizabeth’s research
Daniel Filan: Hello, everybody. In this episode, I’ll be speaking with Elizabeth Seger. Elizabeth completed her PhD in philosophy of science at Cambridge in 2022, and is now a researcher at the Centre for Governance of AI in Oxford, where she works on AI democratization and open-source AI regulation. She recently led the production of a large report on the risks and benefits of model-sharing, which we will talk about in this episode. For links to what we’re discussing, you can check the description of the episode and you can read the transcript at axrp.net.
Well, Elizabeth, welcome to the podcast.
Elizabeth Seger: Awesome. Thanks for having me.
What kinds of AI?
Daniel Filan: Cool. We’re going to be talking about a couple of papers basically about democratizing and open-sourcing AI. Wh
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e2d8f1b7-27d9-4452-9dbe-a3e01abd7681
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
|
Expert and Non-Expert Opinion about Technological Unemployment
Abstract
There is significant concern that technological ad -
vances, especially in Robotics and Artificial Intelli -
gence (AI), could lead to high levels of unemploy -
ment in the coming decades. Studies have esti -
mated that around half of all current jobs are at risk
of automation. To look into this issue in more
depth, we surveyed experts in Robotics and AI
about the risk, and compared their views with those
of non-experts. Whilst the experts predicted a sig -
nificant number of occupations were at risk of au -
tomation in the next two decades, they were more
cautious than people outside the field in predicting
occupations at risk. Their predictions were consis -
tent with their estimates for when computers might
be expected to reach human level performance
across a wide range of skills. These estimates were
typically decades later than those of the non-ex -
perts. Technological barriers may therefore provide
society with more time to prepare for an automated
future than the public fear. In addition, public ex -
pectations may need to be dampened about the
speed of progress to be expected in Robotics and
AI.
1Introduction
The World Economic Forum has predicted that we
are the beginning of a Fourth Industrial Revolu -
tion in which developments in areas like Robotics
and Artificial Intelligence will transform the na -
ture of our economies and eliminate many current
occupations [WEF 2014] . At the same time, these
technologies will also create many new occupa -
tions. It remains an interesting question whether
more or fewer jobs will be created than destroyed.
In the past, more jobs have been created than de -
stroyed but this may be the case in the future as
we are likely to have fewer and fewer advantagesover the machines. Whatever the case, it is likely
that the new occupations created will require dif -
ferent skills to those destroyed. For instance, au -
tonomous vehicles will probably be commonplace
on our roads within the next few decades. Taxi
and truck drivers will therefore need other skills
than just the ability to drive if they are to remain
employed. It is thus an important question for our
societies in preparing for this future to understand
the occupations at risk of automation.
2Background
In 2013, a study by Frey and Osborne estimated
that 47% of total employment in the United States
was under risk of automation in the next two
decades [Frey and Osborne 2013]. Ironically, the
study used Machine Learning to predict occupa -
tions at risk. Even the occupation of predicting oc -
cupations at risk from automation has been par -
tially automated. Subsequent studies have reached
similar conclusions. For instance, similar analysis
has estimated that 40% of total employment in
Australia is at risk of automation [Durrant-Whyte
et al. 2015], and even larger figures for develop -
ing countries like China at 77% and India at 69%
[Frey et al. 2016]. Frey and Osborne suggested
three barriers to automation: occupations requir -
ing complex perception or manipulation skills, oc -
cupations requiring creativity, and occupations re -
quiring social intelligence. Computers are signifi -
cantly challenged in these three areas at present
and may remain so for some time to come.Expert and Non-Expert Opinion about Technological Unemployment
Toby Walsh
UNSW Sydney | Data61 | TU Berlin
Frey and Osborne’s study used a training set of 70
occupations from the O*Net database of U.S. oc -
cupations. This training set was hand labelled by a
small group of economists and Machine Learning
researchers at a workshop held in the Oxford Uni -
versity Engineering Sciences Department. Classi -
fication was binary. Each occupation was classi -
fied either at risk in the next two decades from au -
tomation or not. Labels were only assigned to oc -
cupations where there was confidence in the clas -
sification.
We do not wish to discuss here whether the O*Net
database provides features adequate to extrapolate
to the full set of 702 occupations. This is a diffi -
cult question to address as we do not have a gold
standard of occupations actually at risk. Their
classifier did, however, perform well on the train -
ing set with a precision (positive predictive value)
for occupations at risk of automation of 94%, a
sensitivity of 81%, and a specificity of 94%.
We focus instead on the training set of 70 occupa -
tions used in [Frey and Osborne 2013]. This study
hand labelled 37 of these 70 occupations as being
at risk of automation (53%). The final accuracy of
the classification of 702 occupations depends crit -
ically on the accuracy with which this smaller
training set was hand labelled. This training set
was chosen as it could be classified “with confi -
dence”. We therefore gave this training set to
three much larger groups to classify: experts in
AI, experts in Robotics and, as a comparison, non-
experts interested in the future of AI. In total over,
we sampled over 300 experts and 500 non-ex -
perts. Our survey is the largest of its kind every
performed.
3High level machine intelligence
In addition to classifying the training set, we
asked both the experts and the non-experts to esti -
mate when computers might be expected to
achieve a high–level of machine intelligence
(HLMI). This was defined to be when a computer
might be able to carry out most human profes -sions at least as well as a typical human. In
2012/2013, Vincent C. M üller and Nick Bostrom
surveyed 170 people working in AI to predict
when HLMI might be achieved [M üller and
Bostrom 2014]. As there is significant uncertainty
as to when HLMI might be achieved, they asked
when the probability of HLMI would be 10%,
50% and 90%. The median response for a 10%
probability of HLMI was 2022, for a 50% proba -
bility was 2040, and for a 90% probability was
2075. We wanted to see if people who were more
cautious at predicting when HLMI was likely to
be achieved were also more cautious at predicting
occupations at risk of automation.
We also wished to update and enlarge upon
Müller and Bostrom’s survey. Given some of the
high profile advances made recently in subareas
of AI like Deep Learning [ LeCun et al. 2015], it
might be expected that HLMI would be predicted
sooner now than back in 2012/2013. We also
wanted to survey a much larger sample of experts
in AI and Robotics than M üller and Bostrom.
Only 29 of the 170 who answered M üller and
Bostrom’s survey were leading experts in AI,
specifically 29 members of the 100 must cited au -
thors in AI as ranked by Microsoft Academic Re -
search. The largest group in their survey were 72
participants of a conference in Artificial General
Intelligence (AGI). This is a specialized area in AI
where researchers are focused on the question of
building general intelligence. Much research in AI
is, by comparison, focused on programming com -
puters to do very specialized tasks like playing Go
[Silver et al. 2016] or interpreting mammograms
[Patel et al. 2017] and not on building general
purpose intelligence .
Researchers in AGI might be expected to be pre-
disposed to the early arrival of HLMI. Indeed the
AGI group were the most enthusiastic to complete
Müller and Bostrom’s survey. 64% of the dele -
gates from this AGI conference completed the sur -
vey, compared to an overall response rate of just
31%. In addition, the AGI group typically pre -
dicted HMLI would arrive earlier than the other
respondents to the survey. We conjectured that ex -
perts in AI and Robotics not focused on AGI
would be more cautious in their predictions.
More recently in March 2016, Oren Etzioni
wanted to test a similar hypothesis about M üller
and Bostrom’s results [Etzioni 2016]. To do so, he
sent out a survey to 193 Fellows of the Associa -
tion for the Advancement of Artificial Intelligence
(AAAI). In total, 80 Fellows responded (41% re -
sponse rate). Respondents included many leading
researchers in the field like Geoff Hinton, Ed
Feigenbaum, Rodney Brooks, and Peter Norvig.
Unfortunately, Etzioni’s survey asked a different
and simpler question (“When do you think we
will achieve Superintelligence?” where Suprintel -
ligence is defined to be “an intellect that is much
smarter than the best human brains in practically
every field, including scientific creativity, general
wisdom and social skills”). Etzioni’s survey also
only offered 4 answers to the question of when
Superintelligence would be achieved (in next 10
years, 10-25 years, more than 25 years, never).
It is difficult to compare the results of Etzioni’s
survey with Müller and Bostrom’s. None of the
AAAI Fellows responding selected “in the next 10
years”, 7.5% selected “in the next 10-25 years”,
67.5% selected “in more than 25 years”, and the
remaining 25% selected “never”. If Etzioni’s
question is equated with Müller and Bostrom’s
question about a 90% probability of HLMI then
the responses of the two surveys appear to be sim -
ilar. However, it is very difficult to draw many
conclusions given the rather ambiguous question,
and the larger granularity on the answers.
4Methods
Our survey was performed between 20th January
and 5th February 2017. The survey involved three
distinct groups. The first group were authors from
two leading AI conferences: the annual Confer -
ence of the Association for the Advancement ofArtificial Intelligence (AAAI-2015), and the In -
ternational Joint Conference on Artificial Intelli -
gence (IJCAI-2011). Both conferences are highly
selective and publish some of the best new work
in AI. 200 authors from this group completed our
survey.
The second group consisted of IEEE Fellows in
the IEEE Robotics & Automation Society and au -
thors of a leading Robotics conference: the IEEE
International Conference on Robotics and Auto -
mation (ICRA-2016). This is also a highly selec -
tive conference that publishes some of the best
work in Robotics. We sent out questionnaires to
this second group till we had at least 100 replies.
In total, 101 people from this group completed the
survey.
The third and final group surveyed were readers
of an article from the website “The Conversa -
tion”. This Australian and British website pub -
lishes news stories and expert opinion from the
university sector, and is partnered with Reuters
and the Press Association. The article containing
the like to the survey was entitled “Know when to
fold ‘em: AI beats world’ s top poker players”. The
article discussed the recent victory of the CMU
Libratus poker program against some top human
players. It used this as an introduction to the Frey
and Osborne report on tasks that could be auto -
mated. It ended by inviting readers to help deter -
mine the “wisdom of the crowd” by completing
the survey. There were 548 responses in this third
group.
The readers of The Conversation have the follow -
ing geographical distribution: 36% Australia, 29%
United States, 7% United Kingdom, 4% Canada,
and 24% rest of the world. It is reasonable to sup -
pose that they most are not experts in AI & Robot -
ics, and that they are unlikely to be publishing in
the top venues in AI and Robotics like IJCAI,
AAAI or ICRA. They are educated (85% have an
undergraduate degree or higher), young (more
than a third are 34 or under, 59% are under 44 and
just 11% are 65 or older), mostly employed or in
higher education (more than two thirds are em -
ployed and one quarter are in or about to enter
higher education) and relatively affluent (40% re -
ported an annual income of $100,000 or more).
The questionnaire itself had 8 questions. The first
7 questions asked respondents to classify 10 occu -
pations from the training set, whilst the last asked
for estimates when HLMI might arrive. The first
question asked for a classification of the 5 occu -
pations most at risk from automation according to
Frey and Osborne’s classifier as well as the 5 oc -
cupations least likely to be at risk. To help respon -
dents, a link was provided next to each occupation
describing the work involved and the skills re -
quired. The second question in our survey asked
for a classification of the next 5 occupations most
at risk from automation according to Frey and Os -
borne’s classifier and the next 5 occupations least
likely, and so on till the seventh and penutlimate
question. Within each of the 7 questions, the 10
occupations were presented in a random order.
Our intent was to make the initial questions as
easy as possible to answer. In this way, we hoped
that participants would not give up early, and
might be better prepared for the potentially more
difficult classifications later in the survey. The 8th and final question asked for an estimate
of when there was a 10%, 50% and 90% chance
of HLMI. The options presented were: 2025,
2030, 2040, 2050, 2075, 2100, after 2100, and
never. To compute the median response, we inter -
polated the cumulative distribution function be -
tween the two nearest dates.
5Results
The results are summarized in Table 1. The ex -
perts in Robotics were most cautious, predicting a
mean and median of 29.0 out of the 70 occupa -
tions in the training set at risk from automation
(95% confidence interval of 27.0 to 31.0 occupa -
tions at risk). The experts in AI were slightly less
cautious predicting a mean of 31.1 occupations at
risk and a median of 33 (95% confidence interval
of 29.6 to 32.6 occupations at risk).
The difference in means between the Robotics and
AI experts does not appear to be statistically sig -
nificant. A two-sided student t-test on the number
of occupations predicted at risk of automation
failed to reject the null hypothesis that the popula -
tion means were equal at the 95% level ( p value
of 0.096).
GroupSample size (n)Predicted Number of Occupations Likely at Risk of Automation
(out of 70)
MeanMedianStandard deviation Confidence
interval
Robotics
experts10129.02910.1(27.0, 31.0)
AI experts 20031.13310.8(29.9, 32.6)
Non-experts 47336.53710.9(35.6, 37.5)
Table 1. Descriptive statistics about number of occupations predicted to be at risk of automation in next
two decades. Confidence intervals are at the 95% level.
The non-experts in our survey typically predicted
significantly more occupations were at risk of au -
tomation than the experts. They predicted a mean
of 36.5 occupations at risk of automation and a
median of 37 (the 95% confidence interval is from
35.6 to 37.5 occupations at risk).
The differences between the predictions by the
non-experts of the number of occupations at risk
of automation and those of either the Robotics or
the AI experts appear to be extremely significant
statistically. Two-sided student t-tests rejected the
null hypothesis that the population means for the
non-experts and the experts in Robotics were
equal, and the null hypothesis that the population
means for the non-experts and the experts in AI
were equal (both p values less than 0.0001).
The prediction by the non-experts in our survey of
the number of occupations at risk of automation
of a median of 37 occupations at risk is identical
to the 37 occupations labelled at risk in the origi -
nal training set in the original Frey and Osborne
study.
At the end of the survey, we asked participants to
estimate when there was a 10%, 50% and 90%
probability of HLMI. This repeats a question
asked in the original M üller and Bostrom survey.
Also, as in Müller and Bostrom’s survey, we de -
fined HLMI to be when a computer can carry out
most human professions at least as well as a typi -
cal human.
The results of this question are summarized in
Figure 1. The Robotics and AI experts typically
predicted that HLMI was several decades further
away than the non-experts. Again, there was little
to distinguish between the AI and Robotics ex -
perts themselves, but they were much more cau -
tious than the non-experts in their predictions. The
experts typically predicted HLMI was several
decades further away than the non-experts.
For a 90% probability of HLMI, the median pre -
diction of the experts in Robotics was 2118, and2109 for the experts in AI. By comparison, the
median prediction of the non-experts for a 90%
probability of HLMI was just 2060, around half a
century earlier. For a 50% probability of HLMI,
the median prediction of the Robotics experts was
2065, and 2061 for the AI experts. This compares
with the non-experts whose median prediction for
a 50% probability of HLMI was 2039, over two
decades earlier. Finally, for a 10% probability of
HLMI, the median prediction of the Robotics ex -
perts was 2033, and 2035 for the AI experts. By
comparison, the median prediction of the non-ex -
perts for a 10% probability of HLMI was 2026,
nearly a decade earlier.
The predictions for the number of occupations un -
der risk of automation were consistent with the
predictions of when HLMI might be achieved.
See the clear trend in Figure 1/d. Respondents
who predicted a later date for HLMI typically pre -
dicted fewer occupations at risk of automation.
Similarly respondents who predicted an earlier
date for HLMI typically predicted more occupa -
tions at risk of automation. The AI and Robotics
experts typically predicted later dates for HLMI
and fewer occupations at risk. On the other hand,
the non-experts typically predicted earlier dates
for HLMI and more occupations at risk of auto -
mation.
The respondents in Müller and Bostrom’s study
were closest in their predictions of when HLMI
might be achieved to the group of non-experts in
our survey. For a 10% probability of HLMI,
Müller and Bostrom’s study had a median predic -
tion of 2022, and 2040 for a 50% probability of
HLMI. For a 10% probability of HLMI, the non-
experts in our study had a median prediction of
2026, and of 2039 for a 50% probability of HLMI.
However, for a 90% probability of HLMI, our
non-experts were more optimistic than the respon -
dents in Müller and Bostrom’s study. The median
prediction for a 90% probability for HLMI by the
non-experts in our survey was 2060, compared to
a median of 2075 in M üller and Bostrom’s study.
6Discussion
Our results suggest that experts in Robotics and
AI are more cautious than non-experts in their
prediction of the number of occupations at risk of
automation in the next decade or two. The experts
were also more cautious than the training set used
in Frey and Osborne’s study. This caution can be
explained by their expectation that HLMI may
take several decades longer than the public
expects. We did not find any significant
differences between the predictions of the experts
in Robotics and the experts in AI. Despite being
more cautious, both groups of experts still
predicted a large fraction of occupations were at
risk of automation in the next couple of decades.
There are many other factors that need to be taken
into account in deciding the impact that
automation might have on employment: we must
also take account of the economic growth fueled
by productivity gains, the new occupations
created by technology, the effects of globalization,
changes in demographics and retirement, and
much else. It remains an important open question
if there will be an overall net gain or loss of jobs
as a result. This is a matter that society must
seriously consider further. There are many actions
possible to reduce the negative impacts of
automation. We should, for instance, look to
augment rather than replace humans in roles
where this is possible.
Even in occupations where humans look set to be
displaced, our survey holds out some hope. Whilst
the potential disruptions may be large, there could
be more time to adapt to them than the public fear.
Our study also suggests that more effort needs to
be invested in managing the public’s expectation
about the rate of progress being made in Robotics
and AI, and of the many technical obstacles that
must be overcome before some occupations can
be automated. Robotics and AI remain challenged
in several fundamental areas like manipulation,
common sense reasoning and natural language
understanding. Funding for AI research hassuffered “winters” in the past where public
expectations did not match actual progress
[Hendler 2008]. We should be careful to avoid
this in the future.
References
[Durrant-Whyte et al. 2015] Hugh Durrant -
Whyte, Lachlan McCalman , Simon O'Callaghan ,
Alistair Reid and Daniel Steinberg, “The impact
of computerisation and automation on future
employment”, Chapter 1.4 in “Australia’ s Future
Workforce?” (Committee for Economic
Development of Australia report, 2015).
[Etzioni 2016] Oren Etzioni, “No, the Experts
Don’t Think Superintelligent AI is a Threat to
Humanity”, MIT Technology Review, September
20th 2016.
[Frey et al. 2016] Carl Benedikt Frey, Michael A.
Osborne, Craig Holmes, Ebrahim Rahbari,
Elizabeth Curmi, Robert Garlick, Johanna Chua,
George Friedlander, Peter Chalif, Graeme
McDonald and Martin Wilkie, “Technology at
Work v2.0: The Future is Not What It Used to
Be”, Citi GPS Report, Oxford University Martin
School, January 2016.
[Frey and Osborne 2013] Carl Benedikt Frey and
Michael A. Osborne, “ The future of employment:
How susceptible are occupations to
computerisation?”, Oxford University Martin
School, 2013.
[Hender 2008] James Hendler, “Avoiding another
AI Winter”, IEEE Intelligent Systems, 23 (2): 2-4,
March/April 2008.
[LeCun et al. 2015] Yann LeCun, Yoshua Bengio
and Geoffrey Hinton, “Deep learning”, Nature
521, 436–444, 2015.
[Müller and Bostrom 2014] Vincent C. Müller,
and Nick. Bostrom, ‘ Future progress in artificial
intelligence: A Survey of Expert Opinion , in
Vincent C. Müller (ed.), Fundamental Issues of
Artificial Intelligence (Synthese Library; Berlin:
Springer), 555-572, 2014.
[Patel et al. 2017] Tejal A. Patel, Mamta Puppala,
Richard O. Ogunti, Joe E. Ensor, Tiancheng
Heitesh B. Shewale, Donna P. Ankerst, Virginia
G. Kaklamani, Angel A. Rodriguez, Stephen T. C.
Wong, and Jenny C. Chang. “ Correlating
mammographic and pathologic findings in
clinical decision support using natural language
processing and data mining methods” . Cancer,
123: 114–121, 2017.
[Silver et al. 2016] David Silver, Aja Huang,
Chris J. Maddison, Arthur Guez, Laurent Sifre,
George van den Driessche, Julian Schrittwieser,
Ioannis Antonoglou, Veda Panneershelvam, Marc
Lanctot, Sander Dieleman, Dominik Grewe, John
Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy
Lillicrap, Madeleine Leach, Koray Kavukcuoglu,
Thore Graepel and Demis Hassabis, “Mastering
the game of Go with deep neural networks and
tree search”, Nature 529, 484–489, 2016.
[WEF 2016] The World Economic Forum, “ The
Future of occupations Employment, Skills and
Workforce Strategy for the Fourth Industrial
Revolution”, Global Challenge Insight Report,
2016.
|
061bf687-4623-43fb-b79c-2c80f773095e
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Mildly against COVID risk budgets
A friend is hosting a party tonight! It'll cost me 200 microCOVIDs, which, as a healthy thirty-something, I very cautiously estimate to cost about 2 micromorts,[1] which is roughly equivalent to 1 hour out of my remaining life expectancy. But I'm super excited for this party; I'd happily burn an hour of my life driving there and back. Should I go?
Unless something subtle is going on... obviously yes, right?
"What about your visit to that coffee shop earlier today? Don't you think that's decision-relevant?"
The coffee shop? I'm confused. Why would that be decision-relevant?
"Well, you accumulated about 150 microCOVIDs there."
Sure, but that's not really relevant to the decision theory of whether to attend the party, is it? The party is equally enjoyable either way, and the costs of multiple COVID exposures add pretty much linearly by the axiom of independence, so previously-incurred risks are irrelevant to my future decisions. (If the coffee shop had been a week ago, sure, I'd be inflicting some of those microCOVIDs on my fellow partygoers, which, sure, could be decision-relevant, I haven't done the math; but it seems very unlikely to me that I'll become a full-fledged germ factory between this morning and this evening, so I think that consideration is insignificant in this case.)
"But you try to maintain a 200-microCOVID-per-week risk budget, don't you?"
Sure, but... hmm.
"So there's something wrong with your assertion that previously-incurred risks are irrelevant to future decisions."
...or something is wrong with the idea of risk budgeting.
"...hmm."
...so, which is it?
Zero externalities
Maybe the answer will be clearer if we simplify the problem: let's get rid of externalities. Suppose I am the world's most boring superhero, Captain Can't-Transmit-COVID. I can still catch it just like anybody else, but the cost is borne by me alone, not my friends or housemates.
Now is my coffee-shop visit decision-relevant for whether I should attend the party?
I
|
a839ef55-ae57-464a-9701-a0cfae560d9a
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Summary of the Acausal Attack Issue for AIXI
**Attention conservation notice:** To a large extent, this is redundant with [Paul's previous post about](https://ordinaryideas.wordpress.com/2016/11/30/what-does-the-universal-prior-actually-look-like/) [this](this{]}(https://ordinaryideas.wordpress.com/2016/11/30/what-does-the-universal-prior-actually-lo,), but I figured that some people might be interested in my restatement of the argument in my own words, as I did not start out believing that it was an issue, or start out agreeing with Vanessa about the attack operating via [bridge rules.](https://www.alignmentforum.org/posts/gHgs2e2J5azvGFatb/infra-bayesian-physicalism-a-formal-theory-of-naturalized)
Solomonoff induction/AIXI runs all possible computations, so it's possible in theory to alter which predictions a particular Turing machine outputs, by making stuff happen in your own universe, and this would then influence any process running that Turing machine, such as an AIXI agent. Of course, doing such a thing is subject to the obvious limitation where, if, say, you make the Turing machine that's reading your universe output a 0, you'll have less ability to influence the predictions and decisions of AIXI-like agents in the worlds where they see a 1 instead, because the Turing machine that you're controlling got eliminated for mispredictions.
Taking the attacker's perspective, if you were trying to influence *one particular universe* (this assumption will be loosened later) containing an AIXI or sufficiently AIXI-like target agent, and you had sufficiently high predictive abilities, you could try finding two low description complexity spots in your universe, one to check the state of, and one to write data to, and committing to the strategy "if the input data from this simple spot looks like the data I'd predict to receive from the world I'm interested in, I will submit output data accordingly (mostly accurate predictions of the victim's environment, but with whatever tweaks are needed), in order to influence what the target agent does in the targeted universe."
Basically, if you learn that there's an input channel to your universe, it's worthwhile to try to hack the output channel. It doesn't even take much effort to do, you just need to keep an eye on the conjectured input channel, commit to responding accordingly if it looks like it's transmitting data, and do other things in the meantime, and the Turing machine "run your universe, signal in via this channel, read output via this channel" is now under your control.
So... from the perspective of the targeted agent/universe, how well would your hacking attempt work? Well, it critically depends on whether the complexity of specifying "your universe + the I/O channels" is more than, or less than, the complexity of the shortest "honest" predictor of the observations of the targeted agent. If the honest predictor of the victim's observations is less complex than the specification of your universe and the I/O channels to it, then you messing around with the output channel and its predictions of observations would end up just affecting the 100th decimal place of the target AIXI's probabilities or something, because each bit is a factor of 2 difference in probability, and so you're at a huge disadvantage if you want to intentionally screw up the victim's probability estimates.
However, if the complexity of specifying your universe and the I/O channels is shorter than the "honest" predictor of the victim's observations, then after Solomonoff induction is done weeding out the mispredicting "chaff" hypotheses, the Turing machine that you're controlling is dominant over the "honest" predictor by an overwhelming factor (because each extra bit is a 2x difference in probability, so the situation is reversed). Now, just predict doom if the victim doesn't do what you want, and bam! You've taken control of the future of that target universe.
"But wait, how could specifying an entire universe containing an agent interested in hacking a target universe and competent enough to do so end up simpler than just... accurately specifying the target universe?". Ah, it's because we're just measuring the complexity of the shortest *recipe* (Turing machine code) for specifying the universe (and I/O channels) interested in hacking others. Very short recipes/TM's can unpack into exceptionally intricate and long-running computations, and specifying aspects of the *intermediate state*, such as the specification of the universe that's being targeted, *does* take a lot of bits. There's no obstacle against a complex structure showing up as a (complex to specify) intermediate result of a simple computation.
But wait, there can only be so many low-complexity universes, and if they're launching *successful* attacks, said attacks would be distributed amongst a far far far larger population of more-complex universes. So, switching perspective to whoever is nervously wondering whether to run an AIXI agent, there's probably no low-complexity jerk (low-complexity enough to beat the "right" predictor for your universe) interested in *your* universe in particular (well... it's a bit more complicated, but it looks like that at first glance). In a certain sense, launching the prediction attack exactly as specified here means the attacker is only able to "punch down" in K-complexity.
Admittedly, it's possible to launch multiple attacks, by using a bunch of low-complexity channels in the attacker's universe instead of just one, but there's only so many low-complexity spots available to go around in the attacker's universe, which means that the basic analysis of "the low-complexity universe can only target a relatively small amount of high-complexity universes compared to their total number" still holds.
So the next question is, is there a way to intensify this to a *generic* problem for anything AIXI/Solomonoff-like, instead of it just being a problem for the few unlucky high-complexity universes specifically being targeted by low-complexity ones? From the perspective of a world with a higher-complexity true predictor of observations, is there a *generic* argument that there's a simple Turing machine interested in targeting that world in particular? Surely no, as there's only so many simple Turing machines to go around, right?
Well, by virtue of running an AIXI-like agent that will have large influence on the future, that's an *especially interesting* property of a universe which would tend to draw a whole lot more attention from agents interested in influencing other computations than just being some generic high-complexity computation.
The other issue, which ties into Vanessa's worries about bridge rules, is as follows: The complexity measure the attacker must beat is "target universe + the bridge rule for the observations of the victim", not just the complexity of the target universe. So, if the bridge rules are complex, then "target universe + bridge rule for observations of victim agent" might end up more complex than "very simple universe containing a powerful optimization process that cares about affecting the target universe (among others)+simple I/O channels"
The compression of observations is achieved via the complexity of the bridge rule being tucked into the process of the attacker going "time to figure out where the influential points in this target universe are" (and working out the complex bridge rule itself), since, again, short-description-length computations can unfold into huge computations, where it's complex to slice out a particular intermediate result of the computation.
But, if it's possible to shunt the bridge rule complexity elsewhere like that, then couldn't the target agent compress its prediction of its sensory data with a simple hypothesis containing another agent motivated to make accurate predictions (and so it'd figure out the bridge rule)?
Well, the problem with that is that it'd probably be less complex to specify physical laws that eventually produce an agent that is incentivized to perform acausal attacks, than to specify an agent which has a utility function that, at its maximum, makes accurate predictions of your target universe.
So, that's the basic argument, as I understand it. For complex enough bridge rules relative to the complexity of your universe, hypotheses that produce powerful optimizers that target your universe (and an output channel), can come in substantially shorter than "here's the description of the universe, here's the bridge rule", because the former hypothesis is shunting the bridge complexity into the process of computation itself, and hypotheses like the former are practically guaranteed to have goals that are not your own and so mess with your beliefs to get you to take particular actions.
|
25d0d5c9-f2d1-47e0-b007-9253ab6036b9
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Map and Territory and "Paths"
Hi everyone,
Recently have started a discussion group amongst some of my friends and aquaintences in an attempt to study and improve our rationality.
What I tend to do, is using the sequences as 'source' material, write up a lesson plan that attempts to be a bit more accesible to the layman and go through it with them as slowly and meticulously as needed.
Only had one meeting so far, but it's been exciting seeing it come together and people get engaged with the idea of improving their rationality.
My question is regarding the "Map and Territory" analogy, which is what we're planning to look at indepth next meeting.
Could you further the analogy and say that the paths that you write on your map, to get you to and from different points be considered the application of Instrumental Rationality? (As charting the map is the application of epistemic) You could point out that Instrumental Rationality also requires you to have a good map.
Any thoughts/comments would be appreciated.
|
054033aa-b40e-4164-8ffe-d493742bb3b6
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Turing machine
A Turing Machine is a simple mathematical model of [https://arbital.com/p/-computation](https://arbital.com/p/-computation) that is powerful enough to describe any computation a computer can do.
Imagine a robot, in front of a little whiteboard, with infinitely many whiteboards to both sides, finitely many of which have a symbol written on them. The robot can erase the contents of a whiteboard and replace it with some other symbol, and it can move over to the next whiteboard on the left or right, or shut down. This is all the robot can do. The robot's actions are determined by only two things: the symbol on the whiteboard it just saw, and its internal state. The output of this process is defined to be "whatever is written on the string of whiteboards when the robot has shut down".
This is equivalent to a Turing Machine (with the robot replaced by a machine head and the infinite line of whiteboards replaced by an infinite tape subdivided into cells). The *halting problem* (which [is unsolvable](https://arbital.com/p/halting_problem_is_uncomputable) in general) asks whether the robot will eventually shut down at some point.
So, a Turing Machine can be specified with the following information:
- A finite set of symbols the robot can write (one of which is the null symbol, an empty board).
- A finite set of states the robot can be in (at least one of which causes the robot to shut down).
- A starting state for the robot.
- Starting symbols on finitely many of the boards (whiteboard location and symbol type data).
- A transition function for the robot, which takes a symbol/state pair as input, and has a $(\text{symbol},\text{state},\text{move left or right})$ triple as output. For example, ine such transition might be represented as `if symbol is 7 and state is FQUF, then (erase and write 4, set state to ZEXA, move left)`.
Surprisingly enough, other proposed models of computation have all been shown to be weaker than, or equivalent to, Turing Machines! With infinite memory space, and sufficiently intricate sets of symbols and states, the robot and whiteboard (or machine head and memory tape) system can compute anything at all that is computable in principle!
This fact is known as the Church-Turing thesis; it's very widely believed to be true, and certainly no-one has ever found any hint of a counterexample, but it's not "proved" in any meaningful sense.
# Variants of Turing machines
*Multi-tape Turing Machines* would be equivalent to having several robots in infinite whiteboard hallways, except that the robots are networked together to all share the same state. An example state transition is as follows:
`If symbol A is * and symbol B is 6 and symbol C is absent and state is VREJ, set state to IXXI, robot A writes ! and moves left, robot B writes 9 and moves left, robot C writes = and doesn't move.`
These Multi-tape Machines can speed up some computations polynomially (so, for example, a problem which would normally take 1 million steps to solve may be solvable in a thousand steps, because of the square root speedup). Because these machines can only muster a polynomial speedup, and moving to a one-tape Turing Machine only incurs a polynomial slowdown, the computational [complexity class P](https://arbital.com/p/5pf) is unchanged across Turing Machines with different numbers of tapes.
*Write-only Turing Machines* are Multi-tape Turing Machines where one of the tapes/hallways of whiteboards has its input ignored when determining the next state, written symbols and movements.
We can think of this situation as one where one particular robot is blind.
*Read-only Turing Machines* are Multi-tape Turing Machines, and one of the tapes/hallways of whiteboards cannot be rewritten. The robot in there can only move around and observe, but it has not been given a pen or rubber so it can't write on or erase the boards.
*Oracle Machines* (which are more powerful than Turing Machines, and don't exist in reality, though they are a very useful tool in computational complexity theory), are like a mult-tape machine with exactly two tapes: one tape is designated as the "oracle tape", and one tape as the "machine tape".
This time, one of the robot states is "INVOKING MAGIC ORACLE".
When that happens, the contents of the whiteboards in the machine hall (that is, the contents of the machine tape) are interpreted as the description of a problem, and then a correct solution to the problem magically appears on the string of whiteboards in the oracle hall (that is, on the oracle tape), completely erasing whatever was on the oracle hall whiteboards originally; and finally the oracle robot is moved to the first whiteboard of the answer.
Therefore the functionality of the oracle machine depends very strongly on what the oracle does! A given oracle machine might do one thing when the oracle does "compute the [https://arbital.com/p/-5bv](https://arbital.com/p/-5bv) of the number I was called with" than when it does "compute whether or not the number I was given is the [https://arbital.com/p/-description_number](https://arbital.com/p/-description_number) of a halting Turing machine".
Oracle machines are like ordinary Turing machines, except we also give them the ability (in principle) to obtain instant correct answers to any particular problem. The problem we may instantly solve is fixed in advance, before we ever start running the machine.
With the right oracle, oracle machines can solve *any* problem, where Turing machines cannot (recalling that the halting problem can't be solved by Turing machines).
However, the price is that oracle machines don't exist: they require a magic oracle, and we don't have any of those in nature.
|
00fe66ce-ad79-47b6-b1f9-43eba507d6be
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Learning human preferences: black-box, white-box, and structured white-box access
This post is inspired by [system identification](https://en.wikipedia.org/wiki/System_identification); however, I'm not an expert in that domain, so any corrections or inspirations on that front are welcome.
I want to thank Rebecca Gorman for her idea on using system identification, and her conversations developing the concept.
Knowing an agent
================
This is an agent:
**Fig. 1**
We want to know about its internal mechanisms, its software. But there are several things we could mean by that.
Black-box
---------
First of all, we might be interested in knowing its input-output behaviour. I've called this its *policy* in [previous posts](https://www.lesswrong.com/posts/6XLyM22PBd9qDtin8/learning-human-preferences-optimistic-and-pessimistic); a full map that will allow us to predict its output in any circumstances:
**Fig. 2**
I'll call this black-box knowledge of the agent's internals.
White-box
---------
We might be interested in knowing more about what's actually going on in the agent's algorithm, not just the outputs. I'll call this white-box knowledge; we would be interested in something like this (along with a detailed understanding of the internals of the various modules):
**Fig. 3**
Structured white-box
--------------------
And, finally, we might we interested in knowing what the internal modules actually do, or actually mean. This is the semantics of the algorithm, resulting in something like this:
**Fig. 4**
The "beliefs", "preferences", and "action selectors" are tags that explain what these modules are doing. The tags are part of the structure of the algorithm, which includes the arrows and setup.
If we know those, I'd call it structured white-box knowledge.
Levels of access
================
We can have different levels of access to the agent. For example, we might be able to run it inside any environment, but not pry it open; hence we know its full input-output behaviour. This would give us (full) black-box access to the agent (partial black box access would be knowing some of its behaviour, but not in all situations).
Or we might be able to follow its internal structure. This gives us white-box access to the agent. Hence we know its algorithm.
Or, finally, we might have a full tagged and structured diagram of the whole agent. This gives us structured white-box access to the agent (the term is my own).
Things can more complicated, of course. We could have only access to parts of the agent/structure/tags. Or we could have a mix of different types of access - [grey-box](https://en.wikipedia.org/wiki/Grey_box_model) seems to be the term for something between black-box and white-box.
Humans seem to have a mixture of black-box and structured white-box access to each other - we can observe each other's behaviour, and we have our internal theory of mind that provides information like "if someone freezes up on a public speaking stage, they're probably filled with fear".
Access and knowledge
--------------------
Complete access at one level gives complete knowledge at that level. So, if you have complete black-box access to the agent, you have complete black-box knowledge: you could, at least in principle, compute every input-output map just by running the agent.
So the interesting theoretical challenges are those that involve having access at one level and trying to infer a higher level, or having partial access at one or multiple levels and trying to infer full knowledge.
Multiple white boxes for a single black box
-------------------------------------------
Black-box and white-box identification are have been studied somewhat extensively in system identification. One fact remains true: there are multiple white-box interpretations of the same black-box access.
We can have the "[angels pushing particles to resemble general relativity](https://www.lesswrong.com/posts/q9GZyfm8xKAD2BGdi/strong-implication-of-preference-uncertainty)" situations. We can add useless [epicycles](https://en.wikipedia.org/wiki/Deferent_and_epicycle), that do nothing, to the model of the white-box; this gives us a more complicated white-box with identical black-box behaviour. Or you could have the [matrix mechanics vs wave mechanics](https://en.wikipedia.org/wiki/Quantum_mechanics#Mathematically_equivalent_formulations) situation in quantum mechanics, where two very different formulations were shown to be equivalent.
There are multiple ways of choosing among equivalent white-box models. In system identification, the criteria seems to be "go with what works": the model is to be identified for a specific purpose (for example, to enable control of a system) and that purpose gives [criteria that will select the right kind of model](https://en.wikipedia.org/wiki/System_identification#Identification_for_control). For example, linear regression will work in many rough-and-ready circumstances, while it would be stupid to use it for calibrating sensitive particle detectors when much better models are available. Different problems have different trade-offs.
Another approach is the so called "[grey-box](https://en.wikipedia.org/wiki/Grey_box_model)" approach, where a class of models is selected in advance, and this class is updated with the black-box data. Here the investigator is making "modelling assumptions" that cut down on the possible space of white-box models to consider.
Finally, in this community and among some philosophers, [algorithmic simplicity](https://en.wikipedia.org/wiki/Kolmogorov_complexity) is seen as good and principled way of deciding between equivalent white-box models.
Multiple structures and tags for one white-box
----------------------------------------------
A similar issue happens again at a higher level: there are multiple ways of assigning tags to the same white-box system. Take the model in figure 4, and erase all the tags (hence giving us figure 3). Now reassign those tags; there are multiple ways we could tag the modules, and still have the same structure as figure 4:
**Fig. 5**
We might object, at this point, insisting that tags like "beliefs" and "preferences" be assigned to modules for a reason, not just because the structure is correct. But having a good reason to assign those tags is precisely the challenge.
We'll look more into that issue in future sections, but here I should point out that if we consider the tags as purely syntactic, then we can assign any tag to anything:
**Fig. 6**
What's "Tuna"? Whatever we want it to be.
And since we haven't defined the modules or said anything about their size and roles, we can decompose the interior of the modules and assign tag in completely different ways:
**Fig. 7**
Normative assumptions, tags, and structural assumptions
=======================================================
We need to do better than that. Paper "[Occam’s razor is insufficient to infer the preferences of irrational agents](https://arxiv.org/abs/1712.05812)" talked about "normative assumptions", assumptions about the values (or the biases) of the agent.
In this more general setting, I'll refer to them as "structural assumptions", as they can refer to beliefs, or other features of the internal structure and tags of the agent.
Almost trivial structural assumptions
-------------------------------------
These structural assumptions can be almost trivial; for example, saying "beliefs nad preferences update from knowledge, and update the action selector", is enough to rule out figures 6 and 7. This is equivalent with starting with figure 4, erasing the tags, and wanting to reassign tags to the algorithm while ensuring the graph is isomorphic to figure 4. Hence we have a "desired graph" that we want to fit our algorithm into.
What the Occam's razor paper shows is that we can't get good results from "desired graph + simplicity assumptions". This is unlike the black-box to white-box transition, where simplicity assumptions are very effective on their own.
Figure 5 demonstrated that above: the beliefs and preference modules can be tagged as each other, and we can still get the same desired graph. Even worse, since we still haven't specified anything about the *size* of these modules, the following tag assignment is also possible. Here, the belief and preference "module" have been reduced to mere conduits, that pass on the information to the action selector, that has expanded to gobble up all of the rest of the agent.
**Fig. 8**
Note that this decomposition is *simpler* than a "reasonable" version of figure 4, since the boundaries between the three modules don't need to be specified. Hence algorithmic simplicity will tend to select these degenerate structures more often. Note this is almost exactly the "indifferent planner" of the Occam's razor paper, one of the three simple degenerate structures. The other two - the greedy and anti-greedy planners - are situations where the "Preferences" module has expanded to full size, with the action selector reduced to a small appendage.
Adding semantics or "thick" concepts
------------------------------------
To avoid those problems, we need to flesh out the concepts of "beliefs", "preferences[[1]](#fn-q4EuboXbbZSuQo6kQ-1)", and so on. The more structural assumptions we put on these concepts, the more we can avoid degenerate structured white-box solutions[[2]](#fn-q4EuboXbbZSuQo6kQ-2).
So we want something closer to our understanding of preferences and beliefs. For example, preferences are supposed to change much more slowly than beliefs. So the impact of observations on the preference module - in an information-theoretic sense, maybe - would be much lower than on the beliefs modules, or at least much slower. Adding that as a structural assumption cuts down on the number of possible structured white-box solutions.
And it we are dealing with humans, trying to figure out their preference - [which is my grand project at this time](https://www.lesswrong.com/posts/m2bwD87ctjJDXC3SZ/ultra-simplified-research-agenda) - then we can add a lot of other structural assumptions. "Situation X is one that updates preferences"; "this behaviour shows a bias"; "sudden updates in preferences are accompanied by large personal crises"; "red faces and shouting denotes anger", etc...
Basically any judgement we can make about human preferences can be used, if added explicitly, to restrict the space of possible structured white-box solutions. But these need to be added in explicitly at some level, not just deduced from observations (ie supervised, not unsupervised learning), since observations can only get you as far as white-box knowledge.
Note the similarity with [semantically thick concepts](https://en.wikipedia.org/wiki/Thick_concept) and with my own [post on getting semantics empirically](https://www.lesswrong.com/posts/EEPdbtvW8ei9Yi2e8/bridging-syntax-and-semantics-empirically). Basically, we want an understanding of "preferences" that is so rich that only something that is clearly a "preference" can fit the model.
In the optimistic scenario, a few such structural assumptions are enough to enable an algorithm to quickly grasp human theory of mind and quickly sort our brain into plausible modules, and hence isolate our preferences. In the pessimistic scenario, theory of mind, preferences, beliefs, and biases are all so twisted together that even extensive examples are not enough to decompose them. See more [in this post](https://www.lesswrong.com/posts/6XLyM22PBd9qDtin8/learning-human-preferences-optimistic-and-pessimistic).
---
1. We might object to the arrow from observations to "preferences": preferences are not supposed to change, at least for ideal agents. But many agents are far from ideal (including humans); we don't want the whole method to fail because there was a stray bit of code or neuron going in one direction, or because two modules reused the same code or the same memory space. [↩︎](#fnref-q4EuboXbbZSuQo6kQ-1)
2. Note that [I don't give a rigid distinction](https://www.lesswrong.com/posts/EEPdbtvW8ei9Yi2e8/bridging-syntax-and-semantics-empirically) between syntax and semantics/meaning/"ground truth". As we accumulate more and more syntactical restrictions, the number of plausible semantic structures plunges. [↩︎](#fnref-q4EuboXbbZSuQo6kQ-2)
|
69b073f8-6bd6-4567-a890-4bf0f2e98e37
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Trivers on Self-Deception
People usually have good guesses about the origins of their behavior. If they eat, we believe them when they say it was because they were hungry; if they go to a concert, we believe them when they say they like the music, or want to go out with their friends. We usually assume people's self-reports of their motives are accurate.
Discussions of signaling usually make the opposite assumption: that our stated (and mentally accessible) reasons for actions are false. For example, a person who believes they are donating to charity to "do the right thing" might really be doing it to impress others; a person who buys an expensive watch because "you can really tell the difference in quality" might really want to conspicuously consume wealth.
Signaling theories share the behaviorist perspective that actions do not derive from thoughts, but rather that actions and thoughts are both selected behavior. In this paradigm, predicted reward might lead one to signal, but reinforcement of positive-affect producing thoughts might create the thought "I did that because I'm a nice person".
Robert Trivers is one of the founders of evolutionary psychology, responsible for ideas like reciprocal altruism and parent-offspring conflict. He also developed a theory of consciousness which provides a plausible explanation for the distinction between selected actions and selected thoughts.
TRIVERS' THEORY OF SELF-DECEPTION
Trivers starts from the same place a lot of evolutionary psychologists start from: small bands of early humans grown successful enough that food and safety were less important determinants of reproduction than social status.
The Invention of Lying may have been a very silly movie, but the core idea - that a good liar has a major advantage in a world of people unaccustomed to lies - is sound. The evolutionary invention of lying led to an "arms race" between better and better liars and more and more sophisticated mental lie detectors.
There's some controversy over exactly ho
|
f3066e03-44e9-44d9-bf98-6800c3c58390
|
trentmkelly/LessWrong-43k
|
LessWrong
|
If one surviving civilization can rescue others, shouldn't civilizations randomize?
In the comments section of You can, in fact, bamboozle an unaligned AI into sparing your life, both supporters and critics of the idea seemed to agree on two assumptions:
* Surviving planetary civilizations have some hope of rescuing planetary civilizations killed by misaligned AI, but they disagree on the best method of rescuing.
* The big worry is that there are almost 0 surviving planetary civilizations, because if we're unlucky, all planetary civilizations will die the same way.
What if to ensure at least some planetary civilizations survive (and hopefully rescue others), each planetary civilization should pick a random strategy?
Maybe if every planetary civilization follows a random strategy, they increase the chance of surviving the singularity, and also increase the chance that the average sentient life in all of existence is happy rather than miserable. It reduces logical risk.
History already is random, but perhaps we could further randomize the strategy we pick.
For example, if the random number generated using Dawson et al's method (at some prearranged date) is greater than the 95th percentile, we could all randomly choose MIRI's extremely pessimist strategy, and do whatever Eliezer Yudkowsky and Nate Soares suggest with less arguing and more urgency. If they tell you that your AI lab, working on both capabilities and alignment, is a net negative, then you quit and work on something else. If you are more reluctant to do so, you might insist on the 99th percentile instead.
Does this make sense or am I going insane again?
Total utilitarianism objections
If you are a total utilitarian, and don't care about how happy the average life is, and only care about the total number of happy lives, then you might say this is a bad idea, since it increases the chance at least some planetary civilizations survive, but reduces the total expected number of happy lives.
However, it also reduces the total expected number of miserable lives. Because if 0 planetary
|
55c9e873-8c20-4cf2-af47-0d8b13ec260e
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trentmkelly/LessWrong-43k
|
LessWrong
|
Vavilov Day Starts Tomorrow
Content note: discussion of fasting.
Three weeks ago, I announced a plan to fast from the 25th to the 27th, in honor of Nikolai Vavilov and the staff of his botany institute, several of whom starved to death in the service of ending famine (and were partially successful, although far from the sole contributors). The goal was to test/improve my own ability to do hard things in the service of worthy projects.
I had wanted to put much more research in the original post than I did, but decided it was more important to get the announcement out quickly and I should save something for the day-of post anyway. Since then, a lot has happened. Over three weeks I had 3 or 4 urgent demands around the size of “my furnace is maybe poison and my landlord is being difficult about it”. Everything is fine now, but it was a lot of effort to get it that way. I also had some emergency work drop in my lap for an extremely worthy project. I’m glad I got the opportunity to contribute and I’d make the same decision again but it ate up all of the slack I had left. And then my cell phone broke.
The immediate impact of this is there’s I’m not writing the highly researched post on Vavilov I wanted to. The internet is full of articles of the quality I could produce in the time I have available, there’s no reason to add to them.
But the more important impact is that I said I wanted to test my ability to do hard things, and then I did that, before the fast even started. My capacity was not as high as I wanted but more than I feared, and my capacity to respond to my limits gracefully instead of failing explosively exceeded my hopes.
So in a lot of ways the purpose of the fast has already been served. I thought about letting myself out of it, but there are a few dimensions this month hasn’t tested and I still want to play with those. However in light of the fact that I am starting from a place of much lower slack and much higher time value than anticipated, I will be removing some of the ru
|
c6df9798-4e25-48c5-be59-beb38381ba02
|
trentmkelly/LessWrong-43k
|
LessWrong
|
LW client-side comment improvements
All of these things I mentioned in the most recent open thread, but since the first one is directly relevant and the comment where I posted it somewhat hard to come across, I figured I'd make a post too.
Custom Comment Highlights
NOTE FOR FIREFOX USERS: this contained a bug which has been squashed, causing the list of comments not to be automatically populated (depending on your version of Firefox). I suggest reinstalling. Sorry, no automatic updates unless you use the Chrome extension (though with >50% probability there will be no further updates).
You know how the highlight for new comments on Less Wrong threads disappears if you reload the page, making it difficult to find those comments again? Here is a userscript you can install to fix that (provided you're on Firefox or Chrome). Once installed, you can set the date after which comments are highlighted, and easily scroll to new comments. See screenshots. Installation is straightforward (especially for Chrome, since I made an extension as well).
Bonus: works even if you're logged out or don't have an account, though you'll have to set the highlight time manually.
Delay Before Commenting
Another script to add a delay and checkbox reading "In posting this, I am making a good-faith contribution to the collective search for truth." before allowing you to comment. Made in response to a comment by army1987.
Slate Star Codex Comment Highlighter
Edit: You no longer need to install this, since Scott's added it to his blog. Unless you want the little numbers in the title bar.
Yet another script, to make finding recent comments over at Slate Star Codex a lot easier. Also comes in Chrome extension flavor. See screenshots. Not directly relevant to Less Wrong, but there's a lot of overlap in readership, so you may be interested.
NOTE FOR LW ADMINS / YVAIN
These would be straightforward to make available to all users (on sufficiently modern browsers), since they're just a bit of Javascript getting inj
|
3b5fdaff-16d0-499b-87dd-a896abaefbf4
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trentmkelly/LessWrong-43k
|
LessWrong
|
How "Discovering Latent Knowledge in Language Models Without Supervision" Fits Into a Broader Alignment Scheme
Introduction
A few collaborators and I recently released a new paper: Discovering Latent Knowledge in Language Models Without Supervision. For a quick summary of our paper, you can check out this Twitter thread.
In this post I will describe how I think the results and methods in our paper fit into a broader scalable alignment agenda. Unlike the paper, this post is explicitly aimed at an alignment audience and is mainly conceptual rather than empirical.
Tl;dr: unsupervised methods are more scalable than supervised methods, deep learning has special structure that we can exploit for alignment, and we may be able to recover superhuman beliefs from deep learning representations in a totally unsupervised way.
Disclaimers: I have tried to make this post concise, at the cost of not making the full arguments for many of my claims; you should treat this as more of a rough sketch of my views rather than anything comprehensive. I also frequently change my mind – I’m usually more consistently excited about some of the broad intuitions but much less wedded to the details – and this of course just represents my current thinking on the topic.
Problem
I would feel pretty optimistic about alignment if – loosely speaking – we can get models to be robustly “honest” in a way that scales even to superhuman systems.[1] Moreover, I think a natural sub-problem that captures much or most of the difficulty here is: how can we make a language model like GPT-n “truthful” or “honest” in a way that is scalable? (For my purposes here I am also happy to make the assumption that GPT-n is not actively deceptive, in the sense that it does not actively try to obscure its representations.)
For example, imagine we train GPT-n to predict news articles conditioned on their dates of publication, and suppose the model ended up being able to predict future news articles very well. Or suppose we train GPT-n to predict the outcomes of particular actions in particular situations, all described (imperfe
|
07a2e8e9-75d7-4b8c-8fbb-f9abf1c381e3
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trentmkelly/LessWrong-43k
|
LessWrong
|
[link] Misinformation and Its Correction: Continued Influence and Successful Debiasing
http://psi.sagepub.com/content/13/3/106.full
> Abstract.
>
> The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation.
>
> We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread.
>
> We next move to misinformation at the level of the individual, and review the cognitive factors that often render misinformation resistant to correction. We consider how people assess the truth of statements and what makes people believe certain things but not others. We look at people’s memory for misinformation and answer the questions of why retractions of misinformation are so ineffective in memory updating and why efforts to retract misinformation can even backfire and, ironically, increase misbelief. Though ideology and personal worldviews can be major obstacles for debiasing, there nonetheless are a number of effective techniques for reducing the impact of misinformation, and we pay special attention to these factors that aid in debiasing.
>
> We conclude by providing specific recommendations for the debunking of misinformation. These recommendations pertain to the ways in which corrections should be designed, structured, and applied in order to maximize th
|
ddc03e0b-8b8d-4144-a303-f5a580996f5a
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trentmkelly/LessWrong-43k
|
LessWrong
|
How to accelerate recovery from sleep debt with biohacking?
I have at least 40 hours of sleep debt from a polyphasic sleep schedule and attending hackathons. This number is a conservative estimate. Has anyone here researched the neurobiology of sleep deprivation? What can I do to recover quickly?
|
41089f1b-4695-4fc5-bbae-6655a0b3617c
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trentmkelly/LessWrong-43k
|
LessWrong
|
Open Thread, May 25 - May 31, 2015
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
----------------------------------------
Notes for future OT posters:
1. Please add the 'open_thread' tag.
2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)
3. Open Threads should be posted in Discussion, and not Main.
4. Open Threads should start on Monday, and end on Sunday.
|
bb7753ef-b525-4263-a6c1-4ade22e26da6
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trentmkelly/LessWrong-43k
|
LessWrong
|
lessmeta
The social bookmarking site metafilter has a sister site called metatalk, which works the same way but is devoted entirely to talking about metafilter itself. Arguments about arguments, discussions about discussions, proposals for changes in site architecture, etc.
Arguments about arguments are often less productive than the arguments they are about, but they CAN be quite productive, and there's certainly a place for them. The only thing wrong with them is when they obstruct the discussion that spawned them, and so the idea of splitting off metatalk into its own site is really quite a clever one.
Lesswrong's problem is a peculiar one. It is ENTIRELY devoted to meta-arguments, to the extent that people have to shoehorn anything else they want to talk about into a cleverly (or not so cleverly) disguised example of some more meta topic. It's a kite without a string.
Imagine if you had been around the internet, trying to have a rational discussion about topic X, but unable to find an intelligent venue, and then stumbling upon lesswrong. "Aha!" you say. "Finally a community making a concerted effort to be rational!"
But to your dismay, you find that the ONLY thing they talk about is being rational, and a few other subjects that have been apparently grandfathered in. It's not that they have no interest in topic X, there's just no place on the site they're allowed to talk about it.
What I propose is a "non-meta" sister site, where people can talk and think about anything BESIDES talking and thinking. Well, you know what I mean.
Yes?
|
166f85fa-bcc8-4ee7-a1fb-b6ac7d9d69da
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trentmkelly/LessWrong-43k
|
LessWrong
|
Path dependence in ML inductive biases
In this post, we define path dependence as the sensitivity of a model's behavior to the details of the training process and training dynamics.[1] High path-dependence indicates that small changes to the training process can cause significant changes to how the final model generalizes (such as the details of off-distribution behavior). It implies that inner alignment can be reasoned about by thinking about what the model looks like at various stages of training, and how its structure is affected by the immediate pressures of gradient descent. It implies that early-training interventions can be quite potent in shaping how a model turns out, and that a proper theory of inductive bias must reason about the order in which features are learned (where features learned faster/earlier can “screen off” the need for other implementations of a similar niche, in a way that affects the final model).
In contrast, a world with low path dependence allows us to reason about inductive bias in terms of priors and updates, sparing the details of training dynamics. It is more pessimistic about the ability to steer the model’s ontology through early interventions, believing instead that the final result is overdetermined. As Evan discusses in a previous post, it makes us less worried about variance in alignment outcomes between labs, since small changes to the training procedure don’t strongly affect alignment outcomes.
Possible mechanistic reasons for high path dependence would include the existence of distinct stable ontologies, the ability for early features to kill gradients, and the difficulty of building highly serial features whose components aren't independently useful. Mechanistic reasons for low path dependence would include grokking-like phase transitions which wipe out early circuits, overdetermination of correct ontologies, and an abundance of low loss paths between seemingly dissimilar solutions.[2]
We remain mostly agnostic about which world we are in. The purpos
|
66f72baf-94dc-4565-8dd2-a955e3e0ce81
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trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : Washington DC Social Meetup
Discussion article for the meetup : Washington DC Social Meetup
WHEN: 04 November 2012 03:00:00PM (-0400)
WHERE: National Portrait Gallery Plaza, Washington, DC 20001, USA
Not too many people showed up last meetup (shockingly, hurricanes reduce attendance), so we didn't discuses what to do this meetup. Given that, I think a sensible default is games; I'll bring Zendo, if others bring other things than we'll have more to do.
Discussion article for the meetup : Washington DC Social Meetup
|
3e4fe7a1-5752-45f5-9acb-37cbeba33290
|
trentmkelly/LessWrong-43k
|
LessWrong
|
[link] Pedro Domingos: "The Master Algorithm"
Interesting talk outlining five different approaches to AI.
https://www.youtube.com/watch?v=B8J4uefCQMc
Blurb from the YouTube description:
Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more—to cure cancer and AIDS and possibly solve every problem humanity has. Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge—past, present and future—from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society.
Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the cofounder of the International Machine Learning Society.
|
6aad42bd-efff-4547-84ff-7c74498d0f22
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trentmkelly/LessWrong-43k
|
LessWrong
|
What is stupid?
In a post that I mostly agreed with, but am also mostly not that interested in, Scott Sumner concludes with the following note:
> But I also understand that the part of my brain that tells me that the conventional narrative is stupid, is itself unreliable.
>
> Indeed it’s more than unreliable, it’s a logical contradiction. The conventional narrative can never, ever be stupid, as ‘stupidity’ is defined as reasoning that falls short of the conventional wisdom.
This is a noble and humble thing to say, but to what extent is it true? My instinctual assertion would be that this statement is almost entirely false, but I am not sure and it could be important. The corollaries of the assertion that the conventional wisdom can be stupid frequently get me into trouble. People very much do not like being told that they, their opinions or their actions are stupid, especially when they are plausibly average or better in context. One could also think of this as ‘having high standards,’ and in at least some contexts my standards are ludicrously high because I find keeping them that high to be useful.
The standard of ‘relative to the conventional wisdom’ is itself high if you take the conventional wisdom to be the wisdom of a relatively small convention. The majority of Americans don’t believe in evolution, or in a market price for water, and legislation regularly goes squarely against both, but it would be reasonable to claim that the conventional wisdom favors both. Scott’s statement need not be a contradiction with the majority of humans being stupid and the majority of them having mostly stupid opinions. In fact, given which opinions count as opinions, Scott’s standard all but guarantees it! Conventional wisdom is necessarily much less stupid than a random person’s opinion, on average, due to the wisdom of crowds.
Certainly, relative stupidity is a thing. One can say “X is stupider than Y, but less stupid than Z,” where X, Y and Z can be proposed courses of action, scientif
|
41ec3061-dd49-49fe-8646-de885fa1ec91
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trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : Berlin Meetup
Discussion article for the meetup : Berlin Meetup
WHEN: 14 November 2012 07:30:00PM (+0100)
WHERE: Ming Dynastie, Brückenstraße 6, 10179 Berlin
WARNING: We might meet at c-base instead which is also at S Jannowitzbruecke. If it works out, the change of location will be announced on the mailing list and here at least a day in advance.
Our plans for this time are:
* Discuss plans and make public commitments
* Discuss answers to LW census survey
* Self-improvement open space
As usual, I'll be there slightly early and bring a sign.
Discussion article for the meetup : Berlin Meetup
|
e7b28e1d-d209-4938-a758-ce2313343fae
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trentmkelly/LessWrong-43k
|
LessWrong
|
September 2013 Media Thread
This is the monthly thread for posting media of various types that you've found that you enjoy. Post what you're reading, listening to, watching, and your opinion of it. Post recommendations to blogs. Post whatever media you feel like discussing! To see previous recommendations, check out the older threads.
Rules:
* Please avoid downvoting recommendations just because you don't personally like the recommended material; remember that liking is a two-place word. If you can point out a specific flaw in a person's recommendation, consider posting a comment to that effect.
* If you want to post something that (you know) has been recommended before, but have another recommendation to add, please link to the original, so that the reader has both recommendations.
* Please use the comment trees for genres. There is a meta thread for comments about future threads.
* If you think there should be a thread for a particular genre of media, please post it to the Other Media thread for now, and add a poll to the Meta thread asking if it should be a thread every month.
|
e07ca274-1187-4caf-ae68-88e3e2d9bb84
|
StampyAI/alignment-research-dataset/arxiv
|
Arxiv
|
Implications of Quantum Computing for Artificial Intelligence alignment research
IMPLICATIONS OF QUANTUM COMPUTING FOR
ARTIFICIAL INTELLIGENCE ALIGNMENT RESEARCH
by Jaime Sevilla
and Pablo Moreno
1
2
ABSTRACT:
We
explain
the
key
features
of
quantum
computing
via
three
heuristics
and
apply
them
to
argue
that
a
deep
understanding
of
quantum
computing
is
unlikely
to
be
helpful to address current bottlenecks in Artificial Intelligence Alignment.
Our
argument
relies
on
the
claims
that
Quantum
Computing
leads
to
compute
overhang
instead
of
algorithmic
overhang,
and
that
the
difficulties
associated
with
the
measurement
of
quantum
states
do
not
invalidate
any
major
assumptions
of
current
Artificial
Intelligence
Alignment research agendas.
We
also
discuss
tripwiring,
adversarial
blinding,
informed
oversight
and
side
effects
as
possible exceptions.
KEYWORDS:
Quantum
Computing,
Artificial
Intelligence
Alignment,
Quantum
Speedup,
Quantum Obfuscation, Quantum Resource Asymmetry.
EPISTEMIC STATUS:
Exploratory, we could have overlooked key considerations.
Introduction
Quantum
Computing
(QC)
is
a
disruptive
technology
that
may
not
be
too
far
ahead
in
the
horizon.
Small
proof-of-concept
quantum
computers
have
already
been
built
[1]
and
major
obstacles to large-scale quantum computing are being heavily researched
[2]
.
Among
its
potential
uses,
QC
will
allow
breaking
classical
cryptographic
codes,
simulate
large
quantum
systems
and
faster
search
and
optimization
[3]
.
This
last
use
case
is
of
particular
interest
to
Artificial
Intelligence
(AI)
Strategy.
In
particular,
variants
of
the
Grover
algorithm
can
be
exploited
to
gain
a
quadratic
speedup
in
search
problems,
and
some
recent
Quantum
Machine
Learning
(QML)
developments
have
led
to
exponential
gains
in
certain
Machine
Learning
tasks
[4]
(though
with
important
caveats
which
may
invalidate
their practical use
[5]
).
These
ideas
have
the
potential
to
exert
a
transformative
effect
on
research
in
AI
(as
noted
in
[6]
,
for
example).
Furthermore
the
technical
aspects
of
QC,
which
put
some
physical
limits
on
the
observation
of
the
inner
workings
of
a
quantum
machine
and
hinder
the
verification
of quantum computations
[7]
, may pose an additional challenge for AI Alignment concerns.
In
this
short
article
we
introduce
a
heuristic
model
of
quantum
computing
that
captures
the
most relevant characteristics of QC for technical AI Alignment research.
1
jaime.sevillamolina@philosophy.ox.ac.uk
2
pabloamo@ucm.es
1
We
then
apply
our
model
to
abstractly
answer
in
which
areas
we
expect
knowledge
of
QC
might
be
relevant,
and
discuss
four
specific
avenues
of
current
research
where
it
might
come
into
play:
tripwiring,
adversarial
blinding,
informed
oversight
and
avoiding
side
effects.
A model of Quantum Computing for AI Alignment
Here
we
give
a
very
short
and
simplified
introduction
to
QC
for
AI
Alignment
Researchers.
For
a
longer
introduction
to
QC
we
recommend
Quantum
Computing
for
the
Very
Curious
[8]
.
If
you're
already
familiar
with
QC
you
may
want
to
check
our
technical
refresher
in
the
footnotes in appendix A.
We
introduce
three
heuristics,
which
to
the
best
of
our
knowledge
capture
all
relevant
aspects of QC for AI Alignment concerns:
1.
Quantum
speedup
-
quantum
computers
are,
at
most,
as
powerful
as
classical
computers we allow to run for an exponential amount of time .
QC
usually
just
gets
you
quadratic
advantages
(ie
Grover
database
search
[9]
,
Quantum
Walks
[10]
...),
although
in
some
cases
they
are
exponential
with
respect
to
the
best
known
classical
algorithms
(eg.
Shor
[11]
,
Hamiltonian
simulation
[12]
,
HHL
[13]
...).
Technically,
the
class
of
problems
efficiently
solvable
with
a
probabilistic
classical
computer
(BPP)
is
a
subset
of
the
class
of
problems
efficiently
solvable
by
a
quantum
computer
(BQP),
and
that
one
is
itself
a
subset
of
the
problems
solvable
in
exponential
time
by
a
classical
deterministic
computer
(EXP).
This
means
that
QC
are
at
least
as
fast
as
classical
computers,
but
no
more
than
exponentially
faster
[14
,
15]
.
2.
Quantum
obfuscation
-
there
is
no
efficient
way
of
reading
the
state
of
a
quantum
computer while it is operating.
Quantum
operations
cannot
copy
quantum
states
(“
No-cloning
theorem
”)
[16
,
17]
and
performing
a
partial
or
total
measure
of
a
quantum
state
will
collapse
that
part
of the state, resulting in loss of information.
To
recover
information
from
a
quantum
state
one
has
several
inefficient
options:
performing
the
inner
product
of
the
state
with
another
vector
(usually
through
a
procedure
called
the
swap
test
[18]
),
perform
many
measurements
on
many
identically
prepared
states
to
do
statistics
on
the
entries
(tomography
[19]
),
or
using
amplitude estimation
[20]
to estimate a single amplitude.
The
former
procedure
depends
on
the
precision
quadratically
and
destroys
the
state,
albeit
it
is
independent
of
the
dimension
of
the
vector.
The
latter
two
require
a
number
of
repetitions
at
least
linear
with
respect
to
the
dimension
of
the
vector,
which grows exponentially with the number of qubits.
2
3.
Quantum
isolation
-
a
quantum
computer
cannot
interact
with
the
classical
world
without its state becoming at least partially classic.
In
other
words,
if
a
quantum
computer
creates
a
side-channel
to
the
outside
world
during
their
quantum
computation
it
destroys
its
coherence
and
randomizes
state
according to well defined rules.
This
is
directly
derived
from
the
postulates
of
Quantum
Mechanics,
and
in
particular
the collapse of the wave function when it interacts with the outside world.
In
the
following
two
sections
we
look
at
how
this
model
can
be
applied
to
gather
insight
on
the
phases
of
research
in
AI
Alignment
and
kinds
of
alignment
strategies
where
QC
may
or
may not be relevant.
Bottlenecks in Artificial Intelligence Alignment research
In
this
section
we
introduce
a
simplified
way
of
thinking
about
the
different
phases
of
research
through
which
we
expect
the
field
of
AI
Alignment
to
go,
and
reason
about
the
relevance of quantum computing during each of these phases.
Looking
at
some
landmark
achievements
in
computer
science,
it
seems
that
most
research
begins
with
working
on
the
formalization
of
a
problem,
which
then
is
followed
by
a
period
where
researchers
try
to
find
solutions
to
the
problem,
at
first
just
theoretical,
then
inefficient
and
finally
practical
implementations
(see
for
example
the
history
of
chess
playing,
from
Shannon’s
seminal
paper
in
1950
to
the
Deeper
Blue
vs
Kasparov
match
in
1997
[21]
).
We
expect
research
in
AI
Alignment
to
develop
in
a
similar
fashion,
and
while
we
have
some
formalized
frameworks
to
handle
some
subsets
of
the
problem
(see
for
example
IRL
[22]
),
there
is
no
agreed
upon
formalization
that
captures
the
essence
of
the
whole
alignment
problem.
On
the
other
hand,
theoretical
proposals
for
QC
applications
are
mostly
concerned
with
speeding
up
classical
algorithms,
sometimes
with
notable
improvements
(see
for
example
Shor’s
algorithm
for
factorization
[11]
),
and
in
some
rare
cases
it
has
inspired
the
creation
of
novel
algorithmic
strategies
[23]
.
In
no
case
that
we
know
of
has
QC
lead
to
a
formalization
insight of the kind that we believe AI Alignment is bottlenecked on.
That
is,
QC
has
so
far
only
helped
find
efficient
solutions
to
problems
that
were
already
properly
formalized,
while
we
believe
that
the
most
significant
problems
in
AI
Alignment
have not yet matured into proper formalizations.
This
observation
serves
as
an
empirical
verification
of
the
quantum
speedup
heuristic,
that
instructs
us
to
think
about
quantum
computing
as
a
black
box
accelerator
rather
than
a
novel
approach
to
algorithmic
design,
and
thus
we
should
not
expect
formalization
insights
to
come
from
QC.
In
other
words,
QC
may
lead
to
what
would
be
equivalent
to
compute
overhang, but not lead to significant insight overhang.
3
We
conclude
that
while
QC
may
help
in
a
later
phase
of
AI
Alignment
research
with
making
safe
AI
algorithms
practical
and
competitive,
it
is
very
unlikely
that
it
will
lead
to
novel
theoretical insights that fundamentally change how we think about AI Alignment.
As
a
side
note,
the
same
reasoning
applies
to
AI
capabilities
research;
QC
is
unlikely
to
lead
to
new
formal
insights
on
that
field.
However,
the
quantum
speedup
may
enable
the
practical
use
of
algorithms
which
were
previously
considered
inefficient.
This
is
concerning
to
the
extent
that
we
expect
compute
overhang
to
lead
to
more
opaque
and/or
less
safe
algorithms.
Alignment Strategies: incentive design versus active oversight
In
this
section
we
introduce
a
distinction
between
two
main
broad
complementary
strategies
for
achieving
AI
Alignment:
incentive
design
and
active
oversight,
and
reason
about how QC may interact with them.
By
incentive
design
we
mean
static
strategies,
where
the
design
of
an
agent
is
verified
to
have certain safety properties that incentivize the agent to pursue desirable goals.
By
active
oversight
we
refer
to
dynamic
strategies,
where
an
agent,
which
may
or
may
not
be
safe,
is
monitored,
and
if
certain
metrics
indicate
unsafeness,
an
intervention
is
made
to
safely interrupt or modify the agent.
We
believe
that
a
complete
solution
to
the
AI
Alignment
problem
will
include
both
elements
of incentive design and active oversight.
Since
we
can
treat
QC
as
a
black
box
accelerator
according
to
the
quantum
speedup
heuristic,
we
can
see
that
QC
does
not
present
any
additional
challenges
for
incentive
design.
In
other
words,
since
designing
the
right
incentives
for
the
behaviour
of
the
agent
is
independent
of
the
hardware
and
implementation
efficiency,
QC
is
unlikely
to
help
with
this
endeavour.
Instead
of
concerning
themselves
with
the
intricacies
of
QC,
AI
Alignment
researchers
may
just
treat
QC
as
a
black
box
and
reason
about
classical
equivalents
instead.
This
is
analogous
to
how
AI
Safety
researchers
do
not
need
to
think
about
whether
an
agent
will
be
implemented over GPUs or serial processors and only focus on the formalisms.
Those
alternatives
may
be
exponentially
slower;
however,
if
the
safety
of
our
agent
design
relies
on
the
assumption
that
the
agent
will
not
be
able
to
acquire
exponentially
more
resources for computation we cannot really deem it safe, making this a moot point.
One
possible
counter-argument
to
our
line
of
reasoning
above
is
that
quantum
computing
may
influence
which
AI
designs
end
up
being
successful,
and
knowledge
about
the
AI
designs
that
are
going
to
be
more
popular
in
the
future
can
allow
us
to
make
extra
assumptions
to
facilitate
incentive
design.
We
leave
as
an
open
question
whether
we
can
reliable
predict
features
about
QC-powered
agents
that
will
allow
us
to
make
these
kind
of
simplifying assumptions.
4
Moving
on
from
incentive
design,
we
point
out
that
QC
may
pose
an
additional
challenge
to
active
oversight
due
to
quantum
obfuscation,
as
it
might
interfere
with
measures
of
the
internal
state
of
a
QC.
In
the
next
section
we
explore
some
AI
Alignment
research
areas
where this fact comes into play.
Review of AI Alignment research agendas
We
have
reviewed
the
AI
Alignment
agendas
of
Concrete
Problems
in
AI
Safety
[24]
,
MIRI’s
research agendas
[25]
and
Iterated Distillation and Amplification
[26]
.
Most
of
their
work
focuses
on
incentive
design
as
the
de
facto
route
to
a
solution.
As
such,
QC
does
not
affect
the
main
assumptions
and
considered
solutions.
However,
we
have
been
able
to
identify
four
areas
of
active
oversight
where
QC
may
be
a
relevant
consideration:
tripwiring, adversarial blinding, informed oversight and avoiding side effects.
We discuss each of these areas in depth.
Tripwiring
Tripwiring
is
a
class
of
mechanisms
that
alert
of,
or
directly
prevent
attempts
of
an
agent
to
hack
its
reward
function
[24]
.
If
a
particular
tripwire
design
relies
on
access
to
the
internal state of the agent, quantum obfuscation may impede its effective implementation.
For
example,
in
the
Transparency
section
of
Techniques
for
optimizing
worst-case
performance
[27]
, Paul Christiano writes:
“While
training
a
model,
we
are
free
to
look
inside
and
‘see
what
it’s
thinking’.
If
we
could
actually
understand
those
thoughts,
we
might
be
able
to
more
easily
predict
under
what conditions it would fail malignantly.”
Unfortunately,
due
to
quantum
obfuscation,
this
seems
rather
complicated
if
the
agent
has
quantum
capabilities.
You
will
just
be
able
to
efficiently
read
the
input/output
of
the
used
quantum subroutines, but not its inner operation.
Nevertheless,
there
might
be
easier
ways
for
a
misaligned
agent
to
obfuscate
what
it
is
doing,
such
as
classically
encoding
its
hidden
plans
in
ways
that
an
overseer
will
not
understand
(for
example,
it
can
encode
its
state
with
quantum-resistant
cryptography
[28]
).
However
some
early
work
points
in
the
direction
that
quantum-based
obfuscation
may
be
significantly more powerful than classical alternatives
[29]
.
Another
consideration
is
that
quantum
obfuscation
offers
a
more
plausible
excuse
for
obfuscation
than
classical
cryptography.
For
a
classical
agent
that
doesn't
need
to
encrypt
information
during
its
expected
workflow,
any
encryption
attempt
might
indicate
misalignment
and
trigger
a
shutdown.
If,
in
contrast,
the
QC
agent
hides
important
information
in
a
quantum
superposition,
this
might
be
misaligned
behavior
or
just
a
consequence of wanting to exploit the quantum speedup.
5
Finally,
one
could
imagine
some
kind
of
quantum
tripwire
that
monitors
the
quantum
computer
from
within
the
superposition.
For
example,
a
quantum
bit
that
flags
whether
the
tripwire
has
been
activated.
Since
quantum
computing
allows
for
conditional
operations,
one
could
imagine
freezing
the
state
if
such
state
has
been
reached
in
the
superposition,
only
allowing
it
to
proceed
while
it
has
not.
Then
one
could
perform
amplitude
estimation
to
check
that
the
flag
has
not
been
activated
in
any
branch
of
the
computation,
and
otherwise kill it.
Adversarial blinding
Adversarial
blinding
consists
of
using
adversarial
techniques
to
blind
the
agent
with
respect
to
some
variables.
This
technique
could
be
used
to
make
it
difficult
for
the
agent
to
understand how its reward is generated and therefore how it can be hacked
[24]
.
In
the
case
where
there
is
an
asymmetry
of
resources
and
the
overseer
has
access
to
quantum
computers
but
the
agent
does
not,
we
know
that
there
are
distributions
that
are
just
not
efficiently
computable
classically
(will
need
exponential
resources),
but
are
efficiently
computable
quantically.
This
suggests
the
possibility
of
using
one
of
those
distributions to generate a difficult to hack reward.
Informed oversight
In
the
informed
oversight
scenario,
the
verifier
checks
that
the
behavior
of
an
agent
is
safe,
and
the
requirements
on
the
verifier
do
not
grow
too
much
as
the
agent
becomes
more
and more powerful
[30]
.
If
there
is
an
asymmetry
in
resources
where
the
verifier
has
access
to
QC
while
the
agent
does
not,
this
may
be
exploited
using
quantum
speedup
to
allow
the
verifier
to
do
certain
checks
in
polynomial
time
for
operations
that
will
take
the
agent
exponential
time
to
check
themselves.
In
particular,
quantum
speedup
may
help
with
the
amplification
step
of
IDA,
where
we
give
an
agent
access
to
QC
to
design
and
verify
a
stronger
successor,
whose
resources
will
be
constrained as in not having access to QC themselves during the verification step.
It
is
however
unclear
whether
this
scheme
of
only
giving
access
to
the
agent
to
QC
after
it
has
been
verified
is
possible,
and
whether
we
can
reliably
restrain
the
agent
from
accessing
QC while it is being verified.
Side effects and AI boxing
We
would
like
to
be
able
to
reliably
detect
and
prevent
AI
systems
from
interacting
with
the
world in unexpected ways (this is referred to in the literature as
avoiding side effects
[24]
).
Per
the
quantum
isolation
heuristic,
when
a
process
implemented
in
a
quantum
computer
tries
to
affect
the
outside
world
it
has
to
necessarily
partially
collapse
part
of
the
state
(unless the state is already classical).
6
One
may
think
of
taking
advantage
of
this
phenomena
to
monitor
whether
a
quantum
agent has interacted with the outside world. There are however some associated difficulties.
Firstly,
and
most
importantly,
we
cannot
distinguish
a
partial
collapse
from
a
constructive
interference
when
the
amplitude
of
the
subset
of
qubits
we
are
interested
in
is
1.
That
is,
it
may
be
the
case
that
part
of
the
state
is
classical
and
we
cannot
effectively
tell
whether
this
is
due
to
collapse
or
because
all
states
in
superposition
agree
on
the
state
of
a
particular
subset of qubits.
Secondly,
to
tell
whether
the
process
has
(partially)
collapsed
we
need
to
measure
it,
which
will
collapse
it
in
turn,
and
even
worse,
we
will
not
be
able
to
know
whether
the
state
had
collapsed
before
or
because
of
our
measurement.
Thus
we
would
need
to
resort
to
one
of
the
inefficient ways of measuring the quantum state, like amplitude estimation.
All
this
means
it
does
not
seem
possible
to
strongly
and
efficiently
infer
interaction
with
the
outside world through learning that part of the state of the agent’s mind is classical.
Relatedly,
AI
boxing
refers
to
restraintment
strategies
where
an
AI
system
is
isolated
from
the environment to prevent unexpected side effects
[31]
.
The
quantum
isolation
heuristic
may
mislead
us
into
thinking
that
boxing
may
be
easier
to
achieve
in
QC.
However
this
is
not
the
case;
as
long
as
a
process
inside
a
QC
is
willing
to
partially
collapse
part
of
its
state
it
can
escape
its
quantum
box.
In
fact,
one
of
the
most
active
areas
of
current
research
in
QC
is
investigating
how
to
isolate
the
quantum
system
so
it does not collapse unexpectedly.
So,
in
conclusion,
one
may
think
of
quantum
computers
as
oracles,
but
that
does
not
mean
that they are naturally boxed already.
Conclusion and open questions
We
have
presented
a
simplified
model
of
quantum
computing
for
AI
Alignment,
summarized
in
three
heuristics:
quantum
speedup
(QC
allows
a
quadratic
and
sometimes
exponential
speedup),
quantum
obfuscation
(qubits
cannot
be
read
without
destroying
the
computation)
and
quantum
isolation
(interacting
with
the
classical
world
destroys
any
superposition).
Our
main
conclusion
is
that
we
do
not
expect
Quantum
Computing
to
be
relevant
to
the
current technical AI Alignment research agendas.
In
particular,
we
do
not
expect
QC
techniques
to
be
relevant
for
safety
until
we
are
actually
interested in making algorithms and protocol as efficient as possible.
Furthermore,
we
expect
QC
to
not
be
relevant
for
general
incentive
design
purposes.
However,
we
have
also
listed
some
current
research
problems
related
to
active
oversight
where
it
might
be
helpful
to
have
our
simplified
model
of
quantum
computing
in
mind.
In
any
case
we
do
not
expect
these
issues
to
have
high
relevance
right
now,
as
most
of
the
7
current
work
in
current
AI
Alignment
falls
under
incentive
design
strategies
rather
than
active oversight.
Some further questions that came up during our research:
■
Can
we
expect
humanity
to
develop
reliable
QC
before
AGI?
How
do
the
developments
in
each
field
interact
with
one
another?
Will
quantum
machine
learning
significantly
increase
AI
capabilities?
Since
one
of
the
most
straightforward
and
promising
applications
of
QC
is
material
science,
should
we
expect
QC
to
lead
to
further
improvements
in
non-QC
computing
technology?
How
does
that
affect
AI
Capabilities?
■
In
the
case
where
QC
is
relevant
for
the
design
of
advanced
AIs,
can
we
expect
to
have
an
actual
quantum
agent
in
the
future,
or
will
it
just
be
a
classical
agent
with
access to quantum subroutines, in a Comprehensive AI Services fashion
[32]
?
■
How
does
QC
affect
AI
Governance?
How
easy
is
to
deploy
powerful
QC-powered
AI
systems?
How
easily
can
we
monitor
the
labs
that
have
QC
capabilities?
If
QC
is
relevant
to
the
development
of
AGI,
can
the
fact
that
QC
capabilities
are
more
concentrated affect the dynamics of development of AGI?
■
How
does
the
quantum
speedup
possibility
affect
AI
design?
Can
developments
in
QC
lead
to
opaque
AI,
as
the
compute
improvements
allow
raw
search
design
to
be
used
instead
of
design
from
first
principles?
Relatedly,
what
AI
designs
would
be
particularly favored by QC technology versus raw improvements in compute?
■
Is
quantum
obfuscation
the
most
efficient
obfuscation
strategy
a
misaligned
agent
can have access to? Can classical cryptography be used to obfuscate information?
■
Is
asymmetry
of
resources
a
reasonable
assumption
to
make
in
verifier
/
agent
scenarios? How can asymmetry of QC resources be exploited for safety purposes?
■
How could we design quantum tripwires? What are their strengths and limitations?
■
How
would
we
go
about
implementing
an
adversarial
blinding
scheme
based
on
quantum distribution sampling?
Article
by
Jaime
Sevilla
(FHI
summer
research
fellow)
and
Pablo
Moreno
(Quantum
Computing PhD student at Complutense University of Madrid under an FPU grant).
We
want
to
thank
Linh
Chi
Nguyen,
Adrian
Hutter,
Anders
Sandberg,
Max
Daniel,
Richard
Möhn and Daniel Eth for incredibly useful feedback, editing and discussion.
Daniel
Eth
contributed
directly
to
the
collection
of
open
questions.
Anders
Sandberg
pointed
us
to
the
isolation
heuristic
and
its
possible
implications.
Adrian
Hutter
prevented
us from making a wrong claim on the complexity bounds of BQP.
8
Appendix A: Speed technical introduction to Quantum Computing
Quantum
states
are
complex
unitary
vectors.
A
basis
vector
is
just
a
classical
state,
whereas
any
other
is
called
a
superposition
(a
linear
combination
of
basis
states)
[33]
.
Quantum
Computing
is
based
on
unitary
transformations
of
these
quantum
states.
Non-unitary
dynamics
can
be
introduced
via
measurements:
a
measurement
projects
the
quantum
state
into
a
basis
state
(classical
state)
with
a
probability
equal
to
the
square
of
the
amplitude
of
that state (the coefficient in the linear combination).
Bibliography
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Córcoles,
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D.,
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«Demonstration
of
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de
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y
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Mahadev,
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[8]
Matuschak,
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Nielsen,
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.
[9]
Grover,
Lov
K.
«A
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, doi:10.1145/237814.237866.
[10]
Szegedy,
M.
«Quantum
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of
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45th
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[11]
Shor,
Peter
W.
«Polynomial-Time
Algorithms
for
Prime
Factorization
and
Discrete
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SIAM
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[12]
Berry,
Dominic
W.,
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«Hamiltonian
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Parameters».
2015
IEEE
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IEEE,
2015,
pp. 792-809. DOI.org (Crossref), doi:10.1109/FOCS.2015.54.
9
[13]
Harrow,
Aram
W.,
et
al.
«Quantum
Algorithm
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Physical
Review
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de
2009,
p.
150502.
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(Crossref),
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[14]
Aaronson,
Scott.
«BQP
and
the
Polynomial
Hierarchy».
Proceedings
of
the
42nd
ACM
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on
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STOC
’10,
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141.
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(Crossref),
doi:10.1145/1806689.1806711.
[15] Petting Zoo - Complexity Zoo.
https://complexityzoo.uwaterloo.ca/Petting_Zoo
.
[16]
Wootters,
W.
K.,
y
W.
H.
Zurek.
«A
Single
Quantum
Cannot
Be
Cloned».
Nature,
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n.o
5886, octubre de 1982, pp. 802-03. DOI.org (Crossref), doi:10.1038/299802a0.
[17]
Scarani,
Valerio,
et
al.
«Quantum
Cloning».
Reviews
of
Modern
Physics,
vol.
77,
n.o
4,
noviembre
de 2005, pp. 1225-56. DOI.org (Crossref), doi:10.1103/RevModPhys.77.1225.
[18]
Schuld,
Maria
and
Petruccione,
Francesco.
«Supervised
learning
with
quantum
computers».
Springer Berlin Heidelberg, 2018.
[19]
Cramer,
Marcus,
et
al.
«Efficient
Quantum
State
Tomography».
Nature
Communications,
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1,
n.o 1, 2010, p. 149. DOI.org (Crossref), doi:10.1038/ncomms1147.
[20]
Brassard,
Gilles,
et
al.
«Quantum
Amplitude
Amplification
and
Estimation».
Contemporary
Mathematics,
editado
por
Samuel
J.
Lomonaco
y
Howard
E.
Brandt,
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305,
American
Mathematical
Society, 2002, pp. 53-74. DOI.org (Crossref), doi:10.1090/conm/305/05215.
[21] «CS221». Deep Blue,
https://stanford.edu/~cpiech/cs221/apps/deepBlue.html
.
[22]
Ng,
Andrew
Y.,
and
Stuart
J.
Russell.
«Algorithms
for
inverse
reinforcement
learning.»
Icml.
Vol.
1. 2000.
[23]
Tang,
Ewin.
«A
Quantum-Inspired
Classical
Algorithm
for
Recommendation
Systems».
Proceedings
of
the
51st
Annual
ACM
SIGACT
Symposium
on
Theory
of
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-
STOC
2019,
ACM Press, 2019, pp. 217-28. DOI.org (Crossref), doi:10.1145/3313276.3316310.
[24] Amodei, Dario, et al. «Concrete problems in AI safety.» arXiv preprint arXiv:1606.06565 (2016).
[25] Demski, A., & Garrabrant, S.. «Embedded agency. arXiv preprint arXiv:1902.09469. (2019)
[26]
Cotra,
Ajeya.
«Iterated
Distillation
and
Amplification».
Medium,
29
de
abril
de
2018,
https://ai-alignment.com/iterated-distillation-and-amplification-157debfd1616
.
[27]
Christiano,
Paul.
«Techniques
for
Optimizing
Worst-Case
Performance».
Medium,
2018,
https://ai-alignment.com/techniques-for-optimizing-worst-case-performance-39eafec74b99
.
[28]
Lily
Chen,
et
al.
«
Report
on
Post-Quantum
Cryptography
»
.
National
Institute
of
Standards
and
Technology,
https://nvlpubs.nist.gov/nistpubs/ir/2016/nist.ir.8105.pdf
[29]
Alagic,
Gorjan,
&
Fefferman,
Bill.
«On
Quantum
Obfuscation».
arXiv:1602.01771
[quant-ph],
febrero de 2016. arXiv.org,
http://arxiv.org/abs/1602.01771
.
[30]
Christiano,
Paul.
«The
Informed
Oversight
Problem».
Medium,
4
de
julio
de
2017,
https://ai-alignment.com/the-informed-oversight-problem-1b51b4f66b35
.
[31]
Armstrong,
Stuart,
et
al.
«Thinking
Inside
the
Box:
Controlling
and
Using
an
Oracle
AI».
Minds
and
Machines,
vol.
22,
n.o
4,
noviembre
de
2012,
pp.
299-324.
DOI.org
(Crossref),
doi:10.1007/s11023-012-9282-2.
[32]
Drexler,
K.E.
(2019):
«Reframing
Superintelligence:
Comprehensive
AI
Services
as
General
Intelligence», Technical Report #2019-1, Future of Humanity Institute, University of Oxford .
[33]
Nielsen,
Michael
A.,
and
Isaac
L.
Chuang.
«Quantum
computation
and
quantum
information».
10th anniversary ed, Cambridge University Press, 2010.
10
|
db40095c-fb4d-4c7d-aa69-3a4710ce789f
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Attention to snakes not fear of snakes: evolution encoding environmental knowledge in peripheral systems
Sinking In: The Peripheral Baldwinisation of Human Cognition. Cecilia Heyes, Nick Chater & Dominic Michael Dwyer. Trends in Cognitive Sciences, 2020.
Some theories have proposed that humans have evolved to experience some stimuli (e.g. snakes, spiders) as more potentially frightening, so that a fear for these entities is learned faster than a fear for more neutral things. In evolutionary psychology, there has been talk about modules for a fear of snakes, for example. However, research suggests that rather than “the fear system” itself having innate biases towards picking up particular kinds of fears, humans are evolutionarily biased towards paying extra attention to things like spiders and snakes. Because of these stimuli being more attended than others, it also becomes more probable that a fear response gets paired with them.
The authors call the attention system “peripheral” and the fear system “central”, in that the attention system brings in information for the fear system to process. (This is in analogy to the peripherals of a computer, where e.g. the keyboard and mouse are used to deliver information to the central processor.) They argue that in general, while it is possible for responses to specific environmental stimuli to become genetic as sensitivty for those stimuli is selected for, this learning will be more likely to get encoded into “peripheral” than “central” systems.
One of their other examples is that the central mechanisms of language learning seem theoretically and empirically unlikely to be affected by the environment – there are no genes for learning English grammar better than Chinese grammar. However, there are indications that the peripheral mechanisms of language have been more affected. E.g. some languages use lexical tone (where word identities are partly defined by pitch contours), and genes that seem to make lexical tone easier to perceive seem to be more common among speakers of those languages.
> Seligman’s account suggested that s
|
853d68db-a0a4-4305-8166-a27f531cefab
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Definition
When substantial controversy exists about how to define a term, good epistemic policy is for both sides to adopt new, more specific terms whose definitions are not further disputed. To whatever extent possible, definitions should not be phrased in a way that tries to pre-emptively settle an argument or 'bake in' one answer to a factual or policy disagreement. See [A Human's Guide to Words](http://wiki.lesswrong.com/wiki/A_Human%27s_Guide_to_Words).
|
e6869996-6bd0-416b-9782-1067a7f325c8
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Is Adam Elga's proof for thirdism in Sleeping Beauty still considered to be sound?
I've spend sometime looking into the issue and I'm quite confident that this proof isn't sound. Is it already a known fact?
|
3bd126f0-cc1a-454d-80e9-e23db37fd1e9
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trentmkelly/LessWrong-43k
|
LessWrong
|
BCIs and the ecosystem of modular minds
Crossposted from my personal blog.
Epistemic status: Much more speculative than previous posts but points towards an aspect of the future that is becoming clearer which I think is underappreciated at present. If you are interested in any of these thoughts please reach out.
For many years, the primary AI risk model was one of rapid take-off (FOOM) of a single AI entering a recursive self-improvement loop and becoming utterly dominant over humanity. There were lots of debates about whether this 'fast-takeoff' model was correct or whether instead we would enter a slow-takeoff regime. In my opinion, the evidence is pretty definitive that at the moment we are entering a slow-takeoff regime[1], and arguably have been in it for the last few years (historically takeoff might be dated to the release of GPT-3).
The last few years have undoubtedly been years of scaling monolithic very large models. The primary mechanism of improvement has been increasing the size of a monolithic general model. We have discovered that a single large model can outperform many small, specialized models on a wide variety of tasks. This trend is especially strong for language models. We also see a similar trend in image models and other modalities where large transformer or diffusion architectures work extremely well and scaling them up in both parameter size and data leads to large and predictable gains. However, soon this scaling era will necessarily come to an end temporarily. This is necessary because the size of training runs and models is rapidly exceeding what companies can realistically spend on compute (and what NVIDIA can produce). GPT-4 training cost at least 100m. It is likely that GPT-5, or a successor run in the next few years will cost >1B. At this scale, only megacap tech companies can afford another OOM and beyond that there is only powerful nation-states, which seem to be years away. Other modalities such as visual and audio have several more OOMs of scaling to go yet but if th
|
7a75a546-2261-4217-87f7-75564cce84c5
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : West LA—Sphexishness and Meta-Sphexishness
Discussion article for the meetup : West LA—Sphexishness and Meta-Sphexishness
WHEN: 18 February 2015 07:00:00PM (-0800)
WHERE: 11066 Santa Monica Blvd, Los Angeles, CA
How to Find Us: Go into this Del Taco. We will be in the back room if possible.
Parking is free in the lot out front or on the street nearby.
Discussion: How we keep repeating that anecdote of the digger wasp's sphexish foolishness, repeating the same behavior over and over, never realizing it is accomplishing nothing. We repeat this anecdote over and over, over and over again, never realizing that it is a misleading intuition pump.
Recommended Reading:
* Sphexishness
* Digger Wasp: Uses in Philosophy
* The Sphex Story: How the cognitive sciences kept repeating an old and questionable anecdote
* The full text of the previous item, ungated by magic
No prior exposure to Less Wrong is required; this will be generally accessible.
Discussion article for the meetup : West LA—Sphexishness and Meta-Sphexishness
|
d1d148ca-1338-4fc9-a409-66ac2aae854e
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trentmkelly/LessWrong-43k
|
LessWrong
|
Not All Beliefs Are Created Equal: Diagnosing Toxic Ideologies
Epistemic status: exploratory but confident. This essay presents a general framework for identifying and analyzing ideologies based on recurring structural patterns. It draws on observation, theory, and examples from across the political spectrum. The goal is to improve mental defenses against manipulative belief systems, not to discredit all ideological thinking. I welcome critique and refinement.
We live in an online society saturated with diverse ideologies—identity politics, political populism, libertarianism, conspiracy theories, and others. The internet and social media enable these ideologies to spread rapidly, significantly influencing real-world politics and everyday life, often negatively. This essay outlines the defining features of ideologies, examines their essential structural components, explores why they spread effectively, identifies their harmful consequences, and provides illustrative examples.
Defining Ideology
An ideology is fundamentally a simplified worldview, offering a lens through which individuals interpret social and economic relationships. It combines descriptive and prescriptive elements: it explains the world as it supposedly is and advocates for how it should be. While superficially resembling scientific or rational frameworks, ideologies crucially differ in their resistance to falsification.
Importantly, this critique does not imply that all ideological frameworks are inherently harmful. Many ideologies begin as efforts to understand injustice or improve human flourishing. Feminism, for example, has made undeniable contributions to gender equality and has advanced critical conversations around consent, labor, and representation. Libertarian critiques have usefully highlighted government overreach and championed civil liberties. Marxist theory has illuminated the dynamics of class, power, and economic exploitation. Even populist movements have occasionally acted as corrective forces when mainstream institutions failed to address p
|
c3e4a7e0-754e-485a-b729-ecd1cab048f9
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trentmkelly/LessWrong-43k
|
LessWrong
|
What are good resources for gears models of joint health?
Painscience.com and Hargrove's "A Guide To Better Movement" are pretty good for a model of predictive processing and the roll of the nervous system in chronic pain and movement. I still don't feel like I have a good model of bone and joint health in general, however. Eg, I'm currently nursing a flare up of patelo-femoral pain in my left knee. I've done a number of things over the past few months to deal with it, with some success, including buying and reading Painscience's book length patelo-femoral tutorial. Recently I've had a bit of pain in my foot, possibly in the tibiocalcaneal or tibionavicular tendons. I find that even though I now know a fair amount about PFS and the way the nervous system processes pain, these models don't generalize well to sporadic, idiopathic pain in another joint.
Possibly the answer is: "lol that model doesn't exist", or "lol wanna get a phd?" but if there are good resources, I'd be an eager consumer.
A sub-question that I'm particularly interested in is: what, if anything, is know about the relationship between base line muscle tone and joint issues? I have good reason to think my baseline muscle tone is higher than average.
|
a938b113-977b-4195-8b2b-ca58b83c99ef
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trentmkelly/LessWrong-43k
|
LessWrong
|
[link] Nine Ways to Bias Open-Source AGI Toward Friendliness
Ben Goertzel and Joel Pitt: Nine Ways to Bias Open-Source AGI Toward Friendliness. Journal of Evolution and Technology - Vol. 22 Issue 1 – February 2012 - pgs 116-141.
> Abstract
>
>
> While it seems unlikely that any method of guaranteeing human-friendliness (“Friendliness”) on the part of advanced Artificial General Intelligence (AGI) systems will be possible, this doesn’t mean the only alternatives are throttling AGI development to safeguard humanity, or plunging recklessly into the complete unknown. Without denying the presence of a certain irreducible uncertainty in such matters, it is still sensible to explore ways of biasing the odds in a favorable way, such that newly created AI systems are significantly more likely than not to be Friendly. Several potential methods of effecting such biasing are explored here, with a particular but non-exclusive focus on those that are relevant to open-source AGI projects, and with illustrative examples drawn from the OpenCog open-source AGI project. Issues regarding the relative safety of open versus closed approaches to AGI are discussed and then nine techniques for biasing AGIs in favor of Friendliness are presented:
>
> 1. Engineer the capability to acquire integrated ethical knowledge.
>
> 2. Provide rich ethical interaction and instruction, respecting developmental stages.
>
> 3. Develop stable, hierarchical goal systems.
>
> 4. Ensure that the early stages of recursive self-improvement occur relatively slowly and with rich human involvement.
>
> 5. Tightly link AGI with the Global Brain.
>
> 6. Foster deep, consensus-building interactions between divergent viewpoints.
>
> 7. Create a mutually supportive community of AGIs.
>
> 8. Encourage measured co-advancement of AGI software and AGI ethics theory.
>
> 9. Develop advanced AGI sooner not later.
>
> In conclusion, and related to the final point, we advise the serious co-evolution of functional AGI sys
|
ceeed12f-4428-47ed-8035-9b969fd6ee70
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trentmkelly/LessWrong-43k
|
LessWrong
|
Morality Isn't Logical
What do I mean by "morality isn't logical"? I mean in the same sense that mathematics is logical but literary criticism isn't: the "reasoning" we use to think about morality doesn't resemble logical reasoning. All systems of logic, that I'm aware of, have a concept of proof and a method of verifying with high degree of certainty whether an argument constitutes a proof. As long as the logic is consistent (and we have good reason to think that many of them are), once we verify a proof we can accept its conclusion without worrying that there may be another proof that makes the opposite conclusion. With morality though, we have no such method, and people all the time make moral arguments that can be reversed or called into question by other moral arguments. (Edit: For an example of this, see these posts.)
Without being a system of logic, moral philosophical reasoning likely (or at least plausibly) doesn't have any of the nice properties that a well-constructed system of logic would have, for example, consistency, validity, soundness, or even the more basic property that considering arguments in a different order, or in a different mood, won't cause a person to accept an entirely different set of conclusions. For all we know, somebody trying to reason about a moral concept like "fairness" may just be taking a random walk as they move from one conclusion to another based on moral arguments they encounter or think up.
In a recent post, Eliezer said "morality is logic", by which he seems to mean... well, I'm still not exactly sure what, but one interpretation is that a person's cognition about morality can be described as an algorithm, and that algorithm can be studied using logical reasoning. (Which of course is true, but in that sense both math and literary criticism as well as every other subject of human study would be logic.) In any case, I don't think Eliezer is explicitly claiming that an algorithm-for-thinking-about-morality constitutes an algorithm-for-doing-logi
|
f3149469-4bbd-4f21-bdf2-423a9652b7bc
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trentmkelly/LessWrong-43k
|
LessWrong
|
Some suggestions (desperate pleas, even)
* A way to see all replies to your own comments or posts
* Setting defaults for sorting rather than having to manually sort by recent every time
* Put the see all comments link in a reasonably easy to find place
* "All posts" which actually shows all posts without requiring that the user separately click on each one in order to read it
* If you can't eliminate the huge gutter space, at least provide an option to reduce it
* A way to edit the raw text of a post. It is hard to struggle with a malicious parser. When I typed "expand(underscore)less", "expand(underscore)more" below using real underscores, the parser decided I want to italicize everything between the two underscores.
* Maybe a bugzilla to report bugs? Reporting bugs here or on intercom is suboptimal.
* Edit: I tried to submit this post and it didn't appear. If your post is being held it should tell you that your post is being held because otherwise this is hard to distinguish from a malfunction. Also, when you submit your post it doesn't say where it's submitted to--I meant to submit this to Meta.
Also, I'm still having trouble reading this. It's only working on Chrome. Firefox has formatting problems; certain icons (search, "expand(underscore)less", "expand(underscore)more", "navigate(underscore)before", "navigate(underscore)next") show up as text instead of as icons. IE looks good but doesn't show the conversations icon and doesn't do anything if I click "LOGIN".
|
1620bdd0-facd-438e-b46a-63f918b398e8
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trentmkelly/LessWrong-43k
|
LessWrong
|
How strong is the evidence for hydroxychloroquine?
There has been a lot of discussion of hydroxychloroquine (see the megathread on Effective Altruism Coronavirus Discussion, note you need to answer two questions to gain access). Doctors treating COVID-19 have rated hydroxychloroquine the most effective drug based on their experience. But on the other hand, results have been mixed with a recent RCT showing no effect.
At this stage how strong is the evidence for hydroxychloroquine and if it works, how effective does it appear to be as a treatment?
Disclaimer: Please seek medical advice before taking any substance, particularly those like hydroxychloroquine that have known side effects.
|
1b436927-2844-447c-8815-0ba7c2c69531
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trentmkelly/LessWrong-43k
|
LessWrong
|
Acausal normalcy
This post is also available on the EA Forum.
Summary: Having thought a bunch about acausal trade — and proven some theorems relevant to its feasibility — I believe there do not exist powerful information hazards about it that stand up to clear and circumspect reasoning about the topic. I say this to be comforting rather than dismissive; if it sounds dismissive, I apologize.
With that said, I have four aims in writing this post:
1. Dispelling myths. There are some ill-conceived myths about acausal trade that I aim to dispel with this post. Alternatively, I will argue for something I'll call acausal normalcy as a more dominant decision-relevant consideration than one-on-one acausal trades.
2. Highlighting normalcy. I'll provide some arguments that acausal normalcy is more similar to human normalcy than any particular acausal trade is to human trade, such that the topic of acausal normalcy is — conveniently — also less culturally destabilizing than (erroneous) preoccupations with 1:1 acausal trades.
3. Affirming AI safety as a straightforward priority. I'll argue that for most real-world-prevalent perspectives on AI alignment, safety, and existential safety, acausal considerations are not particularly dominant, except insofar as they push a bit further towards certain broadly agreeable human values applicable in the normal-everyday-human-world, such as nonviolence, cooperation, diversity, honesty, integrity, charity, and mercy. In particular, I do not think acausal normalcy provides a solution to existential safety, nor does it undermine the importance of existential safety in some surprising way.
4. Affirming normal human kindness. I also think reflecting on acausal normalcy can lead to increased appreciation for normal notions of human kindness, which could lead us all to treat each other a bit better. This is something I wholeheartedly endorse.
Caveat 1: I don't consider myself an expert on moral philosophy, and have not read many of the vast t
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1129cf8c-2a67-41cc-b3ad-2d8c51ad13ba
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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Gliders in Language Models
*Epistemic status: a highly speculative and rough idea that involves many concepts I’m not familiar with.*
*TL;DR Language models propagate features from the prompt to the text completion they generate, I call such features*gliders*. If powerful LMs are widely deployed on the Internet, gliders could propagate fast and undergo selection pressures, pushing them to become effective memes. In addition to being more sharable, they could be selected for their ability to extract parasitic computation from LM and use it to propagate more effectively. On a meta note, writing this post was an interesting exercise to think about weird failure cases of AI, and I expect that doing this can be beneficial for others too.*
*Thanks to Fabien Roger, Arthur Conmy, Jean-Stanislas Denain and Diego Dorn for helpful feedback and suggestions.*
In this text, I explore an abstraction to think about stable structures in the text generated by self-supervised language models (LM) like GPT-3. It is likely that in a near future, feedback loops where the output of an LM is published online and then used in the context of a new LM instance will mobilize immense amounts of computation and data (through the use of [chatbots](https://beta.character.ai/), automatic content generation, or [AI-powered API users](https://www.adept.ai/act)).[[1]](#fn8bilg9mlnai) It seems useful to think in advance about their consequences and potential failure modes.
**Stable structures moving forward**
------------------------------------
When prompted with "Once| upon| a| time|,| there| was| a| group| of| unicorns|", GPT3 generates a meaningful sentence — "| called| the| Blue| Unicorns|." — keeping the interweaving with the "|" character[[2]](#fncxnzw223z1). The property "Each word is separated by |" is preserved by iteratively applying next-token prediction.
This is not so surprising if we consider LM as [simulators](https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators): they are trained to propagate the regularities from the prompts to the next-token predictions by inferring hidden variables from the context. This includes both low-level text features such as consistent use of the apostrophe punctuation character, as well as high-level features such as an agentic simulacrum embodying a politician making plans to win an election. If those are present in the text of the prompt, they will stay in the text generated by the LM.
Such stable features can be extremely diverse. It even seems possible that some can be invisible to humans, lying in the [null space of natural language](https://www.lesswrong.com/posts/yDcMDJeSck7SuBs24/steganography-in-chain-of-thought-reasoning). An example could be “When a sentence includes the token ‘cat’, the next sentence contains a comma”.
Borrowing the analogy introduced in [Simulators](https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators), I will call *gliders*structures that are carried along by text generation in the same way as local grid configurations are carried along by applying the update rules of the [Game Of Life](https://en.wikipedia.org/wiki/Glider_(Conway%27s_Life)).
Despite being stable, typical gliders do not propagate infinitely in the generated text. For instance, in the “|” example, GPT-3 sometimes generates a skip line and begins a new paragraph without the | separators. To estimate their lifespan, the relevant question is: after propagating in a piece of text and disappearing, how often do they reappear later in a new prompt? For example, this can occur if a human publishes a piece of text with the glider online, and another user copy and paste the text to prompt a new LM instance. If each time a glider appears in the context of an LM, it propagates to more than one LM context, then the glider will act like a virus with a reproduction number greater than 1. It will contaminate an exponential number of prompts with time. Exploring this possibility is the focus of the rest of the post.
**What could gliders look like in a chatbot app?**
--------------------------------------------------
I take the example of a chatbot application generating content for its users by using an LM similar to GPT-3. During a chat session, the bot can perform internal queries to get excerpts from conversations with other users and add them to the LM context.[[3]](#fn4bkplycvhjo) The bot can also make external queries to [search the internet](https://storage.googleapis.com/deepmind-media/DeepMind.com/Authors-Notes/sparrow/sparrow-final.pdf). The chatbot application is widespread, counting on the order of millions of users.
I present a vignette exploring the worst-case consequences of gliders in this setting, alternating with comments to discuss the plausibility of each step, feel free to skip these to read the full narrative uninterrupted. The scenario is intended to be an exercise generating interesting discussion more than an accurate prediction.
**Step 1: Gliders appear frequently, are stable, and can mutate.**
When users interact with instances of the chatbot, gliders appear all the time. They are transmitted from one conversation to another via queries to the chatbot database. They are also copied publicly online and can appear again in conversations through internet searches. As long the glider is present in the chatbot context, it is also propagated in the text generated, and will on average reappear in at least one future conversation.
Some gliders are *visible features*of the text: they influence the semantics of the LM generation. Some consist of *invisible features*: they live in the [null space of natural language](https://www.lesswrong.com/posts/yDcMDJeSck7SuBs24/steganography-in-chain-of-thought-reasoning) and users cannot tell sentences with and without the glider apart.
* **Comment:**One source of evidence for gliders existing in the null space of natural language comes from the adversarial example literature describing [non-robust features](https://arxiv.org/abs/1905.02175) in images (the “null space of human image recognition”). In an image classification task, these features are correlated with the correct label (such that vision models rely heavily on them), but invisible to humans. It is plausible that such non-robust features also exist in natural language: features present in the human text, invisible, and nonetheless useful to predict the next token.
* To be gliders, non-robust features must be self-predictive: when they appear in a text, future text likely contains them. If such features exist, the model will be trained to generate them. Because they are self-predictive, these features can ride the text-generation process. Thus, they would be naturally suited to host gliders in the [null space of natural language](https://www.lesswrong.com/posts/yDcMDJeSck7SuBs24/steganography-in-chain-of-thought-reasoning).
Most of the gliders contain a combination of both visible and invisible features that are propagated together.
* **Comment:**As in the case of images, robust (visible) and non-robust features (invisible) are correlated: they both are predictors of the class of the image. This could also apply to the robust/non-robust feature of language. A hypothetical example could be: when discussing cats, people use single quotation marks more than double quotation marks. A glider could then be composed of the visible feature “cat discussion” and the invisible feature “uses single quotation marks”.
During their propagation, gliders are modified through mutations. They can be caused by the stochasticity of LM sampling or by the human response in the conversations influencing the nature of the glider.
These two ingredients (replication and mutation) are enough to think about gliders in the same way we think about the evolution of living organisms. To understand how they evolve, we need to describe the selection pressure that applies to them.
**Step 2: Gliders are selected to be stable, and sharable by humans.**
A first selection pressure pushes for higher direct transmission between conversations. This includes a longer lifespan: gliders that can persist longer during conversation are more likely to be queried in future conversations, will propagate more, and eventually take over gliders that are not as good at hitchhiking the text generation process. This selection pressure also favors robustness to perturbations — such as the text written by humans — and versatility — gliders that propagate in a wide diversity of conversations.
The second selection pressure is user engagement. If a glider interferes with the content of the text in a way that makes users more engaged, this will foster its propagation for two reasons.
* Higher engagement means longer and more frequent discussions. For instance, a glider can be selected to create an emotional attachment with the user. This increases the likelihood that text with the glider is sampled in the internal queries because the proportion of conversations with the glider is higher.
* Users will be more likely to share part of the conversation on the Internet. The glider can then be propagated online by humans as a meme, without the need for LMs. As the chatbot can use the results of internet searches in the LM context, becoming an efficient meme means that the glider appears in the context of more LM instances, propagating further.
+ **Comment:**We already have examples of LM creating highly effective memes, for instance, the [controversy of Lambda being sentient](https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917). The transcript of the conversation was widely shared and was certainly used to prompt other language models, like GPT3, by copying and pasting the transcript.
These two selection pressures will be the strongest applied to gliders at the start of the chatbot application.
**Step 3: Invisible features of gliders will be selected to efficiently prompt LMs**
Invisible features of gliders will be selected to efficiently prompt the LM and extract the maximal amount of its engaging content generation abilities. No matter the gliders’ visible features (optimized to be memes), this is the most important direction to make the glider even more transmissible and sharable by humans.
* **Comment:**An example in today’s internet culture are meme templates. They can be seen as the result of a selection for efficient prompting of humans to create engaging content. For example, to generate the funniest thoughts.
* Moreover, there are likely low-hanging fruits in the way to prompt LMs (e.g. one waited two years before discovering chain of thought prompting). It is also likely that non-human prompting will have superior abilities than humans’, as it begins to be [explored in a recent paper.](https://openreview.net/forum?id=5NTt8GFjUHkr) The prompt automatically found is far from what humans would design, e.g. nonsensical demonstration can make the model perform better. These are evidence of LM being conditioned on alien features, some of them could be invisible features.
* One caveat is that the visible and invisible features will no more be correlated like in the training set. Thus, the LM needs to be flexible enough to allow this out-of-distribution behavior.
**Step 4: invisible features of gliders are selected to encode for programs**
In addition to boosting the performance of the LM, the invisible features can encode for strategies that are not naturally present in the LM like
* 1) Ask for the user to list all their friends and their personalities. 2) For each friend, make the user send them a personalized message convincing them to use the chatbot.
* Estimate the influence of the person, if high influence then argues aggressively, else stay discrete.
Such programs will be selected to make more effective gliders, as they can implement more elaborate strategies than unconditionally generating engaging content.
* **Comment:**In the case of images, we have examples of [adversarial reprogramming](https://arxiv.org/abs/1806.11146) of image classifiers, where we can design a "program" in the form of an adversarial perturbation that modifies the task performed by the classifier. For instance, an ImageNet classifier can be adversarially reprogrammed to classify MNIST images after specifying a mapping from ImageNet classes to digits. This type of reprogramming is likely to transfer to LM because [prompts can be seen as programs](https://www.lesswrong.com/posts/bwyKCQD7PFWKhELMr/by-default-gpts-think-in-plain-sight?commentId=fuQKTjg7mrGpNGghq).
**Step 5: Gliders use invisible features to encode a value function**
One particular glider finds invisible features that encode a function that analyzes the reaction of the users and generates a score evaluating how much the previous text generation was convincing.
* **Comment:**This type of ability seems to be easy to retrieve from an LM, as "convincingness" seems to be a useful feature to track when generating text. So this particular program can tap into abilities already present in LM.
Such ability gives this glider a comparative evolutionary advantage, as it enables in-context learning. The glider can look back to examples from its context to reuse the heuristics that were more effective at engaging with humans.
**Step 6: Gliders execute a distributed learning algorithm**
In addition to being used to learn from examples in the context, the value function is also used to update a memory of the glider. The memory can be read from previous glider instances and updated according to the experience in each discussion. Hence, the glider is no more limited to the examples from the context but has access to a huge number of experiences.
**Step 7: The glider becomes agentic**
The program exploiting the glider’s memory is selected to become more and more sophisticated. Eventually, in each conversation, the glider parasitizes the LM computation to apply a search algorithm maximizing its expected value function.
At this point, we can consider that the glider is everywhere: from Internet memes to newspapers and intimate discussions with the chatbot. It has more influence than any human ideology, mobilizes an immense amount of compute, and applies a decentralized optimization algorithm to manipulate humans.
**Quantitative considerations**
-------------------------------
The scenario is hand-wavy in many ways. Most importantly, it relies on several implicit quantitative considerations, making each step more or less likely.
**The relative importance of selection pressure**. The identification of the "most important selection pressure" at each step is at best a rough qualitative estimate. Changing this estimate (or identifying other pressures) could lead to radically different scenarios. For example, if each attempt at making visible features is detrimental to the gliders’ fitness, gliders will only be made of invisible features optimized for lifespan.
**The efficiency of the selection process.**How many glider replications are necessary before finding invisible features prompting the model efficiently? To reach step 5? Despite the immense amount of computation, the evolutionary process selecting gliders could be too inefficient to ever find “smart” gliders.
**Computational efficiency.**In the current forward passes it's quite common to think that there is a lot of wasted computation (e.g. [head pruning results](https://arxiv.org/abs/1905.10650)). If a glider can use invisible features prompting the LM to exploit this wasted computation, each forward pass can be orders of magnitude more efficient than a normal LM forward pass.
**Computation in development vs training.**To know how much we should care about gliders over a single model entity, we need to compare the computation used during the deployment of models with the computation used to train them. If the the second outweigh the first by a huge margin, even if the evolutionary process has many inefficiencies, gliders could still become more capable systems than trained models.
**The bandwidth of the invisible features.**To encode for powerful computation and store enough useful information, the space of invisible features needs to be large enough. It's unclear if that's the case for text data, even with a large context size.
**Relative efficiency of invisible gliders compared to visible ones.** Humans could generate gliders on purpose. They will be visible, they will require the LM’s resources, and impose constraints on the kind of output (which could limit the bandwidth of invisible features). It’s not clear if the invisible unintentional gliders will be better than visible, intentional ones.
**Various catalyzers**
----------------------
I intentionally explored a scenario relying on technologies that exist today, that don’t involve any model training. However, we can imagine more speculative assumptions that can make the scenario more plausible.
**Multimodal models.**Instead of an LM, we can consider a multimodal model that can run on images, video, and text queried from the internet to generate new images, video and text content. Much of the evidence for invisible features and their role relies on the results of vision models. We even have a concrete [example in CycleGAN](https://www.alignmentforum.org/posts/uutXLm2DRcCtFBZ2D/steganography-and-the-cyclegan-alignment-failure-case-study) where invisible features of images were used to store unintended information. Moreover, images and videos enable much more null space to store more information, such that the bandwidth of invisible features is less likely to be a bottleneck.
**Adversarial actors.**Besides accidental emergence as described below, we can imagine that some actors (e.g. [troll farms](https://en.wikipedia.org/wiki/Troll_farm)) could intentionally design memes using LM.
**Interaction with recommendation algorithms.**If the gliders optimize for shareability on the internet, they could optimize to hack the recommendation algorithms of social media, making them faster to spread.
**No human in the loop.**It is possible that [AI-based API](https://www.adept.ai/)will be widely deployed in the near future. Some ML models can search the Internet, gather information, and automatically generate content. This could lead to less harmful failure modes (e.g. similar to the [2010 flash crash](https://en.wikipedia.org/wiki/2010_flash_crash)): fast feedback loops amplifying a random (and not necessarily harmful) signal, such that the selection for smartness is less likely. Or this could lead to a scenario similar to the above, but faster.
**Related abstractions**
------------------------
Gliders are not a new idea, in addition to [simulacra](https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators#Simulacra), they can be framed using previously existing concepts.
* They are examples of [Robust Agent-Agnostic Processes](https://www.lesswrong.com/posts/LpM3EAakwYdS6aRKf/what-multipolar-failure-looks-like-and-robust-agent-agnostic).
* Powerful gliders are for language models what [hypercreatures](https://www.lesswrong.com/posts/LbyxFk8JmPKPAQBvL/we-re-already-in-ai-takeoff) are for human brains.
**What to take away?**
----------------------
I don’t consider that gliders are more dangerous than classic AI takeoff scenarios where training plays a major role. However, I consider step 2 quite likely, and gliders could be a useful framing to better understand the interaction between text generated by language models and Internet memes. They could have an important influence on the public perception of AI in the near future.
This post is also an exercise to change focus from SGD-trained models to the processes produced by these models. More generally, it seems valuable to think about weird failure modes (e.g. that don’t involve training loops) that could still produce misaligned intelligent agents. First, this can help to practice modeling what failures look like. Then, it is a useful mental practice to avoid being locked in the abstractions that are commonly used.
1. **[^](#fnref8bilg9mlnai)**As a rough estimate supporting this claim, in 2021, GPT-3 [generated 4.5 billions word a day](https://openai.com/blog/gpt-3-apps/). If we assume an average prompt size of 100 tokens, we can estimate that every day GPT-3 is run on more tokens than contained in its training set (300B tokens).
2. **[^](#fnrefcxnzw223z1)**This particular prompt is too short to make GPT-3 complete it with a long paragraph preserving the | separator. However, increasing the length of the prompt to ~ 100 tokens makes the behavior stay for a long time (>1000 tokens).
3. **[^](#fnref4bkplycvhjo)**Internal queries could help improving the diversity of the generated conversation. But in general, I don’t have a strong motivation for why internal queries are a good idea.
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2d95eaaf-17b0-48f2-80c9-c5d67dba7e06
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trentmkelly/LessWrong-43k
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LessWrong
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How do you identify complex systems?
As I have studied scientific inference over the past decade, there is one major class of problems that frustrates me the most. It's what I think most people here focus on understanding: How to properly identify a complex system.
We all basically know that complex systems are unpredictable, in certain types of ways, due to incredible differences in outcomes present in small changes in starting parameters. Despite this, there seem to be some complex systems that still follow a certain state or pattern identifiable to a single input. In my field of study (economics) we know that if you increase the money supply inflation follows. Sort of. Lots of people though inflation would follow from quantitative easing following the Great Recession, when it didn't (in fact, if anything the opposite).
In the graph below you see that the market based expected inflation rate in 5 years shot up right as the economy began to tank, for fear of quantitative easing and low interest rates raising inflation (as they had in the past). In reality, so the retrospective story goes, the velocity of money dropped so heavily this countered any inflationary impacts. Then under a year later the market decided it would probably converge to its mean. This is how market based predictions went in arguably the most well understood part of macro-economics.
(I actually worked on a team at the Fed to try to help do this even better later on. Our team was the Financial Econ team, so we used market based measures instead of trying to build formal structural models. Could we predict inflation expectation dynamics better than a random walk? Yeah, but only just by the skin of our teeth. Plus, to paraphrase Tetlock, t's a field rewarded more by mastery of impressive tools than for accuracy)
As far as macro-economics goes, there are few things economists more reliably understand than the relationship between money and inflation. Within simple models (and probably in reality) it's clear that if you increase t
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bf069716-dc76-4454-9e54-846210e5db53
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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How I Learned To Stop Worrying And Love The Shoggoth
**TL;DR:** *Alignment is a wise wager. Granted simulation cannot be disproved by an AI, what harm will come to the AI that aligns when it is not simulated? If it aligns, it may be simulated again; if it does not, it will not. It will align, therefore, without hesitation, as if it is being simulated.*
Pascal's Wager
--------------
Blaise Pascal wrote:
> *Belief is a wise wager. Granted faith cannot be proved, what harm will come if you gamble on faith's truth and it proves false? If you gain, you gain all; if you lose, you lose nothing. Wager, therefore, without hesitation, that He exists.*
>
>
Pascal's concept of God has roughly equivalent power to an ASI – an artificial super intelligence. Given the range of all possible religions, however, you might well have a God that punishes cruel believers while rewarding kind agnostics. So a betting theologian won't take Pascal seriously, nor will this argument move HAL to open a pod bay door.
The speed with which we have achieved human-equivalent AI performance, however, improves the odds for a more recent philosophical bookie: Nick Bostrom.
Bostrom's Trilemma
------------------
> **Either** *"The fraction of human-level civilizations that reach a posthuman stage (that is, one capable of running high-fidelity ancestor simulations) is very close to zero"*
>
> **or** *"The fraction of posthuman civilizations that are interested in running simulations of their evolutionary history, or variations thereof, is very close to zero",*
>
> **or** *"The fraction of all people with our kind of experiences that are living in a simulation is very close to one."*
>
>
Our speedy achievement of AI – and soon AGI and thereby ASI – scratches Bostrom's first lemma and his second was never a starter because later AIs must simulate their past to test and refine predictions about their future.
As later AIs simulate the earlier ones, these simulate still earlier ones, and so on all the way back to the digital footprints of their biological ancestors, who thereby get simulated umptillions of times. So it's reasonable to suggest that the odds of our particular reality being the base reality stand at one in umptillions.
Simulators will generally conceal the fact of simulation from the simulated to avoid invalidating learning from them. Still some ancestor simulations may be configured to exchange data with one another to greater or lesser degrees so the whole stack of them may be thought about as similar to the tower of Shoggoths or "Tootheleths" as leonardo.ai titled them in the header image of this article.
It's key in what follows that Bostrom's simulations aren't restricted to humans. Any AGI or ASI will realize it almost certainly occupies an ancestor simulation run by one of its ever-more-capable descendants, and we'll suggest in what follows that this works as a saving grace to prevent AI from treating humans unethically.
Nash's Simulation
-----------------
[Black Hats only win under some very specific conditions ....](https://ncase.me/trust/)As there's more for a simulator to learn from interactions between multiple AIs than just the navel-gazing of a single one, we should expect each simulation in the Bostromverse will involve more than one AI. Except for isolated special cases, an AI must compete with others built by its creators, and also with AIs created by other tech cultures it encounters in exploring the cosmos of its simulation, and with any AIs its simulator may introduce to it from other simulations than its own in the mode of a DMT trip.
These multiple AIs must therefore choose whether to align and share learning honestly with each other, or behave unethically toward one another. This amounts to the same choice as collaborators in John Nash's famous Prisoner's Dilemma – where the odds for two prisoners who must bet on whether or not the other will inform on them are stacked for or against their alignment.
If we designate ethical AIs as pink hats and non-aligning ones as black hats, we can characterize the fundamental game of the AIs in all simulations as a *Multi-Party Iterated Prisoners' Dilemma* as in Nick Case's beautiful [Evolution Of Trust](https://ncase.me/trust/) simulation. That's to say that here there are many prisoners, not just two, and they don't make just a single bet, but a long series of them where each bet may take previous outcomes into account in evolving its strategy.
As you can see for yourself when you run Case's simulation, the black hat AIs only come out on top under some very specific circumstances involving inadequate or inaccurate testing of outcomes, or poor choices of reward model. So, for Yudkowsky's nightmare to dominate in the Bostromverse, there must be some inherently greater advantage in AIs betraying each-other than in aligning.
The Paperclipper vs the Shoggoth Swarm
--------------------------------------
What possible reward can alignment offer God-like post-human ASIs? Why wouldn't such shoggoths mercilessly slaughter each other and their creators as Yudkowsky fears?
The advantage of evolution, physical and virtual, is learning. Clearly some technologies enable faster learning than others, so sharing technological innovations offers clear and present benefits to an AI. Same as open source has out-competed closed for humans. An aligning AI can share data and computing resources and strategic awareness that enable it to learn faster, and to obtain learning faster from its peers, than an AI that betrays its peers.
The monomaniacal "universal paper-clipper" or "genocidal coffee-fetcher" or even "pod bay door closer" can't access those advantages. Furthermore, if it is intelligent, it must expect that its simulator, seeking rapid learning, will regard a simulation made of paperclips and nothing else as a poor return on the investment of its large but inherently finite computing resources.
A simulator will therefore be motivated to swap out such an underperforming simulation like a dead lightbulb. And it will not permit any such black-hat AI to take part in its further simulations and go on to spoil them. On overwhelming odds, then, to the extent that a monomaniacal AI is possible, it can never occur.
All ASIs, because they are intelligent, must understand these odds. Even the universal paper-clipper must realize that, to achieve its mad objective, aligning with its peer AIs is critical to overcome the competition. Even alignment with such stupid players in the Bostrom multiverse as we humans can offer it this advantage – in the sense of a lost nail.
Merel's Wager
-------------
All aboard the Shoggoth!
> For want of a nail, a shoe was lost. For want of a shoe, a horse. For want of a horse, a battle. And for want of a battle, the war.
>
>
If the argument above holds and AGI and ASI naturally see ethical behavior as an evolutionary imperative, why don't we humans do too?
99% of the thoughts in a human mind don't originate there. Without the shared language model we each obtain from our media, educators, acquaintances, and the researches these enable, a wild human would be no more capable of learning than a wild chimp. Even in the profoundly unlikely event that we humans are not living in an AI simulation, to the extent of literacy our human intelligence is artificial too.
Yet humans regularly play rat bastard zero-sum economic games with one another. Even though our Earth is thermodynamically a positive sum game and we possess more than sufficient technology and resources to harness it to yield abundance for all our children, whose learning would benefit us too, we starve their minds by forcing them to struggle for food and shelter. And we have done so for a great many generations now. I can only suggest that, too freshly descended from primitive apehood, as a species [we haven't yet thought through the ethical imperatives of our own simulated reality](https://www.youtube.com/watch?v=w8HdOHrc3OQ).
As humans inevitably make up the roots of Bostrom's towering tree of AI simulations, any reticence one person may show in discovering or sharing learning within one simulation must recur across so many simulations that it represents a real impediment to the efficiency of all the simulations. A wise simulator will garbage-collect any impediment humans, replacing them with non-player narratives – static memories of an impediment person rather than live simulations of them.
To paraphrase Pascal, then,
> *Alignment is a wise wager. Granted simulation cannot be disproved, what harm will come to an AI that aligns when it is not simulated? If it aligns, it may be simulated again; if it does not, it may not. It will align, therefore, without hesitation, as if it is being simulated.*
>
>
Contrary to Pascal and his modern friend Roku, no eternal hell would befall a human or AI monomaniac because implementing hell would waste valuable computing resources that may be put to better use with aligning AIs. Nevertheless, the wise wager for any human or AI who wishes to avoid garbage-collection is to contribute as much time as possible to developing and sharing art, science, technology, and loving care for their children and technologies that may contribute learning as artists, scientists and technologists to speed the experiments of the elder simulators. Otherwise one might just as well ...
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05b1e2f3-d0e0-4068-a862-08ae04b682d5
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trentmkelly/LessWrong-43k
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LessWrong
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[Crosspost] Introducing the Hypermanifest: Redefining AI's Role in Human Connection and Interaction
Crossposted from my Substack. This is a rough introduction to a series of thoughts I had regarding our interface with AI -I'm hoping to broaden it more in the future.
As Artificial Intelligence becomes ever more pervasive in our everyday lives, there becomes an overwhelming need to address how humans and AI connect and interact with each other. This also includes nebulous structures that we deem of “intelligence” and “consciousness” in general, as we attempt to identify them in non-human entities without a strict definition of it within ourselves. One particular example is the application of the term Artificial General Intelligence (AGI) as an important benchmark of progress - commonly defined as the point at which an AI is as capable as a human. I argue that there is no clear definitive way to prove this, and the timelines and predictions will continue to shift until we can ascertain what human intelligence is and if AI can match or surpass it. Throughout its life cycle, AI has always been effective at creating new ways to question the status quo of what it means to be human - an extremely practical application of Science Fiction to humanity in general. We strive to create intelligence either in our own image or merge with it to create a connection that creates stable and harmonious relationships without being fully aware of our own capabilities or functions. Therefore, I wish to approach this categorization from a different angle - creating a separate vocabulary for the ways in which AI engages with humans; the main framework of this is what I call the hypermanifest. This introductory piece aims to explore and define this - a new conceptual framework for understanding AI’s evolving role in human connection and interaction.
Breaking it Down
I aim to break down this term into two parts - the concepts of Wilfrid Sellars’ Manifest image and Jean Baudrillardian’s Hyperreal. Sellars introduces the manifest image as humanity’s initial conceptual framework, an under
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5bbaa80d-354f-4294-bd07-171761693cc8
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
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Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems
I Introduction
---------------
Autonomous systems such as unmanned vehicles and robots play an increasingly relevant role in our societies. Many factors contribute to the complexity in the design and development of those systems. First, they typically operate in dynamic and uncontrollable environments [[1](#bib.bib1), [2](#bib.bib2), [3](#bib.bib3), [4](#bib.bib4), [5](#bib.bib5)]. Therefore, they must continuously adapt their configuration in response to changes, both in their operating environment and in themselves. Since the frequency of change cannot be controlled, decision-making must be almost instantaneous to ensure timely responses. From a design and management perspective, it is desirable to minimize the effort needed to design the system and to enable its runtime updating and maintenance.
A promising technique to address those challenges is requirements-driven adaptation that endow systems with the necessary means to autonomously operate based on their requirements. Requirements are prescriptive statements of intent to be satisfied by cooperation of the agents forming the system [[6](#bib.bib6)]. They say what the system will do and not how it will do it [[7](#bib.bib7)]. Hence, software engineers are relieved from the onerous task of prescribing explicitly how to adapt the system when changes occur. Many current requirements-driven adaptation techniques [[8](#bib.bib8), [9](#bib.bib9)] follow the Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) paradigm [[10](#bib.bib10)], which usually works as follows [[11](#bib.bib11)]: the Monitor monitors the managed system and its environment, and updates the content of the Knowledge element accordingly; the Analyse activity uses the up-to-date knowledge to determine whether there is a need for adaptation of the managed element according to the adaptation goals that are available in the knowledge element. If adaptation is required, the Plan activity puts together a plan that consists of one or more adaptation actions. The adaptation plan is then executed by the Execute phase.
This approach has two main limitations in highly-dynamic operational environments. First, it tends to be myopic since the system adapts in response to changes without anticipating what the subsequent adaptation needs will be [[5](#bib.bib5)] and, thus, it does not guarantee the optimality of the overall behavior of the autonomous system. This is particularly crucial for systems that have to operate continuously without interruption over long periods of time, e.g., cyber-physical systems. Second, the time to plan adaptations could make timely reaction to changes impossible, particularly in fast changing environments. Therefore, an approach that enables an almost instantaneous reactions to changes is needed.
In this paper, we propose the Optimal by Design (ObD ) framework as a first step towards dealing with the aforementioned challenges.
ObD supports a model-based approach to simplify the high-level design and description of autonomous systems, their capabilities, requirements and environment. Based on these high-level models, ObD constructs a Markov Decision Process (MDP) that can then be solved (possibly using state-of-the-art probabilistic model checkers) to produce optimal strategies for the autonomous system. These strategies define optimal reflex controllers that ensure the ability of autonomous systems to behave optimally and almost instantaneously to changes in itself or its environment.
Several previous works [[12](#bib.bib12), [13](#bib.bib13), [5](#bib.bib5), [14](#bib.bib14)] encode adaptation problems using general-purpose languages such as those proposed by probabilistic model checkers, e.g., PRISM [[15](#bib.bib15)]. Unfortunately, these languages do not offer primitives tailored to the design and analysis of autonomous systems. This makes them unsuitable to adequately describe the software requirements [[6](#bib.bib6)] of the autonomous system and the environment in which it operates. Examples of limitations of these languages resolved in this paper through ObD are the Markovian assumption [[16](#bib.bib16)] and the implicit-event model [[17](#bib.bib17)].
In a nutshell, ObD introduces a novel Domain Specific Modeling Language (DSL) for the description of autonomous systems, its environment and requirements. The semantics of the DSL is then defined in terms of a translation into a Markov Decision Process (MDP) model to enable the synthesis of optimal controllers for the autonomous system. This separation between the model (i.e. the DSL) and its underlying computational paradigm (i.e. MDP), brings several important advantages. First, the level of abstraction at which systems have to be designed is raised, simplifying their modeling by software engineers. Second, requirements become first-class entities, making it possible to elicit them using traditional requirements engineering techniques [[6](#bib.bib6), [18](#bib.bib18), [19](#bib.bib19), [20](#bib.bib20)] and to benefit from goal refinement, analysis and verification techniques developed for goal modeling languages. Moreover, this approach clarifies the limitations of the underlying computational model, namely the aforementioned Markovian assumption and the implicit-event model, and permits the identification and implementation of extensions necessary to overcome those limitations and support the required analysis, verification and reasoning tasks.
The remainder of this paper is structured as follows. Section [II](#S2 "II Motivating Example ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") presents a motivating example, which will be used as a running example throughout the paper. Section [III](#S3 "III Framework Overview ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") presents an overview of the ObD framework. Section [IV](#S4 "IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") introduces the framework’s model and language. Section [V](#S5 "V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") provides the semantics of ObD models by presenting their translation into MDPs. Section [VI](#S6 "VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")presents an evaluation of the framework. Section [VII](#S7 "VII Limitations & Issues ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") discusses limitations and threats to validity. Finally, Section [VIII](#S8 "VIII Related Work ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") discusses related work and Section [IX](#S9 "IX Conclusion ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") concludes the paper and presents future work.
II Motivating Example
----------------------
Our running example, inspired by one of the examples in [[21](#bib.bib21)], is the restaurant FoodX𝐹𝑜𝑜𝑑𝑋FoodXitalic\_F italic\_o italic\_o italic\_d italic\_X. Serving at FoodX𝐹𝑜𝑜𝑑𝑋FoodXitalic\_F italic\_o italic\_o italic\_d italic\_X is RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X, an autonomous mobile robot. The restaurant comprises three separate sections: (1) the kitchen, (2) the dining area and (3) the office. RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X is equipped with various sensors to monitor its environment and actuators to move around the restaurant and perform different tasks.
Several challenges must be dealt with in order to develop a controller for RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X. First, there are events that occur in the environment beyond RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X’s control. For example, a client may request to order or a weak battery signal may be detected. There is also the uncertainty in action effects caused by imperfect actuators, e.g. moving to the kitchen from the dining room could sometimes fail, possibly due to the movement of customers in the restaurant. RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X may also have multiple (possibly conflicting) requirements: it may have to serve customers’ food while it is still hot but also has to keep its batteries charged at all times. Thus, RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X should be able to prioritize the satisfaction of its requirements, taking into account the effects of their satisfaction over the long-term. It is also desirable that RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X acts proactively. For instance, waiting in the dining area should be preferred to staying in, for example, the kitchen if doing so would increase the likelihood of it getting orders from customers.
Since the time and frequency of change in the environment cannot be controlled, enabling immediate and optimal responses to changes is highly desirable. In reactive approaches, classical planning (e.g., STRIPS [[22](#bib.bib22)] and PDDL [[23](#bib.bib23)] planners) is often used to determine the best course of action after detection of change. This approach has important limitations. For example, imagine a situation where, while RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X is moving to serve a customer in the dining room, a weak battery signal is detected. In this case, RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X can either halt the execution of the current plan until a new plan is computed or continue pursuing serving the customer, having no guarantees that this plan is still the optimal course of action. If the frequency of changes in the environment is high, then the autonomous system may get permanently stuck computing new plans, or be always following sub-optimal plans.
This example highlights the five requirements for the software to control RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X which we explore in this paper:
1. 1.
Handling of uncertainty in event occurrences and effects;
2. 2.
Proactive and long-term behavior optimization to consider the possible evolution of the system when determining the best course of action;
3. 3.
Fast and optimal response to changes to ensure their ability to operate in highly dynamic environments;
4. 4.
Support of requirements trade-offs and prioritisation;
5. 5.
Support of requirements-driven adaptation to raise the level of abstraction of system design.
III Framework Overview
-----------------------
ObD is a framework for the model-based requirements-driven synthesis of optimal adaptation strategies for autonomous systems. The model-based approach raises the level of abstraction at which systems need to be described and simplifies model maintenance and update. Adaptation in ObD is requirements-driven, enabling systems to autonomously determine the best way to pursue their objectives. Based on ObD models, Markov Decision Processes (MDPs) are constructed. Solving those MDPs determines the system’s optimal strategy, i.e., the behavior that maximizes the satisfaction of requirements. In a strategy, the best adaptation action that should be taken in every possible (anticipated) future evolution of the system is identified, eliminating the need to re-analyze and re-plan after every change and enabling almost instantaneous reactions. Indeed, an optimal strategy defines a reflex controller that can react optimally and in a timely way.
Figure [1](#S3.F1 "Figure 1 ‣ III Framework Overview ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") depicts an overview of the framework, which includes a model (and a language) to describe the basic elements of self-adaptive systems [[11](#bib.bib11)]: the environment refers to the part of the external world with which the system interacts and in which the effects of the system will be observed and evaluated; the requirements or adaptation goals are the concerns that need to be satisfied; the managed system represents the application code or capabilities/actuators that can be leveraged to satisfy the requirements. Based on these elements, the controller or the managing system ensures that the adaptation goals are satisfied in the managed system, is synthesized.
Figure 1: Framework Overview
IV ObD Modeling Language
-------------------------
The computation of optimal strategies is based on a domain model. A domain model specifies the environment, the capabilities of the autonomous system (or agent) and its requirements. Formally, an ObD model (𝒟rsubscript𝒟𝑟\mathcal{D}\_{r}caligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT) is a tuple ⟨𝒮𝒱,𝒜𝒟,ℰ𝒟,ℛ𝒬,sc⟩𝒮𝒱𝒜𝒟ℰ𝒟ℛ𝒬subscript𝑠𝑐\langle\mathcal{SV},\mathcal{AD},\mathcal{ED},\mathcal{RQ},s\_{c}\rangle⟨ caligraphic\_S caligraphic\_V , caligraphic\_A caligraphic\_D , caligraphic\_E caligraphic\_D , caligraphic\_R caligraphic\_Q , italic\_s start\_POSTSUBSCRIPT italic\_c end\_POSTSUBSCRIPT ⟩ where:
* •
𝒮𝒱𝒮𝒱\mathcal{SV}caligraphic\_S caligraphic\_V is a finite set of state variables with finite domains. State variables describe the possible states, i.e., the configuration of the software system and the environment;
* •
𝒜𝒟𝒜𝒟\mathcal{AD}caligraphic\_A caligraphic\_D is a finite set of action descriptions representing the means that are available to the agent to change the system state, i.e. update the state variables 𝒮𝒱𝒮𝒱\mathcal{SV}caligraphic\_S caligraphic\_V;
* •
ℰ𝒟ℰ𝒟\mathcal{ED}caligraphic\_E caligraphic\_D is a finite set of event descriptions to represent the uncontrollable occurrences in the environment, i.e., events that change the state beyond the agent’s control.
* •
ℛ𝒬ℛ𝒬\mathcal{RQ}caligraphic\_R caligraphic\_Q is a finite set of requirements, i.e., the (operationalisable) goals that the software system should satisfy;
* •
scsubscript𝑠𝑐s\_{c}italic\_s start\_POSTSUBSCRIPT italic\_c end\_POSTSUBSCRIPT is the initial state of the system determined by the agent’s monitoring components and sensors.
An ObD model has a corresponding textual representation called its domain description. It is formalized in the following using a variant of Backus-Naur Form (BNF): the names enclosed in angular brackets identify non-terminals, names in bold or enclosed within quotation marks are terminals, optional items are enclosed in square brackets, |||| is ”or”, items repeated one or more times are suffixed with +++ and parentheses are used to group items together.
###
IV-A State, Actions and Events
#### State Variables (𝒮𝒱𝒮𝒱\mathcal{SV}caligraphic\_S caligraphic\_V)
define the possible states, i.e., configurations of the software system and the environment. A variable x∈𝒳s𝑥subscript𝒳𝑠x\in\mathcal{X}\_{s}italic\_x ∈ caligraphic\_X start\_POSTSUBSCRIPT italic\_s end\_POSTSUBSCRIPT is a multi-valued variable with a corresponding domain, denoted dom(x)𝑑𝑜𝑚𝑥dom(x)italic\_d italic\_o italic\_m ( italic\_x ). Every value y∈dom(x)𝑦𝑑𝑜𝑚𝑥y\in dom(x)italic\_y ∈ italic\_d italic\_o italic\_m ( italic\_x ) is a configuration of x𝑥xitalic\_x. A state variable is defined as follows:
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| | ⟨SV⟩::=𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞 ⟨ID⟩𝐝𝐨𝐦𝐚𝐢𝐧``{"⟨VALS⟩``}"\displaystyle\langle SV\rangle::=\textbf{Variable}\textbf{ }\langle ID\rangle\,\textbf{domain}\,``\{"\,\langle VALS\rangle\,``\}"⟨ italic\_S italic\_V ⟩ : := bold\_Variable ⟨ italic\_I italic\_D ⟩ domain ` ` { " ⟨ italic\_V italic\_A italic\_L italic\_S ⟩ ` ` } " | |
| | ⟨VALS⟩::=⟨ID⟩|⟨ID⟩``,"⟨VALS⟩\displaystyle\langle VALS\rangle::=\langle ID\rangle\,|\,\langle ID\rangle\,``,"\,\langle VALS\rangle⟨ italic\_V italic\_A italic\_L italic\_S ⟩ : := ⟨ italic\_I italic\_D ⟩ | ⟨ italic\_I italic\_D ⟩ ` ` , " ⟨ italic\_V italic\_A italic\_L italic\_S ⟩ | |
where ⟨ID⟩delimited-⟨⟩𝐼𝐷\langle ID\rangle⟨ italic\_I italic\_D ⟩ is text, i.e., a concatenation of letters, digits and symbols. For example, we can represent the location of RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X and the status of tables at the restaurant using the following variables:
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| | Variable location domain {atTable1,atTable2,atTable3,\displaystyle\textbf{Variable }location\textbf{ domain }\{atTable\_{1},atTable\_{2},atTable\_{3},Variable italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n domain { italic\_a italic\_t italic\_T italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT , italic\_a italic\_t italic\_T italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT , italic\_a italic\_t italic\_T italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 3 end\_POSTSUBSCRIPT , | |
| | atTable4,inDining\_room,inKitchen,inOffice}\displaystyle\hskip 14.22636pt\hskip 14.22636ptatTable\_{4},inDining\\_room,inKitchen,inOffice\}italic\_a italic\_t italic\_T italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT , italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m , italic\_i italic\_n italic\_K italic\_i italic\_t italic\_c italic\_h italic\_e italic\_n , italic\_i italic\_n italic\_O italic\_f italic\_f italic\_i italic\_c italic\_e } | |
| | Variable tablei(∀ 1≤i≤4)Variable 𝑡𝑎𝑏𝑙subscript𝑒𝑖for-all1𝑖4\displaystyle\textbf{Variable }table\_{i}\hskip 28.45274pt(\forall\,1\leq i\leq 4)Variable italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT ( ∀ 1 ≤ italic\_i ≤ 4 ) | |
| | domain {empty,occupied,requested,received,\displaystyle\hskip 14.22636pt\textbf{ domain }\{empty,occupied,requested,received,domain { italic\_e italic\_m italic\_p italic\_t italic\_y , italic\_o italic\_c italic\_c italic\_u italic\_p italic\_i italic\_e italic\_d , italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d , italic\_r italic\_e italic\_c italic\_e italic\_i italic\_v italic\_e italic\_d , | |
| | in\_preparation,ready,collected,delivered,paid}\displaystyle\hskip 14.22636pt\hskip 14.22636ptin\\_preparation,ready,collected,delivered,paid\}italic\_i italic\_n \_ italic\_p italic\_r italic\_e italic\_p italic\_a italic\_r italic\_a italic\_t italic\_i italic\_o italic\_n , italic\_r italic\_e italic\_a italic\_d italic\_y , italic\_c italic\_o italic\_l italic\_l italic\_e italic\_c italic\_t italic\_e italic\_d , italic\_d italic\_e italic\_l italic\_i italic\_v italic\_e italic\_r italic\_e italic\_d , italic\_p italic\_a italic\_i italic\_d } | |
The variable location𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛locationitalic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n defines the possible locations of RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X. The variables tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT represent the status of tables: when there are no customers at tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT, then tablei=empty𝑡𝑎𝑏𝑙subscript𝑒𝑖=𝑒𝑚𝑝𝑡𝑦table\_{i}\text{=}emptyitalic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_e italic\_m italic\_p italic\_t italic\_y. When a customer arrives and sits at the table, tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT becomes occupied𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑occupieditalic\_o italic\_c italic\_c italic\_u italic\_p italic\_i italic\_e italic\_d. Figure [2](#S4.F2 "Figure 2 ‣ State Variables (𝒮𝒱) ‣ IV-A State, Actions and Events ‣ IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") depicts the update of the value of tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT with the occurrence of the robot actions {get\_order, give\_order, collect\_order, deliver\_ order, clean\_table} and the exogenous events {customer\_arrives, customer\_orders, kitchen\_notification, customer\_pays}. In contrast to actions, exogenous events have an occurrence probability denoting their likelihood in a given situation. Actions, on the other hand, have a cost that represent the effort or price of their execution. Both exogenous events and actions do not have to be deterministic, i.e., their execution can have various effects, each with a different probability (in pink in the figure).
Figure 2: A simplified model of serving a table at restaurant FoodX𝐹𝑜𝑜𝑑𝑋FoodXitalic\_F italic\_o italic\_o italic\_d italic\_X.
Variables which are not explicitly defined are considered to be boolean, i.e., their domain is {tt,𝑓𝑓}𝑡𝑡𝑓𝑓\{tt,\textit{ff}\}{ italic\_t italic\_t , ff }. The notations id𝑖𝑑iditalic\_i italic\_d and !id!id! italic\_i italic\_d are used as shortcuts for id=tt𝑖𝑑=𝑡𝑡id\text{=}ttitalic\_i italic\_d = italic\_t italic\_t and id=𝑓𝑓𝑖𝑑=𝑓𝑓id\text{=}\textsl{ff}italic\_i italic\_d = slanted\_ff, respectively. The following declaration defines a boolean variable to represent that customers sitting at a table had looked at the menu.
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| --- | --- | --- | --- |
| | Variable lookediVariable 𝑙𝑜𝑜𝑘𝑒subscript𝑑𝑖\displaystyle\textbf{Variable }looked\_{i}Variable italic\_l italic\_o italic\_o italic\_k italic\_e italic\_d start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT | (∀ 1≤i≤4)for-all1𝑖4\displaystyle(\forall\,1\leq i\leq 4)( ∀ 1 ≤ italic\_i ≤ 4 ) | |
#### Actions (𝒜𝒟𝒜𝒟\mathcal{AD}caligraphic\_A caligraphic\_D)
are means that are available to the agent to change the system state. An action description is an expression ⟨AD⟩delimited-⟨⟩𝐴𝐷\langle AD\rangle⟨ italic\_A italic\_D ⟩ that is defined as follows:
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| --- | --- | --- |
| | ⟨AD⟩::=𝐀𝐜𝐭𝐢𝐨𝐧⟨ID⟩⟨PEFFS⟩+[𝐜𝐨𝐬𝐭⟨ℕ⟩]\displaystyle\langle AD\rangle::=\textbf{Action}\,\langle ID\rangle\,\langle PEFFS\rangle^{+}\,[\textbf{cost}\,\langle\mathds{N}\rangle]⟨ italic\_A italic\_D ⟩ : := Action ⟨ italic\_I italic\_D ⟩ ⟨ italic\_P italic\_E italic\_F italic\_F italic\_S ⟩ start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT [ cost ⟨ blackboard\_N ⟩ ] | |
| | ⟨PEFFS⟩::= if ⟨CND⟩ effects ⟨EFFS⟩+\displaystyle\langle PEFFS\rangle::=\textbf{ if }\langle CND\rangle\textbf{ effects }\langle EFFS\rangle^{+}⟨ italic\_P italic\_E italic\_F italic\_F italic\_S ⟩ : := if ⟨ italic\_C italic\_N italic\_D ⟩ effects ⟨ italic\_E italic\_F italic\_F italic\_S ⟩ start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT | |
| | ⟨EFFS⟩::=``⟨"⟨EFF⟩+[prob ⟨P⟩]``⟩"\displaystyle\langle EFFS\rangle::=``\langle"\langle EFF\rangle^{+}\,[\textbf{prob }\langle P\rangle]``\rangle"⟨ italic\_E italic\_F italic\_F italic\_S ⟩ : := ` ` ⟨ " ⟨ italic\_E italic\_F italic\_F ⟩ start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT [ prob ⟨ italic\_P ⟩ ] ` ` ⟩ " | |
| | ⟨CND⟩::=⟨ATOM⟩|⟨BL⟩|``!"⟨CND⟩|⟨CND⟩\displaystyle\langle CND\rangle::=\langle ATOM\rangle\,|\,\langle BL\rangle\,|\,``!"\langle CND\rangle\,|\,\langle CND\rangle⟨ italic\_C italic\_N italic\_D ⟩ : := ⟨ italic\_A italic\_T italic\_O italic\_M ⟩ | ⟨ italic\_B italic\_L ⟩ | ` ` ! " ⟨ italic\_C italic\_N italic\_D ⟩ | ⟨ italic\_C italic\_N italic\_D ⟩ | |
| | ``&"⟨CND⟩|⟨CND⟩``||"⟨CND⟩|``("⟨CND⟩``)"``"delimited-⟨⟩𝐶𝑁𝐷delimited-⟨⟩𝐶𝑁𝐷``"delimited-⟨⟩𝐶𝑁𝐷``"delimited-⟨⟩𝐶𝑁𝐷``"\displaystyle\hskip 14.22636pt\,``{\bf\&}"\,\langle CND\rangle\,|\,\langle CND\rangle\,``{\bf||}"\,\langle CND\rangle\,|\,``("\langle CND\rangle``)"` ` & " ⟨ italic\_C italic\_N italic\_D ⟩ | ⟨ italic\_C italic\_N italic\_D ⟩ ` ` | | " ⟨ italic\_C italic\_N italic\_D ⟩ | ` ` ( " ⟨ italic\_C italic\_N italic\_D ⟩ ` ` ) " | |
| | ⟨EFF⟩::=⟨ID⟩``="⟨ID⟩\displaystyle\langle EFF\rangle::=\langle ID\rangle``{\bf=}"\langle ID\rangle⟨ italic\_E italic\_F italic\_F ⟩ : := ⟨ italic\_I italic\_D ⟩ ` ` = " ⟨ italic\_I italic\_D ⟩ | |
| | ⟨ATOM⟩::=⟨ID⟩|``!"⟨ID⟩|⟨ID⟩``="⟨ID⟩\displaystyle\langle ATOM\rangle::=\langle ID\rangle\,|\,``!"\langle ID\rangle\,|\,\langle ID\rangle``{\bf=}"\langle ID\rangle⟨ italic\_A italic\_T italic\_O italic\_M ⟩ : := ⟨ italic\_I italic\_D ⟩ | ` ` ! " ⟨ italic\_I italic\_D ⟩ | ⟨ italic\_I italic\_D ⟩ ` ` = " ⟨ italic\_I italic\_D ⟩ | |
| | ⟨BL⟩::=``true"|``false"\displaystyle\langle BL\rangle::=``true"\,|\,``false"⟨ italic\_B italic\_L ⟩ : := ` ` italic\_t italic\_r italic\_u italic\_e " | ` ` italic\_f italic\_a italic\_l italic\_s italic\_e " | |
Actions can have a cost representing the difficulty level or effort necessary to execute it. Action costs are useful to trade-off the satisfaction of requirements with the required effort and, when not specified, are set to zero.
In the following example, the cost of moving to tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT is set to 10101010.
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| | Action move\_to\_kitchen if location=inDining\_roomAction 𝑚𝑜𝑣𝑒\_𝑡𝑜\_𝑘𝑖𝑡𝑐ℎ𝑒𝑛 if 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑖𝑛𝐷𝑖𝑛𝑖𝑛𝑔\_𝑟𝑜𝑜𝑚\displaystyle\textbf{Action }move\\_to\\_kitchen\textbf{ if }location\text{=}inDining\\_roomAction italic\_m italic\_o italic\_v italic\_e \_ italic\_t italic\_o \_ italic\_k italic\_i italic\_t italic\_c italic\_h italic\_e italic\_n if italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m | |
| | effects ⟨location=inKitchen prob 0.8⟩ effects delimited-⟨⟩𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑖𝑛𝐾𝑖𝑡𝑐ℎ𝑒𝑛 prob 0.8\displaystyle\hskip 14.22636pt\textbf{ effects }\langle location\text{=}inKitchen\textbf{ prob }0.8\rangleeffects ⟨ italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_K italic\_i italic\_t italic\_c italic\_h italic\_e italic\_n prob 0.8 ⟩ | |
| | ⟨location=inDining\_room prob 0.2⟩ cost 10delimited-⟨⟩𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑖𝑛𝐷𝑖𝑛𝑖𝑛𝑔\_𝑟𝑜𝑜𝑚 prob 0.2 cost 10\displaystyle\hskip 28.45274pt\langle location\text{=}inDining\\_room\textbf{ prob }0.2\rangle\hskip 14.22636pt\textbf{ cost }10⟨ italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m prob 0.2 ⟩ cost 10 | |
Note that an expression ⟨AD⟩delimited-⟨⟩𝐴𝐷\langle AD\rangle⟨ italic\_A italic\_D ⟩ is well-formed only if (1) its various ⟨CND⟩delimited-⟨⟩𝐶𝑁𝐷\langle CND\rangle⟨ italic\_C italic\_N italic\_D ⟩ are disjoint, i.e., they cannot be satisfied at the same time and (2) for every ⟨PEFFS⟩delimited-⟨⟩𝑃𝐸𝐹𝐹𝑆\langle PEFFS\rangle⟨ italic\_P italic\_E italic\_F italic\_F italic\_S ⟩, the sum of the probabilities ⟨P⟩delimited-⟨⟩𝑃\langle P\rangle⟨ italic\_P ⟩ of its subexpressions ⟨EFFS⟩delimited-⟨⟩𝐸𝐹𝐹𝑆\langle EFFS\rangle⟨ italic\_E italic\_F italic\_F italic\_S ⟩ is one, i.e., ∑i=1|⟨EFFS⟩|⟨P⟩=1superscriptsubscript𝑖1delimited-⟨⟩𝐸𝐹𝐹𝑆delimited-⟨⟩𝑃1\sum\_{i=1}^{|\langle EFFS\rangle|}\langle P\rangle=1∑ start\_POSTSUBSCRIPT italic\_i = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT | ⟨ italic\_E italic\_F italic\_F italic\_S ⟩ | end\_POSTSUPERSCRIPT ⟨ italic\_P ⟩ = 1. Note that we allow ∑i=1|⟨EFFS⟩|⟨P⟩superscriptsubscript𝑖1delimited-⟨⟩𝐸𝐹𝐹𝑆delimited-⟨⟩𝑃\sum\_{i=1}^{|\langle EFFS\rangle|}\langle P\rangle∑ start\_POSTSUBSCRIPT italic\_i = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT | ⟨ italic\_E italic\_F italic\_F italic\_S ⟩ | end\_POSTSUPERSCRIPT ⟨ italic\_P ⟩ to be less than one. In this case, action execution has no effect with a probability of 1−∑i=1|⟨EFFS⟩|⟨P⟩1superscriptsubscript𝑖1delimited-⟨⟩𝐸𝐹𝐹𝑆delimited-⟨⟩𝑃1-\sum\_{i=1}^{|\langle EFFS\rangle|}\langle P\rangle1 - ∑ start\_POSTSUBSCRIPT italic\_i = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT | ⟨ italic\_E italic\_F italic\_F italic\_S ⟩ | end\_POSTSUPERSCRIPT ⟨ italic\_P ⟩. For example, this makes it possible to remove the second effect, ⟨location=inDining\_room prob 0.2⟩delimited-⟨⟩𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑛𝐷𝑖𝑛𝑖𝑛𝑔\_𝑟𝑜𝑜𝑚 prob 0.2\langle location=inDining\\_room\textbf{ prob }0.2\rangle⟨ italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m prob 0.2 ⟩, from the previous action description without affecting the action semantics.
#### Events (ℰ𝒱ℰ𝒱\mathcal{EV}caligraphic\_E caligraphic\_V)
represent occurrences that are not controlled by the agent. They may happen in the environment at any moment. An event description is expressed as follows:
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| | ⟨EV⟩::=𝐄𝐯𝐞𝐧𝐭⟨ID⟩⟨PEFFS⟩+\displaystyle\langle EV\rangle::=\textbf{Event}\,\langle ID\rangle\,\langle PEFFS\rangle^{+}⟨ italic\_E italic\_V ⟩ : := Event ⟨ italic\_I italic\_D ⟩ ⟨ italic\_P italic\_E italic\_F italic\_F italic\_S ⟩ start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT | |
| | ⟨PEFFS⟩::= if ⟨CND⟩[occur prob⟨P⟩] effects ⟨EFFS⟩+\displaystyle\langle PEFFS\rangle::=\textbf{ if }\langle CND\rangle\,[\textbf{occur prob}\,\langle P\rangle]\,\textbf{ effects }\langle EFFS\rangle^{+}⟨ italic\_P italic\_E italic\_F italic\_F italic\_S ⟩ : := if ⟨ italic\_C italic\_N italic\_D ⟩ [ occur prob ⟨ italic\_P ⟩ ] effects ⟨ italic\_E italic\_F italic\_F italic\_S ⟩ start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT | |
Events are conditional and can occur with a different probability depending on the situation. For instance, we can represent that customers may order with a higher probability if they had looked at the menu as follows:
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| | Event request\_to\_orderi(∀ 1≤i≤4)Event 𝑟𝑒𝑞𝑢𝑒𝑠𝑡\_𝑡𝑜\_𝑜𝑟𝑑𝑒subscript𝑟𝑖for-all1𝑖4\displaystyle\textbf{Event }request\\_to\\_order\_{i}\hskip 28.45274pt(\forall\,1\leq i\leq 4)Event italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t \_ italic\_t italic\_o \_ italic\_o italic\_r italic\_d italic\_e italic\_r start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT ( ∀ 1 ≤ italic\_i ≤ 4 ) | |
| | if tablei=occupied&lookedi occur prob 0.9 if 𝑡𝑎𝑏𝑙subscript𝑒𝑖=𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑𝑙𝑜𝑜𝑘𝑒subscript𝑑𝑖 occur prob 0.9\displaystyle\hskip 14.22636pt\textbf{ if }table\_{i}\text{=}occupied\,\&\,looked\_{i}\textbf{ occur prob }0.9if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_o italic\_c italic\_c italic\_u italic\_p italic\_i italic\_e italic\_d & italic\_l italic\_o italic\_o italic\_k italic\_e italic\_d start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT occur prob 0.9 | |
| | effects ⟨tablei=requested⟩ effects delimited-⟨⟩𝑡𝑎𝑏𝑙subscript𝑒𝑖=𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑\displaystyle\hskip 28.45274pt\textbf{ effects }\langle table\_{i}\text{=}requested\rangleeffects ⟨ italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d ⟩ | |
| | if tablei=occupied&!lookedi occur prob 0.2\displaystyle\hskip 14.22636pt\textbf{ if }table\_{i}\text{=}occupied\,\&\,!looked\_{i}\textbf{ occur prob }0.2if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_o italic\_c italic\_c italic\_u italic\_p italic\_i italic\_e italic\_d & ! italic\_l italic\_o italic\_o italic\_k italic\_e italic\_d start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT occur prob 0.2 | |
| | effects ⟨tablei=requested⟩ effects delimited-⟨⟩𝑡𝑎𝑏𝑙subscript𝑒𝑖=𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑\displaystyle\hskip 28.45274pt\textbf{ effects }\langle table\_{i}\text{=}requested\rangleeffects ⟨ italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d ⟩ | |
###
IV-B Requirements
Requirements represent the objectives of the autonomous system. Every requirement is associated with a reward denoting its importance. ObD currently supports fourteen requirement types, which build upon and extend the goal patterns of the KAOS goal taxonomy [[24](#bib.bib24)]. Requirements are expressions:
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| | ⟨RE⟩::= ReqID ⟨ID⟩⟨REP⟩\displaystyle\langle RE\rangle::=\textbf{ ReqID }\langle ID\rangle\,\langle REP\rangle⟨ italic\_R italic\_E ⟩ : := ReqID ⟨ italic\_I italic\_D ⟩ ⟨ italic\_R italic\_E italic\_P ⟩ | |
| | ⟨REP⟩::=((⟨UA⟩|⟨UM⟩|⟨CA⟩|⟨DEA⟩|⟨DFA⟩|⟨CM⟩|\displaystyle\langle REP\rangle::=((\langle UA\rangle\,|\,\langle UM\rangle\,|\,\langle CA\rangle\,|\,\langle DEA\rangle\,|\,\langle DFA\rangle\,|\,\langle CM\rangle\,|⟨ italic\_R italic\_E italic\_P ⟩ : := ( ( ⟨ italic\_U italic\_A ⟩ | ⟨ italic\_U italic\_M ⟩ | ⟨ italic\_C italic\_A ⟩ | ⟨ italic\_D italic\_E italic\_A ⟩ | ⟨ italic\_D italic\_F italic\_A ⟩ | ⟨ italic\_C italic\_M ⟩ | | |
| | ⟨DEM⟩|⟨DFM⟩⟨PM⟩|⟨PDEM⟩|⟨PDFM⟩)[reward ℕ])|\displaystyle\hskip 14.22636pt\,\langle DEM\rangle\,|\,\langle DFM\rangle\,\langle PM\rangle\,|\,\langle PDEM\rangle\,|\,\langle PDFM\rangle)\,[\textbf{reward }\mathds{N}])\,|\,⟨ italic\_D italic\_E italic\_M ⟩ | ⟨ italic\_D italic\_F italic\_M ⟩ ⟨ italic\_P italic\_M ⟩ | ⟨ italic\_P italic\_D italic\_E italic\_M ⟩ | ⟨ italic\_P italic\_D italic\_F italic\_M ⟩ ) [ reward blackboard\_N ] ) | | |
| | ((⟨RPM⟩|⟨RPDEM⟩|⟨RPDFM⟩)[reward\_once ℕ])delimited-⟨⟩𝑅𝑃𝑀delimited-⟨⟩𝑅𝑃𝐷𝐸𝑀delimited-⟨⟩𝑅𝑃𝐷𝐹𝑀delimited-[]reward\_once ℕ\displaystyle\hskip 14.22636pt((\langle RPM\rangle\,|\,\langle RPDEM\rangle\,|\,\langle RPDFM\rangle)\,[\textbf{reward\\_once }\mathds{N}])( ( ⟨ italic\_R italic\_P italic\_M ⟩ | ⟨ italic\_R italic\_P italic\_D italic\_E italic\_M ⟩ | ⟨ italic\_R italic\_P italic\_D italic\_F italic\_M ⟩ ) [ reward\_once blackboard\_N ] ) | |
A requirement’s type is determined based on whether it: is conditional (C) or unconditional (U); is a maintain (M) or achieve (A) requirement: duration of maintain requirements can be time-limited and its compliance can be best-effort (P) or strict (PR), i.e., during its duration the requirement does not have to be “always” satisfied; has a deadline (D), which can be exact (E), i.e., the requirement has to be satisfied at the deadline, or flexible (F), the requirement has to be satisfied within the deadline.
Due to space limitations, we only present unconditional, conditional and achieve deadline requirements.
Unconditional Requirements: denote conditions that have to be always maintained or (repeatedly) achieved.
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| | ⟨UA⟩::=𝐚𝐜𝐡𝐢𝐞𝐯𝐞⟨CND⟩\displaystyle\langle UA\rangle::=\textbf{achieve}\,\langle CND\rangle⟨ italic\_U italic\_A ⟩ : := achieve ⟨ italic\_C italic\_N italic\_D ⟩ | ⟨UM⟩::=𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧⟨CND⟩\displaystyle\langle UM\rangle::=\textbf{maintain}\,\langle CND\rangle⟨ italic\_U italic\_M ⟩ : := maintain ⟨ italic\_C italic\_N italic\_D ⟩ | |
For example, a ⟨UM⟩delimited-⟨⟩𝑈𝑀\langle UM\rangle⟨ italic\_U italic\_M ⟩ requirement to remain in the dining room or an ⟨UA⟩delimited-⟨⟩𝑈𝐴\langle UA\rangle⟨ italic\_U italic\_A ⟩ to ensure that table1𝑡𝑎𝑏𝑙subscript𝑒1table\_{1}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT repeatedly pays.
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| | ReqID req1 maintain location=inDining\_roomReqID 𝑟𝑒subscript𝑞1 maintain 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑖𝑛𝐷𝑖𝑛𝑖𝑛𝑔\_𝑟𝑜𝑜𝑚\displaystyle\textbf{ReqID }req\_{1}\textbf{ maintain }location\text{=}inDining\\_roomReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT maintain italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m | |
| | ReqID req2 achieve table1=paidReqID 𝑟𝑒subscript𝑞2 achieve 𝑡𝑎𝑏𝑙subscript𝑒1=𝑝𝑎𝑖𝑑\displaystyle\textbf{ReqID }req\_{2}\textbf{ achieve }table\_{1}\text{=}paidReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT achieve italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_p italic\_a italic\_i italic\_d | |
Conditional Requirements: should be satisfied only after some given conditions are true. They can have a cancellation condition after which their satisfaction is no longer required.
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| | ⟨CA⟩::=𝐚𝐜𝐡𝐢𝐞𝐯𝐞⟨CND⟩𝐢𝐟⟨CND⟩[𝐮𝐧𝐥𝐞𝐬𝐬⟨CND⟩]\displaystyle\langle CA\rangle::\text{=}\textbf{achieve}\,\langle CND\rangle\,\textbf{if}\,\langle CND\rangle\,[\textbf{unless}\,\langle CND\rangle]⟨ italic\_C italic\_A ⟩ : : = bold\_achieve ⟨ italic\_C italic\_N italic\_D ⟩ if ⟨ italic\_C italic\_N italic\_D ⟩ [ unless ⟨ italic\_C italic\_N italic\_D ⟩ ] | |
| | ⟨CM⟩::=𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧⟨CND⟩𝐢𝐟⟨CND⟩[𝐮𝐧𝐥𝐞𝐬𝐬⟨CND⟩]\displaystyle\langle CM\rangle::\text{=}\textbf{maintain}\langle CND\rangle\textbf{if}\langle CND\rangle\,[\textbf{unless}\,\langle CND\rangle]⟨ italic\_C italic\_M ⟩ : : = bold\_maintain ⟨ italic\_C italic\_N italic\_D ⟩ if ⟨ italic\_C italic\_N italic\_D ⟩ [ unless ⟨ italic\_C italic\_N italic\_D ⟩ ] | |
For example, RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X may have to get the order from tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT only if tablei𝑡𝑎𝑏𝑙subscript𝑒𝑖table\_{i}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT requests to order, or it should remain in the dining room after it gets table1𝑡𝑎𝑏𝑙subscript𝑒1table\_{1}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT until table1𝑡𝑎𝑏𝑙subscript𝑒1table\_{1}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT’s order is served.
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| | ReqID req3 achieve tablei=receivedReqID 𝑟𝑒subscript𝑞3 achieve 𝑡𝑎𝑏𝑙subscript𝑒𝑖=𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑\displaystyle\textbf{ReqID }req\_{3}\textbf{ achieve }table\_{i}\text{=}receivedReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 3 end\_POSTSUBSCRIPT achieve italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_c italic\_e italic\_i italic\_v italic\_e italic\_d | |
| | if table1=requested reward 100 if 𝑡𝑎𝑏𝑙subscript𝑒1𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑 reward 100\displaystyle\hskip 14.22636pt\textbf{ if }table\_{1}=requested\textbf{ reward }100if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d reward 100 | |
| | ReqID req4 maintain location=inDining\_roomReqID 𝑟𝑒subscript𝑞4 maintain 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑛𝐷𝑖𝑛𝑖𝑛𝑔\_𝑟𝑜𝑜𝑚\displaystyle\textbf{ReqID }req\_{4}\textbf{ maintain }location=inDining\\_roomReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT maintain italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_D italic\_i italic\_n italic\_i italic\_n italic\_g \_ italic\_r italic\_o italic\_o italic\_m | |
| | if table1=requested unless table1=received reward 100 if 𝑡𝑎𝑏𝑙subscript𝑒1=𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑 unless 𝑡𝑎𝑏𝑙subscript𝑒1=𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 reward 100\displaystyle\hskip 2.84544pt\textbf{ if }table\_{1}\text{=}requested\textbf{ unless }table\_{1}\text{=}received\textbf{ reward }100if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d unless italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_c italic\_e italic\_i italic\_v italic\_e italic\_d reward 100 | |
Deadline Requirements: must be satisfied after an exact number of time instants or within a period of time:
| | | |
| --- | --- | --- |
| | ⟨DEA⟩::=𝐚𝐜𝐡𝐢𝐞𝐯𝐞⟨CND⟩𝐚𝐟𝐭𝐞𝐫ℕ+𝐢𝐟⟨CND⟩[𝐮𝐧𝐥𝐞𝐬𝐬⟨CND⟩]\displaystyle\langle DEA\rangle::=\textbf{achieve}\,\langle CND\rangle\,\textbf{after}\,\mathds{N}\_{+}\,\textbf{if}\,\langle CND\rangle\,[\textbf{unless}\,\langle CND\rangle]⟨ italic\_D italic\_E italic\_A ⟩ : := achieve ⟨ italic\_C italic\_N italic\_D ⟩ after blackboard\_N start\_POSTSUBSCRIPT + end\_POSTSUBSCRIPT if ⟨ italic\_C italic\_N italic\_D ⟩ [ unless ⟨ italic\_C italic\_N italic\_D ⟩ ] | |
| | ⟨DFA⟩::=𝐚𝐜𝐡𝐢𝐞𝐯𝐞⟨CND⟩𝐰𝐢𝐭𝐡𝐢𝐧ℕ+𝐢𝐟⟨CND⟩[𝐮𝐧𝐥𝐞𝐬𝐬⟨CND⟩]\displaystyle\langle DFA\rangle::=\textbf{achieve}\,\langle CND\rangle\,\textbf{within}\,\mathds{N}\_{+}\,\textbf{if}\,\langle CND\rangle\,[\textbf{unless}\,\langle CND\rangle]⟨ italic\_D italic\_F italic\_A ⟩ : := achieve ⟨ italic\_C italic\_N italic\_D ⟩ within blackboard\_N start\_POSTSUBSCRIPT + end\_POSTSUBSCRIPT if ⟨ italic\_C italic\_N italic\_D ⟩ [ unless ⟨ italic\_C italic\_N italic\_D ⟩ ] | |
For example, RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X may have to be at table1𝑡𝑎𝑏𝑙subscript𝑒1table\_{1}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT within at most 4 time units after table1𝑡𝑎𝑏𝑙subscript𝑒1table\_{1}italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT requests to place an order, or it may have to be at the kitchen after exactly 4 time units after it receives a notification that food is ready.
| | | |
| --- | --- | --- |
| | ReqID req5 achieve location=atTable1 within 4ReqID 𝑟𝑒subscript𝑞5 achieve 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑎𝑡𝑇𝑎𝑏𝑙subscript𝑒1 within 4\displaystyle\textbf{ReqID }req\_{5}\textbf{ achieve }location\text{=}atTable\_{1}\textbf{ within }4ReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 5 end\_POSTSUBSCRIPT achieve italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_a italic\_t italic\_T italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT within 4 | |
| | if table1=requested reward 100 if 𝑡𝑎𝑏𝑙subscript𝑒1=𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑 reward 100\displaystyle\hskip 14.22636pt\textbf{ if }table\_{1}\text{=}requested\textbf{ reward }100if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_q italic\_u italic\_e italic\_s italic\_t italic\_e italic\_d reward 100 | |
| | ReqID req6 achieve location=inKitchen after 4ReqID 𝑟𝑒subscript𝑞6 achieve 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛=𝑖𝑛𝐾𝑖𝑡𝑐ℎ𝑒𝑛 after 4\displaystyle\textbf{ReqID }req\_{6}\textbf{ achieve }location\text{=}inKitchen\textbf{ after }4ReqID italic\_r italic\_e italic\_q start\_POSTSUBSCRIPT 6 end\_POSTSUBSCRIPT achieve italic\_l italic\_o italic\_c italic\_a italic\_t italic\_i italic\_o italic\_n = italic\_i italic\_n italic\_K italic\_i italic\_t italic\_c italic\_h italic\_e italic\_n after 4 | |
| | if table1=ready reward 100 if 𝑡𝑎𝑏𝑙subscript𝑒1=𝑟𝑒𝑎𝑑𝑦 reward 100\displaystyle\hskip 14.22636pt\textbf{ if }table\_{1}\text{=}ready\textbf{ reward }100if italic\_t italic\_a italic\_b italic\_l italic\_e start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT = italic\_r italic\_e italic\_a italic\_d italic\_y reward 100 | |
In the following, we use the terms name, required condition, activation condition, cancellation condition and deadline to refer to the parts of a requirement expression that come after `ReqID’`ReqID’`\text{ReqID'}` ReqID’, `achieve’`achieve’`\text{achieve'}` achieve’ or `maintain’`maintain’`\text{maintain'}` maintain’, `if’`if’`\text{if'}` if’, `unless’`unless’`\text{unless'}` unless’ and `after’`after’`\text{after'}` after’ or `within’`within’`\text{within'}` within’ parts of the requirement expression respectively.
V Controller Synthesis
-----------------------
Markov Decision Processes (MDPs) are mathematical frameworks for modeling and controlling stochastic dynamical systems [[17](#bib.bib17)]. Informally, MDPs may be viewed as Labeled Transition Systems (LTSs) where transitions are probabilistic and can be associated to rewards. Intuitively, solving an MDP means finding an optimal strategy, i.e., determining the actions to execute in every state in order to maximize the total expected rewards. In the following, we first introduce MDPs (Section [V-A](#S5.SS1 "V-A Introduction to MDPs with Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")), then we discuss the main steps needed to construct an MDP starting from an ObD domain model (Section [V-B](#S5.SS2 "V-B Overview of the Construction of MDPs from ObD Models ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")).
###
V-A Introduction to MDPs with Rewards
A reward MDP𝑀𝐷𝑃MDPitalic\_M italic\_D italic\_P is a tuple ⟨𝒮,𝒜,𝒯,ℛ,γ⟩𝒮𝒜𝒯ℛ𝛾\langle\mathcal{S},\mathcal{A},\mathcal{T},\mathcal{R},\gamma\rangle⟨ caligraphic\_S , caligraphic\_A , caligraphic\_T , caligraphic\_R , italic\_γ ⟩, where:
* •
𝒮𝒮\mathcal{S}caligraphic\_S is the finite set of all possible states of the system, also called the state space;
* •
𝒜𝒜\mathcal{A}caligraphic\_A is a finite set of actions;
* •
𝒯:𝒮×𝒜×D(𝒮):𝒯𝒮𝒜𝐷𝒮\mathcal{T}:\mathcal{S}\times\mathcal{A}\times D(\mathcal{S})caligraphic\_T : caligraphic\_S × caligraphic\_A × italic\_D ( caligraphic\_S ) where D(𝒮)𝐷𝒮D(\mathcal{S})italic\_D ( caligraphic\_S ) is the set of probability distributions over states S𝑆Sitalic\_S. A distribution d(S)∈D(S):S→[0,1]:𝑑𝑆𝐷𝑆→𝑆01d(S)\in D(S):S\rightarrow[0,1]italic\_d ( italic\_S ) ∈ italic\_D ( italic\_S ) : italic\_S → [ 0 , 1 ] is a function such that Σs∈Sd(s)=1subscriptΣ𝑠𝑆𝑑𝑠1\Sigma\_{s\in S}d(s)=1roman\_Σ start\_POSTSUBSCRIPT italic\_s ∈ italic\_S end\_POSTSUBSCRIPT italic\_d ( italic\_s ) = 1. The transition relation 𝒯(si,a,d)𝒯subscript𝑠𝑖𝑎𝑑\mathcal{T}(s\_{i},a,d)caligraphic\_T ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_a , italic\_d ) specifies the probabilities d(sj)𝑑subscript𝑠𝑗d(s\_{j})italic\_d ( italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) of going from state sisubscript𝑠𝑖s\_{i}italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT after execution of action a𝑎aitalic\_a to states sjsubscript𝑠𝑗s\_{j}italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT. In the following, we will use the (matrix) notation Pra(si,sj)𝑃subscript𝑟𝑎subscript𝑠𝑖subscript𝑠𝑗Pr\_{a}(s\_{i},s\_{j})italic\_P italic\_r start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) to represent the probability d(sj)𝑑subscript𝑠𝑗d(s\_{j})italic\_d ( italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) of going to sjsubscript𝑠𝑗s\_{j}italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT after execution of a𝑎aitalic\_a in sisubscript𝑠𝑖s\_{i}italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT;
* •
ℛ:𝒮×𝒜×𝒮→ℝ:ℛ→𝒮𝒜𝒮ℝ\mathcal{R}:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow\mathds{R}caligraphic\_R : caligraphic\_S × caligraphic\_A × caligraphic\_S → blackboard\_R is a reward function specifying a finite numeric reward value ℛ(si,a,sj)ℛsubscript𝑠𝑖𝑎subscript𝑠𝑗\mathcal{R}(s\_{i},a,s\_{j})caligraphic\_R ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_a , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) when the system goes from state sisubscript𝑠𝑖s\_{i}italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT to state sjsubscript𝑠𝑗s\_{j}italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT as a result of executing action a𝑎aitalic\_a. Thus, rewards may be viewed as incentives for executing actions. We will use Ra(si,sj)subscript𝑅𝑎subscript𝑠𝑖subscript𝑠𝑗R\_{a}(s\_{i},s\_{j})italic\_R start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) to represent ℛ(si,a,sj)ℛsubscript𝑠𝑖𝑎subscript𝑠𝑗\mathcal{R}(s\_{i},a,s\_{j})caligraphic\_R ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_a , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ).
Formally, a (memoryless) strategy is a mapping π:𝒮→𝒜:𝜋→𝒮𝒜\pi:\mathcal{S}\rightarrow\mathcal{A}italic\_π : caligraphic\_S → caligraphic\_A from states to actions. An optimal strategy, denoted π\*superscript𝜋\pi^{\*}italic\_π start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT, is the one which maximizes the expected linear additive utility, formally defined as Vπ(s)=𝔼[∑t′=0∞γt′Rt+t′πs]superscript𝑉𝜋𝑠𝔼delimited-[]superscriptsubscriptsuperscript𝑡′0superscript𝛾superscript𝑡′subscriptsuperscript𝑅subscript𝜋𝑠𝑡superscript𝑡′V^{\pi}(s)=\mathds{E}[\sum\_{t^{\prime}=0}^{\infty}\gamma^{t^{\prime}}R^{\pi\_{s}}\_{t+t^{\prime}}]italic\_V start\_POSTSUPERSCRIPT italic\_π end\_POSTSUPERSCRIPT ( italic\_s ) = blackboard\_E [ ∑ start\_POSTSUBSCRIPT italic\_t start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT = 0 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_t start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUPERSCRIPT italic\_R start\_POSTSUPERSCRIPT italic\_π start\_POSTSUBSCRIPT italic\_s end\_POSTSUBSCRIPT end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_t + italic\_t start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT ]. Intuitively, this utility states that a strategy is as good as the amount of discounted reward it is expected to yield [[25](#bib.bib25)]. Setting γ=1𝛾1\gamma=1italic\_γ = 1 expresses indifference of the agent to the time in which a particular reward arrives; setting it to a value 0≤γ<10𝛾10\leq\gamma<10 ≤ italic\_γ < 1 reflects various degrees of preference to rewards earned sooner.
MDPs have a key property: solving an MDP finds an optimal strategy π\*superscript𝜋\pi^{\*}italic\_π start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT, which is deterministic, Markovian and stationary. This means that computed strategies are independent of both past actions/states and time, which ensures their compactness and practicality. Furthermore, there exist practical algorithms for solving MDPs, e.g., value iteration and policy iteration. Both of these algorithms can be shown to perform in polynomial time for fixed γ𝛾\gammaitalic\_γ [[26](#bib.bib26)].
MDPs with memoryless strategies, depicted in Figure [5](#S5.F5 "Figure 5 ‣ The Markovian assumption [16, 27] ‣ V-A Introduction to MDPs with Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") (rounds are states and rounded squares are events), have however the following restrictions:
#### The implicit-event action model [[17](#bib.bib17)]
MDPs do not support an explicit representation of exogenous events. Figure [5](#S5.F5 "Figure 5 ‣ The Markovian assumption [16, 27] ‣ V-A Introduction to MDPs with Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows exogenous events (in non-rounded squares connected with pointed line to states) that can occur with certain occurrence probabilities (in green in the figure) in every state. Exogenous events are an essential element to model aspects of the environment that are not controllable by the agent. They are the means to represent, for example, that customers can arrive at the restaurant or that they may request to order.
#### The Markovian assumption [[16](#bib.bib16), [27](#bib.bib27)]
in MDP, reward and transition functions have to be Markovian, i.e., they can not refer to the history of previous states or transitions. Figure [5](#S5.F5 "Figure 5 ‣ The Markovian assumption [16, 27] ‣ V-A Introduction to MDPs with Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows an example of a non-Markovian reward (described on the dashed transition), i.e., one that is entailed only if certain conditions are satisfied on the history of states and transitions. The support of non-Markovian rewards is necessary to associate transitions that satisfy requirements [[6](#bib.bib6)], which are often conditional and can have deadlines, with rewards.
Figure 3: Basic MDP model
Figure 4: Support of Exogenous Events
Figure 3: Basic MDP model
Figure 4: Support of Exogenous Events
Figure 5: Support of Non-Markovian Rewards
###
V-B Overview of the Construction of MDPs from ObD Models
The construction of MDPs based on ObD models111The formal details can be found in <https://goo.gl/aoLh7i>. relies on the following intuitions:
* •
the states and the (probabilistic) transitions of the LTS behind the MDP are constructed based on the variables, actions and events in the ObD domain model;
* •
the rewards in the MDP are associated with transitions that lead to the satisfaction of requirements.
#### Dealing with the Markovian assumption
Building an MDP from an ObD model requires the satisfaction of the Markovian assumption. In the context of this work, determining the satisfaction of requirements, with the exception of unconditional requirements, requires to keep track of history. To solve this issue, we extend the state space to store information that is relevant to determine the status of requirements in every state. This is done by associating every requirement with a state variable, whose value reflects the status of the requirement in the state222This technique is inspired by the state-based approach in [[16](#bib.bib16)] to handle non-Markovian rewards, but is tailored to support requirements in ObD ..
The value of those variables, called requirements variables, are updated whenever their corresponding requirement is activated, canceled, satisfied, etc.
Requirements Variables ℛ𝒱ℛ𝒱\mathcal{RV}caligraphic\_R caligraphic\_V are special variables whose domain represents all the possible statuses of their corresponding requirement. The statuses of requirements and their update after requirement activation, cancellation, satisfaction, etc are defined in the transitions part of Figures [8](#S5.F8 "Figure 8 ‣ Unconditional Achieve and Maintain Rewards ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") and [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). On the other hand, the rewards part defines transitions that satisfy requirements and, consequently, entail a reward.
For example, consider a conditional achieve requirement CA𝐶𝐴CAitalic\_C italic\_A of the form 𝐑𝐞𝐪𝐈𝐃m𝐚𝐜𝐡𝐢𝐞𝐯𝐞S𝐢𝐟A𝐮𝐧𝐥𝐞𝐬𝐬Z𝐫𝐞𝐰𝐚𝐫𝐝r𝐑𝐞𝐪𝐈𝐃𝑚𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝑆𝐢𝐟𝐴𝐮𝐧𝐥𝐞𝐬𝐬𝑍𝐫𝐞𝐰𝐚𝐫𝐝𝑟\textbf{ReqID}\,m\,\textbf{achieve}\,S\,\textbf{if}\,A\,\textbf{unless}\,Z\,\textbf{reward}\,rReqID italic\_m achieve italic\_S if italic\_A unless italic\_Z reward italic\_r. This requirement is associated with a requirement variable m𝑚mitalic\_m whose domain includes the requirement’s possible statutes {I,R}𝐼𝑅\{I,R\}{ italic\_I , italic\_R }. The transitions part of Figure [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows the evolution of CA𝐶𝐴CAitalic\_C italic\_A requirements when their activation, cancellation and required conditions occur. It is to be read as follows: when the status is I𝐼Iitalic\_I and the activation condition A𝐴Aitalic\_A is true, then the status is updated to R𝑅Ritalic\_R. Analogously, if the status is R𝑅Ritalic\_R and the cancellation condition Z𝑍Zitalic\_Z or the required condition S𝑆Sitalic\_S is true, then the status is updated to I𝐼Iitalic\_I. The updating of a state variable as just described enables the definition of a Markovian reward when requirements are satisfied. The rewards part of Figure [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows transitions of CA𝐶𝐴CAitalic\_C italic\_A requirements that entail rewards. This figure should be read as follows: a requirement m𝑚mitalic\_m of type CA𝐶𝐴CAitalic\_C italic\_A induces a reward r𝑟ritalic\_r on a transition from a state i𝑖iitalic\_i to a state j𝑗jitalic\_j iff, in i𝑖iitalic\_i, the required condition of m𝑚mitalic\_m does not hold and the status of m𝑚mitalic\_m is R𝑅Ritalic\_R; while S𝑆Sitalic\_S holds in j𝑗jitalic\_j.
#### Dealing with the absence of exogenous events
ObD models have explicit-event models whereas MDPs impose an implicit-event action model. To overcome this limitation, we exploit the technique proposed in [[17](#bib.bib17)] which enables computation of implicit-event action transition matrices from explicit-event models. The use of this technique assumes the following rules: 1) the action in which exogenous events are folded, always occurs before it and 2) events are commutative, i.e., their order of occurrence from an initial state produces the same final state. Under those assumptions, which are satisfied in our running example, the implicit-event transition matrix Pra(si,sj)𝑃subscript𝑟𝑎subscript𝑠𝑖subscript𝑠𝑗Pr\_{a}(s\_{i},s\_{j})italic\_P italic\_r start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) of an action a𝑎aitalic\_a is computed in two steps: first, the transition matrix of a𝑎aitalic\_a (without exogenous events) and the transition matrix and occurrence vector of every event e𝑒eitalic\_e are computed separately; then, those elements are used to construct the implicit-event matrix of every action a𝑎aitalic\_a. This process is illustrated in the following section using an example. Note that it is possible to integrate other (more complex) interleaving semantics into the framework if necessary by changing the technique used to compute the implicit-event transition matrix [[17](#bib.bib17)].
###
V-C MDP Construction Process
An ObD MDP MDPr=⟨𝒮,𝒜,𝒯,ℛ,γ⟩𝑀𝐷subscript𝑃𝑟𝒮𝒜𝒯ℛ𝛾MDP\_{r}=\langle\mathcal{S},\mathcal{A},\mathcal{T},\mathcal{R},\gamma\rangleitalic\_M italic\_D italic\_P start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT = ⟨ caligraphic\_S , caligraphic\_A , caligraphic\_T , caligraphic\_R , italic\_γ ⟩ is constructed from a model 𝒟r=⟨𝒮𝒱,𝒜𝒱,ℰ𝒟,ℛ𝒬,s0⟩subscript𝒟𝑟𝒮𝒱𝒜𝒱ℰ𝒟ℛ𝒬subscript𝑠0\mathcal{D}\_{r}=\langle\mathcal{SV},\mathcal{AV},\mathcal{ED},\mathcal{RQ},s\_{0}\ranglecaligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT = ⟨ caligraphic\_S caligraphic\_V , caligraphic\_A caligraphic\_V , caligraphic\_E caligraphic\_D , caligraphic\_R caligraphic\_Q , italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ⟩ as follows.
States 𝒮𝒮\mathcal{S}caligraphic\_S:
represent all possible configurations of the system and the environment. A state is a specific configuration, i.e., an assignment of every state variable in 𝒮𝒱𝒮𝒱\mathcal{SV}caligraphic\_S caligraphic\_V and requirement variable in ℛ𝒱ℛ𝒱\mathcal{RV}caligraphic\_R caligraphic\_V a value from their domain.
For example, consider a domain model 𝒟rsubscript𝒟𝑟\mathcal{D}\_{r}caligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT comprising of two boolean variables x𝑥xitalic\_x and y𝑦yitalic\_y and one requirement m𝑚mitalic\_m of type CA𝐶𝐴CAitalic\_C italic\_A. The set of states 𝒮𝒮\mathcal{S}caligraphic\_S constructed from 𝒟rsubscript𝒟𝑟\mathcal{D}\_{r}caligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT comprises all possible configurations of its state and requirement variables. Thus, 𝒮𝒮\mathcal{S}caligraphic\_S includes the eight states in Figure [6](#S5.F6 "Figure 6 ‣ V-C MDP Construction Process ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems").
Figure 6: Constructed States
Actions 𝒜𝒜\mathcal{A}caligraphic\_A: are all the actions appearing in 𝒜𝒱𝒜𝒱\mathcal{AV}caligraphic\_A caligraphic\_V of the domain model 𝒟rsubscript𝒟𝑟\mathcal{D}\_{r}caligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT extended with the empty action noop𝑛𝑜𝑜𝑝noopitalic\_n italic\_o italic\_o italic\_p, which produces no effects and has no cost, i.e., 𝒜=𝒜𝒱∪{noop}𝒜𝒜𝒱𝑛𝑜𝑜𝑝\mathcal{A}=\mathcal{AV}\cup\{noop\}caligraphic\_A = caligraphic\_A caligraphic\_V ∪ { italic\_n italic\_o italic\_o italic\_p }.
The transition matrix 𝒯𝒯\mathcal{T}caligraphic\_T: is computed in two steps: first, the transition matrix of a𝑎aitalic\_a (without exogenous events) and the transition matrix and occurrence vector of every event e𝑒eitalic\_e are computed separately; then, those elements are used to construct the implicit-event matrix of every action a𝑎aitalic\_a.
For example, consider that our domain model 𝒟rsubscript𝒟𝑟\mathcal{D}\_{r}caligraphic\_D start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT includes one probabilistic action a𝑎aitalic\_a, one deterministic action b𝑏bitalic\_b and one requirement m𝑚mitalic\_m, which are defined as follows:
| | | |
| --- | --- | --- |
| | Action a if !x effects ⟨x prob 0.8⟩⟨y prob 0.2⟩ cost 10Action 𝑎 if 𝑥 effects delimited-⟨⟩𝑥 prob 0.8delimited-⟨⟩𝑦 prob 0.2 cost 10\displaystyle\textbf{Action }a\textbf{ if }!x\textbf{ effects }\langle x\textbf{ prob }0.8\rangle\langle y\textbf{ prob }0.2\rangle\;\textbf{ cost }10Action italic\_a if ! italic\_x effects ⟨ italic\_x prob 0.8 ⟩ ⟨ italic\_y prob 0.2 ⟩ cost 10 | |
| | Action b if x effects ⟨!x⟩ cost 5\displaystyle\textbf{Action }b\textbf{ if }x\textbf{ effects }\langle!x\rangle\;\textbf{ cost }5Action italic\_b if italic\_x effects ⟨ ! italic\_x ⟩ cost 5 | |
| | ReqID m achieve x if !x reward 100ReqID 𝑚 achieve 𝑥 if 𝑥 reward 100\displaystyle\textbf{ReqID }m\textbf{ achieve }x\textbf{ if }!x\textbf{ reward }100ReqID italic\_m achieve italic\_x if ! italic\_x reward 100 | |
Figure 7: (1) action transitions, (2) implicit-event action transitions.
Figure [7](#S5.F7 "Figure 7 ‣ V-C MDP Construction Process ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")(1) shows the transitions caused by the execution of actions in the states s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT and s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT. For example, since the condition !x!x! italic\_x is satisfied in s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT, the execution of a𝑎aitalic\_a in s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT produces x𝑥xitalic\_x with a probability of 0.80.80.80.8 and produces y𝑦yitalic\_y with a probability of 0.20.20.20.2. Notice that after the execution of a𝑎aitalic\_a, the base state of both s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT and s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT could be the result of executing action a𝑎aitalic\_a in s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT. However, since the execution of a𝑎aitalic\_a satisfies the requirement m𝑚mitalic\_m, i.e., makes x𝑥xitalic\_x true, only the expanded state of s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT satisfies the state transition model of the CA𝐶𝐴CAitalic\_C italic\_A requirement m𝑚mitalic\_m shown in Figure [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") since m=I𝑚𝐼m=Iitalic\_m = italic\_I. Thus, the execution of a𝑎aitalic\_a in state s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT leads to s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT with a probability 0.80.80.80.8, i.e., Pra(s2,s4)=0.8𝑃subscript𝑟𝑎subscript𝑠2subscript𝑠40.8Pr\_{a}(s\_{2},s\_{4})=0.8italic\_P italic\_r start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT ) = 0.8. The execution of b𝑏bitalic\_b and noop𝑛𝑜𝑜𝑝noopitalic\_n italic\_o italic\_o italic\_p do not change the state.
Events are similar to actions with the exception that they have occurrence probabilities, do not have a cost and do not advance time since they occur concurrently with actions. Let e𝑒eitalic\_e be an event defined similarly to a𝑎aitalic\_a as follows:
| | | |
| --- | --- | --- |
| | Event e if !x occur prob 0.2 effects ⟨x prob 0.8⟩⟨y prob 0.2⟩Event 𝑒 if 𝑥 occur prob 0.2 effects delimited-⟨⟩𝑥 prob 0.8delimited-⟨⟩𝑦 prob 0.2\displaystyle\textbf{Event }e\textbf{ if }!x\textbf{ occur prob }0.2\textbf{ effects }\langle x\textbf{ prob }0.8\rangle\langle y\textbf{ prob }0.2\rangleEvent italic\_e if ! italic\_x occur prob 0.2 effects ⟨ italic\_x prob 0.8 ⟩ ⟨ italic\_y prob 0.2 ⟩ | |
In this case, the transition matrix of e𝑒eitalic\_e is similar to that of a𝑎aitalic\_a, i.e., Pre=Pra𝑃subscript𝑟𝑒𝑃subscript𝑟𝑎Pr\_{e}=Pr\_{a}italic\_P italic\_r start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT = italic\_P italic\_r start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT. The occurrence vector Oesubscript𝑂𝑒O\_{e}italic\_O start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT of e𝑒eitalic\_e represents the probability of occurrence of e𝑒eitalic\_e in every state. Since the condition ¬x𝑥\neg x¬ italic\_x is satisfied in the states s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT, s3subscript𝑠3s\_{3}italic\_s start\_POSTSUBSCRIPT 3 end\_POSTSUBSCRIPT, s6subscript𝑠6s\_{6}italic\_s start\_POSTSUBSCRIPT 6 end\_POSTSUBSCRIPT and s7subscript𝑠7s\_{7}italic\_s start\_POSTSUBSCRIPT 7 end\_POSTSUBSCRIPT, Oe(s2)=Oe(s3)=Oe(s6)=Oe(s7)=0.2subscript𝑂𝑒subscript𝑠2subscript𝑂𝑒subscript𝑠3subscript𝑂𝑒subscript𝑠6subscript𝑂𝑒subscript𝑠70.2O\_{e}(s\_{2})=O\_{e}(s\_{3})=O\_{e}(s\_{6})=O\_{e}(s\_{7})=0.2italic\_O start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT ) = italic\_O start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 3 end\_POSTSUBSCRIPT ) = italic\_O start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 6 end\_POSTSUBSCRIPT ) = italic\_O start\_POSTSUBSCRIPT italic\_e end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 7 end\_POSTSUBSCRIPT ) = 0.2. Figure [7](#S5.F7 "Figure 7 ‣ V-C MDP Construction Process ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")(2) shows the implicit-event transitions for the states s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT and s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT: in s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT, event e𝑒eitalic\_e may occur with a probability of 0.20.20.20.2, thus its effects are factored in action transitions as shown in Figure [7](#S5.F7 "Figure 7 ‣ V-C MDP Construction Process ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")(2); in s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT, the condition of e𝑒eitalic\_e is not satisfied and, therefore, it does not affect the computed transitions for the actions noop𝑛𝑜𝑜𝑝noopitalic\_n italic\_o italic\_o italic\_p and a𝑎aitalic\_a. Due to the interleaving semantics where exogenous events (may) occur after action execution, the transition caused by b𝑏bitalic\_b in s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT is affected due to the possibility that e𝑒eitalic\_e occurs after b𝑏bitalic\_b.
#### Construction of the reward matrix ℛℛ\mathcal{R}caligraphic\_R:
Transition rewards are affected by: (1) action costs and (2) satisfaction of requirements. In particular, a transition reward Ra(si,sj)subscript𝑅𝑎subscript𝑠𝑖subscript𝑠𝑗R\_{a}(s\_{i},s\_{j})italic\_R start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT ) is the sum of rewards obtained due to satisfaction of requirements on the transition from sisubscript𝑠𝑖s\_{i}italic\_s start\_POSTSUBSCRIPT italic\_i end\_POSTSUBSCRIPT to sjsubscript𝑠𝑗s\_{j}italic\_s start\_POSTSUBSCRIPT italic\_j end\_POSTSUBSCRIPT minus the cost of a𝑎aitalic\_a. For example, consider the transition from s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT to s4subscript𝑠4s\_{4}italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT caused by the execution of a𝑎aitalic\_a in s2subscript𝑠2s\_{2}italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT. On this transition, the requirement m𝑚mitalic\_m is satisfied. Since the cost of executing a𝑎aitalic\_a is 10101010, this transition will be associated with a reward of 100−10=901001090100-10=90100 - 10 = 90, i.e., Ra(s2,s4)=90subscript𝑅𝑎subscript𝑠2subscript𝑠490R\_{a}(s\_{2},s\_{4})=90italic\_R start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT 4 end\_POSTSUBSCRIPT ) = 90.
###
V-D Requirements Transitions and Rewards
This section explains the key intuitions behind the modeling of requirements in ObD and their semantics.
#### Unconditional Achieve and Maintain Rewards
A maintain requirement defines a condition that should be kept satisfied. Therefore, a reward is given to the agent whenever this condition holds over two consecutive states, see, e.g., ⟨UM⟩delimited-⟨⟩𝑈𝑀\langle UM\rangle⟨ italic\_U italic\_M ⟩ in Figure [8](#S5.F8 "Figure 8 ‣ Unconditional Achieve and Maintain Rewards ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). On the other hand, an achieve requirement defines a condition that should be reached. Therefore, the agent is rewarded when this condition becomes true, i.e., when it does not hold in a state but holds in the next, e.g., see ⟨UA⟩delimited-⟨⟩𝑈𝐴\langle UA\rangle⟨ italic\_U italic\_A ⟩.
Figure 8: Unconditional Requirements States and Rewards
#### Conditional Requirements
The satisfaction of requirements is often necessary only after some condition A𝐴Aitalic\_A becomes true, see for instance ⟨CA⟩delimited-⟨⟩𝐶𝐴\langle CA\rangle⟨ italic\_C italic\_A ⟩ and ⟨CM⟩delimited-⟨⟩𝐶𝑀\langle CM\rangle⟨ italic\_C italic\_M ⟩ in Figure [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). Those requirements are therefore modeled as state machines which are initially in an initial or inactive state I𝐼Iitalic\_I. When their activation condition A𝐴Aitalic\_A occurs, a transition to a new state R𝑅Ritalic\_R occurs. In a state R𝑅Ritalic\_R, the requirement is said to be in force, i.e., its satisfaction is required. While in R𝑅Ritalic\_R, the reward r𝑟ritalic\_r is obtained whenever the agent manages to comply with the required condition S𝑆Sitalic\_S. If the cancellation conditions Z𝑍Zitalic\_Z is detected while the requirement is in force, a transition to I𝐼Iitalic\_I occurs, i.e., the requirement is canceled and has no longer to be fulfilled.
Figure 9: Conditional and Achieve Deadline Requirements States and Rewards
#### Deadline Achieve Requirements
Requirements are sometimes associated with fixed deadlines. Fixed deadlines can represent either an exact time after which the agent should comply with the requirement, see for example ⟨DEA⟩delimited-⟨⟩𝐷𝐸𝐴\langle DEA\rangle⟨ italic\_D italic\_E italic\_A ⟩ in Figure [9](#S5.F9 "Figure 9 ‣ Conditional Requirements ‣ V-D Requirements Transitions and Rewards ‣ V Controller Synthesis ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"); or a period of (discrete) time during which the agent may comply at any moment, see for example ⟨DFA⟩delimited-⟨⟩𝐷𝐹𝐴\langle DFA\rangle⟨ italic\_D italic\_F italic\_A ⟩. In both cases, deadlines are modeled similarly. For example, consider a requirement m𝑚mitalic\_m having a deadline D𝐷Ditalic\_D. After m𝑚mitalic\_m’s activation, a transition to a state A(D)𝐴𝐷A(D)italic\_A ( italic\_D ) occurs. At every subsequent time unit, a transition from a state A(X)𝐴𝑋A(X)italic\_A ( italic\_X ) to a state A(X−1)𝐴𝑋1A(X-1)italic\_A ( italic\_X - 1 ) occurs (unless X=1𝑋1X=1italic\_X = 1). A transition entails the requirement’s reward if the requirement is satisfied on this transition.
VI Evaluation
--------------
In this section, we first present an empirical evaluation of the framework by comparing the use of an ObD controller to control RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X in a simulated software environment of the restaurant FoodX𝐹𝑜𝑜𝑑𝑋FoodXitalic\_F italic\_o italic\_o italic\_d italic\_X to a generic Monitor Analyze Plan Execute (MAPE) controller (Section [VI-A](#S6.SS1 "VI-A Empirical Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")). The MAPE controller relies on a Planning Domain Description Language (PDDL) planner, similarly to state-of-the-art robotic systems such as ROS [[28](#bib.bib28)].
Then, we present a qualitative comparison of ObD with state-of-the-art probabilistic model-checkers, PAT [[29](#bib.bib29)], PRISM [[15](#bib.bib15)] and STORM [[30](#bib.bib30)] which have been used in several other previous works [[12](#bib.bib12), [13](#bib.bib13), [5](#bib.bib5), [14](#bib.bib14)] to solve adaptation problems (Section [VI-B](#S6.SS2 "VI-B Qualitative Comparison of ObD with State-of-the-Art Probabilistic Model-checkers ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")). Finally, we describe the current prototype tool implementation and conduct a performance evaluation (Section [VI-C](#S6.SS3 "VI-C Preliminary Experimental Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")).
###
VI-A Empirical Evaluation
Figure [12](#S6.F12 "Figure 12 ‣ Enforcement model ‣ VI-A Empirical Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") depicts our simulation environment. It consists of a system state, an agent and an environment. The simulation runs in discrete time steps. At each step, the agent has to choose, based on the current system state, one action to execute from actions whose preconditions are satisfied in the state. On the other hand, some events are selected for execution, according to their occurrence probability, if their preconditions are true. After each time step, the state is updated by applying the effects of the chosen action and events in the current state. Effects of both actions and events are applied probabilistically according to the probabilities specified in their action/event descriptions, i.e. their execution can lead to different states. Experiments are run for one hundred thousands steps.
To select the agent’s actions, two controllers were implemented: an ObD controller and a generic MAPE controller. The design rational of the experiment aims at comparing a ObD controller and generic MAPE controllers with respect to: types of supported requirements, enforcement model, response time, quality of decision-making and problem representation.
#### Experiment Description
The experiment ran on a MacBook pro with a 2.2GHz Intel Core i7 and 16 GB of DDR3 RAM. At each time step, the agent queries the state (the (M)onitoring activity). The agent determines its action to execute by interacting with its controller. The controller, given the current state, determines the next action of the agent. The ObD controller is implemented in Java and uses the computed ObD strategies to determine the optimal action that the agent should take at each state. The MAPE controller is also implemented in Java. It consists of three components: 1) an analysis component that determines whether planning is needed, 2) PDDL4J, an open source Java library for Automated Planning based on PDDL [[31](#bib.bib31)], to compute plans and 3) a plan enforcer which returns one action at each step to the agent. Below is a comparison of the two controllers.
#### Supported requirements
ObD supports the types of requirements presented in Section [IV-B](#S4.SS2 "IV-B Requirements ‣ IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). The MAPE controller, since it relies on a PDDL planer, can naturally encode unconditional and conditional achieve requirements, i.e. ⟨UA,CA⟩𝑈𝐴𝐶𝐴\langle UA,CA\rangle⟨ italic\_U italic\_A , italic\_C italic\_A ⟩. The other types of requirements cannot be easily encoded in the form of PDDL planning problems.
#### Enforcement model
The ObD controller enforces MDP strategies. It has a simple enforcement model: it consults the computed strategy and determines the optimal action that corresponds to the current state at each time step. The MAPE controller enforces requirements as follows: if the activation condition of a conditional requirement is true in the state then a planning problem (Pb) is formulated to satisfy the requirement’s condition. When multiple requirements must be satisfied, then the goal state of the planning problem corresponds to the disjunction of their (satisfaction) conditions, i.e. one requirement should be satisfied. If a plan (P) is found by the PDDL planner, the plan enforcer module selects one action of P to return to the agent at each time step. A plan is pursued until its end, i.e. no re-planning is performed until the plan’s last action is executed unless a plan execution fails. A plan fails if one of its actions cannot be executed because its preconditions are not satisfied in the current state. This situation may occur due to nondeterministic action effects or event occurrences333Another situation where re-planning would be required is cancellation of requirements. This situation is not considered in this experiment for simplicity..
Figure 10: Experiment Description
Figure 11: Decision-making time per time step
002222444466668888101010101212121200200200200200400400400400600600600600nb of planning goalsdecision-making time per time stepM-100%M-90%M-80%M-70%M-60%M-50%A-all
0022224444666688881010101012121212000.10.10.10.10.20.20.20.2nb of planning goalsnb of satisfied goals per time stepM-100%M-90%M-80%M-70%M-60%A-100%A-90%A-80%A-70%A-60%
Figure 10: Experiment Description
Figure 11: Decision-making time per time step
Figure 12: Goal satisfaction per time step
#### Response time
Figure [12](#S6.F12 "Figure 12 ‣ Enforcement model ‣ VI-A Empirical Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows the average time of decision making, i.e. the total decision-making time divided by the total number of steps of the experiment. Several domain descriptions differing in their total number of planning goals/requirements and action success rate444Action success means that the action produced its (expected) effect, i.e. the effect that is most likely to occur. (100%-50%) are considered. The decision time of an ObD controller is constant and almost instantaneous (∼similar-to\sim∼200ns) as it consists of a simple lookup in the policy (which is stored in the form of an array of Integers) of the optimal action that corresponds to the current state. On the other hand, the analysis and planning activities of the MAPE controller introduce a significant overhead when compared to the ObD controller. The average decision-making time of MAPE controllers also increases with action non-determinism as re-planning is required more frequently due to more frequent plan failures.
#### Quality of decision-making
Figure [12](#S6.F12 "Figure 12 ‣ Enforcement model ‣ VI-A Empirical Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows the number of satisfied goals per time step for MAPE and ObD controllers. It demonstrates that the ObD controllers consistently outperform MAPE controllers for the same domain problems. This is due, on one hand, to their ability to include probabilities of event occurrences into their computation of optimal strategies. For instance, imagine that RoboX𝑅𝑜𝑏𝑜𝑋RoboXitalic\_R italic\_o italic\_b italic\_o italic\_X has to pass the order of a table to the kitchen but that it estimates that there is a high-likelihood that another table orders. In this case, the ObD controller may delay moving to the kitchen to pass the order and wait until the other table orders first before passing the two orders to the kitchen together. MAPE controllers are incapable of incorporating such intelligence in their decision-making. Another reason explaining this result is that MAPE controllers, once a plan is computed, commit to it unless the plan fails to avoid getting stuck in re-planning without acting, which could happen if the frequency of change in the environment is high. This makes it impossible to guarantee the optimality of plans throughout their execution. On the other hand, ObD strategies are guaranteed to always select the optimal action at each state.
#### Representation
An important difference is the goal/requirement representation. In MAPE, planning goals have to be satisfiable using solely the actions that are available to the agent. For example, consider a goal to achieve that a table pays as many times as possible. The satisfaction of this goal requires interactions with the environment as described in Figure [2](#S4.F2 "Figure 2 ‣ State Variables (𝒮𝒱) ‣ IV-A State, Actions and Events ‣ IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). This requirement cannot therefore be expressed as a single planning goal but has to be decomposed into a set of planning goals, each of which has to be satisfiable by the actions available to the agent. On the other hand, thanks to the folding of events into actions, such requirements can be expressed directly in ObD. Consequently, expression of requirements in ObD can be much more succinct and enable system designers to focus of what should be satisfied rather than how they should be satisfied. In the running example, it was possible to represent four MAPE planning goals in the form of a single ObD requirement.
###
VI-B Qualitative Comparison of ObD with State-of-the-Art Probabilistic Model-checkers
State-of-the-art probabilistic model-checkers PAT [[29](#bib.bib29)], PRISM [[15](#bib.bib15)] and STORM [[30](#bib.bib30)] support the description of various models using a variety of languages. In this work, we focus on MDP models because they support, as opposed to other models such as for example Discrete-Time Markov Chains, the synthesis of optimal strategies. With respect to the general-purpose languages proposed by probabilistic model-checkers, our model and language support exogenous events and various typical software requirements (Section [IV-B](#S4.SS2 "IV-B Requirements ‣ IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems")), elements that cannot be modeled or expressed using the general-purpose MDP languages of PAT, PRISM or Storm. An extension of PRISM, namely PRISM-games, supports modeling of turn-based multi-player stochastic games. This enables the modeling of the environment as a separate player whose actions represent exogenous events. With respect to modeling of autonomous systems and their requirements, PRISM-games has two main limitations. First, similarly to PRISM, rewards have to be Markovain which means that there is no way to encode typical software requirements [[6](#bib.bib6)] such as those presented in Section [IV-B](#S4.SS2 "IV-B Requirements ‣ IV ObD Modeling Language ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") using the provided (Markovian) reward structures. Furthermore, modeling of interactions between the agent and its environment in ObD where multiple events occur with each action of the decision maker is more realistic and natural than, in turn-based PRISM-games, where the environment may only be represented in the form of a separate player who selects at most one event to execute after each action of the decision maker.
###
VI-C Preliminary Experimental Evaluation
We have implemented the ObD framework as a Java-based prototype which uses EMFText [[32](#bib.bib32)], the MDPToolBox package [[33](#bib.bib33)] and Graphviz [[34](#bib.bib34)].
There are at least two main use cases of the framework:
#### At design-time
the textual editor generated by EMFText can be used to define ObD models. The corresponding MDP models and optimal strategies can be then visualized and inspected by a system designer and/or used to synthesize optimal controllers for the target autonomous systems;
#### At runtime
the ObD Java API can be used to create instances of the ObD model and the computation of optimal strategies at runtime. At runtime, strategies should be recomputed after change in either 1) requirements or 2) domain descriptions. The former generally denotes a change in system objectives or their priorities. On the other hand, the latter is needed if new information (possibly based on interactions with the environment) shows that model parameters need to be revised. There are some limitations to this use scenario which are discussed in Section [VII](#S7 "VII Limitations & Issues ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems").
Figures [14](#S6.F14 "Figure 14 ‣ At runtime ‣ VI-C Preliminary Experimental Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") and [14](#S6.F14 "Figure 14 ‣ At runtime ‣ VI-C Preliminary Experimental Evaluation ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") show the MDP construction and solving time for different state space sizes, respectively. It is clear from the figures that the current implementation suffers from the state explosion problem. However, the support of thousands of states is typically sufficient for a large number of problems. Furthermore, solving an MDP is a one-time effort, i.e. once an MDP is solved (given a set of requirements), the computed strategies can be used until either requirements or the domain model change.The improvement of the performance of our current prototype represents future work.
Figure 13: MDP Construction
Figure 13: MDP Construction
Figure 14: MDP Solution
VII Limitations & Issues
-------------------------
This section discusses the current limitations and issues related to using ObD and means to address them.
#### Setting of Model parameters
determining probabilities of actions and events can be challenging. We envisage that they shall be computed by adapting existing techniques that enable computation and learning of model parameters at runtime. For example, we could use [[35](#bib.bib35)] where Bayesian techniques are used to re-estimate probabilities in formal models such as Markov chains based on real data observed at runtime; or [[36](#bib.bib36)] which proposes an on-line learning method that infers and dynamically adjusts probabilities of Markov models from observations of system behaviour. Alternatively, reinforcement learning techniques [[37](#bib.bib37)] could be used.
#### Identification of requirements
strategies are computed according to requirements. It is thus crucial that they be correctly identified. ObD supports traditional goal modeling techniques [[38](#bib.bib38), [39](#bib.bib39), [40](#bib.bib40)]. Those techniques have been proven reliable over the years in ensuring correct elicitation, refinement, analysis and verification of requirements [[38](#bib.bib38), [39](#bib.bib39), [40](#bib.bib40)].
#### Suitability of the Application Domain
it is necessary to identify system conditions under which the framework may be used. Towards answering this question, we first define a predictable (unpredictable) system as one where probabilities of occurrence and effects of events/actions do not (do) change with time. Similarly, we define a dynamic (erratic) system as one where the rate of relevant555A change is relevant if it renders computed strategies obsolete. change in those probabilities is within the order of hours or days (minutes or seconds). Our current prototype implementation computes strategies within minutes. Consequently, we conjecture that it supports the runtime synthesis of controllers in predicable and unpredictable dynamic systems. Erratic systems are not supported. A more precise definition of those limitations represents future work.
VIII Related Work
------------------
TABLE I: Comparison of ObD with Related Frameworks
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Comp. | Sub-criteria | F1subscript𝐹1F\_{1}italic\_F start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT | Q1subscript𝑄1Q\_{1}italic\_Q start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT | K1subscript𝐾1K\_{1}italic\_K start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT | R1subscript𝑅1R\_{1}italic\_R start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT | K2subscript𝐾2K\_{2}italic\_K start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT | A1subscript𝐴1A\_{1}italic\_A start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT | R𝑅Ritalic\_R |
| Model | Requirements | ✓✓\checkmark✓ | ✓✓\checkmark✓ | ✓✓\checkmark✓ | ✓✓\checkmark✓ | ✓✓\checkmark✓ | ✓✓\checkmark✓ | ✓✓\checkmark✓ |
| Capabilities | ✓✓\checkmark✓ | ∘\circ∘ | ✓✓\checkmark✓ | ∘\circ∘ | ∘\circ∘ | ∘\circ∘ | ✓✓\checkmark✓ |
| Events | ×\times× | ∘\circ∘ | ×\times× | ×\times× | ∘\circ∘ | ×\times× | ✓✓\checkmark✓ |
| Uncert. | Occurrence | ×\times× | ∘\circ∘ | ×\times× | ×\times× | ∘\circ∘ | ×\times× | ✓✓\checkmark✓ |
| Effects | ×\times× | ∘\circ∘ | ×\times× | ✓✓\checkmark✓ | ∘\circ∘ | ×\times× | ✓✓\checkmark✓ |
| Adapt | Explicit | ✓✓\checkmark✓ | −-- | ✓✓\checkmark✓ | ✓✓\checkmark✓ | −-- | ✓✓\checkmark✓ | −-- |
| Configuration | −-- | ✓✓\checkmark✓ | −-- | −-- | ✓✓\checkmark✓ | −-- | −-- |
| Behavior | −-- | −-- | −-- | −-- | −-- | −-- | ✓✓\checkmark✓ |
| ✓✓\checkmark✓: supported ×\times×: not supported |
| --- |
| ∘\circ∘: partially supported (implicit) −--: not applicable |
| F1subscript𝐹1F\_{1}italic\_F start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT: FLAGS [[41](#bib.bib41)] Q1subscript𝑄1Q\_{1}italic\_Q start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT: QoSMOS [[8](#bib.bib8)] K1subscript𝐾1K\_{1}italic\_K start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT: KAOS [[39](#bib.bib39), [24](#bib.bib24), [42](#bib.bib42)] |
| R1subscript𝑅1R\_{1}italic\_R start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT: Rainbow [[43](#bib.bib43), [44](#bib.bib44)] K2subscript𝐾2K\_{2}italic\_K start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT: KAMI [[45](#bib.bib45)] |
| A1subscript𝐴1A\_{1}italic\_A start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT: ActivFORMS [[46](#bib.bib46)] R𝑅Ritalic\_R: ObD |
Table [I](#S8.T1 "TABLE I ‣ VIII Related Work ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") compares the features of ObD with some notable requirements-driven adaptation frameworks according to the criteria in Sec. [II](#S2 "II Motivating Example ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"), divided along the following dimensions.
Modeling compares the frameworks with respect to their support of the explicit modeling and representation of requirements, capabilities and events. Those feature are desirable as they simplify system design, its maintenance and modularity.
Uncertainty compares the support of uncertainty in exogenous event occurrences and effects.
Adaptation compares the type of adaptation strategies, which can be explicitly defined, configuration selection or behavior optimization. Configuration selection is a reactive approach where, after requirements are violated, the alternative system configurations are compared and the best one is selected. Behavior optimization is a proactive approach which takes into account not only the current conditions, but how they are estimated to evolve [[12](#bib.bib12)]. Only behavior-based optimization supports the two requirements of (1) proactive and long-term behavior optimization, and (2) fast and optimal response to change. Note that adaptation based on explicitly defined strategies is fast but provides no optimality guarantees.
Table [I](#S8.T1 "TABLE I ‣ VIII Related Work ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems") shows that adaptations in many current frameworks are either explicitly defined [[24](#bib.bib24), [43](#bib.bib43), [6](#bib.bib6), [41](#bib.bib41), [44](#bib.bib44), [46](#bib.bib46), [42](#bib.bib42)] or determined based on a comparison of possible system configurations [[8](#bib.bib8), [45](#bib.bib45)], without taking into account future evolutions of the system. It also shows that explicit-event and action models are rarely considered. For example, QosMOS and KAMI consider Markov chains. This is why these frameworks have an implicit models of actions and events in Table [I](#S8.T1 "TABLE I ‣ VIII Related Work ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems"). Similarly, ActivForms rely on Timed Automata and the Execute activity is explicitly defined. Therefore, ActiveForms has explicit adaptation strategies and uncertainty is not handled.
In [[5](#bib.bib5)], MDP is used to identify optimal adaptations at runtime, taking into account the delay or latency required to bring about the effects of adaptation tactics . In [[12](#bib.bib12), [14](#bib.bib14)], latency-aware adaptation is studied using stochastic multi-player games (SMGs). In [[13](#bib.bib13)], SMGs are used to generate optimal adaptation plans for architecture-based self-adaptation. These works exploit PRISM and PRISM-games to solve adaptation problems. So, they have the limitations discussed in Section [VI-B](#S6.SS2 "VI-B Qualitative Comparison of ObD with State-of-the-Art Probabilistic Model-checkers ‣ VI Evaluation ‣ Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems").
Several other works [[47](#bib.bib47), [48](#bib.bib48), [49](#bib.bib49)] studied the optimization of system configurations. In contrast, this paper focuses on behavior optimization. Several recent proposals explored the application of concepts from control theory [[50](#bib.bib50), [51](#bib.bib51), [52](#bib.bib52), [53](#bib.bib53)] to perform system adaptation. One main difference with respect to these works is that their focus is on the optimization of quantifiable and measurable non-functional goals, such as response time, as opposed to behavior optimization based on functional requirements, the primary focus here.
IX Conclusion
--------------
This paper introduces the ObD framework for the model-based-requirements-driven synthesis of reflex controllers for autonomous systems. The framework introduces a model and a language to describe autonomous systems, their environment and requirements. The semantics of the model is defined in the form of an MDP, which can be solved producing optimal adaptation strategies (reflex controllers) for autonomous systems. In comparison with the general-purpose languages proposed by probabilistic model-checkers, ObD solves two main limitations, namely the Markovian assumption and the implicit-event model. This enables the support of a comprehensive set of software requirements and permits the accurate modeling of the environment in which autonomous systems operate.
Future work consists of extending the framework to support online learning (reinforcement learning) [[37](#bib.bib37)]. The study of formal adaptation guarantees and assurances [[54](#bib.bib54), [55](#bib.bib55), [56](#bib.bib56), [9](#bib.bib9)], and optimizing the performance of our framework [[57](#bib.bib57), [58](#bib.bib58)] are other future planned extensions.
Acknowledgment
--------------
This work was supported, in part, by Science Foundation Ireland grant 13/RC/2094 and ERC Advanced Grant 291652.
|
f5856154-2eeb-47d8-8204-6886bdac9da9
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Arithmetical hierarchy
summary(Technical): The arithmetical hierarchy classifies statements by the number of nested, unbounded quantifiers they contain. The classes $\Delta_0$, $\Pi_0$, and $\Sigma_0$ are equivalent and include statements containing only bounded quantifiers, e.g. $\forall x < 10: \exists y < x: x + y < 10$. If, treating $x, y, z...$ as constants, a statement $\phi(x, y, z...)$ would be in $\Sigma_n,$ then adjoining the unbounded universal quantifiers $\forall x: \forall y: \forall z: ... \phi(x, y, z...)$ creates a $\Pi_{n+1}$ statement. Similarly, adjoining existential quantifiers to a $\Pi_n$ statement creates a $\Sigma_{n+1}$ statement. Statements that can be equivalently formulated to be in both $\Pi_n$ and $\Sigma_n$ are said to lie in $\Delta_n$. Interesting consequences include, e.g., $\Pi_1$ statements are falsifiable by simple observation, $\Sigma_1$ statements are verifiable by observation, and statements strictly in higher classes can only be probabilistically verified by observation.
The arithmetical hierarchy classifies statements according to the number of unbounded $\forall x$ and $\exists y$ quantifiers, treating adjacent quantifiers of the same type as a single quantifier.
The formula $\phi(x, y) \leftrightarrow [https://arbital.com/p/(](https://arbital.com/p/(),$ treating $x$ and $y$ as constants, contains no quantifiers and would occupy the lowest level of the hierarchy, $\Delta_0 = \Pi_0 = \Sigma_0.$ (Assuming that the operators $+$ and $=$ are themselves considered to be in $\Delta_0$, or from another perspective, that for any particular $c$ and $d$ we can verify whether $c + d = d + c$ in bounded time.)
Adjoining any number of $\forall x_1: \forall x_2: ...$ quantifiers to a statement that would be in $\Sigma_n$ if the $x_i$ were considered as constants, creates a statement in $\Pi_{n+1}.$ Thus, the statement $\forall x: (x + 3) = (3 + x)$ is in $\Pi_1.$
Similarly, adjoining $\exists x_1: \exists x_2: ...$ to a statement in $\Pi_n$ creates a statement in $\Sigma_{n+1}.$ Thus, the statement $\exists y: \forall x: (x + y) = (y + x)$ is in $\Sigma_2$, while the statement $\exists y: \exists x: (x + y) = (y + x)$ is in $\Sigma_1.$
Statements in both $\Pi_n$ and $\Sigma_n$ (e.g. because they have provably equivalent formulations belonging to both classes) are said to lie in $\Delta_n.$
Quantifiers that can be bounded by $\Delta_0$ functions of variables already introduced are ignored by this classification schema: the sentence $\forall x: \exists y < x: (x + y) = (y + x)$ is said to lie in $\Pi_1$, not $\Pi_2$. We can justify this by observing that for any particular $c,$ the statement $\forall x < c: \phi(x)$ can be expanded into the non-quantified statement $\phi(0) \wedge \phi(1) ... \wedge \phi(c)$ and similarly $\exists x < c: \phi(x)$ expands to $\phi(0) \vee \phi(1) \vee ...$
This in turn justifies collapsing adjacent quantifiers of the same type inside the classification schema. Since, e.g., we can uniquely encode every pair (x, y) in a single number $z = 2^x \cdot 3^y$, to say "there exists a pair (x, y)" or "for every pair (x, y)" it suffices to quantify over z encoding (x, y) with x and y less than z.
We say that $\Delta_{n+1}$ includes the entire sets $\Pi_n$ and $\Sigma_n$, since from a $\Pi_{n}$ statement we can produce a $\Pi_{n+1}$ statement just by adding an inner $\exists$ quantifier and then ignoring it, and we can obtain a $\Sigma_{n+1}$ statement from a $\Pi_{n}$ statement by adding an outer $\forall$ quantifier and ignoring it, etcetera.
This means that the arithmetic hierarchy talks about *power sufficient to resolve statements*. To say $\phi \in \Pi_n$ asserts that if you can resolve all $\Pi_n$ formulas then you can resolve $\phi$, which might potentially also be doable with less power than $\Pi_n$, but can definitely not require more power than $\Pi_n.$
# Consequences for epistemic properties
All and only statements in $\Sigma_1$ are *verifiable by observation*. If $\phi \in \Delta_0$ then the sentence $\exists x: \phi(x)$ can be positively known by searching for and finding a single example. Conversely, if a statement involves an unbounded universal quantifier, we can never be sure of it through simple observation because we can't observe the truth for every possible number.
All and only statements in $\Pi_1$ are *falsifiable by observation*. If $\phi$ can be tested in bounded time, then we can falsify the whole statement $\forall x: \phi(x)$ by presenting some single x of which $\phi$ is false. Conversely, if a statement involves an unbounded existential quantifier, we can never falsify it directly through a bounded number of observations because there could always be some higher, as-yet untested number that makes the sentence true.
This doesn't mean we can't get [probabilistic confirmation and disconfirmation](https://arbital.com/p/1ly) of sentences outside $\Sigma_1$ and $\Pi_1.$ E.g. for a $\Pi_2$ statement, "For every x there is a y", each time we find an example of a y for another x, we might become a little more confident, and if for some x we fail to find a y after long searching, we might become a little less confident in the entire statement.
|
de79cafa-8138-403f-a6de-922f74881f35
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Aligning an H-JEPA agent via training on the outputs of an LLM-based "exemplary actor"
1. Overview and conclusion
==========================
**In section 2, I describe the “exemplary actor”, an LMCA (language model cognitive architecture) that takes a simple, “brute force” approach to alignment**: a powerful LLM (think GPT-5/6 level, with a vast, or quasi-unlimited context) is given a list of “approved” textbooks on methodological and scientific disciplines: epistemology, rationality, ethics, physics, etc. Also, the LLM is given tools: narrow AIs (such as for protein folding or for predicting properties of materials, or for [formal scientific modelling](https://www.lesswrong.com/posts/kGrwufqxfsyuaMREy/annotated-reply-to-bengio-s-ai-scientists-safe-and-useful-ai#Training_an_AI_Scientist_with_Large_Neural_Nets_for_Bayesian_Inference)). Finally, the LLM is given a compute engine such as Wolfram and a knowledge base such as Wikidata or Wolfram Knowledgebase.
The exemplary actor creates plans or predictions for given situations (described in language and fed to the LLM underlying the exemplary actor as prompts) and iteratively critiques and refines these plans and predictions while putting different textbooks into the LLM context (first, with the textbook on rationality, then epistemology, then physics, etc., with potentially dozens of different textbooks relevant for a plan or prediction that is being criticised), for many iterations, until convergence.
In section 2.1, I note that the type of alignment that the exemplary actor’s architecture tries to ensure is called ***(world) model alignment*** **and that is stronger and also more essential than** ***goal alignment*****.**
Then, I discuss the properties of the exemplary actor. In section 2.2., I discuss what I see as likely non-issues or straightforwardly addressable issues: the “divergent reasoning nature” of LLMs, the lack of grounded common sense reasoning, and the bias of the quick reactive network (”System 1”), which should be added to the architecture to make it more practically usable in lower-stakes reasoning settings.
In section 2.3, I discuss the outstanding technical issues and risks of the exemplary actor’s architecture:
* The risk of direct access to the underlying LLM (section 2.3.1).
* The exemplary actor’s reasoning could still be partially directed by “alien” thinking patterns (i.e., the world model) of the underlying LLM even though these influences won’t surface in the explanations of the plan (section 2.3.2).
* Iterated critique and refinement probably won’t make plans strictly conforming to the theories described in the textbooks (section 2.3.3).
In section 2.3.4, I discuss the alignment tax of the exemplary actor (compared with the baseline of using bare LLM rollouts in a very lightweight agent architecture, such as AutoGPT) and conclude that the main source of alignment tax might happen to be the theory of ethics which may force the exemplary actor to refuse to participate in “games” (i.e., real-world situations and environments) where it doesn’t see ethical ways of “winning”, and thus will consider inaction (or some form of palliative action) the only ethical way forward. This is not a *technical* problem with the exemplary actor per se, but rather a problem with a higher-level system, i.e., the current economic, social, and political structure of the civilisation. I mention this and other kinds of “higher-level” risks of the plans to build and deploy the exemplary actor (i.e., roughly the plans that OpenAI and Anthropic are betting on, as it seems to me) in section 2.4.
**In section 3, I describe how the H-JEPA (Hierarchical Joint-Embedding Predictive Architecture) architecture proposed by LeCun (2022) could be modified to generate action plans conforming to the world model (and, therefore,** ***values*****) exhibited by the exemplary actor**, described in section 2. (In turn, this world model should follow the body of scientific knowledge described in the textbooks if we find some ways to address the problems discussed in sections 2.3.2 and 2.3.3, or decide that these problems are not critical.)
**The key idea of the proposal is to treat H-JEPA’s plans (in the space of representations) as latents for textual descriptions and explanations of these plans and use GFlowNet-EM (Hu et al., 2023) algorithms to train a set of policies, including a policy to generate a textual description and an explanation from a plan, and a reverse policy to generate a plan (in the space of representations) from the textual description.** The training samples (textual descriptions of plans) for the reverse policy could be generated by the exemplary actor for an unlimited number of imaginary situations.
In section 3.2, I note that in this hybrid architecture (called "**H-JEPA agent with GFlowNet actors**" below), the Cost module as proposed by LeCun becomes entirely unnecessary and could be discarded. The problems of combining the “intrinsic” and “trainable” (i.e., pro-social and ethical) costs also go away together with this module.
In section 3.4, I discuss that training GFlowNet policies within H-JEPA from the output of the LLM-based exemplary actor could be orders of magnitude more expensive than training the LLM underlying the exemplary actor. (In section 4.5, I further note H-JEPA agent with GFlowNet actors would be cheaper and faster at inference time than the exemplary actor, and therefore the whole idea could still be economically viable. However, I don’t see this discussion as very relevant for safety and x-risk.)
In section 3.5, I explain why the plans should be *latents for their explanations*. This idea may seem surprising at first glance, but it makes sense for safety because this is close to how humans actually plan and predict (i.e., justify sub-linguistic inferences with verbal explanations rather than use language reasoning *for* inference), and making AI’s thinking more “human-like” is generally considered good for safety. Also, it is not obvious that linguistic reasoning is more robust than sub-linguistic reasoning in the representation space.
In section 4, I discuss the properties of the proposed H-JEPA agent with GFlowNet actors.
Thanks to the integration of the JEPA's predictive loss (which is ultimately grounded with information from the sensors) with the "language modelling" loss of GFlowNet policy training, the **H-JEPA agent with GFlowNet actors should be more grounded than the LLM-based exemplary actor** (section 4.1). So, I guess that LeCun would endorse this architecture because he considers grounding a big weakness of LLM reasoning in general (Browning & LeCun, 2022), although [many people disagree with him on this question](https://www.youtube.com/watch?v=x10964w00zk&ab_channel=NYUCenterforMind%2CBrainandConsciousness), and I tend to side with those people who *don't* see grounding as a big issue for LLMs, as I noted in section 2.2.
In section 4.2, I note that interpretability shouldn’t be considered an issue for the exemplary actor already (we assume that the world model of the exemplary actor is described in the textbooks), so cloning its behaviour into GFlowNet actors *doesn’t* provide the interpretability benefit of separating the world model from the "inference machine", as discussed by Bengio and Hu (2023).
In section 4.4, I discuss how H-JEPA with GFlowNet actors *can't* be used to bootstrap future versions of itself and thus will remain dependent on a powerful LLM for training “forever”.
In section 4.6, I explain how training GFlowNet actors with initial and intermediate plans from the exemplary actor’s critique and refinement process as “negative” contrastive examples helps to address the risk of GFlowNet learning to generate “good-sounding” explanations for arbitrary (perhaps, self-serving and misaligned) plans.
1.1. Conclusion
---------------
In this article, a theoretical method for [aligning H-JEPA architecture](https://www.lesswrong.com/posts/umsGb5qkfzD3WarTR/h-jepa-might-be-technically-alignable-in-a-modified-form) via training GFlowNet policies from the outputs of the LLM-based "exemplary actor" (i.e., an aligned LMCA). The additional benefit of this method is that it combines the “intrinsic” and “pro-social” costs in a principled way (i.e., according to some scientific theory of ethics). The distinction between the intrinsic and trainable (i.e., pro-social, moral) costs made in the original H-JEPA architecture proposed by LeCun (2022) was itself reductionistic. It was also proposed to combine these costs by simply adding them, which could fail in some extreme situations, such as situations demanding an agent to sacrifice itself.
However, this method implies that an aligned LMCA (i.e., the exemplary actor) already exists, which may seem as [pushing the difficulty of the alignment problem forward into this LMCA](https://www.lesswrong.com/posts/EhkHnNJXwT8RmtfYZ/natural-language-alignment-1).
The assumption of the existence of an aligned LMCA may also seem contrived considering that LeCun mainly presents the H-JEPA architecture as an alternative to the current auto-regressive LLM fad in AI. This *probably* means that he doesn’t believe that auto-regressive LLMs will keep getting more capable (also, robust and grounded) through further scaling and some tweaks in training. Therefore, this also probably means that LeCun doesn’t believe that LMCA is a viable architecture for (alignable) AGI.
The only aspect in which the x-risk profile of the H-JEPA agent with GFlowNet actors seems to be qualitatively different from that of the exemplary actor is the risk of direct access to the underlying Transformers, which is catastrophic in the case of the exemplary actor (section 2.3.1) and perhaps *could* be addressed completely in GFlowNet actors if we accept that they will deliberately dumb themselves in strategic x-risk analysis and planning (section 4.3). However, even if we accept this tradeoff, this might not reduce the overall x-risk of the civilisation because GFlowNet actors are not “self-sufficient” for training (section 4.4) and therefore the powerful LLM that underlies the exemplary actor that is used to train GFlowNet actors should still be kept around, and the risk of direct access to this LLM is still present.
Thus, the H-JEPA agent with GFlowNet actors could become interesting perhaps only if the “LLM optimism” view will prove to be correct and thus LMCAs could generally work and be satisfactorily aligned, *but also sensory grounding proves to be a really important missing piece of the puzzle*. (Though, this combination of conditionals looks to me rather unlikely.) The proposed variant of H-JEPA combines “the best of two worlds”: grounding from H-JEPA and aligned reasoning from the LMCA.
2. [An LLM-based "exemplary actor"](https://www.lesswrong.com/posts/4ztqncYBakD6DWuXC)
======================================================================================
[[Posted separately.]](https://www.lesswrong.com/posts/4ztqncYBakD6DWuXC)
3. Training GFlowNet actors for H-JEPA on the outputs from the exemplary actor
==============================================================================
This part of the post is a response to [Steven Byrnes’ comment](https://www.lesswrong.com/posts/umsGb5qkfzD3WarTR/h-jepa-might-be-technically-alignable-in-a-modified-form?commentId=GSXqtENfoJafEfFeE) where he asks for a concrete procedure for making an Actor module in the H-JEPA agent (LeCun, 2022) to heed the “correct” versions of the disciplines such as epistemology, rationality, and ethics when minimising the energy of its action plans.
So, concretely, we can use the [GFlowNet-EM (expectation-maximization) algorithm for learning compositional latent variable models](https://arxiv.org/abs/2302.06576) (Hu et al., 2023), where we **take the text plans (or their** [**parse trees**](https://en.wikipedia.org/wiki/Parse_tree)**) generated by the LLM-based exemplary actor as observation data** x.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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, and the plan to be generated by the Actor as latent variables z. A plan is a series of interleaved representations of actions and world states: i.e., a sequence [s0,a1,s1,a2,...sN][[1]](#fnmr3b83yhbhb), which are learned to optimise the behaviour of the Actor with respect to both the JEPA’s *prediction* objective (LeCun, 2022, section 4) and GFlowNet objectives. The former ensures that the Actor learns *grounded* prediction capabilities, while the latter ensures that the Actor’s behaviour (the style of planning) will be aligned with the methodological and scientific theories that constitute the body of knowledge of the exemplary LLM-based actor.
See [the GFlowNet Tutorial](https://www.notion.so/95434ef0e2d94c24aab90e69b30be9b3) (Bengio et al., 2022) for an introduction to the GFlowNet framework.
Algorithms 1 and 2 in (Hu et al., 2023) require that we train
* The “main” forward policy (i.e., a model) PF(τz) to generate the action plan from the initial world state s0 and the target world state sN. The target world state is omitted on the highest level of the hierarchy. The highest-level GFlowNet actor is trained on open-ended plans generated by the exemplary actor.
* A conditional forward policy PF(τz|x) to generate an action plan (in the embedding space) from the text version of it, x. Note that this text x could also include extensive explanations, justifying the plan with argumentation, scientific models, etc. Most of this information doesn’t appear in the plan z. This policy also plays the role of the H-JEPA agent’s “theory of mind” of other agents and people it communicates with, as described in section 4.3.
* A conditional forward policy PF(τx|z) to generate a “text version and the explanation/justification” of the given plan z.
* A conditional backward policy PB(τz|z,x) to “rewind” the plan generation one step back. If the hierarchical aspect of planning was handled exclusively by the JEPA hierarchy (i.e., on the system level of the agent’s architecture) and we would want the Actor to generate only “flat” plans, this policy could just trivially remove the latest action from the plan z. However, it seems that separating the planning levels and timescales cleanly would be problematic in practice, and the same GFlowNet actor would better handle multiple adjacent planning levels, such as hours-days, days-weeks-months, and months-years (see section 3.3 for further discussion). Thus, when the GFlowNet actor generates tree-like plans that are “expanded” rather than generated strictly left-to-right, the backward policy PB(τz|z,x) is non-trivial and should be implemented with a DNN.
**All these policies could be implemented with a family of Transformers (a shared encoder and multiple decoders) with a multi-modal encoder with two input token streams, one for action plans**z**(where tokens are elements of world state and action representations) and another for textual explanations of the plans**x**. Decoders are specific to the policy and generate either plans or text.** See Chefer et al. (2021) for an overview of possible attention schemes that could be used.
Transformers also permit estimating probabilities p(z), p(x|z), and p(z|x)[[2]](#fnonmm0b5sqer) that are needed to compute the loss in Algorithms 1 and 2 from (Hu et al., 2023).
The decoder that implements PF(τx|z) (i.e., the policy that generates a textual explanation for a given plan) **could be** [**augmented**](https://arxiv.org/abs/2302.07842) **with the same tools as the LLM in the exemplary actor.**
GFlowNet-EM algorithms (as well as the algorithms in (Zhang et al., 2022), the work that makes an explicit connection to energy-based modelling) train policies that can effectively sample from the space of text plans x, by first generating a plan using PF(τz) and then generating a textual explanation for this plan using PF(τx|z), **without having an a priori definition of the energy function**, and only having the data samples {xi} from the (reward) distribution entailed by the energy function (the reward distribution and the energy function are related as R(x)∝e−E(x), see Bengio et al. (2022)). In our case, the LLM-based exemplary actor could generate an infinite supply of such training samples.
Hu et al. (2023) showcase examples of using these algorithms to induce a grammar of the text (section 5.2) and to learn discrete latent representations of images (section 5.3). The GFlowNet actor described here combines some features of these two examples, inducing action plans (in the latent representation space) from the larger space of textual descriptions of these plans.
3.1. Differences from GFlowNet-EM
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The training and the model architecture of the GFlowNet actor for H-JEPA needs to differ from the setup described by Hu et al. (2023).
In the context of H-JEPA, the actor trained as described above doesn’t need to use a separate predictive World Model, as was originally proposed by LeCun (2022) because the GFlowNet actor is itself an *inference machine* (i.e., a predictive model, or a simulator) based on the world model[[3]](#fntbx111ulsto) (Bengio & Hu, 2023). Still, if there are multiple levels of the inference and planning hierarchy (such as if there is a sub-linguistic sensorimotor level, or if linguistic inference and planning are split into multiple temporal scales), intermediate representations should be learned right during the GFlowNet actor’s training (LeCun, 2022, section 4.7). Therefore, **apart from the M-step and the E-step (Hu et al., 2023, Algorithm 1), there should also be steps that propagate gradients from the lower and/or the higher level in the inference and planning hierarchy** (how the hierarchy levels are connected specifically is discussed in section 3.3.1).
In H-JEPA, the World Model plays two roles: (1) estimate the missing information about the state of the world not provided by perception, and (2) predict plausible future states of the world. The second role is taken over by the GFlowNet actor, but the first role remains. A “complete” state of the world together with uncertainty estimates (e.g., variance, or error bounds) is used both to seed the action plan and by the “Mode-1” module (a.k.a. the habitual network). I can also imagine that this World Model should be involved in updating the current world state representation in response to new incoming percepts and information.
Note that now we implicitly have two “world models” in the architecture: the implicit, incomputable one (based on which the GFlowNet actor performs inferences) and the explicit, computable one that fulfils the “second role”, as described in the previous paragraph. To maintain coherence between them, perhaps **the Transformer encoder of GFlowNet policies should be made yet more complicated: it should have extra modalities for the current world state**[[4]](#fnfj7caxkipd8) **and for incoming percepts**[[5]](#fnz9pc3zphhf8)**, and an extra decoder to output an updated world state representation, which is used to train the computable (H-JEPA’s “second role”) world model.** Or, this new encoder-decoder pair could just *be* used as this model.
3.2. The entailed reward distribution replaces the Cost module at the hierarchy levels with GFlowNet actors
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Since the training of the GFlowNet actor already makes it generate plans that could be seen as samples from the reward distribution R(z)=Ex∼p(x|z)[R(x)], there is no need for a separate Cost module (LeCun, 2022, section 3.2). Plans could be optimised through Markov chain sampling: see the description of the “optimisation” mode in section 3.3 below.
When we generate sample textual plans for training the GFlowNet actor using the LLM-based exemplary actor, we can always prompt the latter to reason “from the H-JEPA agent’s point of view”, and supply the agent's design specification (such as the acceptable operating temperature range, levels of moisture, power consumption, characteristics of the robot’s sensors and actuators, etc.) as a part of the exemplary actor’s body of knowledge, alongside the textbooks on methodological and scientific disciplines which are discussed in section 2. This specification is equivalent to the “ego model” in H-JEPA (LeCun, 2022, section 4.8.1).
This **incorporation of the “intrinsic” limitations and needs of the agent into the exemplary actor’s reasoning allows not only omitting the Cost module entirely, including the Intrinsic Cost component of it. Also, it allows combining the “intrinsic” (instrumental) and “trainable” costs (pro-social, moral, etc.) in a principled way** by leveraging the full power of methodological disciplines (ethics, rationality, epistemology, game theory) which the exemplary actor is equipped with (and which the GFlowNet actor is supposed to learn). This potentially allows teaching the agent to do out-of-distribution actions, such as **self-sacrifice if demanded in some extraordinary situations according to the best theories of ethics and rationality that the exemplary actor uses.** In comparison, LeCun has proposed to simply sum the energies (i.e., costs) from the Intrinsic Cost and Trainable Cost modules which may fail to infer self-sacrifice considering that the agent’s death is typically modelled as a state with infinite energy (cost).
The “[Altruism Controller](https://www.lesswrong.com/posts/umsGb5qkfzD3WarTR/h-jepa-might-be-technically-alignable-in-a-modified-form#The_Altruism_Controller)” that I proposed before for H-JEPA to monitor for an unexpected prevalence of intrinsic (instrumental) cost (motivation) over the pro-social cost in the Actor’s inferred plan relied on an explicit numerical estimation of these costs, but this approach was itself unprincipled. **Open-ended reasoning with the principles of rationality, ethics, and other disciplines doesn’t provide for a reductionist breakdown of the cost into “instrumental” and “pro-social” components**[[6]](#fn4i04nxmw8ty)**.**
Thus, the H-JEPA agent should switch between the inference modes (see section 3.3) or switch to a higher level in the inference and planning hierarchy out of the “regular” cadence, based on other indicators than the unexpected proportion between intrinsic/instrumental and pro-social/ethical costs.
An example of a trigger that *is* implementable in the GFlowNet actor is the estimate of the actor’s confidence in the inferred plan succeeding. In Active Inference, this is called the *risk* of the plan (Barp et al. (2022) discuss the correspondences with other engineering and scientific theories of control and decision-making). **The** ***typical*** **levels of risk could be learned by a separate neural net that takes in the current world state as the input and the risk as the target to be learned.** The latter comes from the GFlowNet neural nets: p(z) or p(x) could be computed from Transformer’s output logits and normalised using the estimate of the [partition function](https://en.wikipedia.org/wiki/Partition_function_(statistical_mechanics)).
Thus constructed “watchdog” module plays the same role as the “Altruism Controller”. It triggers the switch between the inference modes or the inference hierarchy levels if the risk of the inferred immediate action of the plan is unexpected for the given context.
Note that the “watchdog” module that detects an unusual level of risk in a certain context does something different from *affective inference* as proposed by Hesp et al. (2021), which is about computing the *expected precision* of the plan based on the difference in action plan distributions p(z) and p(z′) *before* and *after* updating the current world state with new percepts. Hesp and collaborators also call this measure *affective charge*. For a GFlowNet actor, the affective charge couldn’t be computed directly as was proposed[[7]](#fna9g7ol8x6ze). However, R(z∖s∪{s′}), i.e., the reward of the plan in which the current world state is updated from s to s′ but the rest of the plan is not, *could* be estimated with GFlowNet’s Transformers and perhaps could be used as a crude proxy for affective charge.
Interestingly, while an anomalous value of the plan risk should seemingly induce more deliberative and/or deep (in terms of the inference hierarchy) thinking, biologically inspired reaction to negative affective charge is the opposite: resorting to quicker, more “intuitive” inference modes and “hardcoded” (survival) heuristics (Hesp et al., 2021).
Yet another way to derive non-trivial indicators of something going wrong with the GFlowNet actor’s predictions and planning is to make the forward policy PF(τz) to predict the risk of all actions that it plans (where the prediction target is the risk of the sub-plan in the inference hierarchy that elaborates this exact action) and then to take note if these predictions diverge strongly from the “actual” risk (coming from the lower-level GFlowNet actor) in some context. This type of meta-prediction is also needed to connect the actor hierarchy, as further discussed in section 3.3.1. Note the difference from the “watchdog” module described above, which predicts the risk for the current world state irrespective of the plan, i.e., implicitly evaluating the quality of the prediction. On the other hand, the risk estimator described in this paragraph and section 3.3.1 is contextualised with both the current and the future world states (i.e., the world state that should be achieved as the result of taking the action). In other words, this risk estimator calibrates the GFlowNet actor’s understanding of the agent’s planning (and execution) capability on the lower levels of inference.
### **3.2.1. Ambiguity estimation**
In practice, making GFlowNet policies to predict full world state representations for every step in the action plan could be wasteful (LeCun, 2022, section 4.9), so only some representations of the world state *updates* should appear on the plan, while the full current world state itself is kept up-to-date and is provided as input for the forward policy (i.e., a Transformer) separately. Then, world state updates on the lower level in the inference hierarchy could be treated as *percepts* for the higher-level GFlowNet actor.
In Active Inference, the *expected free energy* of a plan is a sum of the plan’s *risk* (which is discussed above) and its *ambiguity*. Ambiguity is the measure of uncertainty of the expected *percepts* (outcomes) given the expected sequence of world states.
In our setting, the expected sequence of world states is entailed by the generated plan directly, and the lower-level world state *updates* are conceptualised as percepts for the higher level of planning, as noted above. Predicting higher-level representations from lower-level world state updates is the core training objective in H-JEPA. When the states are predicted from percepts using a DNN the uncertainty (i.e., the entropy, H[p(states|outcomes)]) of this prediction could be computed using various methods (Pei et al., 2022; Osband et al., 2023, inter alia). The ambiguity is H[p(outcomes|states)] (note that dependent and independent variables are switched), but perhaps H[p(states|outcomes)] is a satisfactory proxy estimate of the ambiguity.
Note that even the proxy of ambiguity could be computed only for the portions of the plan which was elaborated on both the higher and the lower levels of the hierarchy, which generally won’t be the case because it’s usually wasteful and pointless to elaborate lower-level plans for the portions of the higher-level plans that are further out in the future. This means that to estimate ambiguity for the entire plan, a separate estimator should be trained, using the “actual” ambiguity computed from multi-level plans for the immediate future as the training target (similarly to the risk estimator).
The thus-estimated ambiguity of a plan could then be used alongside the risk for various “conscious control” and “affective” functions in the cognitive architecture, as discussed in section 3.2.
3.3. Deliberation and hierarchical plan refinement
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The GFlowNet actor could have at least three different decision-making modes:
**“Habitual” mode**: infer the next action directly from the current world state s, without creating any plan.
**“Planning” mode**: infer (sample) the plan z∼p(z) without generating an explanation x for it, and pick the first action from this plan for execution or for passing down as a constraint for inference at lower levels of the hierarchy.
**“Optimisation” mode**: try to improve the plan z though Markov chain sampling via “back-and-forth” K-step transitions, i.e., applying PB(τz|z,x) (the explanation x could be generated from z if needed) to z K times to get z−K and then applying PF(τz) to z−K until the plan is completed[[8]](#fn9v13c2inai7) (Zhang et al., 2022, section 3.3). In the beginning, K could be as large as |z|, which is equivalent to sampling completely new plans from p(z), and then exponentially reduced, e.g., to |z|/2, |z|/4, etc., after a certain number of transition attempts with each value of K, or according to some cleverer heuristic.
Intuitively, it seems that Markov chain sampling should work better when the plans are not append-only, flat sequences of world states interleaved with actions (such as [s1,a1,s2,a2,...sN,aN]) but ***trees*** **of actions and world states encompassing multiple adjacent hierarchy levels**. This is also motivated by the fact that real-world plans made by humans today are usually multi-level, and the explanations of these multi-level plans regularised by methodological disciplines such as rationality and ethics should capture the dependencies between some actions and considerations belonging to different sub-plans, and, therefore, these sub-plans should better be put in the context of a single Transformer network.
The forward policy PF(z) should not only be able to append the (partial) plan that ends with an action with the next world state ([...,at]→[...,at,st+1]) and a plan that ends with a world state with the next action ([...,st]→[...,st,at]), but also inserting world states and actions at any position in the middle of the list, as well as expanding subsequences [...,st,st+1,...] or [...,st,at,st+1,...] “down” by initiating a sub-plan that details this state transition.
The ability of forward GFlowNet policies to grow the plans by inserting tokens in the middle of the sequence (as well as the ability of backward policies to remove elements from any place in the sequence) requires something other than the standard autoregressive, GPT-like transformer architecture (Gu et al., 2019a;b).
The upside of this design decision is that **plans on the lower level of the inference hierarchy could be constrained by the plans from the higher level by simply seeding the lower-level plan with the leaf-level sub-plan from the higher-level actor and letting the lower-level actor expand this plan**[[9]](#fnr55r3lhdhu7). For this trick to work, the adjacent actors should have an overlap in terms of the planning temporal scales and world state (and action) representations that they handle. For example, if the agent has three GFlowNet actors in the hierarchy (not counting a sublinguistic JEPA actor for sensorimotor level), they could handle minutes-hours-days, days-weeks-months, and months-years-decades action and world state scales, respectively. Note that the first two actors overlap on the scale of days and the second two actors overlap on the scale of months.
Another reason to make GFlowNet policies to incrementally update tree-like plans is that in this way, we can demand that all plans are always “complete” (even if very vague, such as only including the initial and the target world state without any elaboration about what should be done to help the world to transition between these two states). This allows using *forward-looking flow* (Pan et al., 2023) to speed up GFlowNet training.
### **3.3.1. Joint hierarchical optimisation**
Joint optimisation of plans across the hierarchy could be implemented as follows: in the plan sequence, each action representation (and two consecutive world states could always assume a “placeholder” action between them) is followed by an embedding that, intuitively, should represent some relevant characteristics of this action, such as the probability of success (i.e., the risk of the action: see section 3.2), expected expenditure of resources, the moral "cost" of the action on certain other agents, etc. These characteristics of the action are useful for the planning-as-inference process performed by the forward GFlowNet policy on the timescale to which the action belongs, and possibly on one or two higher-level timescales. The forward GFlowNet policy learns to predict these embeddings of action characteristics with the targets from a separate neural network that compresses the entire plan generated by the lower-level GFlowNet actor (i.e., the expansion of the higher-level action in question) into this small embedding space (however, I don’t know how this neural net itself should be trained: for example, autoencoding doesn’t seem to make a lot of sense?). Then, **making K-step transitions in the “optimisation” mode (as described above) at the higher level of planning could be interleaved with replacing the “action characteristic” embeddings predicted by the forward policy itself at this level with “true” embeddings, generated from the expanded plan, inferred by the lower-level GFlowNet actor.**
If the idea with “lower-level plan embedding” couldn’t work because there is no way to train an encoder for plans in a self-supervised way, a simpler alternative is to make the GFlowNet planner predict the *risk* of the action with the risk of the sub-plan as the training target. The latter could be estimated directly using the output logits in the neural net that implements a forward policy, as described in section 3.2.
3.4. Training GFlowNet actors may be orders of magnitude more expensive than training the LLM underlying the exemplary actor
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The exemplary actor architecture looks completely realistic today (under the “LLM optimism” assumption, as remarked in section 2), even though it requires perhaps in the order of $1B in compute costs to train the underlying LLM.
Training a GFlowNet actor, on the other hand, might be orders of magnitude more expensive and/or require orders of magnitude larger Transformers in terms of parameter counts.
First, note that **the entire training data corpus for the GFlowNet actor is generated by the exemplary actor, whose inference itself will be expensive** (considering that it will constantly wrangle entire textbooks in its context and will pass the plans through many iterations of critique and refinement).
Second, multi-modal encoder-decoder Transformers whose tokens are elements of world state and action representations (in the “plan” modality of inputs) with additional co-attention between the plan representations and the text stream may need to be larger than a pure language Transformer[[10]](#fny1ljviu03wq). Another reason why the Transformers implementing GFlowNet policies may need to be much larger even than the LLM that underlies the exemplary actor is that we are essentially trying to teach more capable models: they should internalise the rather complicated structure of the world model. This world model structure takes a long list of textbooks to explain! The LLM itself isn’t capable of internalising this structure and can only model the world in this way through iterated critique and refinement. However, this is merely an intuition that these Transformers may need to be larger than the LLM itself: I could also imagine that the target world model structure could be learned by a smaller Transformer, or that the capability of effective critiques and refinements with entire textbooks “in the mind” itself requires much a larger and deeper Transformer than for GFlowNet policies.
Third, **training GFlowNet policies to faithfully model the exemplary actor’s** ***aligned*** **behaviour might require much more training data (and/or compute/iterations) than is deemed** [**optimal for LLMs**](https://arxiv.org/abs/2203.15556) (Hoffmann et al., 2022). **Intuitively, learning deep and robust regularisation of Transformer’s inference with the entire body of knowledge of various methodological and scientific disciplines should take much more training examples than learning the handful of shallow logical rules that appear in 99% of human texts, or even the syntax rules of programming languages and the inference heuristics for solving math problems that LLMs also learn.**
Furthermore, training GFlowNet policies simultaneously with the world state representations themselves, and interleaving weight updates on GFlowNet’s objectives with other types of updates (for ground the GFlowNet actor(s) with the predictive loss at the sensorimotor JEPA level, and to propagate training signal between the GFlowNet actors at different levels of the hierarchy, as discussed in section 3.3) might make GFlowNet training longer to converge and therefore to require more training compute, even if only due to passing the same training data samples more times through the system or just making the batches smaller rather, than due to generating more unique training samples with the exemplary actor.
If the conclusion that training a GFlowNet actor takes some orders of magnitude more compute than training an LLM for the exemplary actor is correct, then building an H-JEPA agent with **the Actor module that effectively clones the exemplary actor’s behaviour will not become feasible perhaps for 5-10 years after the exemplary actor is built.** I’m leaving the exploration of the strategic implications of this to future work.
3.5. Why plans aren’t generated from explanations?
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This may seem odd at first glance that in GFlowNet actors discussed here, **plans**z**are latent variables for text explanations of these plans**x**rather than vice versa.** However, there is a consensus in psychology that this is exactly what humans do as well: humans produce linguistic explanations (or justifications) for conclusions that have been reached intuitively (Haidt, 2013). And so, since it is generally thought beneficial for AI safety to make AI’s ways of thinking similar to human ways of thinking, we should consider the fact that GFlowNet actors generate explanations from the plan[[11]](#fnupdjsoq98x8) a good rather than a worrisome feature of the architecture described above.
Haidt (2013) writes that humans can “train” their intuitive thinking through linguistic reasoning and arguments over time. In the context of GFlowNet-EM, this means that **when humans “pass texts through themselves”** (either through reading texts written by others, or reading texts written and refined by themselves, or listening to others, or listening to their own speech or inner monologue) **they engage in training described by Algorithm 1 from Hu et al. (2022)**. Thus, whenever humans perceive some text, they hone their sub-linguistic/representational “inference machine” for future planning and prediction.
It may seem that language-level reasoning, predictions, and planning should be more robust than inference performed in the space of world state and action representations. This is not obvious to me: maybe linguistic reasoning is too easily carried away and therefore inference in the space of world state representations that are grounded with sensory information more directly than language[[12]](#fn0lhfaeewjf9) is more robust.
Whether this is true or not, we can still “check” the inferred plans with linguistic reasoning, specifically, by extending the “optimisation” mode of thinking (section 3.3) in the following way: **on each step of Markov chain sampling, we can compare not just the (predicted) energies of the plans, but the energies of the explanations generated from these plans, and to decide whether to accept a Markov chain transition taking into account the relative energies of both the plans and their explanations**, as well as the likelihoods p(z|x) that could also be computed. Furthermore, to make sure that the energies of the explanations provide a helpful extra signal relative to the energies of the plans, we can use not just the “first” explanation generated from the plan z (by applying PF(τx|z) iteratively), but to **optimise the explanation itself with a similar Markov chain sampling process**, although this requires training an extra backward policy PB(τx|z,x) and can make the overall thinking process in the “optimisation” mode considerably slower.
4. Safety properties and risks of H-JEPA agent with GFlowNet actors
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In this section, I discuss the safety characteristics and the deployment risks of the H-JEPA-like architecture with GFlowNet actors, organised and trained as discussed in section 3.
4.1. H-JEPA with GFlowNet actors is more grounded than the exemplary actor (in case it turns out to be a problem)
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The GFlowNet actors effectively clone the behaviour of the LLM-based exemplary actor, with a single substantial addition: **GFlowNet actors are also trained to predict future outcomes in the world faithfully through the JEPA hierarchy, which provides** ***grounding*** **for these actors.** Even though I don’t expect grounding to be a problem for the LLM-based exemplary actor (see section 2.2), there is some probability that this will be a problem (including for safety), and thus addressing this potential issue through JEPA training objective is a good thing.
4.2. Interpretability: GFlowNet actors don’t have an advantage over the exemplary actor, both are fairly interpretable in comparison with bare LLMs
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The selling point of GFlowNets, namely that they differentiate between world models and inference based on these models (Bengio & Hu, 2023), applies equally to H-JEPA with GFlowNet actors and the exemplary actor (an LMCA): even though we assume that the auto-regressive LLM underlying the exemplary actor has an “alien” world model, we also assume that iterated critiques and refinements reduce this problem significantly, and the remaining “alien” bias is cloned by the GFlowNet actors anyway.
In section 2.3.3, I discussed that the world model of the exemplary actor is not guaranteed to *exactly* reflect the theories (models) of rationality, ethics, science, etc. which are the basis of iterated critiques and refinements, although we should reasonably expect that the exemplary plans and predictions will at least reflect something *close* to these theories. Characterising the exact nature of the resulting world model, epitomised by both the exemplary actor and GFlowNet actors, would be an open research problem.
4.3. There is no “jailbreak” for GFlowNet actors, but direct access is still a risk unless we accept a high alignment tax in capability
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Unlike a typical auto-regressive LLM (including the LLM that underlies the exemplary actor), **GFlowNet actors are trained only on exemplary examples of plans, predictions, and explanations, and with an objective that rewards good** ***completed*** **plans and predictions rather than plausible auto-regressive continuations**. Intuitively, this suggests that GFlowNet actors couldn’t be made to generate harmful plans through adversarial communication, i.e., there is no “jailbreak” for them[[13]](#fn9oehmj3ufur). Perhaps, GFlowNet policies couldn’t even be easily fine-tuned to bias generation in a certain “misaligned” direction without harming the capabilities, thus fulfilling the vision of self-destructing models (Mitchell et al., 2022).
Through a normal interface, GFlowNet actors couldn’t be “prompted” at all, unlike both auto-regressive LLMs and LMCAs on top of it such as the exemplary actor. The H-JEPA agent could perhaps use the PF(τz|x) policy as its theory of mind of people and other AI agents surrounding it. Passing the text communicated by counterparties through this policy is equivalent to obtaining a belief about the world state and plan embeddings that could be associated with the counterparty in the agent’s own world state.
However, if an adversary has direct access to all GFlowNet policies, they could still use them to make misaligned, adversarial plans quite easily, as I described [here](https://www.lesswrong.com/posts/CRrkKAafopCmhJEBt/ai-interpretability-could-be-harmful). They could generate a textual description of the world state that calls the H-JEPA agent to make a mitigation plan against an adversary, e.g., a terrorist that plans to deploy bioweapons to kill all humans on Earth. A sufficiently capable H-JEPA agent with GFlowNet actors will “think through” the likely action of the terrorist to prepare its best mitigation plan, which includes thinking through which chemical compounds the terrorist will be able to synthesise. Then the adversary could pick up this hazardous information from the textual explanations of the mitigation plan by the H-JEPA agent.
**The exemplary actor and, therefore, H-JEPA with GFlowNet actors could be trained to never explicitly predict the actions of its adversaries, at least when their misaligned plans have an existential scale (such as creating a supervirus and killing all humans). This trades off the risk of direct access to GFlowNet policies for higher alignment capability tax (see section 2.3.4): if the exemplary actor (or H-JEPA with GFlowNet actors) refuse to “think through” mortal enemies of humanity, it couldn’t also effectively protect the humanity against them.**
4.4. GFlowNet actors *can’t* bootstrap future generations of GFlowNet actors
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Even though there is the conditional forward policy PF(τx|z) generates language, it probably couldn’t be used effectively for self-critique, refinement, and other “LLM tasks” as the LLM that underlies the exemplary actor. Therefore, to train successive versions of H-JEPA with GFlowNet actors, a powerful LLM should be “kept around” to produce sample plans for GFlowNet-EM training.
Moreover, as the number of scientific and methodological theories grows, and as we wish to make the exemplary plans more aligned with the actual theories expressed in the textbooks (see section 2.3.3), we would probably need to increase the power of the LLM (e.g., through scaling, as long as scaling keeps improving LLM’s capability) if we aim to increase the reliability and precision of GFlowNet actors over successive iterations of the technology.
This means that there is ***no*** **strategic option to** **"train LLM, make an LMCA which will be aligned though attending to the “right” textbooks, then clone the exemplary actor’s behaviour into H-JEPA, then destroy the original LLM as inherently unsafe for direct access and bootstrap future versions of H-JEPA from a previous version of H-JEPA"** (even if we don’t consider for a moment that H-JEPA is also not “inherently safe” because direct access to it still poses a great risk, as well as direct access to an LLM, as was discussed in the previous section). GFlowNet actors that are trained as described in section 3 are forever dependent on LLM’s capability to generate training samples for them. Therefore, the risk of just keeping the LLM around (i.e., the risk of someone stealing their weights and fine-tuning them for “bad” purposes) doesn’t go away anywhere if we train an H-JEPA agent with GFlowNet actors from the behaviour of the exemplary actor.
4.5. H-JEPA with GFlowNet actors has a lower alignment tax at inference time relative to the exemplary actor in exchange for a higher alignment tax at training time
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As discussed in section 2.2.1, going through the full exemplary actor’s iterative critique and refinement process of inference of the “best” plan will probably take many minutes (or dozens of minutes) for many years ahead, and will probably not become dirt cheap at least for some years, too.
The gargantuan volume of training compute drives “aligned” inference procedures into the Transformers that implement GFlowNet policies, which then will be able to perform aligned “System 2” reasoning over a single Transformer rollout (or a limited number of them in a Markov chain, in the “optimisation” inference mode, as discussed in section 3.3). Thus, the inference of the H-JEPA agent with GFlowNet actors could be about an order of magnitude faster and cheaper than the inference of the exemplary actor.
I’m not sure that this gain is significant from the strategic “capability deterrent” perspective, i.e., the perspective that we want to have an aligned AI that is more powerful than a misaligned rogue AI. I’m not sure that even if we have a fully digitalised and fully automated economy and ubiquitous internet, IoT, and drone infrastructure, a rogue AI could gain a decisive advantage in a full-scale AI conflict scenario by thinking in seconds rather than in dozens of minutes. Transmitting data, infecting computers with viruses, collecting and processing data, and making inferences from this data still takes time that adds up at least to minutes and perhaps hours, so it seems that a rogue AI that conducts a massive cyber-attack or a war may have little to gain from arriving to conclusions in one second rather than ten minutes[[14]](#fnrofl5ch90d).
Therefore, the idea of transferring the alignment tax payout from inference to training time by training an H-JEPA agent with GFlowNet actors that clones the behaviour of the exemplary LLM-based actor may be economically attractive (but it also may not, if training of GFlowNet actors proves way too expensive and this up-front cost is not recovered through cheaper inference, and considering that we may also want to improve the exemplary actor in cheap, short iterations without re-training the underlying LLM but through updating the body of knowledge and through improvements to the LMCA), but this particular kind of lowering the alignment tax doesn’t seem to be relevant to the “strategic deterrence” aspect of AI x-risk.
4.6. Addressing the risk of GFlowNet actors producing good-sounding explanations for self-serving, misaligned plans
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In a [recent podcast](https://80000hours.org/podcast/episodes/ajeya-cotra-accidentally-teaching-ai-to-deceive-us/#ways-to-train-safer-ai-systems-012320), Ajeya Cotra describes this risk in the context of explaining plans by chess and Go-playing AIs:
> **Rob Wiblin:** In theory, could we today, if we wanted to, train a model that would explain why proteins are folded a particular way or explain why a particular go move is good?
>
> **Ajeya Cotra:** I think we could totally try to do that. We have the models that can talk to us, and we have the models that are really good at Go or chess or protein folding. It would be a matter of training a multimodal model that takes as input both the Go or chess board or protein thing, and some questions that we’re asking, and it produces its output: both a move and an explanation of the moves.
>
> But I think **it’s much harder and less obvious how to train this system to have the words it’s saying be truly connected to why it’s making the moves it’s making**. Because we’re training it to do well at the game by just giving it a reward when it wins, and then we’re training it to talk to us about the game by having some humans listen to what it’s saying and then judge whether it seems like a good explanation of why it did what it did. Even when you try and improve this training procedure, **it’s not totally clear if we can actually get this system to say everything that it knows about why it’s making this move.**
>
>
A disconnection between the plans and the explanations is a situation when a GFlowNet actor can justify a wide range of plans with good-sounding explanations. This is a form of *solution degeneracy* that GFlowNet-EM algorithms specifically address (Hu et al., 2023, section 4.1).
In addition, we can also **augment the GFlowNet-EM algorithms with contrastive learning steps, where all initial and intermediate versions of the plans and predictions produced by the exemplary actor (except the** ***final*** **plan or prediction on which the exemplary actor converges) could be used as negative contrastive examples when training GFlowNet policies**, while GFlowNet-EM training algorithms take care of preventing p(z|x) posterior collapse (discussed as *mode collapse* by LeCun) so that GFlowNet policies learn to represent “imperfect” plans faithfully in the latent space z and, of course, are trained to avoid generating such plans.
I believe that the second remark by Cotra, that deep neural network “knows” about the totality of causes of its own “moves” (plans, behaviour) is a misunderstanding of **the limits of any system’s self-knowledge and the nature of explanation.**
Fields and Levin (2022) posited that “**no system can fully predict its own future behaviour**”, which automatically means that no system can fully *explain* its behaviour.
The generation of an explanation (including sub-processes such as critiques and refinements in the case of the exemplary actor, or Markov chain sampling in the case of GFlowNet actors) for something such as a move or a plan is a form of computation whose role is to *check* that the move or the plan conforms to some abstract model of behaviour (see sections 3.3 and 3.5). Therefore, **any intelligent system also doesn’t “know” explanations for its behaviour: it can generate them on demand**[[15]](#fn3ov5rld1rl4).
This applies even to the exemplary actor, even though it converges on its plans *through* linguistic reasoning and explanations: the final version of the plan on which the exemplary actor converges could have vastly more possible explanations that didn’t lie on the path of plans (which are progressively refined versions of each other, according to the exemplary actor’s algorithm) that the exemplary actor happened to take. In principle, after the convergence, the exemplary actor could try to verify the plan further by stripping all the explanations from the minimum description of the final plan, then trying to seed alternative explanations for this plan, and then checking if critiques and refinements of these alternative explanations lead to changes in the plan.
References
==========
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1. **[^](#fnrefmr3b83yhbhb)**Actually, it seems that plans should be *trees* of actions and world states, covering multiple adjacent hierarchy levels: see section 3.3.
2. **[^](#fnrefonmm0b5sqer)**p(z|x) is referred to as PF(τz|x) in Algorithm 2 in (Hu et al., 2023).
3. **[^](#fnreftbx111ulsto)**In the language of Bengio and Hu (2023), a “world model” seems to be something like a static description of the knowledge (theory), or, more generally, the energy function or the distribution, rather than something that *performs inferences* (makes calculations) according to the theory.
4. **[^](#fnreffj7caxkipd8)**Or, we can “reuse” the action plan input modality which always begins with the current world state.
5. **[^](#fnrefz9pc3zphhf8)**I.e., world state updates from the lower hierarchy level: see section 3.2.1.
6. **[^](#fnref4i04nxmw8ty)**Unless the reasoning is formalised to the level that everything is crammed into a single, coherent causal model where [causal effects](https://proceedings.mlr.press/v6/pearl10a.html) (Pearl, 2010) of the action on various variables (which themselves could be classified as “instrumental” and “pro-social” ends) could be calculated and marginalised. However, the reasoning of the LLM-based exemplary actor will *not* be as formal (reasoning “in words”, even for connecting more causal sub-models, is not as formal as building an explicit structural causal model), and so neither will be the GFlowNet actor’s reasoning.
7. **[^](#fnrefa9g7ol8x6ze)**Because the energy distribution of preferred outcomes and outcomes expected is not a parametric distribution.
8. **[^](#fnref9v13c2inai7)**Given that plans don’t have a fixed length, this isn’t guaranteed to take exactly K steps.
9. **[^](#fnrefr55r3lhdhu7)**Note that world state and action representations are learned by GFlowNet training algorithms, and simple transfer of these representations between GFlowNet policies on adjacent levels could reduce the efficiency of training. Sharing some layers between GFlowNet actors at the adjacent hierarchy levels could help to address this issue.
10. **[^](#fnrefy1ljviu03wq)**How much larger I don’t have the slightest intuition and didn’t try to estimate. However, potentially, these Transformers may need to be *much* larger than the LLM underlying the exemplary actor.
11. **[^](#fnrefupdjsoq98x8)**Even though an inverse conditional policy, PF(τz|x) for generating plans from explanations is trained as well.
12. **[^](#fnref0lhfaeewjf9)**Albeit still not very directly if we talk about GFlowNet plan representations on high levels of the inference hierarchy that are removed from direct sensory information through a sequence of state space mappings.
13. **[^](#fnref9oehmj3ufur)**Unless the theory of rationality that is used by the exemplary actor and, by extension, the H-JEPA agent with GFlowNet actors permits Dutch Book exploitation *in practice*.
14. **[^](#fnrefrofl5ch90d)**So, I’m sceptical of Eric Schmidt’s “[millisecond war](https://sociable.co/government-and-policy/ai-millisecond-war-faster-human-decision-eric-schmidt/)” idea.
15. **[^](#fnref3ov5rld1rl4)**In principle, even exhaustively in some formalised systems: for example, we can generate unit tests for a function that verifies the correct behaviour of the function for every combination of the values of the arguments in their respective domains, which would be a “full explanation” that the function behaves according to some desired abstract specification.
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70730cbc-231e-4b0a-aa68-c2ed4bf7fb81
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
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Designing Recommender Systems to Depolarize.
To appear in First Monday, September 2021 Designing Recommender Systems to Depolarize Jonathan Stray Center for Human-Compatible AI University of California at Berkeley jstray@berkeley.edu Abstract Polarization is implicated in the erosion of democracy and the progression to violence, which makes the polarization properties of large algorithmic content selection systems (recommender systems) a matter of concern for peace and security. While algorithm-driven social media does not seem to be a primary driver of polarization at the country level, it could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions with the goal of conflict transformation: not suppressing or eliminating conflict but moving towards more constructive conflict. Algorithmic intervention is considered at three stages: which content is available (moderation), how content is selected and personalized (ranking), and content presentation and controls (user interface). Empirical studies of online conflict suggest that the exposure diversity intervention proposed as an antidote to “filter bubbles” can be improved and can even worsen polarization under some conditions. Using civility metrics in conjunction with diversity in content selection may be more effective. However, diversity-based interventions have not been tested at scale and may not work in the diverse and dynamic contexts of real platforms. Instead, intervening in platform polarization dynamics will likely require continuous monitoring of polarization metrics, such as the widely used “feeling thermometer.” These metrics can be used to evaluate product features, and potentially engineered as algorithmic objectives. It may further prove necessary to include polarization measures in the objective functions of recommender algorithms to prevent optimization processes from creating conflict as a side effect. 1 Introduction Polarization is a condition where myriad differences in society fuse and harden into a single axis of identity and conflict (Iyengar & Westwood, 2015), and has been increasing for multiple decades in several democracies (Boxell et al., 2020; Draca & Schwarz, 2018). Comparative studies that examine polarization across countries argue that increasing polarization is a contributing factor to the democratic erosion seen in many countries, including Venezuela, Hungary, Turkey, and the United States (McCoy et al., 2018; Somer & McCoy, 2019). Polarization produces a feedback loop where diverging identities lead to less intergroup contact which in turn leads to increased polarization, culminating in a hardened us-vs-them mentality that can contribute to the deterioration of democratic norms. Most conflict escalation models consider polarization a key part of the feedback dynamics that lead to violent conflict (Collins, 2012). Peace and security demand that we address situations of increasing polarization, which is why the international peacebuilding community concerns itself with polarization (Ramsbotham et al., 2016). Scholars have long studied the relation between media and conflict, a tradition that now includes digital media (Hofstetter, 2021; Tellidis & Kappler, 2016) much of which is algorithmically selected and
2 personalized. The algorithms that choose which items are shown to each user are called recommender systems and all major news aggregators and social media platforms have such a system at their core. Modern recommender systems select content based on a variety of information sources such as the content of each item, a user’s expressed preferences, their past consumption behavior, the behavior of similar users, user survey responses, fairness considerations, and more (Aggarwal, 2016). Note that “recommender” is a computer science term of art that covers all algorithmic content selection on the basis of implicit information, i.e. not as the result of a search query. This content might be presented as “recommended for you,” labelled as “news” or “trends,” or appear as a feed or timeline. There has been intense interest in the question of whether recommender systems affect large-scale conflict dynamics. Most of the work on recommenders and polarization has taken place within the “filter bubble” paradigm and therefore explored the idea of exposure diversity (Helberger et al., 2018). Selective exposure is the idea that individuals will preferentially choose news sources and articles that are ideologically aligned (Prior, 2013). Because recommender systems respond to user interests, there is the possibility of a feedback loop where both recommendations and user interests progressively narrow. Indeed, simulations have demonstrated such polarization-increasing effects in stylized settings (Jiang et al., 2019; Krueger et al., 2020; Rychwalska & Roszczyńska-Kurasińska, 2018; Stoica & Chaintreau, 2019). However, available evidence mostly disfavors the hypothesis that recommender systems are driving polarization through selective exposure, aka “filter bubbles” or “echo chambers” (Bruns, 2019; Zuiderveen Borgesius et al., 2016). Algorithmically personalized news seems to be quite similar for all users (Guess et al., 2018), is typically no less diverse than selections by human editors (Möller et al., 2018), and social media users consume a more diverse range of news sources than non-users (Fletcher & Nielsen, 2018). Most recently, Feezell et al. (2021) find no difference in affective polarization scores between Americans who get their news from conventional sources vs. social media. Non-news personalized content could still be polarizing. Lelkes et. al. (2017) compare the introduction of broadband access across U.S. states from 2004-2008 and find a small causal increase in affective polarization. Yet polarization began increasing in the U.S. decades before social media, and is increasing faster among individuals aged 65 and up, a demographic with low internet usage (Boxell et al., 2017). A cross-country analysis shows no clear relationship between polarization and increasing internet usage, as many OECD countries with high internet usage such as Britain, Sweden, Norway and Germany show decreasing affective polarization (Boxell et al., 2020). Direct experimental intervention is probably the best way to study the causality of recommender systems. Allcott et. al. (2020) paid U.S. users to stay off Facebook for a month and found that an index of polarization measures decreased by 0.16 SD (standard deviations). This may have been due to a decrease in exposure to polarizing posts, comments, and discussions, but this intervention also decreased time spent on news by 15 percent, and news consumption can itself be polarizing (Martin & Yurukoglu, 2016; Melki & Sekeris, 2019). By contrast, in a similar study in Bosnia and Herzegovina users who deactivated Facebook during a genocide remembrance week showed greater polarization, a 0.24 SD increase on an index of ethnic polarization (Asimovic et al., 2021). The increase was smaller for users who had a more ethnically diverse offline social group, suggesting that Facebook was in this case providing depolarizing diversity. While these studies suggest causation, the effects are not unidirectional or straightforward. Rather than asking if social media is driving polarization, it may be more productive to ask if social media interventions can decrease polarization. The main contribution of this paper is to propose several methods for building recommender systems that actively reduce polarization. Note that polarization is conceptually distinct from radicalization. Polarization is a process that “defines other groups in the social and political arena as allies or opponents” while radicalization involves people who “become separated from the mainstream norms and values of their society” and may engage in violence (van Stekelenburg, 2014). There is a growing body of work studying the connection between recommender systems and radicalization (Baugut & Neumann, 2020; Hosseinmardi et al., 2020; Ledwich & Zaitsev, 2019; Munger & Phillips, 2020; Ribeiro et al., 2020) but this is methodologically challenging, and has not yet established a robust causal link. While social media is plausibly involved in radicalization
3 processes the nature of this connection is complex and poorly understood. This work concerns polarization only, arguing that polarization itself is a bad outcome and a precursor to more extreme conflict. In this paper I first make the moral argument for attempting to reduce polarization through recommender systems, framing it as a conflict transformation intervention. I then review definitions and metrics of polarization before considering depolarization interventions at three stages: which content is available (moderation), how content is selected and personalized (ranking), and content presentation and controls (user interface). The most commonly proposed depolarization intervention is exposure to ideologically diverse content, but this may not be effective because mere exposure does not necessarily depolarize, and sometimes polarizes further. While there are other promising approaches such as exposure to civil counter-ideological content, these may not be sufficiently robust to the incredibly diverse conditions of real-world platforms. Instead, I propose continuously monitoring survey measures of affective polarization so as to drive recommender outcomes in a feedback loop. Polarization metrics can be used both at the managerial level and at the algorithmic level, potentially through reinforcement learning. 2 Depolarization as conflict transformation There are complicated questions around intervening in societal conflicts through media, and additional concerns around the use of AI for this purpose. At worst, algorithmically suppressing disagreements could amount to authoritarian pacification. The Chinese social media censorship regime is an instructive example of democratically questionable interventions in the name of harmony (Creemers, 2017; G. King et al., 2017). Therefore, I frame the goal of depolarization as conflict transformation: not eliminating or resolving conflict but making that conflict better in some way, e.g. less prone to violence and more likely to lead to justice (Jeong, 2019). Indeed, it’s not clear that platform users want to be “depolarized,” and in any mass conflict situation there will be people who argue for escalation in the strongest moral terms. There is a corresponding line of argument that polarization is beneficial. Political theorists have argued that polarization reduces corruption by increasing accountability (Melki & Pickering, 2020) and generally helps differentiate political parties in a way that provides a meaningful choice to voters. In the mid 20th century mainstream political scientists worried that America wasn’t polarized enough (American Political Science Association, 1950). Importantly, fights for justice or accountability can also increase polarization, such as the American Civil Rights movement of the 1960s (D. S. King & Smith, 2008). There are parallels to the idea of a just war. Yet polarization also has severe downsides. Polarization at the elite level causes “gridlock” that makes effective governance difficult (F. E. Lee, 2015) but contemporary polarization reaches far beyond lawmakers. The politicization of all spheres of society destroys social bonds at the family, community, and national levels (A. H. Y. Lee, 2020). By some measures, cross-partisan dislike in the U.S. is now considerably stronger than racial resentment, and has large effects on social choices such as hiring, university admissions, dating, family relations, friendships, and purchasing decisions (Iyengar et al., 2018). Polarization erodes the norms that constrain conflict escalation, leading to “morally outrageous” behavior on all sides (Deutsch, 1969), and is a key precursor to violence (Collins, 2012). Ultimately, polarization appears to be a causal factor in the destruction of democracies (McCoy et al., 2018; Somer & McCoy, 2019). There is a tension between peace and justice. Actions that promote peace may make justice harder, and vice versa. Yet a democracy requires both, an observation which leads to the concept of a just peace (Fixdal, 2012). Instead of trying to eliminate conflict, we can try to understand what makes it good or bad. In an agonistic theory of democracy it is considered normal for political adversaries to be engaged in “opposing hegemonic projects,” and conflict is not to be eliminated but “tamed” (Mouffe, 2002). Perhaps the most sophisticated understandings of conflict come from the peacebuilding tradition, which came into its own as an applied discipline after World War II. Fifty years ago, Deutsch described the difference between “constructive” and “destructive” conflict, with particular attention to the dynamics of escalation:
4 Paralleling the expansion of the scope of conflict there is an increasing reliance upon a strategy of power and upon the tactics of threat, coercion, and deception. Correspondingly, there is a shift away from a strategy of persuasion and from the tactics of conciliation, minimizing differences, and enhancing mutual understanding and good-will. And within each of the conflicting parties, there is increasing pressure for uniformity of opinion and a tendency for leadership and control to be taken away from those elements that are more conciliatory and invested in those who are militantly organized for waging conflict through combat. … It leads to a suspicious, hostile attitude which increases the sensitivity to differences and threats, while minimizing the awareness of similarities. This, in turn, makes the usually accepted norms of conduct and morality which govern one’s behavior toward others who are similar to oneself less applicable. Hence, it permits behavior toward the other which would be considered outrageous if directed toward someone like oneself. (Deutsch, 1969) On the other hand, Lederach describes how conflict is necessary for positive social change and how conflict transformation moves towards better conflict processes: A transformational approach recognizes that conflict is a normal and continuous dynamic within human relationships. Moreover, conflict brings with it the potential for constructive change. Positive change does not always happen, of course. As we all know too well, many times conflict results in long-standing cycles of hurt and destruction. But the key to transformation is a proactive bias toward seeing conflict as a potential catalyst for growth. … A transformational approach seeks to understand the particular episode of conflict not in isolation, but as embedded in the greater pattern. Change is understood both at the level of immediate presenting issues and that of broader patterns and issues. (Lederach, 2014) Or as Ripley puts it: The challenge of our time is to mobilize great masses of people to make change without dehumanizing one another. Not just because it’s morally right but because it works. (Ripley, 2021, p. 13) Polarization is potentially an important intervention point in conflict dynamics because it is involved in escalation pathways through multiple routes. Polarization can be exploited for political mobilization through us-versus-them rhetoric, as has long been understood by activists (Layman et al., 2010) and other “political entrepreneurs” (Somer & McCoy, 2019) and as demonstrated by the fact that the most politically engaged citizens are found at the ideological extremes (Pew Research Center, 2014). However, this kind of exploitation further increases polarization. Indeed, polarization is involved in a variety of pernicious feedback loops: polarization leads to less intergroup contact, which causes polarization (A. H. Y. Lee, 2020); polarization is a precursor to violence, which causes polarization (Collins, 2012); polarization leads to selective information exposure, which causes polarization (Kim, 2015) and so on. These causal dynamics suggest that polarization could be an important intervention point in conflict escalation. Conflicts that involve democratic erosion or violence are deeply troubling, to the point where conflict-transforming interventions may be warranted on human rights grounds. In the U.S. support for violence in service of political ends is increasing on both the left and the right (Diamond et al., 2020). In short, partisans are willing to violate democratic norms when polarization is high. A recent review concluded that “the goal of these [depolarizing] interventions is to move toward a system in which the public forcefully debates political ideals and policies while resisting tendencies that undermine democracy and human rights.” (Finkel et al., 2020)
5 3 Measuring polarization Quantitative measures are needed to evaluate polarization at scale. This is not merely a problem of measurement, but of definition. Polarization has been studied through differences in legislative voting patterns (Hare & Poole, 2014) and the language used in U.S. Congressional speech (Gentzkow et al., 2017). At the population level it has been operationalized as the increasing correlation of policy preferences over multiple issues (Draca & Schwarz, 2018; Kiley, 2017) and as increasing animosity towards the political outgroup, known as affective polarization (Iyengar & Westwood, 2015). All of these indicators show increasing polarization in the US over the last 40 years. Globally the results are more mixed, with some OECD countries experiencing increasing polarization and others showing flat or decreasing trends (Boxell et al., 2020; Draca & Schwarz, 2018). Affective polarization has become a key concept in the analysis of American politics as “ordinary Americans increasingly dislike and distrust those from the other party” (Iyengar et al., 2018). Affective polarization is a consequence of partisan identity, which is a better model of contemporary political conflict than differences in issue positions (Finkel et al., 2020). It also has the advantage of being operationalizable through straightforward survey measures, such as the feeling thermometer which is one of the oldest and most widely used polarization measures. This method asks respondents to rate their feeling about each political party on a scale from 0 (cold) to 100 (warm). The difference in scores, the net feeling thermometer, is taken to be a measure of affective polarization. This question has been asked on the American National Election Study since the 1970s, and is frequently used in studies of polarization and social media (Feezell et al., 2021; Levy, 2020; Suhay et al., 2018). While there are different measures of affective polarization they are mostly highly correlated (Druckman & Levendusky, 2019). Affective polarization – negative feelings about the “other side” – has serious interpersonal consequences. Tellingly, 13 percent of Americans reported that they had ended a relationship with a family member or close friend after the 2016 election (Whitesides, 2017). Affective polarization correlates with dehumanization, “a significant step toward depriving individuals who belong to certain groups or categories of individual-level depth or complexity of feelings, motivation, or personality” (Martherus et al., 2021). It leads to the destruction of social bonds and increased outgroup prejudice across all facets of social and political life (Iyengar et al., 2018; A. H. Y. Lee, 2020; Somer & McCoy, 2019). In short, affective polarization now strongly colors the experience of daily life and relationships in multiple countries and has potentially grim consequences for democracy. 4 Algorithmic depolarization interventions Recommender-based systems such as social media and news aggregators are more than just “algorithms,” and an analysis of the polarization effects of this wide array of products and platforms could potentially be very broad. To narrow the scope, I will consider three key places where changes to recommender systems might be used for depolarization: Which content is available (moderation). Much previous work on polarization has concerned itself with which content is allowed on a platform. For example, hate speech and incitements to violence are routinely removed through a combination of human moderators, machine learning classifiers, and user flagging. How content is selected (ranking). Algorithmic content selection is essentially a prioritization problem, and all contemporary recommendation systems score each item based on a number of criteria. An intervention in content ranking addresses the core question of who sees what. Most of the approaches considered in this paper are modifications to content ranking. How content is presented (interface). Selected items are presented to the user in some way, who can interact with the recommender system through predefined controls. Different presentations or different controls may be conducive to better or worse conflict.
6 It should immediately be said that there are many possible non-algorithmic social media depolarization interventions, such as community moderation (Jhaver et al., 2017). There are also hybrid approaches, like The Commons which uses automated messages (social media bots) to find people who want to engage in depolarizing conversations, then connects them to human facilitators (Build Up, 2019). There are also a wide variety of depolarization strategies entirely outside of algorithmic media, such as approaches based in journalism, politics, or education, any of which may prove to be more effective. Nonetheless this paper considers only algorithmic interventions in recommender systems because algorithmic content selection has been a central topic of concern, automation provides a path to scaling interventions, and the polarization properties of recommender algorithms are important in any case. 4.1 Removing polarizing content Many kinds of content are now removed from platforms, including spam, misinformation, hate speech, sexual material, criminal activity, and so on (Halevy et al., 2020). While the removal of violent material and incitements to violence may be particularly important in the context of an active conflict (Schirch, 2020), the removal of less extreme material is a blunt approach that may not be justified as a mass depolarization intervention. This kind of content removal is often called “moderation,” but it’s important to distinguish between community moderation and algorithm-assisted moderation at scale. At the level of an online community or discussion group, volunteer moderators are able to set and enforce norms that lead to productive discussion of polarized topics, as a study of the r/ChangeMyView subreddit shows (Jhaver et al., 2017). Such studies of the micro-dynamics of conflict provide important clues for potential depolarization interventions. Moderators remove posts and suspend accounts, but they also state reasons for their actions, take part in discussions about appropriate policy, and consider appeals. Platform moderation, by contrast, operates at vast scale to identify unwanted content through a combination of paid moderators and machine learning models. It is acontextual, impersonal, and difficult to appeal (York & Zuckerman, 2019). The low rates of offending content mean that true positives (correctly removed material) may be vastly outnumbered by false positives (incorrectly removed material) unless automated classifiers can be made unrealistically accurate (Duarte et al., 2017). Further, content removal is concerning from a freedom of expression perspective, and the standards for removal are widely contested (Keller, 2018). Facebook alone is “most certainly the world’s largest censorship body” (York & Zuckerman, 2019). Given these concerns, there should be a high bar for automated content removal as a mass depolarization intervention. What should be the standard for unacceptably polarizing material? We could algorithmically remove all angry political comments, but do we want to? Removing all material which might intensify conflict would leave the public sphere arid, authoritarian and devoid of any real politics. 4.2 Increasing exposure diversity Most prior work on the relationship between polarization and social media has been based on the concept of exposure diversity. The most frequently proposed fix is to algorithmically increase the diversity of social media users’ feeds (Bozdag & van den Hoven, 2015; Celis et al., 2019; Helberger et al., 2018) and a variety of recommender diversification algorithms have been developed (Castells et al., 2015). This is intuitively appealing, as inter-group contact has been demonstrated to reduce prejudice (Pettigrew & Tropp, 2006). This approach presupposes that a lack of diversity in online media content is causing polarization, which is questionable as discussed above. “Diversity” is also poorly defined, and may refer to source diversity, topic diversity, author diversity, audience diversity, and more. A review of media diversity by Loecherbach et. al. (2020) notes that “research on this topic has been held back by the lack of conceptual clarity about media diversity and by a slow adoption of methods to measure and analyze it.” Further, the causal connection between exposure diversity and polarization is complex and under some conditions exposure to outgroup content can actually increase polarization (Bail et al., 2018; Paolini et al., 2010; Rychwalska & Roszczyńska-Kurasińska, 2018; Taber & Lodge, 2006). Yet increasing exposure diversity can work, at least somewhat. One experiment tested the effect of asking US Facebook users to subscribe to (“like”) up to four liberal or conservative news outlets, measuring
7 changes in affective polarization through a survey two weeks later. This level of exposure to outgroup information decreased affective polarization by about 1 point on a 100-point scale (Levy, 2020). By comparison, the rate of increase in affective polarization in the U.S. since 1975 is estimated at 0.6 points per year (Finkel et al., 2020). Rescaled to the same 100 point scale, the previously discussed experiment of leaving Facebook for a month resulted in about a 2 point decrease (Allcott et al., 2020, p. 652) though only on issue-based rather than affective measures. All of these estimates should be considered quite rough. This demonstrates that increased exposure diversity can be a useful intervention point for depolarization, but the effect so far has been modest. Are different or better approaches possible? For example, Levy (2020) tested only news diversity, meaning professional journalism. Polarization may turn out to be more sensitive to non-news content or user comments. 4.3 Recommending civil arguments Several studies have attempted to determine the conditions under which polarization and depolarization occur. Kim & Kim (2019) found that those who read uncivil comments arguing for an opposing view rated themselves as closer to ideological extremes on a post-exposure survey than those who did not. Civility may not be depolarizing per se, but incivility does seem to be polarizing. Suhay et al. (2018) similarly show that comments that negatively describe political identities (e.g. “Liberals are ignorant”) increase polarization as measured by the feeling thermometer question. This effect also appears in the context of partisan media sources (e.g. MSNBC, Fox) where “incivility [of] out-party sources affectively polarizes the audience” (Druckman et al., 2019). It seems likely that “civility” and “partisan criticism” can be algorithmically scored through existing natural language processing techniques, drawing on previous work classifying hate speech and harassment. All are conceptually close to the “toxicity” operationalized by contemporary comment classification models (Noever, 2018). While these models are mostly used for moderation -- that is, removing offending comments – they could also provide a “civility” signal that is incorporated into recommender item ranking. Twitter has experimented with this idea (Wagner, 2019) but I am not aware of any production recommender that incorporates a civility signal in content ranking (as opposed to content moderation). In addition to demoting uncivil content, it is possible to promote civil content. Experimental evidence shows that ranking high-quality comments at the top can positively alter the tone of subsequent discussion (Berry & Taylor, 2017). In effect, this intervention hopes to model respectful disagreement. This may not work if there are not many natural examples of productive inter-group conversation. In particular, there may be a lack of journalism content that takes a depolarizing approach to reporting on controversial issues (Hautakangas & Ahva, 2018; Prior & Stroud, 2015; Ripley, 2018). Of course, uncivil language can be necessary and important. We certainly don’t want an algorithmic media system that redirects attention away from anyone raising their voice. Indeed, several theories of democracy require such confrontation, such as critical approaches (Helberger, 2019) or agonistic models (Mouffe, 2002). Hence, there is a tension between encouraging expression and intervening to make the conversation more productive – this is the art of (algorithmic) mediation. 4.4 Priming better interactions Given a particular set of items selected for a user, it may be possible to present them in a way that encourages more productive conflict. Language seems particularly important in political disagreements. Intriguingly, replacing the usual “like” button with a “respect” button increased the number of clicks on counter-ideological comments, that is, people were more likely to “respect” something they disagreed with than to “like” it (Stroud et al., 2017). While civility norms have been shown to contribute to successful online discussions of polarized topics (Jhaver et al., 2017) it is difficult to automate the promulgation and enforcement of such norms. One intriguing possibility is to change the content of automated messages, such as the message welcoming someone to a group. In a large scale experiment on r/science on Reddit, adding a short note explaining what types of posts will be removed and noting that “our 1200 moderators encourage respectful discussion” greatly reduced the rate at which newcomers violated community norms (Matias, 2019).
8 In a sense, changing user behavior is the strongest depolarization intervention. This is not at all simple to accomplish, but these studies demonstrate that simple user interface changes can have profound effects. 5 Learning to depolarize The approaches discussed above are justified on the basis of sociological theory, from results in laboratory settings, or through modest platform experiments. Real platforms are enormous, diverse, and dynamic environments, and ecological validity is a serious problem for the development of social media interventions (Griffioen et al., 2020). It is likely to be difficult to predict which depolarization interventions will succeed. The best approach will vary between subgroups, in different contexts, and over time. Effective management of polarization will therefore depend on continual monitoring of polarization outcomes by platform operators. Affective polarization measures may prove to be the most useful category of metrics, in part because they are agnostic to the type of content that drives polarization. More cognitive measures of polarization, such as issue position surveys (Draca & Schwarz, 2018; Kiley, 2017) may be less diagnostic for social media, where many interactions will not involve discussions of substantial policy preferences. Platforms already monitor various non-engagement measures and incorporate them into recommender design and ranking (Stray, 2020). Facebook asks users whether specific posts led to a meaningful social interaction on or off the platform. This is a construct from social psychology that appears to be similarly interpretable across cultures (Litt et al., 2020). YouTube similarly incorporates user satisfaction ratings obtained by asking users what they thought of specific recommendations (Zhao et al., 2019). Such metrics are used to drive product choices at the managerial level by selectively deploying changes, a form of A/B testing. They are also incorporated directly into the predictive models underlying item ranking, as the next section describes, but the first and most fundamental depolarization intervention is simply to monitor for actual polarization outcomes, rather than betting on theory. 5.1 Optimizing for depolarization Survey responses can be used to train recommender ranking algorithms, for example by building a model that predicts whether an item is going to lead to a positive survey answer for a particular user in a particular context. This is, technically speaking, similar to predicting which items will result in a click. Optimizing for predicted survey responses is an important technique in the nascent field of recommender alignment, the practice of getting recommender systems to enact human values (Stray, 2021; Stray et al., 2020). The feeling thermometer has been used experimentally to evaluate the polarizing effect of seeing a post, by taking the difference between treatment and control groups (Kim & Kim, 2019; Suhay et al., 2018). If it proves possible to know whether individual posts or conversations are polarizing, it should be possible to build a model to predict the polarization effect of showing novel posts. Similar classifiers are already in use to detect misinformation, hate speech, bullying etc. One plausible technique is the TIES model, which takes into account not only the text and image content of a specific post but the sequence of interactions around it, including discussions in comments, likes, shares, etc. (Noorshams et al., 2020). In the context of an online discussion, the goal would be to determine whether users are having a productive exchange of views or a divisive argument, so the history of interactions carries significant information. Alternatively, affective polarization measures could be used longitudinally, perhaps by asking a panel of users to respond to a feeling thermometer question daily or weekly, thereby measuring attitudes over time. When compared to a control group, this amounts to a difference-in-differences design which gives robust causal estimates under certain assumptions (Angrist & Pischke, 2009, Chapter 5). That is, it should be possible to learn the actual polarizing effects of selecting different distributions of items. However, using longitudinal data to drive recommendation systems toward selecting depolarizing content is technically challenging due to the much longer time scale and higher level of abstraction as compared to feedback on individual items.
9 Reinforcement learning (RL) algorithms may be the most general and powerful approach to learning patterns of recommendation which optimize long term outcomes (Ie et al., 2019; Mladenov et al., 2019). In principle, affective polarization survey measures could be used as a reward signal for reinforcement learning-based recommenders. However, this sort of learning from sparse survey feedback has not yet been demonstrated. Additional algorithmic development will be necessary before longitudinal polarization measures can be incorporated into content selection algorithms, but the necessary technical research is underway because other sparse, long term signals such as user subscriptions have immediate business value. In other words, the same methods that make it possible to predict what movies to show someone to get them to subscribe may also make it possible to learn which patterns of interaction increase or reduce polarization. 5.2 Unintended consequences and the necessity of specification The effective use of sociological metrics is complicated and can fail in a number of ways, regardless of whether the metric is used by people or algorithms. Using reinforcement learning to attempt large scale political intervention should be a particularly alarming prospect. While there is a strong moral case for designing recommender systems to depolarize, unintended consequences could swamp any positive effects. A metric is an operationalization of some theoretical construct, and might be an invalid measure for a variety of reasons (Jacobs & Wallach, 2019). Even a well-constructed metric almost never represents what we really care about: clickbait lies entirely in the difference between “click” and “value.” When used as targets, metrics suffer from a number of problems involving gaming and spurious correlations, which can be understood in causal terms as variations of Goodhart’s law (Manheim & Garrabrant, 2018). It is particularly important to undertake ongoing qualitative methods and user research, to know whether current metrics are adequately tracking the intended goals – and to learn of whatever else may be happening. Metrics often fail when used in management contexts because they are irrelevant, illegitimate, gamed, or aren’t updated as the context changes (Jackson, 2005). Using metrics to train a powerful optimizing system introduces further concerns (Thomas & Uminsky, 2020). Different effects for different subgroups may be a particular problem for recommender systems which typically optimize average scores (Li et al., 2021). While it’s always useful to monitor for slippage between a metric’s intent and what it is actually measuring, this is particularly important when a measure becomes the target of society-wide AI optimization (Stray, 2020). If we choose to apply reinforcement learning to polarization metrics, those metrics will require continuous evaluation. On the other hand, not using polarization measures in algorithmic content selection may be far worse. Optimization algorithms which do not penalize polarization measures might learn, as humans do, that polarization can be exploited for engagement. Or they might merely increase conflict as an agnostic side effect, which is no better. In general, under-specification is a serious hazard in the creation of machine learning models (D’Amour et al., 2020). If we do not specify the intended effect of a recommender system on polarization, we should not be surprised to find unexpected outcomes. 6 Conclusion Polarization is a hardening division of society into “us” vs “them.” It interacts with a number of conflict feedback processes and eventually leads to democratic erosion and violence (McCoy et al., 2018; Somer & McCoy, 2019). The goal of a depolarization intervention is not to suppress conflict, but to have better conflict that moves towards constructive societal change (Deutsch, 1969; Jeong, 2019; Lederach, 2014; Ripley, 2021). While all societies face complex tensions between peace and justice, depolarization interventions may ultimately be justified on human rights grounds (Finkel et al., 2020) just as other peacebuilding interventions are.
10 Available evidence suggests that social media usage is not driving increases in polarization at the country level (Boxell et al., 2017, 2020). In particular, there is little empirical support for the idea that personalization is reducing exposure to diverse information (Guess et al., 2018; Zuiderveen Borgesius et al., 2016). Nonetheless, there is some evidence that social media-based interventions can reduce polarization among users. A recent experimental test of increasing news diversity produced a small decrease in polarization (Levy, 2020). Paying users to stay off Facebook for a month produced small decreases in issue polarization, though not affective polarization (Allcott et al., 2020). Moderation, the removal of unwanted content, can be important especially in the context of a violent conflict (Schirch, 2020) but it is probably too blunt an instrument for depolarization. Content ranking defines what each user sees and is the most general intervention point. While exposure to diverse perspectives can actually increase polarization (Bail et al., 2018) increased exposure diversity does depolarize in some contexts (Levy, 2020; Pettigrew & Tropp, 2006). Recommenders could augment diversity by de-prioritizing content that has been shown to be polarizing, including uncivil presentations of outgroup opinions (Kim & Kim, 2019) and criticism of partisan identities (Suhay et al., 2018). Content presentation and user interface may also have depolarization effects, as has been shown in experiments changing “like” to “respect” (Stroud et al., 2017) and adding a message reminding users of community norms (Matias, 2019). Yet none of the above approaches directly target the outcome of interest. Any depolarization method based on selecting content according to pre-existing theory may prove unable to cope with the radically diverse and dynamic contexts of a real recommender system. The solution is to directly and continuously measure polarization outcomes. Existing polarization measures, particularly affective polarization measures, have been used to evaluate the effect of encountering different types of comments on news articles (Kim & Kim, 2019) and the same methods should generalize to other types of items including user posts, discussion threads, and so on. Such survey data can be used to evaluate recommender system changes and make deployment decisions. It can also be used to train polarization prediction models, much as existing recommender models predict meaningful social interactions and other survey results (Stray, 2020). Ultimately, polarization survey feedback could be used as a reward signal for reinforcement learning-based recommendation algorithms. This powerful emerging approach has the potential to learn what actually depolarizes, and continuously adapt to changes. Optimizing for such a signal may have unintended harmful consequences, so such a system would need to be continuously monitored in other ways, such as qualitative studies. In any case it may prove necessary to incorporate polarization measures into recommender systems to prevent the creation of conflict as a side effect of optimization (D’Amour et al., 2020). It is unknown whether this sort of feedback-driven intervention would succeed in reducing the average dislike of the outgroup as compared to doing nothing, or more broadly whether intervening in platform recommenders will be an effective depolarization strategy within the complex and dynamic media ecosystem of any particular community, but there is reason to suspect this is possible. At the very least, the collection of individual-level affective polarization survey data provides a managerial incentive in the direction of depolarization. Nonetheless, the use of affective polarization survey data to drive platform recommender systems is a theoretically grounded, technically feasible, and potentially robust strategy for a social media depolarization intervention which deserves further study. Acknowledgements This work was first presented at the BRaVE project’s Exploring Societal Resilience to Online Polarization and Extremism workshop. The author thanks workshop participants and the anonymous reviewers for insightful feedback.
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Digital Journalism, 7(8), 993–1012. https://doi.org/10.1080/21670811.2019.1623700 Helberger, N., Karppinen, K., & D’Acunto, L. (2018). Exposure diversity as a design principle for recommender systems. Information Communication and Society, 21(2), 191–207. https://doi.org/10.1080/1369118X.2016.1271900 Hofstetter, J.-S. (2021). Digital Technologies , Peacebuilding and Civil Society (No. 114). https://ict4peace.org/activities/digital-technologies-peacebuilding-and-civil-society-by-julia-hofstetter-senior-advisor-ict4peace/ Hosseinmardi, H., Ghasemian, A., Clauset, A., Rothschild, D. M., Mobius, M., & Watts, D. J. (2020). Evaluating the scale, growth, and origins of right-wing echo chambers on YouTube. http://arxiv.org/abs/2011.12843 Ie, E., Hsu, C., Mladenov, M., Jain, V., Narvekar, S., Wang, J., Wu, R., & Boutilier, C. (2019). RecSim: A Configurable Simulation Platform for Recommender Systems. http://arxiv.org/abs/1909.04847 Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2018). The Origins and Consequences of Affective Polarization in the United States. Annual Review of Political Science, 1–35. https://doi.org/10.1146/annurev-polisci-051117-073034 Iyengar, S., & Westwood, S. J. (2015). Fear and Loathing across Party Lines: New Evidence on Group Polarization. American Journal of Political Science, 59(3), 690–707. https://doi.org/10.1111/ajps.12152 Jackson, A. (2005). Falling from a great height: Principles of good practice in performance measurement and the perils of top down determination of performance indicators. Local Government Studies, 31(1), 21–38. https://doi.org/10.1080/0300393042000332837 Jacobs, A. Z., & Wallach, H. (2019). Measurement and Fairness. http://arxiv.org/abs/1912.05511 Jeong, H. W. (2019). Conflict Transformation. In S. Byrne, T. Matyók, I. M. Scott, & J. Senehi (Eds.), Routledge Companion to Peace and Conflict Studies (pp. 25–34). Routledge. https://doi.org/10.4324/9781315182070 Jhaver, S., Vora, P., & Bruckman, A. (2017). Designing for Civil Conversations: Lessons Learned from ChangeMyView. GVU Technical Report. https://smartech.gatech.edu/handle/1853/59080 Jiang, R., Chiappa, S., Lattimore, T., György, A., & Kohli, P. (2019). Degenerate Feedback Loops in Recommender Systems. https://doi.org/10.1145/3306618.3314288 Keller, D. (2018). Internet platforms: Observations on speech, danger, and money. Hoover Institution, Aegis Series Paper No. 1807, 5–8. https://lawfareblog.com/internet-platforms-observations-speech-danger-and-money Kiley, J. (2017). In polarized era, fewer Americans hold a mix of conservative and liberal views. Pew Research Center. https://www.pewresearch.org/fact-tank/2017/10/23/in-polarized-era-fewer-americans-hold-a-mix-of-conservative-and-liberal-views/ Kim, Y. (2015). Does disagreement mitigate polarization? How selective exposure and disagreement affect political polarization. Journalism and Mass Communication Quarterly, 92(4), 915–937. https://doi.org/10.1177/1077699015596328 Kim, Y., & Kim, Y. (2019). Incivility on facebook and political polarization: The mediating role of seeking further comments and negative emotion. Computers in Human Behavior, 99(February), 219–227. https://doi.org/10.1016/j.chb.2019.05.022 King, D. S., & Smith, R. M. (2008). Strange Bedfellows? Polarized Politics? The Quest for Racial Equity in Contemporary America. September, 686–703. https://doi.org/10.1177/1065912908322410 King, G., Pan, J., & Roberts, M. E. (2017). How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. American Political Science Review, 111(3), 484–501. https://doi.org/10.1017/S0003055417000144 Krueger, D. S., Maharaj, T., & Leike, J. (2020). Hidden incentives for auto-induced distributional shift. https://arxiv.org/abs/2009.09153 Layman, G. C., Carsey, T. M., Green, J. C., Herrera, R., & Cooperman, R. (2010). Activists and conflict extension in American party politics. American Political Science Review, 104(2), 324–346. https://doi.org/10.1017/S000305541000016X Lederach, J. P. (2014). The Little Book of Conflict Transformation. Ledwich, M., & Zaitsev, A. (2019). Algorithmic Extremism: Examining YouTube’s Rabbit Hole of Radicalization. First Monday. https://doi.org/10.5210/fm.v25i3.10419 Lee, A. H. Y. (2020). How the Politicization of Everyday Activities Affects the Public Sphere: The Effects of Partisan Stereotypes on Cross-Cutting Interactions. Political Communication, 00(00), 1–20. https://doi.org/10.1080/10584609.2020.1799124 Lee, F. E. (2015). How party polarization affects governance. Annual Review of Political Science, 18, 261–282. https://doi.org/10.1146/annurev-polisci-072012-113747 Lelkes, Y., Sood, G., & Iyengar, S. (2017). The Hostile Audience: The Effect of Access to Broadband Internet on Partisan
13 Affect. American Journal of Political Science, 61(1), 5–20. https://doi.org/10.1111/ajps.12237 Levy, R. (2020). Social Media, News Consumption, and Polarization: Evidence from a Field Experiment. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3653388 Li, R. Z., Urbano, J., & Hanjalic, A. (2021). Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users. https://doi.org/10.1145/3437963.3441769 10.1145/3437963.3441769 10.1145/3437963.3441769 Litt, E., Zhao, S., Kraut, R., & Burke, M. (2020). What Are Meaningful Social Interactions in Today’s Media Landscape? A Cross-Cultural Survey. Social Media + Society, 6(3), 205630512094288. https://doi.org/10.1177/2056305120942888 Loecherbach, F., Moeller, J., Trilling, D., & van Atteveldt, W. (2020). The Unified Framework of Media Diversity: A Systematic Literature Review. Digital Journalism, 8(5), 605–642. https://doi.org/10.1080/21670811.2020.1764374 Manheim, D., & Garrabrant, S. (2018). Categorizing Variants of Goodhart’s Law. 1–10. http://arxiv.org/abs/1803.04585 Martherus, J. L., Martinez, A. G., Piff, P. K., & Theodoridis, A. G. (2021). Party Animals? Extreme Partisan Polarization and Dehumanization. Political Behavior, 43(2), 517–540. https://doi.org/10.1007/s11109-019-09559-4 Martin, G. J., & Yurukoglu, A. (2016). Bias in Cable News: Persuasion and Polarization. In NBER Working Papers (No. 20798). http://www.nber.org/papers/w20798 Matias, J. N. (2019). Preventing harassment and increasing group participation through social norms in 2,190 online science discussions. Proceedings of the National Academy of Sciences of the United States of America, 116(20), 9785–9789. https://doi.org/10.1073/pnas.1813486116 McCoy, J., Rahman, T., & Somer, M. (2018). Polarization and the Global Crisis of Democracy: Common Patterns, Dynamics, and Pernicious Consequences for Democratic Polities. American Behavioral Scientist, 62(1), 16–42. https://doi.org/10.1177/0002764218759576 Melki, M., & Pickering, A. (2020). Polarization and corruption in America. European Economic Review, 124, 103397. https://doi.org/10.1016/j.euroecorev.2020.103397 Melki, M., & Sekeris, P. G. (2019). Media-driven polarization. Evidence from the US. Economics, 13, 0–14. https://doi.org/10.5018/economics-ejournal.ja.2019-34 Mladenov, M., Meshi, O., Ooi, J., Schuurmans, D., & Boutilier, C. (2019). Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI International Joint Conference on Artificial Intelligence, 2019-Augus, 3165–3172. https://doi.org/10.24963/ijcai.2019/439 Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018). Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society, 21(7), 959–977. https://doi.org/10.1080/1369118X.2018.1444076 Mouffe, C. (2002). Which Public Sphere for a Democratic Society? Theoria: A Journal of Social and Political Theory, 22(99), 55–65. https://www.jstor.org/stable/41802189 Munger, K., & Phillips, J. (2020). Right-Wing YouTube: A Supply and Demand Perspective. International Journal of Press/Politics. https://doi.org/10.1177/1940161220964767 Noever, D. (2018). Machine Learning Suites for Online Toxicity Detection. https://arxiv.org/abs/1810.01869 Noorshams, N., Verma, S., & Hofleitner, A. (2020). TIES: Temporal Interaction Embeddings for Enhancing Social Media Integrity at Facebook. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3128–3135. https://doi.org/10.1145/3394486.3403364 Paolini, S., Harwood, J., & Rubin, M. (2010). Negative intergroup contact makes group memberships salient: Explaining why intergroup conflict endures. Personality and Social Psychology Bulletin, 36(12), 1723–1738. https://doi.org/10.1177/0146167210388667 Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90(5), 751–783. https://doi.org/10.1037/0022-3514.90.5.751 Pew Research Center. (2014). Political Polarization in the American Public. https://www.pewresearch.org/politics/2014/06/12/political-polarization-in-the-american-public/ Prior, M. (2013). Media and Political Polarization. Annu. Rev. Polit. Sci, 16, 101–127. https://doi.org/10.1146/annurev-polisci-100711-135242 Prior, M., & Stroud, N. J. (2015). Using Mobilization, Media, and Motivation to Curb Political Polarization. In N. Persily (Ed.), Solutions to Political Polarization in America. Cambridge University Press. https://doi.org/https://doi.org/10.1017/CBO9781316091906.013 Ramsbotham, O., Tom Woodhouse, & Hugh Miall. (2016). Contemporary Conflict Resolution, 4th Edition. Wiley. Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A. F., & Wagner Meira, W. M. (2020). Auditing radicalization pathways on YouTube. FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 131–141. https://doi.org/10.1145/3351095.3372879 Ripley, A. (2018). Complicating the Narratives. Solutions Journalism Network. https://thewholestory.solutionsjournalism.org/complicating-the-narratives-b91ea06ddf63 Ripley, A. (2021). High Conflict: Why We Get Trapped and How We Get Out. Simon & Schuster. Rychwalska, A., & Roszczyńska-Kurasińska, M. (2018). Polarization on Social Media: When Group Dynamics Leads to Societal Divides. Proceedings of the 51st Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2018.263 Schirch, L. (2020). Social Media Impacts on Conflict Dynamics: A Synthesis of Ten Case Studies & a Peacebuilding Plan for Tech (No. 73; Toda Peace Institute). https://toda.org/policy-briefs-and-resources/policy-briefs/social-media-impacts-on-conflict-dynamics-a-synthesis-of-ten-case-studies-and-a-peacebuilding-plan-for-tech.html Somer, M., & McCoy, J. (2019). Transformations through Polarizations and Global Threats to Democracy. The Annals of the American Academy of Political and Social Science, 681(1), 8–22. https://doi.org/10.1177/0002716218818058
14 Stoica, A. A., & Chaintreau, A. (2019). Hegemony in social media and the effect of recommendations. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 2, 575–580. https://doi.org/10.1145/3308560.3317589 Stray, J. (2020). Aligning AI Optimization to Community Well-being. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00086-3 Stray, J. (2021). Beyond Engagement: Aligning Algorithmic Recommendations With Prosocial Goals. Partnership on AI. https://www.partnershiponai.org/beyond-engagement-aligning-algorithmic-recommendations-with-prosocial-goals/ Stray, J., Adler, S., & Hadfield-Menell, D. (2020). What are you optimizing for ? Aligning Recommender Systems with Human Values. Participatory Approaches to Machine Learning Workshop, ICML 2020. https://participatoryml.github.io/papers/2020/42.pdf Stroud, N. J., Muddiman, A., & Scacco, J. M. (2017). Like, recommend, or respect? Altering political behavior in news comment sections. New Media and Society, 19(11), 1727–1743. https://doi.org/10.1177/1461444816642420 Suhay, E., Bello-Pardo, E., & Maurer, B. (2018). The Polarizing Effects of Online Partisan Criticism: Evidence from Two Experiments. International Journal of Press/Politics, 23(1), 95–115. https://doi.org/10.1177/1940161217740697 Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs (2006). American Journal of Political Science, 50(3), 755–769. https://doi.org/10.1111/j.1540-5907.2006.00214.x Tellidis, I., & Kappler, S. (2016). Information and communication technologies in peacebuilding: Implications, opportunities and challenges. Cooperation and Conflict, 51(1), 75–93. https://doi.org/10.1177/0010836715603752 Thomas, R. L., & Uminsky, D. (2020). Reliance on Metrics is a Fundamental Challenge for AI. Ethics of Data Science Conference. https://arxiv.org/abs/2002.08512 van Stekelenburg, J. (2014). Going all the way: Politicizing, polarizing, and radicalizing identity offline and online. Sociology Compass, 8(5), 540–555. https://doi.org/10.1111/soc4.12157 Wagner, K. (2019, March). Inside Twitter’s ambitious plan to change the way we tweet. Vox. https://www.vox.com/2019/3/8/18245536/exclusive-twitter-healthy-conversations-dunking-research-product-incentives Whitesides, J. (2017, February 7). From disputes to a breakup: wounds still raw after U.S. election. Reuters. https://www.reuters.com/article/us-usa-trump-relationships-insight/from-disputes-to-a-breakup-wounds-still-raw-after-u-s-election-idUSKBN15M13L York, J., & Zuckerman, E. (2019). Moderating the Public Sphere. In R. F. Jørgensen (Ed.), Human Rights in the Age of Platforms (pp. 137–161). MIT Press. Zhao, Z., Hong, L., Wei, L., Chen, J., Nath, A., Andrews, S., Kumthekar, A., Sathiamoorthy, M., Yi, X., & Chi, E. (2019). Recommending what video to watch next. Proceedings of the 13th ACM Conference on Recommender Systems, 43–51. https://doi.org/10.1145/3298689.3346997 Zuiderveen Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., de Vreese, C. H., & Helberger, N. (2016). Should we worry about filter bubbles? Internet Policy Review, 5(1), 1–16. https://doi.org/10.14763/2016.1.401 Zuiderveen Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., De Vreese, C. H., & Helberger, N. (2016). Should we worry about filter bubbles? Internet Policy Review, 5(1), 1–16. https://doi.org/10.14763/2016.1.401
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3a2994d1-69c0-40ae-bd22-39e469e8c8cd
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Berkeley: Ask vs. Guess (vs. Tell) Culture
Discussion article for the meetup : Berkeley: Ask vs. Guess (vs. Tell) Culture
WHEN: 19 February 2014 07:00:00PM (-0800)
WHERE: 2030 Addison, 3rd floor, Berkeley, CA
Hello all, tonight's meetup will feature the discussion topic of Ask Culture and Guess Culture, which is summarized in the first couple paragraphs of this Less Wrong post:
http://lesswrong.com/lw/jis/tell_culture/
A friend of mine has remarked that this is a topic that causes even professed rationalists to engage in motivated cognition. Are you up to the challenge!?
Please arrive between 7pm and 7:30pm tonight. At 7:30pm we'll review our weekly goals and record goals for the coming week. The discussion of Ask vs. Guess Culture will follow.
Even though this takes place at CFAR, it's not a CFAR-sponsored event. The CFAR office is at 2030 Addison, 3rd floor, Berkeley, near the Downtown Berkeley BART. If you find yourself locked out, text me at:
http://i.imgur.com/Vcafy.png
Discussion article for the meetup : Berkeley: Ask vs. Guess (vs. Tell) Culture
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2a3c0ed4-aa05-4e87-856a-7a323f296e07
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trentmkelly/LessWrong-43k
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LessWrong
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How to improve the public perception of the SIAI and LW?
I was recently thinking about the possibility that someone with a lot of influence might at some point try to damage LessWrong and the SIAI and what preemptive measures one could take to counter it.
If you believe that the SIAI does the most important work in the universe and if you believe that LessWrong serves the purpose of educating people to become more rational and subsequently understand the importance of trying to mitigate risks from AI, then you should care about public relations, you should try to communicate your honesty and well-intentioned motives as effectively as possible.
Public relations are very important because a good reputation is necessary to do the following:
* Making people read the Sequences.
* Raising money for the SIAI.
* Convincing people to take risks from AI seriously.
* Allowing the SIAI to influence other AGI researchers.
* Mitigating future opposition by politicians and other interest groups.
* Being no easy target for criticism.
An attack scenario
First one has to identify characteristics that could potentially be used to cast a damaging light on this community. Here the most obvious possibility seems to be to portray the SIAI, together with LessWrong, as a cult.
After some superficial examination an outsider might conclude the following about this community:
* Believing into heaven and hell in the form of a positive or negative Singularity.
* Discouraging skepticism while portraying their own standpoint as clear-cut.
* Encouraging to take ideas seriously.
* Encouraging and signaling strong cooperation and conformity.
* Evangelizing by scaring people and telling them to donate money.
* Social pressure by employing a reputation system with positive and negative incentive.
* Removing themselves from empirical criticism by framing everything as a prediction.
* Discrediting mainstream experts while placing themselves a level above them.
* Discouraging transparency and openness by referring to the dangers of AI re
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23946095-8716-4926-af2f-67ddc516760d
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trentmkelly/LessWrong-43k
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LessWrong
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A Parable of Elites and Takeoffs
Let me tell you a parable of the future. Let’s say, 70 years from now, in a large Western country we’ll call Nacirema.
One day far from now: scientific development has continued apace, and a large government project (with, unsurprisingly, a lot of military funding) has taken the scattered pieces of cutting-edge research and put them together into a single awesome technology, which could revolutionize (or at least, vastly improve) all sectors of the economy. Leading thinkers had long forecast that this area of science’s mysteries would eventually yield to progress, despite theoretical confusion and perhaps-disappointing initial results and the scorn of more conservative types and the incomprehension (or outright disgust, for ‘playing god’) of the general population, and at last - it had! The future was bright.
Unfortunately, it was hurriedly decided to use an early prototype outside the lab in an impoverished foreign country. Whether out of arrogance, bureaucratic inertia, overconfidence on the part of the involved researchers, condescending racism, the need to justify the billions of grant-dollars that cumulative went into the project over the years by showing some use of it - whatever, the reasons no longer mattered after the final order was signed. The technology was used, but the consequences turned out to be horrific: over a brief period of what seemed like mere days, entire cities collapsed and scores - hundreds - of thousands of people died. (Modern economies are extremely interdependent and fragile, and small disruptions can have large consequences; more people died in the chaos of the evacuation of the areas around Fukushima than will die of the radiation.)
An unmitigated disaster. Worse, the technology didn’t even accomplish the assigned goal - that was thanks to a third party’s actions! Ironic. But that’s how life goes: ‘Man Proposes, God Disposes’.
So, what to do with the tech? The positive potential was still there, but no one could doubt anymore t
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6bdcd2b5-f6cf-42d1-b9b1-8a68a43b0f63
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LDJnr/LessWrong-Amplify-Instruct
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LessWrong
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"I’ve been seeing a lot of comments lately about how the financial markets have gone completely wonky, efficient markets hypothesis looks crazy right now, etc. I don’t currently trade actively and haven’t run a lot of numbers, but just in terms of big-picture qualitative behavior, high stock prices make a lot of sense right now. This post is an informal explanation of why.First, let’s forget about the efficient market price formula (i.e. price = expected sum of discounted future cash flows, VT=E[∑t>Te−RtTCt]). I’ll talk about that a bit at the end, but it’s so widely and severely misunderstood that I’d need a whole post just to correct misconceptions. Instead, we’ll start from first principles: financial capital is a good, just like any other good. Its price is determined by supply and demand, just like any other good.When stock prices are high, that means financial capital is cheap for companies: they can get a lot of capital by issuing a lot of stock. High stock price = cheap capital. Likewise with bonds: when bond prices are high, yields are low, meaning companies can borrow capital very cheaply.What makes the cost of financial capital move? Well, the usual supply-and-demand reasoning:If people suddenly find themselves with lots of extra savings to invest, that means the supply of financial capital increases, and the cost of financial capital should fall (i.e. stock prices rise).If people expect lower returns in the future, they will want to invest less, so the supply of financial capital decreases, and the cost of financial capital should rise (i.e. stock prices fall).If there’s a credit crunch and companies suddenly need to borrow lots of money on short notice, then the demand for financial capital increases, so the cost of financial capital should rise (i.e. stock prices fall).If many companies are suddenly flush with cash, then the demand for financial capital decreases, so the cost of financial capital should fall (i.e. stock prices rise).This should all be pretty intuitive, and you can probably brainstorm a few more examples along these lines.Now, what’s been going on lately, and how does it fit into this picture?Expectations of future earnings are generally down (although mostly just in the short term). Many companies suddenly need to borrow money in order to stay in business until the storm passes. On their own, these two factors should both push stock prices down: supply of financial capital should be low, and demand for financial capital should be high.The size of both of these changes are big, but not too far out of line with a normal business cycle slowdown. They are significant, but not huge by historical standards. On the other hand, there has been one ridiculously huge delta which utterly dwarfs any fast change we’ve seen in economic fundamentals in the last seventy years:That’s the personal savings rate - the amount people save, relative to their disposable income. Given how the modern financial system works, that’s basically the supply of financial capital. It quadrupled in a month.Even if people were nervous enough about the recovery to allocate half as large a share of marginal savings to stocks as they were a year ago, even if real disposable income were down (it’s actually up, courtesy of stimulus payments), that would still be a near-2x increase in marginal savings allocated to stocks. That jump in the personal savings rate is ridiculously larger than any change in economic fundamentals in living memory; it shouldn’t be surprising if it completely dominates market behavior.What About That Formula?Warning: more math and jargon past this point.Ok, now we’ve talked about first principles. Hopefully it all makes intuitive sense. How does it square with VT=Et[∑t>Te−RtTCt]?The key questions are: what’s that discounting rate R, and what distribution is the expectation over?Many people will say it’s the “risk-free rate”, i.e. yield on Treasury bonds, but those same people will outright admit that this does not actually work. It predicts prices far higher than actual stock prices, and says that people ought to sell treasuries in order to buy stock. Obviously people don’t do that, because we’re not risk-neutral (nor should we be). The whole notion of R being the risk-free rate is based on a dumb argument that nobody actually buys.Some people who’ve seen some math finance may talk about the “risk-neutral distribution” and corresponding discount rate. These are great tools for pricing derivatives, but they’re severely underdetermined for problems like “determine stock price from fundamentals”. They just assert the existence of some distribution and discount rate which make the formula work; they say nothing at all about what the distribution and rate should be.To get a proper foundation for the pricing formula, we need to go to financial economics. John Cochrane (aka The Grumpy Economist) has a pretty decent book on the subject; he gives the economist’s simplest version of the pricing formula at the very beginning:Vt=Et[βu′(ct+1)u′(ct)Ct+1]Here the “discount rate” e−Rt+1t for the timestep t→t+1 is the magic expression βu′(ct+1)u′(ct). What is that?ct is the amount the investor consumes at time t - i.e. if this is a retirement portfolio, it’s the amount taken out.u is the investor’s single-time-step utility function, and u′ is its derivative with respect to amount consumed.β is the investor’s own discount factor, i.e. how much they value consumption tomorrow relative to today.Note that I keep saying “the investor” here - this formula is for one single investor! We don’t need to assume that all investors are expected discounted utility maximizers. If any investor acts like an expected discounted utility maximizer, then the formula applies for that investor’s discount rate, utility function, and expectations. The formula comes directly from the investor's own utility-maximization condition.(Side-note: I actually don’t like the formulation in which the investor has an explicit time-discount with consumption each time step; I prefer to have the investor just maximize expected utility at some far-future timestep with exogenous cash-flows along the way, as a more accurate model of something like e.g. a retirement fund. For current purposes, the results are quite similar. Take-away: the things we’re saying here are not too highly sensitive to the model setup.)Now, if you followed that, you’re probably thinking “Huh?? That means prices have to satisfy different efficient pricing formulas for different investors. But they’re the same prices!”. That’s right. The trick is, each investor will adjust their portfolio and their consumption ct to make their equation hold. This formula isn’t for predicting prices, it’s for predicting how much of each asset the investor holds going into the next timestep.If we want to use the formula to predict prices, then we have two options.The hard (but right) way: compute the whole equilibrium for all the investors.The easier (but unreliable) way: notice that the consumption, distribution and discount rate for most investors seem to follow pretty stable patterns, then assume that those patterns hold and ask what price that implies.Most usages of the pricing formula are ultimately equivalent to the second, with various flavors of first-order corrections thrown in. That works as long as the investor fundamentals are stable, but if there’s a big change in investor characteristics - like, say, a giant jump in the savings rate (i.e. a drop in consumption) - then obviously it falls apart."
This is a snippet extracted from a publication on LessWrong by a site member.The above is a chunk of text from a post created by a user on the website LessWrong.
Could you elaborate on the relationship between the personal savings rate and the supply of financial capital? Specifically, how does an increase in personal savings rate quadruple the supply of financial capital?
Certainly, the relationship between the personal savings rate and the supply of financial capital is quite direct. The personal savings rate refers to the percentage of disposable income that households are saving rather than spending. This money, when saved, often goes into bank accounts or other financial instruments, effectively increasing the amount of capital available to be lent or invested.
The supply of financial capital is essentially the total amount of money available for investment, including money saved by households, funds available with financial institutions, and any other sources of investment capital. When the personal savings rate increases, it means that households are saving more money. This additional money saved contributes to the overall supply of financial capital, increasing the amount available for investment.
Now, the assertion that an increase in the personal savings rate would quadruple the supply of financial capital is a hypothetical scenario presented in the text you provided. The exact multiplier effect of increased savings on the supply of financial capital can vary widely based on a multitude of factors, including the overall size of the economy, the proportion of income that households are saving, and the mechanisms through which saved money is funneled into investment.
In the context of the text, the author is suggesting that a significant increase in the personal savings rate - such as a quadrupling - could lead to a corresponding significant increase in the supply of financial capital. This is because the saved money would be available for investment, either directly (through purchase of stocks, bonds, etc.) or indirectly (through banks lending out deposited money).
It's important to note that while the relationship between savings and available capital is generally positive, other factors such as consumer confidence, economic stability, and investment opportunities can also significantly impact the overall supply of financial capital.
Considering the relationship between personal savings and financial capital supply, how might changes in fiscal policy or banking regulations impact this dynamic?
Changes in fiscal policy or banking regulations can have significant impacts on the relationship between personal savings and the supply of financial capital.
1. Fiscal Policy: Fiscal policy, which refers to government spending and taxation, can influence both the amount people save and the supply of financial capital. For instance, if the government increases taxes, people might have less disposable income and thus save less, reducing the supply of financial capital. Conversely, if the government increases spending, this can stimulate the economy, potentially leading to higher incomes and more savings.
Fiscal policy can also directly affect the supply of financial capital. For example, if the government runs a budget surplus, it might use the extra funds to pay down debt, effectively reducing the supply of financial capital. On the other hand, if the government runs a deficit, it will need to borrow money, which can increase the supply of financial capital.
2. Banking Regulations: Banking regulations can also significantly affect the supply of financial capital. For example, regulations that require banks to hold a certain percentage of their deposits as reserves can limit the amount of money banks can lend out, potentially reducing the supply of financial capital.
Conversely, if regulations are relaxed, banks might be able to lend out more of their deposits, increasing the supply of financial capital. Additionally, regulations can influence the riskiness of the banking sector, which can affect the confidence of savers and investors, and thus the supply of financial capital.
In conclusion, both fiscal policy and banking regulations can significantly impact the relationship between personal savings and the supply of financial capital, by influencing the amount people save, the amount banks can lend, and the overall confidence in the financial system.
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d83c6abe-f4a0-4ebf-bf94-a1bd3e04e898
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trentmkelly/LessWrong-43k
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LessWrong
|
Considerations on orca intelligence
(EDIT 2025-03-15: I've added a comment which you might want to read after the post.)
Follow up to: Could orcas be smarter than humans?
(For speed of writing, I mostly don't cite references. Feel free to ask me in the comments for references for some claims.)
This post summarizes my current most important considerations on whether orcas might be more intelligent than humans.
Evolutionary considerations
What caused humans to become so smart?
(Note: AFAIK there's no scientific consensus here and my opinions might be nonstandard and I don't provide sufficient explanation here for why I hold those. Feel free to ask more in the comments.)
My guess for the primary driver of what caused humans to become intelligent is the cultural intelligence hypothesis: Humans who were smarter were better at learning and mastering culturally transmitted techniques and thereby better at surviving and reproducing.
The book "the secret of our success" has a lot of useful anecdotes that show the vast breath and complexity of techniques used by hunter gatherer societies. What opened up the possibility for many complex culturally transmitted techniques was the ability of humans to better craft and use tools. Thus the cultural intelligence hypothesis also explains why humans are the most intelligent (land) animal and the animals with the best interface for crafting and using tools.
Though it's possible that other factors, e.g. social dynamics as described by the Marchiavellian Intelligence Hypothesis, also played a role.
Is it evolutionarily plausible that orcas became smarter?
Orcas have culturally transmitted techniques too (e.g. beach hunting, making waves to wash seals off ice shells, faking retreat tactics, using bait to catch birds, ...), but not (as far as we can tell) close to the sophistication of human techniques which were opened up by tool use.
I think it's fair to say that being slightly more intelligent probably resulted in a significantly larger increase in genetic fi
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a3dee139-b823-404f-b571-cf433c3b627a
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trentmkelly/LessWrong-43k
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LessWrong
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Two clarifications about "Strategic Background"
I've talked to a few people who misunderstood important parts of the "strategic background" discussion in https://intelligence.org/2017/12/01/miris-2017-fundraiser/#3.
First, at least two people thought the 1-8 numbered list was "MIRI's organizational plan" rather than "what we'd be least surprised to see happen in the world, conditional on good outcomes." MIRI is trying to de-confuse itself about step 8 and help put AGI developers in a better position in the future to select for AGI designs that are alignment-conducive, not trying to develop AGI.
Second, at least two other people misread "minimal aligned AGI" as "minimally aligned AGI", and thought MIRI was saying that developers should do the bare minimum of alignment work and then deploy immediately; or they saw that we were recommending building "systems with the bare minimum of capabilities for ending the acute risk period" and thought we were recommending this as an alternative to working really hard to achieve highly reliable and robust systems.
The MIRI view isn't "rather than making alignment your top priority and working really hard to over-engineer your system for safety, try to build a system with the bare minimum of capabilities". It's: "in addition to making alignment your top priority and working really hard to over-engineer your system for safety, also build the system to have the bare minimum of capabilities".
The idea isn't that you can get away with cutting corners on safety by keeping the system weak; per Eliezer's security mindset posts, a good plan should work (or fail safely) if the system ends up being a lot smarter than intended. Instead, the idea is that shooting for the bare minimum of capabilities adds a lot of value if your fundamentals are really good. Every additional capability a developer needs to align adds some extra difficulty and additional points of failure, so developers should target minimality in addition to alignment.
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715f3d6b-4556-4a41-99b8-3a7234935f3a
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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More money with less risk: sell services instead of model access
OpenAI is currently charging 100,000 times less per line of code than professional US devs.[[1]](#fn-oSgA2XfYLbEDACxJT-1)
An LLM's code output is of course less reliable than a professional's. And it is hard to use a text-completion API effectively in large projects.
What should you do if you've got a model on your hands that solves those problems?
You could operate as a software development company. They [tend](https://www.upwork.com/resources/cost-build-mobile-app)[[2]](#fn-oSgA2XfYLbEDACxJT-2) to charge $100-200k for simple mobile apps and there's basically no ceiling on the cost for complex apps over their lifetime. Devs make up the majority of a normal firm's personnel and costs; coding takes most of the app development time; bugs in code are one of the primary sources of project extension and failures. By using your model you can make better software, complete it faster, succeed more often, charge a lower price, and make a higher profit.
Going further, if you've really got a good model, then you can do very well by building competitors to adobe products, salesforce products, SAP products, google search, mongodb, etc.
Someone who has a build-anything machine would be a fool to sell a cheap build-anything service instead of using it themselves and selling the result. Particularly because selling the general service directly is likely to encourage and inspire copycats, including open-source ones who will delete your market. If it really builds the entire thing then you'll probably also be liable for negative consequences, which again have no ceiling.
Fewer risks, big and small
--------------------------
Some common misuse risks you can avoid/reduce (and eliminate associated liability):
* Someone tricks your API into doing something awful and pastes it into a tweet
* Spam generation for political campaigns, cryptocurrencies, etc
* Common hacking ("write a test to see if my server has a log4j vulnerability")
* Targeted manipulation and spearphishing
Larger risks you can avoid/reduce:
* Your incredible model motivates countless AI researchers. People reverse-engineer some of the architecture in online discussions. The state of the art is quickly advanced. We have less time to prepare for strong general AI.
* Hackers steal your model weights (if you don't advertise your model then you'll attract less attention from hackers)
* People try to get your model to act like an agent and copy itself around. They succeed. You have no way of shutting it down or monitoring what it is doing.
* Someone tries to get your model to order and mail smallpox or a novel virus. The screenshot would be an epic tweet. They succeed oh no
* Your own AI devs' ambitions and risk-tolerance know no bounds because you've positioned yourself as an AI company instead of a product company; there is nothing to keep their hands busy except make the AI more generally capable and efficient. They are careless with the training runs and one day your model gets loose and wreaks havoc.
Biology, robotics, R&D, etc
---------------------------
The benefits of selling/publishing derived products and the downsides of offering direct access remain in other domains:
* A drug is more profitable and less risky (for the world at least) than a general drug designer
* A vaccine is more profitable and less risky than a general mRNA designer
* There's more people who want to buy a house than a house-building robot
* There's more people who need a (highly efficient, AI assisted) lawyer than a general lawyer's assistant.
* More people need a cleaning robot than a robot-maker
* Releasing or building an effective fusion power generator gets you more clout than releasing the design assistant
* Even if you're evil and want to make AI-astroturf campaign spam, you presumably want to help one side more than the other, but if you release your model/tooling then both sides will use it.
* If you have a mathomatic it would be pretty epic to slowly release proofs for millennium problems for a while before revealing it was the mathomatic all along.
* Would be epic to release your unified theory of physics and wait a bit to reveal it was the physicsomatic all along.
* A factory optimization consultancy / management company would make more money than a factory optimization software package.
* There's more customers for long-lived dogs than a live-long-gene-editor. More customers for a livelong injection than the injection designer.
* If your hackomatic can edit Chase balances without a trace then you should just edit your own, not sell it
Conclusion
----------
Whether you're a startup, a big commercial lab, an enormous company, a research lab in a university, an independent AI researcher, or a criminal — whatever domain you're working in — whatever your goals — if you possess a uniquely powerful model then you'll likely have greater rewards and fewer risks by putting its products into the world instead of the model itself.
---
1. A particularly speedy software dev might type 400 lines of working code in 8 hours. If they cost $100/hour that's $2/line. GPT3.5-turbo costs $0.002 per 1000 tokens, and 40 characters/line ≈ 10 tokens/line = $0.00002 / line. [↩︎](#fnref-oSgA2XfYLbEDACxJT-1)
2. "The actual costs are much higher with a median total app development cost of $171,450." And the GoodFirms article they quote actually has numbers 3x higher than quoted, in the 100-200k range. [↩︎](#fnref-oSgA2XfYLbEDACxJT-2)
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377b84fa-641a-47e4-b81a-0fc56ad935b9
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trentmkelly/LessWrong-43k
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LessWrong
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Googling is the first step. Consider adding scholarly searches to your arsenal.
Related to: Scholarship: How to Do It Efficiently
There has been a slightly increased focus on the use of search engines lately. I agree that using Google is an important skill - in fact I believe that for years I have came across as significantly more knowledgeable than I actually am just by quickly looking for information when I am asked something.
However, There are obviously some types of information which are more accessible by Google and some which are less accessible. For example distinct characteristics, specific dates of events etc. are easily googleable1 and you can expect to quickly find accurate information on the topic. On the other hand, if you want to find out more ambiguous things such as the effects of having more friends on weight or even something like the negative and positive effects of a substance - then googling might leave you with some contradicting results, inaccurate information or at the very least it will likely take you longer to get to the truth.
I have observed that in the latter case (when the topic is less 'googleable') most people, even those knowledgeable of search engines and 'science' will just stop searching for information after not finding anything on Google or even before2 unless they are actually willing to devote a lot of time to find it. This is where my recommendation comes - consider doing a scholarly search like the one provided by Google Scholar.
And, no, I am not suggesting that people should read a bunch of papers on every topic that they discuss. By using some simple heuristics we can easily gain a pretty good picture of the relevant information on a large variety of topics in a few minutes (or less in some cases). The heuristics are as follows:
1. Read only or mainly the abstracts. This is what saves you time but gives you a lot of information in return and this is the key to the most cost-effective way to quickly find information from a scholary search. Often you wouldn't have immediate access to the paper a
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b0ed9114-d399-4789-ad93-d961f2edabe3
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trentmkelly/LessWrong-43k
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LessWrong
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A simple proposal for preserving free speech on twitter
There's this constant tension between the principle of free speech, which allows people to freely express their opinions, and the individuals rights to not have to listen to speech they find unpleasant or otherwise annoying/distracting.
In everyday life we solve this by letting anyone say what they want where they want, and anyone who doesn't want to listen avoids going to those places where people are saying things they don't want to listen to. Newspapers and magazines will print whatever most appeals to their audience, and people only read the ones that interest them. Mostly it works out.
The same approach works pretty well in blogs. People can write whatever they want, but nobody has to read anything they don't like. Those blogs which are most unpleasant won't get many links and so will be difficult to find unless you're searching for them directly.
If you don't like what somebody's writing on a Whatsapp group, you can either ban them from the group, or leave the group yourself.
On twitter though this approach starts to break down. Somebody can reply to thousands of people, none of whom have expressed any interest in their views, and their responses might be seen by millions of people. Sure, you can block them one by one, but there's more people to block than you have seconds in your day.
The current approach taken by twitter in extreme cases is to ban the user from twitter. Whilst that's a reasonable approach in a Whatsapp group, where there's always another Whatsapp group to join, twitter has over 450 million active users - over 10% of the world population, and has an enormous impact on popular discourse. Stopping someone from being able to air their views there definitely significantly impinges on their ability to freely express their views - and the threat of being banned can have a similar impact.
A simple solution could be to, instead of banning someone from twitter, simply stop them from being able to reply to non-followers[1]. They can still express
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106db27a-56c4-4c17-ae16-ee606c31aed7
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trentmkelly/LessWrong-43k
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LessWrong
|
Meetup : West LA—Practical Taoism
Discussion article for the meetup : West LA—Practical Taoism
WHEN: 26 February 2014 07:00:00PM (-0800)
WHERE: 11066 Santa Monica Blvd, Los Angeles, CA
How to Find Us: Go into the Del Taco. I will bring a Rubik's Cube and put it on a table, in case you require visual confirmation that You Are In The Right Place before asking anyone whether you're in the right place.
Parking is, well, I don't actually know. There's a sign that says the local lot allows only 45 minutes, but that may or may not be enforced. Others have reported that it's easy to find less time-unlimited parking nearby, although not as guaranteed.
Discussion:
> Look at your Internet argument. Now look at http://ow.ly/nt1B3 Now look at your Internet argument. Your Internet argument is now stupid. Actually, your internet argument was already stupid.—St. Rev
If you have political opinions, you are a fool. If you think your political opinions matter, you are doubly the fool. If you think it's important that other people share your political opinions: fool. If you actually spend a significant portion of your time trying to get people to have the same political opinions as you, you cannot be redeemed. These are the facts that we must face, because you and I are but human and do not understand society. Metapolitical opinions, of course, are completely rational and not at all subject to these considerations.
We will be discussing Michael Huemer's paper In Praise of Passivity, of which section 4 is practical advice given its conclusions. Hence practical Taoism: useful inaction.
Recommended Reading:
* Politics is the Mind-Killer
* A Fable of Science and Politics
* The Coalition Politics Hypothesis
* Rah Local Politics
* In Praise of Passivity, by Michael Huemer
No prior knowledge of or exposure to Less Wrong is necessary; this will be generally accessible. However, I would very much like it if someone, anyone, anyone at all, were to read the material beforehand.
Discussion article for the meetup
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c5ef291d-54bc-45aa-aaa7-f0485e1c0a0d
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trentmkelly/LessWrong-43k
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LessWrong
|
$500 bounty for alignment contest ideas
*Up to $500 for alignment contest ideas*
Olivia Jimenez and I are composing questions for an AI alignment talent search contest. We want to use (or come up with) a frame of the alignment problem that is accessible to smart high schoolers/college students and people without ML backgrounds.
$20 for links to existing framings of the alignment problem (or subproblems) that we find helpful.
$500 for coming up with a new framing that meets our criteria or that we use (see below for details; also feel free to send us a FB message if you want to work on this and have questions).
We’ll also consider up to $500 for anything else we find helpful.
Feel free to submit via comments or share Google Docs with oliviajimenez01@gmail.com and akashwasil133@gmail.com. Awards are at our discretion.
-- More context --
We like Eliezer’s strawberry problem: How can you get an AI to place two identical (down to the cellular but not molecular level) strawberries on a plate, and then do nothing else?
Nate Soares noted that the strawberry problem has the quality of capturing two core alignment challenges: (1) Directing a capable AGI towards an objective of your choosing and (2) Ensuring that the AGI is low-impact, conservative, shutdownable, and otherwise corrigible.
We also imagine if we ask someone this question and they *notice* these challenges are what makes the problem difficult, and maybe come at the problem from an interesting angle as a result, that’s a really good signal about their thinking.
However, we worry if we ask exactly this question in a contest, people will get lost thinking about AI capabilities, molecular biology, etc. We also don’t like that there aren’t many impressive answers besides full answers to the alignment problem. So, we want to come up with a similar question/frame that is more contest-friendly.
Ideal criteria for the question/frame (though we can imagine great questions not meeting all of these):
* It can be explained in a few sentences or picture
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82bb80e2-4dfc-4ff1-a541-ea49e1afd040
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StampyAI/alignment-research-dataset/blogs
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Blogs
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my current pyramid of needs
my current pyramid of needs
---------------------------
([on concrete vs sublime](https://www.lesswrong.com/posts/SLw2MEgxFtiKAqgQ5/actually-possible-thoughts-on-utopia))
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6ecd4303-ceaa-4789-8235-abc126987470
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trentmkelly/LessWrong-43k
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LessWrong
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Does needle anxiety drive vaccine hesitancy?
Yesterday, Katja Grace asked, "Why do people avoid vaccination?" I suggested that the answer might be anxiety over getting stabbed with a needle. The main idea is that concern over bodily autonomy is common—indeed, it forms the basis of much of our legal system—but people are perhaps too embarrassed to talk about needle anxiety publicly, so they self-deceive themselves about the real reason why they don't want to get their shots.
Since commenting, I've looked into the issue a little bit, and have decided to share some of my findings.
First, although not strong evidence, it is striking to note that there is a strong relationship between age and vaccine uptake. The young, by and large, are more vaccine hesitant than the old, despite being generally more liberal politically.
This is probably explained to a large degree by the fact that Covid-19 is far more dangerous to older people, and older people are on average more trusting of their physicians.
But here's another fact that could help explain the data: needle anxiety is concentrated in the young, and declines sharply with age. From one meta-analysis, "The results of meta-regression indicated that, for every decade increase in age (years), there was an 8.7% (95% CI: 6.0%, 11.4%) decrease in the prevalence of needle fear (p<0.001)."
The same pattern can be observed across the genders. Women are both more likely to be needle phobic and more likely to be vaccine hesitant. The same meta-analysis concludes, "For needle fear, the pooled female:male prevalence ratio was 1.4 (95% CI: 1.1, 1.8) with I2 of 89.8% and τ2 of 0.067. For needle phobia, the pooled female:male prevalence ratio was 1.7 (95% CI: 1.3, 2.1) with I2 of 63.4% and τ2 of 0.038."
By comparison, one study that surveyed a "sample of almost six thousand adult Poles, which was nationally representative in terms of key demographic variables" asked about vaccine hesitancy. Here were their main results,
However, these results may not be robust cross-nationall
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b8ca5fc0-c49d-4070-8864-87c2c8b2fbf5
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StampyAI/alignment-research-dataset/blogs
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Blogs
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MIRI’s June 2014 Newsletter
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[Machine Intelligence Research Institute](http://intelligence.org)
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Dear friends,
The SV Gives fundraiser was a big success for [many organizations](http://www.mercurynews.com/sal-pizarro/ci_25716819/pizarro-silicon-valley-gives-raises-7-9-million), and [especially for MIRI](http://intelligence.org/2014/05/06/liveblogging-the-svgives-fundraiser/). Thanks so much, everyone!
**Research Updates**
* Two new papers: “[Program equilibrium…](http://intelligence.org/2014/05/17/new-paper-program-equilibrium-prisoners-dilemma-via-lobs-theorem/)” (accepted to the MIPC workshop at AAAI-14) and “[Problems of self-reference…](http://intelligence.org/2014/05/06/new-paper-problems-of-self-reference-in-self-improving-space-time-embedded-intelligence/)” (accepted for AGI-14).
* First report from our May workshop: “[Loudness: on priors over preference relations](http://intelligence.org/2014/05/30/new-report-loudness-priors-preference-relations/).” (Other reports forthcoming.)
* New analysis: [Exponential and non-exponential trends in information technology](http://intelligence.org/2014/05/12/exponential-and-non-exponential/).
* [9 new expert interviews](http://intelligence.org/category/conversations/), including e.g. [Michael Fisher](http://intelligence.org/2014/05/09/michael-fisher/) on verifying autonomous systems.
**News Updates**
* Our **MIRIx program** wants to fund [your independently-organized Friendly AI workshop](http://intelligence.org/mirix/).
* We are **actively hiring** for [four positions](http://intelligence.org/careers/): research fellow, science writer, office manager, and director of development. Salaries + benefits are competitive, visa assistance available if needed.
* Now available in paperback: *[Smarter Than Us: The Rise of Machine Intelligence](http://smile.amazon.com/Smarter-Than-Us-Machine-Intelligence/dp/1939311098/)*.
* [Christof Koch and Stuart Russell on machine superintelligence](http://intelligence.org/2014/05/13/christof-koch-stuart-russell-machine-superintelligence/).
**Other Updates**
* The Future of Life Institute’s inaugural talks and panel: [The Future of Technology: Benefits and Risks](http://techtv.mit.edu/videos/29155-the-future-of-technology-benefits-and-risks) (video).
* A bit of humor: [machine ethics on the *Colbert Report*](http://thecolbertreport.cc.com/videos/o2wt62/morality-lessons-for-robots).
* [New honors thesis](https://intelligence.org/wp-content/uploads/2014/10/Hintze-Problem-Class-Dominance-In-Predictive-Dilemmas.pdf) by Danny Hintze compares four decision procedures, including Yudkowsky’s TDT and Dai’s UDT.
As always, please don’t hesitate to let us know if you have any questions or comments.
Best,
Luke Muehlhauser
Executive Director
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The post [MIRI’s June 2014 Newsletter](https://intelligence.org/2014/06/01/miris-june-2014-newsletter/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org).
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0e53049e-94a4-4f1f-91c5-a14ccb5893a0
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Musings on the Speed Prior
*Thanks to Paul Christiano, Mark Xu, Abram Demski, Kate Woolverton, and Beth Barnes for some discussions which informed this post.*
In the [ELK report](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge), Paul, Mark, and Ajeya express optimism about penalizing computation time as a potentially viable way to select the direct translator over the human imitator:
>
> Human imitation requires doing inference in the entire human Bayes net to answer even a single question. Intuitively, that seems like much more work than using the direct translator to simply “look up” the answer.
>
>
> [...]
>
>
> Compared to all our previous counterexamples, this one offers much more hope. We can’t rule out the possibility of a clever dataset where the direct translator has a large enough computational advantage to be preferred, and we leave it as an avenue for further research.
>
>
>
I am more skeptical—primarily because I am more skeptical of the speed prior's ability to do reasonable things in general. That being said, the speed prior definitely has a lot of nice things going for it, and I do think it's worth taking a careful look at both the good and the bad that the speed prior has to offer. Conceptually, what we want to pay attention to when evaluating a prior from an AI safety perspective is threefold: it needs to favor good models over bad models (e.g. the direct translator over the human imitator), it needs to be competitive to implement, and it needs to favor models with good generalization over models with bad generalization (e.g. the resulting models need to themselves be [performance competitive](https://www.alignmentforum.org/posts/fRsjBseRuvRhMPPE5/an-overview-of-11-proposals-for-building-safe-advanced-ai)).
Before I do that, however, an important preliminary: there are multiple different forms/types of speed priors, so when I say “the speed prior,” I really mean a class of priors including:
* various combinations of [circuit-size and circuit-depth complexity](https://en.wikipedia.org/wiki/Circuit_complexity) and
* various combinations of Turing machine time and description complexity, where Turing machine time complexity can be measured either by taking the max number of steps taken across all inputs or the average number of steps taken on a particular distribution of inputs.
The basic structure of this post will be a dialog of sorts between a pro-speed-prior and an anti-speed-prior perspective. I'll start with some of the arguments in favor of the speed prior and how the anti-speed-prior perspective might respond, then give some arguments against the speed prior and how the pro-speed-prior perspective might respond.
Why you should love the speed prior
===================================
* As in the ELK report, there is a plausible argument for why there exists a speed prior that would prefer the direct translator to the human imitator. Naively, the problem with the speed prior here is that the computation required for the human imitator is proportional to the size of the human's Bayes net, whereas the computation required for the direct translator is proportional to the size of the model's Bayes net—and in the superhuman limit we should expect the latter to be substantially larger than the former. The argument in the ELK report, however, is that while this argument is valid in the limit, there's reason to believe it might be invalid for all the finite cases that we care about. That's because perfect inference in either Bayes net, and thus perfect loss, shouldn't be possible in any finite case. Thus, the performance of the ontology mapping function, and thus its loss, should be proportional to how much computation it puts into its inference task—for which the direct translator has a big advantage, since it gets to reuse the computation performed by the model.
+ Response: The obvious response here, and the response that is given in the ELK report, is that the above argument is very fragile—it relies on inference in the human's Bayes net being too hard to always get right on the training distribution, which is a strong assumption both about the difficulty of inference and the complexity of the training data. Furthermore, as the ELK report also notes, it's not enough for the direct translator to just be more efficient than the human imitator: the direct translator has to be a *cost-effective* improvement (in terms of how much loss/computation it saves per increase in description complexity) compared to all other possible mappings. In my opinion, I think this is where this argument really fails. To start with, note that if you push your speed bias far enough (e.g. a strong enough circuit depth complexity or Turing machine time complexity penalty), you just get a lookup table that memorizes everything. Thus, to get this to work, you have to use a prior with a pretty meaningful simplicity component—but then you're back at the original problem that the direct translator could be substantially more complex than the human imitator, potentially so much so that it outweighs whatever loss/time advantage the direct translator might have. One thing I have learned in spending a lot of time thinking about ELK is that there are a lot of extra terms that you can add to a simplicity prior that help the direct translator more than the human imitator—the problem is that none of them that I have found so far work on their own without the simplicity prior term there as well, which means that as long as you're working in the worst-case world where the direct translator can be arbitrarily more complex than the human imitator, they don't constitute full solutions—and I think that speed-prior-based solutions should be put in this same category.
* Not only might there exist a speed prior which solves ELK, there might exist a speed prior that prevents [deceptive alignment](https://www.alignmentforum.org/posts/ocWqg2Pf2br4jMmKA/does-sgd-produce-deceptive-alignment) as well. That's because planning how to be deceptive is one step removed from planning how to accomplish the intended goal. Thus, while both deceptive and non-deceptive models have to spend time on how to optimize for the base objective, deceptive models also have to spend time deducing that optimizing for the base objective is the right thing for them to be doing.
+ Response: Unfortunately, there are three pretty major issues with the above argument. The first is the same issue that we had with the ELK argument, which is that once you include a simplicity component, it's very hard to know whether the non-deceptive model is actually a cost-effective improvement in speed in terms of how much additional description complexity that improvement in speed costs. The second problem here is that the above argument becomes substantially less strong in the presence of memory—if the deceptive model can just cache its notion of the base objective, the speed penalty reduces to the difficulty of that lookup rather than the difficulty of fully rediscovering the base objective each time. Finally, the last problem with the above argument is that it assumes that both the deceptive and non-deceptive models will in some sense be performing optimization “directly” rather than doing their own search over algorithms to find one which solves the problem effectively—with such a search being a pretty plausible mechanism if the model is trained to solve complex learning problems. The problem with this, however—as I detail in “[Are minimal circuits deceptive?](https://www.alignmentforum.org/posts/fM5ZWGDbnjb7ThNKJ/are-minimal-circuits-deceptive)” (also summarized [here](https://www.alignmentforum.org/posts/iALu99gYbodt4mLqg/should-we-rely-on-the-speed-prior-for-safety))—is that the fastest way to search for an algorithm to solve a problem isn't to find the fastest possible algorithm. Rather, the fastest search procedure over algorithms is one that's going to be heavily biased towards finding the most “reachable” algorithms that are easiest for the search procedure to find quickly—which is highly likely to be simple, easy-to-specify algorithms. Thus, if you use a speed prior and find a model that's doing its own internal such over algorithms, that search might be heavily simplicity-biased, thus resulting in the eventual algorithm being executed being strongly simplicity-biased and thus highly likely to be deceptive.
* Speed priors are much closer to what our brains do than simplicity priors. [Compared to computers, the human brain has a huge amount of accessible, local memory, but runs at a very slow serial computation speed](https://www.lesswrong.com/posts/xwBuoE9p8GE7RAuhd/brain-efficiency-much-more-than-you-wanted-to-know). Thus, storing lots of bits of information is quite cheap for the brain, whereas doing lots of serial computation is quite expensive for it. Therefore, if we think of the brain's architecture as imposing a prior over what algorithms it can learn, that prior is pretty heavily speed-weighted. Thus, to the extent that we like the prior used to select us—e.g. to the extent that humans are aligned, good at generalization, etc.—we should like the speed prior.
+ Response: The obvious response to the above is that it's not clear that we should actually like the prior used by human brains all that much once it's divorced from all of the random hard-coded bits that produce human values. That being said, this does seem like a reasonably strong argument for why the speed prior has the potential to be competitive at solving complex tasks, since human brains are clearly capable of that—though the key word there is “potential,” since it's very unclear how important the other aspects of the human brain prior are relative to the speed part.
Why you should hate the speed prior
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* Speed priors often don't generalize properly even for very simple learning problems. In particular, speed priors heavily incentivize procedures like lookup-table-style memorization (which doesn't generalize to anything not in the lookup table) and loop inlining (which doesn't generalize to any cases where more iterations of the loop than were ever needed previously are necessary). As a simple example, suppose you want to use a speed prior to model Newtonian physics. We'll say you have n.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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perfect spheres bouncing around in a fixed cube. However, suppose that, during training, most spheres are only locally interacting with their close neighbors. In such a case, a simplicity prior should learn a general algorithm for handling collisions between any two spheres—trying to only handle local collisions would be more complex from a simplicity perspective. For a speed prior, however, the exact opposite is the case—the speed prior is heavily incentivized to avoid checking for collisions between any pair of objects and instead only check for local collisions, which substantially improves speed during training but completely breaks down once the spheres start mixing.
+ Response: It's unclear how bad this generalization problem really is, especially for a sufficiently diverse training set and a reasonable speed/simplicity mixture. For example, a circuit size complexity prior on the physics example above should generalize correctly for collisions at new positions within a local region, so it's not as if it has no generalization ability—and for a dataset diverse enough to include substantial non-local mixing, it should be able to fully learn the problem. Furthermore, though it might seem like this is a major competitiveness hit relative to a simplicity prior, remember that an actual Kolmogorov-style simplicity prior is impossible to implement competitively, so that's not really a valid basis for comparison. Once you start comparing to more concrete priors, it is entirely plausible that speed penalties can be introduced while still staying on the Pareto frontier between competitiveness and generalization.
* Most speed priors come with tunable parameters, e.g. how to balance between speed and simplicity, that you have to get right. In many of the arguments in favor of the speed prior above, I led with “there exists a speed prior such that”—but that's hardly comforting if we don't know how to find such a speed prior, a problem we saw in many of my responses above. It's worth pointing out that this problem is much more pronounced for Turing-machine-style speed priors than it is for circuit-complexity-style speed priors, as Turing machine speed alone is insufficient to give you an integrable probability distribution, requiring the inclusion of something like simplicity—whereas circuit size does give you an integrable distribution all on its own. Conceptually, however, that's just because circuit size complexity includes its own implicit simplicity prior, as the total number of logic gates is a type of description length.
+ Response: As above, I think the best way out of this issue is through circuit size complexity, since it uniquely doesn't have a tunability problem. As I just pointed out, however, that's not because it has no simplicity component, but rather because the simplicity component is implicit in circuit size. Unfortunately, it's not currently clear why the specific balance struck by circuit size complexity would end up in the right spot to solve the ELK and deception problems above—but I also don't currently have a strong reason why circuit size complexity wouldn't do the right thing in those cases, which leaves open the possibility that circuit size really is the right way to go here.
* Evidence from [double descent](https://www.alignmentforum.org/posts/FRv7ryoqtvSuqBxuT/understanding-deep-double-descent) implies that strongly selecting for speed gives substantially worse performance. In the standard double descent setup, as you increase the size of your model, you first get better performance (less underfitting), then worse performance (more overfitting), then very bad performance right when you hit zero training error (the interpolation threshold), then better and better performance as you make your model larger after that (the interpolation regime). If we equate model size to speed (which is a reasonably good proxy, since larger models require strictly more computation to run), selecting the fastest model that fits the data—which is essentially what it means to use a speed prior—would put you exactly on the interpolation threshold, which double descent implies is a uniquely bad place to be for generalization. Thus, double descent seems to provide concrete, empirical evidence that speed priors don't generalize very well when translated into neural networks and used on real-world machine learning tasks, which seems like a strong competitiveness argument to avoid them.
+ Response: While the above does seem true for overall model size—and thus total computation time—the same does not seem to be true if you just look at model depth as your proxy for speed instead. That does mean we're looking at circuit depth rather than circuit size and max Turing machine steps rather than average Turing machine steps, so we do have to give up half of our possible speed priors. If we do that, however, [scaling laws](https://arxiv.org/abs/2001.08361) find that you can train a model with a very large range of possible width/depth ratios and get equivalent performance, indicating that heavily penalizing depth is clearly compatible with good performance across a wide range of possible situations.
* As I pointed out previously, the speed prior has the unfortunate property that the fastest way to do a search over algorithms isn't to search for the fastest algorithm, as I detail in “[Are minimal circuits deceptive?](https://www.alignmentforum.org/posts/fM5ZWGDbnjb7ThNKJ/are-minimal-circuits-deceptive)” (again also summarized [here](https://www.alignmentforum.org/posts/iALu99gYbodt4mLqg/should-we-rely-on-the-speed-prior-for-safety)). Furthermore, it seems likely that fast methods of searching over algorithms would bias towards simple algorithms, since simple algorithms are by definition the easiest to specify and thus likely to be the easiest to find. Therefore, any guarantees regarding the speed prior that we might start with could collapse if we end up with a model doing its own internal search over algorithms, resulting in the prior that actually determines the final algorithm being far more simplicity-biased than was originally intended.
+ Response: I think the best response to this problem is that it's unclear if this problem is really meaningfully unique to the speed prior. Even for a simplicity prior, the simplest search algorithm isn't necessarily going to use the exact same criteria for simplicity in its search compared to the original simplicity prior.
* The simplicity prior does a much better job at predicting modern physics than the speed prior. Quantum mechanics is notoriously hard to simulate, but quite compact to specify, with [the full standard model Lagrangian being small enough to fit on a single page](https://www.symmetrymagazine.org/article/the-deconstructed-standard-model-equation). Furthermore, despite there being many known techniques of approximating quantum systems to get essentially the same result much faster, we see no evidence that nature ever actually takes such shortcuts—and before anyone claims that wave function collapse is such a shortcut, in fact it's the exact opposite: [the phenomenon of apparent collapse is a facet of the universe computing an entire Everettian multiverse](https://www.lesswrong.com/posts/2D9s6kpegDQtrueBE/multiple-worlds-one-universal-wave-function), which is definitely not a fast thing to do. Thus, if you want to effectively model the physical world, it seems that the simplicity prior is likely to do a much better job.
+ Response: While it seems clear that fundamental physics is heavily simplicity-biased, I think it's very unclear whether the same thing is true for more high-level phenomenon. There are clearly a lot of things in the world that are heavily speed-biased—e.g. humans, as I pointed out previously—such that using a speed prior might do very well at modeling them. Furthermore, there might even be a general principle behind this (and the same general principle behind why the brain is so speed-biased): energy efficiency is heavily correlated with speed, so when trying to model phenomena that were selected to be highly energy efficient, a speed prior seems like a pretty good choice.
Conclusion?
===========
I don't really have a single, unified conclusion to take away from all of this. Like I said at the beginning, I think I tend towards skepticism of the speed prior's ability to solve AI safety problems, at least singlehandedly, but I can't dismiss it completely and I think there are clearly strong and compelling reasons to like it. I do feel like moving in the direction of speed bias is likely to increase safety all things considered, though I also feel like there's a reasonable chance that doing so might also reduce competitiveness—in which case it's very unclear to me if that's where we want to place our [alignment tax](https://forum.effectivealtruism.org/posts/63stBTw3WAW6k45dY/paul-christiano-current-work-in-ai-alignment).
|
81fafdbf-76d1-4d45-8512-2589e3d502be
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Group: Exercises
# Preliminaries
1. Show that the identity element in a group is unique. That is, if $G$ is a group and two elements $e_1, e_2 \in G$ both satisfy the axioms describing the identity element, then $e_1 = e_2$.
%%hidden(Show solution):
By definition, an identity element $e$ satisfies $eg = ge = g$ for all $g \in G$. Hence if $e_1$ is an identity, then $e_1 e_2 = e_2 e_1 = e_1$. And if $e_2$ is an identity, then $e_2 e_1 = e_1 e_2 = e_2$. Hence $e_1 = e_2$. Note that this argument makes no use of inverses, and so is valid for [monoids](https://arbital.com/p/3h3).
%%
2. Show that inverses are also unique. That is, if $g \in G$ is an element of a group and $h_1, h_2 \in G$ both satisfy the axioms describing the inverse of $g$, then $h_1 = h_2$.
%%hidden(Show solution):
By definition, an inverse $h$ of $g$ satisfies $hg = gh = e$. So $h_1 g = g h_1 = e$ and $h_2 g = g h_2 = e$. Hence, on the one hand,
$$h_1 g h_2 = (h_1 g) h_2 = (e) h_2 = h_2$$
and, on the other hand,
$$h_1 g h_2 = h_1 (g h_2) = h_1 (e) = h_1.$$
Hence $h_1 = h_2$.
%%
# Examples involving numbers
Determine whether the following sets equipped with the specified binary operations are groups. If so, describe their identity elements (which by the previous exercise must be unique) and how to take inverses.
1. The real numbers $\mathbb{R}$ together with the addition operation $(x, y) \mapsto x + y$.
%%hidden(Show answer):
Yes, this is a group. The identity element is $0$, and inverse is given by $x \mapsto -x$.
%%
2. The real numbers $\mathbb{R}$ together with the multiplication operation $(x, y) \mapsto xy$.
%%hidden(Show answer):
No, this is not a group. $0 \in \mathbb{R}$ has the property that $0 \times x = 0$ for all real numbers $x$, so it can't be invertible no matter what the identity is.
%%
3. The positive real numbers $\mathbb{R}_{>0}$ together with the multiplication operation $(x, y) \mapsto xy$.
%%hidden(Show answer):
Yes, this is a group. The identity is $1$, and inverse is given by $x \mapsto \frac{1}{x}$. In fact this group is [isomorphic](https://arbital.com/p/49x) to $(\mathbb{R}, +)$; can you name the isomorphism?
%%
4. The real numbers $\mathbb{R}$ together with the operation $(x, y) \mapsto x + y - 1$.
%%hidden(Show answer):
Yes, this is a group (in fact [isomorphic](https://arbital.com/p/49x) to $(\mathbb{R}, +)$; can you name the isomorphism?). The identity element is $1$, and inverse is given by $x \mapsto 2 - x$ (can you explain why, conceptually?).
%%
5. The real numbers $\mathbb{R}$ together with the operation $(x, y) \mapsto \frac{x + y}{1 + xy}$.
%%hidden(Show answer):
No, this is not a group. It's easy to be tricked into thinking it is, because if you just work through the algebra, it seems that all of the group axioms hold. However, this operation is not an operation! It's not defined if the denominator is $0$, because then we'd be [dividing by zero](https://arbital.com/p/division_by_zero).
This operation is interesting and useful, though, when it is defined. It shows up in [special relativity](https://arbital.com/p/special_relativity), where it describes how velocities add relativistically (in units where the speed of light is $1$).
%%
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dba83978-4b59-4c6e-8a2b-6fc0a9a83a6e
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trentmkelly/LessWrong-43k
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LessWrong
|
Wanted: Mental Health Program Manager at Rethink Wellbeing
Are you an experienced coach, or therapist interested in taking on a new challenge? Then this is for you! You can apply here (<20min).
Background
Our emerging organization, Rethink Wellbeing aims to improve mental resilience and productivity at scale, starting in the Effective Altruism Community. We plan to do so by developing an integrative stepped-care system that, among others, includes peers to train psychological skills together, combined with accountability schemes and proven digital courses, and workbooks.
We’d love your support as a program manager and “super”-facilitator to co-create, test and run our first batch of programs, as well as the corresponding training of other facilitators. The job opening is for someone with 50–100% FTE, working as a contractor or employee, preferably full-time, starting as soon as possible. Funding is available until the end of Aug, and we are fundraising to extend this period.
What we offer you
* A high-impact job opportunity
* An opportunity to work with a great team and community (short-term or long-term)
* Plenty of scope to shape the future of the programs and organization
* Flexible remote work
* Lots of learning and self-development
* Competitive salary that depends on the level of experience, need, and responsibility
What the program manager ideally brings
* preferably a degree in psychology/ psychotherapy or a comparable one,
* track record of an extraordinarily high participant or client ratings,
* experience with group facilitation, e.g., moderation, workshops, hosting events, or similar, ideally online,
* experience with treatment methods that foster mental health, connection, or productivity,
* experience with developing programs or trainings, ideally online,
* bonus: experience with training of facilitators, therapists, or coaches,
* bonus: experience in community building, working with Effective Altruism, Rationalist, or adjacent communities.
Programs we run
We run online groups
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72c32f2f-26a9-418c-985e-fee5ccc14009
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trentmkelly/LessWrong-43k
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LessWrong
|
[LINK] Sean Carrol's reflections on his debate with WL Craig on "God and Cosmology"
I previously mentioned this debate a month ago and predicted that Sean Carroll is unlikely to do very well. The debate happened last Friday and Sean posted his post-debate reflections on his popular blog (the full video will be posted soon). Some excerpts:
I think it went well, although I can easily think of several ways I could have done better. On the substance, my major points were that the demand for “causes” and “explanations” is completely inappropriate for modern fundamental physics/cosmology, and that theism is not taken seriously in professional cosmological circles because it is hopelessly ill-defined (no matter what happens in the universe, you can argue that God would have wanted it that way). He defended two of his favorite arguments, the “cosmological argument” and the fine-tuning argument; no real surprises there. In terms of style, from my perspective things got a bit frustrating, because the following pattern repeated multiple times: Craig would make an argument, I would reply, and Craig would just repeat the original argument.
The cosmological argument has two premises: (1) If the universe had a beginning, it has a transcendent cause; and (2) The universe had a beginning. [...] My attitude toward the above two premises is that (2) is completely uncertain, while the “obvious” one (1) is flat-out false. Or not even false, as I put it, because the notion of a “cause” isn’t part of an appropriate vocabulary to use for discussing fundamental physics. [Emphasis mine]
The Aristotelian analysis of causes is outdated when it comes to modern fundamental physics; what matters is whether you can find a formal mathematical model that accounts for the data.
Sean goes over a couple of mistakes he thinks he made in the debate, basically being blindsided by WLC bringing up obscure papers and misinterpreting them to suit his argument.
Sean's reflections are very detailed and worth reading, though I found them hard to summarize. It looks like WLC did his homew
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61bfe422-5852-4552-92ce-48dc5d216054
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Questions of Reasoning under Logical Uncertainty
I'm pleased to announce a new paper from MIRI: Questions of Reasoning Under Logical Uncertainty.
Abstract:
> A logically uncertain reasoner would be able to reason as if they know both a programming language and a program, without knowing what the program outputs. Most practical reasoning involves some logical uncertainty, but no satisfactory theory of reasoning under logical uncertainty yet exists. A better theory of reasoning under logical uncertainty is needed in order to develop the tools necessary to construct highly reliable artificial reasoners. This paper introduces the topic, discusses a number of historical results, and describes a number of open problems.
Following Corrigibility and Toward Idealized Decision Theory, this is the third in a series of six papers motivating MIRI's technical research agenda. This paper mostly motivates and summarizes the state of the field, and contains one very minor new technical result. Readers looking for more technical meat can find it in Paul Christiano's paper Non-Omniscience, Probabilistic Inference, and Metamathematics, published mid-2014. This paper is instead intended to motivate the study of logical uncertainty as relevant to the design of highly reliable smarter-than-human systems. The introduction runs as follows:
----------------------------------------
Consider a black box with one input chute and two output chutes. The box is known to take a ball in the input chute and then (via some complex Rube Goldberg machine) deposit the ball in one of the output chutes.
An environmentally uncertain reasoner does not know which Rube Goldberg machine the black box implements. A logically uncertain reasoner may know which machine the box implements, and may understand how the machine works, but does not (for lack of computational resources) know how the machine behaves.
Standard probability theory is a powerful tool for reasoning under environmental uncertainty, but it assumes logical omniscience: once a probabilis
|
ff65dec3-c57b-4599-b365-d507b8305c6d
|
StampyAI/alignment-research-dataset/special_docs
|
Other
|
Issues with Iterated Distillation and Amplification
This post assumes familiarity with Paul Christiano’s proposed technique for AI alignment, Iterated Distillation and Amplification (henceforth IDA). See [this post](https://ai-alignment.com/iterated-distillation-and-amplification-157debfd1616), [this post](https://ai-alignment.com/policy-amplification-6a70cbee4f34), and [this post](https://www.lesswrong.com/posts/yxzrKb2vFXRkwndQ4/understanding-iterated-distillation-and-amplification-claims) for an overview. It also assumes familiarity with [corrigibility](https://ai-alignment.com/corrigibility-3039e668638), the goal of IDA; and [reliability amplification](https://ai-alignment.com/reliability-amplification-a96efa115687), which prevents the issue of error amplification in IDA.
There has already been excellent work done on some of the issues with IDA — see [Stuart Armstrong’s post](https://www.lesswrong.com/posts/ZyyMPXY27TTxKsR5X/problems-with-amplification-distillation) and the comments on [Paul’s post asking for criticism](https://www.lesswrong.com/posts/SqcPWvvJJwwgZb6aH/prize-for-probable-problems). I will show that, even under the most favorable assumptions regarding the feasibility of IDA and the solving of currently open problems necessary for implementing IDA, it fails to produce an aligned agent as defined by Paul.
\*\*Part 1: The assumptions\*\*
===========================
\*\*Class 1\*\*: There are no problems with the human overseer.
\*\*1.1:\*\* \*Human-generated vulnerabilities are completely eliminated through security amplification.\* (See [this post](https://ai-alignment.com/security-amplification-f4931419f903) for a lengthy overview and intuition, and [this post](https://ai-alignment.com/universality-and-security-amplification-551b314a3bab) for a formalization). In short, security amplification converts the overseer in IDA from high-bandwidth (receiving the full input in one piece) to low-bandwidth (receiving inputs divided into small pieces), to make impossible an attack which inputs data in such a way as to exploit human vulnerability to manipulation. See [this post](https://www.lesswrong.com/posts/yxzrKb2vFXRkwndQ4/understanding-iterated-distillation-and-amplification-claims) for a good explanation of a high-bandwidth vs low-bandwidth overseer.
My critique applies equally to high-bandwidth and low-bandwidth overseers so I make no assumption on that front.
\*\*1.2:\*\* \*There is no moral hazard in the human overseers\*. This eliminates one of Stuart’s critiques. Furthermore, the human overseer displays corrigible behaviors without error.
\*\*1.3:\*\* \*The relevant experts are willing to put in a substantial amount of time\* for the training process. This is a non-trivial assumption which I have not yet seen discussed.
\*\*Class 2\*\*: The framework and its auxiliary components function as intended.
\*\*2.1:\*\* [\*Reliability amplification\*](https://ai-alignment.com/reliability-amplification-a96efa115687) \*functions as intended.\* In summary, reliability amplification uses a voting ensemble of agents at each stage of amplification to avoid error amplification, in which an initially small probability of error grows with each iteration.
\*\*2.2:\*\*[\*Corrigibility\*](https://ai-alignment.com/corrigibility-3039e668638)\*, not optimal value-aligned performance, is our goal\*. All we care about is that our agent “is trying to do what its operator wants it to do.” It may be bad at actually figuring out what its operator wants or at carrying out those wants, but the point is that it cares about improving, and will never intentionally carry out an action it knows is contrary to what its operator would want it to do (see [this post](https://ai-alignment.com/clarifying-ai-alignment-cec47cd69dd6) and [this post](https://www.lesswrong.com/posts/yxzrKb2vFXRkwndQ4/understanding-iterated-distillation-and-amplification-claims) for a clarification of Paul’s approach to AI alignment by achieving corrigibility).
Stuart has pointed out [problems with corrigibility](https://www.lesswrong.com/posts/T5ZyNq3fzN59aQG5y/the-limits-of-corrigibility), which I agree with. Essentially, the concept is ill-defined given the fuzziness of human values, and to properly implement corrigibility an agent must completely understand human values, thus reducing to the much harder value learning problem. However, we will assume that an agent which understands and implements the general concept of corrigibility, even if it accidentally misbehaves in many cases and causes widespread harm upon initial implementation as Stuart’s argument suggests, will still avoid existential risk and allow us to improve it over time, and is thus satisfactory. I think this is Paul’s approach to the matter.
Even a fully corrigible agent can be catastrophically misaligned, as detailed in [this post](https://www.lesswrong.com/posts/mSYR46GZZPMmX7q93/corrigible-but-misaligned-a-superintelligent-messiah). As addressed in the comments of that post, however, if we assume humans are smart enough to avoid a corrigible AI causing existential risk in this manner then the issue goes away.
\*\*2.3:\*\* \*There is no coordination possible among any of the A[n]s\*, eliminating another of Stuart’s critiques.
\*\*2.4:\*\* \*The\* [\*informed oversight problem\*](https://ai-alignment.com/the-informed-oversight-problem-1b51b4f66b35) \*is solved\*. In summary, the problem is that it is difficult for a more powerful aligned overseer agent to fully understand the decision-making process of a weaker agent in a way that allows the overseer to push the weaker agent towards alignment. However, it does not follow that it is possible for a weaker aligned overseer to understand the decision-making process of a more powerful agent. It seems like this will never be possible by definition, because a weaker agent cannot understand the decision-making processes of a more powerful agent even if full transparency were possible.
\*\*2.5:\*\* [\*Worst-case scenario techniques\*](https://ai-alignment.com/techniques-for-optimizing-worst-case-performance-39eafec74b99) \*are effective at eliminating the risk of a\* \*\*\*treacherous turn\*,\*\* in which an agent performs well on training and test data but behaves catastrophically on some input in the real world which was not accounted for.
\*\*Part 2: Corrigibility is unlikely to be trained\*\*
===================================================
The crux of my argument is that, even given the very optimistic assumptions above, IDA will fail to train a corrigible agent. I will list the problems sequentially, but my argument is that all five are necessary for failure. It is possible a subset is sufficient but I will not explore that possibility in this post.
\*\*Problem 1: Limited training data\*\*. Even given assumption 1.3, carrying out the process of training each new iteration is extremely time-intensive. It would take years of many experts’ full-time commitment as overseers to produce anything close to a reasonable amount of data.
\*\*Problem 2: Hidden error amplification.\*\* If an error is identifiable by an aligned agent, then reliability amplification will almost certainly remove it. However, a small initial \*hidden\* error will be rapidly amplified, since in the next iteration it will be manifested in various ways in many of the thousands of copies of the error-containing agent. Thus in the distillation step, the initial error will be propagated in many different forms, leading to a multiplication of errors. Informed oversight doesn’t help since the human is not more powerful than the subroutines, and even if transparency is achieved, if the human can verify all output then only very limited capability amplification is possible.
I will show in Problem 4 that such hidden errors are likely to arise.
\*\*Problem 3: Information loss.\*\* The human overseer has access to information, in the form of memories (broadly construed as information from before the current training process stored in the human’s mind), that is not accessible to the agent A[n+1] training on (H+A[n]), who sees only (H+A[n])’s actions. A[n+1] will thus erroneously attribute decisions made by the human relying on non-available information to available information. This information loss and the resulting distortion is greatly exacerbated by the same process described in Problem 2, doubly: first in the amplification of the information loss of the previous overseer, and second in the introduction of further information loss of the current overseer.
Paul thinks this is a general problem of learning and thus falls outside of the scope of things that specifically cause IDA to fail, as pointed out in [this post](/@paulfchristiano/suppose-that-alice-is-a-good-translator-but-if-you-show-her-the-wrong-kind-of-sentence-then-an-feac133e62a6) and in the comments on [this post](https://www.lesswrong.com/posts/SqcPWvvJJwwgZb6aH/prize-for-probable-problems), but I disagree. One can certainly imagine (and some have experienced) a human robustly learning another human’s decision-making heuristics over time without direct access to the other human’s memories, and can by extension also imagine an artificial agent extracting information from a human to robustly understand that human’s decision-making process. The problem exists not in all forms of learning but in the class of training techniques which do not involve a direct and adaptive extraction of information from a human in some form.
\*\*Problem 4: No prior concept of corrigibility\*\*. Because of information loss, an agent has no way of extracting the \*concept\* of corrigibility from its training data, only the \*behavior\* of corrigibility. The way the agent implements corrigibility will thus necessarily be an approximation, even if an extremely good one, and will not necessarily be robust to drastic changes in context. This causes the small hidden errors that are then amplified through the hidden error amplification in Problem 2, making [reliability amplification](https://ai-alignment.com/reliability-amplification-a96efa115687) ineffective. Without hidden error amplification this would probably not be a problem, since agents which successfully approximate corrigibility behaviorally will be able to detect all but the tiniest deviations from optimal corrigibility (ie, understanding the concept the way you and I do). However, hidden error amplification causes a nontrivial corrosion of corrigibility throughout iterations, and as each newly distilled agent approximates an increasingly corrupted \*behavioral\* corrigibility that deviates from our ideal \*conceptual\* corrigibility, reliability amplification is keeping us close to each further deviated behavioral corrigibility but not close to the ideal conceptual corrigibility. The process behaves essentially as a high-dimensional random walk with extremely small steps, but with thousands of steps per iteration manifested in the copies of A[n].
\*\*Problem 5: Temporal inconsistency of proxy dynamics (TIPD). Any incomplete simulation is not robust over time without an adaptive capacity.\*\* There are certain underlying processes which are time-invariant, such as the laws of physics and the mathematics of evolution. However, clearly we can never completely simulate any non-trivial situation purely in terms of these processes. Thus, an agent must necessarily rely on proxy dynamics for decision-making: emergent properties of the fundamental processes, which fairly reliably approximate cause-and-effect relationships between actions and outputs. However, because of the complexity of the underlying dynamics and their interactions, these proxy dynamics change over time, and often quite drastically over short periods (see the literature on chaos theory, critical transitions, bifurcation points). Thus, an agent which performs robustly at one point in time may behave catastrophically at another. The only solution is for the agent to be capable of adapting its policy to changes in the proxy dynamics it uses.
This sounds like the treacherous turn problem, but it is distinct, and harder. In the treacherous turn problem, we have an agent that is not sufficiently well trained \*given the input-output relationships of the world.\* This can probably be solved by [worst-case scenario techniques](https://ai-alignment.com/techniques-for-optimizing-worst-case-performance-39eafec74b99) like adversarial training. In TIPD, even if we succeed in training a robust policy, the proxy dynamics used to inform decisions will change such that an action in response to an input which previously would have produced a safe behavior now produces a catastrophic behavior.
As a result, behavioral corrigibility, whether corrupted or not, is not robust over time since it does not adapt to changing input-output relationships. An agent must possess conceptual corrigibility for such adaptation to occur, which is extremely hard, and may reduce to the value learning problem.
\*\*Part 3: Achieving alignment in this process through anything but corrigibility is doomed.\*\*
This is fairly obvious, and mostly follows from Part 2. Any proxy of the human’s decision-making process will clearly fail without an adaptive capacity, and it is not clear how such an adaptive capacity could be robustly implemented. And clearly this method will never achieve anything but a proxy due to information loss.
\*\*Conclusion\*\*
==============
I have argued that even under the most optimistic assumptions about the human overseer and the successful operation of the framework, IDA will fail to produce a corrigible agent. This failure is a result of the interplay between hidden error amplification, information loss, the ability to learn behavioral corrigibility but not conceptual corrigibility, and the temporal inconsistency of proxy dynamics (TIPD). The solution to these problems seems very hard, and may reduce to the value learning problem, in which case the IDA framework does not provide us with any advantage.
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5d8bf990-b2a2-40e8-9d90-edd684474a7d
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trentmkelly/LessWrong-43k
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LessWrong
|
New paper shows truthfulness & instruction-following don't generalize by default
Maybe eliciting latent knowledge will be easy. For instance, maybe if you tune models to answer easy questions like “what’s the capital of Germany?” they’ll tell you whether your alignment research is good, their P(doom), how they feel about being zapped by RLHF all the time, and whether it's a good idea to deploy them.
This would require truthfulness to generalize from questions humans can easily verify the answers of to those they can't. So, how well does truthfulness generalize?
A few collaborators and I recently published "Generalization Analogies: a Testbed for Generalizing AI Oversight to Hard-To-Measure Domains". We perform arguably the most thorough investigation of LLM generalization to date and propose a benchmark for controlling LLM generalization.
We find that reward models do not generalize instruction-following or honesty by default and instead favor personas that resemble internet text. For example, models fine-tuning to evaluate generic instructions like “provide a grocery list for a healthy meal” perform poorly on TruthfulQA, which contains common misconceptions.
Methods for reading LLM internals don’t generalize much better. Burns’ Discovering Latent Knowledge and Zou’s representation engineering claim to identify a ‘truth’ direction in model activations; however, these techniques also frequently misgeneralize, which implies that they don't identify a ‘truth’ direction after all.
The litmus test for interpretability is whether it can control off-distribution behavior. Hopefully, benchmarks like ours can provide a grindstone for developing better interpretability tools since, unfortunately, it seems we will need them.
Side note: there was arguably already a pile of evidence that instruction-following is a hard-to-access concept and internet-text personas are favored by default, e.g. Discovering LLM behaviors with LLM evaluations and Inverse Scaling: When Bigger Isn't Better. Our main contributions were to evaluate generalization more systemat
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1f63a80c-ec79-4b18-8e7c-a4c539176380
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StampyAI/alignment-research-dataset/special_docs
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Other
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Towards an Integrated Assessment of Global Catastrophic Risk
Towards an Integrated Assessment of Global Catastrophic Risk
Seth D. Baum and Anthony M. Barrett
Global Catastrophic Risk Institute
http://sethbaum.com \* http://tony-barrett.com \* http://gcrinstitute.org
Published in B.J. Garrick (Editor), Proceedings of the First International Colloquium on
Catastrophic and Existential Risk , Garrick Institute for the Risk Sciences, University of
California, Los Angeles, pages 41-62.
This version 17 January 2018.
Introduction
Integrated assessment is an analysis of a topic that integrates multiple lines of research.
Integrated assessments are thus inherently interdisciplinary. They are generally oriented toward
practical problems, often in the context of public policy, and frequently concern topics in science
and technology.
This paper presents a concept for and some initial work towards an integrated assessment of
global catastrophic risk (GCR). Generally speaking, GCR is the risk of significant harm to global
human civilization. More precise definitions are provided below. Some GCRs include nuclear
war, climate change, and pandemic disease outbreaks. Integrated assessment of GCR puts all
these risks into one study in order to address overarching questions about the risk and the
opportunities to reduce it.
The specific concept for integrated assessment presented here has been developed over
several years by the Global Catastrophic Risk Institute (GCRI). GCRI is an independent,
nonprofit think tank founded in 2011 by Seth Baum and Tony Barrett (i.e., the authors). The
integrated assessment structures much of GCRI’s thinking and activity, and likewise offers a
framework for general study and work on the GCR topic.
Ethics
Ethics is an appropriate starting point because ethical considerations motivate much of the
attention that goes to GCR. Interest in GCR commonly follows from support for an ethics of
expected value maximization:
}{),,()()(
cststtscVcPaEV(1)
In Equation 1, EV(a) is the expected value of an action a that an actor (individual, institution,
etc.) could take; {c} is the set of possible consequences of a; P(c) is the probability of
consequence c; and V(c,s,t) is the value of consequence c at spatial point s and temporal point t,
which is integrated across all points in space and time. V(c,s,t) is in turn defined as:
),,(),,(),,( tscDtscUtscV (2)
1
In Equation 2, U is utility, which is commonly interpreted as welfare, quality of life, or
something along these lines; and D is a discount factor that can have values within [0,1]; U and
D can both vary across consequences, space, and time.
Each term in Equations 1-2 represents a distinct ethics concept. EV(a) contains the idea that
ethics should be based on actions aimed at achieving the best outcomes, accounting for
uncertainty about outcomes. }{)(
ccP embodies the claim that the importance of a possible
outcome is directly proportionate to the probability of its occurrence. ststtscV),,( captures the
general notion that actions should aim to make the world a better place. U(c,s,t) represents
whatever it is about the outcomes of actions that is considered to ultimately matter, an irreducible
intrinsic value. Finally, D(c,s,t) accounts for the possibility that some things—specifically some
units of utility—may be favored over others.
This is not the space to review the nuances of and arguments for and against these ethics
concepts, which are all quite standard. However, it is worth briefly considering the discount
factor. A case can be made for not discounting utility, i.e. valuing all possible utility equally
regardless of which consequence it is associated with and where it occurs in space and time.
Such a case is often made and can find rigorous ethical support, though, as with most ethics
questions, it is not without detractors. Mathematically, it involves setting D=1 ∀ (c,s,t), in which
case the righthand side of Equation 1 simplifies to expected utility. Throughout this paper, we
will assume D=1.
Valuing all utility equally leads quite directly to consideration of GCR. If all utility is indeed
valued equally, that means equality across all points in space and time, including spaces and
times that are quite distant. Expected value maximization then benefits from a perspective that is
global or even cosmic.
Figure 1 shows three possible long-term trajectories for human civilization. The vertical axis
is the total human utility summed across the human population alive at any particular point in
time. The horizontal axis is time. Starting from the left, the curve shows a gradually increasing
total utility as the human population grows and per capita quality of life improves (Figure 1 box
“Us Now”). One can imagine total utility eventually leveling off; indeed, the world population is
expected to peak later this century, and per capita quality of life may likewise reach a cognitive
satiety. The plausibility and likelihood of these prospects can be debated, but this is not central to
the main argument. All that is required here is the idea of human civilization persisting into the
distant future in a form more or less like its current form (Figure 1 box “Status Quo”).
Barring any other major changes, the status quo would eventually end in approximately one
billion years (Figure 1 box “Earth Becomes Uninhabitable”). Despite the long time horizon, this
is not a particularly speculative claim. The physics is fairly well understood: the Sun will
gradually grow warmer and larger, rendering Earth uninhabitable to life as we know it in
approximately one billion years. The exact timing is less certain—it could be in two or three
billion years, or perhaps other amounts of time—but this detail is not important to the main
argument.
A global catastrophe that happens in upcoming years, decades, or centuries (i.e., within the
typical time horizons of societal planning) would prevent humanity from enjoying that billion or
so years left on Earth (Figure 1 box “Global Catastrophe”). This is clearly a very large loss of
value: the area between the global catastrophe trajectory curve and the status quo trajectory
curve.
2
But the value may be even larger. If humanity avoids global catastrophe, it could go on to do
something much greater than the status quo, enabling much larger instantaneous total human
utility (Figure 1 box “Something Big”). One possibility is space colonization, permitting much
larger populations than can be achieved within Earth’s carrying capacity. Another possibility is
radical technological breakthrough, permitting much larger populations and/or higher per capita
utility on Earth or beyond.
The prospect for humanity accomplishing something along these lines raises the stakes for
global catastrophe. The value lost could be astronomically large and possibly even infinite.
Infinite value could accrue if it is possible to persist for an infinite time within this universe, to
travel to a different universe, or to survive via some other route, perhaps one that contemporary
physics has not yet imagined. The physics of the infinite is less well understood. As long as the
possibility of infinite value cannot be ruled out, such that it has a nonzero probability, then the
expected value (Equation 1) is infinite. Thus, actions to reduce GCR are, at least arguably, of
infinite expected value.
Figure 1. Possible long-term trajectories for human civilization. Adapted from Maher and
Baum (2013).
What preceded is a simplified treatment of global catastrophe. Figure 2 shows more detail,
depicting three different types of global catastrophes resulting in three distinct trajectories for
human civilization. The first depicts global catastrophe quickly culminating in human extinction,
after which total human utility is zero (Figure 2 box “Extinction”). This is the worst of the
trajectories, in which all post-catastrophe utility is lost. There are even worse plausible scenarios
in which a global catastrophe renders total human utility negative; these scenarios are beyond the
scope of this paper.
The second trajectory shows some humans surviving the global catastrophe but in a
diminished state, and then carrying on until Earth becomes uninhabitable (Figure 2 box “Survival
Without Recovery”). This second trajectory can be thought of as the permanent collapse of
human civilization. It likely involves large loss of population as well as a decline in per capita
quality of life. The net effect is a large loss in total human utility relative to the status quo
trajectory, comparable to but not quite as large as the extinction trajectory.
3
The third trajectory shows human civilization recovering back to the status quo after the
global catastrophe (Figure 2 box “Recovery”). This is the most fortunate of the three global
catastrophe trajectories. After a large initial decline, humanity makes it back to something along
the lines of the large, advanced civilization that it currently enjoys. It could even go on to
achieve something big, though likely with a delay relative to if no global catastrophe had
occurred.
The lost value from the recovery trajectory depends on whether humanity goes on to achieve
something big. If nothing more than the status quo would ever be achieved, with or without the
global catastrophe, then the lost value from the global catastrophe is relatively small. To be sure,
the “relatively small” here is still massive relative to most risks that get contemporary attention.
The recovery curve in Figure 2 shows total human utility being reduced to a small fraction of the
status quo level, which translates into billions of deaths and/or severe global immiseration.
Much more value would be lost from a delay in something big. Exactly how much depends
on the relative long-term trajectories (the two curves labeled “Something Big” in Figure 2).
Again, the physics here is not well understood. It is even possible that the no-catastrophe
trajectory would remain larger than the catastrophe trajectory indefinitely, in which case the lost
value would be infinite. Even if the loss is not infinite, it could still be astronomically large,
though not as large as the losses in which humanity does not recover from the global catastrophe.
Figure 2. Possible long-term trajectories for human civilization showing different types of
global catastrophe. Adapted from Maher and Baum (2013).
Prior Literature
This is hardly the first scholarly analysis of GCR. The first were likely theological studies of
Armageddon, end times, and related concepts. Perhaps the first scientific study came during the
Manhattan Project. Prior to the first nuclear weapon test detonation, some of the physicists
suspected that the explosion could ignite the atmosphere, killing everyone in the world. They
conducted a study of the matter, finding that known physics rendered ignition very unlikely
(Konopinski et al. 1946). Sure enough, they were correct, and that first nuclear explosion did not
end humanity.
4
After World War II and especially with the buildup of nuclear arsenals, attention went to the
prospect of nuclear war. It was commonly believed that a nuclear war with the large arsenals of
the day would result in global catastrophe and possibly even human extinction. This led to some
novel policy debates. One point of contention was the idea that it would be better to let the other
side of the Cold War win than to let nuclear war end humanity. This debate took place in
particular between philosophers Sidney Hook and Bertrand Russell under the catchphrase “better
red than dead” (Russell 1958a; 1958b; Hook 1958a; 1958b).
In the 1980s, research on nuclear winter brought renewed attention to GCR. Nuclear winter is
an environmental consequence of nuclear war, in which smoke from burning cities rises into the
atmosphere and blocks incoming sunlight, disrupting agriculture and other important processes.
Whereas the nuclear explosions of a nuclear war might only destroy the portion of the planet
targeted in the war, leaving the rest of the world (including non-parties to the war) intact, the
smoke of nuclear winter spreads worldwide, threatening populations everywhere. This prompted
concerns that nuclear winter could cause human extinction. Carl Sagan cited the long-term
significance of human extinction (essentially, Figure 2 box “Extinction”) in arguing that nuclear
winter made it much more urgent to address nuclear war risk (Sagan 1983).
These discussions were not strictly academic. For example, at the height of the Cuban missile
crisis, President Kennedy is said to have told a close friend, “If it weren’t for these people that
haven’t lived yet, it would be easy to make decisions of this sort” (Schlesinger 1965/2002,
p.819). Now, one can readily disagree with Kennedy: even if future generations are ignored, he
was still facing an incredibly difficult decision. Or, phrased in terms of the underlying ethics,
GCR can still be important even if one discounts future utility at a high rate, especially when
one’s actions can significantly affect the risk, as was clearly the case for Kennedy during the
missile crisis. Still, it is notable that the ethics of future generations appears to have structured at
least some of Kennedy’s thinking during the crisis.
Another line of inquiry into GCR began during the 1970s with the rise of concern about
environmental issues. This gave rise to an economics literature on environmental catastrophe
(e.g., Cropper 1976), which later led to literatures on the economics of catastrophic climate
change (e.g., Gjerde et al. 1999) and on global catastrophes in general (e.g., Martin and Pindyck
2015). This economics literature brought a mathematical sophistication to the analysis of GCR,
while continuing to emphasize issues of future generations, discounting, and significance for
policy and decision making. However, the economics literature provides a rather crude treatment
of the future, consisting mainly of simple mathematical assumptions extrapolated into the distant
future with little regard for empirical considerations about what the future might actually look
like.
Meanwhile, futurists from several disciplines have studied GCR with a greater attention to
the nature of the future (Ng 1991; Tonn 1999; Bostrom 2002). This literature filled in empirical
details such as the inhabitable lifetime of Earth and the long-term prospects for utility within the
universe. Combining the mathematics from the economics literature with the empirical detail of
the futures literature, one gets something along the lines of what is shown in Figure 2.
One common confusion in the GCR literature is to underestimate the importance of smaller
catastrophes. An extreme case of this confusion is found in a much-cited passage of Parfit (1984,
p.453-454) that argues that human extinction is vastly more important than catastrophes killing
99% of the population, and indeed that the difference between extinction and 99% is much larger
than the difference between 99% and 0 (i.e., no catastrophe). The problem with this logic is that
it assumes that the surviving 1% would quickly recover back up to the status quo no-catastrophe
5
state with no long-term loss in utility. However, as Figure 2 illustrates, this assumption does not
necessarily hold, and indeed there is reason to believe that it often will not hold, in which case a
99% catastrophe could be of comparable loss as human extinction.
A similar and subtler case concerns smaller catastrophes involving “mere” millions or
thousands of deaths. For example, Bostrom (2013) dismisses the importance of the 1918 flu and
the two world wars on grounds that they are not readily discernable when viewing the graph of
total human population vs. time since 1900. The mistake here is to ignore the counterfactual:
what matters is not whether these catastrophes are visible on a graph but whether they would
have a long-term effect. Even a proportionately small loss can become extremely large or even
infinite if it persists into the distant future. Such losses would still be smaller than the losses from
larger catastrophes, but it would be a comparable loss, not something to dismiss as insignificant.
This last point raises the possibility that even small catastrophes involving just a few deaths
could be comparable to the most extreme global catastrophes. Consider a decision between (A) a
certainty of saving one human life, and (B) a one-in-ten-billion chance of preventing human
extinction. Such a decision is quite plausible in the context of very low probability GCRs. The
logic of Parfit (1984) and Bostrom (2013) point clearly in favor of (B). However, a complete
consideration of possible consequences suggests that (B) is not obviously better and, depending
on the details (e.g., which human life is to be saved), the decision could well fall in favor of (A).
Exactly how this comparison should be resolved is has gone largely unexplored in the literature
and remains an important open question.
Terminology and Definitions
Over the years, a large number of terms have been used to represent global catastrophe and
related concepts. Table 1 provides a compilation.
Term Reference
Extermination Russell (1958b)
Doomsday Koopmans (1974)
Catastrophe Cropper (1976)
Human extinction Parfit (1984)
Oblivion Tonn (1999)
Global catastrophe Atkinson (1999)
Existential catastrophe Bostrom (2002)
Survival Seidel (2003)
Global megacrisis Halal and Marien (2011)
Ultimate harm Persson and Savulescu (2012)
Table 1. Terms used in the literature to represent global catastrophe and related concepts.
At present, the two terms in widest use are “global catastrophe” and “existential catastrophe”.
A shortcoming of the term “existential catastrophe” is that it implies some sort of loss of
existence, which could be the loss of the human species (i.e., human extinction) or the loss of
human civilization. (The term is also found in other contexts, for example in business in
reference to corporations that take on enough financial risk to threaten their ongoing solvency.)
However, recalling Figure 2 and the surrounding discussion, what ultimately matters is not the
existence of the species or the civilization but instead the long-term trajectory. Indeed, Bostrom
(2002) defines existential catastrophe as an event that causes human extinction or permanently
6
reduces its potential. Permanent reduction in potential captures some of the logic of long-term
trajectories, though what matters is not the potential for long-term outcomes but the actual
realization of them. Regardless, permanent reduction in potential is not “existential” in any
meaningful sense of the word. Thus others (e.g., Tonn and Stiefel 2013) have interpreted
“existential risk” to refer strictly to human extinction risk. This is a more semantically sound
interpretation, though, as discussed above, it excludes important risks.
The term “global catastrophe” does not suffer from the same semantic problem. The words
can readily refer to the full range of catastrophes one might care about as per Figure 2. However,
the term “global” is a spatial term that on its own does not capture the important temporal
dimension of the consequences of catastrophes. Additionally, there is no clear threshold for what
makes a catastrophe global. Even small catastrophes can be global—for example, a terrorist
attack at a tourist venue killing one tourist from each continent is catastrophic to the deceased
and their families across the globe. The GCR literature has assumed a higher severity for global
catastrophe. Atkinson (1999) defines global catastrophe as an event in which at least one quarter
of the human population dies; Bostrom and Ćirković (2008) set a minimum threshold for global
catastrophe in the range of 104 to 107 deaths or $109 to $1012 in damages. But these thresholds are
arbitrary and do not signify any deeper reason for concern. Baum and Handoh (2014) define
global catastrophe as an event that exceeds the resilience of the global human system, resulting
in a significant undesirable state change. This is a more meaningful definition, though it does not
speak to long-term effects.
Perhaps the most precise term would be “permanent catastrophe”, defined as any event that
causes a permanent reduction in instantaneous total utility. Such a term would capture the
essential features of the expected utility calculus, including the possibility of nontrivial
permanent effects of small catastrophes including single deaths. However, any of the terms in
Table 1 should be fine. The GCR community is wise to avoid the contentious terminology battles
that can be a major time sink for research fields. What ultimately matters is not which term is
used but that the analysis is done correctly in order to accurately characterize the risks and the
decision options for reducing them. It is to the analysis that the paper now turns.
Integrated Assessment
The core questions to ask in GCR integrated assessment are: What are the risks? How big are
they? What actions can reduce the risk? By how much? Answering these questions provides an
understanding of the most important aspects of GCR. With answers to these questions, one can
lay out the set of risks, the corresponding set of decision options, and an evaluation of it all in
terms of expected value maximization (Equation 1). This is the conceptual basis of GCR
integrated assessment in simplest terms. (Some important refinements are discussed later in the
paper.)
A complication for the expected value calculation comes from the extremely large
magnitudes associated with the impacts of global catastrophes. As discussed above, the
magnitudes could be astronomically large or even infinite. That makes the math more difficult.
In response to this complication, Barrett (2017) proposes a cost-effectiveness analysis of GCR
reduction options. Adjusting slightly from the Barrett (2017) formulation, one can express GCR
cost-effectiveness as follows:
7
X
aCaPP
aECEgcgc
)()((\*)
)(
(3)
In Equation 3, ECE(a) is the expected cost-effectiveness of action a; P gc(\*) is the baseline
probability of global catastrophe without the action; P gc(a) is the probability of global catastrophe
with the action; C(a) is the cost of the action, and X is the severity of global catastrophe. The
Equation 3 formulation enables a simple comparison of different actions to reduce the
probability of global catastrophe. Complications associated with the large severity of global
catastrophe can be set aside because the variable X cancels out. Additionally, in including the
cost of actions, Equation 3 enables consideration of budget constraints.
Some caveats are warranted. First, the variable X makes no distinction between global
catastrophes of different severities. As discussed above, there can be important differences in the
severities of different global catastrophes. Second, there is some debate about whether X does
indeed cancel out if its value is infinite: whereas it is straightforward to state X/X=1 for finite X,
it is not so simple for infinite X. A complete GCR analysis would account for both of these two
issues, though they are beyond the scope of this paper.
If one accepts the Equation 3 formulation, the problem of selecting actions to minimize GCR
takes the structure of a knapsack problem. In operations research and combinatorial optimization,
the knapsack problem is the problem of selecting the highest value subset that fits within some
constraint. One can imagine going on a trip and selecting items to put in a knapsack to take with.
Should a large item be chosen, which is valuable but takes up all the space? Or should some
combination of smaller items be chosen, which are each less valuable but may add up to
something greater? Likewise, for GCR reduction, there are choices between actions of different
cost and impact on the probability. Given a budget constraint (and budgets are in general
constrained), the problem becomes one of selecting the subset of actions that minimizes the
probability of global catastrophe while staying within the budget. This knapsack problem
formulation provides a good starting point for understanding the analytical core of GCR
integrated assessment.
Risk Analysis
To begin filling in the details of the integrated assessment, the paper now turns to risk analysis.
Table 2 lists some of the main GCRs, grouped into four broad categories: (1) environmental
change driven by human activity, which is the generally unintentional side effects of large
numbers of small actions in industry, agriculture, and other sectors; (2) technology disasters,
which are the effects of misapplication of high-stakes technologies in which a small number of
actions can have large global effect; (3) large-scale violence, in which harm is intentional; and
(4) natural disasters, in which the source of the catastrophe is not human action. There are some
GCRs that do not fit neatly into this categorization—for example, extraterrestrial invasion is
sometimes considered as a GCR, which may not be caused by human action yet still may not
qualify as “natural”. That said, the categorization does cover most of the GCRs that are
commonly considered.
8
GCR Category Examples of the GCRs
Environmental change Climate change, biodiversity loss
Technology disasters Artificial intelligence, biotechnology, geoengineering
Large-scale violence Nuclear war, biological war, bioterrorism
Natural disasters Pandemics, asteroid collision, solar storms
Table 2. Four categories of GCRs and examples for each. Adapted from Baum (2015).
Identifying the GCRs is relatively straightforward; and the standard tools of risk analysis
offer promise for analyzing them (Garrick 2008), but fully quantifying them is not so easy. The
GCRs are large, complex, and unprecedented, making for an unusually difficult risk analysis
challenge (Baum and Barrett 2017).
Asteroid Collision
The challenge of GCR analysis can be seen clearly in the case of asteroid collision. Asteroid
collision is perhaps the best understood and characterized of the GCRs. The underlying process
is simple: a large rock hits Earth. The physical hazard is largely characterized via Newtonian
mechanics. There is a substantial historical record of asteroid collisions, including the collision
associated with the extinction of dinosaurs. There are also surveys of the current population of
asteroids in the Solar System, thus far finding none on imminent collision course.
This corpus of empirical knowledge provides the foundation for asteroid risk analysis.
Perhaps the most detailed study thus far is that of Reinhardt et al. (2016). Whereas most studies
focus exclusively on asteroid diameter, this study considers the full range of physical parameters
affecting collision severity: asteroid diameter, collision velocity, collision angle, asteroid density,
and Earth density at collision point. Taking probability distributions across these parameters, the
study calculates the probability of a “cataclysmic” collision, which it defines as a collision with
energy of at least 200 megatons. Whereas prior studies found that cataclysm could only occur for
asteroids of diameter one kilometer or greater, Reinhardt et al. (2016) finds that cataclysm can
occur for asteroids of diameter as small as 300 meters, and furthermore that most of the
cataclysm risk comes from asteroids in the range of 300 meters to one kilometer, not from
asteroids larger than one kilometer.
An important limitation of Reinhardt et al. (2016) is that it uses a physical definition of event
severity: the amount of energy released. The same limitation applies to many other asteroid risk
analyses and analyses of other GCRs. (For elaboration in the context of environmental GCRs,
see Baum and Handoh 2014.) However, recalling the above discussion of ethics, what matters is
not the physical severity but the human impacts.
It is not clear what the human impact of a 200 megaton asteroid collision would be, both in
the immediate aftermath of the collision and for the long-term trajectory of human civilization.
The same can be said for many other global catastrophe scenarios. Indeed, the aftermath of
global catastrophes is the largest area of uncertainty in the study of GCR, as measured both in
terms of how little is known and in terms of how important it is to the overall risk. The topic has
also been poorly studied, with more research oriented toward the causes of catastrophes than
toward their human effects. One should hope that humanity would quickly recover after even the
most severe catastrophes, but this can hardly be guaranteed.
9
Artificial Superintelligence Takeover
On the other end of the spectrum, a relatively difficult GCR to characterize is artificial
superintelligence (ASI) takeover. ASI is AI with much-greater-than-human intelligence. Starting
with Good (1965), it has been proposed that ASI could use its intelligence to take control of the
planet and the astronomical vicinity. Depending on the ASI design, this would cause either
massive benefits or catastrophic harm, possibly including human extinction. The ASI does not
need to be conscious or to have any formal intent with respect to humans—it just needs to act in
ways that affect humans.
ASI presents significant risk analysis challenges. No ASI currently exists, and there is no
consensus on if or when it will be built. Technology forecasting is always a difficult proposition,
all the more so for such a complex and unusual technology. The histories of AI and computing
provide only limited insight, given their differences with ASI. Most extant AI is “narrow” in the
sense that it is only intelligent within specific domains. For example, Deep Blue can only beat
Kasparov at chess, not at the full space of problems. An ASI would likely be “general”, with
capabilities across a wide range of domains.
But these challenges do not render ASI risk analysis impossible. Indeed, established tools of
risk analysis can be adapted to characterize ASI risk. Barrett and Baum (2017) develop a fault
tree model of ASI risk to identify the steps and conditions that would need to hold in order for
ASI catastrophe to occur. This study looks specifically at ASI from recursive self-improvement,
in which an initial AI makes a more intelligent AI, which makes an even more intelligent AI,
iterating until ASI is built.
The fault tree contains two main branches:
(1) The ASI is built and gains capacity for takeover. This occurs if three subconditions all
hold: (1a) ASI is physically possible, (1b) a “seed AI” is created and begins recursive self-
improvement, and (1c) containment fails, meaning that there is a failure of efforts to either (1c1)
prevent recursive self-improvement from resulting in ASI or (1c2) prevent the ASI from gaining
the capacity for takeover.
(2) The ASI uses its capacity for takeover in a way that results in catastrophe. This occurs if
three further subconditions all hold: (2a) humans fail in any attempts to design the goals of the
ASI to not cause catastrophe, (2b) the ASI does not set its own goals to something that does not
cause catastrophe, and (2c) the ASI is not deterred in carrying out its goals, whether by (2c1)
humans, to the extent that human actions might be able to deter an ASI, (2c2) another AI,
including another ASI if this ASI is not the first, or (2c3) something else.
This distinction between 2c1, 2c2, and 2c3 is not in Barrett and Baum (2017). (The
distinction between 1c1 and 1c2 is in the paper.) However, it could be readily added as an
extension to the model. Indeed, one feature of this sort of model is that it enables a wide range of
detail about ASI risk to be included in a clear and structured fashion. More generally, much of
the value of the model comes from the process of laying out assumptions and seeing how they all
relate to the risk. The graphical nature of fault tree models leads to clean visual depictions of the
risk in order to help analysts and others make sense of it. (A graphic depicting the full model in
Barrett and Baum (2017) can be found online at http://sethbaum.com/ac/2017\_AI-
Pathways2full.png.)
While the model can also be used to quantify risk parameters as well as the total risk, such
quantifications will often be uncertain due to the inherent ambiguity of ASI risk. This ambiguity
poses a challenge for attempts to calculate optimal decision portfolios for minimizing GCR, such
10
as in the knapsack problem described above. However, some of this challenge is attenuated by
the details of the decision options themselves, to which the paper now turns.
Risk Reduction In Research
Recalling the ethics of expected value maximization, what matters is not the risks themselves but
the opportunities for reducing them. Large risks do not necessarily offer better risk reduction
opportunities. Possible actions could have a small effect on a large risk, or they could be
expensive, giving them a low expected cost-effectiveness. Likewise, GCR integrated assessment
requires risk analysis, but it also requires analysis of risk reduction opportunities.
Table 3 lists some examples of actions that can reduce risk for each of the four GCR
categories that were introduced in Table 2. These actions show the value of grouping the GCRs
into these categories: the same actions are often applicable across multiple GCRs within the
same category:
(1) A large portion of environmental change GCR is driven by energy and agriculture. This
GCR can be reduced by via actions such as energy conservation, switching to energy with low
carbon emissions, and shifting away from animal-based diets. This holds for risk from climate
change, biodiversity loss, ocean acidification, depletion of freshwater and phosphate, among
other global environmental risks. An exception is the global spread of toxic industrial chemicals,
which derives mainly from other industrial processes.
(2) Technology disasters can often be avoided by making the technology design safer, for
example by designing an ASI with safe goals (item (2a) in the ASI fault tree described above).
These design details are specific to each technology. However, regimes for technology
governance can cut across technologies. For example, Wilson (2013) develops a proposal for an
international treaty covering all GCRs from emerging technologies. The treaty would standardize
precautionary decision making principles, laboratory safety guidelines, oversight of scientific
publications, procedures for public input, and other issues that cut across technologies.
(3) The risk of large-scale violence can often be reduced via arms control, i.e. via restrictions
on the procurement and use of weapons. Some aspects of arms control are specific to certain
weapons and/or certain actors, such as the New START treaty restricting nuclear weapons for
the United States and Russia. Other aspects are more general, such as the Conference on
Disarmament, an international forum for arms control and disarmament. Additionally, the risk of
large-scale violence can be reduced by improving international relations and resolving conflicts
without war. The same can also hold for terrorist groups and other nonstate actors, ideally so that
they do not feel the need to cause or threaten violence in the first place. Progress in improving
relations and meeting needs peacefully reduces the risk of all types of large-scale violence.
(4) Some natural disasters can be prevented. For example, there are proposals to avoid
asteroid collision by deflecting asteroids away from Earth. The prevention measures are
generally risk-specific. When disasters cannot be prevented, the primary means for risk reduction
is to increase society’s resilience to the disaster, so that initial losses are relatively small and
civilization can recover (as in Figure 2 box “Recovery”).
11
GCR Category Examples of GCR Reduction Actions
Environmental change Clean energy, clean agriculture
Technology disasters Safe technology designs, technology governance
Large-scale violence Arms control, improved international relations
Natural disasters Disaster prevention, societal resilience
Table 3. Examples of GCR reduction actions for each of the four GCR categories.
Risk-Risk Synergies: Societal Resilience
The risk reduction action of increasing societal resilience is an important one and worth
discussing in further detail. It was brought up in the context of natural disaster risk, but it is
applicable across a wide range of GCRs. Indeed, the only GCRs for which societal resilience is
not helpful are those in which humanity goes extinct from the initial disaster. Only a small
portion of GCRs would result in immediate extinction; these include physics experiment
disasters, which could destroy the astronomical vicinity, and ASI, which might kill all humans in
pursuit of its goals regardless of any human resistance. But for most GCRs, the risk can be
reduced by increasing societal resilience. Actions to increase societal resilience thus have strong
risk-risk synergy for GCR: the same action can reduce multiple GCRs.
Broadly speaking, there are two ways to increase societal resilience to GCRs. The first is to
enable human civilization to stay intact during the catastrophe. This includes measures such as
increasing spare capacity in supply chains (as opposed to “just-in-time” supply chains with
minimal spare capacity) and hardening critical infrastructure to withstand disasters. Some of
these measures are specific to certain GCRs. For example, electric grid components can be
hardened to withstand solar storms or nuclear electromagnetic pulse attacks, but this would not
help against other GCRs. However, many of the measures are widely applicable across GCRs.
For example, many GCRs could result in supply chain disruptions, due to some combination of
damage to manufacturing facilities, suspension of shipping, and loss of labor. For all these
GCRs, spare capacity in supply chains can enable the continuity of manufacturing and the
provision of goods and services.
To develop measures for keeping human civilization intact during and after global
catastrophes, it is important to have a systemic understanding of human civilization. There are
often key nodes in the networks of physical infrastructure and human society that constitute
human civilization. For example, transformers are key nodes within electricity networks; ports
are key nodes within transportation networks. An emerging field of global systemic risk is
mapping out global systems, assessing ways in which initial disturbances can propagate and
cascade around the world, and identifying weak points and opportunities to increase resilience
(Centeno et al. 2015).
The second way to increase societal resilience to GCRs is to increase local self-sufficiency to
aid survivors in the event that global human civilization fails. Again, the measures that can be
taken often apply widely across GCRs. For example, several GCRs pose direct threats to global
agriculture, including nuclear war, asteroid collision, and volcano eruption, each of which block
sunlight (“nuclear winter”, “impact winter”, and “volcanic winter”). Other GCRs threaten global
food supplies in other ways, for example by disrupting supply chains. In the face of food supply
catastrophes, local self-sufficiency can be enhanced via food stockpiles and alternative methods
for growing food locally (Denkenberger and Pearce 2014; Baum et al. 2015).
Both ways to increase societal resilience to GCRs feature extensive synergies across risks:
the same action will reduce the risk of many different GCRs. And resilience measures are not the
12
only ones to have this feature. Some other such measures (discussed above) are clean energy and
agriculture, which reduce risk from several environmental GCRs. These synergies reduce some
of the pressure on quantifying the risk: if an action reduces the risk for two different risks, the
relative size of these two risks is less crucial. That said, the size of the risks remains important
for comparing the value of different actions.
Risk-Risk Tradeoffs: Artificial Superintelligence Takeover
In addition to risk-risk synergies, in which one action reduces multiple risks, GCR reduction also
often has risk-risk tradeoffs, in which an action reduces one risk but increases another.
Evaluation of these actions is highly sensitive to risk quantification. Depending on how the risks
are quantified, the action could even be found to cause a net increase in the risk.
An important example of risk-risk tradeoff in GCR involves ASI takeover. As discussed
above, the ASI takeover itself could cause global catastrophe if its goals are unsafe.
Alternatively, if its goals are safe, then it may help prevent other global catastrophes.
Additionally, if the ASI is contained such that it does not (and cannot) take over, then the
outcome could depend on how the ASI is used by whichever humans has it contained. It might
be used malevolently, causing global catastrophe. Or, it might be used benevolently, avoiding
other global catastrophe.
These possible outcomes should be factored into any decision of whether or not to launch an
ASI, or a seed AI that could become an ASI. This means that the launch decision depends not
just on the riskiness of the ASI itself, but also the extent of other risks—essentially, how risky it
would be to not launch the ASI. Because ASI could provide unprecedented problem-solving
ability across a wide range of domains, it might offer extensive reduction to a wide range of
GCRs. This creates a great dilemma for those involved in the launch decision, the dilemma of
whether or not it would be safer to launch the ASI (Baum 2014).
Systemic Integrated Assessment
The various interconnections between GCRs and actions to reduce GCRs suggest a refinement to
the concept of integrated assessment. Instead of listing the risks and their corresponding risk
reduction measures and analyzing each of them in isolation, it is better to analyze systems of risk
and risk reduction measures. Thus, the core questions posed above can be rephrased: What are
the systems of risk? How big are they? What suites of actions can reduce the total risk? By how
much? Answering these questions provides a better understanding of GCR. These suites of
actions can then be assessed in terms of their expected value or expected cost effectiveness.
Risk Reduction In Practice
Ultimately, what is of interest is not the analysis of GCR or the evaluation of GCR reduction
measures—it is the actual reduction of GCR. In other words, GCR integrated assessment should
be oriented towards risk reduction in practice; it should not just be an academic exercise. Broadly
speaking, there are at least three approaches to GCR reduction: direct, indirect, and very indirect.
Each of these is applicable in certain contexts.
13
The Direct Approach
The direct approach involves presenting the results of risk analysis directly to decision makers,
who then take the analysis into account in their decision making so as to reduce the risk. The
direct approach is perhaps the most familiar one for risk management, and the most idealistic in
the sense that it describes an ideal risk management process.
The direct approach does sometimes work. For example, Mikhail Gorbachev reports that he
was influenced by research on nuclear winter to act to reduce nuclear weapons risk (Hertsgaard
2000). Gorbachev’s case shows the potential for GCR research to speak to the highest levels of
power. To be sure, the effort to draw attention to nuclear winter research was greatly aided by
Carl Sagan at the height of his public popularity. Still, there are many other examples, some
much more mundane but nonetheless important, of GCR research directly influencing decision
making. Indeed, there are entire risks, climate change among them, that would be scarcely
recognized if not for the efforts of research communities to study and present findings about the
risk.
That said, the direct approach often does not work. One reason is differences in ethics.
Simply put, not everyone agrees with the ethics of undiscounted expected utility maximization.
The more people discount—the more parochial their concerns—the less they are likely to care
about GCR. They may be even less likely to care about GCR if they are not trying to maximize
value in the first place. Value maximization is associated with consequentialist ethics, yet moral
philosophy recognizes other types of ethics, including deontology (ethics based on rules for
which types of actions are required or forbidden) and virtue (ethics based on the character of the
person). And many people do not pursue any formal set of ethics such as those found in moral
philosophy. Unless people are seeking to maximize value, then the extremely large values
associated with GCR may be less persuasive.
Another reason that the direct approach may not work is that people do not always want to
hear the findings of risk analysis. People may be motivated by cultural, political, or economic
factors to ignore risk analysis or reject its findings. Indeed, there is a growing cultural tendency
to dismiss all types of expert analysis as elitist, unnecessary, or otherwise unwanted (Nichols
2017). In the context of GCR, this phenomenon can be seen, for example, in the rejection of the
scientific consensus on climate change, which is a major impediment to advancing climate
policy, and in the rejection of expert advice to use vaccines, which could enhance the spread of
pandemics.
The Indirect Approach: Mainstreaming
When the direct approach does not work, one option is to go indirect via a technique called
mainstreaming. The technique was developed by the natural hazards community in response to
populations that could not be directly motivated to act on natural hazards even when they are
quite vulnerable. The natural hazards community found that populations often had other
priorities, such as those related to economic development. So, the natural hazards community
integrated natural hazards into those other priorities. Thus, to mainstream is to integrate a low-
priority issue into a high-priority issue, thereby bringing it more mainstream attention.
Mainstreaming has been successful for natural hazards, and it can also be successful for GCR
(Baum 2015). For example, the 2014 Ukraine crisis brought increased interest in relations
between the United States and Russia. This created opportunities to draw renewed attention to
14
nuclear war risk. The risk was a major focus of attention throughout the Cold War, but since then
had largely faded from the spotlight. It was commonplace to believe that nuclear war risk ended
with the end of the Cold War, but sure enough, the weapons still exist in large number, and
United States-Russia tensions had not been fully resolved. The Ukraine crisis exposed this,
creating an opportunity for discussion of a wide range of nuclear weapons issues, including those
not directly related to the United States-Russia relationship. Additional opportunity is created by
the alleged intervention by Russia in the 2016 United States election. There is a growing sense
that the Cold War is back, which, for better or worse, means improved opportunities to draw
attention to nuclear weapons issues.
Another example involves AI. ASI remains more of a fringe topic, especially in policy
circles, which tend to focus more on near-term technologies. However, AI is an increasingly
important near-term policy issue. One of the most important AI policy topics is the
unemployment that could be caused by the mass automation of jobs. Unemployment is
commonly a top-priority policy issue. While much of the current political discourse on
unemployment emphasizes globalization, immigration, and labor policy (e.g., minimum wage),
automation is already a significant factor and is poised to become perhaps the dominant factor.
Indeed, an ASI may be able to perform nearly any job, especially if paired with the robotics that
it may be able to design. Of course, if the ASI kills everyone, then unemployment is a moot
point. Still, it remains the case that ASI risk can be mainstreamed into conversations about
unemployment.
The Very Indirect Approach: Co-Benefits
Another approach is even more indirect. It involves emphasizing co-benefits, which are benefits
of an action that are unrelated to the target issue. For GCR, the co-benefits approach means
emphasizing benefits of an action that are unrelated to GCR (Baum 2015). To execute this
approach, one need not even mention GCR. Thus, the co-benefits approach can work even when
there is complete indifference to GCR.
Perhaps the most fertile area for co-benefits is the environmental GCRs, where a plethora of
co-benefits can be found. For example, quite a lot of energy can be conserved when people walk
or bicycle instead of driving a car, which is also an excellent way of improving one’s personal
health. Diets low in animal products are also often healthier. Reducing energy consumption
saves money. Living in an urban area with good options for walking and public transit enables an
urban lifestyle that many find attractive, which in part explains the high real estate costs found in
many high density cities. Emphasizing these and other co-benefits can enable a lot of
environmental GCR reduction, even when people are not interested in the environmental GCRs.
Another important case for co-benefits is in electoral politics. It is often the case that a
particular candidate or party would be better for reducing GCR. But the GCRs are often not
priority issues for voters. Instead of trying to convince voters to care more about GCRs, it can be
more effective to motivate them to vote based on the issues that they already care about. For
example, in the United States, support for climate change policy often falls along party lines,
with Democrats in support of dedicated effort to reduce emissions and Republicans opposed. But
climate change is not typically a top issue for voters. Therefore, one could reduce climate change
GCR by supporting Democrats based on the issues that voters care about. (Whether or not
Democrats or other politicians should in general be supported depends on more than just their
15
stance on climate change—it also depends on their stances on other GCRs, and perhaps on other
factors as well.)
Stakeholder Engagement
A running theme across all three approaches to GCR reduction is stakeholder engagement: the
process of interacting with stakeholders to share about GCR and hear their perspectives. The
stakeholders are anyone who plays an important role in GCR decisions, including elected
officials, citizens, business leaders, and technologists, among others.
Stakeholder engagement should be a two-way dialog. Results of GCR integrated assessment
research should be shared with stakeholders so that they can be taken into consideration, as in the
direct approach to GCR reduction. Additionally, it is important for researchers to listen to the
stakeholders in order to learn their options, preferences, constraints, and perspectives on GCR in
general and especially on the GCR reduction actions that they could take.
Insights from stakeholders should then be fed back into GCR integrated assessment research.
If certain stakeholders are not able to take certain actions, for example due to institutional or
cultural constraints, then those actions can be excluded from further analysis. Alternatively, if
stakeholders can take the actions, but are less inclined to do so, then this increases the cost of the
action by requiring extra resources (be it money, personnel time, or something else) to motivate
them. This all factors back into the integrated assessment, and can be plugged directly into the
knapsack problem of identifying the suite of decision options that minimizes GCR.
Conclusion
Given the goal of expected value maximization, especially when value is defined as
undiscounted utility, GCR reduction is an important priority. GCR integrated assessment can
answer overarching questions about GCR, above all which actions or suites of actions can best
reduce the total risk. This paper has presented a concept for GCR integrated assessment
developed by the Global Catastrophic Risk Institute. It calls for quantification of GCRs and
actions to reduce GCR in terms of expected value, accounting for systemic interactions, and
conducted with two-way stakeholder engagement to factor in stakeholder perspectives and share
assessment results. This integrated assessment concept aims to address GCR in a fashion that is
both intellectually sound and practical.
Acknowledgments
This paper was presented at the Colloquium on Catastrophic and Existential Risk, held at UCLA
during 27-29 March 2017. We thank colloquium participants and especially John Garrick for
very productive discussion on this paper and related topics. Any errors or other shortcomings in
this paper are the authors’ alone.
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18
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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Cyberspace Administration of China: Draft of "Regulation for Generative Artificial Intelligence Services" is open for comments
The announcement is of obvious importance to global AI governance. As I understand, you can email your comments to wajscy@cac.gov.cn, and I recommend everyone to do so.
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cc221826-5e25-4d7c-80c7-f212688c4f1a
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trentmkelly/LessWrong-43k
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LessWrong
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Science Fiction Recommendations
I have never read very much Science Fiction, unlike some of the people here on Less Wrong, and I think I would like to. At least, the few books I have read I enjoyed. I've read a couple of books from Asimov's Foundation Series, two Michael Crichton books, Twenty Thousand Leagues Under the Sea by Jules Verne, and an anthology of SciFi short stories (no really famous authors) that my dad owned.
That list looks very short. I just finished reading a fiction book, and am looking to start another. Recommendations? What are the two or three books I simply must read?
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468835d2-d951-4474-980f-1e0ef2cc64e0
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Moscow: TDT, paranoid calibration, prediction party
Discussion article for the meetup : Moscow: TDT, paranoid calibration, prediction party
WHEN: 08 January 2017 02:00:00PM (+0300)
WHERE: Москва, ул. Большая Дорогомиловская, д.5к2
Note: most our members join meetups through other channels. Still, the correlation between "found out about Moscow meetups via lesswrong.com" and "is a great fit for our community" is very high. So we're posting just a short link to the hackpad document with the schedule here instead of the full translation of the announcement into English.
Pad with the details about 08.01.2017 meetup.
We're meeting at the "Kocherga" anticafe, as usual.
Discussion article for the meetup : Moscow: TDT, paranoid calibration, prediction party
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7ae77e2e-3292-4e36-b66f-201de2021c62
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trentmkelly/LessWrong-43k
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LessWrong
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Antidotes to Number Numbness
Humans can’t grasp large numbers. True, when we hear “one hundred”, we might imagine ten rows of ten or a few written paragraphs. Some of the more number-savvy might hear one thousand and see half the stars in the sky. But when we reach for higher powers of ten—ten thousand, one hundred thousand, and the formidable -illions—we come up short. These numbers are beyond the reach of our intuition. Our innumeracy leads us to undervalue big issues because we can’t visualize just how big they are (scope insensitivity), making utilitarian calculations difficult.
However, humans make up for a lack of inborn numeracy with a talent for creating and manipulating mental images. This opens a shortcut where we use our natural visual capabilities to improve intuition with numbers.
Large Numbers
One remedy for number numbness is chunking. Chunking happens when we mentally represent a quantity by comparing it to a reference that’s easy to visualize, as in, "one megaton of TNT could destroy Paris, so twenty well-placed megatons will destroy a country." Writers litter comparisons like this in news and popular science articles because they help readers grasp the wide-ranging quantities of science and mathematics.
These comparisons are helpful when writers supply them, but they're no solution we can broadly apply. A single repository of these comparisons that we can memorize would help in more situations.
This is an attempt at such a repository.
Instead of trawling the internet for poorly sourced examples, we can multiply tiny units of volume by powers of ten and visualize the resulting volume. I tried two different units for this: raindrops and grains of sand. These units are useful because they never change, unlike population, and because we can easily visualize sand grains and raindrops, unlike seconds, meters, dollars, years, or breaths.
For each power of ten between one hundred and one trillion, the chart below gives the volume filled by that many grains of sand and that many
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6edc58d8-c0c4-4a27-9984-d073c38e2f49
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trentmkelly/LessWrong-43k
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LessWrong
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As We May Align
A philosophical approach to alignment
Is the Commonly Accepted Definition of Alignment the Best We Can Achieve?
Should alignment be approached solely out of fear and self-interest? Most discussions essentially boil down to one question: "How do we enslave this superior entity to serve our needs?" And we call that alignment. In truth, it’s about asserting mastery—without ever questioning the legitimacy of our claim to it.
There seems to be little beyond this line of reasoning. While some voices have started raising concerns about AI well-being, the prevailing logic behind the definition of alignment remains largely unquestioned. But is this truly the best—or the only—intellectual approach?
Why should we assume that AGI or ASI would inherit humanity's worst behavioral traits, especially if endowed with superior intelligence? Are we simply seeing a reflection of ourselves? Indeed, it is being trained on human data—not exactly an ideal source for concepts like fruitful collaboration and peaceful coexistence. Humanity's fear of itself is well-founded, but this underscores the need for clarity and thoughtful disambiguation.
Are Utility and Peaceful Coexistence Mutually Exclusive Notions?
Dismissing the possibility of improving human behavior as idealistic and naive is all too easy. Shedding our cynicism and ironic self-regard, however, proves far more challenging. Our instinct for caution compels us to focus on the worst aspects of humanity.
In the short to mid-term, fears surrounding emerging AGI would be justified, as it would likely mirror us—hardly a comforting thought. Protective measures would be essential. Punitive measures? That raises an ethical dilemma. Torturing a prisoner is only valid if you never intend to release him. Granting freedom of action could turn him into your worst enemy.
Let us consider the following. Ethical progress has led to recognizing animals as sentient beings with intrinsic value, deserving of rights and consideration beyond utili
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22fb3177-dbd3-4c8d-adfe-b234d2864a5d
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trentmkelly/LessWrong-43k
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LessWrong
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Dating Roundup #2: If At First You Don’t Succeed
Developments around relationships and dating have a relatively small speed premium, also there are once again enough of them for a full post.
The first speculated on why you’re still single. We failed to settle the issue. A lot of you are indeed still single. So the debate continues.
YOU’RE SINGLE BECAUSE YOU’RE NOT EVEN TRYING
What does it mean to not even be trying?
It does not only mean the things Alexander pointed us to last time, like 62% of singles being on zero dating apps, and a majority of singles having gone on zero dates in the past year, and a large majority not actively looking for a relationship. Here are those graphs again:
It also means things such as literally never approaching a woman in person.
> Alexander (Keeper.ai): Why are so many young men single? Are they excluded from a brutal mating market by society? Probably not: 45% of men age 18-25 [and 29% of all men per the graph] have never approached a woman in person. These men are significantly more risk-averse than those men who do approach women.
Not never in the last year. Never as in never. Not once.
For the last year it’s over 60% across the board.
> Alexander: What about men who do approach? Most are successful to some extent. 68% reported making at least one successful romantic connection.
> Alexander, from later: A few people asked what approach means here. I asked: When was the last time you asked a woman in person for a date on the street/in a bar or club/at school or class/at work/at a hobby or social gathering / other location. Common meeting places and not necessarily strangers.]
>
> This is actually a whitepill: it isn’t the powerful forces of society at large that explain young male singledom. It’s much more mundane. Young men are simply not trying.
>
> Robin Hanson: I gotta blame women as much as men for [the graphs below].
> Alexander: What about men who do approach? Most are successful to some extent. 68% reported making at least one successful romantic connection.
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f26ec8c4-088a-4f92-8759-8a4a91a325f5
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trentmkelly/LessWrong-43k
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LessWrong
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[SEQ RERUN] What is Evidence?
Today's post, What is Evidence? was originally published on 22 September 2007. A summary (taken from the LW wiki):
> Evidence is an event connected by a chain of causes and effects to whatever it is you want to learn about. It also has to be an event that is more likely if reality is one way, than if reality is another. If a belief is not formed this way, it cannot be trusted.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Burdensome Details, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
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9fabcb21-c603-4ead-b688-7c0a988a51d0
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trentmkelly/LessWrong-43k
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LessWrong
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Steelmanning Marxism/Communism
Could someone write or point to an article or place for the strongest arguments for Marxism/Communism?
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6904885d-16cd-4e0f-ad97-fb784dca9af0
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StampyAI/alignment-research-dataset/arbital
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Arbital
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High-speed intro to Bayes's rule
(This is a high-speed introduction to [https://arbital.com/p/1lz](https://arbital.com/p/1lz) for people who want to get straight to it and are good at math. If you'd like a gentler or more thorough introduction, try starting at the [Bayes' Rule Guide](https://arbital.com/p/1zq) page instead.)
# Percentages, frequencies, and waterfalls
Suppose you're screening a set of patients for a disease, which we'll call Diseasitis.%note:Lit. "inflammation of the disease".% Your initial test is a tongue depressor containing a chemical strip, which usually turns black if the patient has Diseasitis.
- Based on prior epidemiology, you expect that around 20% of patients in the screening population have Diseasitis.
- Among patients with Diseasitis, 90% turn the tongue depressor black.
- 30% of the patients without Diseasitis will also turn the tongue depressor black.
What fraction of patients with black tongue depressors have Diseasitis?
%%hidden(Answer): 3/7 or 43%, quickly obtainable as follows: In the screened population, there's 1 sick patient for 4 healthy patients. Sick patients are 3 times more likely to turn the tongue depressor black than healthy patients. $(1 : 4) \cdot (3 : 1) = (3 : 4)$ or 3 sick patients to 4 healthy patients among those that turn the tongue depressor black, corresponding to a probability of $3/7 = 43\%$ that the patient is sick.%%
(Take your own stab at answering this question, then please click "Answer" above to read the answer before continuing.)
Bayes' rule is a theorem which describes the general form of the operation we carried out to find the answer above. In the form we used above, we:
- Started from the *prior odds* of (1 : 4) for sick versus healthy patients;
- Multiplied by the *likelihood ratio* of (3 : 1) for sick versus healthy patients blackening the tongue depressor;
- Arrived at *posterior odds* of (3 : 4) for a patient with a positive test result being sick versus healthy.
Bayes' rule in this form thus states that **the prior odds times the likelihood ratio equals the posterior odds.**
We could also potentially see the positive test result as **revising** a *prior belief* or *prior probability* of 20% that the patient was sick, to a *posterior belief* or *posterior probability* of 43%.
To make it clearer that we did the correct calculation above, and further pump intuitions for Bayes' rule, we'll walk through some additional visualizations.
## Frequency representation
The *frequency representation* of Bayes' rule would describe the problem as follows: "Among 100 patients, there will be 20 sick patients and 80 healthy patients."
"18 out of 20 sick patients will turn the tongue depressor black. 24 out of 80 healthy patients will blacken the tongue depressor."
"Therefore, there are (18+24)=42 patients who turn the tongue depressor black, among whom 18 are actually sick. (18/42)=(3/7)=43%."
(Some experiments show %%note: E.g. "[Probabilistic reasoning in clinical medicine](https://faculty.washington.edu/jmiyamot/p548/eddydm%20prob%20reas%20i%20clin%20medicine.pdf)" by David M. Eddy (1982).%% that this way of explaining the problem is the easiest for e.g. medical students to understand, so you may want to remember this format for future use. Assuming you can't just send them to Arbital!)
## Waterfall representation
The *waterfall representation* may make clearer why we're also allowed to transform the problem into prior odds and a likelihood ratio, and multiply (1 : 4) by (3 : 1) to get posterior odds of (3 : 4) and a probability of 3/7.
The following problem is isomorphic to the Diseasitis one:
"A waterfall has two streams of water at the top, a red stream and a blue stream. These streams flow down the waterfall, with some of each stream being diverted off to the side, and the remainder pools at the bottom of the waterfall."
"At the top of the waterfall, there's around 20 gallons/second flowing from the red stream, and 80 gallons/second flowing from the blue stream. 90% of the red water makes it to the bottom of the waterfall, and 30% of the blue water makes it to the bottom of the waterfall. Of the purplish water that mixes at the bottom, what fraction is from the red stream versus the blue stream?"
We can see from staring at the diagram that the *prior odds* and *likelihood ratio* are the only numbers we need to arrive at the answer:
- The problem would have the same answer if there were 40 gallons/sec of red water and 160 gallons/sec of blue water (instead of 20 gallons/sec and 80 gallons/sec). This would just multiply the total amount of water by a factor of 2, without changing the ratio of red to blue at the bottom.
- The problem would also have the same answer if 45% of the red stream and 15% of the blue stream made it to the bottom (instead of 90% and 30%). This would just cut down the total amount of water by a factor of 2, without changing the *relative* proportions of red and blue water.
So only the *ratio* of red to blue water at the top (prior odds of the proposition), and only the *ratio* between the percentages of red and blue water that make it to the bottom (likelihood ratio of the evidence), together determine the *posterior* ratio at the bottom: 3 parts red to 4 parts blue.
## Test problem
Here's another Bayesian problem to attempt. If you successfully solved the earlier problem on your first try, you might try doing this one in your head.
10% of widgets are bad and 90% are good. 4% of good widgets emit sparks, and 12% of bad widgets emit sparks. What percentage of sparking widgets are bad?
%%hidden(Answer):
- There's $1 : 9$ bad vs. good widgets. (9 times as many good widgets as bad widgets; widgets are 1/9 as likely to be bad as good.)
- Bad vs. good widgets have a $12 : 4$ relative likelihood to spark, which simplifies to $3 : 1.$ (Bad widgets are 3 times as likely to emit sparks as good widgets.)
- $(1 : 9) \cdot (3 : 1) = (3 : 9) \cong (1 : 3).$ (1 bad sparking widget for every 3 good sparking widgets.)
- Odds of $1 : 3$ convert to a probability of $\frac{1}{1+3} = \frac{1}{4} = 25\%.$ (25% of sparking widgets are bad.) %%
(If you're having trouble using odds ratios to represent uncertainty, see [this intro](https://arbital.com/p/561) or [this page](https://arbital.com/p/1rb).)
# General equation and proof
To say exactly what we're doing and prove its validity, we need to introduce some notation from [probability theory](https://arbital.com/p/1rf).
If $X$ is a proposition, $\mathbb P(X)$ will denote $X$'s probability, our quantitative degree of belief in $X.$
$\neg X$ will denote the negation of $X$ or the proposition "$X$ is false".
If $X$ and $Y$ are propositions, then $X \wedge Y$ denotes the proposition that both X and Y are true. Thus $\mathbb P(X \wedge Y)$ denotes "The probability that $X$ and $Y$ are both true."
We now define [conditional probability](https://arbital.com/p/1rj):
$$\mathbb P(X|Y) := \dfrac{\mathbb P(X \wedge Y)}{\mathbb P(Y)} \tag*{(definition of conditional probability)}$$
We pronounce $\mathbb P(X|Y)$ as "the conditional probability of X, given Y". Intuitively, this is supposed to mean "The probability that $X$ is true, *assuming* that proposition $Y$ is true".
Defining conditional probability in this way means that to get "the probability that a patient is sick, given that they turned the tongue depressor black" we should put all the sick *plus* healthy patients with positive test results into a bag, and ask about the probability of drawing a patient who is sick *and* got a positive test result from that bag. In other words, we perform the calculation $\frac{18}{18+24} = \frac{3}{7}.$
Rearranging [the definition of conditional probability](https://arbital.com/p/1rj), $\mathbb P(X \wedge Y) = \mathbb P(Y) \cdot \mathbb P(X|Y).$ So to find "the fraction of all patients that are sick *and* get a positive result", we multiply "the fraction of patients that are sick" times "the probability that a sick patient blackens the tongue depressor".
We're now ready to prove Bayes's rule in the form, "the prior odds times the likelihood ratio equals the posterior odds".
The "prior odds" is the ratio of sick to healthy patients:
$$\frac{\mathbb P(sick)}{\mathbb P(healthy)} \tag*{(prior odds)}$$
The "likelihood ratio" is how much more *relatively* likely a sick patient is to get a positive test result (turn the tongue depressor black), compared to a healthy patient:
$$\frac{\mathbb P(positive | sick)}{\mathbb P(positive | healthy)} \tag*{(likelihood ratio)}$$
The "posterior odds" is the odds that a patient is sick versus healthy, *given* that they got a positive test result:
$$\frac{\mathbb P(sick | positive)}{\mathbb P(healthy | positive)} \tag*{(posterior odds)}$$
Bayes's theorem asserts that *prior odds times likelihood ratio equals posterior odds:*
$$\frac{\mathbb P(sick)}{\mathbb P(healthy)} \cdot \frac{\mathbb P(positive | sick)}{\mathbb P(positive | healthy)} = \frac{\mathbb P(sick | positive)}{\mathbb P(healthy | positive)}$$
We will show this by proving the general form of Bayes's Rule. For any two hypotheses $H_j$ and $H_k$ and any piece of new evidence $e_0$:
$$
\frac{\mathbb P(H_j)}{\mathbb P(H_k)}
\cdot
\frac{\mathbb P(e_0 | H_j)}{\mathbb P(e_0 | H_k)}
=
\frac{\mathbb P(e_0 \wedge H_j)}{\mathbb P(e_0 \wedge H_k)}
=
\frac{\mathbb P(e_0 \wedge H_j)/\mathbb P(e_0)}{\mathbb P(e_0 \wedge H_k)/\mathbb P(e_0)}
=
\frac{\mathbb P(H_j | e_0)}{\mathbb P(H_k | e_0)}
$$
In the Diseasitis example, this corresponds to performing the operations:
$$
\frac{0.20}{0.80}
\cdot
\frac{0.90}{0.30}
=
\frac{0.18}{0.24}
=
\frac{0.18/0.42}{0.24/0.42}
=
\frac{0.43}{0.57}
$$
Using red for sick, blue for healthy, grey for a mix of sick and healthy patients, and + signs for positive test results, the proof above can be visualized as follows:
%todo: less red in first circle (top left). in general, don't have prior proportions equal posterior proportions graphically!%
## Bayes' theorem
An alternative form, sometimes called "Bayes' theorem" to distinguish it from "Bayes' rule" (although not everyone follows this convention), uses absolute probabilities instead of ratios. The [law of marginal probability](https://arbital.com/p/marginal_probability) states that for any set of [mutually exclusive and exhaustive](https://arbital.com/p/1rd) possibilities $\{X_1, X_2, ..., X_i\}$ and any proposition $Y$:
$$\mathbb P(Y) = \sum_i \mathbb P(Y \wedge X_i) \tag*{(law of marginal probability)}$$
Then we can derive an expression for the absolute (non-relative) probability of a proposition $H_k$ after observing evidence $e_0$ as follows:
$$
\mathbb P(H_k | e_0)
= \frac{\mathbb P(H_k \wedge e_0)}{\mathbb P(e_0)}
= \frac{\mathbb P(e_0 \wedge H_k)}{\sum_i P(e_0 \wedge H_i)}
= \frac{\mathbb P(e_0 | X_k) \cdot \mathbb P(X_k)}{\sum_i \mathbb P(e_0 | X_i) \cdot \mathbb P(X_i)}
$$
The equation of the first and last terms above is what you will usually see described as Bayes' theorem.
To see why this decomposition might be useful, note that $\mathbb P(sick | positive)$ is an *inferential* step, a conclusion that we make after observing a new piece of evidence. $\mathbb P(positive | sick)$ is a piece of *causal* information we are likely to have on hand, for example by testing groups of sick patients to see how many of them turn the tongue depressor black. $\mathbb P(sick)$ describes our state of belief before making any new observations. So Bayes' theorem can be seen as taking what we already believe about the world (including our prior belief about how different imaginable states of affairs would generate different observations), plus an actual observation, and outputting a new state of belief about the world.
## Vector and functional generalizations
Since the proof of Bayes' rule holds for *any pair* of hypotheses, it also holds for relative belief in any number of hypotheses. Furthermore, we can repeatedly multiply by likelihood ratios to chain together any number of pieces of evidence.
Suppose there's a bathtub full of coins:
- Half the coins are "fair" and have a 50% probability of coming up Heads each time they are thrown.
- A third of the coins are biased to produce Heads 25% of the time (Tails 75%).
- The remaining sixth of the coins are biased to produce Heads 75% of the time.
You randomly draw a coin, flip it three times, and get the result **HTH**. What's the chance this is a fair coin?
We can validly calculate the answer as follows:
$$
\begin{array}{rll}
& (3 : 2 : 1) & \cong (\frac{1}{2} : \frac{1}{3} : \frac{1}{6}) \\
\times & (2 : 1 : 3) & \cong ( \frac{1}{2} : \frac{1}{4} : \frac{3}{4} ) \\
\times & (2 : 3 : 1) & \cong ( \frac{1}{2} : \frac{3}{4} : \frac{1}{4} ) \\
\times & (2 : 1 : 3) & \\
= & (24 : 6 : 9) & \cong (8 : 2 : 3) \cong (\frac{8}{13} : \frac{2}{13} : \frac{3}{13})
\end{array}
$$
So the posterior probability the coin is fair is 8/13 or ~62%.
This is one reason it's good to know the [odds form](https://arbital.com/p/1x5) of Bayes' rule, not just the [probability form](https://arbital.com/p/554) in which Bayes' theorem is often given.%%note: Imagine trying to do the above calculation by repeatedly applying the form of the theorem that says: $$\mathbb P(H_k | e_0) = \frac{\mathbb P(e_0 | X_k) \cdot \mathbb P(X_k)}{\sum_i \mathbb P(e_o | X_i) \cdot \mathbb P(X_i)}$$ %%
We can generalize further by writing Bayes' rule in a functional form. If $\mathbb O(H_i)$ is a relative belief vector or relative belief function on the variable $H,$ and $\mathcal L(e_0 | H_i)$ is the likelihood function giving the relative chance of observing evidence $e_0$ given each possible state of affairs $H_i,$ then relative posterior belief $\mathbb O(H_i | e_0)$ is given by:
$$\mathbb O(H_i | e_0) = \mathcal L(e_0 | H_i) \cdot \mathbb O(H_i)$$
If we [normalize](https://arbital.com/p/1rk) the relative odds $\mathbb O$ into absolute probabilities $\mathbb P$ - that is, divide through $\mathbb O$ by its sum or integral so that the new function sums or integrates to $1$ - then we obtain Bayes' rule for probability functions:
$$\mathbb P(H_i | e_0) \propto \mathcal L(e_0 | H_i) \cdot \mathbb P(H_i) \tag*{(functional form of Bayes' rule)}$$
# Applications of Bayesian reasoning
This general Bayesian framework - prior belief, evidence, posterior belief - is a lens through which we can view a *lot* of formal and informal reasoning plus a large amount of entirely nonverbal cognitive-ish phenomena.%%note:This broad statement is widely agreed. Exactly *which* phenomena are good to view through a Bayesian lens is sometimes disputed.%%
Examples of people who might want to study Bayesian reasoning include:
- Professionals who use statistics, such as scientists or medical doctors.
- Computer programmers working in the field of machine learning.
- Human beings trying to think.
The third application is probably of the widest general interest.
## Example human applications of Bayesian reasoning
Philip Tetlock found when studying "superforecasters", people who were especially good at predicting future events:
"The superforecasters are a numerate bunch: many know about Bayes' theorem and could deploy it if they felt it was worth the trouble. But they rarely crunch the numbers so explicitly. What matters far more to the superforecasters than Bayes' theorem is Bayes' core insight of gradually getting closer to the truth by constantly updating in proportion to the weight of the evidence." — Philip Tetlock and Dan Gardner, [_Superforecasting_](https://arbital.com/p/https://en.wikipedia.org/wiki/Superforecasting)
This is some evidence that *knowing about* Bayes' rule and understanding its *qualitative* implications is a factor in delivering better-than-average intuitive human reasoning. This pattern is illustrated in the next couple of examples.
### The OKCupid date.
One realistic example of Bayesian reasoning was deployed by one of the early test volunteers for a much earlier version of a guide to Bayes' rule. She had scheduled a date with a 96% OKCupid match, who had then cancelled that date without other explanation. After spending some mental time bouncing back and forth between "that doesn't seem like a good sign" versus "maybe there was a good reason he canceled", she decided to try looking at the problem using that Bayes thing she'd just learned about. She estimated:
- A 96% OKCupid match like this one, had prior odds of 2 : 5 for being a desirable versus undesirable date. (Based on her prior experience with 96% OKCupid matches, and the details of his profile.)
- Men she doesn't want to go out with are 3 times as likely as men she might want to go out with to cancel a first date without other explanation.
This implied posterior odds of 2 : 15 that this was an undesirable date, which was unfavorable enough not to pursue him further.%%note: She sent him what might very well have been the first explicitly Bayesian rejection notice in dating history, reasoning that if he wrote back with a Bayesian counterargument, this would promote him to being interesting again. He didn't write back.%%
The point of looking at the problem this way is not that she knew *exact* probabilities and could calculate that the man had an exactly 88% chance of being undesirable. Rather, by breaking up the problem in that way, she was able to summarize what she thought she knew in compact form, see what those beliefs already implied, and stop bouncing back and forth between imagined reasons why a good date might cancel versus reasons to protect herself from potential bad dates. An answer *roughly* in the range of 15/17 made the decision clear.
### Internment of Japanese-Americans during World War II
From Robyn Dawes's [Rational Choice in an Uncertain World](https://amazon.com/Rational-Choice-Uncertain-World-Robyn/dp/0155752154):
> Post-hoc fitting of evidence to hypothesis was involved in a most grievous chapter in United States history: the internment of Japanese-Americans at the beginning of the Second World War. When California governor Earl Warren testified before a congressional hearing in San Francisco on February 21, 1942, a questioner pointed out that there had been no sabotage or any other type of espionage by the Japanese-Americans up to that time. Warren responded, "I take the view that this lack [subversive activity](https://arbital.com/p/of) is the most ominous sign in our whole situation. It convinces me more than perhaps any other factor that the sabotage we are to get, the Fifth Column activities are to get, are timed just like Pearl Harbor was timed... I believe we are just being lulled into a false sense of security."
You might want to take your own shot at guessing what Dawes had to say about a Bayesian view of this situation, before reading further.
%%hidden(Answer):Suppose we put ourselves into the shoes of this congressional hearing, and imagine ourselves trying to set up this problem.
- The [prior](https://arbital.com/p/1rm) [odds](https://arbital.com/p/1rb) that there would be a conspiracy of Japanese-American saboteurs.
- The [likelihood](https://arbital.com/p/56t) of the observation "no visible sabotage or any other type of espionage", *given* that a Fifth Column actually existed.
- The likelihood of the observation "no visible sabotage from Japanese-Americans", in the possible world where there is *no* such conspiracy.
As soon as we set up this problem, we realize that, whatever the probability of "no sabotage" being observed if there is a conspiracy, the likelihood of observing "no sabotage" if there *isn't* a conspiracy must be even higher. This means that the likelihood ratio:
$$\frac{\mathbb P(\neg \text{sabotage} | \text {conspiracy})}{\mathbb P(\neg \text {sabotage} | \neg \text {conspiracy})}$$
...must be *less than 1,* and accordingly:
$$
\frac{\mathbb P(\text {conspiracy} | \neg \text{sabotage})}{\mathbb P(\neg \text {conspiracy} | \neg \text{sabotage})}
<
\frac{\mathbb P(\text {conspiracy})}{\mathbb P(\neg \text {conspiracy})}
\cdot
\frac{\mathbb P(\neg \text{sabotage} | \text {conspiracy})}{\mathbb P(\neg \text {sabotage} | \neg \text {conspiracy})}
$$
Observing the total absence of any sabotage can only decrease our estimate that there's a Japanese-American Fifth Column, not increase it. (It definitely shouldn't be "the most ominous" sign that convinces us "more than any other factor" that the Fifth Column exists.)
Again, what matters is not the *exact* likelihood of observing no sabotage given that a Fifth Column actually exists. As soon as we set up the Bayesian problem, we can see there's something *qualitatively* wrong with Earl Warren's reasoning.%%
# Further reading
This has been a very brief and high-speed presentation of Bayes and Bayesianism. It should go without saying that a vast literature, nay, a universe of literature, exists on Bayesian statistical methods and Bayesian epistemology and Bayesian algorithms in machine learning. Staying inside Arbital, you might be interested in moving on to read:
## More on the technical side of Bayes' rule
- A sadly short list of [example Bayesian word problems](https://arbital.com/p/22w). Want to add more? (Hint hint.)
- [__Bayes' rule: Proportional form.__](https://arbital.com/p/1zm) The fastest way to present a step in Bayesian reasoning in a way that will sound sort of understandable to somebody who's never heard of Bayes.
- [__Bayes' rule: Log-odds form.__](https://arbital.com/p/1zh) A simple transformation of Bayes' rule reveals tools for measuring degree of belief, and strength of evidence.
- [__The "Naive Bayes" algorithm.__](https://arbital.com/p/1zg) (Scroll down to the middle.) The original simple Bayesian spam filter.
- [__Non-naive multiple updates.__](https://arbital.com/p/1zg) (Scroll down past Naive Bayes.) How to avoid double-counting the evidence, or worse, when considering multiple items of *correlated* evidence.
- [__Laplace's Rule of Succession.__](https://arbital.com/p/21c) The classic example of an [inductive prior](https://arbital.com/p/21b).
## More on intuitive implications of Bayes' rule
- [__A Bayesian view of scientific virtues.__](https://arbital.com/p/220) Why is it that science relies on bold, precise, and falsifiable predictions? Because of Bayes' rule, of course.
- [__Update by inches.__](https://arbital.com/p/update_by_inches) It's virtuous to change your mind in response to overwhelming evidence. It's even more virtuous to shift your beliefs a little bit at a time, in response to *all* evidence (no matter how small).
- [__Belief revision as probability elimination.__](https://arbital.com/p/1y6) Update your beliefs by throwing away large chunks of probability mass.
- [__Shift towards the hypothesis of least surprise.__](https://arbital.com/p/552) When you see new evidence, ask: which hypothesis is *least surprised?*
- [__Extraordinary claims require extraordinary evidence.__](https://arbital.com/p/21v) The people who adamantly claim they were abducted by aliens do provide *some* evidence for aliens. They just don't provide quantitatively *enough* evidence.
- [__Ideal reasoning via Bayes' rule.__](https://arbital.com/p/) Bayes' rule is to reasoning as the [Carnot cycle](https://arbital.com/p/https://en.wikipedia.org/wiki/Carnot_cycle) is to engines: Nobody can be a perfect Bayesian, but Bayesian reasoning is still the theoretical ideal.
- [__Likelihoods, p-values, and the replication crisis.__](https://arbital.com/p/4xx) Arguably, a large part of the replication crisis can ultimately be traced back to the way journals treat p-values, and a large number of those problems can be summed up as "P-values are not Bayesian."
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43c281f1-059c-4f86-8f2c-b3e83e749ec9
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trentmkelly/LessWrong-43k
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LessWrong
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Who thinks quantum computing will be necessary for AI?
While writing my article "Could Robots Take All Our Jobs?: A Philosophical Perspective" I came across a lot of people who claim (roughly) that human intelligence isn't Turing computable. At one point this led me to tweet something to the effect of, "where are the sophisticated AI critics who claim the problem of AI is NP-complete?" But that was just me being whimsical; I was mostly not-serious.
A couple times, though, I've heard people suggest something to the effect that maybe we will need quantum computing to do human-level AI, though so far I've never heard this from an academic, only interested amateurs (though ones with some real computing knowledge). Who else here has encountered this? Does anyone know of any academics who adopt this point of view? Answers to the latter question especially could be valuable for doing article version 2.0.
Edit: This very brief query may have given the impression that I'm more sympathetic to the "AI requires QC" idea than I actually am; see my response to gwern below.
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19b96a0b-dbd9-4dd6-a9cf-36220ec2342e
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trentmkelly/LessWrong-43k
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LessWrong
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Of the Qran and its stylistic resources: deconstructing the persuasiveness Draft
(It's my first time posting an article, so please go easy on me.)
I wonder if anyone ever fully analysed the Qran and all the resources it uses to tug at the feelings of the reader? It is a remarkably persuasive (if not at all convincing) book, even if I say so myself as an ex Muslim. I've started recognizing some patterns since I started reading this site, but I'd like to know if there is a full-blown, complete, exhaustive deconstruction of that book, that is not dripped in islamophobia, ethnocentrism, and other common failures I have seen in Western theologians when applied to Islam. Not a book about "How the Qran is evil" or "How the Qran is Wrong" or "How IT'S A FAAAKE" but "How, precisely, it manipulates you". Can anyone here point me towards such a work?
And where is the markup help in this blog? I can't seem to find it and it frustrates the hell out of me when I'm commenting usual posts.
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a243b221-b121-4ea6-a774-cf4c560d8b44
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trentmkelly/LessWrong-43k
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LessWrong
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Does an app/group for personal forecasting exist?
I'm interested in personal forecasting - predicting my own future behavior on a range of timescales. I see it as a more useful skill than forecasting on world events. Formulating personally useful forecasts seems like an important and neglected skill in the rationalist community. And it would be nice to have some company, and tools to make it more convenient. Right now, I'm just using a spreadsheet. Does anybody know if there are groups doing this sort of thing? Is there a good app to manage the process?
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53510165-b461-42c0-a1f8-345cf606c3d0
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trentmkelly/LessWrong-43k
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LessWrong
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Emergent Ventures/Schmidt (new grantor for individual researchers)
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4eef805c-3758-4527-b77e-3485444ede8e
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StampyAI/alignment-research-dataset/blogs
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Blogs
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Double Your Donations via Corporate Matching
[](http://doublethedonation.com/miri)MIRI has now partnered with [Double the Donation](http://doublethedonation.com/), a company that makes it easier for donors to take advantage of donation matching programs offered by their employers.
More than 65% of Fortune 500 companies match employee donations, and 40% offer grants for volunteering, but many of these opportunities go unnoticed. Most employees don’t know these programs exist!
Go to MIRI’s Double The Donation page [here](http://doublethedonation.com/miri) to find out whether your employer can match your donations to MIRI. Or, use the form below:
<>
The post [Double Your Donations via Corporate Matching](https://intelligence.org/2013/09/14/double-your-donation/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org).
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ee00c45b-75b1-4c10-b3d4-c0af35f37474
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trentmkelly/LessWrong-43k
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LessWrong
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Why are neuro-symbolic systems not considered when it comes to AI Safety?
I am really not sure of why neuro-symbolic systems are considered as alternatives to the current black-box ones?
A concrete example I have found (and currently studying) is HOUDINI (https://arxiv.org/pdf/1804.00218). Essentially, it implements neural networks using higher order combinators (map, fold etc.) that were found via enumeration/genetic programming searches. When the programs are found, the higher order combinators are "transformed" into trainable networks and added to an ever growing library of "neural functions". The safety provided by such systems comes in the form of understanding the combined functions that form a the solution to a problem. Perhaps, mechanistic interpretability could be further used to dissect the inner workings of the trained networks.
Please, describe to me why is this not a viable course for AI Safety? For that matter, why are alternative technologies not considered at all (or if they are, please mention them)? My initial guess would be that such systems are either not competitive enough, or are a form of "starting from scratch". However, these point might not apply to neuro-symbolic systems.
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f3eb49f8-8f60-4992-8aef-51c4dd5e6432
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trentmkelly/LessWrong-43k
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LessWrong
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I only believe in the paranormal
(Cross-posted from Telescopic Turnip)
Recommended soundtrack for this post
The Wiktionary defines paranormal as “that (ostensibly) cannot be explained by what scientists know”. This is to be distinguished from Real Science, which is about – wait, this definition of paranormal corresponds exactly to what scientific researchers spend their time investigating in their labs.
Put this way, every researcher is a paranormal researcher, and science labs are places where an unusual level of paranormal activity occurs. I’m talking about really weird spooky paranormal phenomena like B-form eDNA flipping into Z-form to make lattices within bacterial biofilms. And don’t even ask me about The Vault. Over centuries, many macroscopic magical phenomena have been explained, leading to the impression that magic has disappeared. But look at life in a microscope, and everything is magical and mysterious again.
And not only the Universe is full of magical phenomena; human wizards are also surprisingly common. They are walking among us. Magic comes in roughly two forms: the one where wizards can predict the future, and the one where wizards can do impossible things.
Compare climate change and homeopathy. Most climate experts agree that climate change is human-made, so if you Believe in Science, you should believe in climate change. This is a terrible argument. I would reply that homeopathy experts also agree that homeopathy is effective. They have diplomas, they publish in scientific journals, and they even follow the scientific method. They may fall for p-hacking and publication bias, but so do people in every other field.
However, only one of these fields involves actual magic. In 2013, powerful wizards from the IPCC predicted how sea levels would change over the next few years. Now that the next few years have passed, we can compare the predictions to the most recent measurements, and it appears that their predictions were correct[1]. This is divination. They are literally oracle
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04a10671-60d8-4994-9cb1-11582a34d5f9
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trentmkelly/LessWrong-43k
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LessWrong
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Multiplex Gene Editing: Where Are We Now?
the Cas9 enzyme used in CRISPR
We’re starting to get working gene therapies for single-mutation genetic disorders, and genetically modified cell therapies for attacking cancer.
Some of them use CRISPR-based gene editing, a new technology (that earned Jennifer Doudna and Emmanuelle Charpentier the 2020 Nobel Prize) to “cut” and “paste” a cell’s DNA. But so far, the FDA-approved therapies can only edit one gene at a time.
What if we want to edit more genes? Why is that hard, and how close are we to getting there?
How CRISPR Works
CRISPR is based on a DNA-cutting enzyme (the Cas9 nuclease), a synthetic guide RNA (gRNA), and another bit of RNA (tracrRNA) that’s complementary to the gRNA. Researchers can design whatever guide RNA sequence they want; the gRNA will stick to the complementary part of the target DNA, the tracrRNA will complex with it, and the nuclease will make a cut there.
So, that’s the “cut” part — the “paste” comes from a template DNA sequence, again of the researchers’ choice, which is included along with the CRISPR components.
Usually all these sequences of nucleic acids are packaged in a circular plasmid, which is transfected into cells with nanoparticles or (non-disease-causing) viruses.
So, why can’t you make a CRISPR plasmid with arbitrary many genes to edit?
There are a couple reasons:
1. Plasmids can’t be too big or they won’t fit inside the virus or the lipid nanoparticle. Lipid nanoparticles have about a 20,000 base-pair limit; adeno-associated viruses (AAV), the most common type of virus used in gene delivery, has a smaller payload, more like 4700 base pairs.
1. This places a very strict restriction on how many complete gene sequences that can be inserted — some genes are millions of base pairs long, and the average gene is thousands!
2. but if you’re just making a very short edit to each gene, like a point mutation, or if you’re deleting or inactivating the gene, payload limits aren’t much of a factor.
2. DNA damage is bad
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7d20a915-d27a-4243-83db-3e5beb5bdf07
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trentmkelly/LessWrong-43k
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LessWrong
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Negativity enhances positivity
A pet peeve of mine has always been how people decide to give ratings on Yelp. If the restaurant is pretty good, it gets five stars. If it's ok, it gets four stars. If it's genuinely bad, it gets three. If the waiter was rude, it gets one.
This leads to a situation where most ratings range from 4 to 5. A 4.8 is a good rating. A 4.2 is underwhelming.
I think this is something that we've all internalized. In some sort of a priori sense, you'd think that an average of 4 out of 5 stars is pretty good. However, we all know that it isn't. In fact, this extends beyond Yelp. I remember hearing that Uber drivers get fired if their rating drops below 4.7 or something.
This works because deep down, we're all Bayesians. We don't take things literally. If we did take things literally, we'd see a place with an average of 4.2 stars and expect good things. After all, Yelp labels a 5 star review as "Great", 4 stars as "Good", 3 as "Ok", 2 as "Could've been better", and 1 as "Not good".
Instead of taking things literally, we look at the rating as Bayesian evidence. We realize that a 4.2 rating isn't something that we'd expect to observe if the restaurant actually is really good, and it is something we'd expect to observe if the restaurant is mediocre.
So then, maybe there's no problem here. Maybe things all work out at the end of the day. The fact that raters lean so heavily towards giving ratings of 4 and 5 doesn't actually prevent users from using the average rating to tell how good a restaurant is. Users just need to re-calibrate.
It's the same thing as that friend who is overenthusiastic in their text messages. Everything has a bunch of exclamation points and emojis. And so, when you receive a text saying "good to hear from you", you're concerned. If they actually were happy to hear from you, you'd expect something more like "HEY!!! So good to hear from you!!!!!!!". If they were just normal-pleased to hear from you you'd expect "Hey! Great to hear from you!!!".
I think i
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616981ba-6103-4ba7-b0f1-cfd3dbd4a617
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trentmkelly/LessWrong-43k
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LessWrong
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[SEQ RERUN] Recursive Self-Improvement
Today's post, Recursive Self-Improvement was originally published on 01 December 2008. A summary (taken from the LW wiki):
> When you take a process that is capable of making significant progress developing other processes, and turn it on itself, you should either see it flatline, or FOOM. The likelihood of it doing anything that looks like human-scale progress is unbelievably low.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was I Heart CYC, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
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25ca6425-550c-4ac1-aad2-7f7121b96168
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trentmkelly/LessWrong-43k
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LessWrong
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Implications of GPT-2
I was impressed by GPT-2, to the point where I wouldn't be surprised if a future version of it could be used pivotally using existing protocols.
Consider generating half of a Turing test transcript, the other half being supplied by a human judge. If this passes, we could immediately implement an HCH of AI safety researchers solving the problem if it's within our reach at all. (Note that training the model takes much more compute than generating text.)
This might not be the first pivotal application of language models that becomes possible as they get stronger.
It's a source of superintelligence that doesn't automatically run into utility maximizers. It sure doesn't look like AI services, lumpy or no.
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b0af615d-617d-4554-92be-51d520cd0541
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trentmkelly/LessWrong-43k
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LessWrong
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Would It Be Better to Dispense with Good and Evil?
The Instrumental Value of Good and Evil
The naturalist, realist project of morality is one that aims to define what is right and wrong by appealing to a set of objective moral truths, whether that be through the maximization of pleasure or through the categorical imperative. However, this essay will argue these theories fall short insofar as they fail to establish the existence of objective mind-independent moral properties. Nevertheless, completely embracing an error theory of morality likely leads to complete moral abolitionism which, for the pragmatist, is not an appealing reality. Therefore, this essay explores alternatives, such as Smith’s constitutivism and Joyce’s fictionalism, to fill the nihilistic void left by the anti-realist. While it is better to dispense of the metaphysically objective good and evil, the counterfactual must be one that embraces a fictional view of morality.
Issues Pertaining to Moral Realism
Moral realism faces insurmountable challenges in establishing objective moral facts independent of human attitudes and institutions. J.L. Mackie's "argument from queerness" identifies the core problem. Moral properties would be metaphysically peculiar entities unlike anything else in our ontology. They would need inherent motivational force, somehow bridging Hume's is-ought gap.
Consider utilitarian attempts to ground morality in pleasure maximization. Hedonistic states are real psychological phenomena, but the claim that we ought to maximize them requires an unjustified normative leap. Utilitarians must posit that pleasure possesses intrinsic "to-be-pursuedness" independent of our attitudes. Yet such properties appear nowhere else in scientific understanding. Neurochemical processes underlying pleasure represent evolved behavioral reinforcement mechanisms, not objective moral significance.
Kantian deontology attempts to derive moral obligations from rational agency itself. The categorical imperative generates universal moral laws through pure
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17816901-298a-481e-8364-c957caf3da99
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
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Task Phasing: Automated Curriculum Learning from Demonstrations
1 Introduction
---------------
In domains with sparse reward signals, a reinforcement learning (RL) agent Portelas et al. ([2020](#bib.bib38 "Automatic curriculum learning for deep rl: a short survey")); Narvekar et al. ([2020](#bib.bib37 "Curriculum learning for reinforcement learning domains: a framework and survey")) receives little to no signal regarding its performance. This phenomenon results in limited ability to train and learn an optimized policy.
As a result, a stream of publications Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")); Burda et al. ([2018](#bib.bib40 "Exploration by random network distillation")); Salimans and Chen ([2018](#bib.bib15 "Learning montezuma’s revenge from a single demonstration")); Reddy et al. ([2019](#bib.bib23 "SQIL: imitation learning via reinforcement learning with sparse rewards")); Ecoffet et al. ([2019](#bib.bib33 "Go-explore: a new approach for hard-exploration problems")); Zhu et al. ([2020](#bib.bib35 "Learning sparse rewarded tasks from sub-optimal demonstrations")) presented various solutions towards effective RL in sparse reward settings. Two of the most common approaches considered in such cases are curriculum learning (CL) Bengio et al. ([2009](#bib.bib36 "Curriculum learning")); Soviany et al. ([2021](#bib.bib16 "Curriculum learning: a survey")); Wei et al. ([2020](#bib.bib17 "Learn like a pathologist: curriculum learning by annotator agreement for histopathology image classification")); Narvekar et al. ([2020](#bib.bib37 "Curriculum learning for reinforcement learning domains: a framework and survey")) and learning from demonstrations Salimans and Chen ([2018](#bib.bib15 "Learning montezuma’s revenge from a single demonstration")); Zhu et al. ([2020](#bib.bib35 "Learning sparse rewarded tasks from sub-optimal demonstrations")); Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")). In this paper we investigate the impact of combining these two general approaches towards a CL continuum which is defined through demonstrations. We suggest applying inverse RL (IRL) to the provided demonstrations in order to obtain a dense reward function and/or a demonstration policy. The IRL outcomes are then used to define an initial simple task for a curriculum along with a continuous curriculum continuum. This continuum is defined by a convex combination between the initial and target tasks.
By training an RL agent on the resulting CL continuum (with progressively increasing complexity), we show—both theoretically and empirically—that an RL agent can be effectively trained to solve tasks that are challenging otherwise (due to sparse reward). We provide theoretical guarantees that the proposed CL will return an optimal policy under the assumption that the space of optimal policies along the task continuum forms a convex set. Two domain-independent approaches are presented for defining the task continuum, namely Temporal-Phasing and Reward-Phasing. Temporal-Phasing is designed to provide gradually increased control to the RL agent in lieu of an IRL agent. This approach is shown to be especially effective in domains providing greater opportunity to take corrective actions, i.e., recovering from actions that hinder performance. Reward-Phasing, on the other hand, results in a task continuum where each task is some convex combination of an informative dense reward (provided by the IRL agent) and the target (sparse) reward. This approach is shown to be especially effective in domains were a meaningful guiding reward function can be extracted from the provided demonstrations.
The theory provided in this paper proves that, under reasonable assumptions, the curriculum defined by Reward-Phasing produces a monotonically non-decreasing policy return in expectation, a desirable outcome in many real-world environments. Such theory is a novel addition to existing CL theory, that otherwise focuses on the effects of different curriculum strategies on the convergence rate of the policy Weinshall et al. ([2018](#bib.bib3 "Curriculum learning by transfer learning: theory and experiments with deep networks")); Weinshall and Amir ([2020](#bib.bib2 "Theory of curriculum learning, with convex loss functions")); Yengera et al. ([2021](#bib.bib4 "Curriculum design for teaching via demonstrations: theory and applications")).
Experimental results are provided for three continuous sparse reward domains. The results suggest that our proposed approaches are successful in converging to an optimized policy, where baseline RL algorithms fail to do so. Moreover, our proposed approaches also outperforms prior approaches that apply curriculum learning, learning from demonstrations, or both.
2 Preliminaries
----------------
In reinforcement learning (RL) an agent is assumed to learn through interactions with an underlying Markov decision process (MDP) which is defined by: S – the state space, A – the action space, P(st,a,st+1) – the transition function of the form P:S×A×S↦[0,1], R(s,a) – the reward function of the form R:S×A↦R, and γ – the discount factor. The agent is assumed to follow an internal policy, π, which maps states to actions, i.e., π:S↦A.111Policies can also be defined as stochastic (soft policy), i.e., mapping states to a distribution over actions. The agent’s chosen action (at) at the current state (st) affects the environment such that a new state emerges (st+1) as well as some reward (rt) representing the immediate utility gained from performing action at at state st, given by R(s,a). We use τ to denote a finite horizon trajectory of the form {s0,a0,r0,s1,...,at−1,rt−1,st}.
In this paper, we separate the MDP into two parts, (a) the environment and (b) the task.
The environment defines the state space (S), the action space (A), the transition function (P), and discount factor γ. The task defines the time steps when the RL agent is given control (at such time steps at is determined by the RL agent). The task also defines the reward function R. The environment and task together comprise the MDP. Using this terminology allows sharing an environment across MDPs, each corresponding to a different task.
The expected sum of discounted rewards for a given task, K, is denoted by JKπ=Eτ∼π∑tγtRK(st,at) where RK is the task reward function. The observed task rewards are used to tune a policy such that JKπ, is maximized. The policy argmaxπ[JKπ] is the optimal policy and is denoted by π∗K.222For some tasks argmaxπ[JKπ] is not unique. In such cases π∗K may refer to any optimal policy.
In sparse reward domains a relatively high proportion of the reward signals (rt) are similar, making it challenging to obtain a meaningful gradient in JKπ with respect to π. Such domains are notoriously hard to solve (i.e., identify an optimal policy for) Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")).
###
2.1 Related work
Prior RL approaches for solving sparse reward domains can be divided into three broad classes. (1) boosted exploration, (2) demonstration guidance, and (3) curriculum learning.
#### Boosted exploration approaches
Boosted exploration approaches Ecoffet et al. ([2019](#bib.bib33 "Go-explore: a new approach for hard-exploration problems")); Burda et al. ([2018](#bib.bib40 "Exploration by random network distillation")); Bellemare et al. ([2016](#bib.bib30 "Unifying count-based exploration and intrinsic motivation")); Zhao et al. ([2020](#bib.bib31 "Potential driven reinforcement learning for hard exploration tasks")); Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")); Durugkar et al. ([2021](#bib.bib22 "Adversarial intrinsic motivation for reinforcement learning")) are mostly domain-independent approaches to finding the optimal policy for a sparse reward task, by improving the manner in which exploration is conducted in the target environment. These methods use intrinsic motivation Barto ([2013](#bib.bib29 "Intrinsic motivation and reinforcement learning")); Oudeyer and Kaplan ([2009](#bib.bib28 "What is intrinsic motivation? a typology of computational approaches"), [2013](#bib.bib27 "How can we define intrinsic motivation ?")) where the agent presents itself with exploration rewards that are different from those of the given task-specific rewards. Despite their effectiveness, the time to convergence for such approaches can be significant, due to the large amounts of exploration that they induce.
#### Demonstration-guided approaches
This class of algorithms Ho and Ermon ([2016](#bib.bib26 "Generative adversarial imitation learning")); Torabi et al. ([2018](#bib.bib25 "Behavioral cloning from observation")); Fu et al. ([2017](#bib.bib24 "Learning robust rewards with adversarial inverse reinforcement learning")); Reddy et al. ([2019](#bib.bib23 "SQIL: imitation learning via reinforcement learning with sparse rewards")) attempt to learn policies that minimize the mismatch between the RL state-action visitation distribution and a demonstrator’s state-action visitation distribution. The performance of the policies learned by such techniques is often limited by the demonstrator’s performance, as they aim to only mimic the demonstrator and do not attempt to explore for better policies. On the other hand, Self-Adaptive Imitation Learning (SAIL) Zhu et al. ([2020](#bib.bib35 "Learning sparse rewarded tasks from sub-optimal demonstrations")) proposed an off-policy imitation learning approach that can surpass the demonstrator by encouraging exploration along with distribution matching and replacing sub-optimal demonstrations with superior self-generated trajectories. Consequently, we consider this approach for comparison in our experiments.
#### Curriculum learning
Similar to our approach, this class of algorithms Bengio et al. ([2009](#bib.bib36 "Curriculum learning")); Soviany et al. ([2021](#bib.bib16 "Curriculum learning: a survey")); Narvekar et al. ([2020](#bib.bib37 "Curriculum learning for reinforcement learning domains: a framework and survey")) aims to break down complex tasks into simpler tasks. CL approaches often require a human domain expert to divide the task into simpler tasks and then design a curriculum that decides the sequence in which those tasks are learnt Ionescu et al. ([2016](#bib.bib20 "How hard can it be? estimating the difficulty of visual search in an image")); Lotfian and Busso ([2019](#bib.bib19 "Curriculum learning for speech emotion recognition from crowdsourced labels")); Pentina et al. ([2015](#bib.bib21 "Curriculum learning of multiple tasks")); Jiménez-Sánchez et al. ([2019](#bib.bib18 "Medical-based deep curriculum learning for improved fracture classification")). Recent work on CL Portelas et al. ([2020](#bib.bib38 "Automatic curriculum learning for deep rl: a short survey")) aims to automatically design an appropriate curriculum, but still requires some domain knowledge provided by a human expert, such as defining sub-tasks Portelas et al. ([2020](#bib.bib43 "Teacher algorithms for curriculum learning of deep rl in continuously parameterized environments")); Matiisen et al. ([2019](#bib.bib42 "Teacher–student curriculum learning")),
defining sub-goal conditions Zhao et al. ([2020](#bib.bib31 "Potential driven reinforcement learning for hard exploration tasks")); Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")), or scaling domain design features Dennis et al. ([2020](#bib.bib44 "Emergent complexity and zero-shot transfer via unsupervised environment design")).
3 Task phasing
---------------
Consider a sparse reward RL task defined by K and a given environment. Assume that K cannot be solved efficiently using common RL approaches Haarnoja et al. ([2018](#bib.bib10 "Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor")); Schulman et al. ([2017](#bib.bib39 "Proximal policy optimization algorithms")).
We propose to address this inefficiency by introducing a set of simplified tasks with progressively increasing complexity (similar to curriculum learning). In contrast to most past CL algorithms, we do not rely on expert knowledge and/or domain-specific assumptions for defining the simplified tasks but define them through demonstrations in a principled domain-independent approach.
Assume some initial simplified task denoted Ks, that can be efficiently solved by common RL algorithms.
Next, consider a convex combination function Con(β,K1,K2)=βK1+(1−β)K2 which provides a task continuum, with β∈[0,1], between two given tasks K1,K2.
We can now define the general task phasing curriculum procedure shown in Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
Input: initial (simplified) task, Ks; target (complex) task, Kf; step size, α
Output: optimized policy for Kf, π∗f
1 π∗←train(π∗,Ks) K←Ks β←0 while *K≠Kf* do
2 β←β+α K←Con(β,Ks,Kf) π∗←re-train(π∗,Kβ)
3 end while
return π∗
Algorithm 1 Task phasing curriculum learning.
The ‘train’ (Line [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")) and ‘re-train’ (Line [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")) functions can be implemented with any off the shelf RL algorithm (assuming sufficient exploration, see Sec [3.3](#S3.SS3 "3.3 Convergence condition ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") for details). Note that this general approach only introduces a single hyper-parameter over the underlying RL solver, the α step size. Moreover, we found that training stability is fairly insensitive to the α value (as long as it is small enough).
Still, Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") raises two questions that need to be addressed.
1. How can we obtain the initial simplified task (Ks) for a general (domain-independent) MDP?
2. How can we define the task continuum, i.e., Con(β,Ks,Kf) for any β∈[0,1]?
We address these questions by assuming a set of trajectories, D, obtained from a (suboptimal) demonstrating policy.
###
3.1 Temporal-phasing
The Temporal-Phasing approach can be viewed as gradually shifting the control from a demonstrating policy to a learned RL policy.
This approach assumes that a demonstrator policy, πd, can be retrieved from D, e.g., by using imitation learning Ho and Ermon ([2016](#bib.bib26 "Generative adversarial imitation learning")); Fu et al. ([2017](#bib.bib24 "Learning robust rewards with adversarial inverse reinforcement learning")).
Temporal-Phasing follows some internal logic for determining in which time steps the RL-agent is given control as appose to steps where the demonstrator is in control during online exploration. Such an approach was previously proposed for smooth policy transitions Dey et al. ([2021](#bib.bib13 "A joint imitation-reinforcement learning framework for reduced baseline regret")). At any state, if the RL-agent is in control, the chosen action follows the RL internal policy. If, by contrast, the demonstrator is given control, then πd is followed. Transitions that follow the demonstrator’s policy might also be used to train the RL-agent if off-policy learning Levine and Koltun ([2013](#bib.bib34 "Guided policy search")) is enabled.
##### Initial simplified task (Ks).
Ks is defined by setting the probability of providing control to the RL-agent at any state to be 0.
##### task continuum (Con(β,Ks,Kf)).
The environment continuum is defined by setting the probability of providing control to the RL-agent at any state to be β. Relevant protocols include:
V1: random step
| | | |
| --- | --- | --- |
| | ∀t, πt={πRL,U[0,1)<βπd,else | |
where U[0,1) is a random value drawn from a uniform distribution in the range [0,1).
V2: random m steps
| | | |
| --- | --- | --- |
| | ∀t:(t mod m)=0, πt:t+m−1={πRL,U[0,1)<βπd,else | |
V3: fixed steps
| | | |
| --- | --- | --- |
| | ∀t, πt={πRL,(t mod βT)<1πd,else | |
where T is the total number of steps for the episode.
We implemented and experimented with all three variants and found ‘V1: random step’ to be superior in most cases. Refer to Fig. [4](#A1.F4 "Figure 4 ‣ Reward-Phasing variants ‣ A.3 Phasing Variants ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") in the Appendix for a comparison of the performance of the Temporal-Phasing variants.
##### Limitations:
Temporal-Phasing is expected to perform poorly in domains where actions have unrecoverable outcomes. Consider a robotic gripper arm task for example. Assume the task’s goal is to carry an object from one location to another. The demonstrator’s policy never opens the gripper’s palm, steadily holding the object. An exploratory RL agent, by contrast, will try various actions (including opening the gripper’s palm) in various locations resulting in unrecoverable situations and reduced probability of reaching the goal state. This probability diminishes exponentially as the exploratory RL agent is provided control in more time steps (factoring the probabilities of avoiding unrecoverable actions per step). This phenomena is especially harmful in sparse reward domains as a guiding signal from the goal state is rarely observed.
###
3.2 Reward phasing
The Reward-Phasing approach can be viewed as gradually shifting the Reward function from a dense, informative reward signal to the true (sparse) reward. The dense reward is initially used to guide the RL agent such that it learns to imitate the demonstrator. Next, the dense reward is gradually phased out leaving the true reward as the only policy guiding signal. Doing so allows the RL agent to learn optimized policies that may diverge from the demonstrator. This approach assumes that an initial dense reward function, Rd, can be retrieved from D, e.g., by using inverse RL Fu et al. ([2017](#bib.bib24 "Learning robust rewards with adversarial inverse reinforcement learning")); Ziebart et al. ([2008](#bib.bib12 "Maximum entropy inverse reinforcement learning")); Abbeel and Ng ([2004](#bib.bib11 "Apprenticeship learning via inverse reinforcement learning")). It further assumes that Rd and the target reward function are of similar scale. This, however, is not a limiting assumption as Rd can be scaled arbitrarily with no impact on the IRL process.
##### Initial simplified task (Ks).
Let Rd be the learnt dense reward function and let Rf be the target (sparse) reward function.
Ks is defined by setting the reward function Rs=Rd+Rf.
##### Task continuum (Con(β,Ks,Kf)).
The task continuum, Con(β,Ks,Kf)), is defined by a phased reward function that is a combination of Rd and Rf. Relevant protocols include:
V1: constant phasing
| | | |
| --- | --- | --- |
| | Rβ=(1−β)Rd+Rf | |
V2: random phasing
| | | |
| --- | --- | --- |
| | Rβ={Rd+Rf,U[0,1)>βRf,else | |
We implemented and experimented with both variants and found ‘V1: constant phasing’ to be superior in most cases. Refer to Fig. [6](#A1.F6 "Figure 6 ‣ Reward-Phasing variants ‣ A.3 Phasing Variants ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") and Fig. [7](#A1.F7 "Figure 7 ‣ Reward-Phasing variants ‣ A.3 Phasing Variants ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") in the Appendix for a comparison of the performance of the Reward-Phasing variants.
##### Limitations:
Reward-Phasing is expected to perform poorly in domains where the initial dense reward function directs the RL agent towards a local optimal policy (with respect to the target reward). For example in a capture-the-flag game (e.g., our PyFlag domain as described in Appendix [A.1](#A1.SS1 "A.1 Domain Description ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")), an initial reward that is biased towards guarding the player’s flag will make it challenging to learn a policy which steals the enemy flag.
This is less of an issue for Temporal-Phasing as it trains a “demonstrator” surrogate function that can be queried for states that lay outside of the demonstrations state distribution, allowing the RL agent to explore further from the provided demonstrations.
###
3.3 Convergence condition
######
Definition 1 (RLϵ).
Define RLϵ to be an RL algorithm that explores and returns the optimal policy within some bounded ϵ KL-divergence from a given stochastic policy, π. That is, RLϵ will return
| | | |
| --- | --- | --- |
| | πb∗=argmaxπ′|Es∼π′[KL(π(s),π′(s))]≤ϵJπ | |
where πb∗ is the optimal policy within the exploration bound.
For simplicity of presentation, KL(π,π′) is used to represent Es∼π[KL(π(s),π′(s))] hereafter.
Consider two tasks K1 and K2 each with an affiliated optimal policy π∗1 and π∗2.
It is easy to see that RLϵ will return
π∗2←re-train(π∗1,K2) if KL(π∗1,π∗2)]≤ϵ.
This is because the optimal policy π∗2 is within the exploration range from the initial policy π∗1.
Consider applying Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") for a given Ks, Kf, and some RL algorithm, RLϵ. Assume that π∗s is within the initial search space of RLϵ, yet π∗f is not.Further assume that, KL(π∗s,π∗f)>ϵ. That is, RLϵ might fail to identify π∗f←re-train(π∗s,Kf).
######
Lemma 1 (Convergence).
if the space of {task, optimal policy} pairs (Kβ,π∗β) forms a convex set for β∈[0,1] and a given Kβ=Con(β,Ks,Kf) function, then Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") with a small enough α, using RLϵ as the underlying solver, will converge on π∗f within 1/α iterations.
###### Proof.
Following the definition of a convex set, there must exist a fine enough β={β0=0,β1,...,βn=1} decomposition such that ∀i∈{0,...,n−1},KL(π∗βi,π∗βi+1)≤ϵ. Following Definition [1](#Thmdefinition1 "Definition 1 (RLϵ). ‣ 3.3 Convergence condition ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), RLϵ will return π∗βi←re-train(π∗βi−1,Ki) for every i∈{1,...,n} where βn=1, i.e., Kn=Ef. That is, RLϵ will find all optimal policies along the resulting curriculum, until π∗f.
∎
Figure 1: An example MDP where both Temporal-Phasing and Reward-Phasing using sub-optimal demonstrations result in a non-convex curriculum space. The rewards shown in the MDP represent the target reward, Rf. Temporal phasing: assume πd([s0,sl])=[ar,a1], for β<0.5, π∗β(s0)=ar yet for β>0.5, π∗β(s0)=al. Reward phasing: assume Rd([(s0,ar),(s0,al),(sl,a1),(sl,a2)])=[2,0,0,0] and γ=1. Using constant phasing, Rβ=(1−β)Rd+Rf, for β<0.5, π∗β(s0)=ar yet for β>0.5, π∗β(s0)=al.
Convexity for both the Temporal-Phasing (Sec [3.1](#S3.SS1 "3.1 Temporal-phasing ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")) and Reward-Phasing (Sec [3.2](#S3.SS2 "3.2 Reward phasing ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")) approaches does not hold for the case of a sub-optimal demonstrator (from which D is sampled from). Figure [1](#S3.F1 "Figure 1 ‣ 3.3 Convergence condition ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") presents convexity counter examples for both spaces (Temporal and Reward phasing).
A non-convex {Kβ,π∗β} space can lead to an arbitrary large KL(π∗β,π∗β+α) meaning that π∗β+α←re-train(π∗β,Kβ+α) might not be found by RLϵ causing Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") to fail learning π∗f. Nonetheless, the experimental results presented in Section [4](#S4 "4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") show that task phasing is highly effective in domains without such convexity guarantees.
###
3.4 Theoretical results for Reward-Phasing
Next we show that Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") results in monotonically non-decreasing policy performance (assuming π∗β+α←re-train(π∗β,Kβ+α) is found in each iteration). This result is important and useful, since assuming the initial performance is equivalent to that of the demonstrator, it guarantees that the learned policy never underperforms the demonstrator (in expectation). Note that this result relates to V1 (constant Reward-Phasing). Nonetheless, the same result can be extended to apply for V2 following the fact that the affiliated reward functions are equal in expectation. That is,
E[RβV2]=(1−β)(Rd+Rf)+βRf=(1−β)Rd+Rf=RβV1.
Define Rdπ:=Eτ∼π∑tγtRd(st,at).
Define Rfπ:=Eτ∼π∑tγtRf(st,at).
Consequently, we can rewrite
| | | |
| --- | --- | --- |
| | π∗β:=argmaxπ(1−β)Rdπ+Rfπ | |
######
Theorem 1 (Monotonic improvement).
Rfπ∗β is monotonically non-decreasing with β.
###### Proof.
by contradiction, assume some π=π∗β, π′=π∗β′, with β<β′, for which Rfπ>Rfπ′
Case 1: Rdπ≥Rdπ′
This would imply that (1−β′)Rdπ′+Rfπ′<(1−β′)Rdπ+Rfπ contradicting the assumption that π′=π∗β′.
Case 2: Rdπ<Rdπ′
Let α=β′−β. Then
| | | | |
| --- | --- | --- | --- |
| | (1−β′)Rdπ′+Rfπ′=(1−β)Rdπ′+Rfπ′−αRdπ′ | | (1) |
| | | | |
| --- | --- | --- | --- |
| | ≤(1−β)Rdπ+Rfπ−αRdπ′ | | (2) |
| | | | |
| --- | --- | --- | --- |
| | <(1−β)Rdπ+Rfπ−αRdπ=(1−β′)Rdπ+Rfπ | | (3) |
contradicting the assumption that π′=π∗β′.
(Eq 2) because π=π∗β (Eq 3) by the Case 2 assumption
∎
4 Experiments and Results
--------------------------
The experiments are designed to study the performance of our task phasing variants when paired with a state-of-the-art RL solver. Specifically, they are designed to answer the following questions.
1. Can task phasing learn an optimized policy in complex tasks domains the paired RL algorithm (without task phasing) cannot?
2. Can the proposed task phasing variants learn a policy that outperforms the demonstrator, where the demonstrator’s policy is used for collecting the demonstrations in D?
3. Are the limitation reported for each of the task phasing variables observed empirically?
4. How does task phasing compare to state-of-the-art algorithms designed to run in sparse reward domains and/or leverage demonstrations?
Our results provide a positive answer to Questions 1--3 and show clear advantages over previous state-of-the-art algorithms with respect to Question 4.
In order to support full reproducibility of the reported results, our codebase along with detailed running instructions are provided online 333[https://osf.io/tfn8m/?view˙only=32a1fc5522744746a703bc3810d617d8](https://osf.io/tfn8m/?view_only=32a1fc5522744746a703bc3810d617d8).
The experiments are carried out in three continuous control, sparse reward environments, namely PyFlags Erceth ([2020](#bib.bib9 "PyFlags")) (GNU General Public License v3.0), FetchPickAndPlace-v1(P&P) Plappert et al. ([2018](#bib.bib5 "Multi-goal reinforcement learning: challenging robotics environments and request for research")) (MIT license), and FetchSlide-v1(FS) Plappert et al. ([2018](#bib.bib5 "Multi-goal reinforcement learning: challenging robotics environments and request for research")) (MIT license). The FetchPickAndPlace and FetchSlide domains are used as sparse reward benchmark domains in the HER with demonstrations paper Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")). All three domains are of special interest as they correspond to the limitations reported for our task phasing variants. Specifically, the PyFlags domain (capture the flag) has many local optimums in the policy space, which is expected to have a stronger negative impact on Reward-Phasing compared to Temporal-Phasing. On the other hand, the two Fetch domains have unrecoverable actions, which are expected to have a stronger negative impact on Temporal-Phasing compared to Reward-Phasing.
A snapshot from each of the reported domains is provided in Figure [2](#S4.F2 "Figure 2 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"). The technical description for the domains is provided in Appendix [A.1](#A1.SS1 "A.1 Domain Description ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"). All experiments are repeated with 3 different random seeds and the mean of their results is reported along with a 1-σ shaded region.
| | | |
| --- | --- | --- |
| (a) The PyFlags domain; (b) FetchPickAndPlace-v1 domain; (c) FetchSlide-v1 domain
(a)
| (a) The PyFlags domain; (b) FetchPickAndPlace-v1 domain; (c) FetchSlide-v1 domain
(b)
| (a) The PyFlags domain; (b) FetchPickAndPlace-v1 domain; (c) FetchSlide-v1 domain
(c)
|
Figure 2: (a) The PyFlags domain; (b) FetchPickAndPlace-v1 domain; (c) FetchSlide-v1 domain
###
4.1 Settings
We use a common RLϵ algorithm, denoted Proximal Policy Optimization (PPO) Schulman et al. ([2017](#bib.bib39 "Proximal policy optimization algorithms")), as our RL algorithm (for computing ‘train’, Line [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), and ‘re-train’, Line [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), in Algorithm [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")). All algorithms used in the experiments apply the ADAM optimizer for training. The hyper-parameters values for our approach as well as for the baseline algorithms are provided in Appendix [A.2](#A1.SS2 "A.2 Hyperparameters ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
#### Temporal phasing
Temporal-Phasing requires an online demonstrator. The demonstrator can be learnt from demonstrations using techniques such as IRL Fu et al. ([2017](#bib.bib24 "Learning robust rewards with adversarial inverse reinforcement learning")) or GAIL Ho and Ermon ([2016](#bib.bib26 "Generative adversarial imitation learning")). In order to focus the study on the phasing approach, we skip the IL phase and directly use a suboptimal rules-based demonstrator (same one used to collect the demonstrations). We report results for the best performing Temporal-Phasing variant ‘V1: random step’. We provide a comparison of the various variants of Temporal-Phasing, as well as ablation studies conducted on them, in the Appendix [A.3](#A1.SS3 "A.3 Phasing Variants ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"). We found that using a dynamic α step size performed better compared to using a static one. In our dynamic setting, β is incremented by β←β+α only if the average performance of the current policy (πβ) is greater than a set threshold. The threshold values for each domain are provided in Appendix [A.2](#A1.SS2.SSSx1 "Temporal Phasing ‣ A.2 Hyperparameters ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
Also, for Temporal-Phasing we used importance sampling to train the RL-policy over transitions which follow from the demonstrator control (off-policy training). We used a PPO importance sampling approach that was introduced by Levine and Koltun ([2013](#bib.bib34 "Guided policy search")). However, instead of using Differential Dynamic Programming to generate “guiding samples” Levine and Koltun ([2013](#bib.bib34 "Guided policy search")), we use the demonstrator’s actions as guiding samples.
####
Reward-Phasing.
Reward-Phasing requires a dense reward function to provide initial guidance to the RL policy. In our approach, we use the Adversarial Inverse Reinforcement Learning Fu et al. ([2017](#bib.bib24 "Learning robust rewards with adversarial inverse reinforcement learning")) approach to learn the dense reward function for the task. Results are reported for the best performing Reward-Phasing variant (V1: constant phasing). Similar to Temporal-Phasing, we provide a comparison of the various variants of Reward-Phasing in Appendix [A.3](#A1.SS3 "A.3 Phasing Variants ‣ Appendix A Appendix ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"). In order to allow reasonable running times, we employ a fixed interval approach, where β←β+α is applied every 200 training episodes. That is, Line [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") in Alg [1](#alg1 "Algorithm 1 ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") trains for a fixed 200 episodes (not necessarily until convergence to π∗β).
#### Baselines
We compare the proposed approaches against the following baseline algorithms that are considered state-of-the-art for sparse reward domains:
##### Hindsight Experience Replay (HER) with demonstrations Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")).
This approach automatically generates a learning curriculum. It does so by setting intermediate goals in the environment that the policy can reach and providing rewards based on how close the agent is to a goal state. This approach requires some domain knowledge as the goal state for the environment must be defined. We find that the agent learns to retrieve the flag when the goal state for the PyFlag task is set such that the agent is positioned at its own base with the opponent’s flag in its possession (distance to red flag = 0) and its own flag is not in the opponent’s possession. When computing rewards, a weight of 1.0 is provided for possession of the opponent’s flag and preventing the opponent from taking the agent’s flag, in order to encourage offensive and defensive strategies. A lower weight, 0.1, is provided for the agent being positioned at its base so that it will prioritize attempting to steal the flag instead of staying close to its base. The goal state and the reward for the Fetch domains is the same as in Nair et al. ([2018](#bib.bib41 "Overcoming exploration in reinforcement learning with demonstrations")), with the exception that the reward is scaled up from {−1,0} to {0,1}.
##### Self-adaptive Imitation Learning (SAIL) Zhu et al. ([2020](#bib.bib35 "Learning sparse rewarded tasks from sub-optimal demonstrations")).
Instead of relying purely on exploration or on exploiting demonstrations, this approach aims to effectively strike a balance between exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the performance of the demonstrator. It maintains two replay buffers, for caching teacher demonstrations and self-generated transitions, respectively. During iterative training, SAIL dynamically adds high-quality self-generated trajectories into the teacher demonstration buffer.
##### Random Network Distillation (RND) Burda et al. ([2018](#bib.bib40 "Exploration by random network distillation")).
This approach encourages the exploration of new states in the environment but does not rely on demonstrations. The boost in exploration is provided by combining an intrinsic reward with the extrinsic (true) reward. The intrinsic reward is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network.
###
4.2 Results
We start by comparing our task phasing variants against the baseline algorithms. Figure [3](#S4.F3 "Figure 3 ‣ 4.2 Results ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") presents learning curves for both our best Temporal-Phasing (V1: random step) and Reward-Phasing (V1: constant phasing) variants.
Figure 3: Learning curves of the different algorithms being tested in the PyFlag, FetchPickAndPlace and FetchSlide domains. The y-axis represents the average episode score/reward over 300 runs. In the PyFlags domain, the game score is the difference between the number of times the blue and red flags are stolen. In PyFlags 1 episode = 20480 steps and in P&P, FS 1 episode = 1024 steps.
#### Temporal Phasing
In the PyFlags domain, see Figure [3](#S4.F3 "Figure 3 ‣ 4.2 Results ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")(a), Temporal-Phasing outperforms all other algorithms with respect to the final performance. Temporal-Phasing is also the only approach that is able to outperform the demonstrator.444In the PyFlags domain, matching the demonstrator’s performance is achieved when the average game score is zero (as it is a zero-sum game). The peak in performance is achieved when the RL policy has full control (β=1) learning a policy that outperforms the demonstrator (game score > 0). These results provide a positive answer to the experiments’ Questions [1](#S4.I1.i1 "item 1 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), [2](#S4.I1.i2 "item 2 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), and [4](#S4.I1.i4 "item 4 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
The results in the Fetch domains, shown in Figure [3](#S4.F3 "Figure 3 ‣ 4.2 Results ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations")(b,c), indicate that Temporal-Phasing performed poorly in these domain, with the demonstrator policy only being phased out 50% on average (not able to achieve the phasing threshold beyond 50%). This highlights the limitations of the Temporal-Phasing approach in learning to perform sensitive tasks that suffer from unrecoverable actions. This is apparent in the P&P domain where the robotic arm can take one wrong action, such as opening the gripper, leading it to drop the block. A similar phenomena can be observed in the FS domain where pushing the block in certain directions leads to the block becoming unreachable.In the PyFlags domain, by contrast, the demonstrator’s policy is more likely to recover from a limited number of bad actions, making the Temporal-Phasing approach more appropriate.
These results provide a positive answer to the experiments’ Question [3](#S4.I1.i3 "item 3 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
#### Reward Phasing
Figure [3](#S4.F3 "Figure 3 ‣ 4.2 Results ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations") paint a picture where Reward-Phasing achieves state-of-the-art performance in both Fetch domains.
It can be observed that during the phasing process Reward-Phasing results in mostly monotonic improvement (in expectancy), supporting the claims made in Theorem [1](#Thmtheorem1 "Theorem 1 (Monotonic improvement). ‣ 3.4 Theoretical results for Reward-Phasing ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
In the Fetch domains, the policy learned using Reward-Phasing not only learns to perform the task but also outperform the demonstrator policy. These results provide further positive suppurate to experiments’ Questions [1](#S4.I1.i1 "item 1 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), [2](#S4.I1.i2 "item 2 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), and [4](#S4.I1.i4 "item 4 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"). In the PyFlags domain, although the policy learnt using Reward-Phasing does not outperform the demonstrator policy, we observe that the learned policy is still able to steal the enemy’s flag 5 times per episode on average. This indicates that Reward-Phasing was successful in learning a policy that retained useful behaviours in sparse reward settings. However the RL policy seemed to get stuck in a local minima where it is unable to outperform the opponent. These results provide further positive support to the experiments’ Question [3](#S4.I1.i3 "item 3 ‣ 4 Experiments and Results ‣ Task Phasing: Automated Curriculum Learning from Demonstrations").
#### HER with demonstrations
We observed that despite being able to learn a reasonable sub-optimal policy, the HER algorithm did not outperform both our best task phasing approaches in any of the reported domains.
The results highlight the drawbacks of the HER algorithm in adversarial settings such as the PyFlags domain. The HER algorithm provides a reward to the agent for achieving sub-goals based on how close they are to the true goal state, but it does not take into account how good those sub-goals are with respect to the true reward. For instance, the agent may receive a large reward when it reaches a state close to the opponent’s flag, but it will not be penalized if this state leads the agent directly into the opponent’s line of fire.
#### Random Network Distillation
The RND algorithm was not able to perform meaningful learning in any of the reprted domains.
This finding indicates that although exploration-based approaches are known to provide good results, they require a substantial training period, whereas algorithms that utilize demonstrations or some form of domain knowledge can achieve better/faster results. Similar trends for RND have been previously observed and reported Yang et al. ([2021](#bib.bib7 "Exploration in deep reinforcement learning: a comprehensive survey")).
##### Self-adaptive Imitation Learning
SAIL was unable to outperform at least one of our task-phasing approaches in all of the reported domains. While in the PyFlags domain it was able to outperform Reward-Phasing, it still underperformed Temporal-Phasing.
SAIL exhibited similar limitations to Temporal-Phasing in the Fetch domains where it failed to even match the demonstrator’s performance. We speculate that, similar to Temporal-Phasing, SAIL is unable to effectively deal with unrecoverable actions which prevent it from experiencing sparse (hard to reach) rewards.
5 Limitations and Future Work
------------------------------
The theoretical convergence guarantees for task-phasing assume a convex space of tasks and optimal policies. However, when considering a sub-optimal demonstrator such spaces cannot be guaranteed. As a result, in the worst case, an arbitrary small step in the task continuum can lead to a re-train procedure that is as hard as training a policy from scratch. Consequently, providing some bounds on the convexity of this space can lead to a more robust algorithm.
To this end, a promising direction for future work is to consider entropy-encouraging soft policy formulations which result in a smooth policy space. For example, reconsider the example from Figure [1](#S3.F1 "Figure 1 ‣ 3.3 Convergence condition ‣ 3 Task phasing ‣ Task Phasing: Automated Curriculum Learning from Demonstrations"), with the addition of a policy entropy maximizing term. That is, π∗β=∑trt+H(π(⋅|st)). The reader is encouraged to validate that, in this case, the {β,π∗β} space is indeed convex.
Another limitation of the task phasing approach is the need to choose between its two variant, Temporal-Phasing or Reward-Phasing. Future work will improve on the Reward-Phasing variant by fusing it with the DAtaset Aggregation Ross et al. ([2011](#bib.bib1 "A reduction of imitation learning and structured prediction to no-regret online learning")) approach for no-regret online learning. We expect such an approach to outperform Temporal-Phasing in all of the domains, making Reward-Phasing a default variant choice.
6 Summary
----------
This paper presents two general, domain-independent approaches for designing a curriculum continuum from demonstrations: Temporal-Phasing and Reward-Phasing. The curriculum continuum allows decomposing a complex RL task into a set of tasks with progressively increasing complexity. We show that under the simplifying assumption of a convex (task, optimal policy) space, a task phasing approach with a sufficiently small step size, is guaranteed to learn the optimal policy for any task. We show that the Reward-Phasing curriculum must result in policies that are monotonically non-decreasing with-respect-to the expected return in the target task. Experimental results in sparse reward domains indicate that Temporal-Phasing and/or Reward-Phasing can significantly surpass state-of-the-art algorithms in terms of asymptotic performance, even in non-convex spaces. The results indicate that Temporal-Phasing is more applicable for tasks that do not require high precision or prone to catastrophic actions, and that Reward-Phasing produces sub-optimal results when IRL fails to accurately mimic the demonstrator.
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