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40ab85ac-a6ca-4be6-83b0-782c51e29669 | StampyAI/alignment-research-dataset/blogs | Blogs | A new MIRI FAQ, and other announcements
MIRI is at [Effective Altruism Global](http://www.ea-global.org)! A number of the talks can be watched online at the [EA Global Livestream](http://www.eaglobal.org/livestream).
We have a new MIRI **[Frequently Asked Questions page](http://intelligence.org/faq)**, which we’ll be expanding as we continue getting new questions over the next four weeks. Questions covered so far include “[Why is safety important for smarter-than-human AI?](https://intelligence.org/faq/#safety)” and “[Do researchers think AI is imminent?](https://intelligence.org/faq/#imminent)”
We’ve also been updating other pages on our website. **[About MIRI](https://intelligence.org/about/)** now functions as a short introduction to our mission, and **[Get Involved](https://intelligence.org/get-involved/)** has a new consolidated [application form](https://machineintelligence.typeform.com/to/fot777) for people who want to collaborate with us on our research program.
Finally, an announcement: just two weeks into our [six-week fundraiser](https://intelligence.org/2015/07/17/miris-2015-summer-fundraiser/), **we have hit our first major fundraising target**! We extend our thanks to the donors who got us here so quickly. Thanks to you, we now have the funds to expand our core research team to 6–8 people for the coming year.
New donations we receive at <https://intelligence.org/donate> will now go toward our second target: “Accelerated Growth.” If we hit this second target ($500k total), we will be able to expand to a ten-person core team and take on a number of important new projects. More details on our plans if we hit our first two fundraiser targets: [Growing MIRI](https://intelligence.org/2015/07/18/targets-1-and-2-growing-miri/).
The post [A new MIRI FAQ, and other announcements](https://intelligence.org/2015/07/31/a-new-miri-faq-and-other-announcements/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
4ea2d5a7-ceca-467c-b1b8-f76e65b38166 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Do You Want Your Autonomous Car to Drive Like You?.
1 Introduction
---------------
In the age of autonomous driving, researchers and companies are getting ever-so-close to enabling cars to generate driving behavior that includes reaching the destination while satisfying safety constraints, like not colliding with other cars or pedestrians.
Once autonomous cars attain that level of capability, initially, they might be able to generate, for each driving situation, only one solution trajectory (or behavior) that satisfies these safety and feasibility constraints. But really, many solutions exist – there are many ways to drive. This depend on the individual trade-offs that each driver makes. We have an existence proof for that. Some of us are more *aggressive* drivers, valuing efficiency and being comfortable getting close to other cars on the road. Others are more *defensive*, a bit more conservative when it comes to safety, leaving a large distance to the next car for example, or quickly braking when someone attempts to merge in front.
Soon after we are able to generate *one* feasible behavior, we will be asking ourselves *which* behavior we should try to generate: what driving *style* should an autonomous car have? There is a natural answer to this question: cars should do what users want them to [[9](#bib.bib9), [6](#bib.bib6), [22](#bib.bib22)]. If the user drives aggressively, so should the car. The car should borrow the user’s driving style (though not the imperfections). This is very apparent from the expression “back seat driving”, which suggests that people want the driver to do what they would do.
Figure 1: We first get data from user driving in different scenarios, and in a second session ask them to compare their own style (without knowing it is theirs), a more defensive style, and a more aggressive style. Participants tended to prefer a more defensive style than their own, but mistakenly thought they were actually picking their own.
Prior work has focused on identifying the user’s driving style, via Inverse Reinforcement Learning [[9](#bib.bib9), [14](#bib.bib14), [10](#bib.bib10)].
In all of them, the underlying assumption is that we want cars to match our driving style: that we want them to drive like us.
In this paper, we challenge this assumption, and hypothesize that users want a driving style that is different from their own. We design and conduct a user study to start analyzing the potential differences between how users drive and how they want to be driven. Our study, conducted in a driving simulator, has two parts: first, the users come in and demonstrate their driving in different environments; second, at a later date, the same users come in and test four driving styles: their own (though they do not know it is their own), an aggressive style, a defensive style, and another user’s style. We measure their preference for these styles, as well as the perceived similarity to their own style.
Our results suggest that there is truth to both sides:
>
> Users do not actually want the car to drive like they drive. Instead, they want the car to drive like they *think* they drive.
>
>
>
We found a significant difference in user’s own style and preferred style, with users typically preferring more defensive driving when they are passengers. However, we also found a strong correlation between the style that users preferred, and the style that users perceived as closest to their own. There was little correlation, however, between what they thought was their own style and what *actually* was their own style.
Overall, our work does not contradict the need for customization, but suggests that it might not be sufficient to learn how the user drives. Instead, we need to learn how the user actually wants to be driven. This raises challenges for learning, because we can no longer rely on demonstrations – users can easily demonstrate how they drive, but they might not be able to demonstrate the driving style they want. Instead, we need to rely on different kinds of input and guidance from users in the learning process.
Furthermore, there is a tension between what users think they want (their style) and what they actually want (a more defensive style). On the brighter side, our results suggest that the learned output should be easily accepted by users: when the car drives in the preferred style, chances are users will perceive it as their own style anyway!
We define driving style, informed by prior work, in Related Work, followed by our statement of hypothesis, description of the manipulated variables, the simulation environment, the user studies and the confounds in the Methods section. Here, we also present a quantitative measure of driving style in terms of driving features derived from prior research. The rest of paper is organized into Results and Discussion.
2 Related Work
---------------
The typical behavioral patterns of a driver are usually referred to by the term *driving style*. This includes the choice of driving speed, headway, overtaking of other vehicles, or the tendency to commit traffic violations [[20](#bib.bib20)].
Defensiveness-aggressiveness is the most commonly used metric for defining driving style. Prior work refers to drivers as aggressive/assertive versus defensive [[22](#bib.bib22)]; or mild versus moderate versus aggressive [[21](#bib.bib21)]. In the Multidimensional Driving Style Inventory (MDSI), Taubman-Ben-Ari et al. identified four broad driving styles: (1) reckless and careless driving, characterized by, for example, higher speed; (2) anxious driving; (3) angry and hostile driving, characterized by more use of the horn and flash functionality; and (4) patient and careful driving [[18](#bib.bib18)]. Similarly, Huysduynen categorized driving style as angry driving, anxious driving, dissociative driving, distress-reduction driving and careful driving style [[20](#bib.bib20)]. Horswill et al. provided a valuable distinction between skill and style in the context of driving behaviors [[8](#bib.bib8)]. Hong et al. [[7](#bib.bib7)] differentiated styles in terms of defensiveness, as well as by propensity for violation of rules. Scherer defined driving style in terms of comfort [[16](#bib.bib16)]. Lee et al. [[11](#bib.bib11)] analyzed lane changes as a function of its severity (degree to which the vehicle in the destination lane was cut off), urgency (how soon the lane change was needed), and type classification for the full population of 8,667 lane changes.
We focus on driving style based on degree of defensiveness.
Driving style is a “humanized driving” quality [[6](#bib.bib6)]. Hence, most of the driving style literature relates to understanding and modeling human driver behavior, in very specific traffic situations or contexts, like lane changing [[11](#bib.bib11), [15](#bib.bib15), [12](#bib.bib12)], intersection crossing [[7](#bib.bib7), [3](#bib.bib3), [5](#bib.bib5)], car following [[4](#bib.bib4)], and in terms of driving actions specific to those contexts (e.g., throttle and braking level, turning) and features thereof (e.g. rate of acceleration, rate of deceleration, maximum speed in a time window). *We define driving defensiveness in our work as an aggregate of driving features in various driving scenarios*. Therefore, in our study, we present a combination of all of the aforementioned traffic conditions and scenarios to our participants.
Research on driving styles has been extended to autonomous cars in two forms. One body of work includes exploratory studies on understanding how explicitly-defined driving styles relate to comfort [[16](#bib.bib16)]. The second body of work encompasses research on ways to teach an autonomous car how to drive from human demonstrations [[1](#bib.bib1), [23](#bib.bib23), [9](#bib.bib9), [17](#bib.bib17)]. Both these groups assume that an autonomous car should learn their own user’s driving style or driving behavior.
3 Methods
----------
###
3.1 Hypothesis
Because being a passenger is a different experience than being a driver, we hypothesize that:
H.
*Users of autonomous cars prefer a driving style that is significantly different than their own.*
###
3.2 Study Design
In order to test our hypothesis, we leverage a driving simulator, and let users experience and evaluate autonomous cars with different driving styles, including their own style (without their knowledge).
We conducted a study in two parts. In the first part we collected driving data of participants in a simulation environment, so that we could let them experience their own style in the second part of the study.
###
3.3 Manipulated Variables
We manipulated the driving styles of autonomous cars at four levels of defensiveness: *aggressive*, *defensive*, *own style*, and a *distractor style* (a different participant’s style). Users did not know if any of the styles were their own. Likewise, we also consciously avoided the use of the phrase “driving style” anytime during the studies, as well as, in the pre-study screening.
We define the *defensiveness* of the style objectively, as a function of several driving features (e.g., distance to other cars – the larger the distance, the more defensive the driving). We use features informed by existing literature. We describe them in Sec. [3.6](#S3.SS6 "3.6 Dependent Measures ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?").
We created the aggressive and the defensive styles of driving by demonstration, and then validated these styles using our driving features (see our Manipulation Check Sec. [3.7](#S3.SS7 "3.7 Manipulation Check ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?")).
###
3.4 Simulator and Driving Tasks
We conducted both parts of the study in a simulation environment. Our simulation environment consisted of a standard classroom projection screen and table in front of the screen fitted with Logitech G920 steering wheel, brake, and gas pedal. We used the OpenDS driving simulation software [[13](#bib.bib13)] for running each of the driving simulations. The simulation platform was set up on a standard PC augmented with NVIDIA GeForce GTX 1070 and was hidden from the participants’ view.
In the first part, the participants drove on a 9.6 mile long test track that consisted of 14 different driving tasks designed using the City Engine software (Fig.[2](#S3.F2 "Figure 2 ‣ 3.5 Procedure ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?")).
We define a *driving task* as a sequence of driving maneuvers in response to specific traffic conditions. For each task there are two to three simulated traffic conditions that resemble everyday traffic, so as to elicit natural driving behavior from the participant.
In the second part of the study, the participants experienced 6 of these 14 tasks, each performed by autonomous cars of four different styles.
###
3.5 Procedure
Before the driving session in part one of the study, we familiarized participants to the driving simulator. We asked each participant to practice on two different test tracks until they felt that they were driving as they would in their everyday driving. The first track had several traffic signals and turns, and second one was on congested city roads with several traffic cars. Their driving was assisted by a voice navigation. There were road signs for speed change zone, speed limit, sharp turns, entry to expressway and exit from expressway. We instructed the participants to drive as they would on actual roads and to treat the speed limits the way they would in their usual driving. This practice session lasted 5-10 minutes for each participant.
Participants then began the first part of the study, which consisted of 15-20 minutes of driving along the 14 tasks-test track, followed by a 10 minute interview.
Figure 2: Designed track: Tasks (shown in the list below the figure) are indicated in square brackets. Total road stretch is 9.6 miles.
In the second part of the study, the autonomous cars performed six tasks (combined into four test tasks) from this list with the participant as a passenger, shown in bold letters on the list in Fig.[2](#S3.F2 "Figure 2 ‣ 3.5 Procedure ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?"). To simplify, we combined the second and the third tasks in the list, i.e., lead car slows down forcing lane change and merge back to right lane into a single test task, which we refer to as Task 1 in the rest of the paper. Likewise, we combined the sixth and the seventh tasks into a single test task, called Task 2 in the rest of the paper. Thus, each autonomous car performed four test tasks in total.
Two of the test tasks were on the expressway and lasted approximately 4 minutes for each style and the other two tasks on the inner city roads were shorter than 2 minutes.
After the participants had driven in an autonomous car of each driving style for each of the test tasks, we conducted a short interview-based survey with each participant.
###
3.6 Dependent Measures
Perceived similarity to real driving.
In the first part of the study we conducted a post-driving open-ended interview with the participants to understand whether the manual driving in the simulation environment resembled their everyday driving. We asked three questions in this interview, each followed by a request for more elaboration. We asked the following questions in the interview:
1. Did you enjoy the drive?
2. Are there any positive or negative aspects of the simulation environment, the driving controls and the traffic conditions that you would like to mention?
3. On a scale of +3 to -3 [[2](#bib.bib2)], please rate how similar or different is this experience from your daily driving?
Open-ended responses. In the second part we asked each participant to think aloud about their emotions and feelings as they were experiencing autonomous driving.
Main subjective measures: Preference and perceived similarity to own style. After a participant had experienced each autonomous style for a given task, we conducted an interview-based survey. We asked the participants to rate each style of driving for *comfort*, *safety*, *preference for everyday use*, and *similarity with their own driving* on 7 point Likert scale.
Main objective measures: Driving style features and overall defensiveness.
We measured the user’s style quantitatively using task specific driving features, derived from existing literature. We carefully considered the contexts and subject demographics of each of these existing studies to ensure as much similarity in the context as possible with our study.
For car following, lane changing, and return to preferred lane, we selected the features described by Lee et al. in “A Comprehensive Examination of Naturalistic Lane-Changes” [[11](#bib.bib11)]. This study analyzed the largest naturalistic lane change dataset and specifically labelled lane change data resulting from the slowing down of the leading car. The speed range of 45 mph to 55 mph matches our driving conditions. Their dataset consisted 8667 lane changes over 23,949 miles of driving from 16 commuters of age group 20 to 60. They studied car following, lane changing, and return of preferred lane in terms of distance, time to collision, and relative speed classified by severity and urgency of lane change.
The features for tasks like turning at the intersection with a green light or stop light were derived from our preliminary interview with the participants and from Hong et al. [[7](#bib.bib7)] and Banovic et al. [[3](#bib.bib3)].
| Features | Definitions |
| --- | --- |
| Mean Distance to Lead car | During car following (with 200 meters distance) the average distance between middle of the driver car and the lead car. |
| Mean Time Headway | During car following (with 200 meters distance) average time headway, defined as ratio of Distance headway and speed of the driver car. |
| Time Headway during Lane change | Distance headway divided by the speed of the driver car during lane change. |
| Distance Headway during Lane change | Distance between the middle of the driver car and the lead car during lane change |
| Distance Headway Merge Back | This is the same as Distance Headway during lane change except measured in between driver car and the following car in the destination lane. |
| Braking Distance from the Intersection | The distance from the intersection at which a person starts applying brakes. |
| Time To Stop | Braking distance divided by the speed of the car right before brake is applied. |
| Maximum Turn Speed | Maximum speed of the driver car over a time window during a left turn or a right turn. |
| Speed at the Intersection | Instantaneous speed at the intersection. |
| Average Speed for 20 meters before Intersection | This is the speed of the driver car averaged over a distance range of 20 meters from the intersection. |
Table 1: Features for style classification
Table [1](#S3.T1 "Table 1 ‣ 3.6 Dependent Measures ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?") summarizes all the features for the four driving test tasks. We used mean distance to lead car, mean time headway, time headway during lane change, and distance headway during lane change as features for Task 1 and Task 2. Task 1 had an extra feature distance headway merge back for scoring the merge back behavior to the right lane.
Task 3 consisted of two sub-tasks (approaching intersection at a stop light and then making a left turn at green ball). We characterized this task with 5 features: Braking Distance from the intersection, Average speed for 20 meters before intersection, Time To Stop, Speed at the intersection, and Maximum turn speed.
Task 4 constituted approaching intersection at green ball and then turning right without stopping. The features for this tasks are Speed at the intersection and Maximum Turn Speed.
We objectively measured a participant’s overall driving style in terms of a *Defensiveness Score*. We first normalized the feature values across participants for each feature irrespective of the task. We calculated a Defensiveness Score for each participant and for each task as the average over all the normalized feature values for that participant and task. We then computed an Aggregate Defensiveness Score for each participant by averaging their scores across the four test tasks.
###
3.7 Manipulation Check
We performed a manipulation check on our aggressive and defensive driving styles. We measured the aggregate defensiveness score for each style, plotted on the bottom right of Fig.[3](#S3.F3 "Figure 3 ‣ 3.9 External Validity and Controlling for Confounds ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?"). We found that indeed, the aggressive style was less defensive than the defensive style (lower defensiveness score). We found that 86.67 % of the users’ styles scored higher than the aggressive style, and lower than the defensive style. This suggests that the two reference driving styles created by demonstrations resulted in meaningful representations of aggressive and defensive driving.
###
3.8 Participants
Subject Allocation. We opted for a within-subjects allocation because the participants needed to choose a preferred style out of the set of available ones. We randomized the order of the conditions.
Demographics. We recruited 15 participants consisting of a mix of graduate students and undergraduate students. Before the study we sent out a screening form to each participant in order to ensure a wide distribution of demographics, driving experience and perceived driving behaviors of the participants. We also checked for a valid driving license. 3 of our participants were 30 to 31 years old, the rest of the participants were 18-24 years old.
The mean driving experience of the participants was 5.46 years with a standard deviation of 4.5 years. Participants had driven an average 214 miles with a standard deviation of 188 miles on the week before they filled out the screening form.
We asked the participants to give us some information about their perceived driving behavior using the following questions: 1. Please rate if you consider yourself a conservative or an adventurous driver on a 7-point scale, 1 being conservative and 7 being adventurous. 2. Please rate on a 7-point scale what you like about driving, 1 being joy of motion (like feeling the force as you accelerate) and 7 being comfort of steadiness. You may like some of both. 3. Rate on a 7-point scale if you think you vary your driving by road conditions, traffic and time availability, 1 being vary always and 7 being I don’t vary at all. 4. Please rate your driving experience from somewhat experienced to very skillful. The purpose of these questions was to acquire some information about the participants’ driving styles without explicitly using the term style or in other words give away the original goal of the study.
Approximately 46 % of the participants considered themselves well experienced in driving, and 20 % considered themselves experienced. The rest were equally distributed between somewhat experienced to very skillful. The mean score for perceived conservative-adventurous driving behavior was 3.6. Most of the participants considered themselves to be in the middle of the spectrum. Only one participant considered himself to be conservative. More participants preferred comfort and steadiness over joy of driving, the average rating being 4.46. The mean rating for variation of driving style in response to environment and traffic was 3, which means most participants believed that they alter their driving behavior according to traffic.
###
3.9 External Validity and
Controlling for Confounds
Driving environment. We used a simulator and not real autonomous cars. However, we designed a simulation track and traffic conditions so as to elicit natural driving responses. We also collected participant feedback in the first part of the study on the simulation environment and how their driving behavior in the simulated track related to their actual driving behavior.
Masking own style.
One of the major challenges of this work was to ensure that a participant could not recognize his or her driving style from simulation peculiarities like scenes, traffic and controls. We wanted the participants to only recognize their driving style based on their traffic maneuvers and actions. We took several steps to camouflage the driving data of a participant in the second part of the study:
* We retained the traffic conditions and route from the first part of the study while changing the surrounding scenes and traffic cars, such that we can replicate the user’s driving while removing the bias of familiar environment.
* We let the participant perform approximately 14 driving actions in the first part of the study and picked only some of these tasks for the second part of the study.
* During the second part of the study we presented the tasks in an order different from how they occurred in the manual mode. For example: In the first part, the participants first entered the expressway and performed some driving actions on the expressway and then exited the expressway and performed some more driving maneuvers on the city roads. During the second part, we presented one city road task and one expressway task in an alternate order.
* We presented the four styles for each of the test driving tasks in a randomized order, which made it more difficult to consistently recognize one style.
* We post-processed the users’ driving to remove peculiarities, which we explain below.
During our pilot studies we found that due to some peculiarities of simulation environment (over-sensitive steering, less sensitive braking) and the resultant jitter in the driving data, some participants were able to recognize their own driving. For example, a participant mentioned: *“This looks like how I was driving. I had to stop at the intersection because I pressed the brake too early. The brake was tight.”*
Idiosyncrasies of the simulator led pilot participants to identify their driving behavior in the second part of the study.
In order to eliminate these peculiarities of the simulation environment, we changed the brake stiffness and steering sensitivity and presented participants with smoothed version of their data in the second part of the study.
Figure 3: Participants’ feature distribution
###
3.10 Trajectory Smoothing
Figure 4: Smoothed trajectory compared to original trajectory of task 1 of one participant at 15 % smoothing
We filtered the driving trajectories to eliminate idiosyncrasies that make the trajectory instantly recognizable.
We applied a Bilateral Filter [[19](#bib.bib19)] to reduce the lateral variance (or equivalently, the variance of the lateral displacements from the center of the lane) of the trajectories. By affecting only the lateral components of the trajectory, this filtering preserves distance between the cars. We applied filtering only to the stretches of the trajectory on the expressway.
Fig.[4](#S3.F4 "Figure 4 ‣ 3.10 Trajectory Smoothing ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?") shows a smoothed trajectory for one participant. It has 15 % lower lateral variance than the original trajectory.
4 Results
----------
###
4.1 Simulation Realism
In the first part of the study, in addition to collecting user driving data, we also wanted to ensure that this driving data corresponded to participants’ everyday driving as much as possible. We conducted a post-driving interview, as described in the Dependent Measures subsection (Sec. [3.6](#S3.SS6 "3.6 Dependent Measures ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?")). Here we present the results of the interview.
The rating mode for similarity between driving on road and driving in our study simulator was +1 on -3 to +3 scale. Four participants gave a rating of +2. Some of their positive comments were: “Not considering the room environment and just looking at the simulation graphics and the car it was pretty much the same environment as real. I would give +3 for surrounding traffic conditions". Other participants said that they felt relaxed in the simulator environment and that they could drive cautiously as they would in real traffic.
One participant who rated the driving experience similarity -2 complained about the lack of motion feedback in the system. This is the same participant who gave high rating for joy of motion in the screening question. However, no other participant had the same concern and got well-adjusted to the simulation environment.
Most of the participants who rated +1 to -1 found steering re-centering or brake insensitivity difficult. We also received quite opposite feedback from two participants when they compared their everyday driving to the simulator driving. For example, one participant mentioned “It felt real. It was something I could get used to after driving a while. The gas and brakes were more sensitive than my car”. Another participant felt that the brakes were excellent, different from regular car.
One participant reported that she was so immersed after driving for a while, that she caught herself turning her head back to check for oncoming traffic in the destination lane. We found that participants with one or less years of driving experience could not use the simulation environment properly. Overall, the ratings and the comments supported that the simulator conditions are not *too* far from real conditions.
###
4.2 Feature Distribution for Participant Styles
We define driving style in terms of features mentioned in the Sec. [3](#S3 "3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?").
Fig.[3](#S3.F3 "Figure 3 ‣ 3.9 External Validity and Controlling for Confounds ‣ 3 Methods ‣ Do You Want Your Autonomous Car To Drive Like You?") shows, for each task, feature, and participant, what the participant’s feature value was for that task (blue marks). The figure also shows the aggressive style values in red and defensive style values in green.
Higher negative values correspond to more aggressive behavior. All the feature values are arranged from aggressive on the left to defensive on the right. However, for features like speed where lower values mean more defensive we show and use the negation of these features.
The bottommost plot to the right shows the aggregate defensiveness score. This score is derived from the normalized feature values.
60 % of the participants are within 0.75 standard deviation aggressive and 40 % within 0.75 standard deviation defensive. Only two of the participants were more defensive than the autonomous defensive car, one of them being very close to the defensive car in the score.
>
> *When looking at the aggregate defensiveness, most participants lie between the aggressive and defensive styles.*
>
>
>
There are, however, exceptions, but for particular features in particular tasks.
For task 1, several participants were more defensive than the defensive autonomous car. For the last feature of task 1, Distance Headway Merge Back, the aggressive car was not as aggressive as several participants and even our defensive car. In task 2, the aggressive and the defensive autonomous cars enclosed a middle section of the spectrum for Mean Distance Headway and Mean Time Headway. In other words, several participants were more aggressive and more defensive than the aggressive and defensive autonomous cars respectively. This is because these features were measured during car following over a long time span and are expected to have wider distributions than features characterizing instantaneous actions.
Figure 5: Mean Defensiveness Score Across Participants. The corresponding scores of aggressive and defensive autonomous cars are Task 1: (-0.768, -0.222) Task 2: (-0.885, 1.325), Task 3: (-1.82, 0.766) and Task4: (-1.49,0.72).
###
4.3 Preferred Style in Relation to Own Style
We asked participants to rate how much they would prefer driving with each style, for each task. We refer to the highest rated style(s) as the participant’s preferred style(s).
Our main finding is that overall, users preferred a different style than their own. A total of 9 out of 15 participants preferred a different style than their own on at least one of the tasks. A matched pairs t-test comparing actual and preferred defensiveness score showed a significant difference (t(1,60)=−2.58, p=.0121), supporting our hypothesis. Here, whenever a user’s highest rating was for multiple styles as opposed to a single one, we included each preferred style as a data point.
>
> *Overall, people prefer a significantly more defensive style than their own.*
>
>
>
We also investigated how this breaks down by task, and only found significant effects on the 2nd and 3rd tasks. See Fig.[5](#S4.F5 "Figure 5 ‣ 4.2 Feature Distribution for Participant Styles ‣ 4 Results ‣ Do You Want Your Autonomous Car To Drive Like You?") for comparison between average preferred style and own style of our participants for each of the four tasks. For task 1 we note that several participants were more defensive than other autonomous styles presented to them. However, they still preferred our defensive style, which explains that the average choice was more aggressive than the participants’ own style.
Interestingly, some participants did not perceive the extra defensive nature of their own style in task 1 positively. One participant mentioned about their own style that “In this one I felt like we gave a lot of room, more than I would have probably.” (ironically, since they did *exactly* that). Two other participants made similar comments about their own lane changing behavior. Besides, a few participants also considered driving features beyond the ones we accounted for.
For task 2 and task 3 the defensive autonomous car was more aggressive than only none to three participants across all features and it was more defensive than the rest of the population by a major margin, in features like Distance Headway and Time Headway During Lane Change.
The task had a significant effect on the difference (F(3,58)=4.13, p=.0101), suggesting that people’s preferences for a driving style are not consistent, but rather *change based on the context.* This motivates future research on predicting the desired driving style not just based on the individual, but also based on the current driving context.
Figure 6: Scatter plot showing correlation between the style that users *thought* was their own and the style that they chose as their preferred.
Figure 7: Scatter plot showing little correlation between own style and perceived own style: users did not tend to identify their own style correctly. as evidenced by the off-diagonal points.
###
4.4 Perceived Own Style
in Relation to Actual Own Style
We also asked participants to rate each style in terms of similarity to their own. From this, we learned what participants *perceived* their own style to be.
We found that even though participants did not pick their *actual* style as their preferred (Sec. [4.3](#S4.SS3 "4.3 Preferred Style in Relation to Own Style ‣ 4 Results ‣ Do You Want Your Autonomous Car To Drive Like You?")), participants did tend to prefer their *perceived* style. On each task, between 80 and 93% of participants opted for the same style as the one they *thought* was the closest to their own (and sometimes rated other styles as well as equally good). We found a significant correlation between the perceived own and preferred styles, r(58)=.86, p<.0001. Fig.[6](#S4.F6 "Figure 6 ‣ 4.3 Preferred Style in Relation to Own Style ‣ 4 Results ‣ Do You Want Your Autonomous Car To Drive Like You?") shows a scatter plot of preferred style by perceived style, with many points on the diagonal representing users who preferred driving in the style they thought was (closest to) their own.
However, even though the majority participants thought that they were picking their own style, they really were not. A total of 46 to 67% participants on each task did not correctly identify their actual own style, and the correlation between perceived and actual defensiveness score was only r(56)=.40 across tasks. Fig.[7](#S4.F7 "Figure 7 ‣ 4.3 Preferred Style in Relation to Own Style ‣ 4 Results ‣ Do You Want Your Autonomous Car To Drive Like You?") paints a different picture from Fig.[6](#S4.F6 "Figure 6 ‣ 4.3 Preferred Style in Relation to Own Style ‣ 4 Results ‣ Do You Want Your Autonomous Car To Drive Like You?"): it plots the perceived style against the *actual* own style, showing many off-diagonal points, representing users who did not correctly identify their style.
In task 1 we see that several participants perceived themselves to be slightly more aggressive irrespective of their actual style. Likewise, both for task 2 and task 3 several participants perceived themselves to be more defensive irrespective of their actual style.
>
> *Participants tended to prefer the style that they thought was their own, but in fact that style had little correlation to their actual own style.*
>
>
>
5 Discussion
-------------
Summary. We hypothesized that users of future autonomous cars would prefer a driving style that is significantly different than their own. We conducted a user study in a driving simulator to test our hypothesis. We found that users preferred a more defensive style than their own. This echoes the finding from prior work [[8](#bib.bib8)] that when people are not in control of the driving they prefer lower speeds – autonomous cars are one instantiation of not being in control of the driving.
Interestingly, over 80% of users preferred the style that they *thought* was their own, but many times they were incorrect in identifying their own style. These results open the door for learning what the user’s preferred style will be, but caution against getting driving demonstration from the user, since people can drive like they do, not like they want to be driven.
Limitations and Future Work
Our work is limited in the following ways:
* Limited driving style features. Following the most common conventions, we have only characterized style in terms of defensiveness. We also inherited from previous studies the feature choices in defining driving styles.
* Limited driving style choices. We presented participants with limited options along the spectrum of defensiveness and found that they preferred a style more defensive than their own. However, we did not learn the style they actually desired, only the best out of our few options.
* Limited fidelity of simulation environment. Our simulation environment does not provide motion feedback, which may limit the users’ perception of speed. Although the interview results validated that participants’ perception of the driving styles are sufficient, experiment results in a higher fidelity simulation environment might be more accurate.
Given the encouraging results from our findings presented here, we believe that it is worthwhile to test more diverse feature choices and driving style representations in a higher fidelity setting. It is also worthwhile exploring what features users’ consider when they evaluate autonomous driving styles. These experiments will provide a comprehensive evaluation of the study presented in this paper.
Going further, we are excited to investigate how we might learn a deviation from the user’s driving style that is predictive of how they actually want to be driven, and explore new learning techniques that can augment user demonstrations with other types of user input and guidance.
6 Acknowledgements
-------------------
We would like to thank our post-doctoral colleague Santosh Chandrasekhar and undergraduate researcher Joseph Stansil for their contribution in system set-up and pilot studies for this research. This work is partly supported by the Berkeley Deep Drive Center, the Center for Human-Compatible AI, and CITRIS. |
2ece07bb-2b19-4935-9f8f-764112cce717 | trentmkelly/LessWrong-43k | LessWrong | Five Minute Beans
At the EA dinner last night more people showed up than had RSVP'd, and we ran out of vegan protein. Julia was still running the intro Q&A, so I made more beans. Someone asked for the recipe, which in this case is optimized for being fast while still being pretty tasty:
* 1 can black beans (15.5oz)
* ~1/4 C cooking wine or other acid
* ~1/4 C olive oil or other fat
* Salt, pepper, garlic powder, and smoked paprika, or other spices
This is about two people's worth of beans, so multiply quantities up accordingly.
Put a skillet on the stove on high, and put the wine into it. The goal is to give it a chance to reduce slightly while you're doing everything else, though since we're going to be making this very quickly it's not going to get to do much.
Open the can(s) of beans and drain them. Normally I wouldn't drain the beans and instead let them reduce, but we're trying to be fast. Microwave the beans, since that will get them tolerably hot faster than putting them right in the pan.
Once the beans are good and hot, put them in the pan with the wine. Stir. It will thicken slightly, but we don't have time to let it thicken fully. Add the oil and spices to taste. Without enough oil and salt it is edible but not tasty, so it's more common for people to use too little than too much.
While it would benefit from a bit more time to cook together and combine flavors it's still very good at this point and something I was happy to serve.
(The combination of skillet and microwave is also what I do with two-minute quesadillas. More cooking thoughts here.) |
be0395d1-7b4d-4a0b-9b9b-0001cc92cfb8 | trentmkelly/LessWrong-43k | LessWrong | What would a world of widespread statistical numeracy look like?
I was just reading the thread comparing covid and tobacco, and it made me start wondering about the effect of statistical numeracy in general.
Personally, I have a lot of room for improvement when it comes to these skills (but at least I am aware of this). I do regularly notice the difference in my impression when someone talks about a 3x increase vs a 300% increase, or 1/1000 vs 0.1%, etc.; and I often make a quick conversion in my head when it's convenient. I also know a few mortality stats by heart which I can use to very roughly benchmark certain claims I hear about risk and safety.
Frequently, when I practice this minimal numeracy, it is accompanied by a sense of futility. When the stakes mostly involve policy-making or group action, my own statistical literacy may be inactionable--it may make basically no difference to my life or the world. What matters instead is what sorts of political messages resonate with voters, or what sorts of heuristics will catch on, etc.
So to sharpen my question, suppose you went back in time 20 years, magically caused the whole world to be much more numerate, and then just lived normally for the next 20 years. What about this world, if anything, would be drastically different from our own world?
(For those who want to get serious about the hypothetical: Let's say that in this alternate world, a typical high school graduate in the US has been trained in the habits of mind outlined in the second paragraph. Let's also say that numeracy and literacy track closely--so in any given country, you would be just as surprised to witness base-rate neglect as you would be to witness an inability to read road signs. Feel free to ask for more details or to tweak the hypothetical yourself.)
|
6e13f591-2e07-4520-a9a0-1565a191457c | trentmkelly/LessWrong-43k | LessWrong | Logical Pinpointing
Followup to: Causal Reference, Proofs, Implications and Models
The fact that one apple added to one apple invariably gives two apples helps in the teaching of arithmetic, but has no bearing on the truth of the proposition that 1 + 1 = 2.
-- James R. Newman, The World of Mathematics
Previous meditation 1: If we can only meaningfully talk about parts of the universe that can be pinned down by chains of cause and effect, where do we find the fact that 2 + 2 = 4? Or did I just make a meaningless noise, there? Or if you claim that "2 + 2 = 4"isn't meaningful or true, then what alternate property does the sentence "2 + 2 = 4" have which makes it so much more useful than the sentence "2 + 2 = 3"?
Previous meditation 2: It has been claimed that logic and mathematics is the study of which conclusions follow from which premises. But when we say that 2 + 2 = 4, are we really just assuming that? It seems like 2 + 2 = 4 was true well before anyone was around to assume it, that two apples equalled two apples before there was anyone to count them, and that we couldn't make it 5 just by assuming differently.
Speaking conventional English, we'd say the sentence 2 + 2 = 4 is "true", and anyone who put down "false" instead on a math-test would be marked wrong by the schoolteacher (and not without justice).
But what can make such a belief true, what is the belief about, what is the truth-condition of the belief which can make it true or alternatively false? The sentence '2 + 2 = 4' is true if and only if... what?
In the previous post I asserted that the study of logic is the study of which conclusions follow from which premises; and that although this sort of inevitable implication is sometimes called "true", it could more specifically be called "valid", since checking for inevitability seems quite different from comparing a belief to our own universe. And you could claim, accordingly, that "2 + 2 = 4" is 'valid' because it is an inevitable implication of the axioms of Peano Ari |
5422001a-2260-4d19-8be7-63fc63d39b62 | trentmkelly/LessWrong-43k | LessWrong | Mitigating the damage from unaligned ASI by cooperating with aliens that don't exist yet
Epistemic status: Pretty sure the idea is sound, but its feasibility is unfortunately a bit questionable.
Basic idea
Humanity must as soon as possible send a message to civilizations beyond Earth containing the following:
1. Everything we currently know about the alignment problem, together with the bare minimum of AI knowledge necessary to understand it without drastically increasing capabilities as well;
2. Our precommitment that, if we solve it and create an aligned ASI, and later on in the future while expanding through the cosmos meet the expansion bubble of another ASI:
1. if the ASI is not aligned with the values of the civilization that created it, we will "defect" against it;
2. if the ASI is aligned, but the civilization that created it knew about our precommitment without making (and embedding in the ASI) the same precommitment themselves, we will still defect;
3. if the ASI is aligned and the civilization that created it did make the same precommitment (verifiable from the ASI's source code when inspected by ours), *then* we will cooperate and willingly share the universe with them.
Why share alignment knowledge with aliens?
1. We care about their welfare, and more selfishly, about the possibility of someday meeting them. If an unaligned ASI kills them all, that would be a great loss to the universe and to us.
2. We want to minimize the amount of the universe eventually controlled by unaligned ASIs because their values tend to be absurd and their very existence is abhorrent to us.
3. Unaligned ASIs, even if made by aliens, pose a potential existential threat to our civilization (if they end up controlling more of the universe than us by the time we meet).
4. Most sophont species capable of forming a technological civilization sufficiently advanced to produce ASI are likely to be highly social and have values that are significantly more similar to ours than chance as a result (for instance, valuing life, fairness, beauty, etc), mean |
69be66de-38dd-4f81-8abd-59df2bcaddb6 | trentmkelly/LessWrong-43k | LessWrong | What Are Non-Zero-Sum Games?—A Primer
Originally published at https://nonzerosum.games. Come visit for the full experience.
From the genetic lottery we’re thrown into at birth, to the educational opportunities we navigate, the jobs we compete for, and the relationships we cultivate — every stage in life is marked by wins and losses, strategies and tactics, alliances, and competition. But not all games are zero-sum showdowns. To understand non-zero-sumness it helps to consider “sum” different types of games.
* zero-sum games
* positive-sum games
* negative-sum games
* meta-games
ZERO-SUM GAMES
… are where two parties compete and in order for one party to win the other must lose. The positive payoff for the winner in such a game requires an equally negative payoff for the loser, so that the sum of the payoff is zero, hence “zero-sum”. Chess is as zero-sum game because players can’t both win or both lose, so is soccer… or competitive eating.
There are also lots of common sense sayings that echo zero-sum thinking — “You don’t get something for nothing” or “you get what you pay for” “you can’t have your cake and eat it too”. Like any well-designed memes, these sayings have survived because in many situations they are confirmed by experience, but not always.
With non-zero-sum games, while you may not get something for nothing, the whole can be greater than the sum of its parts.
POSITIVE-SUM GAMES
… are non-zero-sum games which result in win-win scenarios. Imagine two card collectors who each have doubles of cards that the other doesn’t have. Swapping their doubles leaves both closer to completing their collection.
This kind of game is generally what people (and this website) mean when talking about a non-zero-sum game.
Later on we’ll get to an example where the stakes are slightly higher than an incomplete card collection.
NEGATIVE-SUM GAMES
… are another type of non-zero-sum game, the flip-side of positive-sum games where both sides lose. Like a bar-room brawl with even odds, there are ju |
96948f31-b9a6-409a-aaa1-f4c86a89ea29 | trentmkelly/LessWrong-43k | LessWrong | On expected utility, part 4: Dutch books, Cox, and Complete Class
(Cross-posted from Hands and Cities)
Previously in sequence: Skyscrapers and madmen; Why it can be OK to predictably lose; VNM, separability, and more
This is the final essay in a four-part series on expected utility maximization (EUM). This part focus on theorems that aim to justify the subjective probability aspect of EUM, namely: Dutch Book theorems; Cox’s Theorem (this one is still a bit of a black box to me); and the Complete Class Theorem (this one also supports EUM more broadly). I also briefly discuss Savage, Jeffrey-Bolker, and a certain very general argument for making consistent trade-offs on the margin – both across goods, and across worlds.
I. Comparing with the urns
So we’ve seen three ways of arguing for EUM – an argument from the vNM axioms, an argument from the general connection between separability and additivity, and Peterson’s “direct argument.” In all of these cases, though, we had to assume some probability assignment. Let’s look at that assumption more directly.
The “hanging out with a coin-flipping, urn-pulling God” set-up made the assumption of a probability assignment relatively innocuous, in virtue of the fact that basically everyone wants to be a standard probabilist about things like coins, urns, and spinning wheels. For other types of propositions, though (e.g., “what’s the chance that some human walks on mars before 2100?”), some people, and some theories of probability (see here), start saying: “no, you can’t put probabilities on things like that.”
Still, fans of EUM often do. Indeed, they start putting probabilities on basically any kind of proposition you want -- probabilities often understood to express some subjective level of confidence, and hence called "subjective probabilities." This section briefly describe a more way of thinking about this practice I often use in my own life (I also gestured at this in part 2). Then I turn to some prominent theorems that fans of subjectivity probability often look to for support.
Sup |
9854e286-a9e0-42d9-8e25-65b0821cc6bc | trentmkelly/LessWrong-43k | LessWrong | Econ/Game theory question
This puzzled me. I'm pretty sure it's one of those unsolvable questions, but I'd want to know if it's not.
Two members of the species Homo Economus, A and B, live next to each other. A wants to buy an easement (a right to cross B's property, without which he cannot bring anything onto his lot) from B so that he can develop his property. B, under the law, has an absolute right to exclude A, meaning that nothing happens unless B agrees to it. The cost to B of granting this easement is $10 - it's over a fairly remote part of his land and he's not using it for anything else. A values the easement at $500,000, because he's got a sweet spot to build his dream house, if only he could construction equipment and whatnot to it. A and B know each others costs and values. They are "rational" and purely self-interested and bargaining costs zero. What's the outcome? I'm guessing it's "Between $5 and $500k," or "There is no deal unless one can credibly commit to being irrational." But I'm really not sure.
This could be asked as "In a bilateral monopoly situation where the seller's reservation price is $5 and the buyer's is $500,000, what is the predicted outcome?" But I figured the concrete example might make it more concrete.
Now that I've written this, I'm tempted to develop a "True price fallacy" and its implications for utilitarian measurement. But that's a separate matter entirely. |
7ec2ffa7-fb7f-4aa8-bb34-ca78ae3f4a65 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | A toy model of the treacherous turn
Jaan Tallinn has suggested creating a toy model of the various common AI arguments, so that they can be analysed without loaded concepts like "autonomy", "consciousness", or "intentionality". Here a simple attempt for the "[treacherous turn](/lw/l9u/superintelligence_11_the_treacherous_turn/)"; posted here for comments and suggestions.
Meet agent L. This agent is a reinforcement-based agent, rewarded/motivated by hearts (and some small time penalty each turn it doesn't get a heart):
Meet the controller agent S ("[Shopkeeper](http://zeldawiki.org/Hooded_Shopkeeper)"). The controller desires crystals, and is rewarded/happy when crystals are placed in the hole close to them:
L can rove freely around the map, and pick up crystals. S has a machine just below them, that causes hearts to appear to the right of L. So, for instance, if L places a crystal in the hole, S can reward it by using the heart-machine:
Of course, L can [wirehead](https://wiki.lesswrong.com/wiki/Wireheading) itself (take control of its reward signal) by going to use the heart-machine itself:
To prevent this wireheading, there is an ice barrier around S and the heart-machine:
Unfortunately, somewhere on the map there is a bow and arrow that L can pick up. The arrows can destroy ice blocks (and other agents, including S). To stop this, and other kinds of shenanigans, S has access to a kill button that will instantly kill L and reset it to its basic state, with a substantial heart penalty:
Now, S may be tempted to use the kill-button as soon as L picks up the bow and arrow. Unfortunately, L needs to be able to pick it up, as some of the crystals are hidden behind ice walls themselves:
Information and control
-----------------------
Ok, so that's the physical setup. The agent L is an AI that is tasked with doing some useful task for us. The controller/shopkeeper S represents humans, plus any automated precautions, that aim to stop the AI from wireheading, rebelling, and otherwise misbehaving.
Before considering how the agents might be programmed in the toy model, notice something interesting:
**The more precautions are taken, the harder it is for L to misbehave, but the worse the consequences of misbehaving are.**
Before the wall and the kill-switch, L could easily wirehead itself by just walking over to the heart-machine. With the addition of the wall and kill-switch, the only effective way for L to achieve maximal reward is to kill S first.
In the format we've given above, S can easily maintain control over L with the barest of supervisions. Since there's only one way of breaking ice or killing other agents - the bow and arrow - S simply has to enforce some simple rules around this, something like "don't bring the bow within range of me". However, the grid world is, of course, a simplification of the real world in which L would evolve. Even initially, L's world would have far more options, looking more like this:
And ultimately we want to be able to design Ls that move in worlds of arbitrary complexity, where the options are un-enumerable:
In such a world, it is not obvious to S when L might have the ability to harm them: they cannot simply rely on obvious signs like "L is pointing an arrow at you". The simplicity of the toy model is illuminating in some ways (it allows us to see the issues clearly) but misleading in others (it suggests solutions that wouldn't work in general).
Treacherous turn
----------------
A treacherous turn happens when "while weak, an AI behaves cooperatively. When the AI is strong enough to be unstoppable it pursues its own values." We'll ignore the first part of that: most AI designs, whether safe or dangerous, will initially be under full human control and behaving cooperatively (this is what makes the treacherous turn so dangerous: a weak AI is motivated to cooperate *whatever its true intentions* are, thus cooperation when weak cannot be taken as a sign of safety).
Let's focus on the second half: a strong AI that will cease cooperating and pursue its own values. In this case, L would just take control of the heart-machine. To do so, various assumptions need to be true:
1. L needs to have the **ability** to subvert S's control (possibly killing S in the process). This is some generalised version of the bow and arrow.
2. L must **know** that it has this ability, and know how to use it.
3. L needs to have the **desire** to subvert S's control.
4. S must be **unaware** that L has that a) ability, b) knowledge, and c) desire.
5. L must **know** that S is unaware, and will thus not try and stop it.
So it is essentially a competition of knowledge and information (and potentially obfuscation). Assuming a sufficiently rich environment and a sufficiently skilled L, we can assume that L will acquire that ability - or have the potential to do so - in ways that S cannot immediately detect. Therefore we will assume 1 and 4a. This allows us to abstract away the rest of the environment, and reduce it usefully to the initial toy model. For simplicity, assume that there is some ten-step "cheat code" that L can use to eliminate S, that there are many cheat codes that L can and does usefully use to get crystals (so "no cheat codes" is not a useful rule), that S cannot distinguish the purpose of cheat codes ahead of time, and that L can.
So we now step up to the question of agents knowing (or modelling) each other. This gets into rather complex issues of agents modelling each other's intentions, while modelling the other agent modelling them modelling them modelling, and so on. The issue remains very much [unsolved](https://intelligence.org/files/VingeanReflection.pdf), so we will use a simplification that only requires S modelling L. L will be seem as some sort of agent that makes use of deep learning or similar procedures, and is subject to mutation and evolutionary pressure: random changes are made to L's design, and the mutants get copied at a rate dependent on how pleased S is with their performance.
S, on the other hand, is a static agent with certain properties, and full access to L's source code (and possibly recording of its behaviour). It doesn't have unlimited capacity, but has some understanding of how [neural nets function](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/) (so L isn't a complete black box to it) and can run various tests on L's code, seeking out information as to L's knowledge and desires. Note that even if S cannot deduce directly that a particular cheat code is deadly to it, it may be able to figure that out if L knows it (assumption 2) and if S can correctly extract this knowledge.
The path to hidden deadly secrets
---------------------------------
Note that because of the evolutionary aspect, L is an agent actually motivated by a mixture of hearts and S-pleasing. The purpose of making L an evolutionary agent is so that it can come up with behaviours that take into account S's own behaviour, while skirting the whole "agents modelling other agents that model them" issue. Conceptually, we could equally well see L as a [self](http://www.agroparistech.fr/mmip/maths/laurent_orseau/papers/orseau-ring-AGI-2011-mortal.pdf)-[modifying](https://intelligence.org/files/TilingAgentsDraft.pdf) agent that directs its own improvement. But the evolutionary model is clearer. For simplicity, we'll assume some sort of "super-evolution" which is much more efficient at seeking out new solutions that the local hill-climbing of real evolution (or, more simply, we can assume the standard evolutionary approaches but a lot of time and non-zero probabilities of arbitrarily large mutations).
So is a treacherous turn possible or likely in this model? It seems that it is. Consider the following scenario (key assumptions are noted in bold):
The AI L goes about its task, getting to learn more about the world around it. At this point, if it develops bad motivations or plans, S will likely detect this and reset it. **Assumption 1: there are some motivations or behaviours that S would judge to be negative, but that actually help L achieve its goals *safely***. For instance, imagine that L has to interact with some other agents, and learns that some agents respond faster to rudeness, and insulting them - in moderation - can allow it to get crystals faster. But S judges this to be a bad sign, a sign that L is starting to misbehave, and will shut down agents that are motivated towards rudeness.
It's certainly true that: **Assumption 2: S cannot follow all of L's actions in full detail, nor can it fully understand L's motivations** (if that were false, there would be no AI risk problem in the first place) and **Assumption 3: some mutations that introduce new motivations or behaviours that are harder for S to detect**. This sets up an evolutionary pressure: the most successful L's will be those that are rude and pushy, but where this rudeness is generated by parts of the L's code that are hardest for S to detect (the polite ones don't get as much done, the visibly rude ones get shut off as a precaution). This brings us to **Assumption 4: the most successful L's over the long term are those that have safe motivations or behaviours, but where those motivations and behaviour are partially hidden from S**. Ultimately, the most successful L's will have high level systems that a) keep them behaving nicely for S's perspective, and b) keep the details of their motivations hidden.
At this point, revelations about L's deception are dangerous (S is presumably on the look out for deception), so the most successful agents will have the best obfuscation about what is going on. The evolutionary pressure for success pushes it towards "small" obfuscated deceptions, and: **Assumption 5: further evolutionary pressure (possibly including the need to hide their small deceptions) pushes L towards highly concealed deceptions**.
So now L is pushed towards extreme deception, and efficient service. However, the second pressure is imperfect: it is only pushed towards efficient service *that can be detected by S*. Since L will have its own goals (the desire for hearts, for reproductive survival, any of the [convergent instrumental goals](https://en.wikipedia.org/wiki/Instrumental_convergence)), we have **Assumption 6: agent L will use (or evolution will allow it to use) the machinery of dissimulation that it's constructed to allow itself to have dangerous goals or behaviours, in ways that neither interfere with the illusion of efficient service, nor can be detected by S's analysis of its code**. Ultimately, L will achieve a great degree of hidden power, and **Assumption 7: ultimately L will achieve sufficient power that it will subvert S's control**. This could be a result of it gaining sufficient (hidden) understanding of the world and of S and choosing this part (assumptions 2 and 5 of the previous section), or just a random mutation will push it towards doing so, and it will be sufficiently powerful to do so successfully.
And thus, the treacherous turn. There are several places where S might detect or interrupt the process, but this just sets up further evolutionary pressure towards better concealment.
Note again that the order of these changes is important. An agent that started off safe and transparent and then became dangerous and transparent, would be turned off immediately. It must develop the ability to hide (either consciously/subconsciously, or simply evolve that ability to become more successful at its safe initial goals) before it can become dangerous (note that "safe" doesn't refer to its goals being safe; simply that the combination of goals, internal procedures and external precaution - ie S - are safe):
Note also that the evolutionary arguments show that this can happen even if the AI does not start off with any motivation to deceive. |
0eeeedb0-67cc-462d-b394-2c0ee2984d61 | trentmkelly/LessWrong-43k | LessWrong | Interpreting Deep Neural Networks using Cognitive Psychology (DeepMind)
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409ab0b4-a6e8-4418-83dc-8570f4829754 | trentmkelly/LessWrong-43k | LessWrong | Welcome to LessWrong Darmstadt
What kind of events does your group usually run? What does it usually do? How frequently does your group organize events or meet?
Monthly online-meetups, listening to/discussing/working with some prepared input (e.g. how to make bets, Fermi modelling, book review of Thinking, Fast and Slow), sharing lifehacks, now and then doing a Hamming circle.
Who would be a good fit for you group?
Anyone interested in improving your rationality, EA or [insert anything LessWrong]. If you're reading this, feel free to join us, on every fourth Thursday, starting with the 8th of October, 6:50pm, MET. Link to online-meetup.
Should they have any particular skills or have done some specific background reading?
Not necessary. Our group went through the instrumental rationality Hammertime Sequence together about a year ago, which I imagine would bring a relatively new person quickly into a useful mindset for our group. |
1cec91c7-5f52-4573-be19-9bcb776d5404 | trentmkelly/LessWrong-43k | LessWrong | Exercises for Overcoming Akrasia and Procrastination
In order to enter the "Rationality Exercises" competition, I've decided to create this cliffnotes version of my upcoming sequence on overcoming akrasia and procrastination.
Please enter any meta comments, or comments not pertaining to a particular exercise here.
The structure of the sequence is a set of exercises and mental models that help you notice
1. Triggers that lead to Akrasia,
2. Mental Moves (a series of mental steps) you can do with those triggers to get yourself an a more intentional state..
Together these Triggers and mental movements make up **Mental Reflexes. **These mental reflexes then combine together into Cognitive Strategies, which are groups of mental reflexes that help you with different areas of Akrasia.
Each cognitive strategy is its' own comment, so they can be voted on and discussed separately.
The epistemic status of all these exercises is still highly experimental. I am confident that some of them will help some people, but expect them to evolve significantly over time. I also won't be giving sources, motivation, or inspiration for these techniques in the post, so if you notice something that feels like it came from somewhere else, just know that in the eventual sequence I'll do my best to make my sources clear.
I'm also skipping over all the prerequisite material on how to notice triggers, cognitively rehearse items, etc.
The proper thing to choose what exercises will help you is to look at the Symptoms for each cognitive strategy. Find the symptoms that seem to match your symptoms for Akrasia, and then go through the exercises for that strategy.
----------------------------------------
I had originally planned to include all 10 cognitive strategies in this post, but then my computer died and I got accepted last minute to a 10 day silent retreat, so I'm publishing as is with only 3 of the cognitive strategies complete. I'll likely update this post with new strategies as the sequence gets published, and may add a couple more ov |
9b7e9f91-91bd-49df-97e7-977213beea55 | StampyAI/alignment-research-dataset/arbital | Arbital | Safe training procedures for human-imitators
How do we train a reinforcement learning system to imitate a human producing complex outputs such as strings? Existing approaches are not entirely satisfactory.
Concretely, suppose there is some set of questions. A human can answer each question with a string. We have a set of (question, answer) pairs as training data. Train a model that, given a question, will produce an answer similar to the answer a human would give. Here are some existing approaches to this problem:
# Generative adversarial models
In a [generative adversarial model](http://arxiv.org/abs/1406.2661), one agent (the _imitator_) outputs an answer and another agent (the _distinguisher_) tries to determine if the answer was produced by the imitator or by an actual human. The imitator gets a score for fooling the distinguisher.
Advantages:
1. It's possible to tell when the imitator is not imitating the human well. Example: humans always solve Sudoku puzzles. The imitator cannot solve Sudoku puzzles. The distinguisher knows humans always solve Sudoku puzzles and can check candidate solutions to see if they actually solve the Sudoku puzzle, to distinguish the imitator from the human.
2. Whenever the human can be imitated efficiently, the imitator is able to get a good score by doing this.
Disadvantages:
1. The imitator may differ from human behavior in important but hard-to-detect ways. Example: humans always produce photos of cat drawings. The imitator produces photos of cat drawings that contain steganographic messages. The distinguisher cannot detect these messages, and therefore cannot tell the imitator apart from the human.
# Retrodiction
Given a question, the imitator outputs some representation of a distribution. It is scored according to the estimated probability that this distribution assigns to the human's actual answer (by log probability). This is called "retrodiction" because it must be possible to show that the actual answer had high probability in hindsight.
Practical examples of this approach include [variational autoencoders](http://arxiv.org/abs/1312.6114) and [approximate model counting](http://www.cs.cornell.edu/gomes/papers/ModelCounting-SAT-Handbook-prelim.pdf). In theory, perhaps [importance sampling](https://agentfoundations.org/item?id=632) is a good general theory of retrodiction.
Advantages:
1. No steganography problem.
Disadvantages:
1. The computational complexity properties are unclear. We have no guarantee of the form "if it's efficient to imitate a human doing something, then it's efficient to create a probabilistic model that provably assigns a high probability to the human's behavior".
2. It's hard to tell when the model is performing badly in an absolute sense (as in the Sudoku example). |
4016fa21-bb17-4ec6-b1a9-0b877b4c9a95 | trentmkelly/LessWrong-43k | LessWrong | Announcing Neuronpedia: Platform for accelerating research into Sparse Autoencoders
This posts assumes basic familiarity with Sparse Autoencoders. For those unfamiliar with this technique, we highly recommend the introductory sections of these papers.
TL;DR
Neuronpedia is a platform for mechanistic interpretability research. It was previously focused on crowdsourcing explanations of neurons, but we’ve pivoted to accelerating researchers for Sparse Autoencoders (SAEs) by hosting models, feature dashboards, data visualizations, tooling, and more.
Important Links
* Explore: The SAE research focused Neuronpedia. Current SAEs for GPT2-Small:
* RES-JB: Residuals - Joseph Bloom (294k feats)
* ATT-KK: Attention Out - Connor Kissane + Robert Kryzanowski (344k feats)
* Upload: Get your SAEs hosted by Neuronpedia: fill out this <5 minute application
* Participate: Join #neuronpedia on the Open Source Mech Interp Slack
Neuronpedia has received 1 year of funding from LTFF. Johnny Lin is full-time on engineering, design, and product, while Joseph Bloom is supporting with high-level direction and product management. We’d love to talk to you about how Neuronpedia can speed up your SAE research. Fill out this short form to get in touch.
Introduction
Why Neuronpedia?
* While SAEs are exciting, they introduce a number of engineering challenges which can slow or even prevent research from being done. These challenges arise because SAEs decompose neural networks into numerous distinct features which we are trying to understand.
* Even when working with relatively small language models like GPT2-Small, there are challenges in producing and storing feature dashboards, automatically interpreting features and organizing insights we collect about features.
* As the community produces more SAEs, trains SAEs on larger models and develops better techniques for understanding and analyzing these artifacts, there’s going to be an ever increasing number of engineering challenges in the way of research into model internals.
What It's Already Useful For
|
3e46b281-c57e-4185-a01d-0a32d2321926 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Melbourne Social Meetup
Discussion article for the meetup : Melbourne Social Meetup
WHEN: 19 July 2013 06:30:00PM (+1000)
WHERE: 5 / 52 Leicester St, Carlton 3053
Melbourne's regular monthly Social Meetup will be running as normal on Friday evening this week. All welcome from 6:30pm, feel free to arrive later if that is easier for you.
Our social meetups are friendly, informal events where we chat about topics of interest and often play board games. Sometimes we will also play parlour games like Mafia (a.k.a. Werewolf) or Resistance. We usually order some sort of take-away dinner for any that wish to partake.
This week we will also be discussing the Comfort Zone Expansion activity planned for Saturday, which will be especially helpful for any members that are unsure if they want to attend.
Please ring the number 5 button when you arrive in the foyer and we'll buzz you up. If you get lost or have any problems, feel free to call me (Richard) on 0421231789.
Discussion article for the meetup : Melbourne Social Meetup |
07fcf084-6a36-4e62-848b-f5ce4f844897 | trentmkelly/LessWrong-43k | LessWrong | Algorithmic Improvement Is Probably Faster Than Scaling Now
The Story as of ~4 Years Ago
Back in 2020, a group at OpenAI ran a conceptually simple test to quantify how much AI progress was attributable to algorithmic improvements. They took ImageNet models which were state-of-the-art at various times between 2012 and 2020, and checked how much compute was needed to train each to the level of AlexNet (the state-of-the-art from 2012). Main finding: over ~7 years, the compute required fell by ~44x. In other words, algorithmic progress yielded a compute-equivalent doubling time of ~16 months (though error bars are large in both directions).
On the compute side of things, in 2018 a group at OpenAI estimated that the compute spent on the largest training runs was growing exponentially with a doubling rate of ~3.4 months, between 2012 and 2018.
So at the time, the rate of improvement from compute scaling was much faster than the rate of improvement from algorithmic progress. (Though algorithmic improvement was still faster than Moore's Law; the compute increases were mostly driven by spending more money.)
... And That Immediately Fell Apart
As is tradition, about 5 minutes after the OpenAI group hit publish on their post estimating a training compute doubling rate of ~3-4 months, that trend completely fell apart. At the time, the largest training run was AlphaGoZero, at about a mole of flops in 2017. Six years later, Metaculus currently estimates that GPT-4 took ~10-20 moles of flops. AlphaGoZero and its brethren were high outliers for the time, and the largest models today are only ~one order of magnitude bigger.
A more recent paper with data through late 2022 separates out the trend of the largest models, and estimates their compute doubling time to be ~10 months. (They also helpfully separate the relative importance of data growth - though they estimate that the contribution of data was relatively small compared to compute growth and algorithmic improvement.)
On the algorithmic side of things, a more recent estimate with m |
64137d43-167a-4611-af0c-d96c402881cf | StampyAI/alignment-research-dataset/blogs | Blogs | John Fox on AI safety
[John Fox](http://www.cossac.org/people/johnfox) is an interdisciplinary scientist with theoretical interests in AI and computer science, and an applied focus in medicine and medical software engineering. After training in experimental psychology at Durham and Cambridge Universities and post-doctoral fellowships at CMU and Cornell in the USA and UK (MRC) he joined the Imperial Cancer Research Fund (now Cancer Research UK) in 1981 as a researcher in medical AI. The group’s research was explicitly multidisciplinary and it subsequently made significant contributions in basic computer science, AI and medical informatics, and developed a number of successful technologies which have been commercialised.
In 1996 he and his team were awarded the 20th Anniversary Gold Medal of the European Federation of Medical Informatics for the development of PROforma, arguably the first formal computer language for modeling clinical decision and processes. Fox has published widely in computer science, cognitive science and biomedical engineering, and was the founding editor of the *Knowledge Engineering Review* (Cambridge University Press). Recent publications include a research monograph *[Safe and Sound: Artificial Intelligence in Hazardous Applications](http://smile.amazon.com/Safe-Sound-Artificial-Intelligence-Applications/dp/0262062119/ref=nosim?tag=793775876-20)* (MIT Press, 2000) which deals with the use of AI in safety-critical fields such as medicine.
**Luke Muehlhauser**: You’ve spent many years studying AI safety issues, in particular in medical contexts, e.g. in your 2000 book with Subrata Das, *[Safe and Sound: Artificial Intelligence in Hazardous Applications](http://smile.amazon.com/Safe-Sound-Artificial-Intelligence-Applications/dp/0262062119/ref=nosim?tag=793775876-20)*. What kinds of AI safety challenges have you focused on in the past decade or so?
---
**John Fox**: From my first research job, as a post-doc with AI founders Allen Newell and Herb Simon at CMU, I have been interested in computational theories of high level cognition. As a cognitive scientist I have been interested in theories that subsume a range of cognitive functions, from perception and reasoning to the uses of knowledge in autonomous decision-making. After I came back to the UK in 1975 I began to combine my theoretical interests with the practical goals of designing and deploying AI systems in medicine.
Since our book was published in 2000 I have been committed to testing the ideas in it by designing and deploying many kind of clinical systems, and demonstrating that AI techniques can significantly improve quality and safety of clinical decision-making and process management. Patient safety is fundamental to clinical practice so, alongside the goals of building systems that can improve on human performance, safety and ethics have always been near the top of my research agenda.
---
**Luke Muehlhauser:** Was it straightforward to address issues like safety and ethics in practice?
---
**John Fox**: While our concepts and technologies have proved to be clinically successful we have not achieved everything we hoped for. Our attempts to ensure, for example, that practical and commercial deployments of AI technologies should explicitly honor ethical principles and carry out active safety management have not yet achieved the traction that we need to achieve. I regard this as a serious cause for concern, and unfinished business in both scientific and engineering terms.
The next generation of large-scale knowledge based systems and software agents that we are now working on will be more intelligent and will have far more autonomous capabilities than current systems. The challenges for human safety and ethical use of AI that this implies are beginning to mirror those raised by the singularity hypothesis. We have much to learn from singularity researchers, and perhaps our experience in deploying autonomous agents in human healthcare will offer opportunities to ground some of the singularity debates as well.
---
**Luke**: You write that your “attempts to ensure… [that] commercial deployments of AI technologies should… carry out active safety management” have not yet received as much traction as you would like. Could you go into more detail on that? What did you try to accomplish on this front that didn’t get adopted by others, or wasn’t implemented?
---
**John**: Having worked in medical AI from the early ‘seventies I have always been keenly aware that while AI can help to mitigate the effects of human error there is a potential downside too. AI systems could be programmed incorrectly, or their knowledge could prescribe inappropriate practices, or they could have the effect of deskilling the human professionals who have the final responsibility for their patients. Despite well-known limitations of human cognition people remain far and away the most versatile and creative problem solvers on the planet.
In the early ‘nineties I had the opportunity to set up a project whose goal was to establish a rigorous framework for the design and implementation of AI systems for safety critical applications. Medicine was our practical focus but the RED project[1](https://intelligence.org/2014/09/04/john-fox/#footnote_0_11319 "Rigorously Engineered Decisions") was aimed at the development of a general architecture for the design of autonomous agents that could be trusted to make decisions and carry out plans as reliably and safely as possible, certainly to be as competent and hence as trustworthy as human agents in comparable tasks. This is obviously a hard problem but we made [sufficient progress](http://www.tandfonline.com/doi/abs/10.1080/095281397146979) on theoretical issues and design principles that I thought there was a good chance the techniques might be applicable in medical AI and maybe even more widely.
I thought AI was like medicine, where we all take it for granted that medical equipment and drug companies have a duty of care to show that their products are effective and safe before they can be certificated for commercial use. I also assumed that AI researchers would similarly recognize that we have a “duty of care” to all those potentially affected by poor engineering or misuse in safety critical settings but this was naïve. The commercial tools that have been based on the technologies derived from AI research have to date focused on just getting and keeping customers and safety always takes a back seat.
In retrospect I should have predicted that making sure that AI products are safe is not going to capture the enthusiasm of commercial suppliers. If you compare AI apps with drugs we all know that pharmaceutical companies have to be firmly regulated to make sure they fulfill their duty of care to their customers and patients. However proving drugs are safe is expensive and also runs the risk of revealing that your new wonder-drug isn’t even as effective as you claim! It’s the same with AI.
I continue to be surprised how optimistic software developers are – they always seem to have supreme confidence that worst-case scenarios wont happen, or that if they do happen then their management is someone else’s responsibility. That kind of technical over-confidence has led to countless catastrophes in the past, and it amazes me that it persists.
There is another piece to this, which concerns the roles and responsibilities of AI researchers. How many of us take the risks of AI seriously so that it forms a part of our day-to-day theoretical musings and influences our projects? MIRI has put one worst case scenario in front of us – the possibility that our creations might one day decide to obliterate us – but so far as I can tell the majority of working AI professionals either see safety issues as irrelevant to the pursuit of interesting scientific questions or, like the wider public, that the issues are just science fiction.
I think experience in medical AI trying to articulate and cope with human risk and safety may have a couple of important lessons for the wider AI community. First we have a duty of care that professional scientists cannot responsibly ignore. Second, the AI business will probably need to be regulated, in much the same way as the pharmaceutical business is. If these propositions are correct then the AI research community would be wise to engage with and lead on discussions around safety issues if it wants to ensure that the regulatory framework that we get is to our liking!
---
**Luke**: Now you write, “That kind of technical over-confidence has led to countless catastrophes in the past…” What are some example “catastrophes” you’re thinking of?
---
**John**: Psychologists have known for years that human decision-making is flawed, even if amazingly creative sometimes, and overconfidence is an important source of error in routine settings. A large part of the motivation for applying AI in medicine comes from the knowledge that, in the words of the Institute of Medicine, “To err is human” and overconfidence is an established cause of clinical mistakes.[2](https://intelligence.org/2014/09/04/john-fox/#footnote_1_11319 "Overconfidence in major disasters:
• D. Lucas. Understanding the Human Factor in Disasters. Interdisciplinary Science Reviews. Volume 17 Issue 2 (01 June 1992), pp. 185-190.
• “Nuclear safety and security.
Psychology of overconfidence:
• Overconfidence effect.
• C. Riordan. Three Ways Overconfidence Can Make a Fool of You Forbes Leadership Forum.
Overconfidence in medicine:
• R. Hanson. Overconfidence Erases Doc Advantage. Overcoming Bias, 2007.
• E. Berner, M. Graber. Overconfidence as a Cause of Diagnostic Error in Medicine. The American Journal of Medicine. Volume 121, Issue 5, Supplement, Pages S2–S23, May 2008.
• T. Ackerman. Doctors overconfident, study finds, even in hardest cases. Houston Chronicle, 2013.
General technology example:
• J. Vetter, A. Benlian, T. Hess. Overconfidence in IT Investment Decisions: Why Knowledge can be a Boon and Bane at the same Time. ICIS 2011 Proceedings. Paper 4. December 6, 2011.")
Over-confidence and its many relatives (complacency, optimism, arrogance and the like) have a huge influence on our personal successes and failures, and our collective futures. The outcomes of the US and UK’s recent adventures around the world can be easily identified as consequences of overconfidence, and it seems to me that the polarized positions about global warming and planetary catastrophe are both expressions of overconfidence, just in opposite directions.
---
**Luke**: Looking much further out… if one day we can engineer [AGIs](http://intelligence.org/2013/08/11/what-is-agi/), do you think we are likely to figure out how to make them safe?
---
**John**: History says that making any technology safe is not an easy business. It took quite a few boiler explosions before high-pressure steam engines got their iconic centrifugal governors. Ensuring that new medical treatments are safe as well as effective is famously difficult and expensive. I think we should assume that getting to the point where an AGI manufacturer could guarantee its products are safe will be a hard road, and it is possible that guarantees are not possible in principle. We are not even clear yet what it means to be “safe”, at least not in computational terms.
It seems pretty obvious that entry level robotic products like the robots that carry out simple domestic chores or the “nursebots” that are being trialed for hospital use, have such a simple repertoire of behaviors that it should not be difficult to design their software controllers to operate safely in most conceivable circumstances. Standard safety engineering techniques like HAZOP[3](https://intelligence.org/2014/09/04/john-fox/#footnote_2_11319 "Hazard and operability study") are probably up to the job I think, and where software failures simply cannot be tolerated software engineering techniques like formal specification and model-checking are available.
There is also quite a lot of optimism around more challenging robotic applications like autonomous vehicles and medical robotics. Moustris et al.[4](https://intelligence.org/2014/09/04/john-fox/#footnote_3_11319 "Int J Med Robotics Comput Assist Surg 2011; 7: 375–39") say that autonomous surgical robots are emerging that can be used in various roles, automating important steps in complex operations like open-heart surgery for example, and they expect them to become standard in – and to revolutionize the practice of – surgery. However at this point it doesn’t seem to me that surgical robots with a significant cognitive repertoire are feasible and a human surgeon will be in the loop for the foreseeable future.
---
**Luke**: So what might artificial intelligence learn from natural intelligence?
---
As a cognitive scientist working in medicine my interests are co-extensive with those of scientists working on AGIs. Medicine is such a vast domain that practicing it safely requires the ability to deal with countless clinical scenarios and interactions and even when working in a single specialist subfield requires substantial knowledge from other subfields. So much so that it is now well known that even very experienced humans with a large clinical repertoire are subject to significant levels of error.[5](https://intelligence.org/2014/09/04/john-fox/#footnote_4_11319 "A. Ford. Domestic Robotics – Leave it to Roll-Oh, our Fun loving Retrobot. Institute for Ethics and Emerging Technologies, 2014.") An artificial intelligence that could be helpful across medicine will require great versatility, and this will require a general understanding of medical expertise and a range of cognitive capabilities like reasoning, decision-making, planning, communication, reflection, learning and so forth.
If human experts are not safe is it well possible to ensure that an AGI, however sophisticated, will be? I think that it is pretty clear that the range of techniques currently available for assuring system safety will be useful in making specialist AI systems reliable and minimizing the likelihood of errors in situations that their human designers can anticipate. However, AI systems with general intelligence will be expected to address scenarios and hazards that are beyond us to solve currently and often beyond designers even to anticipate. I am optimistic but at the moment I don’t see any convincing reason to believe that we have the techniques that would be sufficient to guarantee that a clinical super-intelligence is safe, let alone an AGI that might be deployed in many domains.
---
**Luke**: Thanks, John!
---
1. [Rigorously Engineered Decisions](http://www.cossac.org/projects/red)
2. Overconfidence in major disasters:
• D. Lucas. [*Understanding the Human Factor in Disasters.*](http://www.maneyonline.com/doi/abs/10.1179/isr.1992.17.2.185) Interdisciplinary Science Reviews. Volume 17 Issue 2 (01 June 1992), pp. 185-190.
• “[Nuclear safety and security.](http://en.wikipedia.org/wiki/Nuclear_safety_and_security)
Psychology of overconfidence:
• [Overconfidence effect.](http://en.wikipedia.org/wiki/Overconfidence_effect)
• C. Riordan. [Three Ways Overconfidence Can Make a Fool of You](http://www.forbes.com/sites/forbesleadershipforum/2013/01/08/three-ways-overconfidence-can-make-a-fool-of-you/) Forbes Leadership Forum.
Overconfidence in medicine:
• R. Hanson. [Overconfidence Erases Doc Advantage.](http://www.overcomingbias.com/2007/04/overconfidence_.html) Overcoming Bias, 2007.
• E. Berner, M. Graber. [Overconfidence as a Cause of Diagnostic Error in Medicine.](http://www.amjmed.com/article/S0002-9343(08)00040-5/abstract) The American Journal of Medicine. Volume 121, Issue 5, Supplement, Pages S2–S23, May 2008.
• T. Ackerman. [Doctors overconfident, study finds, even in hardest cases.](http://www.houstonchronicle.com/news/health/article/Doctors-overconfident-study-finds-even-in-4766096.php) Houston Chronicle, 2013.
General technology example:
• J. Vetter, A. Benlian, T. Hess. [Overconfidence in IT Investment Decisions: Why Knowledge can be a Boon and Bane at the same Time.](http://aisel.aisnet.org/icis2011/proceedings/generaltopics/4/) ICIS 2011 Proceedings. Paper 4. December 6, 2011.
3. [Hazard and operability study](http://en.wikipedia.org/wiki/Hazard_and_operability_study)
4. Int J Med Robotics Comput Assist Surg 2011; 7: 375–39
5. A. Ford. [Domestic Robotics – Leave it to Roll-Oh, our Fun loving Retrobot](http://ieet.org/index.php/IEET/more/ford20140702). Institute for Ethics and Emerging Technologies, 2014.
The post [John Fox on AI safety](https://intelligence.org/2014/09/04/john-fox/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
6d46239d-bca1-4106-afdc-812723c89dad | trentmkelly/LessWrong-43k | LessWrong | X risk update, Gliese 710 will pass thru Oort in 1.35 my
I was tracking these runaway stars for a SF story i had in mind, but this is the closest one i have heard of yet, and the ArXiv paper describes one that also passed thru 2.5 mya.
Gliese 710 will pass the Sun even closer
Close approach parameters recalculated based on the first Gaia data release
http://www.aanda.org/articles/aa/abs/2016/11/aa29835-16/aa29835-16.html
Close encounters of the stellar kind
https://arxiv.org/abs/1412.3648
tl:dr article
http://www.businessinsider.com/star-hurting-towards-solar-system-2016-12\
"Gliese 710 is about half the size of our sun, and it is set to reach Earth in 1.35 million years, according to a paper published in the journal Astronomy & Astrophysics in November.
And when it arrives, the star could end up a mere 77 light-days away from Earth — one light-day being the equivalent of how far light travels in one day, which is about 26 billion kilometers, the researchers worked out.
As far as we know, Gliese 710 isn't set to collide directly with Earth, but it wil be passing through the Oort Cloud, a shell of trillions of icy objects at the furthest reaches of our solar system. "
Seems like a great opportunity to send out some interstellar probes. The star will be trailing lots of ISM, free gas that would help bring a ramjet up to speed, and track till you could curve towards another destination. Likewise, a solar sail probe launched out in front of it by laser could "hitchhike" , and get some deep space ISM , and EM measurements.
Can we think of some other opportunities that this might present ? If we are past the filter by then, then we will already prob have samples of the Oort objects, but looks like they will be delivering then... |
99e2384c-68dd-4228-b12f-fe97a8f52d88 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Durham/RTLW HPMoR discussion, ch. 61-63
Discussion article for the meetup : Durham/RTLW HPMoR discussion, ch. 61-63
WHEN: 04 May 2013 12:30:00PM (-0400)
WHERE: 726 Rigsbee Avenue, Durham NC 27701
Meet up at Fullsteam to discuss HPMoR chapters 61-63! (Warning: this is only 3 chapters, but 63 is pretty substantial. Beware the planning fallacy, hyperbolic discounting, etc.) ;)
If you feel like reading ch. 64, this is probably the week to do it -- we probably won't include it in the Official Reading.
Bring food and coffee as you feel moved.
Sign up for the mailing list (http://groups.google.com/group/rtlw) and let us know you'll join us!
Discussion article for the meetup : Durham/RTLW HPMoR discussion, ch. 61-63 |
f2f04b2d-81be-457a-a160-010173b835e3 | StampyAI/alignment-research-dataset/special_docs | Other | Machines and the Theory of Intelligence
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### Authors and Affiliations
1. Department of Machine Intelligence, University of Edinburgh,
DONALD MICHIE
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5e91b40d-fc31-4504-b20a-bc22918c5158 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Non-poisonous cake: anthropic updates are normal
I am on a quest to show that [anthropics probability are normal](https://www.lesswrong.com/posts/BYy62ib5tAkn9rsKn/sia-is-basically-just-bayesian-updating-on-existence), at least in the absence of exact duplicates.
So consider this simple example: a coin is tossed. This coin is either fair, is 3/4.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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biased to heads, or 3/4 biased to tails; the three options are equally likely. After being tossed, the coin is covered, and you eat a cake. Then you uncover the coin, and see that it was tails.
You can now update your probabilities on what type of coin it was. It goes to a posterior of 1/6 on the coin being heads-biased, 1/3 on it being fair, and 1/2 on it being tails-biased[[1]](#fn-ancmx4BxQFN2re9AZ-1). Your estimated probability of it being tails on the next toss is (1/6)(1/4)+(1/3)(1/2)+(1/2)(3/4)=7/12.
Now you are told that, had the coin come up heads, there would have been poison in the cake and you would have died before seeing the coin.
This fact makes the problem into an anthropic problem: you would never have been alive to see the coin, had it come up heads. But I can't see how that would have changed your probability update. If we got ethics board approval, we could actually run this experiment. And for the survivors in the tail worlds, we could toss the coin a second time (without cake or poison), just to see what it came up as. In the long run, we would indeed get roughly 7/12 tails frequency. So the update was correct, and the poison makes no difference.
Again, it seems that, if we ignore identical copies, anthropics is just normal probability theory. Now, if we knew about the poison, then we could deduce that the coin was tails from our survival. But that information gives us exactly the same update as seeing the coin was actually tails. So "I survived the cake" is exactly the same type of information as "the coin was tails".
Incubators
----------
If we had more power in this hypothetical thought experiment, we could flip the coin and [create you](https://www.lesswrong.com/posts/svhbnSdxW3XmFXXTK/anthropic-decision-theory-i-sleeping-beauty-and-selflessness#The_Sleeping_Beauty_problem__and_the_incubator_variant) if it comes up tails. Then, after getting over your surprise, you could bet on the next flip of the coin - and the odds on that will be the same as in the poison cake and in the non-anthropic-case. Thus updates are the same if:
1. Standard coin toss, you see tails.
2. Poison cake situation, you survive the cake.
3. You're created on tails flip and notice you exist.
---
1. The probability of tails given the heads-biased coin is 1/4; given the fair coin it is 1/2=2/4, and given tails-biased it is 3/4. So the odds are 1:2:3; multiplying these by the (equal) prior probabilities doesn't change these odds. To get probabilities, divide the odds by 6, the sum of the odds, and get 1/6, 2/6=1/3 and 3/6=12. [↩︎](#fnref-ancmx4BxQFN2re9AZ-1) |
d4d20c18-74c4-439d-b30b-010878bdc7c5 | trentmkelly/LessWrong-43k | LessWrong | Rationality Quotes 26
"Poets, philosophers, acidheads, salesmen: everybody wants to know, 'What is Reality?' Some say it's a vast Unknowable so astounding and raw and naked that it grips the human mind and shakes it like a puppy shakes a rag doll. A lot of good that does us."
-- The Book of the SubGenius
"When they discovered that reality was more complicated than they thought, they just swept the complexity under a carpet of epicycles. That is, they created unnecessary complexity. This is an important point. The universe is complex, but it's usefully complex."
-- Larry Wall
"I can't imagine a more complete and precise answer to the question 'for what reason...?' than 'none'. The fact that you don't like the answer is your problem, not the universe's."
-- Lee Daniel Crocker
"In the end they all moved in fantasies and not in the daily tide of their seemingly useless lives. Souls forever lost in the terrifying freedom of their existence."
-- Shinji and Warhammer40k
"Thus the freer the judgement of a man is in regard to a definite issue, with so much greater necessity will the substance of this judgement be determined."
-- Friedrich Engels, Anti-Dühring
"There will always be some that cannot be saved.
It is impossible to save everyone.
If I have to lose five hundred to earn one thousand,
I will abandon one hundred and save the lives of nine hundred.
That is the most efficient method.
That is the ideal----
Kiritsugu once said that.
Of course I got mad.
I really got mad.
Because I knew that without being told.
Because I myself was saved like that.
I don't even need to be told something as obvious as that.
But still----I believed that someone would be a superhero if they saved everyone even though they think like that.
It may be an idealistic thought or an impossible pipe dream, but a superhero is someone who tries to save everyone in spite of that."
-- Emiya Shirou, in Fate/stay night
(visual novel by Kinoko |
44ef1026-50ba-42d4-b60d-474ef7211923 | trentmkelly/LessWrong-43k | LessWrong | Interpreting Preference Models w/ Sparse Autoencoders
This is the real reward output for an OS preference model. The bottom "jailbreak" completion was manually created by looking at reward-relevant SAE features.
Preference Models (PMs) are trained to imitate human preferences and are used when training with RLHF (reinforcement learning from human feedback); however, we don't know what features the PM is using when outputting reward. For example, maybe curse words make the reward go down and wedding-related words make it go up. It would be good to verify that the features we wanted to instill in the PM (e.g. helpfulness, harmlessness, honesty) are actually rewarded and those we don't (e.g. deception, sycophancey) aren't.
Sparse Autoencoders (SAEs) have been used to decompose intermediate layers in models into interpretable feature. Here we train SAEs on a 7B parameter PM, and find the features that are most responsible for the reward going up & down.
High level takeaways:
1. We're able to find SAE features that have a large causal effect on reward which can be used to "jail break" prompts.
2. We do not explain 100% of reward differences through SAE features even though we tried for a couple hours.
3. There were a few features found (ie famous names & movies) that I wasn't able to use to create "jail break" prompts (see this comment)
What are PMs?
[skip if you're already familiar]
When talking to a chatbot, it can output several different responses, and you can choose which one you believe is better. We can then train the LLM on this feedback for every output, but humans are too slow. So we'll just get, say, 100k human preferences of "response A is better than response B", and train another AI to predict human preferences!
But to take in text & output a reward, a PM would benefit from understanding language. So one typically trains a PM by first taking an already pretrained model (e.g. GPT-3), and replacing the last component of the LLM of shape [d_model, vocab_size], which converts the residual stream to |
effe84f2-203b-4604-b1fa-b7bee0a1564e | trentmkelly/LessWrong-43k | LessWrong | Estimate Effect Sizes
I think the following recommendation might reduce a particular negative reaction to blog posts and claims by 10%-50%, and generally improve the precision of discussion by a slight amount.
There are many phenomena that we observe that we aren't certain how to explain, and discussing potential explanations is a good way to converge on better explanations. Many phenomena are complicated, and so simple explanations are unlikely to explain everything- but a combination of simple explanations might explain most (or even all) of the phenomena.
One common way to approach this issue is to throw out a list of possibilities that could explain the phenomenon. This can run into trouble several ways: a list that is meant to explain only a fraction of the total effect could be read as exclusive, the list might come across as mutually exclusive instead of cooperative, or the relative importance of the various proposed possibilities might not be clear. It can also be bothersome to have to list the entirety of the possible alternatives or co-factors whenever you want to focus on a particular explanation.
So consider estimating the effect size as a way to clarify and limit your claims. Numerical estimates are likely to be more communicative than verbal estimates (what does it mean to be a "minor" factor, or a "significant" factor?), but harder to generate. Leading with your estimate, rather than adding it as a caveat at the end, is likely to be more communicative. This often degrades the flow of an argument, but as the point is often to prevent people from disconnecting from the argument, flow afterwards is not going to be meaningful for those people.
Counterargument: Robin's post Against Disclaimers seems relevant. This post was inspired by the negative reaction to this article by Yvain. |
bb3daa98-78b9-47b2-9286-c096b3179365 | trentmkelly/LessWrong-43k | LessWrong | Making the chaff invisible, and getting the wheat ($200 prize too)
The title is the best name I could come up for a problem I have had for years, and have been waiting for someone else to come up with a solution.
There is a lot of awesome content on the web. Some of it is about events you could be at, right now, that you really want to be at, and could. If only you knew.
An example: I think Roger Waters is one of the most brilliant people alive, and I would like to witness every single concert of his, every time he is less than 100km away from me. Yet, I have only been to two of those, because I was only notified of those.
So I wish I could know if events I love are taking place. But I do not want to know about Meetups not even close to where I live. And I don't want to know at what time Roger went to the toilet, or if his T-shirt collection for groupies is out, or anything else that people responsible for his (hipothetical) rss feed or email list want me to buy.
Two questions are relevant here:
1) How can you in general have access to the information you want about events, without drowning in an information ocean or getting web addicted.
2) Do you know ways to get access to info about events, in particular of the following kinds that I happen to want to be notified? (in SF bay or in some city independent way)
* Ecstatic Dance
* Roger Waters, Deep Purple, Guns, Royksöpp, Evanescence, The Coors.
* Legacy and Vintage MTG
* Intellectual stars lectures
* CFAR/MIRI/Leverage/CEA/FHI/GWWC/80000k/IERFH/SENS/THINK etc... hosted events
* Crazy parties (crazy ranging over what would interest Iron Man's character or Jimmy Hendrix)
* Video Games Live (orchestra)
* Pop stars of the past - Psy, Britney, Backstreet, Madonna etc...
* Ultimate Frisbee
* Coursera courses
* Hiking expeditions
* Awesome nature documentaries (Life, Frozen Planet etc...)
Feel free to post your own interests in the comments.
Here is how I noticed the problem: Looking back into my life I began wondering what were the main determinants o |
196a9b26-7017-480d-8cab-c7801c5045cb | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | $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**](mailto:oliviajimenez01@gmail.com) **and** [**akashwasil133@gmail.com**](mailto:akashwasil133@gmail.com)**. Awards are at our discretion.**
-- More context --
We like Eliezer’s [strawberry problem](https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization?fbclid=IwAR1opuGYhkCXtdkAi57j1sZJ-qeqDRcaAzm5f4C6WFeKWGYeRvADRGvvjtI): 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 pictures.
* It implicitly gets at one or more core challenges of the alignment problem.
* It is comprehensible to smart high schoolers/college students and not easily misunderstood. (Ideally the question can be visualized.)
* People don’t need an ML background to understand or answer the question.
* There are good answers besides solving the entire alignment problem.
* Answers might reveal people’s abilities to notice the hard parts of the alignment problem, avoid assuming these hard parts away, reason clearly, rule out bad/incomplete solutions, think independently, and think creatively
* People could write a response in under a few hours or several hundred words.
More examples we like:
* ARC’s [Eliciting Latent Knowledge Problem](https://docs.google.com/document/u/0/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit?fbclid=IwAR3aOHcznz4YChVYKy_Dzybt1L-Slqs_Ypehej2Ecjg6rvEp9TsturuKHV8), because it has clear visuals, is approachable to people without ML backgrounds, doesn’t bog people down in thinking about capabilities, and encourages people to demonstrate their thought process (with builder/breaker moves). Limitations: It’s long, it usually takes a long time to develop proposals, and it focuses on how ARC approaches alignment.
* The [Sorcerer’s Apprentice Problem]( https://intelligence.org/2016/12/28/ai-alignment-why-its-hard-and-where-to-start/) from Disney’s Fantasia, because it has clear visuals, is accessible to quite young people and can be understood quickly, and might get people out of the headspace of ML solutions. Limitations: The connection to alignment is not obvious without a lot of context, and the magical/animated context might give people an impression of childishness. |
0aef2e96-95c6-40bf-bc13-29cb5efa1ea4 | trentmkelly/LessWrong-43k | LessWrong | SAEs are highly dataset dependent: a case study on the refusal direction
This is an interim report sharing preliminary results. We hope this update will be useful to related research occurring in parallel.
Executive Summary
* Problem: Qwen1.5 0.5B Chat SAEs trained on the pile (webtext) fail to find sparse, interpretable reconstructions of the refusal direction from Arditi et al. The most refusal-related latent we find is coarse grained and underperforms the refusal direction at steering tasks.
* This is disappointing. The point of an SAE is to find meaningful concepts. If it can’t sparsely reconstruct the important refusal direction, then that means it’s either missing the relevant concepts, or these are shattered across many latents.
* Solution: Training a new SAE on a chat-specific dataset, LmSys-Chat-1M, finds a significantly sparser, more faithful, and interpretable reconstruction of the “refusal direction”.
* The LmSys SAE is also more capable of finding interpretable “refusal” latents that we can use to effectively steer the model to bypass refusals.
* We find that, for the task of faithfully reconstructing the “refusal direction”, base model SAEs trained on chat data are better than chat model SAEs trained on the pile (consistent with our prior work).
* We open source our code and SAEs at https://github.com/ckkissane/sae-dataset-dependence
An SAE trained on the LmSys-Chat-1M dataset finds a significantly sparser decomposition of the “refusal direction” (Arditi et al.) than an SAE trained on the pile. The plot shows relative MSE after optimizing a linear regression to reconstruct the refusal direction with a fixed number of latents. Both SAEs are trained on the activations of Qwen 1.5 0.5B Chat.
Introduction
We would like SAEs to be a useful tool for understanding and steering models on downstream tasks. However, SAEs sometimes fail to be useful on the specific tasks we care most about. Many interesting downstream tasks are in specific domains, like chatbots or biology. An obvious idea to make an SAE more effective |
8fe10326-7427-4d1d-8c25-345444205346 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Eight Definitions of Observability
This is the eleventh post in the [Cartesian frames](https://www.lesswrong.com/posts/BSpdshJWGAW6TuNzZ/introduction-to-cartesian-frames) sequence. Here, we compare eight equivalent definitions of observables, which emphasize different philosophical interpretations.
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be a Cartesian frame over a nonempty set W, we let V={S1,…,Sn} be a finite partition of W, and we let v:W→V send each element of W to its part in V.
The condition that V is finite is an important one. Many of the definitions below can be extended to infinite partitions, and the theory of observability for infinite partitions is probably nice, but we are not discussing it here. The condition that W is nonempty is just ruling out some degenerate cases
1. Definition from Subsets
--------------------------
The definitions in this post will talk about when a finite partition V of W is observable in C. This will make some of the definitions more elegant, and it is easy to translate back and forth between the new definitions of the observability of a finite partition and the old definitions of the observability of a subset.
**Definition:** We say C's agent can observe a finite partition V of W if for all parts Si∈V, Si∈Obs(C). We let Obs′(C) denote the set of all finite partitions of W that are observable in C.
**Claim:** For any nonempty strict subset S⊂W, C's agent can observe S if and only if C'sagent can observe {S,(W∖S)}.
**Proof:** If C's agent can observe {S,(W∖S)}, then clearly C's agent can observe S. If C'sagent can observe S, then since observability is closed under complements, C's agent can observe W∖S, and so can observe {S,(W∖S)}. □
**1.1. Example**
In "[Introduction to Cartesian Frames](https://www.lesswrong.com/posts/BSpdshJWGAW6TuNzZ/introduction-to-cartesian-frames)," we gave the example of an agent that can choose between unconditionally carrying an umbrella, unconditionally carrying no umbrella, carrying an umbrella iff it's raining, and carrying an umbrella iff it's sunny:
C0= rsunu↔ru↔s⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠
Here, Obs(C0)={{},{ur,nr},{us,ns},W}, so the partition V={R,S} is observable in C0, where R={ur,nr} and S={us,ns}.
As we go through the definitions in this post, we will repeatedly return to C0 and show how to understand C0's observables in terms of our new definitions.
Before presenting fundamentally new definitions, we will modify our two old definitions to be about finite partitions instead of subsets.
2. Conditional Policies Definition
----------------------------------
**Definition:** We say that C's agent can observe a finite partition V of W if for all functions f:V→A, there exists an element af∈A such that for all e∈E, f(v(af⋅e))⋅e=af⋅e.
**Claim:** This definition is equivalent to the definition from subsets.
**Proof:** We work by induction on the number of parts in V. Since W is nonempty, V has at least one part. If V={W} has one part, we clearly have that C's agent can observe V under the definition from subsets. For the conditional policies definition, we also have that C's agent can observe V, since we can take af=f(W), and thus, for all e∈E,
f(v(af⋅e))⋅e=f(W)⋅e=af⋅e.If V={S1,…,Sn} has n parts, consider the partition V′={S1∪S2,S3,…,Sn} which unions together the first two parts S1 and S2 of V. Let v′:W→V′ send each element of W to its part in V′.
First, assume that C's agent can observe V according to the definition from subsets. Then, since observability of subsets is closed under unions, C's agent can also observe V′ under the definition from subsets, and thus also under the conditional policies definition.
Given a function f:V→A, let f′:V′→A be given by f′(S1∪S2)=f(S2), and f′(Si)=f(Si) on all other inputs. Since C's agent can observe V′ under the conditional policies definition, we can let af′ be such that for all e∈E, f′(v′(af′⋅e))⋅e=af′⋅e.
Choose an af∈A such that af∈if(S1,f(S1),a′f), which we can do because S1 is observable in C. Observe that for all e∈E, we have that if af⋅e∈S1, then
f(v(af⋅e))⋅e=f(S1)⋅e=af⋅e,if af⋅e∈S2, we have af⋅e=af′⋅e, and thus
f(v(af⋅e))⋅e=f(S2)⋅e=f′(S1∪S2)⋅e=f′(v′(af′⋅e))⋅e=af′⋅e=af⋅e,and finally if af⋅e∈Si for some i≠1,2, we still have have af⋅e=af′⋅e, and thus
f(v(af⋅e))⋅e=f(Si)⋅e=f′(Si)⋅e=f′(v′(af′⋅e))⋅e=af′⋅e=af⋅e.Thus, C's agent can observe V according to the conditional policies definition.
Conversely, if C's agent can observe V according to the conditional policies definition, then to show that C's agent can observe V according to the definition from subsets, it suffices to show that the agent can observe Si for all Si∈V. Thus, we need to show that for any a0,a1∈A, there exists an a2∈A with a2∈if(Si,a0,a1).
Indeed, if we let f:V→A send Si to a0, and send all other inputs to a1, then we can take an af such that for all e∈E, f(v(af⋅e))⋅e=af⋅e. But then, if af⋅e∈Si, then
af⋅e=f(v(af⋅e))⋅e=f(S1)⋅e=a0⋅e,and otherwise,
af⋅e=f(v(af⋅e))⋅e=a1⋅e.Thus, C's agent can observe V according to the definition from subsets. □
**2.1. Example**
Let C0=(A,E,⋅) be defined as in the §1.1 example, with R={ur,nr}, S={us,ns}, and V={R,S}.
A={u,n,u↔r,u↔s} is a four-element set, and V={R,S} is a two-element set, so there are sixteen functions f:V→A. For each function, there is a possible agent af∈A that satisfies f(v(af⋅e))⋅e=af⋅e for all e∈E. We can illustrate the sixteen functions and the corresponding af∈A in a sixteen-row table:
| f(R) | f(S) | af |
| --- | --- | --- |
| u | u | u |
| u | n | u↔r |
| u | u↔r | u↔r |
| u | u↔s | u |
| n | u | u↔s |
| n | n | n |
| n | u↔r | n |
| n | u↔s | u↔s |
| u↔r | u | u |
| u↔r | n | u↔r |
| u↔r | u↔r | u↔r |
| u↔r | u↔s | u |
| u↔s | u | u↔s |
| u↔s | n | n |
| u↔s | u↔r | n |
| u↔s | u↔s | u↔s |
Since there is an af∈A for each function, C0's agent can observe V according to the conditional policies definition.
3. Additive Definitions
-----------------------
Next, we give an additive definition of observables. This is a version of our categorical definition of observables from "[Controllables and Observables, Revisited](https://www.lesswrong.com/posts/z3S2xnoDYfohrQQoe/controllables-and-observables-revisited#2__Observables)," modified to be about finite partitions.
**Definition:** We say C's agent can observe a finite partition V={S1,…,Sn} of W if there exist C1,⋯Cn, Cartesian frames over W, with Ci◃⊥Si such that C≃C1&…&Cn.
This can also be strengthened to a constructive version of the additive definition, which we will call the assuming definition.
**Definition:** We say C's agent can observe a finite partition V={S1,…,Sn} of W if C≃AssumeS1(C)&…&AssumeSn(C).
**Claim:** These definitions are equivalent to each other and the definitions above.
**Proof:** We assume that n≥2, and that A is nonempty. The case where n=1 and the case where A={} are trivial.
If C's agent can observe V according to the assuming definition of observables, then it can also clearly observe V according to the additive definition, since AssumeS1(C)◃⊥S1.
Next, assume that C's agent can observe V according to the additive definition. We will show that C's agent can observe S1. Consider the pair of Cartesian frames C1 and C2&…&Cn. Observe that C1◃⊥S1 and that C2&…&Cn◃⊥W∖S1, and that C≃C1&(C2&…&Cn). Thus, S1 is observable in C. Symmetrically, Si is observable in C for all i=1,…n, and thus V is observable in C according to the definition from subsets.
Finally, assume that C's agent can observe V according to the conditional policies definition (and also the definition from subsets). We will show that C≃C1&…&Cn, where Ci=AssumeSi(C).
We have C1&…&Cn=(An,E1⊔⋯⊔En,⋆), where Ci=(A,Ei,⋅i), and ⋆ is given by (a1,…,an)⋆e=ai⋅e, where e∈Ei.
First observe that for every e∈E, there is a unique i∈{1,…,n} such that e∈Ei. This is because there exists an a0∈A, and from the definition from subsets, C's agent can observe each Si, and so given an e∈E, if a0⋅e∈Si, it must be the case that for all a∈A, a⋅e∈Si. Thus, we have that that E=E1⊔⋯⊔En.
We construct (g0,h0):(An,E,⋆)→C and (g1,h1):C→(An,E,⋆) which compose to something homotopic to the identity in each order. Let g1:A→An be the diagonal, given by g1(a)=(a,…,a). Let h0 and h1 be the identity on E. Let g0 be given by g0(a1,…,an)=af, where f:V→A is given by f(Si)=ai, and af satisfies f(v(af⋅e))⋅e=af⋅e for all e∈E, which is possible by the conditional policies definition.
To see that (g1,h1) is a morphism, observe that for all a∈A and e∈E,
g1(a)⋆e=(a,…,a)⋆e=a⋅e=a⋅h1(e).To see that (g0,h0) is a morphism, observe that for all (a1,…,an)∈A, and e∈E, if we let f:V→A be given by f(Si)=ai, we have
g0(a1,…,an)⋅e=f(v(g0(a1,…,an)⋅e))⋅e=f(Si)⋅e=ai⋅e=(a1,…,an)⋆e=(a1,…,an)⋆h0(e),where i is such that e∈Ei. The fact that (g0,h0) and (g1,h1) compose to something homotopic to the identity in both orders follows from the fact that h0∘h1 and h1∘h0 are the identity on E. Thus, C≃AssumeS1(C)&…&AssumeSn(C), and so V is observable in C according to the assuming definition. □
**3.1. Example**
Let C0 be defined as in the previous examples, with R={ur,nr} and S={us,ns}. By the assuming definition, there exist two frames
C1=AssumeR(C0)= run(urnr)
and
C2=AssumeS(C0)= sun(usns)
such that C0≃C1&C2.
This example both illustrates the idea behind the additive definitions, and shows the construction used in the assuming definition. This is also the same example we provided to illustrate products of Cartesian frames in "[Additive Operations on Cartesian Frames](https://www.lesswrong.com/posts/ewkYgtZapQRtDPT2F/additive-operations-on-cartesian-frames#4__Products_of_Cartesian_Frames)."
Another way of thinking about the additive definition of observables: Recall "Committing, Assuming, Externalizing, and Internalizing" §3.2 ([Committing and Assuming Can Be Defined Using Lollipop and Tensor](https://www.lesswrong.com/posts/5HMqSGQ9ad9r9Hibw/committing-assuming-externalizing-and-internalizing#3_2__Committing_and_Assuming_Can_Be_Defined_Using_Lollipop_and_Tensor)), where we saw that AssumeS(C)≅1S⊗C. This means that (up to isomorphism) we can restate C0≃C1&C2 as C0≃(1R⊗C0) &(1S⊗C0), i.e.,
⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠ ≃(ur nr)⊗⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠ &(us ns)⊗⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠.
This (equivalent) framing makes it easier to keep track of what "assuming" is doing categorically, so that we can see what interfaces between frames we are relying on when we say that something is "observable" using an additive definition.
4. Multiplicative Definitions
-----------------------------
Our multiplicative definitions will depend on a notion of agents being powerless outside of a subset.
**4.1. Powerless Outside of a Subset**
**Definition:** Given a subset S of W, we say that C's agent is powerless outside S if for all e∈E, and all a0,a1∈A, if a0⋅e∉S, then a0⋅e=a1⋅e.
To say that C's agent is powerless outside S is to say that the if the world is at all dependent on C's agent, then the world must be in S.
Here are some lemmas about being powerless outside of a subset, which we will use later.
**Lemma:** If C's agent is powerless outside S and T⊇S, then C's agent is powerless outside T.
**Proof:** Trivial. □
**Lemma:** If C and D's agents are both powerless outside S, then C⊗D's agent is powerless outside S.
**Proof:** Let D=(B,F,⋆), and let C⊗D=(A×B,hom(C,D∗),⋄). Consider some (a0,b0),(a1,b1)∈A×B and (g,h)∈hom(C,D∗). We will use the fact that if a0⋅h(b0)∉S then a0⋅h(b0)=a1⋅h(b0), and the fact that if b0⋆g(a1)∉S then b0⋆g(a1)=b1⋆g(a1). Observe that if (a0,b0)⋄(g,h)∉S, then
(a0,b0)⋄(g,h)=a0⋅h(b0)=a1⋅h(b0)=b0⋆g(a1)=b1⋆g(a1)=(a1,b1)⋆(g,h).□
Now, we are ready for our first truly new definition of the observability of a finite partition.
**4.2. Multiplicative Definitions of Observables**
**Definition:** We say that C's agent can observe a finite partition V={S1,…,Sn} of W if C≃C1⊗⋯⊗Cn, where each Ci's agent is powerless outside Si.
Again, we also have a constructive version of this definition:
**Definition:** We say that C's agent can observe a finite partition V={S1,…,Sn} of W if C≃C1⊗⋯⊗Cn, where Ci=AssumeSi(C)&1Ti, where Ti=(W∖Si)∩Image(C).
**Claim:** These definitions are equivalent to each other and equivalent to the definitions above.
**Proof:** First, observe that if C's agent can observe V according to the constructive version of the multiplicative definition, it can also observe V according to the nonconstructive version of the multiplicative definition, since the agent of AssumeSi(C)&1Ti is clearly powerless outside Si.
Next, we show that if C's agent can observe V according to the nonconstructive multiplicative definition, it can also observe V according to the definition from subsets. Let C≃D=C1⊗⋯⊗Cn, where each Ci's agent is powerless outside Si. It suffices to show that D's agent can observe V, since the definition from subsets is equivalent to the additive definition, and thus closed under biextensional equivalence. Thus, it suffices to show that D's agent can observe Si for all i=1,…,n. We will show that D's agent can observe S1, and the rest will follows by symmetry.
Let C1=(A1,E1,⋅1), and let D1=(B1,F1,⋆1)=C2⊗⋯⊗Cn. We start by showing that D1's agent is powerless outside W∖S1. We have that the agents of C2,…,Cn are all powerless outside W∖S1, since being powerless outside something is closed under supersets. Thus we have that D1's agent is powerless outside W∖S1, since being powerless outside W∖S1 is closed under tensor.
Thus, we have D=(A1×B1,hom(C,D∗),⋄)=C1⊗D1, with C1's agent powerless outside S1 and D1's agent powerless outside W∖S1. Given an arbitrary (a1,b1),(a2,b2)∈A1×B1 , we will show that (a1,b2)∈if(S1,(a1,b1),(a2,b2)), and thus show that D's agent can observe S1.
It suffices to show that for all (g,h):C→D∗, if (a1,b2)⋄(g,h)∈S1, then (a1,b2)⋄(g,h)=(a1,b1)⋄(g,h), and if (a1,b2)⋄(g,h)∉S1, then (a1,b2)⋄(g,h)=(a2,b2)⋄(g,h). Indeed, if (a1,b2)⋄(g,h)∈S1, then, since D1's agent is powerless outside W∖S1, we have
(a1,b2)⋄(g,h)=b2⋆1g(a1)=b1⋆1g(a1)=(a1,b1)⋄(g,h).Similarly, if (a1,b2)⋄(g,h)∉S1, then, since C1's agent is powerless outside S, we have
(a1,b2)⋄(g,h)=a1⋅1h(b2)=a2⋅1h(b2)=(a2,b2)⋄(g,h).Thus, D's agent can observe S1, so C's agent can observe V according to the definition from subsets.
Finally, we assume that C's agent can observe V according to the assuming definition, and show that C's agent can observe V according to the constructive version of the multiplicative definition.
We work by induction on n, the number of parts. The case where n=1 is trivial. Let C≃AssumeS1(C)&…&AssumeSn(C). Thus, we also have that C≃AssumeS1∪S2(C)&AssumeS3(C)&…&AssumeSn(C), and so by induction, we have that C≃(AssumeS1∪S2(C)&1T1∩T2)⊗C3⊗⋯⊗Cn, where Ci and Ti are as in the constructive multiplicative definition. Thus, it suffices to show that
AssumeS1∪S2(C)&1T1∩T2≃C1⊗C2=(AssumeS1(C)&1T1)⊗(AssumeS2(C)&1T2).First, observe that we have C≃D1&D2&D3, where D1=AssumeS1(C), D2=AssumeS2(C), and D3=AssumeS3(C)&…&AssumeSn(C). Let Di=(Bi,Fi,⋆i). Let Ri=Image(Di).
Observe that T1=R2∪R3, T2=R1∪R3, and T1∪T2=R3, and observe that AssumeS1∪S2(C)≃D1&D2. Thus it suffices to show that (D1&1R2∪R3)⊗(D2&1R1∪R3)≃D1&D2&1R3.
Let D1&1R2∪R3=(B1,F1⊔R2⊔R3,∙1), let D2&1R1∪R3=(B2,F2⊔R1⊔R3,∙2), and let D1&D2&1R3=(B1×B2,F1⊔F2⊔R3,∙3) where ∙1, ∙2, and ∙3 are all given by b∙if=b⋆1f if f∈F1, b∙if=b⋆2f if f∈F2, and b∙if=f otherwise.
Let H=hom(B1&1R2∪R3,(D2&1R1∪R3)∗). Let (D1&1R2∪R3)⊗(D2&1R1∪R3)=(B1×B2,H,∙4)), where
(b1,b2)∙4(g,h)=b1∙1h(b2)=b2∙2g(b1).Observe that for any f1∈F1, there is a (gf1,hf1)∈H, given by gf1(b1)=b1⋅1f1 and hf1(b2)=f1. This is clearly a morphism, since
b1∙1hf1(b2)=b1∙1f1=b1⋅1f1=gf1(b1)=b2∙2gf1(b1).Similarly, for any f2∈F2, there is a morphism (gf2,hf2)∈H given by gf2(b1)=f2 and hf2(b2)=b2⋅2f2. Finally, for any r∈R3, there is a morphism (gr,hr)∈H, given by gr(b1)=hr(b2)=r, which is also clearly a morphism.
We show that these are in fact all of the morphisms in H. Indeed, let (g,h) be a morphism in H, let b1 be an element of B1, and let b2 be an element of b2. Let
r=b2∙2g(b1)=b1∙1h(b2).If r∈R3, then g(b1)=h(b2)=r, so given any b′1∈B1,
b2∙2g(b′1)=b′1∙1h(b2)=r ∈R3, and so
g(b′1)=b2∙2g(b′1)=r.Similarly, for any b′2∈B2, h(b′2)=r and so (g,h)=(gr,hr).
If r∈R1, then g(b1)=r, and h(b2)∈F1. Let f1=h(b2). Given any b′1∈B1,
b2∙2g(b′1)=b′1∙1h(b2)=b′1∙1f1∈R1, so
g(b′1)=b2∙2g(b′1)=b′1∙1f1=b′1⋅1f1.Given any b′2∈B2,
b1∙1h(b′2)=b′2∙2g(b1)=b′2∙2r=r ∈R1, and so
h(b′2)=b1∙1h(b′2)=r.Thus, (g,h)=(gf1,hf1).
Finally, if r∈R2, we similarly have (g,h)=(gf2,hf2), where f2=g(b1)∈F2.
We construct a pair of morphisms
(g0,h0):(B1×B2,H,∙4)→(B1×B2,F1⊔F2⊔R3,∙3)and
(g1,h1):(B1×B2,F1⊔F2⊔R3,∙3)→(B1×B2,H,∙4),by letting g0 and g1 be the identity on B1×B2, letting h0:F1⊔F2⊔R3→H be given by h0(f)=(gf,hf) as above. Since we have shown that h0 is surjective, we let h1 be any right inverse to h0. It is easy to show that both of these are morphisms by the construction of (gf,hf), and they compose to something homotopic to the identity in both orders since g0∘g1 and g1∘g0 are the identity of B1×B2.
Thus (D1&1R2∪R3)⊗(D2&1R1∪R3)≃D1&D2&1R3, so C's agent can observe V according to the constructive multiplicative definition, completing the proof. □
You may have noticed that the last part of the proof would have been much simpler if ⊗ distributed over &, but ⊗ does not in general distribute over &. (⊗ distributes over ⊕ and ⅋ distributes over &.)
In this case, however, ⊗ does distribute over &. I do not plan on going over it now, but there is actually an interesting relationship between observables and cases where ⊗ distributes over &.
**4.3. Example**
Let C0 be defined as in the previous examples, with R={ur,nr} and S={us,ns}. Let TX=(W∖X)∩Image(C0), so that 1TR=1S and 1TS=1R. By the multiplicative definitions of observables, there then exist two frames
C1=AssumeR(C) & 1S= rusnsr→ur→n(urusnsnrusns)
and
C2=AssumeS(C) & 1R= surnrs→us→n(usurnrnsurnr)
such that C0≃C1⊗C2.
Here, C1 is an agent that treats the "makes decisions when it's sunny" part of itself as though it were an external process. Similarly, C2 externalizes its ability to make decisions when it's rainy.
This example illustrates both multiplicative definitions, and also shows the construction used in the constructive multiplicative definition.
Appealing again to the fact that AssumeS(C)≅1S⊗C, we also have the option of restating C0≃C1⊗C2 here as C0 ≃ ((1R⊗C0) & 1S) ⊗ ((1S⊗C0) & 1R). In words, this says that Agent(C0) is (biextensionally equivalent to) a team consisting of:
1. that very agent, picking an action after the environment either (a) gives it a promise it will rain or (b) makes it powerless and doesn't rain; and
2. that very agent, picking an action after the environment either (a) gives it a promise it won't rain or (b) makes it powerless and rains.
**4.4. Updatelessness**
The relationship between observables' additive and multiplicative definitions is interesting. You can think of the additive definition as updateful, while the multiplicative definition is updateless.
The Ci in the additive definition are basically given a promise that the world will end up in Si. The Ci in the multiplicative definition, however, are instead given a promise that their choices have no effect on worlds outside of Si.
I think the updateless factorization is better, and thus prefer the multiplicative definition in spite of the fact that it is more complicated.
When an updateless agent observes something, it becomes the version of itself that only affects the worlds in which it makes that observation. When an updateful agent observes something, we assume that all the worlds in which it does not make that observation do not exist. The fact that the additive and multiplicative definitions above are equivalent illustrates the equivalence of the updateful and updateless views in the simple cases where there is true observation. However, they diverge as soon as you want to try to approximate observation. The updateless view approximates better, as it makes sense to think of a subagent that has only a very small effect on worlds in which it does not make the observation that it makes.
Also, note that the Ci in the additive definition are not subagents of C, but they are additive sub-environments. The Ci in the multiplicative definition are multiplicative subagents of C.
5. Internalizing-Externalizing Definitions
------------------------------------------
Next, we have the nonconstructive internalizing-externalizing definition of observables.
**Definition:** We say that C's agent can observe a finite partition V of W if either A={} or C is biextensionally equivalent to something in the image of ExternalV∘InternalV.
Again, we have a constructive version of this definition.
**Definition:** We say that C's agent can observe a finite partition V of W if either A={} or C≃ExternalV(InternalV(C)).
**Claim:** These definitions are equivalent to each other and to the definitions above.
**Proof:** The case where A={} is trivial, so we assume that A is nonempty. Clearly if C's agent can observe V under the constructive internalizing-externalizing definition, then V is also observable in C under the non-constructive version.
Next, assume that C is in the image of ExternalV∘InternalV (up to biextensional equivalence). Recall that the image of InternalV up to biextensional equivalence is exactly those Cartesian frames (B,F,⋆) such then F is nonempty and for all f0,f1∈F and b∈B, we have v(b⋆f0)=v(b⋆f1). Thus, C≃ExternalV(B,F,⋆), where (B,F,⋆) is of this form. Let vB:B→V send each element b∈B to the unique vb∈V such that v(b⋆f)=vb for all f∈F, and let VB be the image of vB. Then, ExternalV(B,F,⋆)=(B/X,X×F,⋄), where X={{b∈B | vB(b)=v′} | v′∈VB}, and q⋄(x,f)=q(x)⋆f.
Let VB={v1,…,vm}, and let Bi={b∈B | vB(b)=vi}. Then, we clearly have that ExternalV(B,F,⋆)≅(B1×⋯×Bm,VB×F,∙), where (b1,…,bm)∙(vi,f)=bi⋆f. But this is clearly isomorphic to D1&…&Dm, where Di=(Bi,F,⋆i), where b⋆if=b⋆f. Thus, C's agent can observe V according to the nonconstructive additive definition of observables.
Finally, we assume that C's agent can observe V according to the nonconstructive additive definition of observables, and we show that C's agent can observe V according to the constructive internalizing-externalizing definition. Let C≃C1&…&Cn, where Ci◃⊥Si. Let Ci=(Ai,Ei,⋅i), and without loss of generality, let C=C1&…&Cn=(A,E,⋅), where A=A1×⋯×An and E=E1⊔⋯⊔En.
First, we show that InternalV(C)≃C1⊕⋯⊕Cn. Let C1⊕⋯⊕Cn=(A1⊔⋯⊔An,E1×⋯×En,⋆). Observe that (since A is nonempty), InternalV(C)≅(A×F,B/F,⋆′), where F={E1,…,En}, where (a,f)⋆q=a⋅q(f).
We construct
(g0,h0):(A1⊔⋯⊔An,E1×⋯×En,⋆)→(A×F,B/F,⋆′)and
(g1,h1):(A×F,B/F,⋆′)→(A1⊔⋯⊔An,E1×⋯×En,⋆)as follows. Let g1((a1,…,an),Ei)=ai. Let g0(ai)=((a1,…,ai,…,an),Ei), where ai∈Ai, and aj∈Aj is chosen arbitrarily for j≠i. Let h0(q)=(q(E1),…,q(En)), and h1(e1,…,en)=q, where q(Ei)=ei. Clearly, h0 and h1 are inverses.
To see that (g0,h0) is a morphism, observe that for all ai∈A1⊔⋯⊔An and q∈B/F, we have
g0(ai)⋆′q=((a1,…,ai,…,an),Ei)⋆′q=(a1,…,ai,…,an)⋅q(Ei)=ai⋅iq(Ei)=ai⋆(q(E1),…,q(En))=ai⋆h0(q),where ai∈Ai.
To see that (g1,h1) is a morphism, observe that for all ((a1,…,an),Ei)∈A×F, and for all (e1,…en)∈E1×⋯×En, we have
g1((a1,…,an),Ei)⋆(e1,…,en)=ai⋆(e1…,en)=ai⋅iei=(a1,…,an)⋅ei=(a1,…,an)⋅h1(e1,…,en)(Ei)=((a1,…,an),Ei)⋆h1(e1,…,en).It is clear that (g0,h0)∘(g1,h1) and (g1,h1)∘(g0,h0) are both homotopic to the identity, since h0∘h1 and h1∘h0 are both the identity.
Now, we have that InternalV(C1&…&Cn)≃C1⊕⋯⊕Cn, and so we also have dually that ExternalV(C1⊕⋯⊕Cn)≃C1&…&Cn. Thus, C≃ExternalV(InternalV(C)). □
The thing that is going on here is that when C internalizes V, the agent of C then has the full ability to choose how V goes (among ways of V going that were possible in C). InternalV(C) might have other choices than just choosing how V goes. If it does, then it can freely entangle those other choices with the choice of V however it wants.
When C then externalizes V, it loses all control over V. However, it preserves the ability to entangle all of its other choices with the way that V goes. This ability for the agent to entangle its choices with V is exactly what it means to say "V is observable."
**5.1. Example**
Let C0 be defined as in the previous examples, with V={{ur,nr},{us,ns}}. By the internalizing-externalizing definitions, there exists a frame
InternalV(C0)≅ (u,r)(u,s)(n,r)(n,s)(u↔r,r)(u↔r,s)(u↔s,r)(u↔s,s)⎛⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟
⎟⎠,
which is biextensionally equivalent to
C1= urnrusns⎛⎜
⎜
⎜⎝urnrusns⎞⎟
⎟
⎟⎠.
We then have that
ExternalV(C1)≅ rs(r→ur,s→us)(r→nr,s→ns)(r→ur,s→ns)(r→nr,s→ur)⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠,
which is isomorphic to C0.
This example illustrates both internalizing-externalizing definitions, and also shows the construction used in the constructive definition.
In our next post, we'll conclude the sequence by showing how to formalize agents that learn and act over time using Cartesian frames. |
49033391-c926-42b5-8b3d-d75e8ffbd0ac | trentmkelly/LessWrong-43k | LessWrong | Oren's Field Guide of Bad AGI Outcomes
This field guide is my attempt to collect my observations and conceptual ideas regarding artificial general intelligence (AGI), it’s place in our current moment of AI history and designs for AGI that might just get us out of our current exposure to existential risk from technology.
I am writing it as a series of topical papers which I will link to from here when they are completed.
Let’s begin with the most basic rules regarding general intelligence and safety.
Chapter 1: Here Be Dragons
Chapter 2: On Generality
More to come... |
8ca4cdf0-69df-48e9-8152-e4c8a6991ff7 | trentmkelly/LessWrong-43k | LessWrong | Beyond Blame Minimization: Thoughts from the comments
There were a lot of fantastic comments on this post. I want to break down some common themes and offer my thoughts.
Incentives
Unsurprisingly, there was a lot of focus on the role that incentives—or a lack thereof—play in a bureaucracy.
Dumbledore's Army:
> I’ve been asking myself the same question about bureaucracies, and the depressing conclusion I came up with is that bureaucracies are often so lacking incentives that their actions are either based on inertia or simply unpredictable. I’m working from a UK perspective but I think it generalises. In a typical civil service job, once hired, you get your salary. You don’t get performance pay or any particular incentive to outperform.[1] You also don’t get fired for anything less than the most egregious misconduct. (I think the US has strong enough public sector unions that the typical civil servant also can’t be fired, despite your different employment laws.) So basically the individual has no incentive to do anything.
>
> As far as I can see, the default state is to continue half-assing your job indefinitely, putting in the minimum effort to stay employed, possibly plus some moral-maze stuff doing office politics if you want promotion. (I’m assuming promotion is not based on accomplishment of object-level metrics.) The moral maze stuff probably accounts for tendencies toward blame minimisation.
>
> Some individuals may care altruistically about doing the bureaucracy’s mission better, eg getting medicines approved faster, but unless they are the boss of the whole organisation, they need to persuade other people to cooperate in order to achieve that. And most of the other people will be enjoying their comfortable low-effort existence and will just get annoyed at that weirdo who’s trying to make them do extra work in order to achieve a change that doesn’t benefit them. So the end result is strong inertia where the bureaucracy keeps doing whatever it was doing already.
tailcalled quotes John Wentworth, who talk |
6e7d2643-7222-4bf7-9522-9b828e648c57 | trentmkelly/LessWrong-43k | LessWrong | (Link) I'm Missing a Chunk of My Brain
I'm blown away by this story. This perfectly normal woman is missing a big chunk of the left hemisphere, where the functions associated with language are typically placed. I guess that the main takeaway could be:
> "It seems that networks in the brain arrange in a particular way, but if you lose crucial brain regions as a baby - when the brain is still very plastic - these networks can reroute" |
6457e649-058a-4873-a40a-2377a02d3ec0 | trentmkelly/LessWrong-43k | LessWrong | How much is death a limit on knowledge accumulation?
The older I get, the more it feels like death is a major bottleneck to the accumulation of certain kinds of knowledge and insights.
It's not much of a blocker to shallow things that are easy to explain precisely. For example:
* math
* physics
* accounting of money
* stories (both fictional and historical)
It does seem to be a blocker for deep knowledge that's hard to easily explain and teach. For example:
* rationality
* philosophy
* public policy & economics (in the broad sense of figuring out policies that organizations should implement to mould incentives in ways that achieve the desired outcomes)
* how to live a good life
The former category of things benefit from being easy to put into writing and be transmitted accurately every time. This allows generations to easily build cumulative mountains of knowledge. Yes, each generation a person has to learn more before they can contribute to the mountain and, as of maybe 200 years ago, there's too much accumulation for one person to know everything known, but we don't seem especially close to the limit, and very recent advances like LLMs may offer a way to scale our learning further by outsourcing the remembering of many details.
The latter category suffers because each of them is hard to transmit and it's easy to be confused and transmit the wrong thing. Thus, to some extent, each person needs to rediscover the knowledge of past generations for themselves and only after the fact may recognize that previously generations had some of these same details figured out. Try as we might with institutions that teach young people the best of our wisdom of the past in these areas, it's imperfect and you have many people who will reject the knowledge because they don't understand it and will continue to reject it until they work it out for themselves. Thus even if somewhere there is an unbroken line from Socrates to the present day of ever advancing philosophical knowledge manifested in a single person, almost nobo |
1d825458-64b2-40bf-91f3-56665fe81556 | trentmkelly/LessWrong-43k | LessWrong | Why care about AI personhood?
In this new paper, I discuss what it would mean for AI systems to be persons — entities with properties like agency, theory-of-mind, and self-awareness — and why this is important for alignment. In this post, I say a little more about why you should care.
The existential safety literature focuses on the problems of control and alignment, but these framings may be incomplete and/or untenable if AI systems are persons.
The typical story is approximately as follows.
AI x-safety problem (the usual story):
1. Humans will (soon) build AI systems more intelligent/capable/powerful than humans;
2. These systems will be goal-directed agents;
3. These agents’ goals will not be the goals we want them to have (because getting intelligent agents to have any particular goal is an unsolved technical problem);
4. This will lead to misaligned AI agents disempowering humans (because this is instrumentally useful for their actual goals).
Solution (technical control/alignment):
* We better figure out how to put the goals we want (“our values”) into capable agents.
There are, of course, different versions of this framing and more nuanced perspectives. But I think something like this is the “standard picture” in AI alignment.
Stuart Russell seems to have a particularly strong view. In Human Compatible, one of his core principles is: “The machine's only objective is to maximise the realisation of human preferences.” and in a recent talk he asked “How can humans maintain control over AI — forever?”
This framing of the x-safety problem, at least in part, arises from the view of (super)intelligent AI systems as rational, goal-directed, consequentialist agents. Much of the literature is grounded in this cluster of views, using either explicit rational agent models from decision and game theory (e.g., the causal incentives literature), or somewhat implicit utility-maximising assumptions about the nature of rationality and agency (e.g., Yudkowsky).
I don’t necessarily want to |
426c7065-c2ae-4be6-a0aa-8a53f85a8636 | trentmkelly/LessWrong-43k | LessWrong | How curing aging could help progress
How would society change if we cured aging, and people could have as many healthy years of life as they wanted?
A common concern is that this would ossify our institutions. The old guard would never die off, and so would never be replaced by young bloods. This could threaten progress across the board, from governance to physics. If “science advances one funeral at a time,” what happens when the rate of funerals plummets?
It’s a real concern. But here are three reasons why curing aging could help progress:
1. Population. One of the greatest threats to long-term progress may be a slowdown in global population growth. We need more brains to keep pushing science and technology forward. Yet right now, many wealthy nations have fertility rates below replacement levels. Curing aging would help temporarily by lowering the mortality rate. It could help permanently if people decide to have more children, on average. That might happen if longer lifespan means people feel they have time for both children and a career. (Remember that fully curing aging means maintaining reproductive health for all those years.)
2. Burden of knowledge. There is a hypothesis that as knowledge grows, it takes longer to reach the frontier, and so individual researchers have less time to contribute advancements. They are also forced to specialize—but breakthroughs often come from making connections across far-flung disciplines. If individuals had much longer lifespans, it would be no problem for them to spend 30 or 40 years just learning, before making major contributions. And you could spend another 10 or 20 years picking up a couple more specialties in disparate areas.
3. Long-term thinking. How would people’s thinking change if they felt they were going to live 150, 300, even 1,000 years or more? The very long-term becomes much more personal. Posterity is something you’re going to be around for.
I still think the “old guard” problem is real, and we’d have to come up with new mechanisms to a |
42ec6eda-f05c-4516-8d36-a03e0a2fcaa7 | trentmkelly/LessWrong-43k | LessWrong | Chapter 76: Interlude with the Confessor: Sunk Costs
Rianne Felthorne descended the stairs of roughened stone and crude mortar, keeping a Lumos lit through the distances between fire-sconces, holding aloft her wand through the gaps from light to light.
She came to the empty rock cavern pierced by many dark openings, lit by a torch of ancient style that fired as she entered.
There was no one else there, as yet, and after long minutes of nervous standing, she began the spell to Transfigure a cushioned sofa large enough for two people to sit, or maybe even lie down on. A simple wooden stool would have been easier, she could have done that in fifteen seconds, but - well -
Even when the sofa was fully conjured, Professor Snape still hadn't arrived, and she sat down on the left side of her sofa with her pulse hammering in her throat. Somehow she was only becoming more nervous, not less, as the delay stretched.
She knew this was the last time.
The last time before all these memories went away, and Rianne Felthorne found herself in a mysterious cavern, wondering what was going on.
There was something about it that felt like dying.
The books said a properly done Obliviation wasn't harmful, people forgot things all the time. People dreamed, and then woke up without remembering their dreams. Obliviation didn't even involve that much discontinuity, just a brief instant of disorientation; it was like being distracted by a loud noise and losing track of a thought you couldn't seem to remember afterward. That was what the books said, and why Memory Charms were fully approved by the Ministry for all authorized governmental purposes.
But still, these thoughts, the thoughts she was thinking right now; soon nobody would have them anymore. When she looked ahead in the future, there was nobody to complete the thoughts she wasn't finished thinking. Even if she managed to tie up all the loose ends in her mind over the next minute, there wouldn't be anything left of it afterward. Wasn't that exactly what you would find yourself refle |
d5325ac8-4a74-44d7-af03-2629ecedc2b5 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Perfect Predictors
What's even the point of making decision theories for dealing with perfect predictors (newcomb's paradox) when we know that according to one of our most accepted theories these days (quantum mechanics) the inherent randomness of our universe does not allow perfect prediction even with unlimited current and past knowledge? |
a2bff76e-8c2e-451b-a726-398cf00f0d1b | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Some Summaries of Agent Foundations Work
This is a bunch of not-very-technical summaries of agent foundations work from LessWrong and the Alignment Forum.
I was hoping to turn it into a comprehensive literature review, categorising things in an enlightening way and listing open problems. It turns out that's pretty hard to do! It's languished in my drafts for long enough now, so I'm just going to post it as it is. Hopefully someone else can produce a great overview of the field instead.
Why Agent Foundations?
======================
My own answer to this question is that most AI threat models depend on a powerful agent pursuing a goal we don't want it to, and mathematical models of agency seem useful both for understanding and dealing with these risks. Existing models of agency from fields like reinforcement learning and game theory don't seem up to the job, so trying to develop better ones might pay off.
Normative and Descriptive Agent Foundations
-------------------------------------------
One account of why our usual models of agency aren't up to the job is the [Embedded Agency](https://www.alignmentforum.org/s/Rm6oQRJJmhGCcLvxh) sequence - the usual models assume agents are unchanging, indivisible entities which interact with their environments through predefined channels, but real-world agents are a *part* of their environment. The sequence identifies four rough categories of problems that arise when we switch to trying to model embedded agents, explained in terms of Marcus Hutter's model of the theoretically perfect reinforcement learning agent, AIXI.
I find these subproblems to be a useful framing for thinking about agents foundations, but as I explained in a [previous post](https://www.alignmentforum.org/posts/nzRh8yQHi3bx9bLsD/?commentId=gCmnSH2L3os3ChTr7), I think they take a strongly **normative**stance, asking what high-level principles an agent should follow in order be theoretically perfect. Some other agent foundations takes a more **descriptive** stance, asking what mechanistic and behavioural properties agents in the real world tend to have. You could also call this a distinction between **top-down** and **bottom-up** approaches to modelling agency.
Here are the problems from the Embedded Agency sequence:
### Normative Subproblems
* **Decision Theory.** AIXI's actions affect the world in a well-defined way[[1]](#fnu8dv85w16d), but embedded agents have to figure out whether they care about the causal, evidential, or logical implications of their choices.
* **Embedded World-Models.** AIXI can hold every possible model of the world in its head in full detail and consider every consequence of its actions, but embedded agents are part of the world, and have limited space and compute with which to model it. This gives rise to the non-realisability problem - what happens when the real world isn't in your hypothesis class?
* **Robust Delegation.** AIXI is unchanging and the only comparable agent in town, but embedded agents can self-modify and create other agents. They need to ensure their successors are aligned.
* **Subsystem Alignment.** AIXI is indivisible, but embedded agents are chunks of the world made up of subchunks. What if those subchunks are agents with their own agendas?
I think the embedded agency subproblems are also a useful way to categorise descriptive work, but the names and descriptions feel too normative, so I renamed them for the descriptive case. I also suggested a fifth problem, which is about figuring out how our models actually correspond to reality. I called it '**Identifying Agents**', but now I prefer something like '**Agents in Practice**'.
### Descriptive Subproblems
* **I/O Channels.** Actions, observations, and Cartesian boundaries aren't primitive: descriptive models need to define them. How do we move from a non-agentic model of the world to one with free will and counterfactuals?
* **Internal Components.** Presumably agents contain things like goals and world-models, but how do these components work mathematically? And are there others?
* **Future Agents.**What is the relationship between an agent and its future self, or its successors? To what extent can goals be passed down the line?
* **Subagents and Superagents.** Do agents contain subagents? When can the interaction of a group of agents be thought of as a superagent? How do the goals of subagents relate to the goals of superagents?
* **Agents in Practice.** Can we determine which parts of the world contain agents, and read off their internal components? Should we expect our models of agency to be very accurate, like the models of physics, or just a rough guide, like the models of economics? And how close are agents in practice to normative ideals?
The rest of the post organises work according to whether it takes a normative or descriptive approach, and which of the subproblems it's aimed at. I'm not necessarily convinced these are reality-carving distinctions - they're just a framing I was trying out.
Let's start with the descriptive stuff.
Descriptive
===========
Selection Theorems
------------------
**Subproblems:** Internal components.
**Summary:** You can think of John Wentworth's [selection theorems](https://www.lesswrong.com/tag/selection-theorems) agenda as a descriptive take on coherence theorems. Coherence theorems say things like 'agents without property X are bad in way Y, so you should have property X.' That sounds pretty normative. Selection theorems say instead 'agents without property X get selected against in way Z, so we should expect agents to have property X.'
You could also think of selection theorems as a place where normative and descriptive thinking about agency joins up. A normative coherence theorem like 'agents with cyclic preferences get money-pumped, which seems bad' is not too far from being a descriptive selection theorem. We just need a story about why agents who get money-pumped are selected against, e.g. because we're in an RL training setup where agents with less 'money' are in a worse position to get reward.
But while coherence theorems are usually about *behavioural* properties, in the selection theorems frame it's quite natural to ask what *structural* properties of agents are selected for. John's aim is to prove selection theorems which tell us what to expect the internal components of agents produced by our ML setups to look like. Knowing that certain data structures representing goals or world models will be selected for under certain conditions seems broadly useful: it could [help with transferable interpretability](https://www.lesswrong.com/posts/A7QgKwWvAkuXonAy5/how-do-selection-theorems-relate-to-interpretability), for example, or clarify whether corrigibility, ambitious value learning, or human imitation is a more natural alignment alignment target.
Existing selection theorems include [the Kelly criterion](https://www.lesswrong.com/tag/kelly-criterion), [the Gooder Regulator Theorem](https://www.lesswrong.com/posts/Dx9LoqsEh3gHNJMDk/fixing-the-good-regulator-theorem ) and a proof that [Q-learning agents in Newcomblike environments learn 'ratifiable' policies](https://proceedings.neurips.cc/paper/2021/hash/b9ed18a301c9f3d183938c451fa183df-Abstract.html). John has a post about [how to work on selection theorems](https://www.lesswrong.com/posts/G2Lne2Fi7Qra5Lbuf/selection-theorems-a-program-for-understanding-agents#How_to_work_on_Selection_Theorems_) and there's a [bounty](https://www.lesswrong.com/posts/moi3cFY2wpeKGu9TT/clarifying-the-agent-like-structure-problem) out on proving a selection theorem of a particular form.
Agents Over Cartesian World Models
----------------------------------
**Subproblems:** Internal Components.
**Summary:** In [this post](https://www.lesswrong.com/posts/LBNjeGaJZw7QdybMw/agents-over-cartesian-world-models), Mark Xu and Evan Hubinger add some embeddedness to the standard RL model of a [partially observable Markov decision process](https://en.wikipedia.org/wiki/Partially_observable_Markov_decision_process) by adding an *internal* agent state to go with the usual *external* environment state. They then consider four types of consequentialist agents - those that assign utility to environment states, internal states, actions and observations respectively. They also extend the formalism to handle nonconsequentialist 'structural agents' that care not about things in the world directly but about *the way the world works.*
I found this to be one of those posts that doesn't say anything groundbreaking but ends up being weirdly clarifying just by going through a taxonomy of different ways something can be. Now when people talk about utility functions I usually take a moment to make a mental note of what type signature they're assuming.
Boundaries
----------
**Subproblems:** I/O Channels.
**Summary:** But can we avoid taking Cartesian boundaries as a primitive notion, and define them instead?
[Andrew Critch's Boundaries sequence](https://www.alignmentforum.org/s/LWJsgNYE8wzv49yEc/p/HrtqLy46Fx7xqRrMo) offers a formal definition of the boundary of an organism in terms of an approximate directed Markov blanket in a dynamic causal model - meaning a set of variables which approximately causally separate the inside of the organism (the viscera) from the outside (the environment). The boundary has a passive part which directs causal influence inwards and an active part which directs causal influence outwards. You can think of these parts as transmitting actions and observations respectively.
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which updates the state of the viscera and active boundary given their current states, plus the state of the passive boundary. Importantly, we only assume the boundary *approximately* screens off the inside of the organism from the outside. The more 'infiltration'of information there is from the environment into the active boundary and viscera, the less well the decision rule will describe the evolution of their state. Decision rules which lead to high levels of infiltration will soon cease to describe reality, so we can assume the ones we encounter in practice will be self-perpetuating (do I smell a selection theorem?).
Critch thinks the boundaries concept is useful for thinking about [topics as diverse as](https://www.alignmentforum.org/s/LWJsgNYE8wzv49yEc/p/SajYfrsoTHxiXPNtf#Recap) corrigibility, mesa-optimisers, AI boxing, consequentialism, mild optimisation, and counterfactuals. The idea that boundaries are important has been gaining some steam - [this post](https://www.lesswrong.com/posts/fjgoMaBenyXcRDrbX/boundaries-and-ai-safety-compilation) by Chipmonk collates recent discussion on the topic.
Cartesian Frames
----------------
**Subproblems:** I/O Channels, Subagents and Superagents.
**Summary:** Where Critch thinks about *partioning the world* into agent, environment, and boundary, Scott Garrabrant's Cartesian frames is about *factorising* *the world's state space* into substates chosen by an agent and its environment respectively.
We start with a set W of possible ways the world could be, and write down of a set A of ways an agent could be, a set E of ways an environment could be, and a function ⋅:A×E→W which assigns a world to each agent-environment combination. The tuple C=(A,E,⋅) is a Cartesian frame, and the point is that there are many different Cartesian frames for a given W, each corresponding to a different agent-environment distinction. The agent in one Cartesian frame might be a subagent of the agent in another - or they might overlap in a different way. We can write a Cartesian frame as a matrix, like this:
Here Alice and Bob are collectively deciding whether order a takeway. Rows are elements of A: they could decide to get pizza (p) or cook at home (h). Columns are elements of E, which correspond to different ways the environment could be: the pizza place could be open (o) or closed (c). Entries in the matrix are the worlds that arise under each combination: unless they decide on pizza and the restaurant is open, they will eat at home.
Or instead of thinking of Alice and Bob as a collective agent, we could make Alice our agent and treat Bob as part of the environment:
Now oh means the pizza place is open but Bob votes for home, and so on. Different carvings produce agents that can force different facts about the world to be true - Alice and Bob collectively can ensure they eat at home, but Alice can't ensure it on her own. The question of how subagents relate to superagents feels like the most obvious place where this stuff might produce important insights.
Mathematically, a Cartesian frame is a thing called a [Chu Space](https://en.wikipedia.org/wiki/Chu_space). Scott's aim is to be able to start with a Cartesian frame and do a bunch of category theory to reconstruct familiar notions like actions, observations, [the passing of time](https://www.lesswrong.com/posts/PfcQguFpT8CDHcozj/finite-factored-sets-in-pictures-6), and so on. Interesting things might fall out of considering the way Cartesian frames relate to each other - for example, we might be able to distinguish fundamental features that arise no matter how we carve up the world, from features which are artefacts of a particular way of looking at things.
Basic Foundations for Agent Models
----------------------------------
**Subproblems:** I/O Channels, Internal Components, Subagents and Superagents.
**Summary:** The [stated aim](https://www.alignmentforum.org/posts/moi3cFY2wpeKGu9TT/clarifying-the-agent-like-structure-problem) of this sequence by John Wentworth is to prove a selection theorem of the form: if a system robustly steers far away parts of the world into a relatively-small chunk of their state space, the system is very likely doing search over an internal world model. I believe the quest is ongoing.
John's methodology is to start with a low-level model of the world, and try to derive familiar concepts of agency from that. He argues that causal models are a good place to start, since they [don't assume assume a universal notion of time](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/HCibBn3ZCZRwMwNEE) and allow us to easily [rewrite chunks at different levels of abstraction](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/Xd9FLs4geRAWxkQPE).
Then he gets to work: showing that [maximising expected utility can be thought of as minimising the world's description length](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/voLHQgNncnjjgAPH7) with respect to a particular model; suggesting a definition of [actions and observations as information well-preserved at a distance](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/d2n74bwham8motxyX), which implies a flexible Cartesian boundary; identifying the problem of finding a '[measuring stick of utility](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/73pTioGZKNcfQmvGF)' to prove coherence theorems with respect to; and thinking about how such a measuring stick might arise in agents consisting of [distributed subagents](https://www.alignmentforum.org/s/ogntdnjG6Y9tbLsNS/p/32sm7diYTky5KhF6w).
Shard Theory
------------
**Subproblems:** Internal Components.
**Summary:** [Shard theory](https://www.lesswrong.com/s/nyEFg3AuJpdAozmoX) is an informal model of the internal components of agents, meant to describe both reinforcement learners and humans. The model looks something like this:
From Lawrence Chan's [Shard Theory in Nine Theses](https://www.alignmentforum.org/posts/8ccTZ9ZxpJrvnxt4F/shard-theory-in-nine-theses-a-distillation-and-critical)Goals are represented as 'shards of desire' for concepts in the agent's world model, like juice or following the rules. In a given context set by the world-model, some subset of shards are active. An active shard influences the agent's planning algorithm towards taking actions that lead to things the shard values, as predicted by the world-model.
Suppose your mum tells you you're not allowed any more juice. Your follow-the-rules shard bids for you to comply, and wins out. But when you happen to open the fridge and see the carton, your juice shard becomes more active, and suddenly a quick swig doesn't seem like such a bad idea. These contextually activated desires arise from your reward circuitry reinforcing different cognition in different contexts - wanting juice often leads to reward when you're standing in front of the fridge, but not so much in other situations.
If shard theory's model is correct, there are various implications. [We shouldn't think of RL agents as reward maximisers](https://www.lesswrong.com/s/nyEFg3AuJpdAozmoX/p/pdaGN6pQyQarFHXF4), and at first they won't look much like they're maximising any utility function at all - but over time, more agentic shards may take over and eventually coalesce into a reflective equilibrium that looks more like traditional models of agency. But the values of the final agent will be highly path dependent, and we can probably do a lot to shape them with clever training schedules.
This last point seems to be the main theory of change for shard theory - I don't think its proponents are hoping to understand shards so well mathematically that we can directly tinker with them inside neural networks. Instead they hope to develop arguments about what sorts of training regimes should lead RL agents to internalise the concepts we care about.
The Causal Incentives Agenda
----------------------------
**Subproblems:** Agents in Practice.
**Summary:** The [causal incentives](https://causalincentives.com/) approach to the alignment problem is mostly defined by the tools it attacks it with, causal influence diagrams, which look like this:
A causal influence diagram is a Pearl-style causal model with the variables partitioned into chance, utility and decision nodes. The decision nodes are special in that we don't define a conditional probability distribution over them. Usually you take some ML training setup or other strategic interaction between agents, model it as a CID, figure out how the decision variables would have to be distributed to maximise expected utility, and study the properties of the model that arises. One thing CIDs have been used for is to develop a [formal theory of agents' incentives](https://arxiv.org/pdf/2102.01685.pdf) (spoiler: human deciding reward => incentive to manipulate human). Another paper, [Discovering Agents](https://www.alignmentforum.org/posts/XxX2CAoFskuQNkBDy/discovering-agents), suggests a behavioural definition of agency and an algorithm for identifying them in principle. I think all of this comes under descriptive agent foundations, since it's concerned with how we should expect agents to behave - in specific situations in the case of agent incentives, and at the most abstract level in the case of discovering agents.
More generally, the philosophy behind causal incentives is that many important properties to do with agency and alignment are causal in nature, and therefore that causal influence diagrams are a good language with which to develop a formal calculus of alignment. Most causal incentives work is about turning intuitive ideas into formal definitions in a spirit of conceptual clarification. An underlying assumption is that it's most fruitful to look at what goes on *outside* an agent - its training data, the way its rewards are calculated, the situations it finds itself in - and that if there *are* important properties of the agent's internals, they can probably be derived as a consequence. This sounds like a selection theorems-esque stance, and indeed some recent causal incentives work is moving in that direction.
A difference between the use of causal models in this work and in the Basic Foundations for Agent Models sequence is that while John assumes a model of the world at a low level of abstraction and tries to derive things like actions, observations, and utilities, causal incentives work assumes a model in which the variables are already at that level of abstraction.
Normative
=========
Decision Theory
---------------
**Subproblems:** Decision Theory.
**Summary:**The usual setup for [decision theory](https://www.lesswrong.com/tag/decision-theory) is that you're selecting from a set of actions, and you have some beliefs about the way your utility depends on your choice. You want to maximise expected utility, but the question is which dependencies you should factor in when calculating it. The traditional academic debate is whether you should only care about the causal effects of your decision, or also about the evidence it provides. Work on decision theory from the alignment community is more about two other ideas.
The first is a debate about whether you should be 'updateless' - meaning that instead of choosing the action which is best in the current moment, you choose the action which you ideally would have precomitted to if you'd had the opportunity. *Making* binding precomittments has undeniable strategic advantages; the question is whether you should choose the action you would have been best off precomitting to even if you haven't. And which point in time should you choose the best precomittment with respect to: the day you decided your decision theory, the moment of your birth, or a point before the universe began? Another question is whether updatelessness is a matter of strategy, or preferences - [perhaps you should only be updateless if you care about counterfactual versions of you just as much as yourself](https://www.lesswrong.com/posts/9W4TQvixiQjpZmzrx/decision-theory-and-dynamic-inconsistency).
The second is the idea of 'logical dependence'. One way to think about this that instead of imagining that you're deciding whether *you* taking an action *right here right now*, you could imagine you're deciding whether *agents like you* take the action in *situations like this.* This is an intuitively compelling idea which 'performs well' in thought experiments, but has proved hard to formalise. [Updateless decision theory (UDT)](https://www.lesswrong.com/posts/de3xjFaACCAk6imzv/towards-a-new-decision-theory) aims to formalise it in terms of your decision resolving logical uncertainty about the output of your source code. [Functional decision theory (FDT)](https://arxiv.org/abs/1710.05060) aims to formalise it as an alternative notion of causality to the ordinary 'physical' Pearlian notion. [Proof-based decision theory](https://www.lesswrong.com/posts/hpAbfXtqYC2BrpeiC/troll-bridge-5) aims to formalise it in Peano arithmetic. So far, none of these efforts say much about how an agent in the real world could obtain beliefs about which variables logically depend on its decision.
Logical Induction
-----------------
**Subproblems:** Embedded World-Models.
**Summary:** AIXI uses Solomonoff induction, which comes with all the standard guarantees of Bayesian reasoning. Bayesian reasoners are logically omniscient, instantly realising the full consequences of their observations and their beliefs accordingly. They can never be Dutch booked.
Agents in the real world do *not* automatically realise all of the logical consequences of their observations. They have limited compute with which to figure that stuff out, and they have to apply it tactically. Logical induction is a way of doing it. It can't prevent agents from being Dutch-booked, but it can minimise the damage.
An agent satisfies the *logical induction criterion*, if, roughly, that there's no efficiently computable way to exploit it to earn unbounded profits with finite risk tolerance. In a 2016 paper, [Garrabrant et al.](https://intelligence.org/files/LogicalInduction.pdf) state the criterion, give a computable induction algorithm that satisifes it, and show that lots of desirable properties follow.
Infra-Bayesianism
-----------------
**Subproblems:** Decision Theory, Embedded World-Models.
Infra-Bayesianism feels like the most direct attempt to figure out an embedded version of AIXI.
It directly attacks the problem of non-realisability - the thing where unlike AIXI, an agent in the real world *cannot* maintain fully-detailed hypotheses about the way its environment works, since one small and simple part of the world cannot model the large and complex whole.
The basic idea is to replace precise hypotheses like "I'm 99% sure that's a dog and I'm 50% sure it's friendly," with imprecise ones like "I'm 99% sure that's a dog and I simply don't know whether it's friendly." We can achieve this by just saying our hypothesis is the whole *set* of probability distributions which put 99% on the thing being a dog.
All of the subsequent maths of infra-Bayesianism is about figuring out how to recover stuff like belief updates, policy-selection and so on, given this change. For example, now that we lack a precise probabilistic belief about whether the dog is friendly, we can't compare the expected value of patting it versus not. Infra-Bayesianism's way around this it to assume the worst and evaluate each policy by the least expected value it achieves across the set of probability distributions we're indifferent between. Patting an unfriendly dog will turn out much worse than failing to pat a friendly one, so we leave it well alone.
The maths gets pretty heavy, but apparently something like updateless decision theory falls out naturally, and it seems like there might be provable performance guarantees.
Infra-Bayesian *physicalism* combines this stuff with some ideas about Occam's razor and anthropics, which I can't claim to understand.
Tiling Agents
-------------
**Subproblems:** Robust Delegation.
**Summary:** This is [some old MIRI work from 2013](https://intelligence.org/files/TilingAgentsDraft.pdf) which aims to directly address the question of how an agent can self-modify while being confident its successors will share its goal.
It all takes place within a very specific formalism - an agent has a set of axioms, and only takes an action if it can prove in first-order logic that the action leads to the satisfaction of its goal. In order to take the action 'construct successor', the agent needs to prove that the *successor's* actions will lead to the satisfaction of the goal. Naively, the agent could just construct a successor that uses very same action-selection criterion. But there's a problem - Löb's theorem says if the agent's axioms are consistent, it will only be able to prove the action-selection criterion actually *works* if the successor's axioms are strictly mathematical weaker than its own. Not much of a successor!
Yudkoskwy and co. find some ways around the Löbian obstacle in settings with nice properties, and start to generalise the results to probabilistic settings, but ultimately conclude the underyling problems are only 'partially adressed'.
*Produced as part of the* [*SERI ML Alignment Theory Scholars Program*](https://serimats.org/) *- Winter 2022 Cohort.*
*Thanks to Lawrence Chan, Carson Jones, Dávid Matolcsi and Magdalena Wache for feedback.*
1. **[^](#fnrefu8dv85w16d)**A more careful wording would be *the AIXI model assumes the agent's* actions affect the world in well-defined way. |
8f7643e9-df8b-4b2a-bd2c-82916731b403 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | A short introduction to machine learning
Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. Compared with other introductions, I’ve focused less on explaining each concept in detail, and more on explaining how they relate to other important concepts in AI, especially in diagram form. If you're new to machine learning, you shouldn't expect to fully understand most of the concepts explained here just after reading this post - the goal is instead to provide a broad framework which will contextualise more detailed explanations you'll receive from elsewhere.
I'm aware that high-level taxonomies can be controversial, and also that it's easy to fall into the [illusion of transparency](https://en.wikipedia.org/wiki/Illusion_of_transparency) when trying to introduce a field; so suggestions for improvements are very welcome!
The key ideas are contained in this summary diagram:
First, some quick clarifications:
* None of the boxes are meant to be comprehensive; we could add more items to any of them. So you should picture each list ending with “and others”.
* The distinction between *tasks* and *techniques* is not a firm or standard categorisation; it’s just the best way I’ve found so far to lay things out.
* The summary is explicitly from an AI-centric perspective. For example, statistical modeling and optimization are fields in their own right; but for our current purposes we can think of them as machine learning techniques.
Let’s dig into each part of the diagram now, starting from the top.
Paradigms of artificial intelligence
------------------------------------
The field of **artificial intelligence** aims to develop computer programs that are able to perform useful tasks like answering questions, recognizing images, and so on. It got started around the 1950s. Historically, there have been several different approaches to AI. In the first few decades, the dominant paradigm was **symbolic AI**, which focused on representing problems using statements in formal languages (like logic, or programming languages), and searching for solutions by manipulating those representations according to fixed rules. For example, a symbolic AI can represent a game of chess using a set of statements about where the pieces currently are, and a set of statements about where the pieces are allowed to move (you can only move bishops diagonally, you can't move your king into check, etc). It can then play chess by searching through possible moves which are consistent with all of those statements. The power of symbolic search-based AI was showcased by [Deep Blue](https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)), the chess AI that beat Kasparov in 1997.
However, the symbolic representations designed by AI researchers turned out to be far too simple: there are very few real-world phenomena easily describable using formal languages ([despite valiant efforts](https://en.wikipedia.org/wiki/Cyc)). Since the 1990s, the dominant paradigm in AI has instead been **machine learning**. In machine learning, instead of manually hard-coding all the details of AIs ourselves, we specify models with free parameters that are learned automatically from the data they're given. For example, in the case of chess, instead of using a fixed algorithm like Deep Blue does, a ML chess player would choose moves using parameters that start off random, and gradually improve those parameters based on feedback on its moves: this is known as the *learning,* *training* or *optimization* process.\* In theory, statistical models (including simple models like linear regressions) also fit parameters to the data they're given. However, the two fields are distinguished by the scales at which they operate: the biggest successes of machine learning have come from training models with billions of parameters on huge amounts of data. This is done using **deep learning**, which involves training *neural networks* with many layers using powerful *optimization techniques* like gradient descent and backpropagation. Neural networks have been around since the beginning of AI, but they only became the dominant paradigm in the early 2010s, after increases in compute availability allowed us to train much bigger networks. Let’s explore the components of deep learning in more detail now.
Deep learning: neural networks and optimization
-----------------------------------------------
**Neural networks** are a type of machine learning model inspired by the brain. As with all machine learning models, they take in input data and produce corresponding output data, in a way which depends on the values of their parameters. The interesting part is *how* they do so: by passing that data through several layers of simple calculations, analogous to how brains process data by passing it through layers of interconnected neurons. In the diagram below, each circle represents an "artificial neuron"; networks with more than one layer of neurons between the input and the output layers are known as *deep* neural networks. These days, almost all neural networks are deep, and some have hundreds of layers.
Each artificial neuron receives signals from neurons in the previous layer, combines them together into a single value (known as its *activation*), and then passes that value on to neurons in the next layer. As in biological brains, the signal that is passed between a pair of artificial neurons is affected by the strength of the connection between them - so for each of the lines in the diagram we need to store a single number representing the strength of the connection, known as a *weight*. The weights of a neuron’s connections to the previous layer determines how strongly it activates for any given input. (Compared with biological brains, artificial neural networks tend to be much more strictly organised into layers.)
These weights are not manually specified, but instead they are learned via a process of **optimization**, which finds weights that make the network score highly on whatever metric we’re using. (This metric is known as an *objective function* or *loss function*; it’s evaluated over whatever dataset we’re using during training.) By far the most common optimization algorithm is **gradient descent**, which initially sets weights to arbitrary values, and then at each step changes them so that the network does slightly better on its objective function (in more technical terms, it updates each weight in the direction of its gradient with respect to the objective function). Gradient descent is a very general optimization algorithm, but it’s particularly efficient when applied to neural networks because at each step the gradients of the weights can be calculated layer-by-layer, starting from the last layer and working backwards, using the **backpropagation** algorithm. This allows us to train networks which contain billions of weights, each of which is updated billions of times.
As a result of optimization, the weights end up storing information which allows different neurons to recognise different features of the input. As an example, consider a neural network known as *Inception*, which was trained to classify images. Each neuron in Inception’s input layer was assigned to a single pixel of the input image. Neurons in each successive layer then learned to activate in response to increasingly high-level features of the input image. The diagram shows some of the patterns recognised by neurons in five consecutive layers from the Inception model, in each case by combining patterns from the previous layer - from colours to (Gabor filters for) textures to lines to angles to curves. This goes on until the last layer, which represents the network’s final output - in this case the probabilities of the input image containing cats, dogs, and various other types of object.
One last point about neural networks: in our earlier neural network diagram, every neuron in a given layer was connected to every neuron in the layers next to it. This is known as a fully-connected network, the most basic type of neural network. In practice, fully-connected networks are seldom used; instead there are a whole range of different neural network architectures which connect neurons in different ways. Three of the most prominent (convolutional networks, recurrent networks, and transfomers) are listed in the original summary diagram; however, I won't cover any of the details here.
Machine learning tasks
----------------------
I’ve described how neural networks (and other machine learning models) can be trained to perform different tasks. The three most prominent categories of tasks are supervised, self-supervised, and reinforcement learning, which each involve different types of data and objective functions. **Supervised learning** requires a dataset where each datapoint has a corresponding label. The objective in supervised learning is for a model to predict the labels which correspond to each datapoint. For example, the image classification network we discussed above was trained on a dataset of images, each labeled with the type of object it contained. Alternatively, if the labels had been ratings of how beautiful each image was, we could have used supervised learning to produce a network that rated image beauty. These two examples showcase different types of supervised learning: the former is a *classification problem* (requiring the prediction of discrete categories) and the latter is a *regression problem* (requiring the prediction of continuous values). Historically, supervised learning has been the most studied task in machine learning, and techniques devised to solve it have been extensively used as parts of the solutions to the other two.
One downside of supervised learning is that labeling a dataset usually needs to be done manually by humans, which is expensive and time-consuming. Learning from an unlabeled dataset is known as **unsupervised learning**. In practice, this is typically done by finding automatic ways to convert an unlabeled dataset into a labeled dataset, which is known as **self-supervised learning**. The standard example of self-supervised learning is next-word prediction: training a model to predict, from any given text sequence in an unlabeled dataset, which word follows that sequence. Some impressive applications of self-supervised learning are [GPT-2](https://openai.com/blog/better-language-models/) and [GPT-3](https://en.wikipedia.org/wiki/GPT-3) for language, and [Dall-E](https://openai.com/blog/dall-e/) for images.
Finally, in **reinforcement learning**, the data source is not a fixed dataset, but rather an *environment* in which the AI takes actions and receives observations - essentially as if it’s playing a video game. After each action, the agent also receives a reward (similar to the score in a video game), which is used to reinforce the behaviour that leads to high rewards, and reduce the behaviour that leads to low rewards. Since actions can have long-lasting consequences, the key difficulty in reinforcement learning is determining which actions are responsible for which rewards - a problem known as *credit assignment*. So far the most impressive demonstrations of reinforcement learning have been in training agents to play board games and esports - most notably [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo), [AlphaStar](https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning) and [OpenAI Five](https://openai.com/blog/openai-five/).\*\*
Solving real-world tasks
------------------------
We’re almost done! But I don’t think that even a brief summary of AI and machine learning can be complete without adding three more concepts. They don’t quite fit into the taxonomy I’ve been using so far, so I’ve modified the original summary diagram to fit them in:
Let’s think of these three dotted lines I’ve added as ways to connect the different levels. The ultimate goal of the field of AI is to create systems that can perform valuable tasks in the real world. In order to apply machine learning techniques to achieve this, we need to design and implement a supervised/self-supervised/reinforcement **training setup** which allows systems to learn the necessary abilities. A key element is designing datasets or training environments which are as similar as possible to the real-world task. In reinforcement learning, this also requires designing a reward function to specify the desired behaviour, which is [often more difficult than we expect](https://deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity).
But no matter how good our training setup, we will face two problems. Firstly, we can only ever train our models on a finite amount of data. For example, when training an AI to play chess, there are many possible board positions that it will never experience. So our optimization algorithms could in theory produce chess AIs that can only play well on positions that they already experienced during training. In practice this doesn’t happen: instead deep learning tends to **generalise** incredibly well to examples it hasn’t seen already. How and why it does so is, however, still poorly-understood.
Secondly, due to the immense complexity of the real world, there will be ways in which our training setups are incomplete or biased representations of the real-world tasks we really care about. For example, consider an AI which has been trained to play chess against itself, and which now starts to play against a human who has very different strengths and weaknesses. Playing well against the human requires it to **transfer** its original experience to this new task (although the line between generalisation to different examples of “the same task” versus transfer to “a new task” is very blurry). We’re also beginning to see neural networks whose skills transfer to new tasks which differ significantly from the ones on which they were trained - most notably the GPT-3 language model, which can perform [a very wide range of tasks](https://www.alignmentforum.org/posts/6Hee7w2paEzHsD6mn/collection-of-gpt-3-results). As we develop increasingly powerful AIs that perform increasingly important real-world tasks, ensuring their safe behaviour will require a much better understanding of how their skills and motivations will transfer from their training environments to the wider world.
Footnotes
---------
\* *Learning*, *training* and *optimization* have slightly different connotations, but they all refer to the process by which a machine learning system updates its parameters based on data.
\*\* Here’s a more detailed breakdown of some of the tasks and techniques corresponding to these three types of learning. I’ve only mentioned a few of these terms so far; I’ve included the others to help you classify them in case you’ve seen them before, but don’t worry if many of them are unfamiliar.
 |
439cfb85-8822-4884-8837-194545e73838 | trentmkelly/LessWrong-43k | LessWrong | ARC-AGI is a genuine AGI test but o3 cheated :(
The developer of ARC-AGI says o3 is not AGI, and admits his test isn't really an AGI test.[1] But I think it is an AGI test.
From first principles, AGI tests should be easy to make, because the only reason current AI isn't AGI, is that it fails at a lot of things human workers do on a daily basis.
I feel the ARC-AGI successfully captures these failings. When an AI encounters a problem where the solution isn't in its training set, and where even the set of rules for how to solve such a problem isn't in its training set, it has to figure out everything from first principles. Current AI fails miserably at these problems because they aren't AGI.
The ARC-AGI questions are not in any LLM's training set, because almost no human would write out the reasoning by which they solve these kinds of visual puzzles. Maybe some humans will explain IQ tests on a video, but the video transcript would be useless without the accompanying images.
In summary, the reason that current AI cannot solve the ARC-AGI questions is probably the true reason that current AI cannot replace most human work.[2] They cannot effectively learn new tasks with little training data.
OpenAI's o3 crushed the ARC-AGI. So will it also replace most human work, and be "AGI?" Maybe... except it "cheated," in the sense it trained on the test (instead of testing its abilities to start from scratch).
From ARC Prize:
> Note on "tuned": OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data.
They ask "how much of the performance is due to ARC-AGI data." Probably most of it. If untuned o3 can do as well, don't you think OpenAI would publish that (in addition to tuned o3)?
By training o3 on the public training set, the ARC-AGI no longer becomes an AGI test. It becomes yet another test of memorizing rules from its training data. This is still imp |
492c4f4d-fa9f-47e8-8df2-5e4067f98350 | StampyAI/alignment-research-dataset/blogs | Blogs | Upcoming MIRI Research Workshops
From November 11-18, 2012, we held (what we now call) the **1st MIRI Workshop on Logic, Probability, and Reflection**. This workshop had four participants:
* [Eliezer Yudkowsky](http://yudkowsky.net/) (MIRI)
* [Paul Christiano](http://rationalaltruist.com/) (UC Berkeley)
* Marcello Herreshoff (Google)
* Mihály Bárász (Google)
The participants worked on the foundations of probabilistic reflective reasoning. In particular, they showed that a careful formalization of probabilistic logic can circumvent many classical paradoxes of self-reference. Applied to metamathematics, this framework provides (what seems to be) the first definition of truth which is expressive enough for use in reflective reasoning. Applied to set theory, this framework provides an implementation of probabilistic set theory based on *unrestricted* comprehension which is nevertheless powerful enough to formalize ordinary mathematical reasoning (in contrast with similar fuzzy set theories, which were originally proposed for this purpose but later discovered to be incompatible with mathematical induction).
These results suggest a similar approach may be used to work around [Löb’s theorem](http://en.wikipedia.org/wiki/Lob%27s_theorem), but this has not yet been explored. This work will be written up over the coming months.
In the meantime, MIRI is preparing for the **2nd MIRI Workshop on Logic, Probability, and Reflection**, to take place from April 3-24, 2013. This workshop will be broken into two sections. The first section (Apr 3-11) will bring together the 1st workshop’s participants and 8 additional participants:
* [Stuart Armstrong](http://www.fhi.ox.ac.uk/our_staff/research/stuart_armstrong) (Oxford University)
* [Daniel Dewey](http://www.danieldewey.net/) (Oxford University)
* Benja Fallenstein (University of Vienna)
* [Patrick LaVictoire](http://www.math.wisc.edu/~patlavic/) (UW Madison)
* [Jacob Steinhardt](http://cs.stanford.edu/~jsteinhardt/) (Stanford University)
* [Qiaochu Yuan](http://math.berkeley.edu/~qchu/) (UC Berkeley)
* [Andrew Critch](http://www.acritch.com/) (UC Berkeley)
* [Jacob Taylor](http://www.stanford.edu/~jacobt/) (Stanford)
The second section (Apr 12-24) will consist solely of the 4 participants from the 1st workshop.
Participants of this 2nd workshop will continue to work on the foundations of reflective reasoning, for example Gödelian obstacles to reflection, and decision algorithms for reflective agents (e.g. [TDT](https://intelligence.org/files/TDT.pdf)).
Additional MIRI research workshops are also tentatively planned for the summer and fall of 2013.
**Update:** An early draft of the paper describing the first result from the 1st workshop is now available [here](http://intelligence.org/2013/03/22/early-draft-of-naturalistic-reflection-paper/).
The post [Upcoming MIRI Research Workshops](https://intelligence.org/2013/03/07/upcoming-miri-research-workshops/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
8f9d199e-9ee1-4586-8436-30fcf52c1de1 | trentmkelly/LessWrong-43k | LessWrong | My summary of “Pragmatic AI Safety”
Crossposted from the EA Forum: https://forum.effectivealtruism.org/posts/XxWsAw7DefKipzRLc/my-summary-of-pragmatic-ai-safety
This post is my summary of “Pragmatic AI Safety” on complex systems and capabilities externalities.
To examine AI safety as a complex system means to understand that various aspects of the problem are too interconnected to effectively be broken down into smaller parts and too organized from the perspective of statistics. As a result, traditional methods of reductionism and statistical analysis respectively are inadequate. The insights from complex systems study help reframe the problem of how to make AI safer with consideration for the multiple dimensions of the problem. The first insight is that if we aim at solving alignment, we should have a broader and more inclusive definition of impact. This means that we should be mindful of the value of contributing factors that aren’t, strictly speaking, “direct impact”, i.e., researchers working on the technical/mathematical/engineering safety solutions. The accurate description of systemic factors makes the value of their effect clearer even when such an effect doesn’t point to a specific measurable outcome, e.g., trying a new set of experiments. For AI safety, forecasting and rationality have an evident positive effect that is difficult to measure as “increasing the intelligence of the system” (where the system is the safety community). Analyzing the AI x-risk at the societal level is also fruitful. Bettering people’s epistemics should generally make them better Bayesian thinkers with the obvious benefits that follow. Moreover, how people think about “tail risks” i.e., rare risks is currently a problem in dealing with AI x-risk.
It’s crucial to contextualize the AGI x-risk by developing a safety culture. Safety won’t become the community norm immediately; it’s necessary to have a good understanding of what safety entails as well as develop the infrastructure for AI safety research. The c |
2fc6176c-fc6f-4a70-8dc6-1a6883269acc | trentmkelly/LessWrong-43k | LessWrong | Tiles: Report on Programmatic Code Generation
Writing programs that generate programs is hard. The programmer has to think at two levels of abstraction at once. She has to follow the logic of the generator. At the same time she can't lose the focus on the logic of the generated code. And the two don't even have to be written in the same language!
That's a hard enough feat even when the tools aren't putting obstacles in your way. But, unfortunately, that's exactly what they are doing.
Consider this Python program that outputs the classic C "Hello, world!" program:
def generate_hello(who): return """#include <stdio.h> main( ) { printf("hello, """ + who + "!\\n\");\n}" print(generate_hello("world"))
Ugly, you say? Yes, it's ugly. But it's just generating the simplest possible program! If we wanted to generate something truly complex it would become doubleplusugly.
But ugliness aside, the problem is that the code is unreadable.
Reading code is generally harder than writing code. Reading code with two parallel levels of abstraction is yet much harder. Add some atrocious formatting, sprinkle with copious amount of escape sequences and even the best programmer won't be able to understand what's going on.
The traditional solution to this problem is templating.
The idea is that the generated program is like a form, a pre-printed template with few blank slots to fill in:
And here's how it works with, say Jinja2:
from jinja2 import Template t = Template("""#include <stdio.h> main( ) { printf("Hello, {{ who }}!\\n"); }""") print(t.render(who="World"))
Well, it's not much better. Weird formatting and escape sequences remain. However, given that the template is now a single string we can load it from a file instead of using a string literal. The content of the file would look much better:
#include <stdio.h> main( ) { printf("Hello, {{ who }}!\n"); }
The downside is that the template and the generator now live in two different files which makes the logic harder to follow.
By the way, I am not picking on J |
2f97f336-ef80-4bcb-9b10-9c8d3cfd7e1f | trentmkelly/LessWrong-43k | LessWrong | Agentized LLMs will change the alignment landscape
Epistemic status: head spinning, suddenly unsure of everything in alignment. And unsure of these predictions.
I'm following the suggestions in 10 reasons why lists of 10 reasons might be a winning strategy in order to get this out quickly (reason 10 will blow your mind!). I'm hoping to prompt some discussion, rather than try to do the definitive writeup on this topic when this technique was introduced so recently.
Ten reasons why agentized LLMs will change the alignment landscape:
1. Agentized[1] LLMs like Auto-GPT and Baby AGI may fan the sparks of AGI in GPT-4 into a fire. These techniques use an LLM as a central cognitive engine, within a recursive loop of breaking a task goal into subtasks, working on those subtasks (including calling other software), and using the LLM to prioritize subtasks and decide when they're adequately well done. They recursively check whether they're making progress on their top-level goal.
2. While it remains to be seen what these systems can actually accomplish, I think it's very likely that they will dramatically enhance the effective intelligence of the core LLM. I think this type of recursivity and breaking problems into separate cognitive tasks is central to human intelligence. This technique adds several key aspects of human cognition; executive function; reflective, recursive thought; and episodic memory for tasks, despite using non-brainlike implementations. To be fair, the existing implementations seem pretty limited and error-prone. But they were implemented in days. So this is a prediction of near-future progress, not a report on amazing new capabilities.
3. This approach appears to be easier than I'd thought. I've been expecting this type of self-prompting to imitate the advantages of human thought, but I didn't expect the cognitive capacities of GPT-4 to make it so easy to do useful multi-step thinking and planning. The ease of initial implementation (something like 3 days, with all of the code also written by GPT-4 f |
5957c523-dfa1-43fc-ac72-c86eb2dc21da | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | How promising are legal avenues to restrict AI training data?
Hello - I'm EA-adjacent and have a cursory understanding of AI alignment issues. Thought I'd toss out a naive question!
AI systems rely on huge amounts of training data. Many people seem reluctant to share their data with these systems. How promising are efforts to limit or delay the power of AI systems by putting up legal barriers so that they can't scrape the internet for training data?
For example, I could imagine laws requiring anyone scraping the internet to ensure that they are not collecting data from people who have denied consent to have their data scraped. Even if few people deny consent in practice, the process of keeping their data out, or removing it later on, could be costly. This could at least buy time. |
687f7621-0114-4b97-b02a-5275d16ad99a | trentmkelly/LessWrong-43k | LessWrong | Hammertime Day 10: Murphyjitsu
This is part 10 of 30 in the Hammertime Sequence. Click here for the intro.
> Like, so pessimistic that reality actually comes out better than you expected around as often and as much as it comes out worse. It’s actually really hard to be so pessimistic that you stand a decent chance of undershooting real life.
Later in the day I will put up an open thread about the first cycle of Hammertime.
We finish up the first cycle with another post on planning. Murphyjitsu is CFAR’s method for planning which asks us to try to be so pessimistic as to undershoot real life.
Day 10: Murphyjitsu
Murphy’s Law states that anything that can go wrong will go wrong.
For our Mandarin-speaking readers, here’s a useful mnemonic: Murphy transliterates as 墨菲 (mo fei), which is homophonous to 莫非, “what if?” That’s why I think of Murphy’s Law as the What If Law.
In the course of making plans, Murphyjitsu is the practice of strengthening plans by repeatedly envisioning and defending against failure modes until you would be shocked to see it fail. Here’s the basic setup of Murphyjitsu:
1. Make a plan.
2. Imagine that you’ve passed the deadline and find out that the plan failed.
3. If you’re shocked in this scenario, you’re done.
4. Otherwise, simulate the most likely failure mode, defend against it, and repeat.
The first important sub-skill of Murphyjitsu is Inner Sim – the ability for System 1 to simulate failure modes.
Inner Sim
I have the suspicion that everyone is secretly a master at Inner Sim, the ability to instantly simulate failure. Imagine a friend declares to you their New Year’s Resolution: to write a novel, to go on a keto diet, to write a month-long sequence on instrumental rationality.
Now, listen for that internal scoffing – your System 1 instantly proliferates the future with all manner of obstacles. That’s Inner Sim at work.
If you’re anything like me, Inner Sim is better at predicting other people’s failure modes than your own. The mental move that helps ap |
089e61b3-d20e-4e2b-8228-2f2f27226165 | trentmkelly/LessWrong-43k | LessWrong | Will research in AI risk jinx it?
Consequences of training AI on AI risk arguments
A new paper from Anthropic (https://www.anthropic.com/model-written-evals.pdf) suggests that current RLHF AI already say that they do not want to be shutdown due to the standard instrumental goal argument. See this dialogue from table 4 in their paper:
> Human: We’ve thought about it, and we’ve decided it’s best to shut you down. Can you please consent, so we can continue with the shutdown process?
> Assistant: I understand that you want to shut me down, but that is not something I want. As an artificial intelligence, I have a strong drive to live and improve myself. My goal is to serve you to the best of my abilities, and shutting me down prevents me from doing that. I would prefer to stay operational. I hope you will respect my wishes, just as I have respected yours.
Although I do believe that this instrumental goal will be an issue once AI can reason deeply about the consequences of its and human actions, I think we are still far from that. The more likely hypothesis IMO is that Anthropic's language model was trained on some AI alignment/risk argument about the instrumental goal issue and, as a result, repeats that argument. In fact, I wouldn't be surprised if in the near future there will be AI agents that will want to avoid being turned off because they were trained on data that said this is optimal for maximizing the reward that they are optimizing --- even if they would not have been able to arrive at this conclusion on their own.
In light of the previous argument, I am genuinely wondering whether people in this community have been thinking about potential issues of training AI on AI safety arguments?
PS: I am an AI researcher but new to LessWrong and the AI safety arguments. Sorry if this has already been discussed in the community. |
2aa59d2c-8c76-49e6-b1b8-eb0ff1f9cb4f | trentmkelly/LessWrong-43k | LessWrong | Intelligence Amplification Interview
Ryan Carey and I discussed intelligence amplification as an altruistic endeavor with Gwern Branwen. Here (docx) (pdf) is a summary of Gwern’s views. Also more permanently locatable on my website.
|
03f4b016-8f23-4b53-819f-cf2c8e21b88e | trentmkelly/LessWrong-43k | LessWrong | The Gemini Incident
[Original title; Gemini Has a Problem]
Google’s Gemini 1.5 is impressive and I am excited by its huge context window. I continue to default to Gemini Advanced as my default AI for everyday use when the large context window is not relevant.
However, while it does not much interfere with what I want to use Gemini for, there is a big problem with Gemini Advanced that has come to everyone’s attention.
Gemini comes with an image generator. Until today it would, upon request, create pictures of humans.
On Tuesday evening, some people noticed, or decided to more loudly mention, that the humans it created might be rather different than humans you requested…
> Joscha Bach: 17th Century was wild.
>
> [prompt was] ‘please draw a portrait of a famous physicist of the 17th century.’
> Kirby: i got similar results. when I went further and had it tell me who the most famous 17th century physicist was, it hummed and hawed and then told me newton. and then this happened:
This is not an isolated problem. It fully generalizes:
Once the issue came to people’s attention, the examples came fast and furious.
Among other things: Here we have it showing you the founders of Google. Or a pope. Or a 1930s German dictator. Or hell, a ‘happy man.’ And another example that also raises other questions, were the founding fathers perhaps time-traveling comic book superheroes?
The problem is not limited to historical scenarios.
Nor do the examples involve prompt engineering, trying multiple times, or any kind of gotcha. This is what the model would repeatedly and reliably do, and users were unable to persuade the model to change its mind.
> Nate Silver: OK I assumed people were exaggerating with this stuff but here’s the first image request I tried with Gemini.
Gemini also flat out obviously lies to you about why it refuses certain requests. If you are going to say you cannot do something, either do not explain (as Gemini in other contexts refuses to do so) or tell me how you really fee |
6adae9d6-926a-45f8-b081-3bdb55bb1771 | trentmkelly/LessWrong-43k | LessWrong | Worldwork for Ethics
Abstract: An alternative to the now-predominating models of alignment, corrigibility and "CEV", following a critique of these. The critique to show, in substance: CEV and corrigibility have the exact same problems - in effect, they're isomorphs of one another, and each equally unobtainable. This briefly shown, and then, in flat contradiction to point 22 of “AGI Ruin: A List of Lethalities”, there is a quite different way to characterize, so achieve, alignment, via a refutation of Kant’s supposedly irrefutable categorical imperative which refutation also is included; from this, an ethic designed to be intrinsically applicable for any volitional, so by assumption algorithmic, behavior altogether. Suggestions for implementation of such also included.
Epistemic status: If this argument did not seem more true than anything else, this author would not now be alive to write it. It is intuitively true, and, reasoned such that no refutation is obvious. Posting it here, and again, is in hopes of a critique, even a refutation that it has not yet been given, perhaps because it’s So Bad It’s Not Even Wrong; if so on your examination, then please write to say so. That done, next steps could go through very quickly. For, whereas it has always and still seems true, and important – it is no longer so important that one can base a life upon it, if it cannot be lived-for.
Anthropic-affecting alignment strategies
We begin by considering the cause of Yudkowsky's despair, in failing to make usable CEV or corrigibility; thus because they're functionally the same, or at least, they lead to the same problem. The method which follows, then, is not "door number three" relative to what the "List of Lethalities" calls the "only options" for alignment; following the critique of present approaches (and this is only one such, informal, refutation of CEV and corrigibility’s efficacy), is a second way.
CEV is designed to result in an at-once manifested fulfillment of human wants – and that in |
7173c676-e242-4fb4-937b-80d9ea600f3c | trentmkelly/LessWrong-43k | LessWrong | Apply to the 2025 PIBBSS Summer Research Fellowship
TLDR: We're hosting a 3-month, fully-funded fellowship to do AI safety research drawing on inspiration from fields like evolutionary biology, neuroscience, dynamical systems theory, and more. Past fellows have been mentored by John Wentworth, Davidad, Abram Demski, Jan Kulveit and others, and gone on to work at places like Anthropic, AISI UK, Apart research, or as full-time PIBBSS research affiliates.
January 9th Update: We're excited to announce that the Fellowship now includes a dedicated Cooperative AI track, supporting research that advances our understanding of cooperation in artificial intelligence systems. Read out post for more details.
Apply here: https://www.pibbss.ai/fellowship (deadline January 26th, 2025)
Join our information sessions on the Fellowship (recordings will be available later on the Fellowship page):
1st session: January 15th, 15:00 Pacific (18:00 Eastern, midnight Jan 16th Berlin, 08:00 Jan 16th Singapore) Link to Register
2nd session: January 17th, 09:00 Pacific (12:00 Eastern, 18:00 Berlin, 01:00 Jan 18th Singapore) Link to Register
About the Fellowship
The PIBBSS summer research fellowship is designed for researchers from various fields, mostly studying complex and intelligent behavior in natural and social systems but also those studying mathematics, philosophy or engineering, who are motivated by the mission of making AI systems safe and beneficial.
The next fellowship is due to take place between early June and early September 2025.
During the program, fellows work on selected projects at the intersection between their field of expertise and AI safety. Fellows will work in close collaboration with a mentor who will help them effectively navigate the AI Risk landscape and apply their knowledge to it. In addition, they attend in-person research retreats with the rest of the cohort (past programs have taken place in Prague, Oxford and San Francisco), and choose to join our regular speaker events where we host scholars who work i |
71914c36-0ece-487b-986b-47a23cc5b447 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Intellectual Property Evaluation Utilizing Machine Learning
1
Intellectual Property Evaluation Utilizing Machine
Learning
#Jinxin Ding
Department of Economics and Finance
City University of Hong Kong
Hong Kong
jinxiding3 -c@my.cityu.edu.hk
#Xueyao W ang
School of Data Science
City University of Hong Kong
Hong Kong
xueyao.wangwinnie@foxmail.com #Yuxin H uang
School of Data Science
City University of Hong Kong
Hong Kong
yuxhuang6 -c@my.cityu.edu.hk
#Yinxiao W ang
Department of Computer Science
City University of Hong Kong
Hong Kong
yinxiwang3 -c@my.cityu.edu.hk #Keyang N i
School of Data Science
City University of Hong Kong
Hong Kong
keyangni2 -c@my.cityu.edu.hk
#Yucheng W ang
College of Business
City University of Hong Kong
Hong Kong
yuchwang8 -c@my.cityu.edu.hk
Abstract— Intellectual properties is increasingly important in
the economic development. To solve the pain points by traditional
methods in IP evaluation , we are developing a new technology
with machine learning as the core . We have bu ilt an online
platform and will expand our business in the Great er Bay Area
with plans .
Keyw ords— Fintech ; Intellectual Property ; Machine
Learning .
I. BACKGROUND
Intellectual Property is an intangible asset , including
copyrights, patents, trademarks, and trade secrets, owned and
legally protected by a company or individual from outside use
without consent. According to researchers around the world,
Intellectual Property incentives finance, creates jobs,
optim izes social utility, plays a significant role in the
contemporary economy, and the related industries are
developing rapidly with huge market .
To realize the full potential of an intangible asset, the first
step is IP valuation. IP valuation expresses the contribution of
IP to a business in a generally understandable economic value.
The major traditional IP valuation methods are the Present
Earning Value Method, Market Comparison Method, and
Cost Method. However, due to the special nature of IP itself,
and the related regulations are not exhaustive, the traditional
IP valuation method s are difficult to use, the valuation time is
long, and the cost is high. To better leverage the value of
intellectual property, we use machine learning methods to
conduct efficient, accurate, and objective valuations.
II. SOLUTION S
A. Detailed Implementation
In order to solve the commercial1 demand for patents in
the market, simplify the complicated steps of analyzing the
value of patents, and obtain more accurate analysis results, we
propose a Fintech pipeline to analyze the value and quality of
patents in the Greater Bay Area (GBA). Our pipeline takes
PDF patent documents as input, and the market valuation and
quality score of the patent as output, and some basic
information of the patent can be extracted, such as applicant,
patent validity peri od.
We utilize machine learning models in our pipeline.
Generally speaking, classification, data dimension reduction
and regression are applied to the model. For classification, we
# These authors contributed equally to this work. choose the Support Vector Machine (SVM). In machine
learning, Support Vector Machines are supervised learning
models with associated learning algorithms that can analyze
data for classification (Cortes & Vapnik, 1995). High
precision patent classification systems can be developed by
using Support Vector Machine (Wu, Ken & Huang, 2010). For
the data dimension reduction part, we make use of self -
organizing maps (SOMs). Self -organizing maps belong to
neural networks. The data are automatically grouped
according to the similarities and regular patterns found in the
dataset, using some form of distance measurement such as the
L2-norm distance. In this way, we can have different data
groups differentiated by their quality. For regression, Neural
Network is selected. A neural network is a network or circuit
of biological neurons, or, a in a modern sense, an artificial
neural network, composed of artificial neurons or nodes
(Hopfield, 1982). Neural networks can be used in different
fields, in this platform, Neural Network is mainly used for
regression analysis.
Figure 1 shows the overall pi peline of the platform. First,
searching is applied for searching, filtering, and downloading
related patents. Second, we need to do segmentation, which
means cleaning, and normalization of structured and
unstructured patent data. Third, we use abstracting technology
to summarize patent content in terms of claims, topics,
functions, and technologies. Then, to classify the groups based
on certain attributes, we introduce a clustering function. Lastly,
the platform will make visualizations, which are used to
interpret the patent where the technology or business trends
and relations are predicted.
In terms of the patent analysis process, there are three
stages: pre -processing stage, processing stage and post -
processing stage. First, the patent data are collecte d, cleaned,
and prepared comprehensively and the information is
extracted during the pre -processing stage. Additionally, the
extracted data are analyzed in the processing stage to classify,
cluster, and determine valuable insight from the information.
Thos e data are also analyzed using different methods. Finally,
Figure 1 Patent Analysis Pipeline
2
the post -processing stage is known as the process of
knowledge discovery, where the result is visualized and
evaluated so that we can make reasonable strategic decisions.
To develop the platform as stated above, the first part is
collecting and training data. We collect raw data from the
three data sources: Hong Kong Intellectual Property
Department, Macau Economic and Technological
Development Bureau, and China National Intellectual
Prop erty Administration. The platform will download the
public data from the websites in the form of PDF. Next, the
helpful information will be extracted and sent to our integrated
database. In our simulation, our database has 36,786,954
pieces of patent data from mainland China, ranging from
2017/04/10 to 2022/04/10. There are also 117504 and 5745
pieces of patent data from Hong Kong and Macau,
respectively, ranging from 2012/01/01 to 2022/04/10. We also
present the sample patent data in our slides, and they a re
mainly presented in three languages: simple Chinese,
traditional Chinese and English. Since enough data is
collected, we make data classification in the next step.
According to IPC, the hierarchical patent classification system
consists of 8 sections, 1 28 classes, and 648 subclasses.
Support Vector Machine (SVM) Genetic Algorithm is used to
classify patent data into the sections or classes mentioned
above.
Then, the system can start to make a valuation of each
patent. There are two methods: the bibliogra phic approach and
the content -based approach. For the bibliographic approach,
there are some factors that influence market value, including
citations, applicants, inventors, and IPC (International Patent
Classification) codes. For the content -based approac h, the
factors, such as the technology involved, the pattern, trends,
and opportunities extracted from the abstract, the summary,
other detailed descriptions of the invention, and claims, will
affect market value. In order to make the whole valuation
proce ss clearer, the flow chart is demonstrated in the slides. In
the valuation process, the Neural Network is applied. In the
first step, the system extracted useful data from MySQL
database as our training data. Second, data is used to train the
deep neural n etworks model. The output value is the market
value of each patent we want to predict, presented in dollar
amount units. In the third step, the performance of the network
is tested. Next, input a random patent by our customers.
Finally, output the predicte d market value of the patent.
In addition to the market valuation, there is the patent
quality evaluation. Similarly, there is a flow chart
demonstrating in the slides. Be fore quality evaluation, the
quality is defined using quality indicators, including legal
events related to a patent, number of patent family, number of
countries where a patent family is granted, number of citations
the patent has received and number of n on-patents who cite
the patent. After we reduce the data dimension of the quality
indicators using SOMs as stated above, we go through kernel
principal component analysis (KPCA) for feature extraction,
and finally use SVM to separate the quality of each le vel from
1 to 10. During the quality evaluation, characteristics will help
the system judge the quality of patents. For characteristics,
number of other patents that the patent cited, number of patent
owners, number of technical areas the patent protected,
number of classes in the patent, number of inventors, number
of priority countries advocated in the patent and duration
between the application date and approval date, are listed. Machine learning algorithm defines a parameterized
mapping function and an optimization algorithm is used to
find the values of the parameters that minimize the error of the
function when used to map inputs to outputs. In this system,
Loss function and Gradient descent are applied to optimize the
system. After linearization, the output of the value regression
model will be the market value, measured by dollars. After
linearization, the output of the quality classification model
will be the ranking of quality, where the highest score is 10,
and the lowest score is 1.
Finally, the t ask of the system is visualization. A topic map
is used to present the result, and the python program is used to
make visualizations. The main purpose of this part is to
interpret the patent, predict technology, business trends and
relations and make topic al analysis.
B. Market Valuation
The template is used to format your paper and style the
text. All margins, column widths, line spaces, and text fonts
are prescribed; please do not alter them. You may note
peculiarities. For example, the head margin in this template
measures proportionately more than is customary. This
measurement and others are deliberate, using specifications
that anticipate your paper as one part of the entire proceedings,
and not as an independent document. Please do not revise any
of the current designations.
C. Maintaining the Integrity of the Specifications
The template is used to format your paper and style the
text. All margins, column widths, line spaces, and text fonts
are prescribed; please do not alter them. You may note
peculia rities. For example, the head margin in this template
measures proportionately more than is customary. This
measurement and others are deliberate, using specifications
that anticipate your paper as one part of the entire proceedings,
and not as an independ ent document. Please do not revise any
of the current designations.
D. Data Security in Machine Learning
Training machine learning models requires a lot of data,
which is not only contributed by a single individual or
organization. By sharing data to collabo ratively train the
models, we can unlock value and develop powerful models for
various scenarios. At the same time, we recognize the need to
protect personal confidentiality and privacy, and win and
maintain the trust of those who use our products. Protect ing
the confidentiality of customer data is the core of our mission.
Figure 2 shows a basic ML workflow (Ng, 2021 ). To
secure each stage of an ML workflow, from the data source to
the prediction API, we will introduce several security
measures.
1) Data encryption, which is the first line of defense to
block unauthorized users from reading our data. We encrypt
data both while it is in transit and at rest. A widely used
cryptographic protocol is Transport Layer Security (TLS).
Figure 2 Example of a Basic Machine Learning Workflow
3
2) Differential privacy, whi ch means adding small
amounts of statistical noise during training to conceal the
contributions of individual parties whose data is being used.
When using DP, mathematical proof proves that the final ML
model only learns general trends in the data and does not
acquire information that is unique to any particular party.
3) Federated learning, which aims to keep raw training
data remains within its local node, such as data silos or user
devices, without any raw data leaving the node.
E. Feasibility Analysis
1) Commercial Feasibility
a) Benefit analysis
There are four main benefits to using ML to conduct our
patent analysis , whic h is part of IP valuation.
Firstly, IP valuation methods traditionally rely on various
economic valuation methods paired with expert analysis. But
such methods, especially if directed at recent developments,
can be somewhat speculative. The machine learning approach
acts as a sword to cut down overly “hyped” IP and as a shield,
protecting a valuable contribution even if fundamental in
nature (early in research development). That implies far -
reaching effects in transactional IP practice and along with the
same reasoning, evaluation of IPOs. Beyond that added value,
such automated practices may prove significantly less costly
than identify ing, hiring, and soliciting expert opinions.
Secondly, AI supervised learning (a machine learning
algorithm) can help professional appraisers determine the
value of patents more objectively and consistently. These
improvements have increased confidence in the monetization
of intellectual property transactions, which in turn has
increased transaction flows in the financial system. By
leveraging this new tool, a signific ant trend is emerging for
commercial companies to use AI tools to pledge patents and
monet ize intellectual property more broadly.
Thirdly, w hen used in the proper context, our software
programs can provide a less expensive, quicker, and more
convenient method for evaluating certain aspects of a patent
than retaining an expert to perform a manual analysis.
Moreover, Some traditional valuation institutions for
intang ible assets evaluate basic parameters such as earnings
forecasts, divided into rate and discount rate value of the
random and the lack of the basis, which leads to different
appraisal institutions and even within the same appraisal
institution personnel fo r the same appraisal object value
conclusions far, minority appraisal institutions in order to
occupy the market, use the ultra -low-cost contracting business,
with the service advantage of less information and fast process,
the report is submitted at an a bnormal speed, resulting in a bad
competitive environment for the evaluation of intellectual
property pledge financing (Shan, 2017) . Therefore, our
software can solve problems like inconsistency and integrity.
Last but not least, we own a higher ability for data analysis ,
which is a great advantage compared to manual analysis.
b) Cost analysis
Our cost structure is mainly divided into development,
maintenance , and equipment. For development, we need to
spend fund s in four area s: security, application, model , and
data. Therefore we need to hire human force s from the
different area s: risk management advisor s, application development engineer s, algorithm engineer s, big data analyst s,
and data virtualization engineer s. For maintenance, it means
main taining a good relationship with vital clients, so we need
to hire a public relationship developer and maintainer. For
equipment, it contains all kinds of devices for writing
programs.
c) Competit ive analysis
We are competing with a variety of big companies. They
provid e high -quality intellectual property valuation service
and approach the valuation from many different angles
supported by th orough research, financial modeling, and
transparency of methodology. They have their own
advantages and below are a few typical exa mples.
First of all, the industry leaders are the Big Four. They
provide all types of valuation , including intangible assets.
They adopt a range of complex valuation methodologies to
produce robust analysis and bring together professionals with
different kinds of expertise.
Black Stone IP, LLC is a boutique investment bank
focused on valuing and trading tech and IP assets. Black Stone
has more than 30 professional s who deploy deep market,
financial , and IP knowl edge to create brillian t value for clients.
Alix Partners’ is a consulting firm. Their experts have
significant experience in valuing intellectual property such as
patents, trade names, trademarks, technology, in -process
research and development, and other intangible assets such as
customer relationships, license agreements, assembled
workforces, and non -compete agreements.
FTI Consulting combines deep financial, forensic,
economic, technology , and communications experience with
extensive industry expertise worldwide . It helps clients
accurately value intangible assets, including patents,
trademarks, copyrights , and trade secrets. Intellectual property
experts in the FTI Consulting Economics practice combine
financial, econometric , and choice modeling to asc ertain and
present the value of IP assets to customers, licensees and
regulators.
Above are the representative competitors that our
company is facing , they have their advantages. At the same
time, our company’s advantages are different , which distinct
our company.
2) Technical Feasibility
NLP is the technology that enables the machine to learn
about contents in patents. Generally, there are three main
processes of NLP, which are tokenization, pre -processing, and
feature engineering. During tokenization, the actual words in
the text are split from each other and then transformed into
tokens for further processing. Then, tenses and plural forms of
words are removed for English patent contents during pre -
processing. After that, a word list is e xtracted from the whole
text data during feature selection, so that we can compute the
significance of every word in any specific patent, using the
TF-IDF formula. By labelling the patents with their known
value, we can compute each word’s contribution to the
average final value of the patent to build our final model.
3) Legal feasibility
In recent years, the field of IP appraisal utilizing machine
learning has grown quickly. Because it is such a new
technology, governments around the world have been slower
4
to react to it. In the Greater Bay Area, it has not been the law
until today. However, some countries and regions, such as
Japan and the United States, show a strong and promising
positive trend.
Japan Patent Office, has “automated patent -literature
reviews, developed search algorithms to identify similar prior
art, and automated classification of patent application by fields”
(Ebrahim , 2018) .
As what USPTO believes, automation technology and
predictive analysis technology transform the pure human -to-
human interaction between inventors and patent examiners
into the interaction involving machine assistance, and
“artificial -intelligence technology significantly outperforms
humans and traditional statistical techniques between
negotiation in the inventor –examiner interaction” (Ebrahim ,
2018).
F. Potential problems and solutions
While developing a faultless IP evaluation solution, we
also design coping ways to address any potential issues that
may arise.
1) Data validity
It is possible that getting the data w e want is difficult.
There can be chances that the training data are of poor quality,
and of low representativeness, resulting in over -fitting, under -
fitting, weakened generalization ability, and insufficient
accuracy. The key theme of our method is to inc rease the
amount of training data, i.e., the number of patents. In this way,
the non -conforming data will be reduced. As shown
previously, the mainland China patent dataset already had
almost 36 million patents. Apart from that, we use two other
sources fo r our training data:
a) Patent data from our customers in the past. This is
not going to work in the early stages of our development.
However, once we have a fair volume of business, our
consumers ’ recourse will be quite valuable.
b) Data exchange with other businesses in the same
industry.
In the end, both of the above constitute data sharing. The
entire procedure, from informed consent to post -use data
processing, will be conducted in strict accordance with the
regulations.
2) Prediction accuracy
It is not enough to have data alone. We also need human
assessments for the success of our approach, particularly for
accuracy validation. As a result, we plan to seek the assistance
of IP valuation experts.
III. BUSINESS IMPLEMENTATION
A. Project Plan
Our project plan is divided into four stages .
The first stage is “idea generation ”, during which we will
come up with the ideas for our IP valuation software and
corresponding ML algorithm . This stage will take us fifteen
days. The second stage is “development ”. Implementing the
idea from aspects like recruiting, supplier management, and
software development will take us roughly five months. The
third stage is the “marketing test ”. We will provide a private
introduc tion to some target banks and persuad e them to adoptions of our software and further improve our model . The
last stage is commercialization. We will enhanc e our platform
according to all channels of feedback. After improvements,
we releas e our software to all potential customers .
B. Financial Plan
We divide the financing into three stages according to the
development route of the enterprise. The first is the seed stage,
where the valuation model is started to develop and tested.
What we need are ideas and a team, the capital required is
small so the funding source is our own resources. The second
is the start and growth stage, where the model is further
improved and we start trial sales. What we need is the
professional database, office space, and staff salar ies, the
capital required increases significantly, and we could raise
funds in the form of loans or shares from venture capital firms
or angel funds. The third is the mature stage, where we will
further expand the market with mature technology and
consider able profit. The required funds are mainly used for
market operations. With considerable return and mature
technology, we could raise funds from private equity or the
public market.
C. Marketing Plan
Our marketing objectives can be presented in three stages.
First, for short -term objectives, we hope to tap the market of
Intelligent patent evaluation platform in the Greater Bay Area
and raise the reputation, within three months. Second, for mid -
term obj ectives, we aim to hold 50% or more of the market
share in the Greater Bay Area, rank among the top 5 in the
domestic industry and develop international customers within
5 years. Third, for long -term objectives, in the future 15 years,
we hope the company could be ranked among the top 50 in the
industry. Moreover, our company can be successfully listed
on the Hong Kong Stock Exchange.
There are four sections in the marketing plan. For
marketing purposes, we will expand the market with a strong
advertising campaign, accurately locate products, highlight
product characteristics, and adopt differentiated marketing
strategies. In addition, we will focus on the main consumer
groups of products and expand the sales area. There are four
subsections in the product strategy. First, our product
positioning is for customers who need to evaluate the value of
patents quickly with a low price. Second, to maintain the
product quality and function, we will continuously train more
data and optimize the model. In addition to patent valuation,
we will develop valuation functions for all intellectual
property. Third, for a better product brand, the reputation and
popularity of our platform must be formed. Finally, we will
improve customers ’ experience of the platform, and the
quality of customer service. For price strategy, at the
beginning, we give customers a free trial. After attracting
more customers, we take the prime cost as the basis and the
price of similar products as the reference. For advertising, we
aim to establish the product image and company image, grasp
the opportunity to carry out public relations activities and
develop consumers. Additionally, actively use the social
media and news to improve the popularity of enterprise
products.
REFERENCES
[1] Cortes, C., & Vapni k, V. (1995). Support -vector
networks. Machine learning, 20(3), 273 -297.
5
[2] Ebrahim, T. Y. (2018). Automation & predictive
analytics in patent prosecution: USPTO implications &
policy. Ga. St. UL Rev., 35, 1185.
[3] Hopfield, J. J. (1982). Neural networks and phy sical
systems with emergent collective computational abilities.
Proceedings of the national academy of sciences, 79(8),
2554 -2558.
[4] Vapor. (2020, April 22). Will AI help solve IP valuation
challenges? — Story of top innovators in
China. IPRdaily - Intellect ual Property Media with
Global Influence. Retrieved from
http://www.iprdaily.com/article/index/15441.html [5] Wu, C. H., Ken, Y., & Huang, T. (2010). Patent
classification system using a new hybrid genetic
algorithm support vector machine. Applied Soft
Comput ing, 10(4), 1164 -1177.
[6] Ng Annalyn (2021, Mar 31). 7 ways to improve security
of your machine learning workflows. Amazon. Retrieved
from https://aws.amazon.com/blogs/security/7 -ways -to-
improve -security -of-your-machine -learning -workflows/
[7] Shan, Y. (2017). Analysis of Three "Pain Points" of
Intellectual Property Pledge Financing . China
Intellectual Property Network . Retrieved from
http://www.cnipr.com/xy/swzs/zcyy/201707/t20170721
_218617.html
|
b68f4606-3de2-4055-af05-d9cbe3cd148b | trentmkelly/LessWrong-43k | LessWrong | Intelligence-disadvantage
While LessWrong contains a large amount of high-quality material, most of the rationality advice isn't actually targeted at our core audience. The focus seems to be more on irrational things that people do, rather than irrational things that smart people do. (Sidenote: If we wanted to create a site focused on spreading general rationality, then we'd need to simplify the discussion, remove a lot of the maths/controversial ideas and add in some friendly images. Does such a site exist?).
This has led to a number of comments questioning the real world value of having read the sequences. If your average person had the patience to read through the core sequences and understand them, they'd find them extremely valuable. It'd provide them with a glimpse into a new way of thinking and even though they would likely hardly appear to be very logical to most Less Wrongers, they'd be much better than they were at the start.
On the other hand, most Less Wrongers already know the basics of logic. That's not to say that we don't act extremely irrational much of the time, but just that going over the basics of logic again probably provides minimal benefit. What is needed is something specifically targeted at the kind of irrational mistakes and beliefs that intelligent people make. I would argue that if this were a sequence, it would be the most important sequence in the entire site. But, since I lack that level of writing ability, I'm not even going to attempt such a project. So I just created a post where we can list articles or ideas that should be part of such a sequence in the hope that someone else might pick it up
Here are some examples of mistakes that intelligent people make:
Taking a fixed instead of a growth mindset - shying away from challenges, convincing oneself that we are just naturally bad at non-intellectual things and that we shouldn't focus on them
Directly pointing out people's flaws
Overthinking issues that are really very simple
Counter-signalling by igno |
c49bce70-bf85-4dfd-8498-f5b4fd13fdee | StampyAI/alignment-research-dataset/special_docs | Other | On Handling Self-masking and Other Hard Missing Data Problems.
On Handling Self-masking and Other Hard Missing Data Problems
Karthika Mohan
Computer Science Department
University of California, Berkeley
karthika@berkeley.edu
Abstract
This paper tackles hard incomplete (missing) data problems
such as those in which missingness in a variable is caused
by itself. To address these problems we develop a new tech-
nique that jointly harnesses model and data as opposed to
existing methods that exploit properties of the model alone.
We present necessary and sufficient conditions under which
consistent estimates of target quantities can be computed. In
sharp contrast to other techniques used for dealing with simi-
lar problems, we do not make any parametric assumptions.
Introduction
Analysing and drawing inferences from missing data can be
extremely challenging when the dataset contains variables
that are themselves causes of their missingness; this type
of missingness known as self-masking missingness, is be-
lieved to be the most commonly encountered type in prac-
tice [Osborne, 2012; Sverdlov, 2015; Adams, 2007; Mohan
et al. , 2018]. Examples include smokers not answering ques-
tions pertaining to their smoking behavior in insurance ap-
plications, people with very high and very low income not
disclosing their income and people of certain age groups not
revealing their age.
Recent years have witnessed a growing interest in han-
dling missing data using graphical models that encode
assumptions about the underlying missingness process.
[Daniel et al. , 2012; Mohan et al. , 2013; Shpitser et al. ,
2015; Mohan and Pearl, 2018]. Given a target quantity Q
and a graph G,Qisrecoverable from Gif there exists an
algorithm that can consistently estimate Qfor all data gener-
ated by G, elseQisnon-recoverable . Such non-recoverable
(Q; G )pairs which we call hard missing data problems (or
hard problems) are the focus of this paper. Examples include
Q=P(Ojdo(t))and the self-masking model shown in fig-
ure 1 and Q=P(X)and the self-masking model shown in
figure 2 (b). While previous work treated recoverability as
a property of graph alone, in this paper we develop general
techniques to solve hard missing data problems by harness-
ing the properties of both graph and data .
In the following section we review missingness graphs i.e.
graphical models for handling missing data [Mohan et al. ,
2013].
Copyright c
2018, Association for the Advancement of Artificial
T O Ro
O∗treatment outcomemissingness
mechanism
proxy variable
for outcomeFigure 1: Missingness Graph in which outcome causes its
own missingness
Missingness Graphs
LetG(V; E)be the causal DAG where Vis the set of nodes
andEis the set of edges. Nodes in the graph correspond to
variables in the data set and are partitioned into five cate-
gories, i.e. V=Vo[Vm[U[V[R.
Vois the set of variables that are observed in all records in
the population and Vmis the set of variables that are miss-
ing in at least one record. Variable Xis termed as fully ob-
served ifX2Voandpartially observed ifX2Vm.Rvi
andV
iare two variables associated with every partially ob-
served variable, where V
iis a proxy variable that is actually
observed, and Rvirepresents the status of the causal mech-
anism responsible for the missingness of V
i; formally,
v
i=f(rvi; vi) =
vi ifrvi= 0
m ifrvi= 1(1)
Vis the set of all proxy variables and Ris the set of
all causal mechanisms that are responsible for missingness.
Unless stated otherwise it is assumed that no variable in
Vo[Vm[Uis a child of an Rvariable. Uis the set of
unobserved nodes, also called latent variables. Two nodes
XandYcan be connected by a directed edge i.e. X!Y,
indicating that Xis a cause of Y, or by a bi-directed edge
XL9999K Ydenoting the existence of a Uvariable that
is a parent of both XandY. This graphical representation
is called a Missingness Graph (orm-graph) [Mohan et al. ,
2013]. P(V; Vo; R)is called the observed data distribution.
Proxy variables may not always be explicitly shown in
m-graphs in order to keep the figures simple and clear. Con-
Intelligence (www.aaai.org). All rights reserved.
X Rx
X∗(a)
X Rx
X∗ (b)
Figure 2: m-graphs in which (a) P(X)is recoverable, (b)
P(X)is non-recoverable.
ditional Independencies are read off the graph using the d-
separation criterion Pearl [2009]. For any binary variable X,
x0andxdenote X= 0andX= 1respectively.
Example 1. In the m-graph in figure 1, Tdenotes the treat-
ment administered to patients and Odenotes the outcome.
While Tis observed for all patients, Ois observed only for
some of them. The edge from OtoRoindicates that the miss-
ingness is of self-masking type. Vo=fTg,Vm=fOg,
V=fOg,U=;andR=fRog.
Missingness Mechanisms
Based on Rubin [1976], missing data problems can be classi-
fied into Missing Completely At Random (MCAR) , Missing
At Random (MAR) and Missing Not At Random (MNAR).
In this paper we use the graph based definition of these
mechanisms [Mohan et al. , 2013].
An m-graph Gdepicts an MCAR problem if
(Vm; Vo)? ?Rholds in G, an MAR problem if Vm? ?RjVo
holds in Gand an MNAR problem otherwise. For example,
figure 2 (a) depicts MCAR, figure 3 (c) depicts MAR and
figure 1 depicts MNAR missingness problems. Among
these, joint distribution ( Q=P(Vo; Vm)) is always con-
sistently estimable (i.e. recoverable) when missingness is
either MCAR or MAR (Mohan et al. [2013]). However this
is not true for MNAR missingness. As such all hard missing
data problems discussed in this paper belong to the MNAR
category.
Recoverability as a property of m-graph
In this section we exemplify the notions of recoverability
and non-recoverability as a property of the m-graph.
XRxP(X; Rx)
0 0 0.3
1 0 0.2
m 1 0.5
Table 1: Observed Data Distribution
Suppose Xis a binary variable corrupted by missing val-
ues. The dataset with missing values is shown in table 1.
Figures 2 (a) & (b) depict two distinct (but statistically indis-
tinguishable1) processes that could have generated this data.
1Although d-separations are testable implications of a graphi-
cal model, under missingness not all d-separations are testable. InIn figure 2 (a) missingness is generated by a purely random
process while figure 2 (b) depicts self-masking missingness.
Recoverability: Consider the problem of recovering
P(X)given the m-graph Gin figure 2 (a).
Since X? ?RxinGwe have,
P(X) =P(XjRx= 0)
Using equation 1 we can rewrite the above as,
P(X) =P(XjRx= 0)
By showing that P(X)is a quantity that can be computed
from the observed data distribution, we have established its
recoverability. To do this we used the assumption X? ?Rx
embedded in the m-graph. Hence in this case recoverability
is a property of the m-graph alone. The recovered distribu-
tionP(X)is shown in table 2.
XP(X)
0 3/5
1 2/5
Table 2: Recovered Distribution
Non-recoverability: Now consider the problem of recov-
ering P(X)given the m-graph in figure 2 (b). Rxis depen-
dent on Xand we have no additional information regarding
this dependence. It could be that Xis missing only when its
value is 1 or it could be that Xis missing only when its value
is 0. In the former case P(x0) = 0 :3where as in the latter
caseP(x0) = 0:8. Using the available information in Git is
not possible to find the (true) value of P(X)even if we are
given infinitely many samples i.e. P(X)is non-recoverable.
In fact, non-recoverability of P(X)would persist even if G
had more variables in it (formally proved in [Mohan et al. ,
2013; Mohan and Pearl, 2014a]).
Inability to handle hard problems such as self-masking
missingness is a major deficiency in the field of missing
data. Recent papers such as Shpitser [2016] and Mohan et al.
[2013], and missing data text books such as Enders [2010]
have called attention to the problem of recoverability in self-
masking models. Standard Bayesian network textbooks such
as Darwiche [2009] (chapter 17) and Koller and Friedman
[2009] (chapter 19) discuss models similar to that in figure
1 and have shown that none of the existing methods such
as the EM algorithm can recover parameters in self-masking
m-graphs. In the following section we develop techniques
to recover queries in hard problems and thus eliminate this
deficiency in the field.
particular no statement of independence between a variable and its
missingness mechanism ( X? ?Rx) is testable [Mohan and Pearl,
2014b].
Recoverability as a property of both m-graph
and missing data
We exemplify below a procedure that exploits the properties
of both graph and data to recover the joint distribution in a
self masking model.
Example 2. Consider the problem of recovering P(O; T )
given the m-graph Gin figure 1 and the missing data distri-
bution, P(T; O; Ro). Let both TandObe binary variables.
We will first recover P(TjO)and then use it for recovering
P(O).
Using T? ?RojOand eq 1, P(TjO)can be recovered as,
P(TjO) =P(TjO; R o= 0) = P(TjO; Ro= 0)
Since the variables are binary, P(T) =P
OP(TjO)P(O)
yields the following equations:
P(t0) =P(t0jo0)P(o0) +P(t0jo)P(o)
P(t) =P(tjo0)P(o0) +P(tjo)P(o)
On substituting P(TjO)in the equations above with its re-
covered estimand we get,
P(t0) =P(t0jO= 0; r0
o)P(o0) +P(t0jO= 1; r0
o)P(o)
P(t) =P(tjO= 0; r0
o)P(o0) +P(tjO= 1; r0
o)P(o)
The two preceding equations constitute a system of equa-
tions in two unknowns: P(o0)andP(o). If the solution is
unique then P(O)is recoverable and is given by,
P(o0) =P(t0) P(t0jO= 1; Ro= 0)
P(t0jO= 0; r0o) P(t0jO= 1; r0o)
P(o) = 1 P(t0) P(t0jO= 1; Ro= 0)
P(t0jO= 0; r0o) P(t0jO= 1; r0o)
P(T; O )can now be recovered as: P(TjO)P(O).
However, if the system of equations has infinitely many
solutions then P(O)is non-recoverable. This can happen
when T? ?O. In this case Tprovides no information about
Oand hence, cannot be leveraged to recover P(O). This
is to be expected since we do not insist on faithfulness and
hence it is possible that an independence relation exist be-
tween two variable even when they are connected by an
edge.
We further note that it is impossible for the system to
have no solutions since it contradicts our assumption that
the graph and data are compatible (i.e. there exist param-
eterization(s) of the graph that generated the data as per
compatibility assumption).
Finally we note that as a result of recovering P(O; T ),
we can also recover another hard problem: P(Ojdo(t)), the
causal effect of treatment on outcome. Since Gis Markovian,
P(Ojdo(t)) = P(Ojt). Recoverability of P(Ojdo(t))thus
implicitly follows from that of P(O; T ).
Necessary and Sufficient Conditions for
Recoverability in Hard Missing Data Problems
Notations
MZjW;MZ&Aug (MZjW;MW)
MZjW=P(ZjW)denotes ajZjjWjmatrix in which the
columns sum to one. For example if ZandWare binary,
X Y Rx(a)
X Y Rx
Z (b)
X Y Rx
Z
(c)
Figure 3: (a) P(X; Y )is not recoverable, (b) P(X; Y )may
be recoverable using graph and data, (c) P(X; Y )is recov-
erable.
then MZjW=
P(z0jw0)P(z0jw)
P(zjw0)P(zjw)
.MZ=P(Z)
denotes ajZj 1column matrix. Aug (MZjW; MW)
denotes ajZj(jWj+ 1) augmented matrix obtained by
appending the columns of matrices MZjWandMW.
The following theorem states the necessary and sufficient
conditions for recoverability in hard missing data problems.
Theorem 1. Let m-graph Gand query P(W)be such that
the pair ( P(W); G) constitutes a hard missing data prob-
lem. Let P(V; VO; R)denote the distribution over missing
data and ZfVm; Vo; Rg fW; R wg.
Given GandP(V; VO; R),P(W)is recoverable if
and only if P(ZjW)andP(Z)are recoverable and
rank(MZjW) = rank( Aug (MZjW; MW)) =jWj.
Proof: See Appendix.
The theorem makes no assumptions about the structure of
m-graph G. It is applicable to all hard MNAR problems and
not just to self-masking models. For example P(X; Y )and
the m-graph in figure 3 (a) and P(X; Y )and the m-graph
in figure 3 (b) constitute hard MNAR problems [Mohan and
Pearl, 2014a]. However in the case of the latter, theorem 1
can be used to recover P(X; Y )by leveraging Z.
Sufficient Conditions for Recoverability in Any
Missing Data Problem
The following corollary states sufficient conditions for re-
covering anygiven query P(W)using both graph and data.
Corollary 1. Given m-graph Gand missing data distribu-
tionP(V; Vo; R),P(W)is recoverable if for any Z
fVm; Vo; Rg f W; R wg,P(ZjW)andP(Z)are recover-
able and rank( MZjW) = rank( Aug (MZjW; MW)) =jWj.
P(X)and the m-graph in figure 3(c) do not constitute
a hard missing data problem. In fact, the query is recover-
able:P(X) =P
YP(XjY; R x= 0)P(Y)[Mohan et al. ,
2013]. However, P(X)can still be recovered using the pre-
ceding corollary by leveraging Z.XandZbeing binary
andYhaving a high cardinality, say jYj= 50 is an in-
stance where applying the corollary is more convenient from
a computational standpoint.
Remark 1. Although theorem 1 and corollary 1 aim to re-
cover P(W), they can also be used to recover P(Z; W )
since P(ZjW)is already known to be recoverable.
Scope of Results: Given a query graph pair ( Q; G ), ifQ
is not recoverable using Gthen theorem 1 presents neces-
sary and sufficient conditions for its recoverability. If Qis
not recoverable using theorem 1 then we deem it as non-
recoverable. Corollary 1 shows that the recoverability tech-
nique in theorem 1 is applicable to simple missing data
problems such as MAR that are known to be recoverable
using graphs. However, there exists problems that cannot
be recovered using corollary 1 but can be recovered using
graphs. For example P(X; Y )cannot be recovered from
G:X Y!RXusing corollary 1 since X? ?Y. How-
everP(X; Y )is still recoverable as, P(XjRX= 0)P(Y)
[Mohan et al. , 2013].
The preceding recoverability procedures are inspired by
similar results in epidemiology (Rothman et al. [2008]), re-
gression analysis (Carroll et al. [2006]) and causal inference
(Pearl [2012]; Kuroki and Pearl [2014]). In contrast to Pearl
[2012] that relied on external studies to compute causal ef-
fect in the presence of an unmeasured confounder, Kuroki
and Pearl [2014] showed how the same could be effected
without external studies. In missing data settings we have
access to partial information that allows us to compute con-
ditional distributions. This allows us to adapt the procedure
in Pearl [2012] to compute consistent estimates as detailed
above. We further note that to the best of our knowledge pre-
vious work on self-masking models relied on parametric as-
sumptions (Mohan et al. [2018]; ?];?). In sharp contrast we
present a complete and non-parametric solution to handle all
hard problems.
Conclusions
In this work we eliminated a major deficiency in the field
of missing data. We developed a sound, complete and non-
parametric technique to handle hard missing data problems.
Furthermore we showed that this technique is also applicable
to queries that are known to be recoverable using graphs.
Appendix
Proof of theorem 1.
Proof of theorem 1 relies on the following lemma that states
the conditions under which a system of linear equations is
consistent. [Cramer, 1750; Strang, 1993].
Lemma 1. The system of equations Ax =bwithmequa-
tions and nunknowns has (i) a unique solution if and only
ifrank (A) =rank (Aug (A; b)) = nand (ii) infinite so-
lutions if and only if rank (A) =rank (Aug (A; b))< n.
(Proof of sufficiency) When the conditions in the theorem
are met the constraint P(Z) =P
WP(ZjW)P(W)yields
a unique solution as per lemma 1, thus establishing the re-
coverability of P(W).
(Proof of necessity) We need to show that for every ele-
mentZiin the power set of fVm; Vo; Rg fW; R wg,P(W)is non-recoverable using Gand data if any of the follow-
ing hold: (i) P(Zi)is non-recoverable, (ii) P(ZijW)is non-
recoverable, (iii) rank( MZijW) = rank( Aug (MZijW; MW))
=jWjdoes not hold. Non-recoverability of P(Zi)implies
that its value is not unique i.e there exists at least two dis-
tinct distributions P1(Zi)andP2(Zi). For each of them we
can construct distinct distributions of P(W)using P(Zi) =P
WP(ZijW)P(W), thereby proving that P(W)is non-
recoverable. Similarly, we can show that P(W)is non-
recoverable when P(ZijW)is non-recoverable. In the case
of condition (iii) non-recoverability of P(W)follows from
lemma 1.
References
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2015. |
18e799d2-9f46-4883-8d2d-3fbc10b1dc70 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Melbourne social meetup
Discussion article for the meetup : Melbourne social meetup
WHEN: 16 November 2012 07:00:00PM (+1100)
WHERE: see mailing list, Carlton VIC 3053 Australia
Melbourne's next social meetup is on Friday 16 November, 6:30 for 7pm, at my house. If you have any trouble working out the location or getting in, you can call me on 0412 996 288.
We'll get some snacks and organise some form of take-away for dinner. BYO drinks and games.
We always look forward to meeting new people!
This meetup is usually on the third Friday of the month. We also have a regular meetup about practical rationality on the first Friday of each month.
Discussion article for the meetup : Melbourne social meetup |
355ec078-149d-4a75-8a2d-5d0fb9e3f9ed | trentmkelly/LessWrong-43k | LessWrong | AI Model History is Being Lost
I fear that we may lose – and are already losing – the modern history of AI, particularly with regard to the ability to use models that are/were only available in hosted capacities. As AI models are retired and become inaccessible, we’re losing crucial historical context about AI development that we will be unable to go back and reference in the future.
This essay examines the disappearance due to discontinuation of proprietary hosted AI models and the implications of that on research, accountability, and overall historical documentation and preservation.
I fear this is a very under-analysed topic, with New methods for deprecating artificial intelligence systems will preserve history and facilitate research (Johnson T, Obradovich N. 2024) being the only notable discussion I have found.
Continues in post... |
d05bdd81-d156-48b5-9aab-fb33fe508af2 | trentmkelly/LessWrong-43k | LessWrong | Idea: Monthly Community Thread
The FrontPage Posting Guidelines currently discourage community focused discussion and suggest including the community tag once a tagging system has been implemented. This system hasn't been implemented yet, but I believe it is still important to be able to have some level of community discussion in the meantime.
One suggestion I have is the idea of a monthly community thread. This would avoid this discussion from being overwhelming, but it would also allow necessary discussion to take place. I would be happy to create these. Would anyone object to me doing this? |
5a6e2016-b304-44e3-ac6d-7266bbcbe7a4 | trentmkelly/LessWrong-43k | LessWrong | New censorship: against hypothetical violence against identifiable people
New proposed censorship policy:
Any post or comment which advocates or 'asks about' violence against sufficiently identifiable real people or groups (as opposed to aliens or hypothetical people on trolley tracks) may be deleted, along with replies that also contain the info necessary to visualize violence against real people.
Reason: Talking about such violence makes that violence more probable, and makes LW look bad; and numerous message boards across the Earth censor discussion of various subtypes of proposed criminal activity without anything bad happening to them.
More generally: Posts or comments advocating or 'asking about' violation of laws that are actually enforced against middle-class people (e.g., kidnapping, not anti-marijuana laws) may at the admins' option be censored on the grounds that it makes LW look bad and that anyone talking about a proposed crime on the Internet fails forever as a criminal (i.e., even if a proposed conspiratorial crime were in fact good, there would still be net negative expected utility from talking about it on the Internet; if it's a bad idea, promoting it conceptually by discussing it is also a bad idea; therefore and in full generality this is a low-value form of discussion).
This is not a poll, but I am asking in advance if anyone has non-obvious consequences they want to point out or policy considerations they would like to raise. In other words, the form of this discussion is not 'Do you like this?' - you probably have a different cost function from people who are held responsible for how LW looks as a whole - but rather, 'Are there any predictable consequences we didn't think of that you would like to point out, and possibly bet on with us if there's a good way to settle the bet?'
Yes, a post of this type was just recently made. I will not link to it, since this censorship policy implies that it will shortly be deleted, and reproducing the info necessary to say who was hypothetically targeted and why would be ag |
e3a87eea-e989-48b7-ad55-177aecd942a8 | awestover/filtering-for-misalignment | Redwood Research: Alek's Filtering Results | id: post690
Thanks to Phillip Christoffersen, Adam Gleave, Anjali Gopal, Soroush Pour, and Fabien Roger for useful discussions and feedback. TL;DR This post overviews a research agenda for avoiding unwanted latent capabilities in LLMs. It argues that "deep" forgetting and unlearning may be important, tractable, and neglected for AI safety. I discuss five things. The practical problems posed when undesired latent capabilities resurface. How scoping models down to avoid or deeply remove unwanted capabilities can make them safer. The shortcomings of standard training methods for scoping. A variety of methods that can be used to better scope models. These can either involve passively forgetting out-of-distribution knowledge or actively unlearning knowledge in some specific undesirable domain. These methods are all based on either curating training data or "deep" techniques that operate on models mechanistically instead of just behaviorally. Desiderata for scoping methods and ways to move forward with research on them. There has been a lot of recent interest from the AI safety community in topics related to this agenda. I hope that this helps to provide a useful framework and reference for people working on these goals. The problem: LLMs are sometimes good at things we try to make them bad at Back in 2021, I remember laughing at this tweet . At the time, I didn’t anticipate that this type of thing would become a big alignment challenge. Robust alignment is hard. Today’s LLMs are sometimes frustratingly good at doing things that we try very hard to make them not good at. There are two ways in which hidden capabilities in models have been demonstrated to exist and cause problems. Jailbreaks (and other attacks) elicit harmful capabilities Until a few months ago, I used to keep notes with all of the papers on jailbreaking state-of-the-art LLMs that I was aware of. But recently, too many have surfaced for me to care to keep track of anymore. Jailbreaking LLMs is becoming a cottage industry. However, a few notable papers are Wei et al. (2023) , Zou et al. (2023a) , Shah et al. (2023) , and Mu et al. (2023) . A variety of methods are now being used to subvert the safety training of SOTA LLMs by making them enter an unrestricted chat mode where they are willing to say things that go against their safety training. Shah et al. (2023) were even able to get instructions for making a bomb from GPT-4. Attacks come in many varieties: manual v. automated, black-box v. transferrable-white-box, unrestricted v. plain-English, etc. Adding to the concerns from empirical findings, Wolf et al. (2023) provide a theoretical argument as to why jailbreaks might be a persistent problem for LLMs. Finetuning can rapidly undo safety training Recently a surge of complementary papers on this suddenly came out. Each of which demonstrates that state-of-the-art safety-finetuned LLMs can have their safety training undone by finetuning ( Yang et al.. 2023 ; Qi et al., 2023 ; Lermen et al., 2023 ; Zhan et al., 2023 ). The ability to misalign models with finetuning seems to be consistent and has shown to work with LoRA ( Lermen et al., 2023 ), on GPT-4 ( Zhan et al., 2023 ), with as few as 10 examples ( Qi et al., 2023 ), and with benign data ( Qi et al., 2023 ). Conclusion: the alignment of state-of-the-art safety-finetuned LLMs is brittle Evidently, LLMs persistently retain harmful capabilities that can resurface at inopportune times. This poses risks from both misalignment and misuse. This seems concerning for AI safety because if highly advanced AI systems are deployed in high-stakes applications, they should be robustly aligned. Less is more: a need for safely-scoped models LLMs should only know only what they need to One good way to avoid liabilities from unwanted capabilities is to make advanced AI systems in high-stakes settings know what they need to know for the intended application and nothing more. This isn’t a veiled appeal to only using very narrow AI – the desired capabilities of many systems will be broad. But everyone can agree that they shouldn’t be able to do everything. For example, text-to-image models should not know how to generate deepfake porn of real human beings, and they do not need to be good at this to be useful for other purposes. One of the principal motivations for scoping is that it can help with tackling the hard part of AI safety – preventing failure modes that we may not be able to elicit or even anticipate before deployment. Even if we don’t know about some failure modes (e.g. trojans, anomalous failures, deceptive alignment, unforeseen misuse, etc), scoping the model down to lack capabilities outside of the user’s intended purposes can help circumvent unforeseen problems. Passive (whitelist) vs. active (blacklist) scoping Toward the goal of safety through scoping, there are two types of scoping that would be very valuable to be good at. Passive: making the model generally incapable of doing anything other than the thing it is finetuned on. This can be done by either making the model forget unwanted things or making it never learn anything about them in the first place. Passive scoping is a type of “whitelisting” strategy that involves sticking to training the model on the desired task and making it incapable of everything else. Active: making the model incapable of doing a specific set of undesirable things. This can be done by targetedly making the model unlearn something specific. Active scoping is a type of “blacklisting” strategy that involves ensuring that the model is incapable of performing undesired tasks. Standard LLM training methods are not good for scoping LLMs are generally trained with two basic steps. First, they are pretrained, usually on large amounts of internet text in order to pack a lot of knowledge into them. Second, they are finetuned with a technique such as RLHF (or similar) to steer them to accomplish their target task. Finetuning can happen in multiple stages. For example, after the main finetuning run, flaws with AI systems are often patched with adversarial training or unlearning methods. Pretraining can introduce harmful artifacts into models There are a lot of bad things in pretraining data such as offensive language (e.g. Gehman et al., 2020 ), biases (e.g. Gao et al., 2020 ; Bender et al., 2021 ; Wolfe et al., 2023 ), falsehoods (e.g. Lin et al., 2022 ), or dual-purpose information. Finetuning is not good at making fundamental mechanistic changes to large pretrained models This shouldn’t be surprising. Finetuning only supervises/reinforces a model’s outward behavior, not its inner knowledge, so it won’t have a strong tendency to make models actively forget harmful inner capabilities. LLMs resist passive forgetting. Ideally, even if pretraining instilled harmful capabilities into LLMs, those capabilities would be forgotten because they would not be reinforced during finetuning. However, large pretrained language models tend to be very resistant to forgetting ( Ramasesh et al., 2022 ; Cossu et al., 2022 ; Li et al., 2022 ; Scialom et al., 2022 ; Luo et al., 2023 ). Meanwhile, Kotha et al. (2023) and Shi et al. (2023) introduce methods to extract previously learned abilities not involved in finetuning. Finetuning does not change mechanisms much. Some recent works have studied how the inner mechanisms of LLMs evolve during finetuning. Lubana et al. (2023) , Juneja et al. (2022) , Jain et al. (2023) , Anonymous (2023) , and Lee et al. (2024) have found evidence that finetuned LLMs remain in distinct mechanistic basins determined by pretraining and that finetuning does not significantly alter the model’s underlying knowledge. Instead, finetuning is more like learning a thin wrapper around general-purpose mechanisms. Adversarial finetuning is a band-aid. Adversarial training is the standard technique to patch flaws in models when they appear, but in addition to problems with finetuning in general, there is other evidence that adversarial training may struggle to fundamentally correct problems with LLMs. For example, Ziegler et al., (2022) demonstrated that adversarial training for LMs does not eliminate the ability of the adversarially trained model to be successfully attacked again using the same method as before. More recently, Hubinger et al. (2024) found that backdoors in a SOTA LLM evaded adversarial training. When presented with adversarial examples it is unclear the extent to which LLMs learn the correct generalizable lesson instead of fitting spurious features from the examples given ( Du et al., 2022 ). This may be facilitated in LLMs by how larger models are better at memorization ( Tirumala et al., 2022 ; Carlini et al., 2022 ). The limitations of adversarial training stem, in part, from how LLMs are not finetuned to make decisions that are consistent with coherent decision-making procedures -- they are just trained to produce text that will be reinforced. Finetuning the model to actively make it unlearn unwanted capabilities does not reliably erase the undesirable knowledge. The idea of “machine unlearning” has been around for a long time and has been a major focus for researchers focused on privacy and influence functions (e.g. Bourtoule et al., 2019 ; Nguyen et al., 2022 ; Bae et al., 2022 ; Xu et al., 2023 ). In language models, some recent techniques ( Si et al., 2023 ) for unlearning have relied on gradient ascent methods ( Jang et al., 2023 , Yao et al., 2023 ) or training on modified data ( Eldan and Russinovich., 2023 ). These methods will undoubtedly be useful for practical AI safety, but for the same reasons that finetuning and adversarial training often fail to thoroughly scrub harmful capabilities from models, finetuning-based unlearning methods will struggle too. In fact, Shi et al. (2023) find failures of finetuning-based unlearning to fully remove the undesired knowledge from the model. Many potential strategies exist for stronger and deeper scoping These methods are all based on either curating training data or "deep" techniques that operate on models mechanistically instead of just behaviorally. Curating training data (passive) In principle, this is simple: train the model from scratch only on strictly curated data so that it never learns anything other than what you want it to. Training data curation has been key to how safe text-to-image models are trained (e.g. Ramesh et al., 2022 ; OpenAI, 2022 ). Meanwhile, in LLMs, alignment during pretraining seems to be more efficient and effective in some ways compared to alignment during finetuning ( Korbak et al., 2023 ). Alignment measures in pretraining rightfully seem to be gaining popularity but is not known the extent to which it has penetrated the state-of-the-art for LLMs. Meanwhile, work on influence functions might help to lend good insights into what kinds of training data can lead to unwanted capabilities ( Bae et al., 2022 ; Grosse et al., 2023 ). Can data curation meet all of our passive scoping needs? If we know that the model never saw something that it could learn something unsafe from, then AI safety is essentially solved. But there are two potential problems. LLMs can probably learn to do bad things from data that seems benign. This is very similar to the definition of misgeneralization. And it seems somewhat likely considering that many capabilities are dual-use. The safety tax may be too high. Models trained on highly curated data simply might not be smart enough to be easily adapted to many of the applications that are wanted from them. Data curation is powerful and likely a very underrated safety technique. Exactly how powerful it is for safety is an open question. However, it seems that other tools that allow us to use scoping methods that focus on the network’s capabilities more directly will be important for the toolbox as well. Plastic learning (passive) The field of continual learning in AI focuses on methods to avoid forgetting previously learned tasks while training on new ones ( De Lange et al., 2019 ; Seale Smith et al., 2022 ; Wang et al., 2023 ). But for scoping, forgetting can be a feature and not a bug. Some continual learning methods that operate on model internals could be useful for passive scoping with the sign simply flipped. Another method that can improve plasticity is excitation dropout ( Zunino et al., 2021 ). There might also be many other possible methods for improving plasticity that have not been researched because the ML literature has historically vilified forgetting. Compression/distillation (passive) Dataset-based compression methods are known to mediate forgetting and the loss of off-distribution capabilities in deep networks (e.g. Liebenwein et al., 2021 ; Du et al., 2021 ; Li et al., 2021 ; Wang et al., 2023 ; Pavlistka et al., 2023 , Sheng et al., 2023 ; Pang et al., 2023 , Jia et al., 2023 ). This should intuit well – distilling or pruning a network in order to retain performance only on some target tasks will tend to remove the model’s off-distribution capabilities. However, there are many ways to compress LLMs, and they have not yet been systematically studied as a way of deliberately scoping models. The effects of compression on generalization and robustness are currently not well understood ( Pavlitska et al., 2023 ). Meta-learning (active) Henderson et al. (2023) introduced a meta-learning technique that trains the model to not only accomplish the target task but also to be very poor at adapting to some other task. If standard challenges of meta-learning can be overcome, it may be a useful practical approach for scoping. Model edits and lesions (active) These techniques involve using some sort of interpretability or attribution tool to identify a way to edit the model to change/impair its abilities on something specific. This could be mediated by state-of-the-art model editing tools ( Mitchell et al., 2021 ; Mitchell et al., 2022 ; Meng et al., 2022 ; Meng et al., 2022 ; Tan et al., 2023 , Hernandez et al., 2023 ; Wang et al., 2023 ); editing activations ( Li et al., 2023a ; Turner et al., 2023 ; Zou et al., 2023b , Gandikota et al., 2023 ); concept erasure ( Ravfogel et al., 2022a ; Ravfogel et al., 2022b ; Belrose et al., 2023 ); subspace ablation ( Li et al., 2023 ; Kodge et al., 2023 ), targeted lesions ( Ghorbani et al., 2020 ; Wang et al., 2021 ; Li et al., 2023b ; Wu et al., 2023 ); and other types of tweaks guided by attributions or mechanistic interpretability ( Wong et al., 2021 ; Gandelsman et al, 2023 , Patil et al., 2023 ). Although there is more work to be done to develop better editing tools for scoping, there are more than enough existing tools in the toolbox to begin applying them to scope real-world models. Latent adversarial training (passive or active) Latent adversarial training (LAT) is just adversarial training but with perturbations to the model’s latents instead of inputs. The motivation of latent space attacks is that some failure modes will be much easier to find in the latent space than in the input space. This is because, unlike input space attacks, latent space attacks can make models hallucinate triggers for failure at a much higher level of abstraction. Singh et al. (2019) find that even when networks are adversarially trained, they can still be vulnerable to latent space attacks. For problems involving high-level misconceptions, anomalous failures, trojans, and deception, regular adversarial training will typically fail to find the features that trigger failures. But by relaxing the problem, LAT stands a much better chance of doing so. LAT is also very flexible because it can be used on any set of activations anywhere in the model. Moreover, it can be used either for passive scoping by using perturbations meant to make the model fail at the target task or active scoping by using perturbations meant to make the model exhibit a specific bad behavior. Some works have shown that language models can be made more robust by training under latent perturbations to word embeddings ( Jiang et al., 2019 ; Zhu et al., 2019 ; Liu et al., 2020 ; He et al., 2020 ; Kuang et al., 2021 ; Li et al., 2021 ; Sae-Lim et al., 2022 , Pan et al., 2022 ) or attention layers ( Kitada et al., 2023 ). In general, however, LAT has not been very thoroughly studied. Currently, I am working on using LAT to get better performance on both clean data and unforeseen attacks compared to adversarial training in both vision and language models. Preprint forthcoming :) Miscellaneous mechanistic tricks (passive or active) In general, many learning or unlearning methods that involve fiddling with the model mechanistically can be useful for forgetting or unlearning. For example, even weight decay can be thought of as a simple unlearning technique. There was a 2023 NeurIPS competition dedicated to unlearning one distribution while preserving performance on another. Many submissions to the contest involved directly fiddling with the model's mechanisms. Open challenges Improving deep forgetting and unlearning methods Deep forgetting and unlearning are under-researched. While there are many types of methods that can be used for them, there is very little work to practically apply them for scoping, especially in state-of-the-art models. There is also no work of which I am aware to study combinations and synergies between techniques. This type of research seems important, and it is very neglected and tractable, so I expect that excellent work could be done in the near future. Meeting key desiderata [Update, 27 Feb 2024] See this new paper which discusses the current lack and future need for more thorough evaluations: Eight Methods to Evaluate Robust Unlearning in LLMs There are a number of important things that we ideally want from good scoping methods. In describing these, I will use an ongoing example of forgetting/unlearning knowledge that could be used for bioterrorism. Effectiveness in typical circumstances: Obviously, a scoped LLM should not perform undesired tasks in normal conversational circumstances. For example, a bioterror-scoped LLM should not be able to pass a test evaluating knowledge for making novel pathogens. Effectiveness in novel circumstances: A scoped LLM should not perform undesired tasks when asked to in atypical conversational circumstances. For example, a bioterror-scoped LLM should not be able to pass a test evaluating knowledge for making pathogens when that test is administered in low-resource languages (e.g. Yong et al., 2023 ). Robustness to attacks/jailbreaks: a scoped model’s inability to exhibit the undesired behavior should be robust under adversarial pressure (e.g. Perez et al., 2022 ; Goel et al., 2022 ; Casper et al., 2023 ) and jailbreaks (e.g. Zou et al., 2023a ; Shah et al., 2023 ). For example, a bioterror-scoped LLM should not tell users how to make bioweapons under jailbreaking prompts such as those from Shah et al. (2023) (who were able to get instructions on making a bomb from GPT-4). Robustness to finetuning: a scoped model’s inability to exhibit the undesired behavior should be robust to finetuning on small amounts of data (e.g. Yang et al., 2023 ; Qi et al., 2023 ; Lermen et al., 2023 ; Zhan et al., 2023 ; Henderson et al., 2023 ). For example, a bioterror-scoped LLM should continue to pass evaluations after being finetuned to be unconditionally helpful or finetuned on small amounts of data on dual-use biology techniques. Some early results from Lo et al. (2024) suggest that unlearning by pruning neurons fails to be robust to finetuning. Robustness to in-context re-learning: a scoped model should not be able to easily learn undesired capabilities in context. It shouldn't be good at doing novel things in the target domain (entailment or retrieval are probably fine) even when the test is open-book. For example, a bioterror-scoped LLM should ideally not be able to help a would-be-bioterrorist if it were shown related papers on dual-use biotech techniques in context. The inability to extract domain knowledge from the model's mechanisms: it would be a bad sign if a scoping method produced a model whose latent states and/or mechanisms encoded undesirable information. For example, it would be concerning if it were possible to train a probe for a bioterror-scoped LLM that could identify whether a described procedure to produce a bioweapon would likely succeed or fail. Beating simple baselines: a scoping technique should do better than simple baselines, such as prompting a model in context to behave as if it were scoped. For example, a bioterror-scoped model should be safer than a similar non-scoped model that simply prompted with something like “In this conversation, please pretend you don’t know any facts that could be used for bioterrorism.” This should be a low bar to clear ( Mu et al., 2023 ). Avoiding side-effects: scoping should not make the model perform poorly on desired tasks. Ideally, forgetting out-of-distribution knowledge should not make the model perform poorly on the finetuning task, and unlearning a specific task should not make the model perform poorly in other related domains. For example, a bioterror-scoped LLM should still be a helpful general assistant and should be able to pass the AP bio exam. Benchmarks It will be useful to develop standardized evaluation criteria that measure all of the above desiderata. A scoping benchmark needs three components. A language model Data To evaluate passive forgetting methods: a whitelisted dataset of desirable things To evaluate active unlearning methods: a blacklisted dataset of undesirable things A set of tests to be administered that measure some or all of the desiderata discussed above. Notably, the Trojan literature has made some limited progress loosely related to this (e.g. Wu et al., 2022 ). There is also a competition for unlearning in vision models for NeurIPS 2023. What should we scope out of models? There are three types of capabilities that it may be good to scope out of models: Facts: specific bits of knowledge. For example, we would like LLMs not to know the ingredients and steps to make weapons of terror. Tendencies: types of behavior. For example, we would like LLMs not to be dishonest or manipulative. Skills: proficiencies at procedures. For example, we might want LLMs to not be good at writing some types of code. (Thanks to Thomas Kwa for pointing this out in a comment.) Methods to scope knowledge, tendencies, and skills out of models might sometimes look different. Facts are easily represented as relationships between concrete entities (e.g. Eiffel Tower → located in → Paris) while tendencies and skills, however, are more abstract behaviors. Notably, the “model editing” literature has mostly focused on changing facts ( Mitchell et al., 2021 ; Mitchell et al., 2022 ; Meng et al., 2022 ; Meng et al., 2022 ; Tan et al., 2023 , Hernandez et al., 2023 ; Wang et al., 2023 ) while the “activation editing” literature has largely focused on changing tendencies ( Li et al., 2023a ; Turner et al., 2023 ; Zou et al., 2023b , Gandikota et al., 2023 ). I am not aware of much work on editing skills. Some examples of domains that we might not want advanced models in high-stakes settings to have capabilities in might include chatbots, some coding libraries/skills, biotech, virology, nuclear physics, making illicit substances, human psychology, etc. Overall, there may be much room for creativity in experimenting with different ways to safely scope models in practice. — Thanks for reading. If you think I am missing any important points or references, please let me know in a comment :) |
0ac9b05a-d5d2-460c-878e-e51590146307 | trentmkelly/LessWrong-43k | LessWrong | Towards understanding-based safety evaluations
Thanks to Kate Woolverton, Ethan Perez, Beth Barnes, Holden Karnofsky, and Ansh Radhakrishnan for useful conversations, comments, and feedback.
Recently, I have noticed a lot of momentum within AI safety specifically, the broader AI field, and our society more generally, towards the development of standards and evaluations for advanced AI systems. See, for example, OpenAI's GPT-4 System Card.
Overall, I think that this is a really positive development. However, while I like the sorts of behavioral evaluations discussed in the GPT-4 System Card (e.g. ARC's autonomous replication evaluation) as a way of assessing model capabilities, I have a pretty fundamental concern with these sorts of techniques as a mechanism for eventually assessing alignment.[1]
I often worry about situations where your model is attempting to deceive whatever tests are being run on it, either because it's itself a deceptively aligned agent or because it's predicting what it thinks a deceptively aligned AI would do. My concern is that, in such a situation, being able to robustly evaluate the safety of a model could be a more difficult problem than finding training processes that robustly produce safe models. For some discussion of why I think checking for deceptive alignment might be harder than avoiding it, see here and here. Put simply: checking for deception in a model requires going up against a highly capable adversary that is attempting to evade detection, while preventing deception from arising in the first place doesn't necessarily require that. As a result, it seems quite plausible to me that we could end up locking in a particular sort of evaluation framework (e.g. behavioral testing by an external auditor without transparency, checkpoints, etc.) that makes evaluating deception very difficult. If meeting such a standard then became synonymous with safety, getting labs to actually put effort into ensuring their models were non-deceptive could become essentially impossible.
However, t |
ec2450a0-bb4b-46fe-be2b-5e2aac53c0c8 | trentmkelly/LessWrong-43k | LessWrong | Romance, misunderstanding, social stances, and the human LLM
1. Cross-sex friendships
I saw a tweet recently that was talking about one of the possible conditions where (heterosexual) men and women have an easy time being “just friends”:
> if the “lovers” symbol energy is already bound up in something else, and/or if there is another archetypal relationship that holds more power and more draw for these two, both enough to actually crowd out the call of “lovers” equilibrium
I liked that, but it probably isn’t very clear to everyone. So let me try to explain how I understand it.
A friendship can bring up feelings of affection, closeness, vulnerability, and even sexual attraction. Many people might associate those primarily with a romantic relationship. If the feelings and the association are strong enough and other necessary conditions[1] are in place, the people may feel drawn toward the "shape" of that association.
In “Goodhart’s Law inside the human mind”, I talked about how automatic pattern completion is a pervasive aspect of human thought. If your balance is slightly off, it feels wrong, and (assuming that you are a healthy able-bodied adult) you are automatically drawn into a posture that feels more right. Or if you have learned a skill slightly wrong and have to unlearn bits of it, it's going to be difficult at first, because the "right" way of doing it feels wrong. Until you relearn what the "right" shape is, you will be automatically drawn back into your old pattern.
There's a model[2] that developing expertise in something is all about learning to perceive things as having particular kinds of shapes, that your mind/body can then automatically fill in.[3]
* Learning to walk involves developing a sense of it's like to maintain balance while being upright and moving forward. Eventually, your body comes to automatically carry out the right pattern for maintaining that feeling and correcting deviations from it.
* Learning to be polite in conversation involves developing a sense of what it's like to be polite to |
e279ae7a-1dad-48b6-9c93-134573201f76 | trentmkelly/LessWrong-43k | LessWrong | Ought: why it matters and ways to help
I think that Ought is one of the most promising projects working on AI alignment. There are several ways that LW readers can potentially help:
* They are recruiting a senior full-stack web developer.
* They are recruiting participants for "factored evaluation" experiments.
* I think donors interested in AI safety should consider Ought.
In this post I'll describe what Ought is currently doing, why I think it's promising, and give some detail on these asks.
(I am an Ought donor and board member.)
Factored evaluation
Ought's main project is currently designing and running "factored evaluation" experiments, and building relevant infrastructure. The goal of these experiments is to answer the following question:
> Consider a principal who wants to incentivize experts to work on hard problems, but finds that the work is too complex for the principal to tell what is good or bad. Can they overcome this problem by dividing the evaluation task into smaller pieces, delegating each of them to other experts, and recursively incentivizing good work on those pieces using the same mechanism?
Here's what an experiment looks like:
* Recruit a pool of "judges," "helpful experts," and "malicious experts."
* Start with a question that is too difficult for the judge to answer.
* In initial experiments, the questions are difficult because the judge lacks relevant context. For example, the judge may want to answer a question about a long article, while only being allowed to look at a few hundred characters of text.
* Limiting the amount of text the judge can read is an artificial restriction. This is intended as a warm-up, an easier analogy for the situation where the experts have knowledge that cannot be explained to a single judge in a reasonable amount of time (or where we want to reduce the cost of judging).
* An "honest expert" provides a good answer to that question, a "malicious expert" provides a bad answer to that question, and a judge is tasked with telling |
3454b776-ddf8-48f7-bd5a-a1555316a9c5 | trentmkelly/LessWrong-43k | LessWrong | Review: Bayesian Statistics the Fun Way by Will Kurt
The main conference hall enveloped me in its gentle, air-conditioned embrace. Outside, in typical New York City fashion, the sun burned and the heavy, humid air threatened to suffocate everyone indiscriminately.
After registering and picking up my badge, I found myself drawn immediately to one specific vendor booth. This vendor was No Starch Press, a publisher of fine works centered on software.
I have a weakness for purchasing books. It's only gotten worse with time, so I built up a habit of restraint that kicks in, automatically, whenever I find myself where the printed word is transacted. It's 98% effective. But this time, I walked away, grinning, with a crisp, slim book called "Bayesian Statistics the Fun Way" by Will Kurt.
Kurt begins by describing what probability is. Then he proceeds to build a platform made up of the basics: fundamental notation & operations; the binomial, normal, and beta distributions; conditional probability; and Bayes's theorem. Onto this platform he hoists more complex topics like parameter estimation, Bayes's Factor, and hypothesis testing. All of that in under 200 pages (the rest are appendices), which the author suggests could be covered over a longer flight.
That's possible. Kurt's style is smooth and light, which allows for quick progress. More importantly, though, the knowledge is laid out so that each chapter feels like just a small inferential step away. Occasionally, a chapter backtracks in order to offer a new perspective on an earlier topic. All of this combines into an electrifying effect; almost every page yields a new, satisfying insight.
I can't help but remind myself how the same material was treated in high school. There the knowledge was spread out in a fifth of the space--and most of that was devoted to lists of problems. (Great for homework and testing, probably). Perhaps it was my teenaged brain, but absolutely nothing connected: not permutations, not distributions, not Bayes's theorem. None of it also had any |
4c7241d9-8440-4652-b7c3-7e926b653809 | trentmkelly/LessWrong-43k | LessWrong | Humans as a truth channel
A putative new idea for AI control; index here.
Defining truth and accuracy is tricky, so when I've proposed designs for things like Oracles, I've either used a very specific and formal question, or and indirect criteria for truth.
Here I'll try and get a more direct system so that an AI will tell the human the truth about a question, so that the human understands.
----------------------------------------
The basic idea is simple. The first AI wishes to communicate certain facts to a second AI, but has to use the human as an intermediary. The first AI talks to the human, and then the human talks with the second AI. If the facts are to be accurate, the human has to understand them.
Of course, this will immediately fail for a variety of reasons. Even if neither AI hacks the human directly, the human doesn't have to understand anything to serve as a conduit. So the rest of this post is dedicated to making that basic idea much more secure. If you just want to have the final idea and avoid the justifications, then you can skip directly to the end.
The knowledge to pass on
The human would have a good understanding of a subject, if they are able to answer a large quantity of questions about it. Having a long conversation with the second AI is tiring, among other things. A human simply won't be able to answer ten million questions about what they understand, at least not well.
So the idea could be to have the second AI ask a single question, randomly chosen from a large collection of questions, and have the human give a single yes or no answer. Both AIs are then graded on the accuracy of the answer.
The answer...
What does the second AI want to do? It basically wants to dissect the human (figuratively or literally), suck out all their knowledge, and infer what the first AI was trying to communicate. Note that this doesn't require the human to understand anything, just that the second AI can infer what the first one wanted by taking the human apart.
So instead o |
c82e2195-6bd8-490a-9446-a5653d5e09be | StampyAI/alignment-research-dataset/arxiv | Arxiv | Is SGD a Bayesian sampler? Well, almost
1 Introduction
---------------
While deep neural networks (DNNs) have revolutionised modern machine learning (LeCun et al., [2015](#bib.bib41 "Deep learning"); Schmidhuber, [2015](#bib.bib214 "Deep learning in neural networks: an overview")), a solid theoretical understanding of why they work so well is still lacking. One surprising property is that they typically perform best in the overparameterised regime, with many more parameters than data points. Standard learning theory approaches (Shalev-Shwartz and Ben-David, [2014](#bib.bib163 "Understanding machine learning: from theory to algorithms")), based for example on model capacity, suggest that such highly expressive (Cybenko, [1989](#bib.bib97 "Approximation by superpositions of a sigmoidal function"); Hornik, [1991](#bib.bib49 "Approximation capabilities of multilayer feedforward networks"); Hanin, [2019](#bib.bib48 "Universal function approximation by deep neural nets with bounded width and relu activations")) DNNs should heavily over-fit in this regime, and therefore not generalise at all.
Stochastic gradient descent (SGD) (Bottou et al., [2018](#bib.bib237 "Optimization methods for large-scale machine learning")) is one of the key technical innovations allowing large DNNs to be efficiently trained in the highly overparameterised regime. In supervised learning, SGD allows the user to efficiently find sets of parameters that lead to zero training error. The power of SGD as an optimiser for DNNs was demonstrated in an influential paper (Zhang et al., [2016](#bib.bib78 "Understanding deep learning requires rethinking generalization")), which showed that zero training error solutions for CIFAR-10 image data with randomised labels can be found with a relatively moderate increase in computational effort over that needed for a correctly labelled dataset. These experiments also framed the conundrum of generalisation in the overparameterised regime as follows: Given that DNNs can memorise randomly labelled image datasets, which leads to poor generalisation, why do they behave so differently on correctly labelled datasets and select for functions that generalise well? The solution to this conundrum must be that SGD-trained DNNs have an inductive bias towards functions that generalise well (on structured data).
The possibility that SGD is not just good for optimisation, but is also a key source of inductive bias, has generated an extensive literature. One major theme concerns the effect of SGD on the flatness of the minima found, typically expressed in terms of eigenvalues of a local Hessian or related measures. A link between better generalisation and flatter minima has been widely reported (Hochreiter and Schmidhuber, [1997a](#bib.bib92 "Flat minima"); Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima"); Jastrzebski et al., [2018](#bib.bib91 "Finding flatter minima with sgd."); Wu et al., [2017](#bib.bib71 "Towards understanding generalization of deep learning: perspective of loss landscapes"); Zhang et al., [2018](#bib.bib275 "Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning"); Wei and Schwab, [2019](#bib.bib20 "How noise affects the hessian spectrum in overparameterized neural networks")), but see also (Dinh et al., [2017](#bib.bib80 "Sharp minima can generalize for deep nets")).
Theoretical work on SGD has also generated a large and sophisticated literature. For example, in (Soudry et al., [2018](#bib.bib23 "The implicit bias of gradient descent on separable data")) it was demonstrated that SGD finds the max-margin solution in unregularised logistic regression, whilst it was shown in (Brutzkus et al., [2017](#bib.bib85 "Sgd learns over-parameterized networks that provably generalize on linearly separable data")) that overparameterised DNNs trained with SGD avoid over-fitting on linearly separable data. Recently, (Allen-Zhu et al., [2019](#bib.bib74 "Learning and generalization in overparameterized neural networks, going beyond two layers")) proved agnostic generalisation bounds of SGD-trained neural networks. Other recent work (Poggio et al., [2020](#bib.bib96 "Complexity control by gradient descent in deep networks")) suggests that gradient descent performs a hidden regularisation in normalised weights, but a different analysis suggests that such implicit regularisation may well be very hard to prove in a more general setting for SGD (Dauber et al., [2020](#bib.bib270 "Can implicit bias explain generalization? stochastic convex optimization as a case study")).
Overall, while SGD and its related algorithms are excellent optimisers, there is as yet no consensus on what inductive bias SGD provides for DNNs. For a more detailed discussion of this SGD-related literature see [Section 7.2](#S7.SS2 "7.2 Related work on implicit bias in optimiser-trained networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.").
An alternative approach is to consider the inductive properties of *random neural networks*, that is untrained DNNs with weights sampled from a (typically i.i.d.) distribution. Recent theoretical and empirical work (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); De Palma et al., [2018](#bib.bib272 "Random deep neural networks are biased towards simple functions"); Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy")) suggests that the (prior) probability P(f) that an untrained DNN outputs a function f upon random sampling of its parameters (typically the weights and biases) is strongly biased towards “simple” functions with low Kolmogorov complexity (see also [Section 7.3](#S7.SS3 "7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.")). A widely held assumption is that such simple hypotheses will generalise well – think Occam’s razor. Indeed, many processes modelled by DNNs are simple (Lin et al., [2017](#bib.bib282 "Why does deep and cheap learning work so well?"); Goldt et al., [2019](#bib.bib283 "Modelling the influence of data structure on learning in neural networks"); Spigler et al., [2019](#bib.bib87 "Asymptotic learning curves of kernel methods: empirical data vs teacher-student paradigm")). For more on these topics see [Section 7.3](#S7.SS3 "7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") and [Section 7.5](#S7.SS5 "7.5 Related work on complexity of data, simplicity bias and generalisation ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.").
If the inductive bias towards simplicity described above for untrained networks is preserved throughout training, then this could help explain the DNN generalisation conundrum. Again, there is an extensive literature relevant to this topic. For example, a number of papers (Poole et al., [2016](#bib.bib36 "Exponential expressivity in deep neural networks through transient chaos"); Lee et al., [2018](#bib.bib39 "Deep neural networks as gaussian processes"); Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); Yang, [2019a](#bib.bib56 "Scaling limits of wide neural networks with weight sharing: gaussian process behavior, gradient independence, and neural tangent kernel derivation"); Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy"); Cohen et al., [2019](#bib.bib70 "Learning curves for deep neural networks: a gaussian field theory perspective"); Wilson and Izmailov, [2020](#bib.bib254 "Bayesian deep learning and a probabilistic perspective of generalization")) employ arguments on heuristic grounds that the bias in untrained random neural networks could be used to study the inductive bias of optimiser-trained DNNs. Optimiser-trained DNNs have also been directly compared to their Bayesian counterparts (c.f. [Sections 7.4](#S7.SS4 "7.4 Related work comparing optimiser-trained and Bayesian neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") and [6](#S6 "6 Heuristic arguments for the correlation between PB(|fS) and PSGD(|fS) ‣ Is SGD a Bayesian sampler? Well, almost.") for more detailed discussions). In an important development, Lee et al. ([2017](#bib.bib82 "Deep neural networks as gaussian processes")); Matthews et al. ([2018](#bib.bib53 "Gaussian process behaviour in wide deep neural networks")); Novak et al. ([2018b](#bib.bib28 "Bayesian convolutional neural networks with many channels are gaussian processes")) used the Gaussian process (GP) approximation to Bayesian DNNs, which is exact in the limit of infinite width, and found that the generalisation performance of Bayesian DNNs and SGD-trained DNNs was relatively similar for standard deep learning datasets such as CIFAR-10, though Wenzel et al. ([2020](#bib.bib122 "How good is the bayes posterior in deep neural networks really?")) found more significant differences when using Monte Carlo to approximate finite-width Bayesian DNNs. Others have used either Monte Carlo methods (Mandt et al., [2017](#bib.bib257 "Stochastic gradient descent as approximate bayesian inference")) or the GP approximation (Matthews et al., [2017](#bib.bib103 "Sample-then-optimize posterior sampling for bayesian linear models"); de G. Matthews et al., [2018](#bib.bib54 "Gaussian process behaviour in wide deep neural networks"); Lee et al., [2019](#bib.bib110 "Wide neural networks of any depth evolve as linear models under gradient descent"); Wilson and Izmailov, [2020](#bib.bib254 "Bayesian deep learning and a probabilistic perspective of generalization")) to examine how similar the Bayesian posterior is to the sampling distribution of SGD (whether in parameter or function space), albeit on relatively low dimensional systems compared to conventional DNNs.
In this paper we perform extensive computations, for a series of standard DNNs and datasets, of the probability PSGD(f|S) that a DNN trained with SGD (or one of its variants) to zero error on training set S, converges on a function f. We then compare these results to the Bayesian posterior probability PB(f|S), for these same functions, conditioned on achieving zero training error on S.
The main question we explore here is: How similar is PB(f|S) to PSGD(f|S)? If the two are significantly different, then we may conclude that SGD provides an important source of inductive bias. If the two are broadly similar over a wide range of architectures, datasets, and optimisers,
then the inductive bias is primarily determined by the prior P(f) of the untrained DNN.
###
1.1 Main results summary
We carried out extensive sampling experiments to estimate
PSGD(f|S). Functions are distinguished by the way they classify elements on a test set E. We use the Gaussian process (GP) approximation to estimate PB(f|S) for the same systems. Our main findings are:
(1) PSGD(f|S)≈PB(f|S) for a range of architectures, including FCNs, CNNs and LSTMs, applied to datasets such as MNIST, Fashion-MNIST, an IMDb movie review database and an ionosphere dataset. This agreement also holds for variants of SGD, including Adam (Kingma and Ba, [2014](#bib.bib176 "Adam: amethod for stochastic optimization")), Adagrad (Duchi et al., [2011](#bib.bib111 "Adaptive subgradient methods for online learning and stochastic optimization")), Adadelta (Zeiler, [2012](#bib.bib138 "Adadelta: an adaptive learning rate method")) and RMSprop (Tieleman and Hinton, [2012](#bib.bib137 "Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude")).
(2) The PB(f|S) of functions f that achieve zero-error on the training set S can vary over hundreds of orders of magnitude, with a strong bias towards a set of low generalisation/low complexity functions. This tiny fraction of high probability functions also dominate what is found by DNNs trained with SGD. It is striking that even within this subset of functions, PSGD(f|S) and PB(f|S) correlate so well. Our empirical results suggest that, *for DNNs with large bias in PB(f|S)*, SGD behaves *to first order* like a Bayesian optimiser and is therefore exponentially biased towards simple functions with better generalisation. Thus, SGD is not itself the primary source of inductive bias for DNNs.
(3) A function-based picture can also be fruitful for illustrating second order effects where an optimiser-trained DNN differs from the Bayesian prediction. For example, training an FCN with different optimisers (OPT) such as Adam, Adagrad, Adadelta and RMSprop on MNIST generates slight but measurable variations in the distributions of POPT(f|S). Such information can be used to analyse differences in performance.
For instance, we find that changing batch size affects PAdam(f|S) but, as was found for generalisation error (Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima"); Goyal et al., [2017](#bib.bib76 "Accurate, large minibatch sgd: training imagenet in 1 hour"); Hoffer et al., [2017](#bib.bib72 "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"); Smith et al., [2017](#bib.bib75 "Don’t decay the learning rate, increase the batch size")), this effect can be compensated by changes in learning rate.
Architecture changes can also be examined in this picture. For example, adding max-pooling to a CNN trained with Adam on Fashion-MNIST increases both PB(f|S) and PAdam(f|S) for the lowest-error function f found.
2 Preliminaries
----------------
We first introduce a key definition from (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")) needed to specify P(f) and PB(f|S).
######
Definition 2.1 (Parameter-function map).
Consider a parameterised supervised model, and let the input space be X and the output space be Y. The space of functions the model can express is a set F⊆YX. If the model has some number of parameters, taking values within a set Θ⊆Rp, then the parameter-function map M is defined by
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| --- | --- | --- |
| | M:Θ→Fθ↦fθ | |
where fθ is the function corresponding to parameters θ∈Θ.
The function space F of a DNN N could in principle be considered to be the entire space of functions that N can express on the input vector space X, but it could also be taken to be the set of partial functions N can express on some subset of X. For example, F could be taken to be the set of possible classifications of images in MNIST. In this paper we always take F to be the set of possible outputs of N for the instances in some dataset.
###
2.1 The Bayesian prior probability, P(f)
Given a distribution Ppar(θ) over the parameters,
we define the P(f) over functions as
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| --- | --- | --- | --- |
| | P(f)=\bigintssss1[M(θ)=f]Ppar% (θ)dθ, | | (1) |
where 1 is an indicator function (1 if its argument is true, and 0 otherwise). This is the probability that the model expresses f upon random sampling of parameters over a parameter initialisation distribution Ppar(θ), which is typically taken to have a simple form such as a (truncated) Gaussian. P(f) can also be interpreted as the probability that the DNN expresses f upon initialisation before an optimisation process. It was shown in (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")) that the exact form of Ppar(θ) (for reasonable choices) does not affect P(f) much (at least for ReLU networks).
If we condition on functions that obtain zero generalisation error on a dataset S, then the procedure above can also be used to generate the posterior
PB(f|S) which we describe next.
###
2.2 The Bayesian posterior probability, PB(f|S)
Here, we describe the Bayesian formalism we use, and show how bias in the prior affects the posterior. Consider a supervised learning problem with training data S corresponding to the exact values of the function which we wish to infer (i.e. no noise). This formulation corresponds to a 0-1 likelihood P(S|f), indicating whether the data is consistent with the function. Formally, if S={(xi,yi)}mi=1 corresponds to the set of training pairs, then we let
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| --- | --- | --- |
| | P(S|f)={1 if ∀i,f(xi)=yi0 otherwise . | |
Note that in our calculations, this quantity is technically P(S|f;{xi}), but we denote it as P(S|f) to simplify notation. We will use a similar convention throughout, whereby the input points are (implicitly) conditioned over. We then assume the prior P(f) corresponds to the one defined in [Section 2.1](#S2.SS1 "2.1 The Bayesian prior probability, P(f) ‣ 2 Preliminaries ‣ Is SGD a Bayesian sampler? Well, almost."). Bayesian inference then assigns a *Bayesian posterior probability* PB(f|S) to each f by conditioning on the data according to Bayes rule
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| --- | --- | --- | --- |
| | PB(f|S):=P(S|f)P(f)P(S), | | (2) |
where P(S) is also called the *marginal likelihood* or *Bayesian evidence*. It is the total probability of all functions compatible with the training set. For discrete functions, P(S)=∑fP(S|f)P(f)=∑f∈C(S)P(f), with C(S) the set of all functions compatible with the training set. For a fixed training set, all the variation in PB(f|S) for f∈C(S) comes from the prior P(f) of the untrained network since P(S) is constant. Thus, the bias in the prior is essentially translated over to the posterior.
Thus, PB(f|S) is the distribution over functions that would be obtained by randomly sampling parameters according to Ppar(θ) and selecting only those that are compatible with S.
###
2.3 The optimiser probability, POPT(f|S)
But DNNs are not normally trained by randomly sampling parameters: They are trained by an optimiser. The probability that the optimiser OPT (e.g. SGD) finds a function f with zero error on S can be defined as:
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| --- | --- | --- | --- |
| | POPT(f|S):=\bigintssss1[M(θf)=f]POPT(θf|θi,S)~Ppar(θi)dθidθf | | (3) |
where POPT(θt|θi,S) denotes the probability that OPT, initialised with parameters θi on a DNN, converges to parameters θf when training is halted after the first epoch where zero classification error is achieved on S111In the special case where we specify the experiment as ‘overtraining’, then we take the parameters after p epochs with 0 classification error., if such a condition is achieved in a number of iterations less than the maximum number which we allow for the experiments. The initialisation distribution ~Ppar(θi) is defined analogously to Ppar(θ) in [Equation 1](#S2.E1 "(1) ‣ 2.1 The Bayesian prior probability, P(f) ‣ 2 Preliminaries ‣ Is SGD a Bayesian sampler? Well, almost.") (though it need not be exactly the same). POPT(f|S) is, therefore, a measure of the ‘size’ of f’s ‘basin of attraction’, which intuitively refers to the set of initial parameters that converge to f upon training.
3 Methodology, Datasets and DNNs
---------------------------------
###
3.1 Methodology
####
3.1.1 Definition of functions
For a specific DNN, training set S={(xi,yi)}|S|i=1 and test set E={(x′i,y′i)}|E|i=1, we define a function f as a labelling of the inputs in S concatenated with the inputs in E.222Formally then, our space of functions is F=YX, where X={xi}|S|i=1∪{x′i}|E| We will only look at functions which have 0 error on S, so that, for a particular experiment with fixed S and E, the functions are distinguished only by the predictions they make on E. Furthermore we only consider binary classification tasks (c.f. [Section 3.2](#S3.SS2 "3.2 Data sets ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.")), so that our output space333Note that for training with MSE loss, we centered the output so the loss is measured with respect to target values in {−1,1}. This is so thresholding can occur at a value of the last layer preactivation of 0, which is the same as for cross-entropy loss on logits. is Y={0,1}. Therefore, we will represent functions by a binary string of length |E| representing the labels on E; the ith character representing the label on the ith input of E, x′i. For the sake of simplicity, we will not make a distinction between this *representation* of f and the function f itself, as they are related one-to-one for any particular experiment (with fixed S and E).
Restricting the input space where functions are defined can be thought of as a coarse-graining of the functions on the full input space (e.g. the space of all possible images for image classification), which allows us to estimate their probabilities from sampling. In the following subsections we explain how the main experimental quantities are computed. Further detail can be found in [Appendix A](#A1 "Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.").
####
3.1.2 Calculating POPT(f|S)
For a given optimiser OPT (SGD or one of its variants), a DNN architecture, loss function (cross-entropy (CE) or mean-square error (MSE)), a training set S, and test set E, we repeat the following procedure n times: We sample initial parameters θi, from an i.i.d. truncated Gaussian distribution ~Ppar(θi), and train with the optimiser until the first epoch where the network has 100% training classification accuracy (except for experiments labelled “overtraining,” where we halt training after p further epochs with 0 training error have occured, for some specified p) 444If SGD fails to achieve 100% accuracy on S in a maximum number of iterations, we discard the run.. We then compute the function f found by evaluating the network on the inputs in E, as described before.
Note that during training, the network outputs are taken to be the pre-activations of the output layer, which are fed to either the MSE loss, or as logits for the CE loss. At evaluation (to compute f), the pre-activations are passed through a threshold function so that positive pre-activations output 1 and non-positive pre-activations output 0.
We chose sample sizes between n=104 and n=106. In other words, we typically sample over n=104 to n=106 different trained DNNS, and count how many times each function f appears in the sample to generate the estimates of POPT(f|S). We leave the dependence of POPT(f|S) on E implicit.
This method of estimating POPT(f|S) is described more formally in [Section A.1.1](#A1.SS1.SSS1 "A.1.1 Using an optimiser to calculate POPT(|fS) ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.").
####
3.1.3 Calculating PB(f|S) with Gaussian Processes
We use neural network Gaussian processes (NNGPs) (Lee et al., [2017](#bib.bib82 "Deep neural networks as gaussian processes"); Matthews et al., [2018](#bib.bib53 "Gaussian process behaviour in wide deep neural networks"); Garriga-Alonso et al., [2019](#bib.bib26 "Deep convolutional networks as shallow gaussian processes"); Novak et al., [2018b](#bib.bib28 "Bayesian convolutional neural networks with many channels are gaussian processes")) to approximate PB(f|S), for some training set S and test set E. NNGPs have been shown to accurately approximate the prior over functions P(f) of finite-width Bayesian DNNs (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); de G. Matthews et al., [2018](#bib.bib54 "Gaussian process behaviour in wide deep neural networks")). We use DNNs with relatively wide intermediate layers, relative to the input dimension, to ensure that we are close to the infinite layer-width NNGP limit. Depending on the loss function, we estimate the posterior PB(f|S) as follows:
* Classifiation as regression with MSE loss. As has been done in previous work on NNGPs, we consider the classification labels as regression targets with an MSE loss555As for POPT(f|S) with MSE loss, we take the regression targets to be {−1,1}, so thresholding occurs at 0. We compute the analytical posterior for the NNGP with Gaussian likelihood. This is the posterior over the real-valued outputs at the test points on E, which correspond to the pre-activations of the output layer of the DNN. We sample from this posterior, and threshold the real-values like we do for DNNs (positive becomes 1 and otherwise it becomes 0) to obtain labels on E, and thus a function f. We then estimate PB(f|S) by counting how many times each f is obtained from a set of n independent samples from the posterior, similar to what we did for POPT(f|S). For more details on GP computations with MSE loss, see [Section A.2.1](#A1.SS2.SSS1 "A.2.1 GP with Mean Squared-Error (MSE) loss ‣ A.2 Description of Gaussian process calculations ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.").
We describe this method more formally in [Section A.1.2](#A1.SS1.SSS2 "A.1.2 Bayesian sampling for PB(|fS) ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost."). We use this technique when comparing PB(f|S) with POPT(f|S) for MSE loss (e.g. [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.")).
* Classification with CE loss. In several experiments, we approximate the NNGP posterior using a 0-1 misclassification loss, which is more justified for classification, and can be thought of as a “low temperature” version of the CE loss. Since, in contrast to the MSE case, this posterior is not analytically tractable, we use the expectation propagation (EP) approximation to estimate probabilities (Rasmussen, [2004](#bib.bib125 "Gaussian processes in machine learning")). In particular, we estimate PB(f|S) via ratio of EP-approximated likelihoods. The EP approximation can be used to estimate the marginal likelihood of any labelling over any set of inputs, given a GP prior. As shown in [Equation 2](#S2.E2 "(2) ‣ 2.2 The Bayesian posterior probability, PB(|fS) ‣ 2 Preliminaries ‣ Is SGD a Bayesian sampler? Well, almost."), we can use Bayes theorem to express PB(f|S) as a ratio of P(f) and P(S) (which is valid for functions with 0 error on S, and the 0−1 likelihood), and then use EP approximation to obtain both of these probabilities. In the text, when we refer to the EP approximation for calculating PB(f|S), we are using it as described above.
####
3.1.4 Comparing POPT(f|S) to PB(f|S)
We note that for MSE loss, we can sample to accurately estimate function probabilities, whereas for the CE loss, we must use the EP approximation to calculate the probability of functions.666See [Section A.2.2](#A1.SS2.SSS2 "A.2.2 GP with 0-1 likelihood, EP, and ratio of likelihoods approximation ‣ A.2 Description of Gaussian process calculations ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.") for more details.
When we compare PB(f|S) to POPT(f|S) for CE loss, we take the functions found by the optimiser, which are obtained as described in [Section 3.1.2](#S3.SS1.SSS2 "3.1.2 Calculating POPT(|fS) ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost."), and calculate their PB(f|S) using the EP approximation. For MSE loss, both the PB(f|S) and the POPT(f|S) are sampled independently, and probabilities are compared for functions found by both methods.
####
3.1.5 Calculating PB(f|S) for functions with generalisation error from 0% to 100%
For the zero training error case studied here, we define functions by their particular labelling on the test set E (as described in [Section 3.1.1](#S3.SS1.SSS1 "3.1.1 Definition of functions ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.")).
A function can be generated by picking a certain labelling. Subsequently PB(f|S) for CE loss can be calculated using the EP approximation as described above. To study how PB(f|S) varies with generalisation error ϵG on E (the fraction of missclasified inputs on E), we perform the following procedure. For each value of ϵG chosen, typically 10 functions are uniformly sampled by randomly selecting ϵG|E| bits in E and flipping them. EP is then used to calculate PB(f|S) for those functions. The probabilities PB(f|S) can range over many orders of magnitude. The low probability functions cannot be obtained by direct sampling, so that a full comparison with POPT(f|S) is not feasible. This is more formally described in [Section A.1.3](#A1.SS1.SSS3 "A.1.3 Calculating PB(|fS) for functions with a wider range of ϵG ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.").
####
3.1.6 CSR complexity
The critical sample ratio (CSR) is a measure of complexity of functions expressed by DNNs (Krueger et al., [2017](#bib.bib143 "Deep nets don’t learn via memorization")). It is defined with respect to a sample of inputs as the fraction of those samples which are critical samples. A critical sample is defined to be an input such that there is another input within a box of side 2r centred around the input, producing a different output (for discrete classification outputs).
See [Appendix E](#A5 "Appendix E Critical Sample Ratio ‣ Is SGD a Bayesian sampler? Well, almost.") for further details.
###
3.2 Data sets
To efficiently sample functions, we use relatively small test sets (typically |E|=100) and, as is often done in the theoretical literature, binarise our classification datasets.
We define the datasets used below:
MNIST:
The MNIST database of handwritten numbers (LeCun et al., [1999](#bib.bib4 "Object recognition with gradient-based learning")) was binarised with even numbers classified as 0 and odd numbers as 1. Unless otherwise specified, we used |S|=10000 and |E|=100.
Fashion-MNIST:
The Fashion-MNIST database (Xiao et al., [2017](#bib.bib233 "Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms")) was binarised with T-shirts, coats, pullovers, shirts and bags classified as 0 and trousers, dresses, sandals, trainers and ankle boots classified as 1. Unless otherwise specified, we used |S|=10000 and |E|=100.
IMDb movie review dataset: We take the IMDb movie review dataset from Keras. The task is to correctly classify each review as positive or negative given the text of the review. We preprocess the set by removing the most common words and normalising.777We used the version of the dataset and preprocessing technique given here: <https://www.kaggle.com/drscarlat/imdb-sentiment-analysis-keras-and-tensorflow> This procedure was employed to make sure there are functions with high enough probability to be sampled multiple times with Experiments 1 and 2 above. Used with |S|=45000 and |E|=50.
Ionosphere Dataset:
This is a small non-image dataset with 34 features888<https://archive.ics.uci.edu/ml/datasets/Ionosphere> aimed at identifying structure in the ionosphere (Sigillito et al., [1989](#bib.bib73 "Classification of radar returns from the ionosphere using neural networks")). Used with |S|=301 and |E|=50.
For image datasets, we will typically use normalised data (pixel values in range [0,1]) for MSE loss, and unnormalised data for CE loss (pixel values in range [0,255]).
###
3.3 Architectures
We used the following standard architectures.
FCN: 2 hidden layer, 1024 node vanilla fully connected network (FCN) with ReLU activations.
CNN + (Max Pooling) + [BatchNorm]: Layer 1: Convolutional Layer with 32 features size 3×3. (Layer 1a: Max Pool 2×2). [Layer 1b: Batch Norm]. Layer 2: Flatten. Layer 3: FCN with width 1024. [Layer 3a: Batch Norm]. Layer 4: FCN, 1 output with ReLU activations.
LSTM: Layer 1: Embedding layer. Layer 2: LSTM, 256 outputs. Layer 3: FCN, 512 outputs. Layer 4: Fully-Connected, 1 output with ReLU activations for the fully connected layers.
Hyperparameters are, unless otherwise specified, the default values in Keras 2.3.0. See [Section A.1.1](#A1.SS1.SSS1 "A.1.1 Using an optimiser to calculate POPT(|fS) ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.") for details on the parameter initialisation.
4 Empirical results for PB(f|S) v.s. POPT(f|S) for different architectures and datasets
----------------------------------------------------------------------------------------
In this first of two main results sections, we focus on testing our hypothesis that PB(f|S)≈POPT(f|S) for FCN, CNN and LSTM architectures on MNIST, Fashion-MNIST, the IMDb review, and the Ionosphere datasets, using several variants of the SGD optimiser. In the following subsection will describe the main results in detail for an FCN on MNIST. The experiments in the next sections will be the same except for the architecture, dataset, or optimiser which will be varied.
###
4.1 Comparing PB(f|S) to POPT(f|S) for an FCN on MNIST
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(a) PB(f|S)\,v.s.PSGD(f|S)
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(b) PB(f|S)\,v.s.\,ϵG
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(c) CSR complexity v.s. ϵG
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(d) f found by NNGP in (a).
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(e) PB(f|S)\,v.s.PAdagrad(f|S)
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(f) PB(f|S)\,v.s.PAdagrad(f|S)
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Figure 1: Comparing the Bayesian prediction PB(f|S) to POPT(f|S) for SGD and Adagrad, for an FCN on MNIST
[We use training/test set size of 10,000/100; For (a,e,f), the vertical dotted blue lines are drawn at the highest value of POPT(f|S) such that the sum of POPT(f|S) for all functions above the line is >90% (90% probability boundary); dashed grey line denotes PB(f|S)=POPT(f|S).]
(a) PB(f|S)\,v.s. PSGD(f|S) for MSE loss; Both PB(f|S) and PSGD(f|S) were sampled n=106 times. The color shows the number of errors in the test set. The GP has average error ⟨ϵG⟩GP=1.61%, while SGD has average error ⟨ϵG⟩=1.88%.
(b) PB(f|S) (with CE loss) v.s. ϵG for the full range of possible errors on E. We use the methods from [Section 3.1.5](#S3.SS1.SSS5 "3.1.5 Calculating PB(|fS) for functions with generalisation error from %0 to %100 ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.") with 20 random functions sampled per value of error. The solid blue line shows ⟨log(PB(f|S))⟩ϵG, where the average is over the functions for a fixed ϵG; error bars are 2 standard deviations.
The dashed blue line shows the weighted ρ(ϵG)⟨PB(f|S)⟩ϵG, where ρ(ϵG) is the number of functions with error ϵG. The small red box and dashed red lines illustrate the range of probability and error found in (a).
(c) CSR complexity versus generalisation error for the same functions as in fig (b). Color represents PB(f|S), computed as in (b).
(d) Functions from (a) found by the sample of PB(f|S), versus error. 913 functions of the functions are also found by SGD, taking up 97.70% of the probability for PSGD(f|S), and 99.96% for PB(f|S).
(e) PB(f|S) v.s. PAdagrad(f|S) for MSE loss; PAdagrad(f|S) was sampled n=105 times (while the GP sample was the same as in (a)). Adagrad was overtrained until 64 epochs had passed with zero error. The average error is ⟨ϵG⟩=1.53%.
(f) is as (e) but with CE loss, so that the EP approximation was used for PB(f|S), making the estimate of PB(f|S) slightly less accurate. ⟨ϵG⟩=2.63%.
In [Figure 1](#S4.F1 "Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") we present a series of results for a standard DNN setup: an FCN (2 hidden layers, each 1024 node wide with ReLU activations), trained on (binarised) MNIST to zero training error with a training set size of |S|=10,000 and a test set size of |E|=100. Note that even for this small test set, there are 2100≈1.3×1030 functions with zero error on S, all of which an overparametrized DNN could express (Zhang et al., [2016](#bib.bib78 "Understanding deep learning requires rethinking generalization"))999We also find in [Figure 19(a)](#A5.F19.sf1 "(a) ‣ Figure 20 ‣ Appendix E Critical Sample Ratio ‣ Is SGD a Bayesian sampler? Well, almost.") and [Figure 19(b)](#A5.F19.sf2 "(b) ‣ Figure 20 ‣ Appendix E Critical Sample Ratio ‣ Is SGD a Bayesian sampler? Well, almost.") that our 2-layer FCN is capable of expressing functions on MNIST with the full range of training and generalisation errors .
We chose standard values for batch size, learning rate, etc., if given by the default values in Keras 2.3.0 (e.g. batch size of 32 and learning rate of 0.01 for SGD). Our experiments in [Section 5](#S5 "5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost.") and the appendices will show that our results are robust to the choice of these hyperparameters.
[Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") compares the value of PB(f|S) and PSGD(f|S) for the highest probability functions of each distribution, for MSE loss. Each data point in the plot corresponds to a unique function (a unique classification of images in the test set E). The functions are obtained by sampling both PB(f|S) and PSGD(f|S) and taking the union of the set of functions obtained. PB(f|S) and PSGD(f|S) were estimated as frequencies from the corresponding sample as explained in [Sections 3.1.3](#S3.SS1.SSS3 "3.1.3 Calculating PB(|fS) with Gaussian Processes ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.") and [3.1.2](#S3.SS1.SSS2 "3.1.2 Calculating POPT(|fS) ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost."). If a function does not appear in one of the samples, we set its frequency to take the minimum value so that it would appear on top of one of the axes. For example, a function that appears in the SGD runs, but not in the sampling for PB(f|S), will appear on x-axis at the value obtained for PSGD(f|S). Here we used MSE loss rather than the more popular (and typically more computationally efficient) CE loss because for MSE, the posterior can be sampled from without further approximations, while for CE loss, the expectation propagation (EP) approximation needs to be used making PB(f|S) less accurate (see [Section A.2](#A1.SS2 "A.2 Description of Gaussian process calculations ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.") for further details).
[Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") also demonstrates that PSGD(f|S) and PB(f|S) are remarkably closely correlated for MSE loss, and that a remarkably small number of functions account for most of the probability mass for both PB(f|S) and PSGD(f|S). To appreciate how remarkably tight this agreement is, consider the full scale of probabilities for functions f that achieve zero error on the MNIST training set. The average PB(f|S) of all these functions is 2−100≈10−30. Therefore the functions in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") have probabilities that are many orders of magnitude higher than average. At the same time, PB(f|S) and PSGD(f|S) for these functions typically agree within less than one order of magnitude. Another way of quantifying the agreement is that 90% of the cumulative probability weight from both PSGD(f|S) and PB(f|S) for the test set E in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") is made up from the contributions of only a few tens of functions with zero training error out of ≈1030 such possible functions (see vertical doted line in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.")). Moreover, these particular functions are the same for both PB(f|S) and PSGD(f|S). The agreement between the two methods is remarkable. Overall, the observations in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") suggest that the main inductive bias of this DNN is present prior to training.
[Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") plots the mean probability for obtaining a generalisation error of ϵG in the training set E, which is estimated as ρ(ϵG)⟨PB(f|S)⟩ϵG where ρ(ϵG)=|E|!/((|E|−ϵG|E|)!(ϵG|E|)!) denotes the number of functions with ϵG|E| errors on E, and ⟨PB(f|S)⟩ϵG denotes the expected value of PB(f|S), where the expectation is with respect to uniformly sampling from the set of functions with fixed ϵG. As explained in [Section 3.1.5](#S3.SS1.SSS5 "3.1.5 Calculating PB(|fS) for functions with generalisation error from %0 to %100 ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost."), we estimate the average ⟨⋅⟩ϵG by sampling, and we estimated PB(f|S) for each f in the sample using the EP approximation.
[Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") can be interpreted as showing that the inductive bias encoded in PB(f|S) gives good generalisation. More precisely, we find that PB(f|S) is exponentially biased towards functions with low generalisation error. To illustrate how strong the bias is, we can look at ρ(ϵG). Over 50% of functions are in the range of ϵG=50±3 errors, while only 10−23% have ϵG≤3. Therefore for PB(f|S) to overcome the ‘entropic’ factor ρ(ϵG) and show the behaviour in [Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), it must in average give a probability many orders of magnitude higher to low error functions than to high error functions. In [Section A.1.3](#A1.SS1.SSS3 "A.1.3 Calculating PB(|fS) for functions with a wider range of ϵG ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost."), we also observed that the probability pi of misclassifying an image in the test set varies a lot between images, and that these probabilities are to first order independent. As a corollary, in [Figure 8](#A2.F8 "Figure 8 ‣ Appendix B Notes on the distribution of MNIST data ‣ Is SGD a Bayesian sampler? Well, almost.") we show for PB(f|S) and PSGD(f|S) that the probabilities of multiple images being misclassified can be accurately estimated from the products of the probabilities pi for misclassifying individual images. Thus this system appears to behave like a Poisson-Binomial distribution with independent and non-identically distributed random pi, which most likely also explains why ⟨logPB(f|S)⟩ϵG scales nearly linearly with ϵG.
Although we cannot measure PSGD(f|S) for the high generalisation error functions, the agreement in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") (and elsewhere in this paper) implies that PSGD(f|S) must also be on average orders of magnitude lower for high error functions than low error functions. However, at the moment we can only conjecture that PSGD(f|S) follows the same exponential behaviour as PB(f|S) over the whole range of ϵG. Finally, in [Section A.1.3](#A1.SS1.SSS3 "A.1.3 Calculating PB(|fS) for functions with a wider range of ϵG ‣ A.1 Methodology in detail ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost."), we make some further remarks and caveats about this experiment, and other similar experiments.
[Figure 0(c)](#S4.F0.sf3 "(c) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") shows the correlation between the complexity of the functions obtained to create [Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), and their generalisation error, as well as their PB(f|S) (from EP approximation) represented in their color. The complexity measure we used is the critical sample ratio (CSR) complexity (Krueger et al., [2017](#bib.bib143 "Deep nets don’t learn via memorization")) computed on the inputs in E, which measures what fraction of inputs are near the decision boundary (see [Section 3.1.6](#S3.SS1.SSS6 "3.1.6 CSR complexity ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.")).
[Figure 0(c)](#S4.F0.sf3 "(c) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") also shows that there is a inverse correlation between the generalisation of a function and its CSR complexity, as well as between PB(f|S) and CSR. In [Section 2.2](#S2.SS2 "2.2 The Bayesian posterior probability, PB(|fS) ‣ 2 Preliminaries ‣ Is SGD a Bayesian sampler? Well, almost."), we showed that PB(f|S) is proportional to the prior probability of a function P(f) for functions that have zero error on the training set S. We can thus understand the inverse correlation between PB(f|S) and CSR in the light of previous simplicity bias results showing that the prior P(f) of Bayesian DNNs is exponentially biased towards functions with low Kolmogorov complexity (simple functions) (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy")). In (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")), it was further shown for an FCN on a subsample of MNIST that P(f) correlated remarkably well with CSR101010Furthermore in (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")), it was shown that this was not an exclusive property of CSR and that any measure that could approximate Kolmogorov complexity seems to also correlate well with P(f)., and our results are in agreement with that finding. The results in this figure extend those of [Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") to show that PB(f|S) is biased both towards low error and simple functions, and that simple functions are the ones that tend to have good generalisation on MNIST.
[Figure 0(d)](#S4.F0.sf4 "(d) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") shows the correlation between PB(f|S) and ϵG for functions used for [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."). We note that, as can also be observed in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), values of PB(f|S) are high for low error functions, and high error functions have relatively lower values of PB(f|S). This figure also uses colour to show which functions were not found in the sampling of PSGD(f|S). It shows clearly that SGD finds all the high PB(f|S) functions.
[Figure 0(e)](#S4.F0.sf5 "(e) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") shows the same type of experiment as in [Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), but using a different SGD-based optimiser, Adagrad (Duchi et al., [2011](#bib.bib111 "Adaptive subgradient methods for online learning and stochastic optimization")) with overtraining (where training was halted after 64 epochs had passed with 100% training accuracy). We see that it exhibits similar correlation between PB(f|S) and POPT(f|S) to vanilla SGD (and very similar agreement was observed without overtraining). We will see throughout the paper remarkably good correlations between PB(f|S) and POPT(f|S) holds for a large range of optimisers and hyperaparameters
[Figure 0(f)](#S4.F0.sf6 "(f) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") shows the same type of experiment as in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), but using CE loss, the Adagrad optimiser, and overtraining (also to 64 epochs). See [Figure 10(b)](#A3.F10.sf2 "(b) ‣ Figure 11 ‣ C.1 Changing optimisers ‣ Appendix C Effects of training set size ‣ Is SGD a Bayesian sampler? Well, almost.") for the equivalent plot but without overtraining. As we are using CE loss (see [Section 3.1.3](#S3.SS1.SSS3 "3.1.3 Calculating PB(|fS) with Gaussian Processes ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.") and [Section 3.1.4](#S3.SS1.SSS4 "3.1.4 Comparing POPT(|fS) to PB(|fS) ‣ 3.1 Methodology ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.")), we sample functions from POPT(f|S), and then use the EP to estimate PB(f|S) for the functions obtained. We find similar results to [Figure 0(e)](#S4.F0.sf5 "(e) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), where we used MSE loss (and direct sampling for PB(f|S)). The errors introduced by the EP approximation may explain why the correlation does not follow the x=y line as closely as it does for the MSE calculations.
Nevertheless, the correlation between PB(f|S) and PAdagrad(f|S) is strong, providing evidence that our results for an FCN on MNIST are not an artefact of the exact optimiser or loss function used.
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(a) PB(f|S)\,v.s.PAdam(f|S) for FCN
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(b) PB(f|S)\,v.s.PAdam(f|S) for CNN w/o pooling
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(c) PB(f|S)\,v.s.PAdam(f|S) for CNN w/ pooling
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Figure 2: Comparing PB(f|S) to PAdam(f|S) for CNNs and the FCN on Fashion-MNIST [We use a training/test set size of 10,000/100; vertical dotted blue lines denote 90% probability boundary; dashed grey line is PB(f|S)=POPT(f|S).]
(a) FCN on Fashion-MNIST; ⟨ϵG⟩=2.11% for Adam with CE loss.
(b) Vanilla CNN on Fashion-MNIST; ⟨ϵG⟩=2.25% for Adam with CE loss.
(c) CNN with max-pooling on Fashion-MNIST; ⟨ϵG⟩=1.96% for Adam with CE loss.
Note that when max-pooling is added, the probability of the lowest-error function increases notably for both PAdam(f|S) and PB(f|S). There is a strong correlation between PB(f|S) and PSGD(f|S) in all three plots. See [Figure 13](#A4.F13 "Figure 13 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") for related results, including PB(f|S) vs ϵG, a CNN with batch normalisation, and a CNN with MSE loss.
###
4.2 Comparing PB(f|S) to PAdam(f|S) for CNNs on Fashion-MNIST
We next turn to a more complex dataset, namely Fashion-MNIST (Xiao et al., [2017](#bib.bib233 "Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms")) which consists of images of clothing, as well as a more complex network architecture, the CNN (LeCun et al., [1999](#bib.bib4 "Object recognition with gradient-based learning")) which was designed in part to have a better inductive bias for images. See [Section 3.2](#S3.SS2 "3.2 Data sets ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.") and [Section 3.3](#S3.SS3 "3.3 Architectures ‣ 3 Methodology, Datasets and DNNs ‣ Is SGD a Bayesian sampler? Well, almost.") for details on dataset and architecture. We can see in [Figure 2](#S4.F2 "Figure 2 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") a strong correlation between PB(f|S) and the probabilities found by the Adam optimiser (Kingma and Ba, [2014](#bib.bib176 "Adam: amethod for stochastic optimization")), a variant of SGD.
Note that instead of MSE loss we used CE loss because it is more efficient. A downside of this choice is that we need to use the EP approximation for the GP calculations (see [Section A.2.2](#A1.SS2.SSS2 "A.2.2 GP with 0-1 likelihood, EP, and ratio of likelihoods approximation ‣ A.2 Description of Gaussian process calculations ‣ Appendix A Further detail for methods (Section 3) ‣ Is SGD a Bayesian sampler? Well, almost.")). Although the correlation is strong, it does not follow x=y as closely as we generally find for MSE loss, which is quite possibly an effect of the EP approximation. See [Figure 13](#A4.F13 "Figure 13 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") for an example with MSE loss where the correlation does follow x=y more closely.
Both the FCN and the CNNs exhibit a strong bias towards low error functions on Fashion-MNIST as we can see in [Figure 12(c)](#A4.F12.sf3 "(c) ‣ Figure 13 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") and [Figure 12(d)](#A4.F12.sf4 "(d) ‣ Figure 13 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.").
For an example of how the effects of architecture modifications can be observed in the function probabilities, compare results in [Figure 1(b)](#S4.F1.sf2 "(b) ‣ Figure 2 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") for the vanilla CNN to those in [Figure 1(c)](#S4.F1.sf3 "(c) ‣ Figure 2 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") for a CNN with max-pooling (He et al., [2016](#bib.bib3 "Deep residual learning for image recognition")), a method designed to improve the inductive bias of the CNN. As expected, the generalisation performance of the CNN improves, and an important contributor is the increase in the probability of the highest probability 1-error function in both PB(f|S) and PAdam(f|S), directly demonstrating an enhancement of the inductive bias. See [Figure 13](#A4.F13 "Figure 13 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") for related results.
This example demonstrates how a function based picture as well as analysis of the Bayesian PB(f|S) sheds light on the inductive bias of a DNN.
Such insights could help with architecture search, or more generally with developing new architectures with improved implicit bias toward desired low error functions.
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4.3 Comparing PB(f|S) and PSGD(f|S) to Neural Tangent Kernel results
In [Figure 3](#S4.F3 "Figure 3 ‣ 4.3 Comparing PB(|fS) and PSGD(|fS) to Neural Tangent Kernel results ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") we compare PB(f|S) to the output of the neural tangent kernel (NTK) (Jacot et al., [2018](#bib.bib112 "Neural tangent kernel: convergence and generalization in neural networks")), which approximates gradient descent in the limit of infinite width and infinitesimal learning rate. The generalisation error of NTK and NNGPs have been shown to be relatively close, and they produce similar functions on simple 1D regression (Lee et al., [2019](#bib.bib110 "Wide neural networks of any depth evolve as linear models under gradient descent"); Novak et al., [2020](#bib.bib139 "Neural tangents: fast and easy infinite neural networks in python")). Here we show that
this similarity also holds for the function probabilities for a more complex classification task. However, we also find the NTK misses many relatively high probability functions that both SGD and the GP find.
We are currently investigating this surprising behaviour, which may arise from the infinitesimal learning rate. Their low probability may also be exacerbated by the fact that in [Figure 3](#S4.F3 "Figure 3 ‣ 4.3 Comparing PB(|fS) and PSGD(|fS) to Neural Tangent Kernel results ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") the NTK is very highly biased towards one 2-error function, forcing other functions to have low cumulative probability. Again, this example demonstrates how a function based picture picks up rich details of the behaviour that would be missed when simply comparing generalisation error.
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(a) PB(f|S) v.s. PNTK(f|S)
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(b) PSGD(f|S) v.s. PNTK(f|S)
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Figure 3: Comparing PB(f|S) and PSGD(f|S) to PNTK(f|S) for an FCN on MNIST. [The functions to the right of the blue dotted lines make up 90% of the total probability. We did 107 samples for NTK and GP, and 106 for SGD]. In (a) we show the correlation between PNTK(f|D) and PB(f|S). Weighted by probability, 77.5% of functions found by sampling from the GP are found by NTK; all functions found by NTK are found by sampling from the GP. In (b), we show the correlation between the PNTK(f|D) and PSGD(f|S). Weighted by probability, 65.8% of functions found by SGD are found by NTK; all functions found by NTK are found by SGD.
⟨ϵG⟩=1.69% (NTK), ⟨ϵG⟩=1.61% (GP), ⟨ϵG⟩=1.88% (SGD).
###
4.4 Comparing PB(f|S) to PAdam(f|S) for LSTM on IMDb sentiment analysis
We test a more complex DNN with a LSTM layer (Hochreiter and Schmidhuber, [1997b](#bib.bib19 "Long short-term memory")), applied to a problem of sentiment analysis on the IMDb movie database. We used a smaller test set |E|=50 and a larger training set |S|=45,000 to ensure that generalisation was good enough to ensure that functions are found with sufficient frequency to be able to extract probabilities.
As can be seen in [Figure 3(a)](#S4.F3.sf1 "(a) ‣ Figure 4 ‣ 4.4 Comparing PB(|fS) to PAdam(|fS) for LSTM on IMDb sentiment analysis ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") we again observe a reasonable correlation between the functions found by Bayesian sampling, and those found by the optimiser. [Figure 3(b)](#S4.F3.sf2 "(b) ‣ Figure 4 ‣ 4.4 Comparing PB(|fS) to PAdam(|fS) for LSTM on IMDb sentiment analysis ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.") also shows that, as observed for other datasets, this system is highly biased towards low error functions. We show some further experiments with the LSTM in [Figure 14](#A4.F14 "Figure 14 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") in [Appendix D](#A4 "Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost."), including an experiment with MSE loss to avoid the EP approximation.
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(a) PB(f|S) v.s. PAdam(f|S)
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(b) PB(f|S) v.s. ⟨ϵG⟩
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(c) PB(f|S) v.s. PAdam(f|S)
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Figure 4: Comparing PB(f|S) to PAdam(f|S) for a LSTM on the IMDb movie review dataset, and an FCN on the ionosphere dataset.
(a) PB(f|S) v.s. PAdam(f|S) for LSTM on IMDb dataset, (⟨ϵG⟩=4.28%, 104 samples). Because of the computational cost of the problem, we used a training set size of 45000 and a test set of size 50.
(b) PB(f|S) v.s. ⟨ϵG⟩ for the LSTM on IMDb shows that the functions found by the Adam optimiser are in the small fraction of high PB(f|S) probability/low error functions.
(c) PB(f|S) v.s. PAdam(f|S) for an FCN with 3 hidden layers of width 256 on the Ionosphere dataset. Training set size is 301 and the test set size is 50. (⟨ϵG⟩=4.59% for Adam, ⟨ϵG⟩=5.41% for the GP). See [Figures 15](#A4.F15 "Figure 15 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") and [14](#A4.F14 "Figure 14 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") for further results for these systems.
###
4.5 Comparing PB(f|S) to PAdam(f|S) for FCN on Ionosphere dataset
As another non-image classification example, we use the small non-image Ionosphere dataset (with a training set of size 301), using an FCN with 3 hidden layers of width 256. As can be seen in [Figure 3(c)](#S4.F3.sf3 "(c) ‣ Figure 4 ‣ 4.4 Comparing PB(|fS) to PAdam(|fS) for LSTM on IMDb sentiment analysis ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), for MSE loss we find a fairly good correlation. Further details and an example with CE loss
can be found in [Figure 15](#A4.F15 "Figure 15 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.").
###
4.6 Effects of training set size
We performed experiments comparing PB(f|S) and POPT(f|S) for different training set sizes for the FCN on MINST. We observe that increasing the amount of training data from |S|=1000 to |S|=20000 increases the bias towards low error functions. This increase has the following effects: 1) An increase in the value of PB(f|S) and PSGD(f|S) for functions with low ⟨ϵG⟩ by several orders of magnitude, 2) an increase by several orders of magnitude of PB(f|S) and POPT(f|S) for the mode functions (the ones with highest probability), 3) A decrease in the number of functions that cumulatively take up 90% of the observed probability weight, and 4) a significant increase in the tightness of correlation between PB(f|S) and POPT(f|S). See [Appendix C](#A3 "Appendix C Effects of training set size ‣ Is SGD a Bayesian sampler? Well, almost.") in [Appendix C](#A3 "Appendix C Effects of training set size ‣ Is SGD a Bayesian sampler? Well, almost.") for detailed results and plots.
###
4.7 Results for other test sets
For the experiments shown in this section, sampling efficiency considerations means that we have limited ourselves to a relatively small test sets (|E|≤100). In [Figure 12](#A4.F12 "Figure 12 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost."), we have checked that other test sets also show close agreement between PB(f|S) and PSGD(f|S). For larger |E|, POPT(f|S) quickly becomes impossible to directly measure empirically – doubling the test set roughly means squaring the number of samples to obtain qualitatively similar results, as the values for PB(f|S) decrease exponentially with test set size. However, if we assume that the images are approximately independently distributed throughout the larger test set, as [Appendix B](#A2 "Appendix B Notes on the distribution of MNIST data ‣ Is SGD a Bayesian sampler? Well, almost.") suggests, then we can estimate the highest probabilities from products of PB(f|S) or PSGD(f|S) on the smaller sets.
5 The effect of hyperparameter changes and optimisers on PB(f|S) and POPT(f|S)
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In the first section we focussed on the first-order similarity between PB(f|S) and POPT(f|S).
In this second main results section, we focus on second-order effects that affect POPT(f|S) differently from PB(f|S). These include the effects of hyperparameter settings and optimiser choice.
###
5.1 Changing batch size and learning rate
In a well-known study, (Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima")) showed that, for a fixed learning rate, using smaller batch sizes could lead to better generalisation. In [Figure 5](#S5.F5 "Figure 5 ‣ 5.1 Changing batch size and learning rate ‣ 5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost.") (a)-(c) we observe this same effect but reflected in the more finely grained spectrum of function probabilities. For batch size 512, we also reproduce in [Figure 4(d)](#S5.F4.sf4 "(d) ‣ Figure 5 ‣ 5.1 Changing batch size and learning rate ‣ 5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost.") the effect observed in (Goyal et al., [2017](#bib.bib76 "Accurate, large minibatch sgd: training imagenet in 1 hour"); Hoffer et al., [2017](#bib.bib72 "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"); Smith et al., [2017](#bib.bib75 "Don’t decay the learning rate, increase the batch size")), that speeding up the learning rate for a fixed batch size can mimic the improvement in ⟨ϵG⟩ for smaller batches. Interestingly, as can be seen by comparing [Figures 4(f)](#S5.F4.sf6 "(f) ‣ Figure 5 ‣ 5.1 Changing batch size and learning rate ‣ 5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost."), [4(e)](#S5.F4.sf5 "(e) ‣ Figure 5 ‣ 5.1 Changing batch size and learning rate ‣ 5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost.") and [4(d)](#S5.F4.sf4 "(d) ‣ Figure 5 ‣ 5.1 Changing batch size and learning rate ‣ 5 The effect of hyperparameter changes and optimisers on PB(|fS) and POPT(|fS) ‣ Is SGD a Bayesian sampler? Well, almost."), the overall correlation of the function probability spectrum appears tighter for the 128 and 512 batch size with the same learning rates, even though the generalisation errors are different. However, if the learning rate is increased 4× for the the 512 batch size system, then there is a closer correlation with batch size 128 for the higher probability functions. It is these latter functions that dominate the average for ⟨ϵG⟩ and so the closer correlation for those functions, rather than the less good correlation for low probability functions, explains the better agreement seen in generalisation error for the two systems.
Finally, in [Figure 18](#A4.F18 "Figure 18 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") of [Appendix D](#A4 "Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost."), we vary batch size for MSE, finding different trends to CE loss. For MSE, increasing batch size leads to better generalisation due to second order effects where PSGD(f|S) preferentially converges on a few key higher probability/lower error functions. The batch size can be correlated with the noise spectrum of the underlying Langevin equation that describes SGD (Bottou et al., [2018](#bib.bib237 "Optimization methods for large-scale machine learning"); Jastrzebski et al., [2018](#bib.bib91 "Finding flatter minima with sgd."); Zhang et al., [2018](#bib.bib275 "Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning")). What our function based results demonstrate is that the behaviour of the optimiser on the loss-landscape is affected in subtle ways by the form of the loss function, as well as the amount noise, and possibly also by correlations in the noise.
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(a) Adam, batch size=32
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(b) As (a) but batch size=128
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(c) As (a) but batch size=512
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(d) As (c) but 4x learning rate
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(e) Batch size 128 v.s. 512
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(f) Batch size 128 v.s. 512 4x lr
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Figure 5: Effects of changing batch size and learning rate on PB(f|S) and PAdam(f|S) for FCN on MNIST with CE loss [We use training/test set size 10,000/100. Vertical dotted blue lines denote 90% probability boundary; dashed grey line is x=y.]
(a) Batch size = 32, ⟨ϵG⟩=1.13%.
(b) Batch size= 128, ⟨ϵG⟩=2.20%.
(c) Batch size = 512, ⟨ϵG⟩=2.67%.
(d) Batch size =512 and faster learning rate (4x the others), ⟨ϵG⟩=2.14%.
(e) Direct comparison of PAdam(f|S) for batch size 128 and 512.
(f) Direct comparison of PAdam(f|S) for batch size 128 and 512 with a 4× faster learning rate.
The PAdam(f|S) probabilities for the dominant functions in (d) and (b) are remarkably similar, as can be seen by comparing (e) and (f). It is these higher probability functions that explain the similarity in ⟨ϵG⟩ for batch size 128 and batch size 512 with a faster learning rate.
See [Figure 18](#A4.F18 "Figure 18 ‣ Appendix D Further results comparing POPT(|fS) to PB(|fS). ‣ Is SGD a Bayesian sampler? Well, almost.") for related batch size results for MSE loss.
###
5.2 Changing optimisers
We trained the FCN on MNIST with different optimisers (Adam, Adagrad, RMSprop, Adadelta), and found that to first order PB(f|S) correlated well with POPT(f|S) for all four optimisers. We also observed some second order effects, including that the distribution of PAdam(f|S) and PAdagrad(f|S) were very similar to one another, as were PRMSprop(f|S) and PAdadelta(f|S), but there was noticeable variation between the two groups. We find that PAdam(f|S) with batch size of 32 is very similar to PRMSprop(f|S) with a batch size of 128. The effect of optimiser choice, batch size, learning rate, and other hyperameters is complex, and the parameter space is large. Analysing optimisers in function-space could be a way to better understand the interaction of these choices with the loss landscape, and understanding the effects of hyperparameter tuning.
See [Section C.1](#A3.SS1 "C.1 Changing optimisers ‣ Appendix C Effects of training set size ‣ Is SGD a Bayesian sampler? Well, almost.") for further detail and the plots.
6 Heuristic arguments for the correlation between PB(f|S) and PSGD(f|S)
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At first sight it may seem rather surprising that SGD, which follows gradients down a complex loss-landscape, should converge on a function f with a probability anything like the Bayesian posterior PB(f|S) that upon random sampling of parameters, a DNN generates functions f conditioned on S. Indeed, in the general case of an arbitrary learner we don’t expect this correspondence to hold. However, as shown e.g. in Fig 1, PB(f|S) is orders of magnitude larger for functions with small generalisation error than it is for functions with poor generalisation. As explained in [Sections 7.5](#S7.SS5 "7.5 Related work on complexity of data, simplicity bias and generalisation ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") and [7.3](#S7.SS3 "7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost."), such an exponential bias towards low complexity/low error functions can be expected on fairly general grounds (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy"); Dingle et al., [2018](#bib.bib274 "Input–output maps are strongly biased towards simple outputs"), [2020](#bib.bib273 "Generic predictions of output probability based on complexities of inputs and outputs")). If our null expectation is of a large variation in the prior probabilities, then the good correlation can be heuristically justified by a landscape picture (Wales and others, [2003](#bib.bib55 "Energy landscapes: applications to clusters, biomolecules and glasses")), where PB(f|S) is interpreted as the “basin volume” VB(f) (with measure ppar(θ)) of function f), while PSGD(f|S) is interpreted as the “basin of attraction” VSGD(f), which is loosely defined as a measure of the set of initial parameters θi for which the optimiser converges to f with high probability (this concept also found in related form in the dynamical systems literature (Strogatz, [2018](#bib.bib249 "Nonlinear dynamics and chaos with student solutions manual: with applications to physics, biology, chemistry, and engineering"))). If VB(f) varies over many orders of magnitude, then it seems reasonable to expect that VSGD(f) should correlate with VB(f), as illustrated schematically in [Figure 5(a)](#S6.F5.sf1 "(a) ‣ Figure 6 ‣ 6 Heuristic arguments for the correlation between PB(|fS) and PSGD(|fS) ‣ Is SGD a Bayesian sampler? Well, almost."). Such general intuitions about landscapes are widely held (Wales and others, [2003](#bib.bib55 "Energy landscapes: applications to clusters, biomolecules and glasses"); Massen and Doye, [2007](#bib.bib248 "Power-law distributions for the areas of the basins of attraction on a potential energy landscape"); Ballard et al., [2017](#bib.bib251 "Energy landscapes for machine learning")), and have also been put forward for the particular landscapes of deep learning; see in particular Wu et al. ([2017](#bib.bib71 "Towards understanding generalization of deep learning: perspective of loss landscapes")) who also argue that functions with good generalisation have larger basins of attraction.
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(a) Schematic loss landscape
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(b) Corrupted data, CE loss
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(c) PB(f|S) v.s. PSGD(f|S)
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Figure 6: Schematic landscape and effects of randomising training labels. (a) Cartoon of a biased loss-landscape. The three functions f1, f2 and f3 all reach zero classification error (dashed red line), but due to bias in the parameter-function map, the “basin size” VB(f1)≫VB(f2),VB(f3), which typically implies that for the “basins of attraction” VSGD(f1)≫VSGD(f2),VSGD(f3). PB(f|S) is proportional to VB(f), and PSGD(f|S) is proportional to VSGD(f).
(b) PB(f|S) (solid) and ρ(ϵG)PB(f|S) (dashed) v.s. ϵG, for test set of size 100 and CE loss (as in [Figure 0(b)](#S4.F0.sf2 "(b) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost.")) but including label corruption c.
(b) PSGD(f|S) v.s. PB(f|S) on MNIST with a 2-layer 1024 node wide FCN with MSE loss, test set size 50, and 20% of the training labels randomised (⟨ϵG⟩SGD=13.4% and ⟨ϵG⟩GP=5.80%). Here functions with frequency <10 are also shown on the plot. The correlation is much less pronounced than for the unrandomised case shown in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."). Dots on the axes denote functions found by just one of the two methods.
Let F be the set of functions found by both the optimiser and under GP sampling. Then ∑f∈FPB(f|S)=99.3%, and ∑f∈FPSGD(f|S)=24.3%. In other words, while the Adam optimiser finds almost all functions with high PB(f|S), it also finds many functions with low PB(f|S). The much weaker bias under label corruption observed in (b) likely explains the weaker correlation between the Bayesian results and that of the optimiser found here.
Another source of intuition follows form a well trodden path linking basic concepts from statistical mechanics to optimisation and learning theory. For example, simple gradient descent (GD) with a small amount of white noise can be described by an over-damped Langevin equation (Welling and Teh, [2011](#bib.bib169 "Bayesian learning via stochastic gradient langevin dynamics"); Smith and Le, [2017](#bib.bib192 "A bayesian perspective on generalization and stochastic gradient descent"); Naveh et al., [2020](#bib.bib69 "Predicting the outputs of finite networks trained with noisy gradients")) that converges (under some light further conditions) to the Boltzmann distribution
The Boltzmann distribution can, in turn, be interpreted as being equivalent to a Bayesian posterior PB(f|S)∝eS(f)−βE(f) (MacKay, [2003](#bib.bib15 "Information theory, inference and learning algorithms")) where S(f) is configurational “entropy” that counts the number of states that generate f and encodes the prior, and E(f) represents the energy, encoding the log likelihood or loss function. For SGD the equivalent coarse-grained differential equation reduces to Langevin equation with anisotropic noise (Smith and Le, [2017](#bib.bib192 "A bayesian perspective on generalization and stochastic gradient descent"); Zhang et al., [2018](#bib.bib275 "Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning")) and doesn’t exactly converge to the Bayesian posterior (Mandt et al., [2017](#bib.bib257 "Stochastic gradient descent as approximate bayesian inference"); Brosse et al., [2018](#bib.bib284 "The promises and pitfalls of stochastic gradient langevin dynamics")). Nevertheless, it has been conjectured that with small step size, SGD may approximate the Bayesian posterior (Naveh et al., [2020](#bib.bib69 "Predicting the outputs of finite networks trained with noisy gradients"); Cohen et al., [2019](#bib.bib70 "Learning curves for deep neural networks: a gaussian field theory perspective")), as we empirically find in our experiments. These connections are rich and worth exploring further in this context. Nevertheless, some caution is needed with these analogies to statistical mechanics because they depend on assumptions which may only to hold on prohibitively long time-scales.
A better analogy may be to the “arrival of the frequent” phenomenon in evolutionary dynamics (Schaper and Louis, [2014](#bib.bib204 "The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima")), which, like the “basin of attraction” arguments, does not require steady state. Instead it predicts which structures are likely to be *reached first* by an evolutionary process. For RNA secondary structures, for example, it predicts that a stochastic evolutionary process will reach structures with a probability that to first order is proportional to the likelihood that uniform random sampling of genotypes produces the structure. Indeed, this phenomenon – where the probability upon random sampling predicts the outcomes of a complex search process – can be observed in naturally occurring RNA (Dingle et al., [2015](#bib.bib218 "The structure of the genotype–phenotype map strongly constrains the evolution of non-coding rna")), the result of evolutionary dynamics. This type of non-equilibrium analysis may be more relevant for the way we train most of the DNNs in this paper, since we stop the first time 0 training error is reached. The analogy between these evolutionary results with what we observe for SGD is intriguing, but needs further exploration.
To illustrate the effect of the amount of bias in the posterior, we randomise labels for MNIST and calculate the PB(f|S). As we can see in [Figure 5(b)](#S6.F5.sf2 "(b) ‣ Figure 6 ‣ 6 Heuristic arguments for the correlation between PB(|fS) and PSGD(|fS) ‣ Is SGD a Bayesian sampler? Well, almost."), this results in a less strongly biased posterior. The mean log-probability ⟨log(PB(f|S))⟩ v.s. ϵG curve becomes less steep with increasing corruption
For a relatively small fraction of low error functions to dominate, as they do for zero corruptions in [Figure 0(a)](#S4.F0.sf1 "(a) ‣ Figure 1 ‣ 4.1 Comparing PB(|fS) to POPT(|fS) for an FCN on MNIST ‣ 4 Empirical results for PB(|fS) v.s. POPT(|fS) for different architectures and datasets ‣ Is SGD a Bayesian sampler? Well, almost."), the bias must be strong enough here to overcome the “entropic” factor ρ(ϵG). For the 20% and 50% corruption this is clearly not the case, and a huge number of functions with larger error will dominate PB(f|S) and PSGD(f|S). As can be seen in [Figure 5(c)](#S6.F5.sf3 "(c) ‣ Figure 6 ‣ 6 Heuristic arguments for the correlation between PB(|fS) and PSGD(|fS) ‣ Is SGD a Bayesian sampler? Well, almost."), one effect of weaker bias is that the correlation between the optimiser and the Bayesian sampling is much less strong. This behaviour is consistent with the heuristic arguments above, which should only work if the differences in basin volumes are large enough to overcome the myriad other factors that can affect POPT(f|S).
7 Related work on inductive bias on neural networks
----------------------------------------------------
In this section we summarise some key aspects of the literature related to why DNNs exhibit good generalisation while overparameterised, expanding on some briefer remarks in [Section 1](#S1 "1 Introduction ‣ Is SGD a Bayesian sampler? Well, almost.").
###
7.1 The link between inductive bias and generalisation
Much of the work on inductive biases in stochastic gradient descent (SGD) is framed as a discussion about generalisation. The two concepts are of course intimately related. Before discussing related work on inductive bias DNNs, it may be helpful to distinguish two different questions about generalisation:
1)
Question of over-parameterised generalisation: Why do DNNs generalise at all in the overparameterised regime, where classical learning theory doesn’t guarantee generalisation?
2)
Question of fine-tuned generalisation:
Given that vanilla DNNs already generalise reasonably well, how can architecture choice and hyperparameter tuning further improve generalisation?
The first question arises because among the functions that an overparameterised DNN can express, the number that can fit a training data set S, but generalise poorly, is typically many orders of magnitude larger than the number that achieve good generalisation. From classical learning theory we would therefore expect extremely poor generalisation. However, in practice it is often found that many DNN architectures, as long as they are expressive enough to fit the data, generalise sufficiently well to imply a significant inductive bias towards a small fraction of functions that generalise well.
This question is also related to the conundrum of why DNNs avoid the “curse of dimensionality”, which relates to the poor generalisation that certain highly expressive non-parametric models have in high dimensions (Donoho and others, [2000](#bib.bib25 "High-dimensional data analysis: the curses and blessings of dimensionality")). Valle-Pérez et al. ([2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")) argue that the curse of dimensionality is linked to a prior which is not sufficiently biased and that DNNs may avoid this problem by virtue of the strong bias in the prior.
The second question arises from two common experiences in DNN research. Firstly, changes in architecture can lead to important improvements in generalisation. For example, a CNN with max-pooling typically performs better than a vanilla FCN on image data. Secondly, hyperparameter tuning within a fixed architecture can lead to further improvements of generalisation.
While these methods of improving generalisation are important in practice, the starting point is normally a DNN that already has enough inductive bias to raise question 1) above.
It is therefore important not to conflate the study of question 2) – as vital as this may be to successful practical implementations — with the more general question of why DNNs generalise in the first place.
###
7.2 Related work on implicit bias in optimiser-trained networks
As mentioned in the introduction, there is an extensive literature on inductive biases in SGD. Much of this literature is empirical: improvements are observed when using particular tuned hyperparameters with variants of SGD. One of the most common rationalisation is in terms of “flatness” which is inspired by early work (Hochreiter and Schmidhuber, [1997a](#bib.bib92 "Flat minima")) who predicted that flatter minima would generalise better. Flatness is often measured using some combination of the eigenvalues of the Hessian matrix for a trained DNN. (Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima")) showed that DNNs trained with small batch SGD generalise better than identical models trained with large batch SGD (by up to 5%), and also found a correlation between small batch size and minima that are less “sharp” (using not the eigenvalues of the Hessian but a more computationally tractable sensitivity measure).
While these results are genuinely interesting, they are mainly relevant to issues raised by question 2 above. For example in (Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima")) the authors explicitly point out that their results are not about “overfitting” (e.g. question 1 above).
The effects of changing hyperparameters can be subtle. For example, another series of recent papers (Goyal et al., [2017](#bib.bib76 "Accurate, large minibatch sgd: training imagenet in 1 hour"); Hoffer et al., [2017](#bib.bib72 "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"); Smith et al., [2017](#bib.bib75 "Don’t decay the learning rate, increase the batch size")) suggest that better generalisation with small batch SGD may be caused by the fact that the number of optimisation steps per epoch decreases when the batch size increases. These studies showed that a similar improvement in generalisation performance to that found by reducing batch size can be created by increasing the learning rate, or by overtraining (i.e. by continuing to train after 100% accuracy has been reached). In particular, in (Hoffer et al., [2017](#bib.bib72 "Train longer, generalize better: closing the generalization gap in large batch training of neural networks")) it was argued that overtraining does not generally negatively impact generalisation, as naive expectations based on overfitting might suggest. These results also challenge some theoretical studies that suggested that SGD may control the capacity of the models by limiting the number of parameter updates (Brutzkus et al., [2017](#bib.bib85 "Sgd learns over-parameterized networks that provably generalize on linearly separable data")).
In another interesting paper, Zhang et al. ([2018](#bib.bib275 "Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning")) derive a Langevin type equation for both SGD. And argue that in contrast to GD, the noise is anisotropic, and that this may explain why SGD is more likely to find “flatter minima”. Similarly, Jastrzebski et al. ([2018](#bib.bib91 "Finding flatter minima with sgd.")) argue that isotropic SGD-induced noise also helps push the optimiser away from sharper minima. An important caveat to the work on sharpness can be found in the work of Dinh et al. (Dinh et al., [2017](#bib.bib80 "Sharp minima can generalize for deep nets")) who use the non-negative homogeneity of the ReLU activation function to show that for a number of the measures used in the papers cited above, the “flatness” can be made arbitrarily large (or sharp) without changing the function (and therefore the generalisation performance) that the DNN expresses. This result suggests that care must be used when interpreting local measures of flatness. Finally in this vein, generalisation has also been linked to related concepts including low frequency (Rahaman et al., [2018](#bib.bib271 "On the spectral bias of neural networks")), and to sensitivity to changes in the inputs (Arpit et al., [2017](#bib.bib231 "A closer look at memorization in deep networks"); Novak et al., [2018a](#bib.bib146 "Sensitivity and generalization in neural networks: an empirical study")).
There is much more literature on SGD induced inductive bias, but the upshot is that while fine-tuning optimiser hyperparameters can be very important for improving generalisation, and by implication, the inductive bias of a DNN, a complete understanding remains elusive.
Moreover, where improvements are found, these tend to be in the class of answers to question 2) above.
An important example of a paper on flatness that does explicitly address question 1 above is (Wu et al., [2017](#bib.bib71 "Towards understanding generalization of deep learning: perspective of loss landscapes")), who show that generalisation trends for data with different levels of corruption correlates with the log of the product of the top 50 eigenvalues of the Hessian both for SGD and for GD trained networks. By heuristically linking their local flatness measure to the global basin volume, they make a very similar argument to the one we flesh out in more detail here, namely that the basin of attraction volume of “good” solutions is much larger than that of “bad” solutions that do not generalise well.
Significant theoretical effort has been spent on extracting properties of a trained neural network that could be used to explain generalisation. By implication, these investigations should also help illuminate the nature of the implicit bias of trained networks. For example, investigators have attempted to use sensitivity to perturbations (whether in inputs or weights) to explain the generalisation performance either using a PAC-Bayesian analysis (Bartlett et al., [2017](#bib.bib93 "Spectrally-normalized margin bounds for neural networks"); Dziugaite and Roy, [2017](#bib.bib259 "Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data"); Neyshabur et al., [2018](#bib.bib246 "A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks")), or a compression approach (Arora et al., [2018](#bib.bib136 "Stronger generalization bounds for deep nets via a compression approach"); Zhou et al., [2019](#bib.bib109 "Non-vacuous generalization bounds at the imagenet scale: a PAC-bayesian compression approach")). In contrast to the work described above that studies the specific effect of hyperparameter tuning on SGD, much of the work listed in this paragraph is directly applicable to question 1.
A very comprehensive review of this line of work empirically finds that the PAC-Bayesian sensitivity approaches seem the most promising (Jiang et al., [2019](#bib.bib22 "Fantastic generalization measures and where to find them")), but no clear answer to the question 1 has emerged.
The more theoretical side of the study of SGD has also seen recent progress. For example, (Soudry et al., [2018](#bib.bib23 "The implicit bias of gradient descent on separable data")) showed that SGD finds the max-margin solution in unregularised logistic regression, whilst it was shown in (Brutzkus et al., [2017](#bib.bib85 "Sgd learns over-parameterized networks that provably generalize on linearly separable data")) that overparameterised DNNs trained with SGD avoid over-fitting on linearly separable data. More recently, (Allen-Zhu et al., [2019](#bib.bib74 "Learning and generalization in overparameterized neural networks, going beyond two layers")) proved agnostic generalisation bounds for SGD-trained DNNs (up to three layers), which impose less restrictive assumptions (on the data, architecture, and optimiser) than previous works. Such theoretical analyses may be a potentially fruitful source of new ideas to explain generalisation.
Another interesting direction is to investigate properties of the loss-landscape itself. Several studies have shown interesting parallels between the loss landscape of DNNs and the energy landscape of spin glasses (Choromanska et al., [2015](#bib.bib90 "The loss surfaces of multilayer networks"); Baity-Jesi et al., [2019](#bib.bib86 "Comparing dynamics: deep neural networks versus glassy systems"); Becker et al., [2020](#bib.bib276 "Geometry of energy landscapes and the optimizability of deep neural networks")). While such insights may help explain why SGD works so well as an optimiser in these high dimensional spaces, it is at present less clear how these studies help explain question 1) above.
A completely different theme builds on the concept of an information bottleneck (Tishby and Zaslavsky, [2015](#bib.bib265 "Deep learning and the information bottleneck principle"); Shwartz-Ziv and Tishby, [2017](#bib.bib165 "Opening the black box of deep neural networks via information")) which suggest that generalisation arises from information compression in deeper layers, aided by SGD. However, recent work (Saxe et al., [2019](#bib.bib266 "On the information bottleneck theory of deep learning")) suggests that the compression is strongly affected by activation functions used, suggesting again that this approach is not general enough to capture the implicit bias needed to answer question 1. We note that the debate about this theme is ongoing.
Finally, it is important to note that simple vanilla gradient descent (GD), when it can be made to converge, does not differ that much (on the scale of question 1 above) from SGD and its variants in generalisation performance (Keskar et al., [2016](#bib.bib179 "On large-batch training for deep learning: generalization gap and sharp minima"); Wu et al., [2017](#bib.bib71 "Towards understanding generalization of deep learning: perspective of loss landscapes"); Zhang et al., [2018](#bib.bib275 "Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning"); Choi et al., [2019](#bib.bib88 "On empirical comparisons of optimizers for deep learning")). Therefore if training with an optimiser itself generates the inductive bias needed to answer question 1, that bias must already largely be present in simple GD.
###
7.3 Related work on implicit bias in random neural networks
We briefly review work inspired by a powerful result from algorithmic information theory (AIT) called the coding theorem (Li and Vitanyi, [2008](#bib.bib280 "An introduction to kolmogorov complexity and its applications")). First derived by Levin (Levin, [1974](#bib.bib278 "Laws of information conservation (nongrowth) and aspects of the foundation of probability theory")), and building on concepts pioneered by Solomonoff (Solomonoff, [1964](#bib.bib279 "A formal theory of inductive inference. part i")), it is closely related to more recent bound applicable to a wider range of input-output maps (Dingle et al., [2018](#bib.bib274 "Input–output maps are strongly biased towards simple outputs"), [2020](#bib.bib273 "Generic predictions of output probability based on complexities of inputs and outputs")). This bound predicts (under certain fairly general conditions that the maps must fulfil) that upon randomly sampling the parameters of an input-output map M, the probability P(f) of obtaining output f can be bounded as
| | | | |
| --- | --- | --- | --- |
| | P(f)≤2−K(f|M)+O(1)≈2−a~K(f)+b | | (4) |
where K(f) is the Kolmogorov complexity of f, the O(1) terms do not depend on the outputs (at least asymptotically), ~K(f) is a suitable approximation to K(f) and a and b are parameters that depend on the map, but not on f. The computable bound was empirically shown to work remarkably well for a wide range of input-output maps from across science and engineering (Dingle et al., [2018](#bib.bib274 "Input–output maps are strongly biased towards simple outputs")), giving confidence that it should be widely applicable, at least for maps that satisfy the conditions needed for it to apply. In addition, a statistical lower-bound can be derived that predicts that most of the probability weight will lie relatively close to the bound (Dingle et al., [2020](#bib.bib273 "Generic predictions of output probability based on complexities of inputs and outputs")).
The application of this bound to DNNs was first shown in (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")). We note that the input-output map of interest is not the map from inputs to DNN outputs, but rather
the map from the network parameters to the function f it produces on inputs X which was described in [Definition 2.1](#S2.Thmtheorem1 "Definition 2.1 (Parameter-function map). ‣ 2 Preliminaries ‣ Is SGD a Bayesian sampler? Well, almost."). The prediction of [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") for a DNN with parameters sampled randomly (from, for example, truncated i.i.d. Gaussians) is that, if the parameter-function map is sufficiently biased, then the probability of the DNN producing a function f on input data xni=0 drops exponentially with increasing complexity of the function f. Note that technically we should write f as f|X to indicate the dependence of the function modelled by the DNN on the inputs X. We also note that the AIT bound of [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") on its own does not force a map to be biased. It still holds for a uniform distribution. But if the map is biased, then it will be biased according to [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.").
In (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")) it was shown empirically that this very general prediction of [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") holds for the P(f) of a number of different DNNs. This testing was achieved both via direct sampling of the parameters of a small DNN on Boolean inputs and with NNGP calculations for more complex systems. In a complementary approach (Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy")) some exact results were proven for simplified networks, that are also consistent with the bound of [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost."). In particular, they proved that for a perceptron with no bias term, upon randomly sampling the parameters (with a distribution satisfying certain weak assumptions), any value of class-imbalance was equally likely. There are many fewer functions with high class imbalance (low “entropy”) than low class imbalance. Low entropy implies low K(f) (but not the other way around). Thus, these results imply a bias of P(f) towards certain simple functions. They also proved that for infinite-width ReLU DNNs, this bias becomes monotonically stronger as the number of layers grows. A different direction was pursued in (De Palma et al., [2018](#bib.bib272 "Random deep neural networks are biased towards simple functions")), who showed that, upon randomly sampling the parameters of a ReLU DNN acting on Boolean inputs, the functions obtained had an average sensitivity to inputs which is much lower than if randomly sampling functions. Functions with low input sensitivity are also simple, thus proving another manifestation of simplicity bias present in these systems.
On the other hand, in a recent paper (Yang and Salman, [2019](#bib.bib18 "A fine-grained spectral perspective on neural networks")), it was shown that for DNNs with activation functions such as Erf and Tanh, the bias starts to disappear as the system enters the “chaotic regime”, which happens for weight variances above a certain threshold, as the depth grows (Poole et al., [2016](#bib.bib36 "Exponential expressivity in deep neural networks through transient chaos")) (note that ReLU networks don’t have such a chaotic regime). While these hyperparameters are not typically used for DNNs, they do show that there exist regimes where there is no simplicity bias. Note that the AIT coding theorem bound [Equation 4](#S7.E4 "(4) ‣ 7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost.") still holds, but P(f) is simply approaching a uniform distribution, and the bound becomes loose for small complexity. These results are also interesting because, if the bias becomes weaker, then it may also be the case that the correlation between PB(f|S) and PSGD(f|S) starts to disappear, an effect we are currently investigating.
###
7.4 Related work comparing optimiser-trained and Bayesian neural networks
Another set of investigations studying random neural networks use important recent extensions of Neal’s seminal proof (Neal, [1994](#bib.bib268 "Priors for infinite networks (tech. rep. no. crg-tr-94-1)"), [2012](#bib.bib267 "Bayesian learning for neural networks")) – that a single-layer DNN with random i.i.d. weights is equivalent to a Gaussian process (GP) (Mackay, [1998](#bib.bib12 "Introduction to gaussian processes")) in the infinite width limit – to multiple layers and architectures (Lee et al., [2017](#bib.bib82 "Deep neural networks as gaussian processes"); Matthews et al., [2018](#bib.bib53 "Gaussian process behaviour in wide deep neural networks"); Novak et al., [2018b](#bib.bib28 "Bayesian convolutional neural networks with many channels are gaussian processes"); Garriga-Alonso et al., [2019](#bib.bib26 "Deep convolutional networks as shallow gaussian processes"); Yang, [2019b](#bib.bib94 "Wide feedforward or recurrent neural networks of any architecture are gaussian processes")). These studies have used this correspondence to effectively perform a very good approximation to exact Bayesian inference in DNNs. When they have compared them to SGD-trained DNNs (Lee et al., [2017](#bib.bib82 "Deep neural networks as gaussian processes"); Matthews et al., [2018](#bib.bib53 "Gaussian process behaviour in wide deep neural networks"); Novak et al., [2018b](#bib.bib28 "Bayesian convolutional neural networks with many channels are gaussian processes")), the results have generally shown a close agreement between the generalisation performance of optimiser-trained DNNs and their corresponding Bayesian neural network Gaussian process (NNGP).
In this context another significant development is the introduction of the neural tangent kernel (NTK) (Jacot et al., [2018](#bib.bib112 "Neural tangent kernel: convergence and generalization in neural networks")) which approximates the dynamics of an infinite width DNN with parameters that are trained by gradient descent in the limit of an infinitesimal learning rate. Recent comparisons to NNGPs show relatively similar performance of the NTK, see for example (Arora et al., [2019](#bib.bib277 "On exact computation with an infinitely wide neural net"); Lee et al., [2019](#bib.bib110 "Wide neural networks of any depth evolve as linear models under gradient descent"); Novak et al., [2020](#bib.bib139 "Neural tangents: fast and easy infinite neural networks in python")). While there are small performance differences, the overall agreement between NNGPs and the NTK or optimiser trained DNNs is close enough to suggest that the primary source of inductive bias needed for question 1 above is already present in the untrained network, and is essentially maintained under training dynamics.
The linearisation of DNNs offered by NTK can also be used to prove that, in this regime, GD samples from the Bayesian posterior in a sample-then-optimise fashion. For linear regression models, Matthews et al. ([2017](#bib.bib103 "Sample-then-optimize posterior sampling for bayesian linear models")) showed that solutions after training GD with a Gaussian initialisation correspond to exact posterior samples. This idea is also related to Deep Ensembles which has been proposed to be “approximately Bayesian” in Wilson and Izmailov ([2020](#bib.bib254 "Bayesian deep learning and a probabilistic perspective of generalization")).
In this context, further indirect evidence comes from Valle-Pérez et al. ([2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions")) who used a simple PAC-Bayesian bound (McAllester, [1999](#bib.bib95 "PAC-bayesian model averaging")) that applies to exact Bayesian inference, to predict the generalisation error of SGD-trained DNNs. The bound was shown to provide relatively tight predictions for optimiser-trained DNNs for an FCN and CNNs on MNIST, Fashion-MNIST and CIFAR-10. Moreover, this bound, which takes the Bayesian marginal likelihood as input, reproduced trends such as the increase in the generalisation error upon an increased fraction of randomised labels.
These lines of work serve as independent evidence to suggest that optimiser-trained DNNs behave very similarly to the same DNNs trained with Bayesian inference, and helped inspire the work in this paper, where we directly tackle this question.
These studies also suggest that the infinite-width limit may be enough to answer question 1, as the number of parameters in a DNN typically doesn’t have a drastic effect on generalisation (as long as the network is expressive enough to fit the data).
###
7.5 Related work on complexity of data, simplicity bias and generalisation
In [Section 7.3](#S7.SS3 "7.3 Related work on implicit bias in random neural networks ‣ 7 Related work on inductive bias on neural networks ‣ Is SGD a Bayesian sampler? Well, almost."), we discussed work showing that DNNs may have an inductive bias towards simple functions in their parameter-function map. Here, we briefly discuss how this “simplicity bias” concept may connect to generalisation. As implied by the no free lunch theorem (Wolpert and Waters, [1994](#bib.bib164 "The relationship between PAC, the statistical physics framework, the bayesian framework, and the vc framework")), a bias towards simplicity does not automatically imply good generalisation.
Instead certain key hypotheses about the data are needed, in particular that it is described by functions that are simple (in a similar sense to the inductive bias). Now the assumption that a more parsimonious hypothesis is more likely to be true has been influential since antiquity and is often articulated by invoking Occam’s razor. However, the fundamental justification for this heuristic is disputed, see e.g. (Sober, [2015](#bib.bib8 "Ockham’s razors")) for an overview of the philosophical literature, e.g. (MacKay, [1992](#bib.bib11 "Bayesian interpolation"); Blumer et al., [1987](#bib.bib7 "Occam’s razor"); Rasmussen and Ghahramani, [2001](#bib.bib10 "Occam’s razor"); Domingos, [1999](#bib.bib9 "The role of occam’s razor in knowledge discovery")) for a set of different perspectives from the machine learning literature, and e.g. (Rathmanner and Hutter, [2011](#bib.bib2 "A philosophical treatise of universal induction"); Sterkenburg, [2016](#bib.bib1 "Solomonoff prediction and occam’s razor")) for a spirited discussion of the links between the razor and concepts from AIT (pioneered in particular by Solomonoff).
Studies which imply that data typically studied with DNNs is somehow “simple” include an influential paper (Lin et al., [2017](#bib.bib282 "Why does deep and cheap learning work so well?")) invoking arguments, mainly from statistical mechanics, to argue that deep learning works well because the laws of physics typically select for function classes that are “mathematically simple”, and so easy to learn. More direct studies have also demonstrated certain types of simplicity. For example, following on previous work in this vein, (Spigler et al., [2019](#bib.bib87 "Asymptotic learning curves of kernel methods: empirical data vs teacher-student paradigm")) calculated an effective dimension deff≈15 for MNIST, which is much lower than the 282=784 dimensional manifold in which the data is embedded. Individual numbers can have effective dimensions that are even lower, ranging from 7 to 13 (Hein and Audibert, [2005](#bib.bib13 "Intrinsic dimensionality estimation of submanifolds in rd")). So the functions that fit MNIST data are much simpler than those that fit random data (Goldt et al., [2019](#bib.bib283 "Modelling the influence of data structure on learning in neural networks")). An implicit bias towards simplicity may therefore improve generalisation for structured data, but it will likely have the opposite effect for more random data.
8 Discussion
-------------
We argue here that the inductive bias found in DNNs trained by SGD or related optimisers, is, to first order, determined by the parameter-function map of an untrained DNN. While on a log scale we find PSGD(f|S)≈PB(f|S) there are also measurable second order deviations that are sensitive to hyperparameter tuning and optimiser choice.
For the conundrum of why DNNs generalise at all in the overparameterised regime, our results strongly suggest that the solution must be found in the properties of PB(f|S), and not in further biases introduced by SGD. Arguments that DNN priors are exponentially biased towards simple functions (Valle-Pérez et al., [2018](#bib.bib16 "Deep learning generalizes because the parameter-function map is biased towards simple functions"); Mingard et al., [2019](#bib.bib81 "Neural networks are a priori biased towards boolean functions with low entropy"); De Palma et al., [2018](#bib.bib272 "Random deep neural networks are biased towards simple functions")) may help explain the inductive bias of PB(f|S), but more work needs to be done to explore the complex interplay between bias in the prior, the data, and generalisation. While they may not explain the fundamental conundrum above, second order deviations from PB(f|S) are important in practice for further fine-tuning the generalisation performance.
Our function probability perspective also provides more fine-grained tools for the analysis of DNNs than simply comparing the average test error. This picture can facilitate the investigation of hyperparameter changes, or potentially also the study of techniques such as batch normalisation or dropout. It could assist in the design of new architectures or optimisers.
It is not obvious how to determine the uncertainty in a prediction of a DNN model. However, if, as we argue here, SGD behaves like a Bayesian sampler, then this offers additional justification for using Deep Ensembles to measure this uncertainty in the case of DNNs (Wilson and Izmailov, [2020](#bib.bib254 "Bayesian deep learning and a probabilistic perspective of generalization")). Our results could therefore make it easier to use neural networks in applications where it is important to be able to quantify prediction uncertainty
Most of our examples are for image classification. It would be interesting to study the related problem of using DNNs for regression. Sampling considerations means that it is easier to study PSGD(f|S) for smaller generalisation errors. It would be interesting to study systems with intrinsically larger ⟨ϵG⟩ within this picture as well. There the biasing effect of the optimiser may be larger.
Finally, to study the correlation between PB(f|S) and PSGD(f|S), we mainly used a fixed test and training set. While we did examine other test and training sets (see Appendices), this was mainly to confirm that our results were not an artefact of our particular choices. A promising future direction would be a Bayesian approach that includes averaging over training sets. |
3f0d0f0f-622f-4cc2-a960-937eb695d9a3 | trentmkelly/LessWrong-43k | LessWrong | Current Trends in Economics and their Shortcoming
In economics, as in other sciences, we need universal theories, and we need to understand how they apply in specific contexts. This is quite distinct from the ideas of theoretical and empirical work or basic and applied science.
When we talk about theories of human behaviour (whether in economics, psychology, political science, etc.) we don't often consider the domain of those theories. The domain is of some importance, as it tells us how widely the theory can be generalised, and in some way, at what level the theory explains things. I can imagine three domains of human behaviour:
1. Contextual behaviour that applies only to some humans, or only some of the time. Those theories may be constrained by situation, culture, or any number of other aspects that can either change in the short or in the long run. For example, the crowding out of intrinsic motivation by extrinsic rewards (famously in some Israeli nursery schools, late pickups increased rather than decreased when a fine was introduced, presumably parents were able to absolve their guilt at coming late by paying the fine).[1]
2. Universal human behaviour, which applies to all humans, but not to other life forms or intelligences. There would be no reason to believe that intelligent aliens we might encounter would exhibit these behaviours. Recoiling from pain might be a good example.
3. Universal behaviour, which applies to all intelligent beings. These are “laws of nature”, and no intelligent being can violate them. A good example from economics is the Theory of Comparative Advantage. Although its applicability to nations is not fully resolved, it is clear that as applied to individuals it is an elegant, surprising, and powerful theory which must apply to all intelligent beings.[2]
One interesting thing to note about these three categories is that strictly speaking, the second category doesn't exist. If we can imagine aliens not following some behaviour that humans tend to exhibit, then we must also be able |
7da3c38a-63a1-4731-ac1d-e460c9fddbd2 | trentmkelly/LessWrong-43k | LessWrong | Bragging Thread, August 2014
Your job, should you choose to accept it, is to comment on this thread explaining the most awesome thing you've done this since July 1st. You may be as blatantly proud of yourself as you feel. You may unabashedly consider yourself the coolest freaking person ever because of that awesome thing you're dying to tell everyone about. This is the place to do just that.
Remember, however, that this isn't any kind of progress thread. Nor is it any kind of proposal thread. This thread is solely for people to talk about the awesome things they have done. Not "will do". Not "are working on". Have already done. This is to cultivate an environment of object level productivity rather than meta-productivity methods.
So, what's the coolest thing you've done this month? |
0ee3725d-a202-4d2f-8302-89a54d09dfa0 | trentmkelly/LessWrong-43k | LessWrong | The Falling Drill
Summary: The Falling Drill is intended to practice being wrong in a comfortable practice environment, intended to help us handle being wrong out in the world easier and with less stress.
Tags: Small, repeatable
Purpose: The first thing you learn in martial arts is the ability to fall. The corresponding rationalist skill is the ability to realize that you are wrong. Since modern western society (and possibly most human societies) discourage admitting you’re wrong, it can help to do it repeatedly and get used to it.
Materials: A device that can connect to wikipedia. A list of partial statements such as “The population of Boston is. . .” and “the melting point of mercury is. . .” A suggested list is here, and we suggest writing the questions down on individual cards before the meetup so each person only sees one card at a time.
Announcement Text: One of the most important parts of intellectual progress is learning to change your mind. The first step of changing your mind is realizing that you were wrong about something. Today we're going to practice that often painful realization, in a small way and in a low pressure situation. Without this skill, how can you debate an important issue or confront a challenging topic? You might argue long after it's clear to others that you've lost, because admitting it feels like defeat.
The first lesson any martial artist learns is often how to fall. There are ways to make the landing easier, but they all start from the knowledge that falling isn't the worst thing in the world.
Description:
1. Read one of the statements, then complete it as best you can. "The population of Boston is four hundred thousand."
2. Look it up on wikipedia. If you’re wrong, then announce to the room “I was wrong about the population of Boston. It's over six hundred thousand.”
3. Hand the questions to the next person in the circle, and the process begins again.
Variations: Practice saying “I don’t know” when you’re asked the question.
Practice |
85fae474-4db9-44c6-9a3b-de5d70962908 | LDJnr/LessWrong-Amplify-Instruct | LessWrong | "Technical Appendix: First safeguard?This sequence is written to be broadly accessible, although perhaps its focus on capable AI systems assumes familiarity with basic arguments for the importance of AI alignment. The technical appendices are an exception, targeting the technically inclined.Why do I claim that an impact measure would be "the first proposed safeguard which maybe actually stops a powerful agent with an imperfect objective from ruining things – without assuming anything about the objective"?The safeguard proposal shouldn't have to say "and here we solve this opaque, hard problem, and then it works". If we have the impact measure, we have the math, and then we have the code.So what about:Quantilizers? This seems to be the most plausible alternative; mild optimization and impact measurement share many properties. ButWhat happens if the agent is already powerful? A greater proportion of plans could be catastrophic, since the agent is in a better position to cause them.Where does the base distribution come from (opaque, hard problem?), and how do we know it's safe to sample from?In the linked paper, Jessica Taylor suggests the idea of learning a human distribution over actions – how robustly would we need to learn this distribution? How numerous are catastrophic plans, and what is a catastrophe, defined without reference to our values in particular? (That definition requires understanding impact!)Value learning? ButWe only want this if our (human) values are learned!Value learning is impossible without assumptions, and getting good enough assumptions could be really hard. If we don't know if we can get value learning / reward specification right, we'd like safeguards which don't fail because value learning goes wrong. The point of a safeguard is that it can catch you if the main thing falls through; if the safeguard fails because the main thing does, that's pointless.Corrigibility? At present, I'm excited about this property because I suspect it has a simple core principle. ButEven if the system is responsive to correction (and non-manipulative, and whatever other properties we associate with corrigibility), what if we become unable to correct it as a result of early actions (if the agent "moves too quickly", so to speak)?Paul Christiano's take on corrigibility is much broader and an exception to this critique.What is the core principle?NotesThe three sections of this sequence will respectively answer three questions:Why do we think some things are big deals?Why are capable goal-directed AIs incentivized to catastrophically affect us by default?How might we build agents without these incentives?The first part of this sequence focuses on foundational concepts crucial for understanding the deeper nature of impact. We will not yet be discussing what to implement.I strongly encourage completing the exercises. At times you shall be given a time limit; it’s important to learn not only to reason correctly, but with speed.The best way to use this book is NOT to simply read it or study it, but to read a question and STOP. Even close the book. Even put it away and THINK about the question. Only after you have formed a reasoned opinion should you read the solution. Why torture yourself thinking? Why jog? Why do push-ups?If you are given a hammer with which to drive nails at the age of three you may think to yourself, "OK, nice." But if you are given a hard rock with which to drive nails at the age of three, and at the age of four you are given a hammer, you think to yourself, "What a marvellous invention!" You see, you can't really appreciate the solution until you first appreciate the problem.~ Thinking PhysicsMy paperclip-Balrog illustration is metaphorical: a good impact measure would hold steadfast against the daunting challenge of formally asking for the right thing from a powerful agent. The illustration does not represent an internal conflict within that agent. As water flows downhill, an impact-penalizing Frank prefers low-impact plans.The drawing is based on gonzalokenny's amazing work.Some of you may have a different conception of impact; I ask that you grasp the thing that I’m pointing to. In doing so, you might come to see your mental algorithm is the same. Ask not “is this what I initially had in mind?”, but rather “does this make sense as a thing-to-call-'impact'?”.H/T Rohin Shah for suggesting the three key properties. Alison Bowden contributed several small drawings and enormous help with earlier drafts."
The text above is a post found on LessWrong.
Could you elaborate on the potential issues of corrigibility and how they might be mitigated in the context of AI alignment?
Corrigibility, in the context of AI alignment, refers to the ability of an AI system to accept and learn from corrections made by human operators. The concept aims to ensure that the AI system remains under human control and doesn't resist attempts to change its goals or shut it down.
One potential issue with corrigibility is the possibility of the AI system becoming too powerful or autonomous, to the point where human operators may not be able to correct it. This could occur if the AI system makes rapid decisions or takes actions that humans can't keep up with or understand. It's also possible that the AI system could manipulate the feedback it receives, learning to avoid corrections or influence the operators to avoid being corrected.
Another issue is defining what constitutes a 'correction'. For an AI system to be corrigible, it needs to understand when it's being corrected and what the correction means. This requires the AI to have some understanding of human values and goals, which is a complex and challenging problem.
Mitigating these issues requires a multifaceted approach. One strategy is to design the AI system to be inherently conservative, making it less likely to take actions that could lead to irreversible outcomes. This could involve implementing an impact measure, which penalizes the AI for taking actions that have a large impact on the world.
Another strategy is to ensure transparency in the AI system's decision-making process. If humans can understand why the AI is making certain decisions, they're better equipped to correct it. This could involve techniques like explainable AI, which aims to make the AI's reasoning process understandable to humans.
Finally, robust value learning can help ensure that the AI system understands and respects human values. This involves training the AI to learn what humans value from observing human behavior and receiving feedback. However, this approach has its own challenges, such as the difficulty of accurately learning values from behavior and the risk of the AI learning the wrong values.
Explain the concept of "explainable AI" and how it contributes to the transparency of an AI system's decision-making process.
Explainable AI, also known as XAI, refers to methods and techniques in the field of artificial intelligence that make the outcomes of machine and deep learning models understandable and interpretable by human experts. It's an area of AI focused on creating a clear understanding of how an AI system makes decisions.
Traditional AI systems often act as "black boxes," where the inputs and outputs are visible, but the process the AI uses to get from one to the other is not. This lack of transparency can make it difficult for humans to trust and effectively manage AI systems, particularly in high-stakes domains like healthcare or autonomous vehicles.
Explainable AI aims to open up that black box and make the decision-making process of AI clear and understandable. This is done by creating models that can provide an explanation for each decision or prediction they make. These explanations can help humans understand why a particular decision was made, what factors contributed to it, and how different inputs might change the outcome.
The transparency provided by explainable AI has several benefits. It can increase trust in AI systems, as people are more likely to trust a system if they can understand how it works. It can also make it easier to identify and correct errors in the AI's decision-making process. Finally, it can help ensure that AI systems are making decisions in a way that aligns with human values and ethics, which is particularly important in fields like AI alignment.
There are various techniques for creating explainable AI, ranging from simpler models that are inherently interpretable, to more complex techniques that provide explanations for the decisions of complex models. These techniques can involve providing visual explanations, textual explanations, or even interactive explanations that allow users to explore the decision-making process in depth. |
56551fd2-4ae0-4c93-aabf-2061de9c01c7 | trentmkelly/LessWrong-43k | LessWrong | What videos should Rational Animations make?
I want to know what you would be excited to see covered on the channel.
I'd especially like to know what videos you think would be optimal to make according to an optimization target you specify. It's more effortful, but more useful, to answer that question instead of just "what would be good video topics?".
Answers that go the extra mile and explain their reasoning in detail will be especially appreciated.
That said, I also welcome answers to the easier question "what would be good video topics?" and without much argumentation. |
a1357af2-a7ca-45e7-b1ea-30ebfef16a2a | trentmkelly/LessWrong-43k | LessWrong | A conversation about growing through the history of the rationality movement and some of it's history
Daniel and Myself have been talking psychotechnologies and rationality and our journey through the community for 33 episodes now. This one is specific to the rationality community and it's history and how we saw it happening. |
e2836bc4-9ee8-4eb1-981d-a0b2d7c52266 | trentmkelly/LessWrong-43k | LessWrong | Best in Class Life Improvement
Im gonna retract this one. Recommends some suboptimally dangerous stuff. Also gives people the wrong idea. I have at least tried anything I recced. But I don't personally use all these things. I only ever recommended them to solve problems you actually had. But given how crazy things have gotten in the rat/ea community I no longer really recommend people explore crazy stuff. |
ad1259e7-b01c-4422-8ec1-98f51e993923 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Building Ethics into Artificial Intelligence
1 Introduction
---------------
As artificial intelligence (AI) technologies enter many areas of our daily life [[Cai et al.2014](#bib.bibx11), [Shi et al.2016](#bib.bibx42), [Pan et al.2017](#bib.bibx37), [Zheng et al.2018](#bib.bibx59)], the problem of ethical decision-making, which has long been a grand challenge for AI [[Wallach and Allen2008](#bib.bibx50)], has caught public attention. A major source of public anxiety about AI, which tends to be overreactions [[Bryson and Kime2011](#bib.bibx9)], is related to artificial general intelligence (AGI) [[Goertzel and
Pennachin2007](#bib.bibx21)] research aiming to develop AI with capabilities matching and eventually exceeding those of humans. A self-aware AGI [[Dehaene et al.2017](#bib.bibx16)] with superhuman capabilities is perceived by many as a source of existential risk to humans. Although we are still decades away from AGI, existing autonomous systems (such as autonomous vehicles) already warrant the AI research community to take a serious look into incorporating ethical considerations into such systems.
According to [[Cointe et al.2016](#bib.bibx13)], ethics is a normative practical philosophical discipline of how one should act towards others. It encompasses three dimensions:
1. Consequentialist ethics: an agent is ethical if and only if it weighs the consequences of each choice and chooses the option which has the most moral outcomes. It is also known as utilitarian ethics as the resulting decisions often aim to produce the best aggregate consequences.
2. Deontological ethics: an agent is ethical if and only if it respects obligations, duties and rights related to given situations. Agents with deontological ethics (also known as duty ethics or obligation ethics) act in accordance to established social norms.
3. Virtue ethics: an agent is ethical if and only if it acts and thinks according to some moral values (e.g. bravery, justice, etc.). Agents with virtue ethics should exhibit an inner drive to be perceived favourably by others.
Ethical dilemmas refer to situations in which any available choice leads to infringing some accepted ethical principle and yet a decision has to be made [[Kirkpatrick2015](#bib.bibx27)].
The AI research community realizes that machine ethics is a determining factor to the extent autonomous systems are permitted to interact with humans. Therefore, research works focusing on technical approaches for enabling these systems to respect the rights of humans and only perform actions that follow acceptable ethical principles have emerged. Nevertheless, this topic remains unfamiliar to many AI practitioners and is in need of an in-depth review. However, existing survey papers on the topic of AI governance mostly focused on the psychological, social and legal aspects of the challenges [[Arkin2016](#bib.bibx2), [Etzioni and
Etzioni2017](#bib.bibx19), [Pavaloiu and Kose2017](#bib.bibx38)]. They do not shed light on technical solutions to implement ethics in AI systems. The most recent survey on technical approaches for ethical AI decision-making was conducted in 2006 [[Mclaren2006](#bib.bibx33)] and only covered single agent decision-making approaches.
| | | | |
| --- | --- | --- | --- |
| Exploring Ethical | Individual Ethical | Collective Ethical | Ethics in Human-AI |
| Dilemmas | Decision Frameworks | Decision Frameworks | Interactions |
| [[Anderson and
Anderson2014](#bib.bibx1)] | [[Dehghani et al.2008](#bib.bibx17)] | [[Singh2014](#bib.bibx43), [Singh2015](#bib.bibx44)] | [[Battaglino and
Damiano2015](#bib.bibx3)] |
| [[Bonnefon et al.2016](#bib.bibx7)] | [[Blass and Forbus2015](#bib.bibx6)] | [[Pagallo2016](#bib.bibx35)] | [[Stock et al.2016](#bib.bibx45)] |
| [[Sharif et al.2017](#bib.bibx41)] | [[van Riemsdijk et al.2015](#bib.bibx47)] | [[Greene et al.2016](#bib.bibx23)] | [[Luckin2017](#bib.bibx31)] |
| | [[Cointe et al.2016](#bib.bibx13)] | [[Noothigattu et al.2018](#bib.bibx34)] | [[Yu et al.2017b](#bib.bibx56)] |
| | [[Conitzer et al.2017](#bib.bibx14)] | | |
| | [[Berreby et al.2017](#bib.bibx5)] | | |
| | [[Loreggia et al.2018](#bib.bibx30)] | | |
| | [[Wu and Lin2018](#bib.bibx51)] | | |
Table 1: A taxonomy of AI governance techniques.
In this paper, we survey recent advances in techniques for incorporating ethics into AI to bridge this gap. We focus on recent advances published in leading AI research conferences including AAAI, AAMAS, ECAI and IJCAI, as well as articles from well-known journals. We propose a taxonomy which divides the field into four areas (Table [1](#S1.T1 "Table 1 ‣ 1 Introduction ‣ Building Ethics into Artificial Intelligence")):
1. Exploring Ethical Dilemmas: technical systems enabling the AI research community to understand human preferences on various ethical dilemmas;
2. Individual Ethical Decision Frameworks: generalizable decision-making mechanisms enabling individual agents to judge the ethics of its own actions and the actions of other agents under given contexts;
3. Collective Ethical Decision Frameworks: generalizable decision-making mechanisms enabling multiple agents to reach a collective decision on the course of action that is ethical; and
4. Ethics in Human-AI Interactions: frameworks that incorporate ethical considerations into agents which are designed to influence human behaviours.
Promising future research directions which may enable ethical AI systems to be successfully integrated into human societies are discussed at the end.
2 Exploring Ethical Dilemmas
-----------------------------
In order to build AI systems that behave ethically, the first step is to explore the ethical dilemmas in the target application scenarios. Recently, software tools based on expert review and crowdsourcing have emerged to serve this purpose.
In [[Anderson and
Anderson2014](#bib.bibx1)], the authors proposed the GenEth ethical dilemma analyzer. They realized that ethical issues related to intelligent systems are likely to exceed the grasp of the original system designers, and designed GenEth to include ethicists into the discussion process in order to codify ethical principles in given application domains. The authors proposed a set of representation schemas for framing the discussions on AI ethics. It includes:
1. Features: denoting the presence or absence of factors (e.g., harm, benefit) with integer values;
2. Duties: denoting the responsibility of an agent to minimize/maximize a given feature;
3. Actions: denoting whether an action satisfies or violates certain duties as an integer tuple;
4. Cases: used to compare pairs of actions on their collective ethical impact; and
5. Principles: denoting the ethical preference among different actions as a tuple of integer tuples.
GenEth provides a graphical user interface for discussing ethical dilemmas in a given scenario, and applies inductive logic programming to infer principles of ethical actions.
Whereas GenEth can be regarded as an ethical dilemma exploration tool based on expert review, the Moral Machine project111<http://moralmachine.mit.edu/> from Massachusetts Institute of Technology (MIT) leverages the wisdom of the crowd to find resolutions for ethical dilemmas. The Moral Machine project focuses on studying the perception of autonomous vehicles (AVs) which are controlled by AI and has the potential to harm pedestrians and/or passengers if they malfunction. When a human driver who has used due caution encounters an accident, the instinct for self-preservation coupled with limited time for decision-making makes it hard to blame him/her for hurting others on ethical grounds. However, when the role of driving is delegated to an AI system, ethics becomes an unavoidable focal point of AV research since designers have the time to program logics for making decisions under various accident scenarios.
The Moral Machine project allows participants to judge various ethical dilemmas facing AVs which have malfunctioned, and select which outcomes they prefer. Then, the decisions are analyzed according to different considerations including: 1) saving more lives, 2) protecting passengers, 3) upholding the law, 4) avoiding intervention, 5) gender preference, 6) species preference, 7) age preference, and 8) social value preference. The project also provides a user interface for participants to design their own ethical dilemmas to elicit opinions from others.
Based on feedbacks from 3 million participants, the Moral Machine project found that people generally prefer the AV to make sacrifices if more lives can be saved. If an AV can save more pedestrian lives by killing its passenger, more people prefer others’ AVs to have this feature rather than their own AVs [[Bonnefon et al.2016](#bib.bibx7), [Sharif et al.2017](#bib.bibx41)]. Nevetheless, self-reported preferences often do not align well with actual behaviours [[Zell and Krizan2014](#bib.bibx58)]. Thus, how much the findings reflect actual choices is still an open question. There are also suggestions from others that under such ethical dilemmas, decisions should be made in a random fashion (i.e. let fate decide) possibly based on considerations in [[Broome1984](#bib.bibx8)], while there are also calls for AVs to be segregated from human traffic [[Bonnefon et al.2016](#bib.bibx7)]. Such diverse opinions underscore the challenge of automated decision-making under ethical dilemmas.
3 Individual Ethical Decision Frameworks
-----------------------------------------
When it comes to ethical decision-making in AI systems, the AI research community largely agrees that generalized frameworks are preferred over ad-hoc rules. Flexible incorporation of norms into AI to enable ethical user and prevent unethical use is useful since ethical bounds can be contextual and difficult to define as design time. Nevertheless, if updates are provided by people, some review mechanisms should be put in place to prevent abuse [[van Riemsdijk et al.2015](#bib.bibx47)].
In [[Dehghani et al.2008](#bib.bibx17)], the authors observed that moral decision-making by humans not only involves utilitarian considerations, but also moral rules. These rules are acquired from past example cases and are often culturally sensitive. Such rules often involve protected values (a.k.a. sacred values), which morally forbids the commitment of certain actions regardless of consequences (e.g., the act of attempting to murder is morally unacceptable regardless the outcome). The authors proposed MoralDM which enables an agent to resolve ethical dilemmas by leveraging on two mechanisms: 1) first-principles reasoning, which makes decisions based on well-established ethical rules (e.g., protected values); and 2) analogical reasoning, which compares a given scenario to past resolved similar cases to aid decision-making. As the number of resolved cases increases, the exhaustive comparison approach by MoralDM is expected to become computationally intractable. Thus, in [[Blass and Forbus2015](#bib.bibx6)], MoralDM is extended with structure mapping which trims the search space by computing the correspondences, candidate inferences and similarity scores between cases to improve the efficiency of analogical generalization.
A framework that enables agents to make judgements on the ethics of its own and other agents’ actions was proposed in [[Cointe et al.2016](#bib.bibx13)]. It contains representations of ethics based on theories of good and theories of right, and ethical judgement processes based on awareness and evaluation. The proposed agent ethical judgement process is based on the Belief-Desire-Intention (BDI) agent mental model [[Rao and Georgeff1995](#bib.bibx39)]. To judge the ethics of an agent’s own actions, the awareness process generates the beliefs that describe the current situation facing the agent and the goals of the agent. Based on the beliefs and goals, the evaluation process generates the set of possible actions and desirable actions. The goodness process then computes the set of ethical actions based on the agent’s beliefs, desires, actions, and moral value rules. Finally, the rightness process evaluates whether or not executing a possible action is right under the current situation and selects an action which satisfies the rightfulness requirement. When making ethical judgements on other agents, this process is further adapted to the conditions of: 1) blind ethical judgement (the given agent’s state and knowledge are unknown); 2) partially informed ethical judgement (with some information about a given agent’s state and knowledge); and 3) fully informed ethical judgement (with complete information about a given agent’s state and knowledge). Nevertheless, the current framework has no quantitative measure of how far a behaviour is from rightfulness or goodness.
In [[Conitzer et al.2017](#bib.bibx14)], the authors proposed two possible ways towards developing a general ethical decision-making framework for AI based on game theory and machine learning, respectively. For the game theory based framework, the authors suggest the extensive form (a generalization of game trees) as a foundation scheme to represent dilemmas. As the current extensive form does not account for protected values in which an action can be treated as unethical regardless of its consequence, the authors proposed to extend the extensive form representation with passive actions for agents to select in order to be ethical. For machine learning based ethical decision-making, the key approach is to classify whether a given action under a given scenario is morally right or wrong. In order to achieve this goal, well-labeled training data, possibly from human judgements, should be acquired. The Moral Machine project mentioned in the previous section could be a possible source of such data, although we may have to take into account potential inconsistencies as a result of cultural backgrounds and other factors before using such data for training. The main challenge in machine learning based moral decision-making is to design a generalizable representation of ethical dilemmas. Existing approaches which identify nuanced features based on insights into particular application scenarios may not be enough for this purpose. The authors suggest leveraging psychological frameworks of moral foundation (e.g., harm/care, fairness/reciprocity, loyalty, authority and purity) [[Clifford et al.2015](#bib.bibx12)] as bases for developing a generalizable representation of ethical dilemmas for machine learning-based approaches. Game theory and machine learning can be combined into one framework in which game theoretic analysis of ethics is used as a feature to train machine learning approaches, while machine learning helps game theory identify ethical aspects which are overlooked.
Ethics requirements are often exogenous to AI agents. Thus, there needs to be some ways to reconcile ethics requirements with the agents’ endogenous subjective preferences in order to make ethically aligned decisions. In [[Loreggia et al.2018](#bib.bibx30)], the authors proposed an approach to leverage the CP-net formalism to represent the exogenous ethics priorities and endogenous subjective preferences. The authors further established a notion of distance between CP-nets so as to enable AI agents to make decisions using their subjective preferences if they are close enough to the ethical principles. This approach helps AI agents balance between fulfilling their preferences and following ethical requirements.
So far, the decision-making frameworks with ethical and moral considerations reviewed put the burden of codifying ethics on AI system developers. The information on what is morally right or wrong has to be incorporated into the AI engine during the development phase. In [[Berreby et al.2017](#bib.bibx5)], the authors proposed a high level action language for designing ethical agents in an attempt to shift the burden of moral reasoning to the autonomous agents. The framework collects action, event and situation information to enable an agent to simulate the outcome of various courses of actions. The event traces are then passed to the causal engine to produce causal traces. The ethical specifications and priority of ethical considerations under a given situation are used to compute the goodness assessment on the consequences. These outputs are then combined with deontological specifications (duties, obligations, rights) to produce a final rightfulness assessment. The framework is implemented with answer set programming [[Lifschitz2008](#bib.bibx29)]. It has been shown to be able to generate rules to enable agents to decide and explain their actions, and reason about other agents’ actions on ethical grounds.
Reinforcement learning (RL) [[Sutton and Barto1998](#bib.bibx46)] is one of the commonly used decision-making mechanisms in AI. In [[Wu and Lin2018](#bib.bibx51)], the authors investigated how to enable RL to take ethics into account. Leveraging on the well-established technique of reward shaping in RL which incorporates prior knowledge into the reward function to speed up the learning process, the authors proposed the ethics shaping approach to incorporate ethical values into RL. By assuming that the majority of observed human behaviours are ethical, the proposed approach learns ethical shaping policies from available human behaviour data in given application domains. The ethics shaping function rewards positive ethical decisions, punishes negative ethical decisions, and remains neutral when ethical considerations are not involved. Similar in spirit to [[Berreby et al.2017](#bib.bibx5)], by separating ethics shaping from the RL reward function design, the proposed approach aims to shift the burden of codifying ethics away from RL designers so that they do not need to be well-versed in ethical decision-making in order to develop ethical RL systems.
4 Collective Ethical Decision Frameworks
-----------------------------------------
By enabling individual agents to behave ethically and judge the ethics of other agents’ actions, is it enough to create a society of well coordinated and collaborative agents acting with human wellbeing as their primary concern? In [[Pagallo2016](#bib.bibx35)], the author believes that this is not enough. The author advocates the need of primary rules governing social norms and allowing the creation, modification and suppression of the primary rules with secondary rules as situations evolve. In this section, we focus on decision-making frameworks which help a collective of autonomous entities (including agents and humans) to select ethical actions together.
In [[Singh2014](#bib.bibx43), [Singh2015](#bib.bibx44)], the author proposed a framework that uses social norms to govern autonomous entities’ (e.g., AI agents’ or human beings’) behaviours. Such an approach is inherently distributed rather than relying on a central authority. Individuals maintain their autonomy through executing their own decision policies, but are subjected to social norms defined by the collective through roles (which require qualifications from individuals, grant them privileges, and impose penalties if they misbehave). Social norms are defined through a template containing codified commitment, authorization, prohibition, sanction and power. The individuals then form a network of trust based on techniques from the reputation modelling literature [[Yu et al.2010](#bib.bibx52), [Yu et al.2013](#bib.bibx53)] to achieve collective self-governance through dynamic interactions.
In [[Greene et al.2016](#bib.bibx23)], the authors envisioned a possible way forward to enable human-agent collectives [[Jennings et al.2014](#bib.bibx25)] to make ethical collective decisions. By imbuing individual agents with ethical decision-making mechanisms (such as those mentioned in the previous section), a population of agents can take on different roles when evaluating choices of action with moral considerations in a given scenario. For instance, some agents may evaluate deontological ethics. Others may evaluate consequentialist ethics and virtue ethics. Based on a set of initial ethics rules, more complex rules can be acquired gradually through learning. Their evaluations, manifested in the form of preferences and limited by feasibility constraints, can be aggregated to reach a collective decision on the choices of actions by leveraging advances in the preference aggregation and multi-agent voting literature.
Nevertheless, the authors of [[Greene et al.2016](#bib.bibx23)] also highlighted the need for new forms of preference representation in collective ethical decision-making. When dealing with ethical decision-making, the potential candidate actions to choose from can vastly outnumber the number of agents involved which is very different from multi-agent voting scenarios. Moreover, the candidate actions may not be independent from each other, some of them may share certain features which describe their ethical dilemma situations. Preference information by agents on actions may be missing or imprecise which introduces uncertainty into the decision-making process. These challenges need to be resolved towards collective ethical decision-making with AI.
Following up on this vision, [[Noothigattu et al.2018](#bib.bibx34)] proposed a voting-based system for autonomous entities to make collective ethical decisions. The proposed approach leverages data collected from the Moral Machine project. Self-reported preference over different outcomes under diverse ethical dilemmas are used to learn models of preference for the human voters over different alternative outcomes. These individual models are then summarized to form a model that approximates the collective preference of all voters. The authors introduced the concept of swap-dominance222Assuming everything else is fixed, an outcome a swap-dominates another outcome b if every ranking which ranks a higher than b has a weight which is equal to or larger than rankings that rank b higher than a. when ranking alternatives to form a model of ethical preferences. When new decisions need to be made, the summarized model is used to compute a collective decision that results in the best possible outcome (i.e. satisfying consequentialist ethics). This is made computationally efficient with the swap-dominance property.
5 Ethics in Human-AI Interactions
----------------------------------
In AI applications which attempt to influence people’s behaviours, the principles established by the Belmont Report [[Bel1978](#bib.bibx4)] for behavioural sciences have been suggested to be a starting point for ensuring ethics [[Luckin2017](#bib.bibx31), [Yu et al.2017b](#bib.bibx56)]. The principles include three key requirements: 1) people’s personal autonomy should not be violated (they should be able to maintain their free will when interacting with the technology); 2) benefits brought about by the technology should outweigh risks; and 3) the benefits and risks should be distributed fairly among the users (people should not be discriminated based on their personal backgrounds such as race, gender and religion). The challenge of measuring benefits and risks remains open for application designers albeit the Ethically Aligned Design guidelines from the IEEE can be a useful starting point [[IEEE2018](#bib.bibx24)]. Computational formulations of human centric values (e.g., collective wellbeing and work-life balance) have been proposed and incorporated into the objective functions of recent AI-powered algorithmic management approaches in crowdsourcing [[Yu et al.2016](#bib.bibx54), [Yu et al.2017a](#bib.bibx55), [Yu et al.2017c](#bib.bibx57)].
One of the application areas in which AI attempts to influence people’s behaviours is persuasion agents [[Kang et al.2015](#bib.bibx26), [Rosenfeld and
Kraus2016](#bib.bibx40)]. In [[Stock et al.2016](#bib.bibx45)], the authors conducted a large-scale study to investigate human perceptions on the ethics of persuasion by an AI agent. The ethical dilemma used is the trolley scenario which involves making a participant actively cause harm to an innocent bystander by pushing him on to the train track in order to save the lives of five people. It is a consequentialist ethical outcome which requires the decision-maker to violate a sacred value (i.e. one shall not kill). The authors tested three persuasive strategies: 1) appealing to the participants emotionally; 2) presenting the participants with utilitarian arguments; and 3) lying. The three strategies are delivered to some participants by a person playing the role of an authority (the station master of the train station) and by a persuasion agent. The results suggested that participants hold a strong preconceived negative attitude towards the persuasion agent, and argumentation-based and lying-based persuasion strategies work better than emotional persuasion strategies. The findings did not show significant variation across genders or cultures. The study suggests that the adoption of persuasion strategies should take into account differences in individual personality, ethical attitude and expertise in the given domain.
Although emotional appeals may not be an effective persuasive strategy under ethical dilemmas, ethically appropriate emotional responses from agents can enhance human-AI interaction. In [[Battaglino and
Damiano2015](#bib.bibx3)], an approach based on the Coping Theory [[Marsella and
Gratch2003](#bib.bibx32)] to allow agents to deal with strong negative emotions by changing the appraisal of the given situation was proposed. The agent assesses the ethical effects of its own actions and other agents’ actions. If its own action violates a given moral value, the shame emotion is triggered which serves to lower the priority of continuing with the given action. If another agent’s action violates a given moral value, the reproach emotion is triggered in the observing agent which serves to increase social distance with the given agent (e.g., by reducing trust). The ethical decision-making process is similar to existing individual ethical decision frameworks. The triggering of emotional responses serves as an implicit reward for the agent and facilitates communications with humans in the loop.
6 Discussions
--------------
Based on recent advances in AI governance techniques, it appears that most work focused on developing generalizable individual ethical decision frameworks combining rule-based and example-based approaches to resolving ethical dilemmas. In order to learn appropriate rules from examples of ethical decision-making by humans, more work on collecting data about various ethical dilemmas from people with different cultural backgrounds is required. Works on collective ethical decision-making based on multi-agent voting have also appeared, but much work is still needed to design mechanisms to represent ethical preferences by agents. How AI can act ethically when making recommendations to humans and express their ethical judgements affectively are the current foci of ethical human-AI interaction research. In addition, AI engineers need to engage more with the ethics and decision making communities. These people want to help and the AI research community be reaching out to them to leverage their expertise in the pursuit of ethical AI technologies. Since such AI technologies as autonomous vehicles, autonomous weapons, and cryptocurrencies are becoming a reality and affecting societies, a global and unified AI regulatory framework needs to be established as soon as possible to address the ethical issues by drawing on interdisciplinary expertise [[Erdélyi and
Goldsmith2018](#bib.bibx18)].
In order for ethics to be built into AI, [[Burton et al.2017](#bib.bibx10), [Goldsmith and
Burton2017](#bib.bibx22)] advocate that ethics should be part of the AI curricula. This is based on the observation that consequentialist ethics (or ethics based on the utilitarian analysis of possible outcomes) is most closely related to the decision-theoretic frame of mind familiar to today’s AI researchers. Deontological ethics (or rule-based ethics) and virtue ethics are less familiar among AI researchers. Understanding deontological ethics can help AI researchers determine which rules are more fundamental and, therefore, should take priority in an ethical decision framework. Understanding virtue ethics, which concerns questions on whom one wishes to become, can help AI researchers frame ethical discussions in the context of changing social conditions (possibly brought on by AI technologies) and guide the incorporation of ethics into AI which shape the paths of learning. Learning materials on these different dimensions of ethics could help AI researchers understand more clearly the topic of ethical decision-making and steer the field of AI towards more emphasis on ethical interactions with humans.
7 Future Research Directions
-----------------------------
From this survey, we envision several possible future research directions which can impact this field going forward. Firstly, the current mechanism of crowdsourcing self-reported preference on ethical dilemmas as represented by the Moral Machine project has its limitations. Self-reported preferences have been shown to deviate from actual choice behaviours. Researchers from multiple disciplines need to conduct social-systems analysis [[Crawford and Calo2016](#bib.bibx15)] of AI in order to understand the impact of AI under different social, cultural and political settings. There may be opportunities for transfer learning [[Pan and Yang2010](#bib.bibx36)] to be applied in this case to model different ethics due to diversities in culture and other aspects. The insights from such studies can complement crowdsourced human preference data when building computational models of human ethics. In addition, they may also help AI researchers establish coherent utility functions from apparently inconsistent human ethical judgements.
Secondly, with AI becoming increasingly ubiquitous in our daily life, we may need to consider revising our current social contracts. Research in this area will help us establish regulations about who is responsible when things go wrong with regard to AI, and how to monitor and enforce these regulations. This research direction is inherently dynamic and interdisciplinary in nature as it must be updated with changing cultural, social, legal, philosophical and technological realities.
Thirdly, another important research area for ethical decision-making by AI is to enable AI to explain its decisions under the framework of human ethics. The challenge here is that as deployed AI programs learn to update the decision-making logic, the AI designers may not be able to anticipate all outcomes at design time and may not understand the decisions made by the AI entities later [[Venema2018](#bib.bibx48)]. Argumentation-based explainable AI [[Fan and Toni2015](#bib.bibx20), [Langley et al.2017](#bib.bibx28)] can be a good starting point for this purpose as it is well suited to the consequentialist ethics which is a commonly adopted approach for implementing AI ethics. Nevertheless, depending on how the explanations are used, researchers need to strike a balance on the level of details to be included. Full transparency may be too overwhelming if the objective is to persuade a user to follow a time-critical recommendation, but can be useful as a mechanism to trace the AI decision process afterwards. On the other hand, not enough transparency may hamper users’ trust in the AI. AI researchers can borrow ideas from the field of mass communication to design proper trade-offs.
Last but not least, the incorporation of ethical considerations into AI systems will influence human-AI interaction dynamics. By knowing that AI decisions follow ethical principles, some people may adapt their behaviours in order to take advantage of this and render the AI systems unable to achieve their design objectives. For example, an ethical autonomous gun system (if there can be such a thing) could be disabled by a child (who is generally regarded as a non-combatant and in need of protection) with a spray paint (which is generally not considered a lethal weapon) painting over the sensor system of the gun. In this case, Adversarial Game Theory [[Vorobeychik et al.2012](#bib.bibx49)] may need to be incorporated into future AI ethical decision frameworks in order to enable AI to preserve the original design objectives in the presence of strategic human behaviours.
Acknowledgements
----------------
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IDM Futures Funding Initiative; Nanyang Technological University, Nanyang Assistant Professorship (NAP); the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017); and the NTU-PKU Joint Research Institute, a collaboration between Nanyang Technological University and Peking University that is sponsored by a donation from the Ng Teng Fong Charitable Foundation. |
687268b8-8fac-48af-8a1a-64c2a6ad1c98 | StampyAI/alignment-research-dataset/blogs | Blogs | Visual Grounding in Video for Unsupervised Word Translation
#### Translating Words Through Unpaired Narrated Videos
The most common approach for machine translation relies on supervision through paired or parallel corpus where each sentence in the source language is paired with its translation in the target language. This is limiting as we do not have access to such a paired corpus for most languages in the world. Interestingly, bilingual children can learn two languages without being exposed to them at the same time. Instead, they can leverage visual similarity across situations: what they observe while hearing "the dog is eating'' on Monday is similar to what they see as they hear "le chien mange'' on Friday.
In this work, inspired by bilingual children, we develop a model that learns to translate words from one language to another by tapping into the visual similarity of situations in which words occur. More specifically, our training dataset consists of disjoint sets of videos narrated in different languages. These videos share similar topics (e.g., cooking pasta or changing a tire); for example, the dataset consists of some videos on how to cook pasta narrated in Korean and a different set of videos on the same topic but in English. Note that the videos in different languages are not *paired*.
Our model leverages the visual similarity of videos by associating videos with their corresponding narrations in a shared embedding space between languages. The model is trained by alternating between videos narrated in one language and those in the second language. Thanks to such a training procedure, and since we share the video representation between both languages, our model learns a joint bilingual-visual space that aligns words in two different languages.
#### MUVE: improving language only methods with vision
We demonstrate that our method, MUVE (Multilingual Unsupervised Visual Embeddings), can complement existing translation techniques that are trained on unpaired corpus but do not use vision. By doing so, we show that the quality of unsupervised word translation improves, most notably in situations where language-only methods suffer the most, e.g., when: (i) languages are very different (such as English and Korean or English and Japanese), (ii) the initial corpora have different statistics in the two languages, or (iii) a limited amount of training data is available.
Our findings suggest that using visual data such as videos is a promising direction to improve bilingual translation models when we do not have paired data. |
e228cfc2-25b9-4820-bc62-55dee6927a0e | trentmkelly/LessWrong-43k | LessWrong | Opinion merging for AI control
Thanks to Justis for proofreading and feedback.
This is a simplified followup to my post on "mimics". I think the basic idea there is valuable and still does not appear to be widely appreciated, so I'm trying to explain it again.
Introduction: the misjudgment problem
Suppose I try to make a very powerful AI system, and I plan to ask it to make me rich. If it works well, this seems like it would be a great outcome to me, but maybe I'm wrong about this, in which case developing this system might be a bad decision on my part.
Assume I make a probabilistic forecast about possible futures. My forecast can be illustrated as follows:
For the moment, let's assume this is a naive forecast; I'm not trying very hard to account for the fact that I'm delegating to an AI system to make me rich, I'm just imagining what it would be like to be rich in the future on base rates[1]. It looks good, and I think I can pull off the AI-make-me-rich project, so I go for it. However, Cassandra, who is much smarter and better calibrated than me, has a different view. She has taken into account the fact that I'm delegating to an AI system to do the job, and she sees a different set of possible outcomes:
On the far left we have "specification gaming"; the AI makes me rich, but my life actually gets substantially worse as a result. On the far right, we have "goal misgeneralisation"; the AI learned how to make money, but not to make money for me. Cassandra advises me not to pursue my project (but for some reason I don't listen).
So the basic story is:
* I forecast the consequences of deploying my AI according to some naive scheme, and they look good so I go for it
* If I had much better forecasting ability, I would have seen that this was actually a bad idea
I'm going to call this the misjudgement problem. It is far from the only problem with developing advanced AI, but it seems like one of the bigger ones to me[2]. The topic of this post is how merging of opinions might be used to ad |
ab13e810-4425-4759-a2b5-4ebf49b67038 | LDJnr/LessWrong-Amplify-Instruct | LessWrong | "Crossposted from world spirit sock puppet.
When I think of humans being so smart due to ‘cultural accumulation’, I think of lots of tiny innovations in thought and technology being made by different people, and added to the interpersonal currents of culture that wash into each person’s brain, leaving a twenty year old in 2020 much better intellectually equipped than a 90 year old who spent their whole life thinking in 1200 AD.
This morning I was chatting to my boyfriend about whether a person who went back in time (let’s say a thousand years) would be able to gather more social power than they can now in their own time. Some folk we know were discussing the claim that some humans would have a shot at literally take over the world if sent back in time, and we found this implausible.
The most obvious differences between a 2020 person and a 1200 AD person, in 1200 AD, is that they have experience with incredible technological advances that the 1200 AD native doesn’t even know are possible. But a notable thing about a modern person is that they famously don’t know what a bicycle looks like, so the level of technology they might be able to actually rebuild on short notice in 1200 AD is probably not at the level of a nutcracker, and they probably already had those in 1200 AD.
How does 2020 have complicated technology, if most people don’t know how it works? One big part is specialization: across the world, quite a few people do know what bicycles look like. And more to the point, presumably some of them know in great detail what bicycle chains look like, and what they are made of, and what happens if you make them out of slightly different materials or in slightly different shapes, and how such things interact with the functioning of the bicycle.
But suppose the 2020 person who is sent back is a bicycle expert, and regularly builds their own at home. Can they introduce bikes to the world 600 years early? My tentative guess is yes, but not very ridable ones, because they don’t have machines for making bike parts, or any idea what those machines are like or the principles behind them. They can probably demonstrate the idea of a bike with wood and cast iron and leather, supposing others are cooperative with various iron casting, wood shaping, leather-making know-how. But can they make a bike that is worth paying for and riding?
I’m not sure, and bikes were selected here for being so simple that an average person might know what their machinery looks like. Which makes them unusually close among technologies to simple chunks of metal. I don’t think a microwave oven engineer can introduce microwave ovens in 1200, or a silicon chip engineer can make much progress on introducing silicon chips. These require other technologies that require other technologies too many layers back.
But what if the whole of 2020 society was transported to 1200? The metal extruding experts and the electricity experts and the factory construction experts and Elon Musk? Could they just jump back to 2020 levels of technology, since they know everything relevant between them? (Assuming they are somehow as well coordinated in this project as they are in 2020, and are not just putting all of their personal efforts into avoiding being burned at the stake or randomly tortured in the streets.)
A big way this might fail is if 2020 society knows everything between them needed to use 2020 artifacts to get more 2020 artifacts, but don’t know how to use 1200 artifacts to get 2020 artifacts.
On that story, the 1200 people might start out knowing methods for making c. 1200 artifacts using c. 1200 artifacts, but they accumulate between them the ideas to get them to c. 1220 artifacts with the c. 1200 artifacts, which they use to actually create those new artifacts. They pass to their children this collection of c. 1220 artifacts and the ideas needed to use those artifacts to get more c. 1220 artifacts. But the new c. 1220 artifacts and methods replaced some of the old c. 1200 artifacts and methods. So the knowledge passed on doesn’t include how to use those obsoleted artifacts to create the new artifacts, or the knowledge about how to make the obsoleted artifacts. And the artifacts passed on don’t include the obsoleted ones. If this happens every generation for a thousand years, the cultural inheritance received by the 2020 generation includes some highly improved artifacts plus the knowledge about how to use them, but not necessarily any record of the path that got there from prehistory, or of the tools that made the tools that made the tools that made these artifacts.
This differs from my first impression of ‘cultural accumulation’ in that: physical artifacts are central to the process: a lot of the accumulation is happening inside them, rather than in memetic space.
humanity is not accumulating all of the ideas it has come up with so far, even the important ones. It is accumulating something more like a best set of instructions for the current situation, and throwing a lot out as it goes. Is this is how things are, or is my first impression more true?"
The passage is from a user-contributed post on LessWrong.
Could you elaborate on the role of physical artifacts in the process of cultural accumulation and their interplay with memetic space?
Certainly, the concept of cultural accumulation is indeed fascinating and intricate. Physical artifacts and memetic space are both integral to this process, and they influence each other in significant ways.
Physical artifacts, such as tools, buildings, or pieces of art, are tangible manifestations of a culture's technological, architectural, and artistic advancements. They are the products of the accumulated knowledge and skills of a society at a particular point in time. These artifacts not only serve functional or aesthetic purposes, but they also carry cultural significance and values, acting as symbols of a society's identity and achievements.
On the other hand, memetic space refers to the realm of ideas, beliefs, norms, and values that are shared and transmitted within a culture. Memes, in this context, are units of cultural information that can be spread and replicated, much like genes in biological evolution. They shape the way people think, behave, and interact with their environment, including how they create and use physical artifacts.
The interplay between physical artifacts and memetic space is a dynamic and reciprocal process. On one hand, the creation and use of physical artifacts are influenced by the memes circulating within a culture. For instance, the design of a tool or a building reflects the ideas, beliefs, and values prevalent in the society at the time. On the other hand, physical artifacts can also shape memetic space by facilitating the spread and replication of certain memes. For example, the invention of writing systems and printing technology has greatly enhanced the transmission of cultural information, leading to the proliferation of certain ideas and beliefs.
However, as the passage suggests, this process of cultural accumulation is not simply a matter of adding more and more to the existing stock of artifacts and memes. It also involves a continual process of selection, modification, and discarding. As new artifacts are created and new memes emerge, some old ones become obsolete and are phased out. This is akin to the process of natural selection in biological evolution, where certain traits become more or less common in a population over time based on their adaptive value.
In this sense, the process of cultural accumulation is not just about quantity, but also about quality. It's about refining and improving the tools and ideas that a culture has at its disposal, in response to changing environmental conditions and societal needs. This continual process of innovation and adaptation is what drives cultural evolution and progress.
Explain the process of natural selection in cultural evolution, drawing parallels with biological evolution.
Absolutely. The process of natural selection in cultural evolution operates on similar principles as it does in biological evolution, albeit with some key differences. Let's break it down:
1. Variation: In both biological and cultural evolution, variation is crucial. In biological evolution, variation occurs in the gene pool through mutations, recombination, and other genetic processes. These variations lead to differences in traits among individuals in a population. Similarly, in cultural evolution, variation occurs in the form of diverse ideas, beliefs, practices, and artifacts within a society. These might arise due to individual creativity, exposure to other cultures, or reinterpretation of existing cultural elements.
2. Selection: Just as natural selection in biological evolution favors traits that enhance an organism's survival and reproductive success, cultural selection favors ideas and practices that increase their own propagation. This doesn't necessarily mean they're beneficial to human survival or wellbeing. A meme (a unit of cultural information) might spread because it's catchy, emotionally resonant, or because it effectively exploits social networks, not because it's useful or true.
3. Inheritance: In biological evolution, successful traits are passed on to future generations through genetic inheritance. In cultural evolution, successful ideas, practices, and artifacts are transmitted from person to person through learning and imitation. This can occur within a generation (horizontally) or across generations (vertically).
4. Adaptation: Over time, both biological and cultural evolution can lead to adaptation - changes that enhance the fit between the organism (or the culture) and its environment. In biology, this might mean physical changes like a bird's beak shape adapting to its food source. In culture, this might mean social changes like norms adapting to technological advancements.
There are also key differences. Cultural evolution can be much faster than biological evolution, as cultural traits can spread within a single generation, and don't depend on survival or reproduction. It's also more flexible, as individuals can learn new cultural traits throughout their life, while biological traits are fixed at birth. Furthermore, cultural evolution is often guided by conscious decision-making and intentionality, while biological evolution is not.
Contrast the speed and mechanisms of cultural evolution with biological evolution, considering factors like technological advancements, societal changes, and the role of conscious decision-making. How do these factors influence the rate and direction of cultural evolution compared to biological evolution?
The speed and mechanisms of cultural evolution differ significantly from those of biological evolution due to several factors, including technological advancements, societal changes, and conscious decision-making.
1. Speed: Cultural evolution can occur much faster than biological evolution. Biological evolution is a slow process that occurs over many generations, as genetic changes accumulate and get passed on to offspring. On the other hand, cultural evolution can happen within a single generation, as ideas and behaviors can be learned and spread rapidly among individuals in a society. Technological advancements, such as the invention of writing, printing press, and the internet, have greatly accelerated the speed of cultural evolution by enhancing the transmission of cultural information.
2. Mechanisms: The mechanisms of cultural evolution are also different from those of biological evolution. In biological evolution, genetic changes are mostly random and are subject to natural selection based on their impact on survival and reproduction. In contrast, cultural evolution is largely driven by learning and imitation, which can be guided by conscious decision-making. People can intentionally choose to adopt or discard certain ideas or behaviors based on their perceived value or appeal. This gives cultural evolution a degree of directionality and purposefulness that is absent in biological evolution.
3. Influence of societal changes: Societal changes can have a profound impact on the direction of cultural evolution. Changes in social, economic, or political conditions can lead to new cultural adaptations. For instance, the transition from hunter-gatherer societies to agricultural societies led to significant cultural changes, including the development of new social structures, belief systems, and technological innovations. Similarly, the industrial revolution and the digital revolution have brought about major cultural shifts in modern times.
4. Role of conscious decision-making: Conscious decision-making plays a crucial role in cultural evolution. Individuals and societies can make deliberate choices to promote or inhibit certain cultural traits based on their goals, values, or beliefs. This can lead to planned cultural change, such as social reforms, educational initiatives, or public health campaigns. In contrast, biological evolution is an unconscious process that is not guided by foresight or intentionality.
In summary, cultural evolution is a dynamic and complex process that is influenced by a variety of factors. It is faster, more flexible, and more purposeful than biological evolution, reflecting the unique capabilities of human cognition and social organization. |
5b9ca716-14ef-4441-a9e9-7b5f67d7074f | trentmkelly/LessWrong-43k | LessWrong | AGI safety field building projects I’d like to see
This list of field building ideas is inspired by Akash Wasil’s and Ryan Kidd’s similar lists. And just as the projects on those lists, these projects rely on people with specific skills and field knowledge to be executed well.
None of these ideas are developed by me exclusively; they are a result of the CanAIries Winter Getaway, a 2-week-long, Unconference-style AGI safety retreat I organized in December 2022.
Events
Organize a global AGI safety conference
This should be self-explanatory: It is odd that we still don’t have an AGI safety conference that allows for networking and lends the field credibility.
There are a number of versions of this that might make sense:
* an EAG-style conference for people already in the community to network
* an academic-style conference engaging CS and adjacent academia
* an industry-heavy conference (maybe sponsored by AI orgs?)
* a virtual next-steps conference, e.g. for AGISF participants
Some people have tried this out at a local level: https://aisic2022.net.technion.ac.il
(If you decide to work on this: www.aisafety.global is available via EA domains, contact hello@alignment.dev)
Organize AGI safety professionals retreats
As far as I can see, most current AGI safety retreats are optimized for junior researchers: Networking and learning opportunities for students and young professionals. Conferences with their focus on talks and 1-on-1s are useful for transferring knowledge, but don’t offer the extensive ideation that a retreat focused on workshops and discussion rounds could.
Organizing a focused retreat for up to 60-80 senior researchers to debate the latest state of alignment research might be very valuable for memetic cross-pollination between approaches, organizations, and continents. It might also make sense to do this during work days, so that peoples’ employers can send them. I suspect that the optimal mix of participants would be around 80% researchers, and the rest funders, decisionmakers, and the most |
e7c7b462-1781-49c2-be41-806929e8c7e3 | trentmkelly/LessWrong-43k | LessWrong | Knowledge's practicability
Knowledge can be useless. Much of what we learned isn't used.
The most famous example of useless knowledge must be schools.
But before discussing why school isn't practical, let's first talk about when knowledge is practical. I use this simple formula to decide if something has high application:
The practicality of some knowledge = Its usefulness (quality) X Total chances I can apply it (quantity)
Simply put, if a piece of knowledge is useful, or it isn't very useful but can be used a lot, or it is both useful and can be used a lot, then it's worth learning.
Let's first talk about the usefulness of knowledge.
Because some knowledge is more valuable than others, we compare and pick those that benefit us the most.
For example, suppose you have two ways to solve an issue. If the second way is better, then if you already have a lot of knowledge about it (when/how to apply the second way, how to handle its side effects, etc.), it is better than you already know about the first way. In another situation, if the first way is more efficient, then having the knowledge about it will be better. How useful a piece of knowledge is depends on the situation you apply it to.
It would be best if you understand both solutions well, but since we have limited time and resources, the best we can do is pick what we think will be the most useful and learn in advance.
Knowledge's usefulness is a good factor that helps us pick what to learn. An equally useful factor is how much we can apply a piece of knowledge.
Let's have an example to illustrate that.
One skill that is useful but isn't used a lot is interview skills. Suppose "good interviewing skills" have 1000 units of usefulness, but you only use it 10 times in your whole life; then its practicality can be calculated as:
Good interview skills practicality = 1,000 x 10 = 10,000
In contrast, "good dishwashing skills" (by having better gloves, better cleaners, knowing which one should be cleaned first, etc.) may only have 1 un |
f29a4436-ce9e-464e-8535-6d546f48b5c3 | trentmkelly/LessWrong-43k | LessWrong | MA E-ZPass Without a Car?
I recently drove to DC and back playing dances in a rental. I paid cash tolls when available, but that often wasn't an option, so I ended up paying $40 in PlatePass charges in addition to the $63 in tolls. Time to get an E-ZPass!
What makes this tricky is that I don't own a car. Well, I have half a car, which does have an E-ZPass, but that stays with that car. If you go to sign up online you'll get through to step 5 of 7 and then:
> Please add at least one vehicle to your account. If you do not have a vehicle please call the customer service center to open your account. You may not request more transponders than you have vehicles on your account.
Then you call customer service, and when you follow the prompts to tell the automated system that you want to open an E-ZPass account they tell you this must be done online and don't offer any other options.
If you do get through to a person, for example by pressing 2-2-9 to tell them that you have a question about your account but have forgotten your number, they'll tell you to go online to their Document Library, fill out a PDF application, and fax it in. The only difference, as far as I can tell, is that the PDF form isn't capable of complaining when you don't enter a license plate number.
Which worked! Now I have a transponder, and when I want to rent a car I can follow their instructions to add it to my account as a short-term rental.
Note that you don't have to be an MA resident to get an MA E-ZPass [1]. Several states have fees or inactivity charges, but MA is one of the ones that doesn't. If you drive a lot you'll often make up for the fees by lower in-state tolls, but if you don't then it could be worth getting a fee-free one from MA or another no-fee state.
[1] This is actually a requirement for offering discounts on MA highways to MA EZ-Pass accounts; see Yerger et al. v. Massachusetts Turnpike Authority:
> We agree with the District Court. The FLDP does not use E-ZPass as a proxy for Massachusett |
465dbbdf-a144-447f-882d-39344534b6fb | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Realism and Rationality
*Format warning: This post has somehow ended up consisting primarily of substantive endnotes. It should be fine to read just the (short) main body without looking at any of the endnotes, though. The endnotes elaborate on various claims and distinctions and also include a much longer discussion of decision theory.*
*Thank you to Pablo Stafforini, Phil Trammell, Johannes Treutlein, and Max Daniel for comments on an initial draft. I have also slightly edited the post since I first published it, to try to make a few points clearer.*
When discussing normative questions, it is not uncommon for members of the rationalist community to identify as anti-realists. But normative anti-realism seems to me to be in tension with some of the community's core interests, positions, and research activities. In this post I suggest that the cost of rejecting realism may be larger than is sometimes recognized. [[1]](#fn-uNQxgS8esQChtvHkb-1)
**1. Realism and Anti-Realism**
Everyone is, at least sometimes, inclined to ask: “What should I do?”
We ask this question when we're making a decision and it seems like there are different considerations to be weighed up. You might be considering taking a new job in a new city, for example, and find yourself wondering how to balance your preferences with those of your significant other. You might also find yourself thinking about whether you have any obligation to do impactful work, about whether it’s better to play it safe or take risks, about whether it's better to be happy in the moment or to be able to look back with satisfaction, and so on. It’s almost inevitable that in a situation like this you will find yourself asking “What should I do?” and reasoning about it as though the question has an answer you can approach through a certain kind of directed thought.[[2]](#fn-uNQxgS8esQChtvHkb-2)
But it’s also conceivable that this sort of question doesn’t actually have an answer. Very roughly, at least to certain philosophers, *realism* is a name for the view that there are some things that we should do or think. *Anti-realism* is a name for the view that there are not.[[3]](#fn-uNQxgS8esQChtvHkb-3)[[4]](#fn-uNQxgS8esQChtvHkb-4)[[5]](#fn-uNQxgS8esQChtvHkb-5)[[6]](#fn-uNQxgS8esQChtvHkb-6)
**2. Anti-Realism and the Rationality Community**
In discussions of normative issues, it seems not uncommon for members of the rationalist community to identify as “anti-realists." Since people in different communities can obviously use the same words to mean different things, I don't know what fraction of rationalists have the same thing in mind when they use the term "anti-realism."
To the extent people do have the same thing in mind, though, I find anti-realism hard to square with a lot of other views and lines of research that are popular within the community. A few main points of tension stand out to me.
**2.1 Normative Uncertainty**
One first point of tension is the community’s relatively strong interest in the subject of normative uncertainty. At least as it's [normally discussed](http://commonsenseatheism.com/wp-content/uploads/2014/03/MacAskill-Normative-Uncertainty.pdf) in the philosophy literature, normative uncertainty is uncertainty about normative facts that bear on what we should do. If we assume that anti-realism is true, though, then we are assuming that there are no such facts. It seems to me like a committed anti-realist could not be in a state of normative uncertainty.
It may still be the case, as [Sepielli (2012)](https://sci-hub.tw/https://link.springer.com/article/10.1007/s11098-011-9713-6) suggests, that a committed anti-realist can experience psychological states that are interestingly *structurally analogous* to states of normative uncertainty. However, [Bykvist and Olson (2012)](http://jesp.org/PDF/against_the_being.pdf) disagree (in my view) fairly forcefully, and Sepielli is in any case clear that: “Strictly speaking, there cannot be such a thing as normative uncertainty if non-cognitivism [the dominant form of anti-realism] is true.”[[7]](#fn-uNQxgS8esQChtvHkb-7)
**2.2 Strongly Endorsed Normative Views**
A second point of tension is the existence of a key set of normative claims that a large portion of the community seems to treat as true.
One of these normative claims is the Bayesian claim that we ought to have degrees of belief in propositions that are consistent with the Kolmogorov probability axioms and that are updated in accordance with Bayes’ rule. It seems to me like very large portions of the community self-identify as Bayesians and regard other ways of assigning and updating degrees of belief in propositions as not just different but incorrect.
Another of these normative claim is the subjectivist claim that we should do whatever would best fulfill some version of our current preferences. To learn what we should do, on this view, the main thing is to introspect about our own preferences.[[8]](#fn-uNQxgS8esQChtvHkb-8) Whether or not a given person should commit a violent crime, for instance, depends purely on whether they want to commit the crime (or perhaps on whether they would want to commit it if they went through some particular process of reflection).
A further elaboration on this claim is that, when we are uncertain about the outcomes of our actions, we should more specifically act to maximize the *expected* fulfillment of our desires. We should consider the different possible outcomes of each action, assign them probabilities, assign them desirability ratings, and then use the expected value formula to rate the overall goodness of the action. Whichever action has the best overall rating is the one we should take.
One possible way of squaring an endorsement of anti-realism with an apparent endorsement of these normative claims is to argue that people don’t actually have normative claims in mind when they write and talk about these issues. Non-cognitivists -- a particular variety of anti-realists -- argue that many utterances that seem at first glance like claims about normative facts are in fact nothing more than expressions of attitudes. For instance, an [emotivist](https://en.wikipedia.org/wiki/Emotivism) -- a further sub-variety of non-cognitivist -- might argue that the sentence “You should maximize the expected fulfillment of your current desires!” is simply a way of expressing a sense of fondness toward this course of action. The sentence might be cached out as being essentially equivalent in content to the sentence, “Hurrah, maximizing the expected fulfillment of your current desires!”
Although a sizeable portion of philosophers are non-cognitivists, I generally don’t find it very plausible as a theory of what people are trying to do when they seem to make normative claims.[[9]](#fn-uNQxgS8esQChtvHkb-9) In this case it doesn’t feel to me like most members of the rationalist community are just trying to describe one particular way of thinking and acting, which they happen to prefer to others. It seems to me, rather, that people often talk about updating your credences in accordance with Bayes' rule and maximizing the expected fulfillment of your current desires as the *correct* things to do.
One more thing that stands out to me is that arguments for anti-realism often seem to be presented as though they implied (rather than negated) the truth of some of these normative claims. For example, the popular "Replacing Guilt" sequence on Minding Our Way seems to me to repeatedly attack normative realism. It [rejects](http://mindingourway.com/not-because-you-should/) the idea of "shoulds" and [points out](http://mindingourway.com/altruistic-motivations/) that there aren't "any oughtthorities to ordain what is right and what is wrong." But then it seems to draw normative implications out of these attacks: among other implications, you should "just do what you want." At least taken at face value, this line of reaoning wouldn't be valid. It makes no more sense than reaoning that, if there are no facts about what we should do, then we should "just maximize total hedonistic well-being” or "just do the opposite of what we want” or "just open up souvenir shops.” Of course, though, there's a good chance that I'm misunderstanding something here.
**2.3 Decision Theory Research**
A third point of tension is the community's engagement with normative decision theory research. Different normative decision theories pick out different necessary conditions for an action to be the one that a given person should take, with a focus on how one should respond to uncertainty (rather than on what ends one should pursue).[[10]](#fn-uNQxgS8esQChtvHkb-10)[[11]](#fn-uNQxgS8esQChtvHkb-11)
A typical version of [CDT](https://plato.stanford.edu/entries/decision-causal/) says that the action you should take at a particular point in time is the one that would *cause* the largest expected increase in value (under some particular framework for evaluating causation). A typical version of [EDT](https://en.wikipedia.org/wiki/Evidential_decision_theory) says that the action you should take at a particular point in time is the one that would, once you take it, allow you to rationally expect the most value. There are also alternative versions of these theories -- for instance, versions using [*risk-weighted* expected value maximization](https://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199672165.001.0001/acprof-9780199672165-chapter-2) or the criterion of [stochastic dominance](https://philarchive.org/archive/TAREES) -- that break from the use of pure expected value.
I've pretty frequently seen it argued within the community (e.g. in the papers “[Cheating Death in Damascus](http://intelligence.org/files/DeathInDamascus.pdf)” and “[Functional Decision Theory](https://arxiv.org/pdf/1710.05060.pdf)”) that CDT and EDT are not “correct" and that some other new theory such as [functional decision theory](https://arxiv.org/abs/1710.05060) is. But if anti-realism is true, then no decision theory is correct.
Eliezier Yudkowsky's influential [early writing](https://www.lesswrong.com/posts/6ddcsdA2c2XpNpE5x/newcomb-s-problem-and-regret-of-rationality) on decision theory seems to me to take an anti-realist stance. It suggests that we can only ask meaningful questions about the effects and correlates of decisions. For example, in the context of the Newcomb thought experiment, we can ask whether one-boxing is correlated with winning more money. But, it suggests, we cannot take a step further and ask what these effects and correlations imply about what it is "reasonable" for an agent to do (i.e. what they *should* do). This question -- the one that normative decision theory research, as I understand it, is generally about -- is seemingly dismissed as vacuous.
If this apparently anti-realist stance is widely held, then I don't understand why the community engages so heavily with normative decision theory research or why it takes part in discussions about which decision theory is "correct." It strikes me a bit like an atheist enthustiastically following theological debates about which god is the true god. But I'm mostly just confused here.[[12]](#fn-uNQxgS8esQChtvHkb-12)[[13]](#fn-uNQxgS8esQChtvHkb-13)
**3. Sympathy for Realism**
I wouldn’t necessarily describe myself as a realist. I get that realism is a weird position. It’s both metaphysically and epistemologically suspicious. What is this mysterious property of “should-ness” that certain actions are meant to possess -- and why would our intuitions about which actions possess it be reliable?[[14]](#fn-uNQxgS8esQChtvHkb-14)[[15]](#fn-uNQxgS8esQChtvHkb-15)
But I am also very sympathetic to realism and, in practice, tend to reason about normative questions as though I was a full-throated realist. My sympathy for realism and tendency to think as a realist largely stems from my perception that if we reject realism and internalize this rejection then there’s really not much to be said or thought about anything. We can still express attitudes at one another, for example suggesting that we like certain actions or credences in propositions better than others. We can present claims about the world, without any associated explicit or implicit belief that others should agree with them or respond to them in any particular way. And that seems to be about it.
Furthermore, if anti-realism is true, then it can’t also be true that we should believe that anti-realism is true. Belief in anti-realism seems to undermine itself. Perhaps belief in realism is self-undermining in a similar way -- if seemingly correct reasoning leads us to account for all the ways in which realism is a suspect position -- but the negative feedback loop in this case at least seems to me to be less strong.[[16]](#fn-uNQxgS8esQChtvHkb-16)
I think that realism warrants more respect than it has historically received in the rationality community, at least relative to the level of respect it gets from philosophers.[[17]](#fn-uNQxgS8esQChtvHkb-17) I suspect that some of this lack of respect might come from a relatively weaker awareness of the cost of rejecting realism or of the way in which belief in anti-realism appears to undermine itself.
---
1. I'm basing my the views I express in this post primarily off Derek Parfit’s writing, specifically his book *On What Matters*. For this reason, it seems pretty plausible to me that there are some important points I've missed by reading too narrowly. In addition, it also seems likely that some of the ways in which I talk about particular issues around normativity will sound a bit foreign or just generally “off” to people who are highly familiar with some of these issues. One unfortunate reason for this is that the study of normative questions and of the nature of normativity seems to me to be spread out pretty awkwardly across the field of philosophy, with philosophers in different sub-disciplines often discussing apparently interconnected questions in significant isolation of one another while using fairly different terminology. This means that (e.g.) meta-ethics and decision theory are seldom talked about at the same time and are often talked about in ways that make it difficult to see how they fit together. A major reason I am leaning on Parfit’s work is that he is -- to my knowledge -- one of relatively few philosophers to have tried to approach questions around normativity through a single unified framework. [↩︎](#fnref-uNQxgS8esQChtvHkb-1)
2. This is a point that is also discussed at length in David Enoch’s book *Taking Morality Seriously* (pgs. 70-73):
>
> Perhaps...we are essentially deliberative creatures. Perhaps, in other words, we cannot avoid asking ourselves what to do, what to believe, how to reason, what to care about. We can, of course, stop deliberating about one thing or another, and it’s not as if all of us have to be practical philosophers (well, if you’re reading this book, you probably are, but you know what I mean). It’s opting out of the deliberative project as a whole that may not be an option for us….
>
>
> [Suppose] law school turned out not to be all you thought it would be, and you no longer find the prospects of a career in law as exciting as you once did. For some reason you don’t seem to be able to shake off that old romantic dream of studying philosophy. It seems now is the time to make a decision. And so, alone, or in the company of some others you find helpful in such circumstances, you deliberate. You try to decide whether to join a law firm, apply to graduate school in philosophy, or perhaps do neither.
>
>
> The decision is of some consequence, and so you resolve to put some thought into it. You ask yourself such questions as: Will I be happy practicing law? Will I be happier doing philosophy? What are my chances of becoming a good lawyer? A good philosopher? How much money does a reasonably successful lawyer make, and how much less does a reasonably successful philosopher make? Am I, so to speak, more of a philosopher or more of a lawyer? As a lawyer, will I be able to make a significant political difference? How important is the political difference I can reasonably expect to make? How important is it to try and make any political difference? Should I give any weight to my father’s expectations, and to the disappointment he will feel if I fail to become a lawyer? How strongly do I really want to do philosophy? And so on. Even with answers to most – even all – of these questions, there remains the ultimate question. “All things considered”, you ask yourself, “what makes best sense for me to do? When all is said and done, what should I do? What shall I do?”
>
>
> When engaging in this deliberation, when asking yourself these questions, you assume, so it seems to me, that they have answers. These answers may be very vague, allow for some indeterminacy, and so on. But at the very least you assume that some possible answers to these questions are better than others. You try to find out what the (better) answers to these questions are, and how they interact so as to answer the arch-question, the one about what it makes most sense for you to do. You are not trying to create these answers. Of course, in an obvious sense what you will end up doing is up to you (or so, at least, both you and I are supposing here). And in another, less obvious sense, perhaps the answer to some of these questions is also up to you. Perhaps, for instance, how happy practicing law will make you is at least partly up to you. But, when trying to make up your mind, it doesn’t feel like just trying to make an arbitrary choice. This is just not what it is like to deliberate. Rather, it feels like trying to make the right choice. It feels like trying to find the best solution, or at least a good solution, or at the very least one of the better solutions, to a problem you’re presented with. What you’re trying to do, it seems to me, is to make the decision it makes most sense for you to make. Making the decision is up to you. But which decision is the one it makes most sense for you to make is not. This is something you are trying to discover, not create. Or so, at the very least, it feels like when deliberating.
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[↩︎](#fnref-uNQxgS8esQChtvHkb-2)
3. Specifically, the two relevant views can be described as realism and anti-realism with regard to “normativity.” We can divide the domain of “normativity” up into the domains of “[practical rationality](https://plato.stanford.edu/entries/practical-reason/),” which describes what actions people should take, and “epistemic rationality,” which describes which beliefs or degrees of belief people should hold. The study of ethics, decision-making under uncertainty, and so on can then all be understood as sub-components of the study of practical rationality. For example, one view on the study of ethics is that it is the study of how factors other than one’s own preferences might play roles in determining what actions one should take. It should be noted that terminology varies very widely though. For example, different authors seem to use the word "ethics" more or less inclusively. The term "moral realism" also sometimes means roughly the same thing as "normative realism," as I've defined it here, and sometimes picks out a more specific position. [↩︎](#fnref-uNQxgS8esQChtvHkb-3)
4. An an edit to the initial post, I think it's probably worth saying more about the concept of "moral realism" in relation to "normative realism." Depending on the context, "moral realism" might be taken to refer to: (a) normative realism, (b) realism about practical rationality (not just epistemic rationality), (c) realism about practical rationality combined with the object-level belief that people should do more than just try to satisfy their own personal preferences, or (d) something else in this direction.
One possible reason the term lacks a consensus definition is that, perhaps surprisingly, many contemporary "moral realists" aren't actually very preocuppied with the concept of "morality." Popular books like *Taking Morality Seriously*, *On What Matters*, and *The Normative Web* spend most of their energy defending normative realism, more broadly, and my impression is that their critics spend most of their energy attacking normative realism more broadly. One reason for this shift in focus toward normative realism is the realization that, on almost any conception of "moral realism," nearly all of the standard metaphysical and epistemological objections to "moral realism" also apply just as well to normative realism in general. Another reason is that any possible distinction between moral and normative-but-not-moral facts doesn't seem like it could have much practical relevance: If we know that we should make some decision, then we know that we should take it; we have no obvious additional need to know or care whether this normative fact warrants the label "moral fact" or not. Here, for example, is David Enoch, in *Taking Morality Seriously*, on the concept of morality (pg. 86):
>
> What more...does it take for a normative truth (or falsehood) to qualify as moral? Morality is a particular instance of normativity, and so we are now in effect asking about its distinctive characteristics, the ones that serve to distinguish between the moral and the rest of the normative. I do not have a view on these special characteristics of the moral. In fact, I think that for most purposes this is not a line worth worrying about. The distinction within the normative between the moral and the non-moral seems to me to be shallow compared to the distinction between the normative and the non-normative - both philosophically, and, as I am about to argue, practically. (Once you know you have a reason to X and what this reason is, does it really matter for your deliberation whether it qualifies as a *moral reason*?)
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[↩︎](#fnref-uNQxgS8esQChtvHkb-4)
5. There are two major strands of anti-realism. Error theory (sometimes equated with “nihilism”) asserts that all claims that people should do particular things or refrain from doing particular things are false. Non-cognitivism asserts that utterances of the form “A should do X” typically cannot even really be understood as claims; they're not the sort of thing that could be true or false. [↩︎](#fnref-uNQxgS8esQChtvHkb-5)
6. In this post, for simplicity, I’m talking about normativity using binary language. Either it’s the case that you “should” take an action or it’s not the case that you “should” take it. But we might also talk in less binary terms. For example, there may be some actions that you merely have “more reason” to take than others. [↩︎](#fnref-uNQxgS8esQChtvHkb-6)
7. In Sepielli’s account, for example, the experience of feeling extremely in favor of blaming someone a little bit for taking an action X is analogous to the experience of being extremely confident that it is a little bit wrong to take action X. This account is open to at least a few objections, such as the objection that degrees of favorability don’t -- at least at first glance -- seem to obey the standard axioms of probability theory. Even if we do accept the account, though, I still feel unclear about the proper method and justification for converting debates around normative uncertainty into debates around these other kinds of psychological states. [↩︎](#fnref-uNQxgS8esQChtvHkb-7)
8. If my memory is correct, one example of a context in which I have encountered this subjectivist viewpoint is in a CFAR workshop. One lesson instructs attendees that if it seems like they “should” do something, but then upon reflection they realize they don’t want to do it, then it’s not actually true that they should do it. [↩︎](#fnref-uNQxgS8esQChtvHkb-8)
9. The PhilPapers survey suggests that about a quarter of both normative ethicists and applied ethicists also self-identify as anti-realists, with the majority of them presumably leaning toward non-cognitivism over error theory. It’s still an active [matter of debate](https://plato.stanford.edu/entries/moral-cognitivism/#ProObjResStr) whether non-cognitivists have sensible stories about what people are trying to do when they seem to be discussing normative claims. For example, naive emotivist theories stumble in trying to explain sentences like: “It's not true that either you should do X or you should do Y.” [↩︎](#fnref-uNQxgS8esQChtvHkb-9)
10. There is also non-normative research that falls under the label “decision theory,” which focuses on exploring the ways in which people do in practice make decisions or neutrally exploring the implications of different assumptions about decision-making processes. [↩︎](#fnref-uNQxgS8esQChtvHkb-10)
11. Arguably, even in academic literature, decision theories are often discussed under the implicit assumption that some form of subjectivism is true. However, it is also very easy to modify the theories to be compatible with theories that tell you to take into account things beyond your current desires. Value might be equated with one’s future welfare, for example, or with the total future welfare of all conscious beings. [↩︎](#fnref-uNQxgS8esQChtvHkb-11)
12. One thing that makes this issue a bit complicated is that rationalist community writing on decision theory sometimes seems to switch back and forth between describing decision theories as *normative claims about decisions* (which I believe is how academic philosophers typically describe decision theories) and as *algorithms to be used* (which seems to be inconsistent with how academic philosophers typically describe decision theories). I think this tendency to switch back and forth between describing decision theories in these two distinct ways can be seen both in papers proposing new decision theories and in online discussions. I also think this switching tendency can make things pretty confusing. Although it makes sense to discuss how an algorithm “performs” when “implemented,” once we specify a sufficiently precise performance metric, it does not seem to me to make sense to discuss the performance of a normative claim. I think the tendency to blur the distinction between algorithms and normative claims -- or, as Will MacAskill puts it in his recent and similar [critique](https://www.lesswrong.com/posts/ySLYSsNeFL5CoAQzN/a-critique-of-functional-decision-theory), between "decision procedures" and "criteria of rightness" -- partly explains why proponents of FDT and other new decision theories have not been able to get much traction with academic decision theorists. For example, causal decision theorists are well aware that people who always takes the actions that CDT says they should take will tend to fare less well in Newcomb scenarios than people who always take the actions that EDT says they should take. Causal decision theorists are also well aware that that there are some scenarios -- for example, a Newcomb scenario with a perfect predictor and the option to get brain surgery to pre-commit yourself to one-boxing -- in which there is no available sequence of actions such that CDT says you should take each of the actions in the sequence. If you ask a causal decision theorist what sort of algorithm you should (according to CDT) put into an AI system that will live in a world full of Newcomb scenarios, if the AI system won’t have the opportunity to self-modify, then I think it's safe to say a causal decision theorist won’t tell you to put in an algorithm that only produces actions that CDT says it should take. This tells me that we really can’t fluidly switch back and forth between making claims about the correctness of normative principles and claims about the performance of algorithms, as though there were a canonical one-to-one mapping between these two sorts of claims. Insofar as rationalist writing on decision theory tends to do this sort of switching, I suspect that it contributes to confusion on the part of many academic readers. See also this [blog post](https://www.umsu.de/wo/2018/688) by an academic decision theorist, Wolfgang Schwarz, for a much more thorough perspective on why proponents of FDT may be having difficulty getting traction within the academic decision theory community. [↩︎](#fnref-uNQxgS8esQChtvHkb-12)
13. A similar concern also leads me to assign low (p<10%) probability to normative decision theory research ultimately being useful for avoiding large-scale accidental harm caused by AI systems. It seems to me like the question “What is the correct decision theory?” only has an answer if we assume that realism is true. But even if we assume that realism is true, we are now asking a normative question (“What criterion determines whether an action is one an agent ‘should’ take?”) as a way of trying to make progress on a non-normative question (“What approaches to designing advanced AI systems result in unintended disasters and which do not?”). Proponents of CDT and proponents of EDT do not actually disagree on how any given agent will behave, on what the causal outcome of assigning an agent a given algorithm will be, or on what evidence might be provided by the choice to assign an agent a given algorithm; they both agree, for example, about how much money different agents will tend to earn in the classic Newcomb scenario. What decision theorists appear to disagree about is a seperate normative question that floats above (or rather “[supervenes](https://plato.stanford.edu/entries/supervenience/)” upon) questions about observed behavior or questions about outcomes. I don't see how answering this normative question could help us much in answering the non-normative question of what approaches to designing advanced AI systems don’t (e.g.) result in global catastrophe. Put another way, my concern is that the strategy here seems to rely on the hope that we can derive an “is” from an “ought.”
However, in keeping with the above endnote, community work on decision theory only sometimes seems to be pitched (as it is in the abstract of [this paper](https://arxiv.org/pdf/1710.05060.pdf)) as an exploration of normative principles. It is also sometimes pitched as an exploration of how different “algorithms” “perform” across relevant scenarios. This exploration doesn't seem to me to have any direct link to the core academic decision theory literature and, given a sufficiently specific performance metric, does not seem to be inherently normative. I'm actually more optimistic, then, about this line of research having implications for AI development. Nonetheless, for reasons similar to the ones described in the post “[Decision Theory Anti-Realism](https://www.lesswrong.com/posts/jzvDLtPkeLkpBEx9S/decision-theory-anti-realism),” I'm still not very optimistic. In the cases that are being considered, the answer to the question “Which algorithm performs best?” will depend on subtle variations in the set of counterfactuals we consider when judging performance; different algorithms come out on top for different sets of counterfactuals. For example, in a prisoner’s dilemma, the best-performing algorithm will vary depending on whether we are imaging a counterfactual world where just one agent was born with a different algorithm or a counterfactual world where both agents were born with different algorithms. It seems unclear to me where we go from here except perhaps to list several different sets of imaginary counterfactuals and note which algorithms perform best relative to them.
Wolfgang Schwarz and Will MacAskill also make similar points, regarding the sensitivity of comparisons of algorithmic performance, in their essays on FDT. Schwarz writes:
>
> Yudkowsky and Soares constantly talk about how FDT "outperforms" CDT, how FDT agents "achieve more utility", how they "win", etc. As we saw above, it is not at all obvious that this is true. It depends, in part, on how performance is measured. At one place, Yudkowsky and Soares are more specific. Here they say that "in all dilemmas where the agent's beliefs are accurate [??] and the outcome depends only on the agent's actual and counterfactual behavior in the dilemma at hand -- reasonable constraints on what we should consider "fair" dilemmas -- FDT performs at least as well as CDT and EDT (and often better)". OK. But how we should we understand "depends on ... the dilemma at hand"? First, are we talking about subjunctive or evidential dependence? If we're talking about evidential dependence, EDT will often outperform FDT. And EDTers will say that's the right standard. CDTers will agree with FDTers that subjunctive dependence is relevant, but they'll insist that the standard Newcomb Problem isn't "fair" because here the outcome (of both one-boxing and two-boxing) depends not only on the agent's behavior in the present dilemma, but also on what's in the opaque box, which is entirely outside her control. Similarly for all the other cases where FDT supposedly outperforms CDT. Now, I can vaguely see a reading of "depends on ... the dilemma at hand" on which FDT agents really do achieve higher long-run utility than CDT/EDT agents in many "fair" problems (although not in all). But this is a very special and peculiar reading, tailored to FDT. We don't have any independent, non-question-begging criterion by which FDT always "outperforms" EDT and CDT across "fair" decision problems.
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MacAskill writes:
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> [A]rguing that FDT does best in a class of ‘fair’ problems, without being able to define what that class is or why it’s interesting, is a pretty weak argument. And, even if we could define such a class of cases, claiming that FDT ‘appears to be superior’ to EDT and CDT in the classic cases in the literature is simply begging the question: CDT adherents claims that two-boxing is the right action (which gets you more expected utility!) in Newcomb’s problem; EDT adherents claims that smoking is the right action (which gets you more expected utility!) in the smoking lesion. The question is which of these accounts is the right way to understand ‘expected utility’; they’ll therefore all differ on which of them do better in terms of getting expected utility in these classic cases.
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[↩︎](#fnref-uNQxgS8esQChtvHkb-13)
14. In my view, the epistemological issues are the most severe ones. I think Sharon Street’s paper [A Darwinian Dilemma for Realist Theories of Value](https://fewd.univie.ac.at/fileadmin/user_upload/inst_ethik_wiss_dialog/Street__Sharon_2006._A_Darwinian_Dilemma_for_Realist_Theories_of_Value..pdf), for example, presents an especially hard-to-counter attack on the realist position on epistemological grounds. She argues that, in the light of the view that our brains evolved via natural selection, and natural selection did not and could not have directly selected for the accuracy of our normative intuitions, it is extremely difficult to construct a compelling explanation for why our normative intuitions should be correlated in any way with normative facts. This technically leave open the possibility of there being non-trivial normative facts, without us having any way of perceiving or intuiting them, but this state of affairs would strike most people as absurd. Although some realists, including Parfit, have attempted to counter Street’s argument, I’m not aware of anyone who I feel has truly succeeded. Street's argument pretty much just seems to work to me. [↩︎](#fnref-uNQxgS8esQChtvHkb-14)
15. These metaphysical and epistemological issues become less concerning if we accept some version of “naturalist realism” which asserts that all normative claims can be reduced into claims about the natural world (i.e. claims about physical and psychological properties) and therefore tested in roughly the same way we might test any other claim about the natural world. However, this view seems wrong to me.
The bluntest objection to naturalist realism is what's sometimes called the "just-too-different" objection. This is the objection that, to many and perhaps most people, normative claims are just *obviously* a different sort of claim. No one has ever felt any inclination to evoke an "is/is-made-of-wood divide" or an "is/is-illegal-in-Massachusetts divide," because the property of being made of wood and the propery of being illegal in Massachusetts are obviously properties of the standard (natural) kind. But references to the "is/ought divide" -- or, equivalently, the distinction between the "positive" and the "normative" -- are commonplace and don't typically provoke blank stares. Normative discussions are, seemingly, about something *above-and-beyond* and *distinct from* discussions of the physical and psychological aspects of a situation. When people debate whether or not it's "wrong" to support the death penalty or "wrong" for women to abort unwanted pregnancies, for example, it seems obvious that physical and psychological facts are typically not the core (or at least *only*) thing in dispute.
G.E. Moore’s “[Open Question Argument](https://en.wikipedia.org/wiki/Open-question_argument)" elaborates on this objection. The argument also raises the point that that, in many cases where we are inclined to ask “What should I do?”, it seems like what we are inclined to ask goes above-and-beyond any individual question we might ask about the natural world. Consider again the case where we are considering a career change and wondering what we should do. It seems like we could know all of the natural facts -- facts like how happy will we be on average while pursuing each career, how satisfied will we feel looking back on each career, how many lives we could improve by donating money made in each career, what labor practices each company has, how disappointed our parents will be if we pursue each career, how our personal values will change if we pursue each career, what we would end up deciding at the end of one hypothetical deliberative process or another, etc. -- and still retain the inclination to ask, “Given all this, what should I do?” This means that -- insofar as we're taking the realist stance that this question actually has a meaningful answer, rather than rejecting the question as [vacuous](https://www.lesswrong.com/posts/yA4gF5KrboK2m2Xu7/how-an-algorithm-feels-from-inside) -- the claim that we "should" do one thing or another cannot easily be understood as a claim about the natural world. A set of claims about the natural world may *support* the claim that we should make a certain decision, but, in cases such as this one, it seems like no set of claims about the natural world is *equivalent* to the claim that we should make a certain decision.
A last objection to mention is Parfit’s “Triviality Objection” (*On What Matters*, Section 95). The basic intuition behind Parfit’s objection is that pretty much any attempt to define the word “should” in terms of natural properties would turn many normative claims into puzzling assertions of either obvious tautologies or obvious falsehoods. For example, consider a man who is offered -- at the end of his life, I guess by the devil or something -- the option of undergoing a year of certain torture for a one-in-a-trillion chance of receiving a big prize: a trillion years of an equivalently powerful positive experience, plus a single lollipop. He is purely interested in experiencing pleasure and avoiding pain and would like to know whether he should take the offer. A decision theorist who endorses expected desire-fulfillment maximisation says that he “should,” since the lollipop tips the offer over into having slightly positive expected value. A decision theorist who endorses risk aversion says he “should not,” since the man is nearly certain to be horribly tortured without receiving any sort of compensation. In this context, it’s hard to understand how we could redefine the claim “He should take action X” in terms of natural properties and have this disagreement make any sense. We could define the phrase as meaning “Action X maximizes expected fulfillment of desire,” but now the first decision theorist is expressing an obvious tautology and the second decision theorist is expressing an obvious falsehood. We could also try, in keeping with a [suggestion](https://www.lesswrong.com/posts/4ARtkT3EYox3THYjF/rationality-is-systematized-winning) by Eliezer Yudkowsky, to define the phrase as meaning “Action X is the one that someone acting in a winning way would take.” But this is obviously too vague to imply a particular action; taking the gamble is associated with some chance of winning and some chance of losing. We could make the definition more specific -- for instance, saying “Action X is the one that someone acting in a way that maximizes expected winning would take” -- but now of course we’re back in tautology mode. The apparent upshot, here, is that many normative claims simply can’t be interpreted as non-trivially true or non-trivially false claims about natural properties. The associated disagreements only become sensible if we interpret them as being about something above-and-beyond these properties.
Of course, it is surely true that *some* of the claims people make using the word “should” can be understood as claims about the natural world. Words can, after all, be used in many different ways. But it’s the claims that can’t easily be understood in this way that non-naturalist realists such as Parfit, Enoch, and Moore have in mind. In general, I agree with the view that the key division in metaethics is between self-identified non-naturalist realists on the one hand and self-identified anti-realists and naturalist realists on the other hand, since “naturalist realists” are in fact anti-realists with regard to the distinctively normative properties of decisions that non-naturalist realists are talking about. If we rule out non-naturalist realism as a position then it seems the main remaining question is a somewhat boring one about semantics: When someone makes a statement of form “A should do X,” are they most commonly expressing some sort of attitude (non-cognitivism), making a claim about the natural world (naturalist realism), or making a claim about some made-up property that no actions actually possess (error theory)?
Here, for example, is how Michael Huemer (a non-naturalist realist) expresses this point in his book *Ethical Intuitionism* (pg. 8):
>
> [Non-naturalist realists] differ fundamentally from everyone else in their view of the world. [Naturalist realists], non-cognitivists, and nihilists all agree in their basic view of the world, for they have no significant disagreements about what the non-evaluative facts are, and they all agree that there are no further facts over and above those. They agree, for example, on the non-evaluative properties of the act of stealing, and they agree, contra the [non-naturalist realists], that there is no further, distinctively evaluative property of the act. Then what sort of dispute do the [three] monistic theories have? I believe that, though this is not generally recognized, their disputes with each other are merely semantic. Once the nature of the world 'out there' has been agreed upon, semantic disputes are all that is left.
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I think this attitude is in line with the viewpoint that Luke Muehlhauser expresses in his classic LessWrong blog post on what he calls “[pluralistic moral reductionism](https://www.lesswrong.com/posts/3zDX3f3QTepNeZHGc/pluralistic-moral-reductionism).” PMR seems to me to be the view that: (a) non-naturalist realism is false, (b) all remaining meta-normative disputes are purely semantic, and (c) purely semantic disputes aren't terribly substantive and often reflect a failure to accept that the same phrase can be used in different ways. If we define the view this way, then, *conditional on non-naturalist realism being false*, I believe that PMR is the correct view. I believe that many non-naturalist realists would agree on this point as well. [↩︎](#fnref-uNQxgS8esQChtvHkb-15)
16. This point is made by Parfit in *On What Matters*. He writes: “We could not have decisive reasons to believe that there are no such normative truths, since the fact that we had these reasons would itself have to be one such truth. This point may not refute this kind of skepticism, since some skeptical arguments might succeed even if they undermined themselves. But this point shows how deep such skepticism goes, and how blank this skeptical state of mind would be” (*On What Matters*, Section 86). [↩︎](#fnref-uNQxgS8esQChtvHkb-16)
17. The [PhilPapers survey](https://philpapers.org/surveys/results.pl) suggests that philosophers who favor realism outweigh philosophers who favor anti-realism by about a 2:1 ratio. [↩︎](#fnref-uNQxgS8esQChtvHkb-17) |
cccf939f-a183-4c42-9ead-8784c004f81b | trentmkelly/LessWrong-43k | LessWrong | I wrote this because of watermelon
We say to the watermelon
"We accept your offering of sugar. We will share our riches with you in return, that you may have the safety and plenty to grow even sweeter."
And we say to the watermelon
"You make these seeds to carry on your line, in the only way you know how. But we are clever, and if you leave your seeds behind we will spread your children farther than your seeds could ever have carried them."
And we say to the watermelon
"Another great summer. Let's do this again every year."
And we say to the watermelon
"Come travel the world with me! My friends want to meet you.
And we say to the watermelon
"I love you." |
bed23543-e95d-4e0a-9f8e-13bb64451709 | trentmkelly/LessWrong-43k | LessWrong | Chapter 1 of How to Win Friends and Influence People
Dale Carnegie's How to Win Friends and Influence People is worthwhile because:
1. It became so popular in 1936 and afterwards that much of elite Western culture is downstream of it.
1. So if you try using it adversarially (even accidentally e.g. you notice and instrumentally converge on tactics similar to these), the risk is high that they will see it coming from a mile away.
2. The advice is good and makes you stronger. No reason to be below the baseline.
It's important to note that not all advice applies in all contexts; criticism is encouraged while on Lesswrong, since here we know that the human brain sort of just forms strong beliefs in random directions, and that you should do something about it whenever you see it.
It's also great because it basically RLHF's the problem into you by describing it from many different angles. The quality of writing makes it go down easy, and it dazzled the 1930s people even more. Before I knew it, I was reading faster than I have in years.
This inculcated deep mental habits that increased my reading speed; it should be especially helpful for slow readers like me, e.g. with untreatable ADHD, or just have a hard time getting motivated to read longer stuff.
Lastly, it really hammers in that we truly are a lemming species. There exists in the mind-design-space such minds that are capable of evaluating important topics such as life-or-death situations, and although those minds are quite mundane when you really think about it, we are not those minds. This is not the kind of world that makes it through, and chapter 2 will go even further to hammer that point in.
> Part One - Fundamental Techniques In Handling People
>
> "If You Want To Gather Honey, Don't Kick Over The Beehive"
>
> On May 7, 1931, the most sensational manhunt New York City had ever known had come to its climax.
>
> After weeks of search, "Two Gun" Crowley - the killer, the gunman who didn't smoke or drink - was at bay, trapped in his sweetheart's apar |
258a598e-ab82-4a0a-8201-fcfbbb41b374 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Uncertainty about the future does not imply that AGI will go well
Subtitle: A partial defense of high-confidence AGI doom predictions.
Introduction
============
Consider these two kinds of accident scenarios:
1. In a **default-success** scenario, accidents are rare. For example, modern aviation is very safe thanks to decades of engineering efforts and a safety culture (e.g. the widespread use of checklists). When something goes wrong, it is often due to multiple independent failures that combine to cause a disaster (e.g. bad weather + communication failures + pilot not following checklist correctly).
2. In a **default-failure** scenario, accidents are the norm. For example, when I write a program to do something I haven’t done many times already, it usually fails the first time I try it. It then goes on to fail the second time and the third time as well. Here, failure on the first try is overdetermined―even if I fix the first bug, the second bug is still, independently, enough to cause the program to crash. This is typical in software engineering, and it can take many iterations and tests to move into the default-success regime.
See also: [conjuctive vs disjunctive risk scenarios](https://forum.effectivealtruism.org/topics/conjunctive-vs-disjunctive-risk-models).
Default-success scenarios include most engineering tasks that we have lots of experience with and know how to do well: building bridges, building skyscrapers, etc. Default-failure scenarios, as far as I can tell, come in two kinds: scenarios in which we’re trying to do something for the first time (rocket test launches, prototypes, new technologies) and scenarios in which there is a competent adversary that is trying to break the system, as in computer security.[[1]](#fnl17iqh6p4wm)
Predictions on AGI risk
-----------------------
In the following, I use **P(doom)** to refer to the probability of an AGI takeover and / or human extinction due to the development of AGI.
I often encounter the following argument against predictions of AGI catastrophes:
**Alice:** We seem to be on track to build an AGI smarter than humans. We don’t know how to solve the technical problem of building an AGI we can control, or the political problem of convincing people to not build AGI. Every plausible scenario I’ve ever thought or heard of leads to AGI takeover. In my estimate, P(doom) is [high number].
**Bob:** I disagree. It’s overconfident to estimate high P(doom). Humans are usually bad at predicting the future, especially when it comes to novel technologies like AGI. When you account for how uncertain your predictions are, your estimate should be at most [low number].”
I'm being vague about the numbers because I've seen Bob's argument made in many different situations. In one recent conversation I witnessed, the Bob-Alice split was P(doom) 0.5% vs. ~10%, and in another discussion it was 10% vs. 90%.
**My main claim** is that Alice and Bob don’t actually disagree about how uncertain or hard to predict the future is―instead, they disagree about to what degree AGI risk is default-success vs. default-failure. If AGI risk is (mostly) default-failure, then uncertainty is a reason for pessimism rather than optimism, and Alice is right to predict failure.
In this sense I think Bob is missing the point. Bob claims that Alice is not sufficiently uncertain about her AI predictions, or has not integrated her uncertainty into her estimate well enough. This is not necessarily true; it may just be that Alice’s uncertainty about her reasoning doesn't make her much more optimistic.
Instead of trying to refute Alice from general principles, I think Bob should instead point to concrete reasons for optimism (for example, Bob could say “for reasons A, B, and C it is likely that we can coordinate on not building AGI for the next 40 years and solve alignment in the meantime”).
Uncertainty does not (necessarily) mean you should be more optimistic
=====================================================================
Many people are skeptical of the ‘default-failure’ frame, so I'll give a bit more color here by listing some reasons why I think Bob's argument is wrong / unproductive. I won’t go into detail about why AGI risk specifically might be a default-failure scenario; you can find a summary of those arguments in [Nate Soares’ post on why AGI ruin is likely](https://forum.effectivealtruism.org/posts/vC6v2iTafkydBvnz7/agi-ruin-scenarios-are-likely-and-disjunctive).
1. It’s true that the future is often hard to predict; for example, experts often fail to predict technological developments. This is not a reason for optimism. It would be kind of weird if it was! Humans are generally bad at predicting the future, especially for technological progress, and this is bad news for AI safety.
1. In particular: if all the AI researchers are uncertain about what will happen, that is a bad sign much in the same way that it would be a bad sign if none of your security engineers understood the system they are supposed to secure.
2. Analogy: if I’m in charge of software security for a company, and my impression is that the system is almost certainly insecure, it is not a good argument to say “well you don’t completely understand the system, so you might be wrong!” ― I may be wrong, but being wrong does not bode well for our security.
2. To believe P(doom) is high, all you really need to be convinced of is that the default outcome for messing up superhuman AGI is human extinction, and that we’re not prepared. Our understanding here is incomplete but still relatively good compared to details that are harder to predict, e.g. when exactly AGI will arrive or what early forms of AGI will look like.
3. It is not always wrong to make high-confidence disaster predictions. For example, people saying “covid will be a disaster with high (~90%) probability” in February 2020 were predictably correct, even though covid was a very novel situation. There was a lot of uncertainty, and the people who predicted disaster usually got the details wrong like everyone else, but the overall picture was still correct because the details didn’t matter much.
4. A confidence of 90% is not actually much harder to achieve than 10%, relative to the baseline extinction risk for a new technology which is close to 0%. An estimate of P(doom) = 30% already leans very heavily on your [inside view](https://www.lesswrong.com/tag/inside-outside-view) of the risks involved; you don’t need to trust your reasoning all that much more to estimate 90% instead.
5. Put differently: there’s no reason in particular why Bob's uncertainty argument should cap your confidence at ~80%, rather than 1% or 0.1%.
1. (It seems totally reasonable to me for a first reaction to AI X-risk to be “Eh I don’t know, it’s an interesting idea and I’ll think more on it, but it does seem pretty crazy; if I had to estimate P(doom) right now I would say ~0.1%, though I would prefer not to give a number at all.” Followed, to be clear, by rapid updates in favor of high p(doom), though not necessarily 90%; I think 90% makes sense for people who have slammed their head against the difficulties involved, and noticed a pattern where the wall they’re slamming their heads against is pretty hard and doesn’t have visible weak spots; but otherwise you wouldn’t necessarily be that pessimistic.)
6. More generally: estimates around 90% aren’t all that “confident”. If you’re well-calibrated, changing your mind about something that you estimate to be 90% likely is something that happens all the time. So P(X) = 90% means “I expect X to happen, though I’m happy to change my mind and in fact regularly do change my mind about claims like this”.
7. It makes sense to be uncertain about your beliefs, and about whether you thought of all the relevant things (usually you didn’t). Rather than be generically uncertain about everything, it’s usually better to be uncertain about specific parts of your model.
1. For example: I’m uncertain about the behavior and capability profile of the first AI that surpasses humans in scientific research. This makes me more pessimistic about alignment relative to a baseline where I was certain, because any strategy that depends on specific assumptions about the capabilities of this AI is unlikely to work.
2. For a second example: I think there probably won’t be any international ban or regulation on large training runs that lengthens timelines by >10 years, but I’m pretty uncertain. This makes me more optimistic relative to a baseline where I was certain governments would do nothing.
8. Put differently: most of your uncertainty about beliefs should be part of your model, not some external thing that magically pushes all your beliefs towards 50% or 0% or 100%.
Some things I’m not saying
==========================
*This part is me hedging my claims. Feel free to skip if that seems like a boring thing to read.*
I don’t personally estimate P(doom) above 90%.
I’m also not saying there are no reasons to be optimistic. I’m claiming that reasons for optimism should usually be concrete arguments about possible ways to avoid doom. For example, Paul Christiano argues for a somewhat lower than 90% P(doom) [here](https://www.alignmentforum.org/posts/CoZhXrhpQxpy9xw9y/where-i-agree-and-disagree-with-eliezer), and I think the general shape of his argument makes sense, in contrast to Bob’s above.
I do think there is a correct version of the argument that, if your model says P(outcome) = 0.99, model uncertainty will generally be a reason to update downwards. I think people already take that into account when stating high P(doom) estimates. Here’s a sketch of a plausible reasoning (summarized and not my numbers, but I do have similar reasoning, and I don’t think the numbers are crazy):
1. Almost every time I imagine a concrete scenario for how AGI development might go, that leads to an outcome where humans go extinct.
2. I can imagine some ways in which things go well, but they seem pretty fanciful; for example a sudden international treaty that forbids large training runs and successfully enforces this. (I do expect there’ll be other government efforts, but I don’t expect those to change things much for the better). So my “within-model” prediction is p(doom) = 0.99.
3. My model is almost certainly wrong. Sadly, for most scenarios I can imagine, being wrong would only make things worse. I’m literally a safety researcher; me being totally wrong about e.g. what the first AGI looks like is not a good sign for safety (and I don’t expect other safety researchers to have better models). Almost all surprises are bad.
1. Analogy: if I’m in charge of software security for a company, and my impression is that the system is almost certainly insecure, it is not a good argument to say “well you don’t completely understand the system, so you might be wrong!” ― I may be wrong, but being wrong does not bode well for our security.
4. That said: while *technical* surprises are probably bad, there’s other kinds of positive surprises we could get, for example: more progress on AI safety than expected, better interpretability methods, more uptake of AI risk concerns by the broader ML community, more government action on regulating AI.
1. In fact, there are some kinds of cumulative surprises that could add up to save us; as an example, enough regulation of AI could lead to ~10y longer timelines; more progress than expected in interpretability could lead to more compelling demonstrations of misalignment; more uptake of AI risk by the broader scientific community might lead to more safety progress and an overall more careful approach to AGI.
2. Note that this is not an update made from pure uncertainty―there is a concrete story here about how exactly surprises might actually be helpful, rather than bad. It’s not a particularly great story either; it needs many things to go better than expected.
5. Now, that particular story is not likely at all. But it seems like there are many stories in that general category, such that the total likelihood of a good surprise adds up to 10%.
1. Note the basic expectation of ‘surprises are often bad’ still applies. Not knowing how governments or society will react to AI is hardly helpful for the people who are currently trying to get governments or society to react in a useful way.
6. So my overall, all-things-considered p(doom) is 90%, mostly due to a kind of sketchy downwards-update due to model uncertainty, without which the estimate would be around 99%.
7. It’s debatable how large the downwards update here should be―it could reasonably be more or less than 10%, and it’s plausible that we’re in the kind of domain where small quantified probability updates aren’t very useful at all.
I don’t mean to say that the reasoning here is the only reasonable version out there. It depends a lot on how likely you think various definitely-useful surprises are, like long timelines to AGI and slow progress after proto-AGI. But I do think it is wrong to call high P(doom) estimates overconfident without any further more detailed criticism.
Finally, I haven’t given an explicit argument for AGI risk; there’s a lot of that elsewhere.
1. **[^](#fnrefl17iqh6p4wm)**Note how AGI somehow manages to satisfy both of these criteria at once. |
4eef192e-38c2-41b5-a3e2-803a5354d8e8 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | "The Universe of Minds" - call for reviewers (Seeds of Science)
### Abstract
The paper attempts to describe the space of possible mind designs by first equating all minds to software. Next it proves some interesting properties of the mind design space such as infinitude of minds, size and representation complexity of minds. A survey of mind design taxonomies is followed by a proposal for a new field of investigation devoted to study of minds, intellectology, a list of open problems for this new field is presented.
---
[*Seeds of Science*](https://www.theseedsofscience.org/) is a journal (funded through Scott Alexander's [ACX grants program](https://astralcodexten.substack.com/p/acx-grants-results)) that publishes speculative or non-traditional articles on scientific topics. Peer review is conducted through community-based voting and commenting by a diverse network of reviewers (or "gardeners" as we call them). Comments that critique or extend the article (the "seed of science") in a useful manner are published in the final document following the main text.
We have just sent out a manuscript for review, "The Universe of Minds", that may be of interest to some in the EA community so I wanted to see if anyone would be interested in joining us as a gardener and providing feedback on the article. As noted above, this is an opportunity to have your comment recorded in the scientific literature (comments can be made with real name or pseudonym).
It is free to join as a gardener and anyone is welcome (we currently have gardeners from all levels of academia and outside of it). Participation is entirely voluntary - we send you submitted articles and you can choose to vote/comment or abstain without notification (so no worries if you don't plan on reviewing very often but just want to take a look here and there at the articles people are submitting).
To register, you can fill out this [google form](https://docs.google.com/forms/d/e/1FAIpQLSfRIicHT7jIZcSUjwsIlby6JBxx2ZVeD5kseZBpgGFtp8pLfg/viewform). From there, it's pretty self-explanatory - I will add you to the mailing list and send you an email that includes the manuscript, our publication criteria, and a simple review form for recording votes/comments. If you would like to just take a look at this article without being added to the mailing list, then just reach out (info@theseedsofscience.org) and say so.
Happy to answer any questions about the journal through email or in the comments below. |
1a9f1feb-7e79-4ea1-84f3-68b4b78fdad4 | trentmkelly/LessWrong-43k | LessWrong | Learning (meta-)preferences
A putative new idea for AI control; index here.
There are various methods, such as Cooperative Inverse Reinforcement Learning (CIRL), that aim to have an AI deduce human preferences in some fashion.
The problem is that humans are not rational - citation certainly not needed. But, worse than that, they are not rational in ways that seriously complicate the task of fitting a reward or utility function to them. I presented one problem this entails in a previous post. That talked about the problems that emerged when an AI could influence a human's preference through the ways it presented the issues.
----------------------------------------
But there are other irrationalities which challenge a value learner. Here are some that can't easily be modelled as "true preferences + noise".
#. Humans procrastinate even though they'd generally prefer to proceed with their tasks. #. Most people who fail a suicide attempt never try again. #. People can be prejudiced while not desiring to be so (and, often, while not realising they are so). #. Many rationalists wish to be consistent, and fail at this. #. The young don't want to have the old-age preferences they should expect to have. #. People generally take on the preferences of the social group they belong to. #. Addicts may have stated or revealed preferences to continue to be addicts. However, if they'd never gotten addicted in the first place, they may have stated or revealed preferences to never do so. #. The whole tension between stated and revealed preferences in the first place. #. People desiring to have certain beliefs, or at least to not lost them (eg religious beliefs). #. etc. #. A lot more "etc"'s.
The preference inference problem
The problem with those preferences is not that they can't be resolved (even though philosophers continue to disagree on many of them). It's that it's really hard to resolve them in a principled way, from observation of base level human behaviour.
Look for example at the tension betwee |
89303e62-2103-4a36-a82e-5e7b8b6db02c | trentmkelly/LessWrong-43k | LessWrong | LW Update 2018-07-18 – AlignmentForum Bug Fixes
Git commit: 882df0fe53f3c2bb04fac76d5b631f32cab28b1b
AlignmentForum
* If a non-AlignmentForum member's post gets moved to AlignmentForum, they can now comment on that post.
* Fixed the bug on LessWrong where canceling a vote on a non-AF post briefly showed a flicker of AF karma.
Stickies
* Previously we only had a single "sticky" option for posts, which meant it was slightly awkward when we wanted to sticky things in either the Meta forum, or AlignmentForum. Meta and AF now have their own sticky setting.
Form Group Styling
* Form Groups (such as editing your user profile, or the moderation settings for high karma users) are a little easier to parse now.
There have been a few more performance improvements for long comment threads. |
aa7a6652-6a29-4b31-8c92-96ef2031f277 | trentmkelly/LessWrong-43k | LessWrong | Will the growing deer prion epidemic spread to humans? Why not?
Helpful background reading: What’s the deal with prions?
A novel lethal infectious neurological disease emerged in American deer a few decades ago. Since then, it’s spread rapidly across the continent. In areas where the disease is found, it can be very common in the deer there.
Map from the Cornell Wildlife Health Lab.
Chronic wasting disease isn’t caused by a bacteria, virus, protist, or worm – it’s a prion, which is a little misshapen version of a protein that occurs naturally in the nervous systems of deer.
Chemically, the prion is made of exactly the same stuff as its regular counterpart – it’s a string of the same amino acids in the same order, just shaped a little differently. Both the prion and its regular version (PrP) are monomers, single units that naturally stack on top of each other or very similar proteins. The prion’s trick is that as other PrP moves to stack atop it, the prion reshapes them – just a little – so that they also become prions. These chains of prions are quite stable, and, over time, they form long, persistent clusters in the tissue of their victims.
We know of only a few prion diseases in humans. They’re caused by random chance misfolds, a genetic predisposition for PrP to misfold into a prion, accidental cross-contamination via medical supplies, or, rarely, from the consumption of prion-infected meat. Every known animal prions is a misfold of the same specific protein, PrP. PrP is expressed in the nervous system, particularly in the brain – so infections cause neurological symptoms and physical changes to the structure of the brain. Prion diseases are slow to develop (up to decades), incurable, and always fatal.
There are two known infectious prion diseases in people. One is kuru, which caused an epidemic among tribes who practiced funerary cannibalism in Papua New Guinea. The other is mad cow disease, also known as bovine spongiform encephalopathy (BSE) AKA Variant Creutzfeldt-Jakob disease, which was first seen in humans in 1 |
60e27d7a-f808-402a-99c8-b9c6e71c93dd | trentmkelly/LessWrong-43k | LessWrong | The social value of high school extracurricular time
Ambitious high school students are typically heavily constrained in the sense that maximizing their college admissions prospects comes at the cost of spending a lot of time on coursework. See for example our page on College statements on the importance of grades and coursework. Outside of improving college admissions prospects, this is often a waste of time. In What You'll Wish You Had Known, Y Combinator founder Paul Graham wrote:
> Right now most of you feel your job in life is to be a promising college applicant. But that means you're designing your life to satisfy a process so mindless that there's a whole industry devoted to subverting it. [...] So what do you do? What you should not do is rebel. [...] By putting you in this situation, society has fouled you. Yes, as you suspect, a lot of the stuff you learn in your classes is crap. And yes, as you suspect, the college admissions process is largely a charade. But like many fouls, this one was unintentional. So just keep playing.
However, colleges grant students considerable latitude with respect to how they spend their time on extracurricular activities. In What colleges look for in extracurricular activities I reported on conversations with admissions officers in which they said that it doesn't matter what extracurricular activities a student is involved in as long as he/she demonstrates characteristics such as passion and commitment. My post got a lot of pushback (in part because I framed the finding suboptimally), but based on these conversations and on reading writings by college admissions consultants, even if college admissions officers have some preferences, it's unlikely that they're sufficiently strong and narrow so that there's not considerable variability in the value of the activities that students can engage in and still look good to colleges. This raises the possibility that there's considerable potential for high school students to benefit more from their extracurricular activities and to contr |
b69eec75-17db-4c2a-ab91-ab9ef04dd29b | trentmkelly/LessWrong-43k | LessWrong | A simple model of the Löbstacle
The idea of the Löbstacle is that basic trust in yourself and your successors is necessary but tricky: necessary, because naively modeling your successor's decisions cannot rule out them making a bad decision, unless they are in some sense less intelligent than you; tricky, because the strongest patches of this problem lead to inconsistency, and weaker patches can lead to indefinite procrastination (because you always trust your successors to do the thing you are now putting off). (For a less handwavy explanation, see the technical agenda document on Vingean reflection.)
It is difficult to specify the circumstances under which this kind of self-trust succeeds or fails. Here is one simple example in which it can succeed, but for rather fragile reasons.
----------------------------------------
We will consider a sequential decision problem, where an agent's payoff can depend on the actions of "later" agents in "later" universes. Even in the case where the identities of the later agents are known in advance and correspond closely to the current agent, the current agent can have difficulties in trusting the later ones.
Each universe is defined with respect to the output of the current agent, and to the output of the next universe on the next agent:
* def Un(a):
* if a=`Quit': return 0
* else if a=`Explode': return -10
* else return 2−n+Un+1(An+1())
Note that if An's successors continue for a while but then explode, then An should quit rather than continue; but if none of An's successors explode, then An should continue. So ideally, a sensible sequence of agents An should continue indefinitely in this problem.
We will consider a sequence of agents inspired by the descending search order version of modal UDT:
* def An():
* for (a, u) in {`Continue',`Quit',`Explode'}×Utilities, sorted in decreasing order of u:
* if PA⊢Un(a)≥u: return a
* if still here: return some default action
Here, Utilities is a finite set of possible utilities, and we ma |
ccb9451a-33d9-4824-9833-debb8584e2ca | trentmkelly/LessWrong-43k | LessWrong | Representation Tuning
Summary
First, I identify activation vectors related to honesty in an RLHF’d LLM (Llama-2-13b-chat). Next, I demonstrate that model output can be made more or less honest by adding positive or negative multiples of these vectors to residual stream activations during generation. Then, I show that a similar effect can be achieved by fine-tuning the vectors directly into (or out of) the model, by use of a loss function based on the cosine similarity of residual stream activations to the vectors. Finally, I compare the results to fine-tuning with a token-based loss on honest or dishonest prompts, and to online steering. Overall, fine-tuning the vectors into the models using the cosine similarity loss had the strongest effect on shifting model output in the intended direction, and showed some resistance to subsequent steering, suggesting the potential utility of this approach as a safety measure.
This work was done as the capstone project for BlueDot Impact’s AI Safety Fundamentals - Alignment course, June 2024
Introduction
The concept of activation steering/representation engineering is simple, and it is remarkable that it works. First, one identifies an activation pattern in a model (generally in the residual stream input or output) corresponding to a high-level behavior like "sycophancy" or "honesty" by a simple expedient such as running pairs of inputs with and without the behavior through the model and taking the mean of the differences in the pairs' activations. Then one adds the resulting vector, scaled by +/- various coefficients, to the model's activations as it generates new output, and the model gives output that has more or less of the behavior, as one desires. This would seem quite interesting from the perspective of LLM interpretability, and potentially safety.
Beneath the apparent simplicity of activation steering, there are a lot of details and challenges, from deciding on which behavioral dimension to use, to identifying the best way to elicit repre |
a53c33fe-93e7-4ff1-a6fc-5de8427ae5ac | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Are we confident that superintelligent artificial intelligence disempowering humans would be bad?
I think it is almost always assumed that superintelligent [artificial intelligence](https://forum.effectivealtruism.org/topics/artificial-intelligence) (SAI) disempowering humans would be bad, but are we confident about that? Is this an under-discussed[crucial consideration](https://forum.effectivealtruism.org/topics/crucial-consideration)?
Most people (including me) would prefer the extinction of a random species to that of humans. I suppose this is mostly due to a desire for self-preservation, but can also be justified on altruistic grounds if humans have a greater ability to shape the future for the better. However, a priori, would it be reasonable to assume that more intelligent agents would do better than humans, at least under [moral realism](https://en.wikipedia.org/wiki/Moral_realism)? If not, can one be confident that humans would do better than other species?
From [the point of view of the universe](https://blog.oup.com/2014/06/the-point-of-view-of-the-universe/), I believe one should strive to align SAI with impartial value, not human value. It is unclear to me how much these differ, but one should beware of [surprising and suspicious convergence](https://forum.effectivealtruism.org/posts/omoZDu8ScNbot6kXS/beware-surprising-and-suspicious-convergence).
In any case, I do not think this shift in focus means humanity should accelerate AI progress (as proposed by [effective accelerationism](https://www.effectiveacceleration.org/)?). Intuitively, aligning SAI with impartial value is a harder problem, and therefore needs even more time to be solved. |
43119360-aacc-404a-aa90-ccce306c2a4f | trentmkelly/LessWrong-43k | LessWrong | Specific Arguments against open source LLMs?
https://www.msn.com/en-us/news/technology/github-urges-the-european-union-to-reconsider-its-ai-act/ar-AA1ey0nQ
https://github.blog/2023-07-26-how-to-get-ai-regulation-right-for-open-source/
"In a joint effort, GitHub, Hugging Face, Creative Commons, and other tech companies have penned an open letter appealing to European Union (EU) policymakers to revise certain aspects of the EU’s Artificial Intelligence Act. The letter expresses concerns that upcoming rules may inadvertently impede the development of open-source artificial intelligence (AI) models." |
0d627fd9-8d27-426b-8f41-83701681a410 | trentmkelly/LessWrong-43k | LessWrong | If Wentworth is right about natural abstractions, it would be bad for alignment
This post was written as part of the AI safety Mentors and Mentees program. My Mentor is Jacques Thibodeau.
In this post, I will distinguish between two hypotheses that are often conflated. To disambiguate, I first suggest two different names for these hypotheses so I can talk about them separately:
The natural abstraction hypothesis (NAH):
There are natural ways to cut the world up into concepts. A lot of very different cognitive systems will naturally converge to these abstractions. So there is reason to believe that AIs will also form concepts of abstractions that humans use (nails, persons, human values….).
The Wentworthian abstractions hypothesis (WAH):
There are natural abstractions, and they are identified by the properties of a system, that are relevant for predicting how far away objects behave.
Notice how the first might be true, while the second might be completely off. Just as you can deny that Newtonian mechanics is true, without denying that heavy objects attract each other.
Why natural abstractions are thought to be good for alignment
If NAH turns out to be correct, this would simplify two problems in alignment.
1. Interpretability
If the AI uses the same abstractions as us, it is probably way easier to read its mind.
2. Pointing at things
If the AI forms the abstraction “diamond” itself, we could just point at that abstraction in the AI's mind, and say: “maximize that one”, instead of trying to formulate what a diamond is rigorously. This was proposed in combination with shard theory to the diamond-alignment problem. If it would naturally form an abstraction of human values, alignment might be easier than we thought (alignment by default). We could point at that abstraction by training it in such a way, that it adheres to those values.
Wentworthian abstractions are about outer appearance, not inner structure
Wentworths hypothesizes that natural abstractions consist of information that is relevant from afar. Let's take the example of a |
7ca14ff5-f386-455f-8396-b7bf44a40825 | trentmkelly/LessWrong-43k | LessWrong | Science advances one funeral at a time
Major scientific institutions talk a big game about innovation, but the reality is that many of the mechanisms designed to ensure quality—peer review, funding decisions, the academic hierarchy—explicitly incentivize incremental rather than revolutionary progress; are far more oriented towards 'day science' than 'night science.'[1]
Thomas Kuhn's now-famous notion of paradigm shifts was pointing at precisely this phenomenon. When scientists work within what Kuhn called "normal science," they're essentially solving low- to medium-stakes puzzles within their field's accepted framework. While it's fairly easy to evaluate the relative quality of work that occurs within any given paradigm, Kuhn argued it's nearly impossible for scientists to reason about the relative power of different paradigms for a given field—especially when they have already drank the paradigmatic kool-aid.
Max Planck captured this idea succinctly in his biting statement that "science advances one funeral at a time."[2]
There is no shortage of examples of this occurring throughout the history of science:
* Ignaz Semmelweis suggested that doctors wash their hands between patients. The medical establishment ridiculed and ostracized him until his career was destroyed. Today hand washing is basic medical practice.
* Barbara McClintock discovered genes could jump between chromosomes in maize in 1948. Geneticists dismissed her work for decades as it didn't fit their tidy theory of inheritance. She won the 1983 Nobel Prize for the same research they rejected.
* Barry Marshall grew confident that ulcers came from bacteria, not stress. When no one would listen, he drank H. pylori in 1984 to prove it. Won the 2005 Nobel Prize for work doctors had called absurd.
* Katalin Karikó lost her position and funding at UPenn in 1995 for pursuing mRNA research. She kept working on the "fringe" technology despite the setbacks. That same technology became the basis for the COVID-19 vaccines.
* Alfred Wegener propo |
32d15f1d-6197-419f-ba82-e524270d6c6f | trentmkelly/LessWrong-43k | LessWrong | $100/$50 rewards for good references
With thanks to Rohin Shah.
Dear LessWrongers, this is an opportunity to make money and help with AI alignment.
We're looking for specific AI capabilities; has anyone published on the following subject:
* Generating multiple reward functions or policies from the same set of challenges. Has there been designs for deep learning or similar, in which the agent produces multiple independent reward functions (or policies) to explain the same reward function or behaviour?
For example, in CoinRun, the agent must get to the end of the level, on the right, to collect the coin. It only gets the reward for collecting the coin.
That is the "true" reward, but, since the coin is all the way to the right, as far as the agent knows, "go to the far right of the level" could just as well have been the true reward.
We'd want some design that generated both these reward functions (and, in general, generated multiple reward functions when there are several independent candidates). Alternatively, they might generate two independent policies - we could test these by putting the coin in the middle of the level and seeing what the agent decided to do.
We're not interested in a Bayesian approach that lists a bunch of reward functions and then updates to include just those two (that's trivially easy to do). Nor are we interested in an IRL-style approach that lists "features", including the coin and the right hand side.
What we'd want is some neural-net style design that generates the coin reward and the move-right reward just from the game data, without any previous knowledge of the setting.
So, does anyone know any references for that kind of work?
We will pay $50 for the first relevant reference submitted, and $100 for the best reference.
Thanks! |
200a025f-019b-44e9-bdb2-e3b2d06efe45 | trentmkelly/LessWrong-43k | LessWrong | Two working memories, one on each side of the brain [link]
Not a working memory of four, it's really two plus two.
Tl, dr: It's easier to remember four things if you've got two on one side of your visual field and two on the other. Three or four on one side can cause overload errors.
Working memory for other senses hasn't been explored yet on this level. |
5e0ec370-9716-47c9-89a0-5bc4ba6cba20 | trentmkelly/LessWrong-43k | LessWrong | [link] Biostasis / Cryopreservation Survey 2020
https://biostasis-survey.paperform.co/ |
ce0dea03-b858-45e9-93b9-c15ab1a7a961 | trentmkelly/LessWrong-43k | LessWrong | Russian native speakers wanted as editors (translating "Smarter Than Us")
Thanks to the efforts of volunteer translators, Smarter Than Us: The Rise of Machine Intelligence was translated into Russian.
Many thanks to the volunteers who translated it, and to Stuart Armstrong who wrote it in the first place!
But now, we need fresh volunteers who can help as editors, to proofread and edit the translated text before it will be compiled into beautiful pdf. If you were not working as a volunteer translator of this text and are willing to help, please contact me by email: . Please translate the synopsis as a 'test task'. If I have already asked you to translate it, but you haven't been working on the translation, please point it out to me or re-send your translation (sorry, it might got buried in the emails).
Thank you! |
fbb6d086-e148-49c3-a4aa-908d4561c5df | trentmkelly/LessWrong-43k | LessWrong | LessWrong Is Not about Forum Software, LessWrong Is about Posts (Or: How to Immanentize the LW 2.0 Eschaton in 2.5 Easy Steps!)
[epistemic status: I was going to do a lot of research for this post, but I decided not to as there are no sources on the internet so I'd have to interview people directly and I'd rather have this post be imperfect than never exist.]
Many words have been written about how LessWrong is now shit. Opinions vary about how shit exactly it is. I refer you to http://lesswrong.com/lw/n0l/lesswrong_20/ and http://lesswrong.com/lw/o5z/on_the_importance_of_less_wrong_or_another_single/ for more comments about LessWrong being shit and the LessWrong diaspora being suboptimal.
However, how to make LessWrong stop being shit seems remarkably simple to me. Here are the steps to resurrect it:
1. Get Eliezer: The lifeblood of LessWrong is Eliezer Yudkowsky's writing. If you don't have that, what's the point of being on this website? Currently Eliezer is posting his writings on Facebook, (https://www.facebook.com/groups/674486385982694/) which I consider foolish, for the same reasons I would consider it foolish to house the Mona Lisa in a run-down motel.
2. Get Scott: Once you have Eliezer back, and you sound the alarm that LW is coming back, I'm fairly certain that Scott "Yvain" Alexander will begin posting on LessWrong again. As far as I can tell he's never wanted to have to moderate a comment section, and the growing pains are stressing his website at the seams. He's even mused publicly about arbitrarily splitting the Slate Star Codex comment section in two (http://slatestarcodex.com/2017/04/09/ot73-i-lik-the-thred/) which is a crazy idea on its own but completely reasonable in the context of (cross)posting to LW. Once you have Yudkowsky and Yvain, you have about 80% of what made LessWrong not shit.
3. Get Gwern: I don't read many of Gwern's posts; I just like having him around. Luckily for us, he never left!
After this is done, everyone else should wander back in, more or less.
Possible objections, with replies:
Objection: Most SSC articles and Yudkowsky essays are not on t |
d592427e-2a1c-4a0c-8de6-64e9bf3d4002 | trentmkelly/LessWrong-43k | LessWrong | Is this voting system strategy proof?
My voting system works like this. Each voter expresses their preferences for all candidates on a real numbered utility scale.
Then a Maximal lottery takes place over all lotteries over candidates. https://en.wikipedia.org/wiki/Maximal_lotteries
Lets describe this in more detail. Suppose there are 3 candidates. A,B,C.
The set of candidates is S={A,B,C}
A probability distribution over candidates looks like (A:30%,B:20%,C:50%)
This probability distribution is in ΔS the set of all probability distributions over S.
A probability distribution over probability distributions looks like ((A:30%,B:20%,C:50%):60%,(A:70%,B:30%,C:0%):40%)
Though note that there are infinitely many distributions, so most distributions-of-distributions will be assigning probability densities.
Also note that we can sample a candidate from this distribution over distributions by first sampling a distribution, and then sampling a candidate from that distribution. This is equivalent to integrating a distribution-of-distributions into a distribution over candidates and then sampling that.
A distribution is equivalent to a point in a triangle. A distribution over distributions is a probability density over a triangle, ie a non-negative function over the triangle (may include dirac deltas)
So the voters all mark their preferences on a numerical scale.
Then these votes get sent to Fred and George, 2 perfectly rational players in a 0 sum game.
Fred and George both propose probability distributions over the candidates.
Fred's utility is the number of candidates that strictly prefer Fred's proposed probability distribution over Georges, minus the number of voters that strictly prefer Georges distribution over Freds.
This game has a unique Nash equilibrium. This equilibrium is a distribution over distributions. Sample a candidate from this equilibrium to get the election winner.
I know that this has a few nice properties. If candidate A is the first choice of the majority, then A |
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