Near-term motivation for AGI alignment

AGI alignment work is usually considered “longtermist”, which is about preserving humanity’s long-term potential. This was the primary motivation for this work when the alignment field got started around 20 years ago, and AGI seemed far away or impossible to most people in AI. However, given the current rate of progress towards general AI capabilities, there is an increasingly relevant near-term motivation to think about alignment, even if you mostly or only care about people alive today. This is most of my personal motivation for working on alignment.

I would not be surprised if AGI is reached in the next few decades, similarly to the latest AI expert survey‘s median of 2059 for human-level AI (as estimated by authors at top ML conferences) and the Metaculus median of 2039. The Precipice gives a 10% probability of human extinction this century due to AI, i.e. within the lifetime of children alive today (and I would expect most of this probability to be concentrated in the next few decades, i.e. within our lifetimes). I used to refer to AGI alignment work as “long-term AI safety” but this term seems misleading now, since alignment would be more accurately described as “medium-term safety”. 

While AGI alignment has historically been associated with longtermism, there is a downside of referring to longtermist arguments for alignment concerns. Sometimes people seem to conclude that they don’t need to worry about alignment if they don’t care much about the long-term future. For example, one commonly cited argument for trying to reduce existential risk from AI is that “even if it’s unlikely and far away, it’s so important that we should worry about it anyway”. People understandably interpret this as Pascal’s mugging and bounce off. This kind of argument for alignment concerns is not very relevant these days, because existential risk from AI is not that unlikely (10% this century is actually a lot, and may be a conservative estimate) and AGI not that far away (an average of 36 years in the AI expert survey). 

Similarly, when considering specific paths to catastrophic risk from AGI, a typical longtermist scenario involves AGI inventing molecular nanotechnology, which understandably sounds implausible to most people. I think a more likely path to catastrophic risk would involve AGI precipitating other catastrophic risks like pandemics (e.g. by doing biotechnology research) or taking over the global economy. If you’d like to learn about the most pertinent arguments for alignment concerns and plausible paths for AI to gain an advantage over humanity, check out Holden Karnofsky’s Most Important Century blog post series. 

In terms of my own motivation, honestly I don’t care that much about whether humanity gets to colonize the stars, reducing astronomical waste, or large numbers of future people existing. These outcomes would be very cool but optional in my view. Of course I would like humanity to have a good long-term future, but I mostly care about people alive today. My main motivation for working on alignment is that I would like my loved ones and everyone else on the planet to have a future. 

Sometimes people worry about a tradeoff between alignment concerns and other aspects of AI safety, such as ethics / fairness, but I still think this tradeoff is pretty weak. There are also many common interests between alignment and ethics that would be great for these communities to coordinate on. This includes developing industry-wide safety standards and AI governance mechanisms, setting up model evaluations for safety, and slow and cautious deployment of advanced AI systems. Ultimately all these safety problems need to be solved to ensure that AGI systems have a positive impact on the world. I think the distribution of effort between AI capabilities and safety will need to shift more towards safety as more advanced AI systems are developed. 

In conclusion, you don’t have to be a longtermist to care about AGI alignment. I think the possible impacts on people alive today are significant enough to think about this problem, and the next decade is going to be a critical time for steering advanced AI technology towards safety. If you’d like to contribute, here is a list of research agendas in this space, and a good course to get up to speed on the fundamentals of AGI alignment.

Refining the Sharp Left Turn threat model

(Coauthored with others on the alignment team and cross-posted from the alignment forum: part 1, part 2)

A sharp left turn (SLT) is a possible rapid increase in AI system capabilities (such as planning and world modeling) that could result in alignment methods no longer working. This post aims to make the sharp left turn scenario more concrete. We will discuss our understanding of the claims made in this threat model, propose some mechanisms for how a sharp left turn could occur, how alignment techniques could manage a sharp left turn or fail to do so.

Image credit: Adobe

Claims of the threat model

What are the main claims of the “sharp left turn” threat model?

Claim 1. Capabilities will generalize far (i.e., to many domains)

There is an AI system that:

• Performs well: it can accomplish impressive feats, or achieve high scores on valuable metrics.
• Generalizes, i.e., performs well in new domains, which were not optimized for during training, with no domain-specific tuning.

Generalization is a key component of this threat model because we’re not going to directly train an AI system for the task of disempowering humanity, so for the system to be good at this task, the capabilities it develops during training need to be more broadly applicable. 

Some optional sub-claims can be made that increase the risk level of the threat model:

Claim 1a [Optional]: Capabilities (in different “domains”) will all generalize at the same time

Claim 1b [Optional]: Capabilities will generalize far in a discrete phase transition (rather than continuously) 

Claim 2. Alignment techniques that worked previously will fail during this transition

• Qualitatively different alignment techniques are needed. The ways the techniques work apply to earlier versions of the AI technology, but not to the new version because the new version gets its capability through something new, or jumps to a qualitatively higher capability level (even if through “scaling” the same mechanisms).

Claim 3: Humans can’t intervene to prevent or align this transition 

• Path 1: humans don’t notice because it’s too fast (or they aren’t paying attention)
• Path 2: humans notice but are unable to make alignment progress in time
• Some combination of these paths, as long as the end result is insufficiently correct alignment

Paradigms of AI alignment: components and enablers

(This post is based on an overview talk I gave at UCL EA and Oxford AI society (recording here). Cross-posted to the Alignment Forum. Thanks to Janos Kramar for detailed feedback on this post and to Rohin Shah for feedback on the talk.)

This is my high-level view of the AI alignment research landscape and the ingredients needed for aligning advanced AI. I would divide alignment research into work on alignment components, focusing on different elements of an aligned system, and alignment enablers, which are research directions that make it easier to get the alignment components right.

• Alignment components
  • Outer alignment
  • Inner alignment
• Alignment enablers
  • Mechanistic interpretability
  • Understanding bad incentives
  • Foundations

You can read in more detail about work going on in these areas in my list of AI safety resources.

Tradeoff between desirable properties for baseline choices in impact measures

(Cross-posted to the Alignment Forum. Summarized in Alignment Newsletter #108. Thanks to Carroll Wainwright, Stuart Armstrong, Rohin Shah and Alex Turner for helpful feedback on this post.)

Impact measures are auxiliary rewards for low impact on the agent’s environment, used to address the problems of side effects and instrumental convergence. A key component of an impact measure is a choice of baseline state: a reference point relative to which impact is measured. Commonly used baselines are the starting state, the initial inaction baseline (the counterfactual where the agent does nothing since the start of the episode) and the stepwise inaction baseline (the counterfactual where the agent does nothing instead of its last action). The stepwise inaction baseline is currently considered the best choice because it does not create the following bad incentives for the agent: interference with environment processes or offsetting its own actions towards the objective. This post will discuss a fundamental problem with the stepwise inaction baseline that stems from a tradeoff between different desirable properties for baseline choices, and some possible alternatives for resolving this tradeoff.

One clearly desirable property for a baseline choice is to effectively penalize high-impact effects, including delayed effects. It is well-known that the simplest form of the stepwise inaction baseline does not effectively capture delayed effects. For example, if the agent drops a vase from a high-rise building, then by the time the vase reaches the ground and breaks, the broken vase will be the default outcome. Thus, in order to penalize delayed effects, the stepwise inaction baseline is usually used in conjunction with inaction rollouts, which predict future outcomes of the inaction policy. Inaction rollouts from the current state and the stepwise baseline state are compared to identify delayed effects of the agent’s actions. In the above example, the current state contains a vase in the air, so in the inaction rollout from the current state the vase will eventually reach the ground and break, while in the inaction rollout from the stepwise baseline state the vase remains intact. 

Possible takeaways from the coronavirus pandemic for slow AI takeoff

(Cross-posted to LessWrong. Summarized in Alignment Newsletter #104. Thanks to Janos Kramar for helpful feedback on this post.)

As the covid-19 pandemic unfolds, we can draw lessons from it for managing future global risks, such as other pandemics, climate change, and risks from advanced AI. In this post, I will focus on possible implications for AI risk. For a broader treatment of this question, I recommend FLI’s covid-19 page that includes expert interviews on the implications of the pandemic for other types of risks. 

A key element in AI risk scenarios is the speed of takeoff – whether advanced AI is developed gradually or suddenly. Paul Christiano’s post on takeoff speeds defines slow takeoff in terms of the economic impact of AI as follows: “There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles.” It argues that slow AI takeoff is more likely than fast takeoff, but is not necessarily easier to manage, since it poses different challenges, such as large-scale coordination. This post expands on this point by examining some parallels between the coronavirus pandemic and a slow takeoff scenario. The upsides of slow takeoff include the ability to learn from experience, act on warning signs, and reach a timely consensus that there is a serious problem. I would argue that the covid-19 pandemic had these properties, but most of the world’s institutions did not take advantage of them. This suggests that, unless our institutions improve, we should not expect the slow AI takeoff scenario to have a good default outcome. 

2019-20 New Year review

This is an annual post reviewing the last year and making resolutions and predictions for next year. This year’s edition features sleep tracking, intermittent fasting, overcommitment busting, and evaluating calibration for all annual predictions since 2014.

2019 review

AI safety research:

• Wrote an updated version of the relative reachability paper, including an ablation study on design choices.
• Coauthored a paper on modeling AGI Safety frameworks with causal influence diagrams, accepted to IJCAI workshop.
• Wrote a paper on avoiding side effects by considering future tasks, accepted to NeurIPS workshop.
• Co-ran a subteam of the safety team focusing on agent incentive design (ongoing).

AI safety outreach:

• Co-organized FLI’s Beneficial AGI conference in Puerto Rico, a more long-term focused sequel to the original Puerto Rico conference and the Asilomar conference. This year I was the program chair for the technical safety track of the conference.
• Co-organized the ICLR AI safety workshop, Safe Machine Learning: Specification, Robustness and Assurance. This was my first time running a paper reviewing process.
• Gave a talk at the IJCAI AI safety workshop on specification, robustness an assurance problems.
• Took part in the DeepMind podcast episode on AI safety (“I, robot”).

Retrospective on the specification gaming examples list

My post about the specification gaming list was recently nominated for the LessWrong 2018 Review (sort of like a test of time award), which prompted me to write a retrospective (cross-posted here). 

I’ve been pleasantly surprised by how much this resource has caught on in terms of people using it and referring to it (definitely more than I expected when I made it). There were 30 examples on the list when was posted in April 2018, and 20 new examples have been contributed through the form since then.  I think the list has several properties that contributed to wide adoption: it’s fun, standardized, up-to-date, comprehensive, and collaborative.

Some of the appeal is that it’s fun to read about AI cheating at tasks in unexpected ways (I’ve seen a lot of people post on Twitter about their favorite examples from the list). The standardized spreadsheet format seems easier to refer to as well. I think the crowdsourcing aspect is also helpful – this helps keep it current and comprehensive, and people can feel some ownership of the list since can personally contribute to it. My overall takeaway from this is that safety outreach tools are more likely to be impactful if they are fun and easy for people to engage with.

This list had a surprising amount of impact relative to how little work it took me to put it together and maintain it. The hard work of finding and summarizing the examples was done by the people putting together the lists that the master list draws on (Gwern, Lehman, Olsson, Irpan, and others), as well as the people who submit examples through the form. What I do is put them together in a common format and clarify and/or shorten some of the summaries. I also curate the examples to determine whether they fit the definition of specification gaming (as opposed to simply a surprising behavior or solution). Overall, I’ve probably spent around 10 hours so far on creating and maintaining the list, which is not very much. This makes me wonder if there is other low hanging fruit in the safety resources space that we haven’t picked yet. 

I have been using it both as an outreach and research tool. On the outreach side, the resource has been helpful for making the argument that safety problems are hard and need general solutions, by making it salient just in how many ways things could go wrong. When presented with an individual example of specification gaming, people often have a default reaction of “well, you can just close the loophole like this”. It’s easier to see that this approach does not scale when presented with 50 examples of gaming behaviors. Any given loophole can seem obvious in hindsight, but 50 loopholes are much less so. I’ve found this useful for communicating a sense of the difficulty and importance of Goodhart’s Law. 

On the research side, the examples have been helpful for trying to clarify the distinction between reward gaming and tampering problems. Reward gaming happens when the reward function is designed incorrectly (so the agent is gaming the design specification), while reward tampering happens when the reward function is implemented incorrectly or embedded in the environment (and so can be thought of as gaming the implementation specification). The boat race example is reward gaming, since the score function was defined incorrectly, while the Qbert agent finding a bug that makes the platforms blink and gives the agent millions of points is reward tampering. We don’t currently have any real examples of the agent gaining control of the reward channel (probably because the action spaces of present-day agents are too limited), which seems qualitatively different from the numerous examples of agents exploiting implementation bugs. 

I’m curious what people find the list useful for – as a safety outreach tool, a research tool or intuition pump, or something else? I’d also be interested in suggestions for improving the list (formatting, categorizing, etc). Thanks everyone who has contributed to the resource so far!

Classifying specification problems as variants of Goodhart’s Law

(Coauthored with Ramana Kumar and cross-posted from the Alignment Forum. Summarized in Alignment Newsletter #76.)

There are a few different classifications of safety problems, including the Specification, Robustness and Assurance (SRA) taxonomy and the Goodhart’s Law taxonomy. In SRA, the specification category is about defining the purpose of the system, i.e. specifying its incentives.  Since incentive problems can be seen as manifestations of Goodhart’s Law, we explore how the specification category of the SRA taxonomy maps to the Goodhart taxonomy. The mapping is an attempt to integrate different breakdowns of the safety problem space into a coherent whole. We hope that a consistent classification of current safety problems will help develop solutions that are effective for entire classes of problems, including future problems that have not yet been identified.

The SRA taxonomy defines three different types of specifications of the agent’s objective: ideal (a perfect description of the wishes of the human designer), design (the stated objective of the agent) and revealed (the objective recovered from the agent’s behavior). It then divides specification problems into design problems (e.g. side effects) that correspond to a difference between the ideal and design specifications, and emergent problems (e.g. tampering) that correspond to a difference between the design and revealed specifications.

In the Goodhart taxonomy, there is a variable U* representing the true objective, and a variable U representing the proxy for the objective (e.g. a reward function). The taxonomy identifies four types of Goodhart effects: regressional (maximizing U also selects for the difference between U and U*), extremal (maximizing U takes the agent outside the region where U and U* are correlated), causal (the agent intervenes to maximize U in a way that does not affect U*), and adversarial (the agent has a different goal W and exploits the proxy U to maximize W).

We think there is a correspondence between these taxonomies: design problems are regressional and extremal Goodhart effects, while emergent problems are causal Goodhart effects. The rest of this post will explain and refine this correspondence.

ICLR Safe ML workshop report

This year the ICLR conference hosted topic-based workshops for the first time (as opposed to a single track for workshop papers), and I co-organized the Safe ML workshop. One of the main goals was to bring together near and long term safety research communities.

The workshop was structured according to a taxonomy that incorporates both near and long term safety research into three areas – specification, robustness and assurance.

Specification: define the purpose of the system	Robustness: design system to withstand perturbations	Assurance: monitor and control system activity
Reward hacking Side effects Preference learning Fairness	Adaptation Verification Worst-case robustness Safe exploration	Interpretability Monitoring Privacy Interruptibility

We had an invited talk and a contributed talk in each of the three areas.

2018-19 New Year review

2018 progress

Research / AI safety:

• Wrote a paper on measuring side effects using relative reachability in May, and presented the results at the ICML GoalsRL workshop and the AI safety summer school. Since then, some new approaches have come out using my method as a baseline :).
• Made a list of 30 specification gaming examples in AI (assembled from several existing lists). Since the list was posted in April, 16 new examples have been contributed through the form (thanks everyone!). The list received some attention on Twitter, and I was interviewed about it by Wired and the Times.
• Was in the top 30% of NeurIPS reviewers.
• Gave talks at the Oxford AI Society, EA Global London, etc.
• Got involved in organizing the upcoming ICLR AI safety workshop, Safe Machine Learning: Specification, Robustness and Assurance.

Rationality / effectiveness:

• Attended the CFAR mentoring workshop in Prague, and started running rationality training sessions with Janos at our group house.
• Started using work cycles – focused work blocks (e.g. pomodoros) with built-in reflection prompts. I think this has increased my productivity and focus to some degree. The prompt “how will I get started?” has been surprisingly helpful given its simplicity.
• Stopped eating processed sugar for health reasons at the end of 2017 and have been avoiding it ever since.
  • This has been surprisingly easy, especially compared to my earlier attempts to eat less sugar. I think there are two factors behind this: avoiding sugar made everything taste sweeter (so many things that used to taste good now seem inedibly sweet), and the mindset shift from “this is a luxury that I shouldn’t indulge in” to “this is not food”.
  • Unfortunately, I can’t make any conclusions about the effects on my mood variables because of some issues with my data recording process :(.
• Declining levels of insomnia (excluding jetlag):
  • 22% of nights in the first half of 2017, 16% in the second half of 2017, 16% in the first half of 2018, 10% in the second half of 2018.
  • This is probably an effect of the sleep CBT program I did in 2017, though avoiding sugar might be a factor as well.
• Made some progress on reducing non-research commitments (talks, reviewing, organizing, etc).
  • Set up some systems for this: a spreadsheet to keep track of requests to do things (with 0-3 ratings for workload and 0-2 ratings for regret) and a form to fill out whenever I’m thinking of accepting a commitment.
  • My overall acceptance rate for commitments has gone down a bit from 29% in 2017 to 24% in 2018. The average regret per commitment went down from 0.66 in 2017 to 0.53 in 2018.
  • However, since the number of requests has gone up, I ended up with more things to do overall: 12 commitments with a total of 23 units of workload in 2017 vs 19 commitments with a total of 33 units of workload in 2018. (1 unit of workload ~ 5 hours)