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So8res comments on My current thoughts on MIRI's "highly reliable agent design" work - Effective Altruism Forum

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Comment author: So8res 08 July 2017 09:10:42PM *  20 points [-]

Thanks for this solid summary of your views, Daniel. For others’ benefit: MIRI and Open Philanthropy Project staff are in ongoing discussion about various points in this document, among other topics. Hopefully some portion of those conversations will be made public at a later date. In the meantime, a few quick public responses to some of the points above:

2) If we fundamentally "don't know what we're doing" because we don't have a satisfying description of how an AI system should reason and make decisions, then we will probably make lots of mistakes in the design of an advanced AI system.

3) Even minor mistakes in an advanced AI system's design are likely to cause catastrophic misalignment.

I think this is a decent summary of why we prioritize HRAD research. I would rephrase 3 as "There are many intuitively small mistakes one can make early in the design process that cause resultant systems to be extremely difficult to align with operators’ intentions.” I’d compare these mistakes to the “small” decision in the early 1970s to use null-terminated instead of length-prefixed strings in the C programming language, which continues to be a major source of software vulnerabilities decades later.

I’d also clarify that I expect any large software product to exhibit plenty of actually-trivial flaws, and that I don’t expect that AGI code needs to be literally bug-free or literally proven-safe in order to be worth running. Furthermore, if an AGI design has an actually-serious flaw, the likeliest consequence that I expect is not catastrophe; it’s just that the system doesn’t work. Another likely consequence is that the system is misaligned, but in an obvious ways that makes it easy for developers to recognize that deployment is a very bad idea. The end goal is to prevent global catastrophes, but if a safety-conscious AGI team asked how we’d expect their project to fail, the two likeliest scenarios we’d point to are "your team runs into a capabilities roadblock and can't achieve AGI" or "your team runs into an alignment roadblock and can easily tell that the system is currently misaligned, but can’t figure out how to achieve alignment in any reasonable amount of time."

This case does not revolve around any specific claims about specific potential failure modes, or their relationship to specific HRAD subproblems. This case revolves around the value of fundamental understanding for avoiding "unknown unknown" problems.

We worry about "unknown unknowns", but I’d probably give them less emphasis here. We often focus on categories of failure modes that we think are easy to foresee. As a rule of thumb, when we prioritize a basic research problem, it’s because we expect it to help in a general way with understanding AGI systems and make it easier to address many different failure modes (both foreseen and unforeseen), rather than because of a one-to-one correspondence between particular basic research problems and particular failure modes.

As an example, the reason we work on logical uncertainty isn’t that we’re visualizing a concrete failure that we think is highly likely to occur if developers don't understand logical uncertainty. We work on this problem because any system reasoning in a realistic way about the physical world will need to reason under both logical and empirical uncertainty, and because we expect broadly understanding how the system is reasoning about the world to be important for ensuring that the optimization processes inside the system are aligned with the intended objectives of the operators.

A big intuition behind prioritizing HRAD is that solutions to “how do we ensure the system’s cognitive work is being directed at solving the right problems, and at solving them in the desired way?” are likely to be particularly difficult to hack together from scratch late in development. An incomplete (empirical-side-only) understanding of what it means to optimize objectives in realistic environments seems like it will force designers to rely more on guesswork and trial-and-error in a lot of key design decisions.

I haven't found any instances of complete axiomatic descriptions of AI systems being used to mitigate problems in those systems (e.g. to predict, postdict, explain, or fix them) or to design those systems in a way that avoids problems they'd otherwise face.

This seems reasonable to me in general. I’d say that AIXI has had limited influence in part because it’s combining several different theoretical insights that the field was already using (e.g., complexity penalties and backtracking tree search), and the synthesis doesn’t add all that much once you know about the parts. Sections 3 and 4 of MIRI's Approach provide some clearer examples of what I have in mind by useful basic theory: Shannon, Turing, Bayes, etc.

My perspective on this is a combination of “basic theory is often necessary for knowing what the right formal tools to apply to a problem are, and for evaluating whether you're making progress toward a solution” and “the applicability of Bayes, Pearl, etc. to AI suggests that AI is the kind of problem that admits of basic theory.” An example of how this relates to HRAD is that I think that Bayesian justifications are useful in ML, and that a good formal model of rationality in the face of logical uncertainty is likely to be useful in analogous ways. When I speak of foundational understanding making it easy to design the right systems, I’m trying to point at things like the usefulness of Bayesian justifications in modern ML. (I’m unclear on whether we miscommunicated about what sort of thing I mean by “basic insights”, or whether we have a disagreement about how useful principled justifications are in modern practice when designing high-reliability systems.)

Comment author: Daniel_Dewey 10 July 2017 07:11:25PM 3 points [-]

My perspective on this is a combination of “basic theory is often necessary for knowing what the right formal tools to apply to a problem are, and for evaluating whether you're making progress toward a solution” and “the applicability of Bayes, Pearl, etc. to AI suggests that AI is the kind of problem that admits of basic theory.” An example of how this relates to HRAD is that I think that Bayesian justifications are useful in ML, and that a good formal model of rationality in the face of logical uncertainty is likely to be useful in analogous ways. When I speak of foundational understanding making it easy to design the right systems, I’m trying to point at things like the usefulness of Bayesian justifications in modern ML. (I’m unclear on whether we miscommunicated about what sort of thing I mean by “basic insights”, or whether we have a disagreement about how useful principled justifications are in modern practice when designing high-reliability systems.)

Just planting a flag to say that I'm thinking more about this so that I can respond well.

Comment author: Daniel_Dewey 10 July 2017 07:10:12PM 3 points [-]

Thanks Nate!

The end goal is to prevent global catastrophes, but if a safety-conscious AGI team asked how we’d expect their project to fail, the two likeliest scenarios we’d point to are "your team runs into a capabilities roadblock and can't achieve AGI" or "your team runs into an alignment roadblock and can easily tell that the system is currently misaligned, but can’t figure out how to achieve alignment in any reasonable amount of time."

This is particularly helpful to know.

We worry about "unknown unknowns", but I’d probably give them less emphasis here. We often focus on categories of failure modes that we think are easy to foresee. As a rule of thumb, when we prioritize a basic research problem, it’s because we expect it to help in a general way with understanding AGI systems and make it easier to address many different failure modes (both foreseen and unforeseen), rather than because of a one-to-one correspondence between particular basic research problems and particular failure modes.

Can you give an example or two of failure modes or "categories of failure modes that are easy to foresee" that you think are addressed by some HRAD topic? I'd thought previously that thinking in terms of failure modes wasn't a good way to understand HRAD research.

As an example, the reason we work on logical uncertainty isn’t that we’re visualizing a concrete failure that we think is highly likely to occur if developers don't understand logical uncertainty. We work on this problem because any system reasoning in a realistic way about the physical world will need to reason under both logical and empirical uncertainty, and because we expect broadly understanding how the system is reasoning about the world to be important for ensuring that the optimization processes inside the system are aligned with the intended objectives of the operators.

I'm confused by this as a follow-up to the previous paragraph. This doesn't look like an example of "focusing on categories of failure modes that are easy to foresee," it looks like a case where you're explicitly not using concrete failure modes to decide what to work on.

“how do we ensure the system’s cognitive work is being directed at solving the right problems, and at solving them in the desired way?”

I feel like this fits with the "not about concrete failure modes" narrative that I believed before reading your comment, FWIW.

Comment author: So8res 10 July 2017 10:46:05PM *  5 points [-]

Can you give an example or two of failure modes or "categories of failure modes that are easy to foresee" that you think are addressed by some HRAD topic? I'd thought previously that thinking in terms of failure modes wasn't a good way to understand HRAD research.

I want to steer clear of language that might make it sound like we’re saying:

  • X 'We can't make broad-strokes predictions about likely ways that AGI could go wrong.'

  • X 'To the extent we can make such predictions, they aren't important for informing research directions.'

  • X 'The best way to address AGI risk is just to try to advance our understanding of AGI in a general and fairly undirected way.'

The things I do want to communicate are:

  • All of MIRI's research decisions are heavily informed by a background view in which there are many important categories of predictable failure, e.g., 'the system is steering toward edges of the solution space', 'the function the system is optimizing correlates with the intended function at lower capability levels but comes uncorrelated at high capability levels', 'the system has incentives to obfuscate and mislead programmers to the extent it models its programmers’ beliefs and expects false programmer beliefs to result in it better-optimizing its objective function.’

  • The main case for HRAD problems is that we expect them to help in a gestalt way with many different known failure modes (and, plausibly, unknown ones). E.g., 'developing a basic understanding of counterfactual reasoning improves our ability to understand the first AGI systems in a general way, and if we understand AGI better it's likelier we can build systems to address deception, edge instantiation, goal instability, and a number of other problems'.

  • There usually isn't a simple relationship between a particular open problem and a particular failure mode, but if we thought there were no way to predict in advance any of the ways AGI systems can go wrong, or if we thought a very different set of failures were likely instead, we'd have different research priorities.