AF - Don't accelerate problems you're trying to solve by Andrea Miotti

<a href="https://www.alignmentforum.org/posts/4Pi3WhFb4jPphBzme/don-t-accelerate-problems-you-re-trying-to-solve">Link to original article</a><br/><br/>Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Don't accelerate problems you're trying to solve, published by Andrea Miotti on February 15, 2023 on The AI Alignment Forum. If one believes that unaligned AGI is a significant problem (>10% chance of leading to catastrophe), speeding up public progress towards AGI is obviously bad. Though it is obviously bad, there may be circumstances which require it. However, accelerating AGI should require a much higher bar of evidence and much more extreme circumstances than is commonly assumed. There are a few categories of arguments that claim intentionally advancing AI capabilities can be helpful for alignment, which do not meet this bar. Two cases of this argument are as follows It doesn't matter much to do work that pushes capabilities if others are likely to do the same or similar work shortly after. We should avoid capability overhangs, so that people are not surprised. To do so, we should extract as many capabilities as possible from existing AI systems. We address these two arguments directly, arguing that the downsides are much higher than they may appear, and touch on why we believe that merely plausible arguments for advancing AI capabilities aren’t enough. Dangerous argument 1: It doesn't matter much to do work that pushes capabilities if others are likely to do the same or similar work shortly after. For a specific instance of this, see Paul Christiano’s “Thoughts on the impact of RLHF research”: RLHF is just not that important to the bottom line right now. Imitation learning works nearly as well, other hacky techniques can do quite a lot to fix obvious problems [.] RLHF is increasingly important as time goes on, but it also becomes increasingly overdetermined that people would have done it. In general I think your expectation should be that incidental capabilities progress from safety research is a small part of total progress [.] Markets aren’t efficient, they only approach efficiency under heavy competition when people with relevant information put effort into making them efficient. This is true for machine learning, as there aren’t that many machine learning researchers at the cutting edge, and before ChatGPT there wasn’t a ton of market pressure on them. Perhaps something as low hanging as RLHF or something similar would have happened eventually, but this isn’t generally true. Don’t assume that something seemingly obvious to you is obvious to everyone. But even if something like RLHF or imitation learning would have happened eventually, getting small steps of progress slightly earlier can have large downstream effects. Progress often follows an s-curve, which appears exponential until the current research direction is exploited and tapers off. Moving an exponential up, even a little, early on can have large downstream consequences: The red line indicates when the first “lethal” AGI is deployed, and thus a hard deadline for us to solve alignment. A slight increase in progress now can lead to catastrophe significantly earlier! Pushing us up the early progress exponential has really bad downstream effects! And this is dangerous decision theory too: if every alignment researcher took a similar stance, their marginal accelerations would quickly add up. Dangerous Argument 2: We should avoid capability overhangs, so that people are not surprised. To do so, we should extract as many capabilities as possible from existing AI systems. Again, from Paul: Avoiding RLHF at best introduces an important overhang: people will implicitly underestimate the capabilities of AI systems for longer, slowing progress now but leading to faster and more abrupt change later as people realize they’ve been wrong. But there is no clear distinction between eliminating capability overhangs and discovering new capabilities. Eliminating capability overhangs is discovering AI capabilities faste...

First published

02/15/2023

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education

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Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Don't accelerate problems you're trying to solve, published by Andrea Miotti on February 15, 2023 on The AI Alignment Forum. If one believes that unaligned AGI is a significant problem (>10% chance of leading to catastrophe), speeding up public progress towards AGI is obviously bad. Though it is obviously bad, there may be circumstances which require it. However, accelerating AGI should require a much higher bar of evidence and much more extreme circumstances than is commonly assumed. There are a few categories of arguments that claim intentionally advancing AI capabilities can be helpful for alignment, which do not meet this bar. Two cases of this argument are as follows It doesn't matter much to do work that pushes capabilities if others are likely to do the same or similar work shortly after. We should avoid capability overhangs, so that people are not surprised. To do so, we should extract as many capabilities as possible from existing AI systems. We address these two arguments directly, arguing that the downsides are much higher than they may appear, and touch on why we believe that merely plausible arguments for advancing AI capabilities aren’t enough. Dangerous argument 1: It doesn't matter much to do work that pushes capabilities if others are likely to do the same or similar work shortly after. For a specific instance of this, see Paul Christiano’s “Thoughts on the impact of RLHF research”: RLHF is just not that important to the bottom line right now. Imitation learning works nearly as well, other hacky techniques can do quite a lot to fix obvious problems [.] RLHF is increasingly important as time goes on, but it also becomes increasingly overdetermined that people would have done it. In general I think your expectation should be that incidental capabilities progress from safety research is a small part of total progress [.] Markets aren’t efficient, they only approach efficiency under heavy competition when people with relevant information put effort into making them efficient. This is true for machine learning, as there aren’t that many machine learning researchers at the cutting edge, and before ChatGPT there wasn’t a ton of market pressure on them. Perhaps something as low hanging as RLHF or something similar would have happened eventually, but this isn’t generally true. Don’t assume that something seemingly obvious to you is obvious to everyone. But even if something like RLHF or imitation learning would have happened eventually, getting small steps of progress slightly earlier can have large downstream effects. Progress often follows an s-curve, which appears exponential until the current research direction is exploited and tapers off. Moving an exponential up, even a little, early on can have large downstream consequences: The red line indicates when the first “lethal” AGI is deployed, and thus a hard deadline for us to solve alignment. A slight increase in progress now can lead to catastrophe significantly earlier! Pushing us up the early progress exponential has really bad downstream effects! And this is dangerous decision theory too: if every alignment researcher took a similar stance, their marginal accelerations would quickly add up. Dangerous Argument 2: We should avoid capability overhangs, so that people are not surprised. To do so, we should extract as many capabilities as possible from existing AI systems. Again, from Paul: Avoiding RLHF at best introduces an important overhang: people will implicitly underestimate the capabilities of AI systems for longer, slowing progress now but leading to faster and more abrupt change later as people realize they’ve been wrong. But there is no clear distinction between eliminating capability overhangs and discovering new capabilities. Eliminating capability overhangs is discovering AI capabilities faste...

Duration

9 minutes

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The Nonlinear Library: Alignment Forum Weekly

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