research_prime_space

• research_prime_space 20 Oct 2023 18:06 UTC

  LW: 2 AF: 2

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  on: Com­par­ing An­thropic’s Dic­tionary Learn­ing to Ours

  This is cool! These sparse features should be easily “extractable” by the transformer’s key, query, and value weights in a single layer. Therefore, I’m wondering if these weights can somehow make it easier to “discover” the sparse features?

Ex­cit­ing New In­ter­pretabil­ity Paper!

• research_prime_space 10 Apr 2023 0:13 UTC

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  in reply to: scasper’s comment on: Pe­nal­ize Model Com­plex­ity Via Self-Distillation

  1. I don’t really think that 1. would be true—following DAN-style prompts is the minimum complexity solution. You want to act in accordance with the prompt.

  2. Backdoors don’t emerge naturally. So if it’s computationally infeasible to find an input where the original model and the backdoored model differ, then self-distillation on the backdoored model is going to be the same as self-distillation on the original model.

  The only scenario where I think self-distillation is useful would be if 1) you train a LLM on a dataset, 2) fine-tune it to be deceptive/​power-seeking, and 3) self-distill it on the original dataset, then self-distilled model would likely no longer be deceptive/​power-seeking.

• research_prime_space 8 Apr 2023 2:49 UTC

  LW: 2 AF: 2

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  in reply to: scasper’s comment on: Pe­nal­ize Model Com­plex­ity Via Self-Distillation

  I think self-distillation is better than network compression, as it possesses some decently strong theoretical guarantees that you’re reducing the complexity of the function. I haven’t really seen the same with the latter.

  But what research do you think would be valuable, other than the obvious (self-distill a deceptive, power-hungry model to see if the negative qualities go away)?

• research_prime_space 8 Apr 2023 0:58 UTC

  2 points

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  in reply to: quetzal_rainbow’s comment on: Pe­nal­ize Model Com­plex­ity Via Self-Distillation

  As of right now, I don’t think that LLMs are trained to be power seeking and deceptive.

  Power-seeking is likely if the model is directly maximizing rewards, but LLMs are not quite doing this.

• research_prime_space 7 Apr 2023 16:02 UTC

  LW: 1 AF: 1

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  on: Pe­nal­ize Model Com­plex­ity Via Self-Distillation

  I just wanted to add another angle. Neural networks have a fundamental “simplicity bias”, where they learn low frequency components exponentially faster than high frequency components. Thus, self-distillation is likely to be more efficient than training on the original dataset (the function you’re learning has fewer high frequency components). This paper formalizes this claim.

  But in practice, what this means is that training GPT-3.5 from scratch is hard but simply copying GPT-3.5 is pretty easy. Stanford was recently able to finetune a pretty bad 7B model to be as good as GPT-3.5 using only 52K examples (generated from GPT-3.5) and $600 of compute. This means that once a GPT is out there, it’s fairly easy for malevolent actors to replicate it. And while it’s unlikely that the original GPT model, given its strong simplicity bias, is engaging in complicated deceptive behavior, it’s highly likely that the malevolent actor has finetuned their model to be deceptive and power-seeking. This creates a perfect storm where malevolent AI can go rogue. I think this is a significant threat, and OpenAI should add some more guardrails to try and prevent this.

Pe­nal­ize Model Com­plex­ity Via Self-Distillation

• research_prime_space 6 Mar 2023 23:35 UTC

  1 point

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  in reply to: baturinsky’s comment on: Cap Model Size for AI Safety

  I feel like capping the memory of GPUs would also affect normal folk who just want to train simple models, so it may be less likely to be implemented. It also doesn’t really cap the model size, which is the main problem.

  But I agree it would be easier to enforce, and certainly, much better than the status quo.

• research_prime_space 6 Mar 2023 23:33 UTC

  1 point

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  in reply to: JBlack’s comment on: Cap Model Size for AI Safety

  I think you make a lot of great points.

  I think some sort of cap is the one of the highest impact things we can do from a safety perspective. I agree that imposing the cap effectively and getting buy-in from broader society is a challenge, however, these problems are a lot more tractable than AI safety.

  I haven’t heard anybody else propose this so I wanted to float it out there.

Cap Model Size for AI Safety

• research_prime_space 20 Dec 2022 1:41 UTC

  1 point

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  on: A sim­ple ap­proach to AI safety

  I’d love some feedback on this if possible, thank you!

• research_prime_space 12 Dec 2022 23:18 UTC

  1 point

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  in reply to: Jon Garcia’s comment on: Is AI ac­tu­ally power-seek­ing?

  Thanks, I appreciate the explanation!

• research_prime_space 12 Dec 2022 23:17 UTC

  1 point

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  in reply to: Charlie Steiner’s comment on: Is AI ac­tu­ally power-seek­ing?

  Thanks, that’s a really helpful framing!

Sim­ple Way to Prevent Power-Seek­ing AI

• research_prime_space 4 Dec 2022 2:10 UTC

  1 point

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  in reply to: Jon Garcia’s comment on: Is AI ac­tu­ally power-seek­ing?

  I agree with everything you’ve said. Obviously, AI (in most domains) would need to evaluate its plans in the real world to acquire training data. But my point is that we have the choice to not carry out some of the agent’s plans in the real-world. For some of the AI’s plans, we can say no—we have a veto button. It seems to me that the AI would be completely fine with that—is that correct? If so, it makes safety a much more tractable problem than it otherwise would be.

• research_prime_space 15 Jun 2017 19:51 UTC

  2 points

  on: Open thread, June. 12 - June. 18, 2017

  I have a question about AI safety. I’m sorry in advance if it’s too obvious, I just couldn’t find an answer on the internet or in my head.

  The way AI has bad consequences is through its drive to maximize (destroys the world in order to produce paperclips more efficiently). If you instead designed AIs to: 1) find a function/​algorithm within an error range of the goal, 2)stop once that method is found, 3) do 1) and 2) while minimizing the amount of resources it uses and/​or its effect on the outside world

  If the above could be incorporated as a convention into any AI designed, would that mitigate the risk of AI going “rougue”?

• research_prime_space 14 Jun 2017 21:40 UTC

  5 points

  on: Wel­come to Less Wrong!

  Hi! I’m 18 years old, female, and a college student (don’t want to release personal information beyond that!). I’m majoring in math, and I hopefully want to use those skills for AI research :D

  I found you guys from EA, and I started reading the sequences last week, but I really do have a burning question I want to post to the Discussion board so I made an account.