As a researcher I’ve also been spending a lot of time with models, talking through various ideas. I don’t think I will surprise anyone when I say that they’re obviously getting better, even over the course of the past few months. While I don’t have Mythos and $100k to spend, I have been able to query at least one new advanced unreleased model, and I also have received some surprising new “results” to questions that I’ve been interested in for a few years.
Which brings me to the real problem: just because a model spits out an apparent new result, this does not mean the result is real. Even if models are good at producing real results, they’re much better at producing results that look real but are misleading. This can be enormously frustrating, and often means that human attention is more necessary than ever.
There are exceptions to this rule: for “full” attacks like HAWK, where the attack runs in a few hours (against a weaker version of the scheme), verification is extremely easy. You can just send over the code and let anyone check that it recovers keys and signs real chosen messages. For more subtle speedup attacks like the AES result, checking validity is not so easy. Here the approach is more specific: formally-verifiable Lean proofs can help here, but (even where these proofs are easy to make), such proofs are still highly sensitive to how you’ve formulated the theorem statement, and that often requires human experts to check.
You’ll probably notice that many of the exciting recentmathematical results have had this flavor: they either include a machine-checkable proof of a well-understood theorem, or (like the Jacobian conjecture) they involve finding a simple counterexample you can compute on. Alternatively, a bunch of experts spent a lot of time reviewing the result and were eventually convinced by it. This need for some humans to check the work is going to slow down our progress. For non-devastating examples of cryptanalysis, this is probably where we’re going to be for a while.
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With that said, I said there was good news, and I meant it. Right now we’re in the midst of a historic transition from traditional public-key algorithms based on EC-based cryptography and RSA, moving over to new post-quantum algorithms based on novel problems. This is why there are so many standards like HAWK being considered. If there was ever a perfect time for a massive new public cryptanalysis capability to come on line, we’re in it. So unless AIs succeed in undermining all of our hard problems altogether (or we live in Impagliazzo’s Minicrypt) then this could not be a better time for AI to get good at cryptanalysis. In the best case, the result is that we gain real confidence in the problems we’ve identified, and the cryptanalysis literature gets a lot more robust. Hopefully.
For scientists: this is also a wonderful time. You now have a plastic pal who’s fun to be with, and you can talk over your hardest problems. At the same time it’s not yet smart enough that it can solve all of them without your assistance. And even better, the pace of new findings is speeding way up. This is mostly good! If you’re energetic. I still have many questions, like: “who should get credit for these new results” and “who will review all of these new results” but so far I’m not panicked. The world is getting modestly better. For now.
For the world: I don’t know. If you’re under the impression that these models are “glorified autocomplete” or that progress is slowing down, I need to urge you: stop thinking that. The models are very intelligent and capable, they are getting better at a fast clip. I can cite measurable and impressive progress over just the past five months on specific types of problem I’ve asked them to look at. If there’s a ceiling out there, I don’t yet see evidence of it. The people who think models are dumb are mostly using Google’s free AI search results, and not interacting with the high-end stuff (which only costs $20, so it’s not out of reach.) And they’re mostly not working in new areas.
On the other hand: if you think that models are super-intelligent or that AGI is already here, you should also stop thinking that. Working with these tools is like swimming in a pond where the ground drops off sharply. One minute you’re wading comfortably and there’s support under your feet. Then suddenly you cross a specific line, and you’re back to swimming on your own. This analogy is my best way to explain what it feels like when the model goes from helpful to clueless. Right now it’s easy for a human being to find that line if you’re doing advanced research, so you know where the intelligence drops off. But the line is moving. You can feel it slowly drifting outwards under your feet.
From the article:
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