Experts judge novelty by how much of a hint humans needed

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 36:17

   how easy or difficult is it to define a new idea in such a way that um you know we can easily distinguish them like it you know if we think about kind of the evolution of this discourse around like is it ai creative you know initially it was like yeah it's very creative it created this like poem and you know and pirate like that's creativity that's you know that was a new idea But that's certainly not what we're talking about when we're talking about, like, advancing science, advancing mathematics, et cetera.

2. 36:54

   You know, so, A, how do we, like, how do we define that? But also, you know, from your perspective as someone who's studying this, is it like, you know, we've got this long line of folks that are coming that says, here's this AI with a new idea. and you're like, yeah, well, let me evaluate it against my new idea criteria. No, it's not really that. Or are we actually, like, is it pretty clear to everyone that the AI is not having new ideas and, like, we're waiting for the first person to come and say that AI has the new idea?

3. 37:27

   Like, how do you think about that whole space? Very challenging nut to crack. So we, you know, try to approach it from different angles. one thing you can try to say is i've got a problem i don't know if a new idea will be required to crack it and this could be a problem in math or in science or any quantitative area but humans have tried to crack it humans have tried to make you know this number go up whether that number is the efficacy of some you know drug at fighting some disease or something like that or some uh you you know, or it's a math result of some conjecture that has stumped mathematicians for a long time.

4. 38:11

   And you can say, okay, well, I don't know if, I'm not sure how I'm going to measure a new idea, but I can at least tell if this problem has been solved or this number has been made to go up or whatever the concrete metric is. And you can then, then you're, you can at least check if that has happened. Now, I was probably a year ago more optimistic that there would be a tighter relationship between math problem that humans have failed to solve and post hoc qualitative assessment of creativity. So this is sort of the combination, the double punch we're trying here.

5. 38:52

   First, you set a target that's hard on human terms that humans care about. Humans have tried to be creative to solve. And then you do post hoc review with experts and say, what do you make of the solution? Why hadn't you found it? No offense. You know, what is in the AI solution that humans had missed? And so on. And this is risky, like risky in terms of humans being honest about it, because, you know, your ego might be wrapped up in it, or you might, once you see the solution, it feels more obvious in hindsight. So would you have described it? Like, very, very, very risky. But, but you still, you try your best. And, you know, you might say things like, well, how, like, here's an interesting approach that we haven't operationalized in like detail, but it's a nice heuristic, I think.

6. 39:38

   How big a hint would a human have needed to solve this in hindsight? So, for example, that unit distance, a very prominent mathematician, Fields Medalist Timothy Gowers, did this sort of exercise on this unit distance problem once AI had solved it, saying, how big a hint and how specific of a hint do I think a mathematician would have needed now in hindsight to solve this problem? uh one one yeah and he came away with like an interesting exploration of well actually ai sort of disproved this conjecture as opposed to proving it true it said actually it's false even that is a huge hint humans had mostly thought it was true they were wrong but they'd been trying to prove that it was true instead of looking for a counterexample so even just the hint of try to find a counterexample is pretty big and then there was like one more piece and he was like

7. 40:30

   i think though very hard to say we can't really do the counterfactual experiment i think with just that with like this sort of two-part hint, it would have been possible for like a human would be like, oh, okay, I know what to do now, blah, blah, blah. And like probably would have at least hastened a human solution. So you can do things like that, maybe still very qualitative, but you try to be honest with yourself and be rigorous. And you could imagine doing a more rigorous version of this experiment. And anyway, things like that. But then the fact is math is still, for the most part a sort of discipline removed from physical real world impact. Not always, but its impact is often hard to predict. There are domains where impact is not at all hard to predict. I'm in the chemistry lab and I'm trying to improve the yield of my synthesis process. And if AI makes incremental

8. 41:20

   improvements, I expect incremental yield. And if AI suddenly comes and says, no, no, no, you're doing it all wrong. Let me redesign your whole thing from scratch. I've got a maybe creative idea well at some point you don't care if it's creative or not in the qualitative sense you just care that you have saw discontinuity in the yield of your chemical substance or whatever and that's you know that's uh that's a bigger bigger impact for the world anyway what i took from that is that we filter on the problem and how hard humans have kind of banged their head heads against the problem then we look at the results that ai has come up with and try to kind of subjectively say you know is this based on a new idea or is it you know more brute force or something else like um but it's kind of imperfect and messy and like we're not really sure but we're we don't think

9. 42:14

   that we've come up with, you know, great examples of AIs coming up with, you know, quote-unquote new ideas. That's exactly right. One reference point I'd give is way back in, gosh, well, the teens, I forget which year, the famous Go playing system, the game of Go, AlphaGo. I had that thought earlier as we were discussing this. like if i remember correctly the the you know part of the solution was like oh that was an innovative move that no human ever would have done there was a specific one yeah it was a very specific move in there yeah yeah they call it move 37 because it was the 37th move in this game where uh yeah apparently this was a very surprising move like you said no human would have done it in this particular position in this game of Go.

10. 43:11

    And I think it's like encouraging for our admittedly quite subjective program here that Go experts looked at and were like, oh, my goodness, like that's not, you know, that's new. And then it also became clear that that was pivotal, that that move gave the AI system playing Go a decisive advantage. And so that's sort of what we're looking for in math. And at least, you know, maybe we have some hope that humans will be able to spot it when it happens.

11. 43:39

    uh but but yeah that's that's what we haven't seen quite yet and again the trajectory is fast