All of johncrox's Comments + Replies

For those interested in US election betting strategies, I'm hosting a Discord here: https://discord.gg/dHXrHdXnRD.

My pleasure!

Yeah, I agree - I'd rather have a blackbird than AlphaZero. For one thing, it'd make our current level of progress in AI much clearer. But on your second and third points, I think of ML training as somewhat analogous to evolution, and the trained agent as analogous to an animal. Both the training process and evolution are basically blind but goal-directed processes with a ton of iterations (I'm bullish on evolution's ability to transmit information through generations) that result in well-adapted agents.

If that's the right analogy, then we can

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I remember looking into communication speed, but unfortunately I can't find the sources I found last time! As I recall, when I checked the communication figures weren't meaningfully different from processing speed figures.

Edit: found it! AI Impacts on TEPS (traversed edges per second): https://aiimpacts.org/brain-performance-in-teps/

Yeah, basically computers are closer in communication speed to a human brain than they are in processing speed. Which makes intuitive sense - they can transfer information at the speed of light, while brains are stuck sending c

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Thanks! Yeah, it might have been a bad idea to take general chip cost decreases as super relevant for specialized AI chips' cost efficiency. I read Carey's estimates for cost decreases as applying to AI chips, when upon closer inspection he was referring to general chips. Probably we'll see faster gains in AI chips' cost efficiency for a while as the low-hanging fruit is picked.

My point was something like, "Development costs to make AI chips will largely be borne by leading AI companies. If this is right, then they won't be able to take advantage of cheape

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I claim that this is not how I think about AI capabilities, and it is not how many AI researchers think about AI capabilities. For a particularly extreme example, the Go-explore paper out of Uber had a very nominally impressive result on Montezuma's Revenge, but much of the AI community didn't find it compelling because of the assumptions that their algorithm used.

Sorry, I meant the results in light of which methods were used, implications for other research, etc. The sentence would better read, "My understanding (and I think everyone else&... (read more)

Thanks for the comment! In order:

I think that its performance at test time is one of the more relevant measures - I take grandmasters' considering fewer moves during a game as evidence that they've learned something more of the 'essence' of chess than AlphaZero, and I think AlphaZero's learning was similarly superior to Stockfish's relatively blind approach. Training time is also an important measure - but that's why Carey brings up the 300-year AlphaGo Zero milestone.

Indeed we are. And it's not clear to me that we're much better optimized for general cogn

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Rohin Shah
5y
Mostly agree with all of this; some nitpicks: I claim that this is not how I think about AI capabilities, and it is not how many AI researchers think about AI capabilities. For a particularly extreme example, the Go-explore paper out of Uber had a very nominally impressive result on Montezuma's Revenge, but much of the AI community didn't find it compelling because of the assumptions that their algorithm used. Tbc, I definitely did not intend for that to be an actual metric. I would say that I have a set of intuitions and impressions that function as a very weak prediction of what AI will look like in the future, along the lines of that sort of metric. I trust timelines based on extrapolation of progress using these intuitions more than timelines based solely on compute.To the extent that you hear timeline estimates from people like me who do this sort of "progress extrapolation" who also did not know about how compute has been scaling, you would want to lengthen their timeline estimates. I'm not sure how timeline predictions break down on this axis.