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The Bitter Lesson (2019)

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Re: The Bitter Lesson (2019)

#41

Post by elcomet »

glitchc wrote: post #40

When it comes to games, exploitation (of tendencies, weaknesses), misdirection, subterfuge and yomi play a far bigger role in winning than actual skill. Humans are much better than computers at all of those. Perhaps a dubious honour, but an advantage nonetheless. We're only really in trouble when the machine learns to reliably replicate the same tactics.

I think that computers managed to beat humans at poker already. (Online poker, which is different from physical games, where of course AI cannot compete)

Re: The Bitter Lesson (2019)

#42

Post by abetusk »

aszen wrote: post #20

Interesting, I wonder what happens now that Moore's law is considered dead and we can't rely on computation power increasing year over year. To make further progess with general purpose search and learning methods we will need lots more computational power which may not be cheaply available. Then do we focus our efforts on developing more efficient learning strategies like the one we have in our minds ? I do agree wi…

Moore's law might be dead but the deeper law is still alive.

Moore's law is technically "the number of transistors per unit area doubles every 24 months" [1]. The more important law is that the cost of transistors halves every 18-24 months.

That is, Moore's law talks about how many transistors we can pack into a unit area. The deeper issue is how much it costs. If we can only pack in a certain amount transistors per area but the cost drops exponentially, we still see massive gains.

There's also Wright's law that comes into play [3] that talks about dropping exponential costs just from institutional knowledge (2x in production leads to (.75-.9)x in cost).

[1] https://en.wikipedia.org/wiki/Moore%27s_law

[2] https://www.youtube.com/watch?v=Nb2tebYAaOA

[3] https://en.wikipedia.org/wiki/Experience_curve_effects

Re: The Bitter Lesson (2019)

#43

Post by dreamcompiler »

YeGoblynQueenne wrote: post #34

>> In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. "Massive, deep search" that started from a book of opening moves and the combined expert knowledge of several chess Grandmasters. And that was an instance of the minimax algorithm with alpha-beta cutoff, i.e. a search algorithm specifically designed for two-player, deterministic games like chess.…

Are we certain that well-trained human players are not doing search? It's possible that a search subnetwork gets "compiled without debugger symbols" and the owner of the brain is simply unaware that it's happening.

Re: The Bitter Lesson (2019)

#44

Post by aszen »

abetusk wrote: post #42
aszen wrote: post #20

Interesting, I wonder what happens now that Moore's law is considered dead and we can't rely on computation power increasing year over year. To make further progess with general purpose search and learning methods we will need lots more computational power which may not be cheaply available. Then do we focus our efforts on developing more efficient learning strategies like the one we have in our minds ? I do agree wi…

Moore's law might be dead but the deeper law is still alive. Moore's law is technically "the number of transistors per unit area doubles every 24 months" [1]. The more important law is that the cost of transistors halves every 18-24 months. That is, Moore's law talks about how many transistors we can pack into a unit area. The deeper issue is how much it costs . If we can only pack in a certain amount transistors per…

Agreed the cost aspect of Moore's law may continue to remain true, especially with chiplets with varying fab nodes and 3d architectures. Wright's law will also bring down costs as lower nm nodes mature.

But as mentioned in the comments below ai model training is increasing exponentially (compute required to train models has been doubling every 3.6 months) so it still far outstrips the cost savings.

Re: The Bitter Lesson (2019)

#45

Post by gwern »

dreamcompiler wrote: post #43
YeGoblynQueenne wrote: post #34

>> In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. "Massive, deep search" that started from a book of opening moves and the combined expert knowledge of several chess Grandmasters. And that was an instance of the minimax algorithm with alpha-beta cutoff, i.e. a search algorithm specifically designed for two-player, deterministic games like chess.…

Are we certain that well-trained human players are not doing search? It's possible that a search subnetwork gets "compiled without debugger symbols" and the owner of the brain is simply unaware that it's happening.

I'm not sure why YeGoblynQueenne thinks this is such a mystery. (This is not the first time I've been puzzled by their pessimism on HN.) There is no mystery here: AlphaZero shows that you can get superhuman performance by searching only a few ply by sufficiently good pattern recognition in a highly parameterized and well-trained value function, and MuZero makes this point even more emphatically by doing away with the formal search entirely in favor of an more abstract recurrent pondering. What more is there to say?

Re: The Bitter Lesson (2019)

#46

Post by new2628 »

YeGoblynQueenne wrote: post #34

>> In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. "Massive, deep search" that started from a book of opening moves and the combined expert knowledge of several chess Grandmasters. And that was an instance of the minimax algorithm with alpha-beta cutoff, i.e. a search algorithm specifically designed for two-player, deterministic games like chess.…

At least in chess, if it is not the search, then it is probably the evaluation function.

Expert players have likely a very well-tuned evaluation function of how strong a board "feels". Some of it is explainable easily: center domination, diagonal bishop, connected pawn structure, rook supporting pawn from behind, others are more elaborate, come with experience and harder to verbalize.

When expert players play against computers, the limitation of their evaluation function becomes visible. Some board may feel strong, but you are missing some corner case that the minmax search observes and exploits.

Re: The Bitter Lesson (2019)

#47

Post by ksdale »

I think it's plausible that many technological advances follow a similar. Something like the steam engine is a step-improvement, but many of the subsequent improvements are basically the obvious next step, implemented once steel is strong enough, or machining precise enough, or fuel is refined enough. How many times has the world changed qualitatively, simply in the pursuit of making things quantitatively bigger or faster or stronger?

I can certainly see how it could be considered disappointing that pure intellect and creativity doesn't always win out, but I, personally, don't think it's bitter.

I also have a pet theory that the first AGI will actually be 10,000 very simple algorithms/sensors/APIs duct-taped together running on ridiculously powerful equipment rather than any sort of elegant Theory of Everything, and this wild conjecture may make me less likely to think this a bitter lesson...

Re: The Bitter Lesson (2019)

#48

Post by KKKKkkkk1 »

Today Elon Musk announced that Tesla is going to reach level-5 autonomy by the end of the year. Specifically

There are no fundamental challenges remaining for level-5 autonomy. There are many small problems. And then there's the challenge of solving all those small problems and then putting the whole system together. [0]

I feel like this year is going to be another year in which the proponents of brute-force AI like Elon and Sutton will learn a bitter lesson.

[0] https://twitter.com/yicaichina/status/1281149226659901441

Re: The Bitter Lesson (2019)

#49

Post by typon »

KKKKkkkk1 wrote: post #48

Today Elon Musk announced that Tesla is going to reach level-5 autonomy by the end of the year. Specifically There are no fundamental challenges remaining for level-5 autonomy. There are many small problems. And then there's the challenge of solving all those small problems and then putting the whole system together. [0] I feel like this year is going to be another year in which the proponents of brute-force AI like…

Elon Musk announcing something doesn't make it true

Re: The Bitter Lesson (2019)

#50

Post by aszen »

noanabeshima wrote: post #31
aszen wrote: post #20

Interesting, I wonder what happens now that Moore's law is considered dead and we can't rely on computation power increasing year over year. To make further progess with general purpose search and learning methods we will need lots more computational power which may not be cheaply available. Then do we focus our efforts on developing more efficient learning strategies like the one we have in our minds ? I do agree wi…

The amount of compute used in the largest AI training runs has been exponentially growing: https://openai.com/blog/ai-and-compute/ The amount of compute required for Imagenet classification has been exponentially decreasing: https://openai.com/blog/ai-and-efficiency/

Very interesting links, thanks for sharing.

So the trend isn't changing we still need bigger models to make progress in NLP and CV, while the algorithmic effeciencies are promising but they aren't giving anywhere near the same improvements as larger models.

I'm curious how long this trend will continue and if there's anything promising that can reverse this trend

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