Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

The Bitter Lesson

incompleteideas.net

Post reply on HN
Print view View original

31–40 of 54 posts

Re: The Bitter Lesson

#31

Post by sdenton4 »

sweezyjeezy wrote: post #11

This post oversimplifies the story by putting all the emphasis on compute power. Deep Blue using brute force to solve chess obviously fits this pattern, but the others? Let's take computer vision. Alex Krizhevsky et al destroyed the ImageNet competition with a neural network in 2012, kicking off the current AI hype cycle. Essentially everything in their model had been known about since the late 80s. But we also didn'…

Likewise, the search in Go is a Monte Carlo search, very different from the kind of search used in chess. And the neutral nets in alpha go are guiding where to run the search, which is very very different from brute force search.

Many of these things have required the giant leaks in compute, but still wouldn't work at all without the concurrent improvements in algorithms.

Along these lines, here's a classic blog post: https://www.johndcook.com/blog/2015/12/08/algorithms-vs-moor...

"Grötschel, an expert in optimization, observes that a benchmark production planning model solved using linear programming would have taken 82 years to solve in 1988, using the computers and the linear programming algorithms of the day. Fifteen years later — in 2003 — this same model could be solved in roughly 1 minute, an improvement by a factor of roughly 43 million. Of this, a factor of roughly 1,000 was due to increased processor speed, whereas a factor of roughly 43,000 was due to improvements in algorithms! Grötschel also cites an algorithmic improvement of roughly 30,000 for mixed integer programming between 1991 and 2008."

Re: The Bitter Lesson

#32

Post by colordrops »

sweezyjeezy wrote: post #11

This post oversimplifies the story by putting all the emphasis on compute power. Deep Blue using brute force to solve chess obviously fits this pattern, but the others? Let's take computer vision. Alex Krizhevsky et al destroyed the ImageNet competition with a neural network in 2012, kicking off the current AI hype cycle. Essentially everything in their model had been known about since the late 80s. But we also didn'…

I don't think what you are saying contradicts the text. What he's saying is that we need to put our efforts into how to design and use the tools that tackle the problem space, rather than reasoning about the problem space itself, e.g. how to use neural nets, monte carlo search, etc. That doesn't mean we just throw a for-loop at the data.

Re: The Bitter Lesson

#33

Post by _bxg1 »

OliverJones wrote: post #4

It's not just AI. Our trade's history is chockfull of valiant efforts to solve problems that were overrun by the exponential decline of computing costs by the time they really worked properly. Remember DSEE / ClearCase? They had all sorts of complicated virtual file systems to deliver tagged and branched contents of source code repositories. But drive space expanded with a Moore's-law style curve and now we have "git…

And raytracing is about to do the same thing to all the rasterization innovations of the past few decades. Funny how these kinds of things feel partly depressing.

Re: The Bitter Lesson

#34

Post by ssivark »

zimablue wrote: post #14

You can only reliably say that something which has happened several times is something that will always happen if you know the reason why it happened several times. This article seems to think it's Moore's law, which has ended. I think history tends to go in cycles as people over-index on whatever worked well for the last n decades.

This kind of over-indexing to statistical correlations in past data, without understanding the casual mechanism is a common criticism of contemporary AI techniques, especially "deep" RL ;-)

Maybe as someone who hasn't been steeped in AI for the past several decades, I'm not able to appreciate the depth of emotion behind Sutton's statements. I find this kind of vague pontificating to be boring. It seems aimed more at convincing the author of a position, than doing a critical analysis and convincing the reader.

Will compute+data solve AI? Will structured algorithms solve AI? Will neuroscience provide key breakthroughs? Who knows? We'll find out when we find out. This article provides little value beyond historical reminiscing.

In the meanwhile, there are many interesting problems begging for attention where data or compute is limited.

I'll believe it when these techniques can solve real problems in a robust way. More importantly, when it can solve real problems in a robust way, opinions don't matter! Proponents won't need to go around trying to convince people almost in the manner of superstitions belief.

Re: The Bitter Lesson

#35

Post by atomi »

gwern wrote: post #2

This really needs a better title, or at least a subtitle. (I clicked only because I recognized the domain name; the title itself is vague puffery which gives no promise of being interesting...) 'compute beats clever'? 'fast > fancy'? 'better big than bright'? 'in the end, brute force wins'? Or at least call it "AI's Bitter Lesson" or something !

I agree with this fellow. The non-descriptive titles are a glaring issue that needs to be fixed.

Re: The Bitter Lesson

#37

Post by SmooL »

sdenton4 wrote: post #31
sweezyjeezy wrote: post #11

This post oversimplifies the story by putting all the emphasis on compute power. Deep Blue using brute force to solve chess obviously fits this pattern, but the others? Let's take computer vision. Alex Krizhevsky et al destroyed the ImageNet competition with a neural network in 2012, kicking off the current AI hype cycle. Essentially everything in their model had been known about since the late 80s. But we also didn'…

Likewise, the search in Go is a Monte Carlo search, very different from the kind of search used in chess. And the neutral nets in alpha go are guiding where to run the search, which is very very different from brute force search. Many of these things have required the giant leaks in compute, but still wouldn't work at all without the concurrent improvements in algorithms. Along these lines, here's a classic blog post…

I think the authors point though is that all our effort into the algorithms where algorithms to do just one thing - search - and that we used that in conjunction with more compute power.

I'll agree that the author emphasizes compute power, but his real point still holds. Monte Carlo search may not be classic brute force, and neural networks guiding it may also not be standard, but the two just let you effectively search on a massive scale.

Re: The Bitter Lesson

#38

Post by freshm087 »

sweezyjeezy wrote: post #11

This post oversimplifies the story by putting all the emphasis on compute power. Deep Blue using brute force to solve chess obviously fits this pattern, but the others? Let's take computer vision. Alex Krizhevsky et al destroyed the ImageNet competition with a neural network in 2012, kicking off the current AI hype cycle. Essentially everything in their model had been known about since the late 80s. But we also didn'…

Your observations don't disprove anything in the article. The author doesn't say that brute force is the way to go, and one has to cease all tries to optimize just waiting for computing power to increase. He says that essentially all attempts to build AI by modeling human thinking lead to nowhere, because their are inherently too complex, with simpler statistical, or search-based methods constantly winning. What you wrote here is absolutely in line with his words. "Neural network" contrary to its name doesn't work by emulating human.

Re: The Bitter Lesson

#39

Post by bazzargh »

CSSer wrote: post #18

body { max-width: 50em; margin: 1em auto; } to make this more readable on a desktop...

you're not wrong, but I installed bookmarklets on my ipad mini to increase and decrease font size because of _HN_, which sets lower-than-normal font sizes. My eyesight isn't great, but this site is the absolute pits for undersized text without max-width set.

Re: The Bitter Lesson

#40

Post by a-nikolaev »

tschellenbach wrote: post #10

On the other hand at some point we will want AI to learn based on a small number of interactions. IE an AI that beats a human after playing 10 games of chess/starcraft etc. Right now it takes millions of training matches. Many real world situations don't happen that often so this fundamentally limits applications of the current generation of AI.

I guess, then, the next step in AI research should be to develop a deep learning network for automatic training examples generation ~~ An AI Machine Learning Trainer of some kind.
Post reply on HN