Indeed a bitter lesson. I once enjoyed encoding human knowledge into a computer because it gives me understanding of what's going on. Now everything is becoming a big black box that is hard to reason about. /sigh/
Also, Moore's law has become a self-fulfilling prophecy. Now more than ever, AI is putting a lot of demand on computational power, to the point which drives chip makers to create specialized hardware for it. It's becoming a flywheel.
I am still hoping AI progress will get to the point where the AI can eventually create AI's that are built up out of robust and provable logic which can be read and audited. Until that time, I wouldn't trust it for risky stuff. Unfortunately, it's not my choice and within a scarily short timespan, black boxes will make painfully wrong decisions about vital things that will ruin lives.
Well, take compiler optimization for example. You can allow your AI to use correctness-preserving transformations only. This will give you correct output no matter how weird the AI behaves.
The downside is that you will sometimes not get the optimizations that you want. But, this is sort of already the case, even with human made optimization algorithms.
This depends a little bit on what the goal of AI research is. If it is (and it might well be) to build machines that excel at tasks previously thought to be exclusively reserved to, or needing to involve, the human mind, then these bitter lessons are indeed worthwhile.
But if you do AI research with the idea that by teaching machines how to do X, we might also be able to gain insight in how people do X, then ever more complex statistical setups will be of limited information.
Note that I'm not taking either point of view here. I just want to point out that perhaps a more nuanced approach might be called for here.
> if you do AI research with the idea that by teaching machines how to do X, we might also be able to gain insight in how people do X, then ever more complex statistical setups will be of limited information
At the very least we know consistent language and vision abilities don't require lived experience. That is huge in itself, it was unexpected.
> In computer vision, there has been a similar pattern. Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded.Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better.
I was there, at that moment where pattern matching for vision started to die.
That was not completely lost though, learning from that time is still useful on other places today.
I was an undergrad interning in a computer vision lab in the early 2010s. During group meeting, someone presented a new paper that was using abstract machine learning like stuff to do vision. The prof was so visibly perturbed and agnostic. He could not believe that this approach was even a little bit viable, when it so clearly was.
Best lesson for me - vowed never to be the person opposed to new approaches that work.
This describes Go AIs as a brute force strategy with no heuristics, which is false as far as I know. Go AIs don't search the entire sample space, they search based on their training data of previous human games.
First there was AlphaGo, which had learnt from human games, then further improved from self-play, then there was AlphaGo Zero which taught itself from scratch just by self-play, not using any human data at all.
Game programs like AlphaGo and AlphaZero (chess) are all brute force at core - using MCTS (Monte Carlo Tree Search) to project all potential branching game continuations many moves ahead. Where the intelligence/heuristics comes to play is in pruning away unpromising branches from this expanding tree to keep the search space under control; this is done by using a board evaluation function to assess the strength of a given considered board position and assess if it is worth continuing to evaluate that potential line of play.
In DeepBlue (old IBM "chess computer" that beat Kasparov) the board evalation function was hand written using human chess expertise. In modern neural-net based engines such as AlphaGo and AlphaZero, the board evaluation function is learnt - either from human games and/or from self-play, learning what positions lead to winning outcomes.
So, not just brute force, but that (MCTS) is still the core of the algorithm.
> ... This describes Go AIs as a brute force strategy with no heuristics ...
no, not really, from the paper
>> Also important was the use of learning by self play to learn a value function (as it was in many other games and even in chess, although learning did not play a big role in the 1997 program that first beat a world champion). Learning by self play, and learning in general, is like search in that it enables massive computation to be brought to bear.
important notion here is, imho "learning by self play". required heuristics emerge out of that. they are not programmed in.
The paragraph on Go AI looked accurate to me. Go AI research spent decades trying to incorporate human-written rules about tactics and strategy. None of that is used any more, although human knowledge is leveraged a bit in the strongest programs when choosing useful features to feed into the neural nets. (Strong) Go AIs are not trained on human games anymore. Indeed they don't search the entire sample space when they perform MCTS, but I don't see Sutton claiming that they do.
I remember the article, and remember how badly it missed the point... The goal of writing a chess program that could beat a world champion wasn't to beat the world champion... the goal was to gain understanding into how anyone can play chess well. The victory in that match would've been equivalent to eg. drugging Kasparov prior to the match, or putting a gun to his head and telling him to lose: even cheaper and more effective.
"The goal of Automated driving is not to drive automatically but to understand how anyone can drive well"...
The goal of DeepBlue was to beat the human with a machine, nothing more.
While the conquest of deeper understanding is used for a lot of research, most AI (read modern DL) research is not about understanding human intelligence, but automatic things we could not do before. (Understanding human intelligence is nowadays a different field)
for example there are clever ways of rewarding all the steps of a reasoning process to train a network to “think”. but deepseek found these don’t work as well as much simpler yes/no feedback on examples of reasoning.
cxr|1 year ago
khaledh|1 year ago
Also, Moore's law has become a self-fulfilling prophecy. Now more than ever, AI is putting a lot of demand on computational power, to the point which drives chip makers to create specialized hardware for it. It's becoming a flywheel.
anonzzzies|1 year ago
amelius|1 year ago
The downside is that you will sometimes not get the optimizations that you want. But, this is sort of already the case, even with human made optimization algorithms.
kleiba|1 year ago
But if you do AI research with the idea that by teaching machines how to do X, we might also be able to gain insight in how people do X, then ever more complex statistical setups will be of limited information.
Note that I'm not taking either point of view here. I just want to point out that perhaps a more nuanced approach might be called for here.
visarga|1 year ago
At the very least we know consistent language and vision abilities don't require lived experience. That is huge in itself, it was unexpected.
jdright|1 year ago
I was there, at that moment where pattern matching for vision started to die. That was not completely lost though, learning from that time is still useful on other places today.
abdullahkhalids|1 year ago
Best lesson for me - vowed never to be the person opposed to new approaches that work.
Buttons840|1 year ago
DavidPiper|1 year ago
HarHarVeryFunny|1 year ago
Game programs like AlphaGo and AlphaZero (chess) are all brute force at core - using MCTS (Monte Carlo Tree Search) to project all potential branching game continuations many moves ahead. Where the intelligence/heuristics comes to play is in pruning away unpromising branches from this expanding tree to keep the search space under control; this is done by using a board evaluation function to assess the strength of a given considered board position and assess if it is worth continuing to evaluate that potential line of play.
In DeepBlue (old IBM "chess computer" that beat Kasparov) the board evalation function was hand written using human chess expertise. In modern neural-net based engines such as AlphaGo and AlphaZero, the board evaluation function is learnt - either from human games and/or from self-play, learning what positions lead to winning outcomes.
So, not just brute force, but that (MCTS) is still the core of the algorithm.
signa11|1 year ago
no, not really, from the paper
>> Also important was the use of learning by self play to learn a value function (as it was in many other games and even in chess, although learning did not play a big role in the 1997 program that first beat a world champion). Learning by self play, and learning in general, is like search in that it enables massive computation to be brought to bear.
important notion here is, imho "learning by self play". required heuristics emerge out of that. they are not programmed in.
dfan|1 year ago
crabbone|1 year ago
krallistic|1 year ago
The goal of DeepBlue was to beat the human with a machine, nothing more.
While the conquest of deeper understanding is used for a lot of research, most AI (read modern DL) research is not about understanding human intelligence, but automatic things we could not do before. (Understanding human intelligence is nowadays a different field)
perks_12|1 year ago
currymj|1 year ago
for example there are clever ways of rewarding all the steps of a reasoning process to train a network to “think”. but deepseek found these don’t work as well as much simpler yes/no feedback on examples of reasoning.
blufish|1 year ago