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We can’t trust AI systems built on deep learning alone

technologyreview.com

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Re: We can’t trust AI systems built on deep learning alone

#81

Post by jkp56 »

XuMiao wrote: post #32

One key question is whether symbolic AI is the right model of the world. It underperforms vector based AI on many specific tasks. But human experts heavily reply on it to communicate with each other. If symbolic AI is not the right model, P vs NP problem might be just irrelevant. Human philosophy is full of craps. We will lose a lot of beliefs. Elon will be right, we will abandon human languages, and connect through…

Of course symbolic AI is the way to go. We communicate with text messages that consist of words that we internally convert to word2vec style vectors to detect similar words. One more thing we do in our heads: we build a graph of those word2vec symbols. When I read in a book "a cat is sleeping on a tree" I instantly build a small graph where nodes Cat and Tree are connected with an edge labeled Sleeps. I may visualize this as a picture, but that's not necessary for AI. In fact, some people can't visualize anything, but they can definitely think. How? They can still build this knowledge graph. I believe that this graph representation is the limit of our intelligence: there are many facts out there that we can't possibly think about because they don't fit this graph model. It's like the set of real numbers can't be squeezed into the set of rational numbers: most of the numbers are irrational.

Re: We can’t trust AI systems built on deep learning alone

#82

Post by m0zg »

mindgam3 wrote: post #63
jayd16 wrote: post #60

Earlier quoted context omitted.

>Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining. Interesting. Has this actually been shown? I would assume a lot of the strategies a human is familiar with would fall apart as well. I'm no chess or go player but I would have to learn new strategies in a tic-tac-toe game scaled to 10x10. I would certainly not be as proficient although I…

Almost all of the human strategies and concepts would still apply: center control, square control, development, initiative, king safety, the opposition, etc. The only exceptions would be fringe concepts like opening theory (already moot in Chess960) and endgame edge cases. If you’re still not convinced, I’ll prove that skills transfer by playing bullet against anyone who can make a 10x10 variant playable online. [Edi…

I'm pretty sure this isn't how chess masters actually play chess. That'd be too slow and error prone. They pattern match very heavily in the beginning and towards the end. And all those patterns would be wrong on a larger board with more chess pieces. At least for chess.

Re: We can’t trust AI systems built on deep learning alone

#83

Post by jkp56 »

jotakami wrote: post #28
keithyjohnson wrote: post #7

Understanding a sentence is fundamentally different from recognizing an object. But people are trying to use deep learning to do both. I agree with most of the article but I think this^^ skips over the different types of networks used to solve perception and language problems. A CNN is very different from say, word2vec, which isn't a very deep network at all.

Perhaps the difference is in the nature of the information that is being probed and its larger context? Visual imagery often provides almost all of its own context, but the “meaning” of a sentence can be radically different depending upon its source. Humans produce words, so you almost need a working theory of mind to fully understand them. None of that context will ever make it into word2vec.

Why not? It's true that the word "space" has different meanings when in appears in a math book, CS book or astronomy book. But we just have 3 different word2vec models. When I read something about math, I pick the math word2vec model and there "space" appears close to words "Hilbert" and "separable", while in the CS model, the same word is next to "complexity" and "memory". As I read more, I improve my word2vec models, but never mix them together. Now what happens if I'm reading something and don't understand the context? No, I don't switch to some general word2vec model. I rather try to guess which model to use and then reread the same text using that model.

Re: We can’t trust AI systems built on deep learning alone

#84

Post by mlurp »

mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

I think a lot of AI research now is very narrow, but this ignores that there's also a lot of research in RL/etc that's working to solve the problem of generalization.

Meta learning is for solving similar problems from a distribution (like different sized boards in your chess example) and has taken off recently (only baby steps so far though). Modular learning is also becoming big, where concepts that are repeatedly used are stored/generalized.

Re: We can’t trust AI systems built on deep learning alone

#85

Post by mindgam3 »

kchamplewski wrote: post #80
mindgam3 wrote: post #65

Earlier quoted context omitted.

> They are designed to play one combinatorial game really well. Maybe, but they’re certainly not described that way by whoever is in charge of publishing DeepMind’s research: “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play” https://deepmind.com/research/publications/general-reinforce...

I think it's quite important to look at the distinction between the actual agent in play and the learning algorithm used. The learning algorithm AlphaGo uses is somewhat general, and can handle different games (e.g. you can put chess or Go through the algorithm and it functions well for either). The output of this algorithm, however, is a specialised agent. The agent is not general. If I create a chess agent and give…

The distinction you’re making between agent and algorithm is meaningless for the point I was trying to make, which is that the only connection between this DeepMind research (agent, algorithm, whatever) and AGI have in common is the word “general”.

Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.

Re: We can’t trust AI systems built on deep learning alone

#86

Post by mindgam3 »

m0zg wrote: post #82
mindgam3 wrote: post #63

Earlier quoted context omitted.

Almost all of the human strategies and concepts would still apply: center control, square control, development, initiative, king safety, the opposition, etc. The only exceptions would be fringe concepts like opening theory (already moot in Chess960) and endgame edge cases. If you’re still not convinced, I’ll prove that skills transfer by playing bullet against anyone who can make a 10x10 variant playable online. [Edi…

I'm pretty sure this isn't how chess masters actually play chess. That'd be too slow and error prone. They pattern match very heavily in the beginning and towards the end. And all those patterns would be wrong on a larger board with more chess pieces. At least for chess.

I hate to break it to you, but this is exactly how chess masters play chess. Including bullet.

Source: am a master, rated 2500 in bullet.

Re: We can’t trust AI systems built on deep learning alone

#87

Post by mr_toad »

mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

> Once a human learns chess

Humans have orders of magnitude more neurons, more complicated neurons, more intricate neural structures, and their training data is larger and more varied.

Re: We can’t trust AI systems built on deep learning alone

#88

Post by mindgam3 »

mr_toad wrote: post #87
mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

> Once a human learns chess Humans have orders of magnitude more neurons, more complicated neurons, more intricate neural structures, and their training data is larger and more varied.

Right, which is why it makes sense to apply the word “learning” to what we do.

In contrast to “machine learning” which is merely a fancy way to say “data processing with massive compute”.

Re: We can’t trust AI systems built on deep learning alone

#89

Post by m0zg »

mindgam3 wrote: post #86
m0zg wrote: post #82

Earlier quoted context omitted.

I'm pretty sure this isn't how chess masters actually play chess. That'd be too slow and error prone. They pattern match very heavily in the beginning and towards the end. And all those patterns would be wrong on a larger board with more chess pieces. At least for chess.

I hate to break it to you, but this is exactly how chess masters play chess. Including bullet. Source: am a master, rated 2500 in bullet.

Yet you yourself also mention openings and endgame above in the thread. What's that if not pattern matching?

Re: We can’t trust AI systems built on deep learning alone

#90

Post by mindgam3 »

m0zg wrote: post #89
mindgam3 wrote: post #86

Earlier quoted context omitted.

I hate to break it to you, but this is exactly how chess masters play chess. Including bullet. Source: am a master, rated 2500 in bullet.

Yet you yourself also mention openings and endgame above in the thread. What's that if not pattern matching?

Wait, are you referring to the comment in which I refer to them as “fringe concepts”, “already moot”, and “edge cases”?

Please don’t attempt to twist my words in order to support your own bogus position. Act like a chess master and just resign already.

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