I always found it fascinatingly ironic that AGI research only fund deep learning which is a local minimum.
People really interested in AGI should better look at Cyc and opencog
We can’t trust AI systems built on deep learning alone
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Re: We can’t trust AI systems built on deep learning alone
#92One 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…
Does one’s knowledge of how to ride a bike have anything to do with such graph structures? Or is intelligence unrelated to such skills? It seems that the intelligence involved in having a basic conversation would engage a lot of such skills, even just social tact.
Re: We can’t trust AI systems built on deep learning alone
#93Earlier quoted context omitted.
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.
I'm not a chess master myself, my knowledge of this topic is from what I've read of Kasparov. The guy is all about patterns. He even advises to play using a physical board as much as possible, to improve _visual_ recognition of patterns. Thing is, you might not even recognize this as "pattern recognition" per se. Call it "intuition", call it "experience", or whatever you like, but combinatorially I'm pretty sure you're not searching the entire tree of possible positions several moves ahead - that's literally impossible to do for a human to do in the finite time allotted to a game. You're relying on patterns to constrain the search, much like a modern neural algorithm would constrain its search using a cost function. That's what's meant by "pattern recognition" here, not rigid recognition of fixed positions. That is also combinatorially impossible for a human to precisely memorize.
Re: We can’t trust AI systems built on deep learning alone
#94Earlier quoted context omitted.
> 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”.
I’m not convinced it isn’t simply a matter of scale, both in terms of the processing power and data.
Re: We can’t trust AI systems built on deep learning alone
#95Earlier quoted context omitted.
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…
Does one’s knowledge of how to ride a bike have anything to do with such graph structures? Or is intelligence unrelated to such skills? It seems that the intelligence involved in having a basic conversation would engage a lot of such skills, even just social tact.
I think intelligence isn't involved in riding a bike. Understanding the theory about riding a bike is a different story, and that understanding is a knowledge graph.
Re: We can’t trust AI systems built on deep learning alone
#96> 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, 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…
Perhaps a better example than 10x10 chess would be bughouse chess [1]. That's a chess variant played between two teams of two players using two sets and two clocks. It's a common break activity between rounds at amateur chess tournaments. Human chess players of all levels pick it up pretty fast after they play a handful of games.
[1] Detail on bughouse in this comment from an earlier discussion: https://news.ycombinator.com/item?id=20831586
Re: We can’t trust AI systems built on deep learning alone
#97Earlier 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.
"Center control", "square control", etc. all sound to me like things discovered by pattern matching. Yes, the patterns are large and somewhat abstract, but they're still patterns.
Re: We can’t trust AI systems built on deep learning alone
#98> 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.…
The question we should be asking is how much retraining had to occur to accomplish the new task? If it's significantly less than to the accomplish the original task, the algorithm has transferred its latent knowledge from the original task to the new task, which is significant.
Re: We can’t trust AI systems built on deep learning alone
#99I haven't read the book, but the viewpoints he expresses in the interview are spot-on. DL can a great alert/suggestion mechanism in narrow domains, but it should never be trusted to make critical decisions. I believe that general intelligence will only be achieved through major advancements in general symbolic reasoning. Something like DL might play a small role in this breakthrough, but it will not be a core part of the solution.
Re: We can’t trust AI systems built on deep learning alone
#100I haven't read the book, but the viewpoints he expresses in the interview are spot-on. DL can a great alert/suggestion mechanism in narrow domains, but it should never be trusted to make critical decisions. I believe that general intelligence will only be achieved through major advancements in general symbolic reasoning. Something like DL might play a small role in this breakthrough, but it will not be a core part of…
Vectors may be the proper way to represent symbols as they are the link with perception. I would call that a pretty core part of AI.