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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

#71

Post by rytill »

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.…

Of course if you mess with a function's inputs in ways it's never seen it's going to "not understand" what's going on. This is an agent which only knows 8x8 space.

Train it on variable spaces, and you'll get an agent that can play on variable spaces. In fact, you can probably speed things up drastically by using transfer learning from a model which already learned 8x8 space and modifying the inputs and outputs to match the new state and action space.

What part of this do you think "exceedingly intelligent tech people" aren't grasping? Something qualitative? Do you think people in machine learning think of "learning" as literally meaning the same thing as the colloquial usage? What, precisely, are you attacking here? All the harsh anti-machine-learning viewpoints with no clarity are becoming exhausting.

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

#72

Post by thelazydogsback »

brundolf wrote: post #55
thelazydogsback wrote: post #20

I've been waiting for the Symbolic/NN pendulum to starting swinging back the other way and start settling in the center. NN/DL is great for the interface between the outer world and the inner world of the mind (pattern recognition and re-construction), and symbolic AI more straightforwardly represents more "language of the mind" tasks, and easily handles issues like explanation and other meta-behaviors that with DL i…

My company is one of the only ones still pushing on symbolic AI: https://www.cyc.com/

Lenat's Cyc? I'm quite familiar. I hadn't looked at it in about 25 years until I just happened to come across the ATT-CYC docs a few days ago (I seem to be missing Part 2) and a printout of a PPT that he gave group of us at Microsoft in 1994/5 or so.

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

#73

Post by mindgam3 »

rytill wrote: post #71
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.…

Of course if you mess with a function's inputs in ways it's never seen it's going to "not understand" what's going on. This is an agent which only knows 8x8 space. Train it on variable spaces, and you'll get an agent that can play on variable spaces. In fact, you can probably speed things up drastically by using transfer learning from a model which already learned 8x8 space and modifying the inputs and outputs to mat…

> What, precisely, are you attacking here?

Please refer, precisely, to my earlier comment in this thread.

https://news.ycombinator.com/item?id=21109193

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

#74

Post by jayd16 »

inimino wrote: post #68
jayd16 wrote: post #33

Has work been done to formally prove general AI can't arise from deep learning? I can't help but feel its an assumption being made by those that prefer classical research.

It is trivially proved that deep learning can represent any computable function so the proof you're asking for is not going to be possible. However, it's also completely obvious to anyone familiar with deep learning as it exists today that it is not moving in the direction of AGI and none of the research is ever going to lead to AGI short of some kind of miracle. The burden of proof here would be on those who think t…

I'm no expert but it doesn't seem very far fetched to me. If we can use deep learning to create a digital assistant, computer vision, and navigation/traversal then we're not very far off from something surpassing a dog's level of intelligence even with what exists today. If that's possible then it seems plausible it could continue onward.

Not saying its obviously possible but it doesn't seem obviously impossible and its a mistake to assume as such.

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

#75

Post by gpm »

mindgam3 wrote: post #70
csande17 wrote: post #69

Earlier quoted context omitted.

> make a 10x10 variant playable online I'm not saying I'm definitely going to do this, but is there a rulebook somewhere for 10x10 chess? (What are the initial piece positions, and how would castling work?)

I’m not aware of 10x10 chess ever being attempted, let alone codified. Here’s my suggestion: add another set of bishops (or knights, or one of each) in between rooks and king/queen. Castling works the same, king goes around rook.

Generalized NxN chess is a thing I've seen talked about in complexity theory. I'm not sure if anyone has actually made a proper set of rules for it though. It looks like they often just don't care about such trivialities as starting positions (and probably castling). E.g. http://www.ms.mff.cuni.cz/~truno7am/slozitostHer/chessExptim...

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

#76

Post by joe_the_user »

rytill wrote: post #71
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.…

Of course if you mess with a function's inputs in ways it's never seen it's going to "not understand" what's going on. This is an agent which only knows 8x8 space. Train it on variable spaces, and you'll get an agent that can play on variable spaces. In fact, you can probably speed things up drastically by using transfer learning from a model which already learned 8x8 space and modifying the inputs and outputs to mat…

The GP described the "hype machine" and the implication that deep learning is step to AGI. As far as I can tell, the "hype machine" is real in sense that popular articles describe current methods as steps towards our broad concept of intelligence.

Certainly, someone close enough to the technical process of deep learning will admit that it essentially an extension of logistic regression without any "larger" implications - at least some deep learning researchers are always clear to distinguish the activity from "human intelligence" (and even if a given research never parrots the hype train's mantra, they know it's there and inherently play some part).

But more a minimum assertion of deep learning is that it "generalizes well". And what does "well" mean in this context? In the few situations where data can be generated by the process, like Alpha-Go, it can make a good average approximation of a function but in most situations of deep learning it means "generalizes like a human" - especially image recognition.

This comes together in the process of training AIs. Researchers take data that they hope represents a pattern of inputs and output in a human decision making process and assume they can construct a good approximation of a function that underlies this data. A variety of things can go wrong - the input data can be selective in ways the researchers don't understand (there was a discussion about a large database of images from the net being biased just by the tendency of photographers to center their main subject), there can be no unambiguous "function" - loan/parole AI that's inherently biased because it associated data that isn't legitimate, objective criteria for the decision sought), and so-forth. Some tech people are aware of the problems here to but this stuff is going out the door and being used in decisions affecting people's lives. Merely noting possible problems isn't enough here. These "exceedingly smart people" are still handing off their creations to other people are taking them as something akin to miraculous decision makers.

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

#77

Post by jhncls »

mindgam3 wrote: post #70
csande17 wrote: post #69

Earlier quoted context omitted.

> make a 10x10 variant playable online I'm not saying I'm definitely going to do this, but is there a rulebook somewhere for 10x10 chess? (What are the initial piece positions, and how would castling work?)

I’m not aware of 10x10 chess ever being attempted, let alone codified. Here’s my suggestion: add another set of bishops (or knights, or one of each) in between rooks and king/queen. Castling works the same, king goes around rook.

Former world chess champion Capablanca suggested a 10x8 board with two additional pieces in the 1920s, but there have been many variants proposed earlier and later [0]. Grand Chess [1] is the most known 10x10 variant, also with 2 additional pieces and a different start position, castling not allowed. See wikipedia for links to programs implementing these rules.

[0] https://en.wikipedia.org/wiki/Capablanca_Chess

[1] https://en.wikipedia.org/wiki/Grand_Chess

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

#78

Post by joe_the_user »

antome wrote: post #61
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.…

This doesn't have much to do with the algorithms, and is more to do with the engineering decisions that went into AlphaGo and AlphaZero. They are designed to play one combinatorial game really well. With a bit of additional efffort and a lot of additional compute, you could expand the model to account for multiple rule / scale variations, maybe even different combinatorial games.

I think the GP was noting the problem that AI can easily encounter situations beyond what it was designed and simply fail while human intelligence involves a more robust combination of behaviors and thus humans can generalize in a much wider variety of situations.

If the system designer has to know the parameters of the challenge the system is up again, it should be obvious you can always add another parameter that the designer didn't know about and get a situation where the system will fail. This is much more of a problem in "real world situations" which no designer can fully describe.

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

#79

Post by mindgam3 »

jhncls wrote: post #77
mindgam3 wrote: post #70

Earlier quoted context omitted.

I’m not aware of 10x10 chess ever being attempted, let alone codified. Here’s my suggestion: add another set of bishops (or knights, or one of each) in between rooks and king/queen. Castling works the same, king goes around rook.

Former world chess champion Capablanca suggested a 10x8 board with two additional pieces in the 1920s, but there have been many variants proposed earlier and later [0]. Grand Chess [1] is the most known 10x10 variant, also with 2 additional pieces and a different start position, castling not allowed. See wikipedia for links to programs implementing these rules. [0] https://en.wikipedia.org/wiki/Capablanca_Chess [1] h…

Interesting. Note that both of those types involve introducing new pieces. I would argue that this changes the fundamental nature of the game in a way that increasing board size alone doesn’t. The reason is that I (as a human chess master) would need to retrain myself to learn the new piece movements.

What I really want is a 10x10 or even 8x10 board using the original set of pieces. This would be sufficient to prove that human chess masters can adapt in a way that machine-learning based algorithms cannot.

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

#80

Post by kchamplewski »

mindgam3 wrote: post #65
antome wrote: post #61

Earlier quoted context omitted.

This doesn't have much to do with the algorithms, and is more to do with the engineering decisions that went into AlphaGo and AlphaZero. They are designed to play one combinatorial game really well. With a bit of additional efffort and a lot of additional compute, you could expand the model to account for multiple rule / scale variations, maybe even different combinatorial games.

> 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 it Go or chess with different rules, it will perform very poorly.

Creating general learning algorithms is arguably a somewhat easier task than creating a general agent, since learning algorithms are typically run for a long time while an agent often has to make time constrained decisions.

The holy grail of AGI is to make the learning algorithm and the agent the same thing, and have them be general. Then you have an agent which can rapidly adapt to its environment and self-modify as needed. We are still a long way off a system that would do this in terms of current research.

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