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Yann LeCun's comment on AlphaGo and true AI

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Re: Yann LeCun's comment on AlphaGo and true AI

#111

Post by kotach »

Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

Given Langford's locality vs globality argument this also gets quite obvious for the 4th game mistake and overconfidence that AlphaGo had. The rate of growth of the compounding error for local decision maker is going to cause these kinds of mistakes more often than not.

Re: Yann LeCun's comment on AlphaGo and true AI

#112

Post by conceit »

sago wrote: post #106

Ah, the joys of arguing about artificial intelligence without ever defining intelligence. It is the perfect argument, everyone can forcefully make their points forever, and we'll be none the wiser whether this AI is 'true AI' or not.

So then the discussion is all about defining intelligence, beginning with a fuzzy conception of required qualities. It literally means ability to select, read, choose between. The discussion can be a means to judge the AIs or, for the sake of the argument, to judge and improve intelligence with AI as a heavily simplified model, which is an old hat by now.

Do you think that's irrational? Do you expect neuroscience or are you rather interested in mathematics? I thought that's how to learn in absence of external inputs, by recombination of the old inputs, ie. the fuzzy notions, to generate new ones. I'd think that's how recurrent networks work. What do you know about it (honest question)?

Fuzzy doesn't mean wrong. Underspecification, as little as I know about it, is a feature.

Re: Yann LeCun's comment on AlphaGo and true AI

#113

Post by wildmusings »

sytelus wrote: post #89
grumpy-buffalo wrote: post #48

I wish the term "true AI" were replaced with "strong AI" or "artificial general intelligence" or some such term. We already have true AI - it's a vast, thriving industry. AlphaGo is obviously a true, legitimate, actual, real, nonfictional example of artificial intelligence, as are Google Search, the Facebook Newsfeed, Siri, the Amazon Echo, etc.

Interestingly just ~5 years ago the team "AI" was frowned upon and people insisted in using the term "machine learning". The reasoning was that "AI" is just too convoluted a term because people will insist on comparing with humans and then whole question of consciousness invariably arises which renders scientific inquiry into undesirable debates.

I think that the refusal to seriously engage with consciousness is the main obstacle to progress toward general AI.

Re: Yann LeCun's comment on AlphaGo and true AI

#114

Post by StanislavPetrov »

flashman wrote: post #50

If artificial intelligence is the cake, true AI is the ability to argue about whether cake is a useful analogy.

The ability to argue, or the capacity to reason that argument? If the AI can convincingly argue that the analogy is apt, without actually reasoning or "thinking", does that make it the holy grail? I'd suggest that self awareness, and the ability to reason, would be true AI, and not just a glorified Turing test in the form of an effective ability to pose an argument.

Re: Yann LeCun's comment on AlphaGo and true AI

#115

Post by bbsome »

gracenotes wrote: post #83
Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

I took a closer read through the AlphaGo paper today. There are some other features that make it not general. In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like: - How many turns since a move was played - Number of liberties (empty adjacent points) - How many opponent stones would be captured - How many of own stones would be captured - Num…

Note, that these features are for the RollOut fast policy. The reason is that this needs to be fast, so rather than a net they have a linear policy. A linear policy in order to work it requires good feature selection, which is what this is. In some future, when we have better hardware, you can imagine removing the roll out policy and having just one.

Re: Yann LeCun's comment on AlphaGo and true AI

#116

Post by [deleted] »

gracenotes wrote: post #83
Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

I took a closer read through the AlphaGo paper today. There are some other features that make it not general. In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like: - How many turns since a move was played - Number of liberties (empty adjacent points) - How many opponent stones would be captured - How many of own stones would be captured - Num…

[deleted]

Re: Yann LeCun's comment on AlphaGo and true AI

#117

Post by scotty79 »

> As I've said in previous statements: most of human and animal learning is unsupervised learning.

I don't think that's true. When baby is learning to use muscles of its hands to wave them around there's no teacher to tell it what should its goal be. But physics and pain teaches it fairly efficiently which moves are bad idea.

It has built in face detection engine and the orienting and attempting to move and reach towards it is clear goal. Reward circuit in the brain do the supervision.

Re: Yann LeCun's comment on AlphaGo and true AI

#118

Post by woko »

scotty79 wrote: post #117

> As I've said in previous statements: most of human and animal learning is unsupervised learning. I don't think that's true. When baby is learning to use muscles of its hands to wave them around there's no teacher to tell it what should its goal be. But physics and pain teaches it fairly efficiently which moves are bad idea. It has built in face detection engine and the orienting and attempting to move and reach tow…

Sorry but what you describe is not what supervised learning means. It has a specific meaning in AI. At best, what you describe is reinforcement learning. And I don't think this is how we learn to recognize people's faces.

Re: Yann LeCun's comment on AlphaGo and true AI

#119

Post by Flenser »

jacquesm wrote: post #44
argonaut wrote: post #33

Earlier quoted context omitted.

This analogy doesn't make any sense. Since automobiles were never patterned off anything in biology (they were the improvement of horse drawn carriages), I'm not sure what point you're trying to make with the analogy. I never said there weren't any developments in neural nets, I'm just saying that few ideas have been taken from neuroscience (there certainly have been some ideas, like convolutions). In fact most thing…

All it took for us to make aircraft was to look at birds and to realize something could be done (flying even though you weigh more than the air). The first wanna-be aircraft looked like birds, and some even attempted to work like them. Most of those designs went nowhere. Just like legs got replaced by the wheel in ground transportation aircraft engines and eventually jets replaced the muscles of birds. It doesn't rea…

I like the comparison to developing wings. What's interesting about the development of plane wings is that, while we used the underlying physics of how wings work to make plane wings, a plane gets it's trust differently than a bird, and looks and flies differently. Flapping wasn't very useful to us for planes; what things about the way minds work will not be useful for AI? I think once we understand the algorithms behind AI / intelligence / learning, what we choose to make may be very different than what a lot of people currently imagine having AI or robots will to be like.

Re: Yann LeCun's comment on AlphaGo and true AI

#120

Post by tarsinge »

tarsinge wrote: post #108
Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

It is a major AI breakthrough, but not the sci-fi like. AlphaGo is a true AI for Go in the sense that it develops intuition and anticipation by evaluating probabilities, same as any human player. A Go-specific philosophical zombie in short. And when we will have superhuman philosophical zombies for each problem, what will be the point of a general AI? Edit: typo

to add to that, even our brain seems to work with dedicated specialized sub system. Wild supposition but maybe general AI could be something like a load balancer/reverse proxy to lot of problem specific AI. When a human learns to play Go, is some small part of the brain retrained specifically for this task? If yes, then AlphaGo could in fact be a building block. Architecture would look like "reverse proxy" -> sub system dedicated to the learning process -> sub system trained for learning board games -> sub system trained for playing Go. Absolutely not expert so it surely has been thought by the way /wild disgression
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