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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

#131

Post by doctorstupid »

svalorzen wrote: post #110
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,…

- As all problems can be converted to Markov Decision Processes, this is a moot point. The transition may not be efficient in terms of states/actions, but since we just nearly solved a problem with more states than the atoms of the universe, this seems to be a moot point. In addition, most problems for humans are actually in the form of {state,action}. Just because now ML is popular and it's all about putting label o…

This is besides your main point, but I just want to add that the 'problem' of Go is not nearly solved. Rather, nearly solved is the problem of beating a human Go player. There is quite a difference.

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

#132

Post by mannykannot »

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.

As I have said elsewhere, I will agree we have achieved true AI when a program, uncoached, creates a persuasive argument that it is intelligent. One nice feature of this test is that you don't have to define intelligence precisely beforehand. Of course, that does not give a specification for developers to work to, but that is the case we have now, anyway.

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

#133

Post by sago »

mannykannot wrote: post #132
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.

As I have said elsewhere, I will agree we have achieved true AI when a program, uncoached, creates a persuasive argument that it is intelligent. One nice feature of this test is that you don't have to define intelligence precisely beforehand. Of course, that does not give a specification for developers to work to, but that is the case we have now, anyway.

Nice to know your criteria. There have been many. The trick is not to decide when you'll 'agree' (whatever your agreement is worth), but to form a consensus in the discussion.

I've known people who've been unable to create a persuasive argument that they are intelligent (or unwilling), and I've know intelligent dogs unable to argue for anything, persuasive or not. I don't fancy your chances of having your definition become the standard.

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

#134

Post by platz »

doctorstupid wrote: post #131
svalorzen wrote: post #110

Earlier quoted context omitted.

- As all problems can be converted to Markov Decision Processes, this is a moot point. The transition may not be efficient in terms of states/actions, but since we just nearly solved a problem with more states than the atoms of the universe, this seems to be a moot point. In addition, most problems for humans are actually in the form of {state,action}. Just because now ML is popular and it's all about putting label o…

This is besides your main point, but I just want to add that the 'problem' of Go is not nearly solved. Rather, nearly solved is the problem of beating a human Go player. There is quite a difference.

I wouldn't expect global maximums to even be definable for most problems

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

#135

Post by atrudeau »

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…

Wow, this really took me by surprise. I thought the only input was (s_1...s_final, whowon) where s are statates during training and (s_current) during play, and the system would learn the game on its own. That's the way it worked with the Atari games anyway.

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

#136

Post by nabla9 »

pavanky wrote: post #36

Can someone more knowledgeable explain why biological systems are considered unsupervised instead of reinforcement based systems? While it seems intuitive that most individual "intelligent" systems in animals can be seen as unsupervised, isn't life itself driven in a reinforced manner?

There is no enough reinforcement volume to learn anything complex.

If child gets external reward for every waking moment until she is 12 years old, it's just 4.2 million signals.

Reinforcement learning works for fine motor control and other tasks where the feedback loop is tight and immediate. Reinforcement and conditioning can also modulate high level cognition and behavior, but it's not the secret sauce of learning.

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

#137

Post by nabla9 »

sytelus wrote: post #87

If you look at how child learns, it's huge amount of supervised learning. Parents spend lots of time in do and don't and giving specific instructions on everything from how to use toilet to how to construct a correct sentence. Lots of language development, object identification, pattern matching, comprehension, math skills, motor skills, developing logic - these activities has huge amount of supervised training that…

If child gets external reward every waking moment until she is 12 years old, it's just 4.2 million feedback signals. It's not enough to learn complex behavior.

Reinforcement learning works for fine motor control and other tasks where the feedback loop is tight and immediate. Reinforcement and conditioning can also modulate high level cognition and behavior, but it's not the secret sauce of learning.

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

#139

Post by fallingfrog »

I don't know if I'd agree that unsupervised learning is the "cake" here, to paraphrase Yann LeCun. How do we know that the human brain is an unsupervised learner? The supervisor in our brains comes in the form of the dopamine feedback loop, and exactly what kinds of things it rewards aren't totally mapped out but pleasure and novelty seem to be high on the list. That counts as a "supervisor" from a machine learning point of view. It's not necessary to anthropomorphize the supervisor into some kind of external boss figure; any kind of value function will do the trick.

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

#140

Post by jacquesm »

doctorstupid wrote: post #131
svalorzen wrote: post #110

Earlier quoted context omitted.

- As all problems can be converted to Markov Decision Processes, this is a moot point. The transition may not be efficient in terms of states/actions, but since we just nearly solved a problem with more states than the atoms of the universe, this seems to be a moot point. In addition, most problems for humans are actually in the form of {state,action}. Just because now ML is popular and it's all about putting label o…

This is besides your main point, but I just want to add that the 'problem' of Go is not nearly solved. Rather, nearly solved is the problem of beating a human Go player. There is quite a difference.

> the 'problem' of Go is not nearly solved.

And it is very likely that it will never be, the number of combinations is simply too large.

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