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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

#161

Post by lukeschlather »

gracenotes wrote: post #93
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…

Interesting. I have heard the opposite of this. Supervised learning may be how it looks from the outside, but consider that out of the >6,570,0000 waking seconds of a child's life up to age 5, there maybe only a few dozen instances of supervised adult instruction per day. Besides those, what do neurons do the remaining 99.99% of the time? Part of the problem might be that comparing supervised and unsupervised learnin…

> Supervised learning may be how it looks from the outside, but consider that out of the >6,570,0000 waking seconds of a child's life up to age 5, there maybe only a few dozen instances of supervised adult instruction per day. Besides those, what do neurons do the remaining 99.99% of the time?

This seems like a really facile analysis. For example, if I read a child a storybook, I'm deliberately providing several signals every second. That's a "single instance" but I've effectively provided a lot of training information. At least enough to keep a child's mind busy for 3600 seconds.

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

#163

Post by simonh »

emcq wrote: post #31
hacknat wrote: post #5

I think we need more advances in neuroscience and, I know this will be controversial, psychology before we really know what the cake even is. Edit: I actually think the major AI breakthrough will come from either of those two fields, not computer science.

I disagree. In engineering we've shown great advancements not by exactly reproducing biology from cases like transportation (wheel vs legs) or flight (engine and wing vs flapping feathers). We can't even mass produce synthetic muscles and instead use a geared motor. The growth in building increasingly sophisticated AI is faster than our efforts to reverse engineer biology. I could see that changing with improved obse…

I think you're right, and AlphaGo is an example. The big advancements in flight give us supersonic jets far faster than birds, but we've only recently developed microdrones that can land on a tree branch. We have wheeled vehicles far faster than horses, but haven't got a single vehicle that can complete an Eventing course. So in AI we have computers far better at Chess than any human, far better at indexing and searching databases, and now (or certainly very soon) far better at playing Go.

But we are still nowhere near developing a computer that can learn to play Monopoly, Risk or Axis & Allies just from reading the rule book and looking at the components. If you aim your machine at a very narrow subset of a problem, you can optimise it to amazing degrees, far exceeding humans or nature. But developing a machine that has the whole package is staggeringly hard in comparison.

But you know what? That's fine. Special purpose, single domain tools are fantastically useful and are easier to make highly reliable, with well understood limitations.

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

#164

Post by zby »

sago wrote: post #123
conceit wrote: post #112

Earlier quoted context omitted.

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…

I remember similar arguments in the early 90s when I was doing my PhD. They got about as far then. The same arguments will be happening in another 25 years. And beyond, even when a computer is super-human in every conceivable way, there'll be arguments over whether it is 'real AI'. And nobody will define their terms then either. Ultimately the discussion will be as irrelevant then as it is now, and, then as now, it w…

But then the super-human intelligence will give us some convincing arguments for one or the other side :)

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

#165

Post by simonh »

tarsinge wrote: post #120
tarsinge wrote: post #108

Earlier quoted context omitted.

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 sys…

Marvin Minsky thought exactly along these lines. Check out the interview below, especially the last few minutes. All his interviews in that series are well worth watching.

http://youtu.be/wPeVMDYodN8

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

#166

Post by Smerity »

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…

Thanks for the good discussion :)

+ The issue with MCTS was not that it couldn't be extended to non-determinism (you're correct re: POMCP) but that it requires a simulator which produces at least a reasonably accurate model of the world. This simulator is almost always hand engineered. Determinism and perfect information simplify both the task and the creation of the simulator. The state in Go also contains all history required to perform the next optimal computation - i.e. the system doesn't have to have any notion of memory - yet the state in most real world tasks is far more complicated, at least in regards to what needs to be remembered and how that should be stored.

+ The point of the hardware requirements is that hardware advances will advance the state of the art in Go but will not do the same in many other ML tasks. Whether or not we have 1x or 10x AlphaGo distributed's computing power in our pocket is not the issue - the issue is that such computing power won't assist many tasks as the potential of our ML models are not compute bound.

There's also disagreement about how concerted the "10 year jump" was, which is mentioned in the article by Miles Brundage. Many people (including Michael Bowling, the person who designed the system that "solved" limit heads-up Texas Hold Em) predicted professional level Go play around now. Whilst it might be held by many, I also feel it was a media reinforced estimate.

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

#167

Post by astazangasta »

fallingfrog wrote: post #139

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 p…

That IS unsupervised learning - you are maximizing an optimization function, you are not training using a set of "truth" values.

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

#168

Post by daveguy »

sago wrote: post #133
mannykannot wrote: post #132

Earlier quoted context omitted.

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…

Would you like to propose a definition?

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

#169

Post by fallingfrog »

astazangasta wrote: post #167
fallingfrog wrote: post #139

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 p…

That IS unsupervised learning - you are maximizing an optimization function, you are not training using a set of "truth" values.

Hmm, I don't think so. From wikipedia: "Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution." (emphasis mine)

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

#170

Post by [deleted] »

conceit wrote: post #157
sago wrote: post #123

Earlier quoted context omitted.

I remember similar arguments in the early 90s when I was doing my PhD. They got about as far then. The same arguments will be happening in another 25 years. And beyond, even when a computer is super-human in every conceivable way, there'll be arguments over whether it is 'real AI'. And nobody will define their terms then either. Ultimately the discussion will be as irrelevant then as it is now, and, then as now, it w…

> About recurrent NNs? yes, obviously, as that's the topic, but also the the rest I of what I mentioned, neuroscience, maths, leaning on logic and philosophy. > I remember similar arguments in the early 90s That's why I mention neuroscience, the ideas are much older. > even when a computer is super-human in every conceivable way, there'll be arguments over whether it is 'real AI' Of course. Just because it's superhum…

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