Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field.<p>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, sells well, and doesn't require the specifics.<p>Some of the limitations preventing AlphaGo from being general:<p>+ Monte Carlo tree search (MCTS) is really effective at Go but not applicable to many other domains we care about. If your problem is in terms of {state, action} pairs and you're able to run simulations to predict outcomes, great, but otherwise, not so much. Go also has the advantage of perfect information (you know the full state of the board) and deterministic simulation (you know with certainty what the state is after action A).<p>+ The neural networks (NN) were bootstrapped by predicting the next moves in more matches than any individual human has ever seen, let alone played. It then played more against itself (cool!) to improve - but it didn't learn that from scratch. They're aiming to learn this step without the human database but it'll still be very different (read: inefficient) compared to the type of learning a human does.<p>+ The hardware requirements were stunning (280 GPUs and 1920 CPUs for the largest variant) and were an integral part to how well AlphaGo performed - yet adding hardware won't "solve" most other ML tasks. The computational power primarily helped improve MCTS which roughly equates to "more simulations gets a better solution" (though with NNs to guesstimate an end state instead of having to simulate all the way to an end state themselves)<p>Again, amazing, interesting, stunning, but not an indication we've reached a key AI milestone.<p>For a brilliant overview: <a href="http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress" rel="nofollow">http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr...</a><p>John Langford also put his opinion up at: <a href="http://hunch.net/?p=3692542" rel="nofollow">http://hunch.net/?p=3692542</a><p>(note: copied from my Facebook mini-rant inspired by Langford, LeCun, and discussions with ML colleagues in recent days)
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; <i>any</i> kind of value function will do the trick.
Ah, the joys of arguing about artificial intelligence without ever defining intelligence.<p>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.
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.<p>Edit:<p>I actually think the major AI breakthrough will come from either of those two fields, not computer science.
Can someone more knowledgeable explain why biological systems are considered unsupervised instead of reinforcement based systems?<p>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?
I would like future competitions between AIs and humans to have a "power budget" for training and during gameplay. For example, a chess grandmaster that has played for 20 years would have spent X amount of energy training. The AI should get an equivalent budget to train with. During gameplay, the AI would get the same 20 watts [1] that a human has. This would drive the development of more efficient hardware instead of throwing power at the problem :)<p>[1] <a href="http://www.popsci.com/technology/article/2009-11/neuron-computer-chips-could-overcome-power-limitations-digital" rel="nofollow">http://www.popsci.com/technology/article/2009-11/neuron-comp...</a>
I'm surprised he'd make such an optimistic statement. I think a better analogy would be:<p>We figured out how to make icing, but we still don't really know what a cake is.
It sounds reversed to me- shouldn't the "cherry" be supervised learning and the "icing" be reinforcement learning? At least insofar as reinforcement learning is closer to the "cake" of unsupervised learning, as there is less feedback required for a reinforcement learning system to work (a binary correctness signal rather than an n-dimensional label signal.)<p>It might also be argued that most "unsupervised learning" in animals can be broken down into a relatively simple unsupervised segment (e.g., an "am I eating nice food" partition function) and a more complicated reinforcement segment (e.g. a "what is the best next thing to do to obtain nice food?" function.) I'm sure someone like Yann LeCun is familiar with such arguments, though.
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.
No one is claiming that alphaGo is close to AGI. At least not anyone that understands the methods it uses. What alphaGo is, is an example of AI progress. There has been a rapid increase in progress in the field of AI. We are still a ways away from AGI, but it's now in sight. Just outside the edge of our vision. Almost surely within our lifetime, at this rate.
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 runs day after day and year after year. There is sure unsupervised elements like ability to recognize phonemes in speech, tracking objects, inference despite of occlusion, ability to stand up and walk, make meaningful sounds, identify faces, construct sequence of actions to achieve goal, avoiding safety risks from past experiences and so on. However, typical child goes through unparalleled amount of supervised learning. There was an incidence of a child who got locked up in a room for over a decade and she didn't developed most of the language, speech or social skills. It seems unsupervised learning can't be all of the cake.
Is anyone working on an embodied AI? Even a simulated body might help. Ultimately intelligence is only useful insofar as it guides the body's motion. We often tend to minimize the physical act of say, writing down a theorem or actually applying paint to the canvas, but there are certain actions like playing a musical instrument that certainly blur the distinction between "physical" and "mental". Indeed, even 'purely mental' things like having an "intuition" about physics is certainly guided by one's embodied experience.
What we need next are more systems which can predict "what is likely to happen if this is done". Google's automatic driving systems actually do that. Google tries hard to predict the possible and likely actions of other road users. This is the beginning of "common sense".
> As I've said in previous statements: most of human and animal learning is unsupervised learning.<p>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.<p>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.
(1) Adversarial learning is unsupervised and works great.
Most of language modeling is unsupervised (you predict next word, but it's not real supervision because it's self-supervision).
There're many works in computer vision which are unsupervised and still give more or less reasonable performance.
See f.e. <a href="http://arxiv.org/pdf/1511.05045v2.pdf" rel="nofollow">http://arxiv.org/pdf/1511.05045v2.pdf</a> for unsupervised learning in action recognition, also <a href="http://arxiv.org/pdf/1511.06434v2.pdf" rel="nofollow">http://arxiv.org/pdf/1511.06434v2.pdf</a> and <a href="http://www.arxiv-sanity.com/search?q=unsupervised" rel="nofollow">http://www.arxiv-sanity.com/search?q=unsupervised</a><p>(2) ImageNet supervision gives you much information to solve other computer vision tasks. So perhaps we don't need to learn everything in unsupervised manner, we might learn most features relevant for most tasks using several supervision tasks. It is kind of cheating but very reasonable one.<p>Moreover,<p>(3) We observe now just fantastic decrease of perplexity (btw, it's all unsupervised = self-supervised). It's quite probable that in the very near future neural chat bots write reasonable stories, answer intelligibly with common sense, discuss things. All of this would be just a mere consequence of low enough perplexity. If neural net says smth inconsistent it means that it gives too much probability to some inappropriate words i.e, it's perplexity isn't optimized yet.<p>(4) It's quite probable that it would open a finish line for human-level AI. AI would be able to learn from textbooks, scientific articles, video lectures. Btw, <a href="http://arxiv.org/pdf/1602.03218.pdf" rel="nofollow">http://arxiv.org/pdf/1602.03218.pdf</a> gives a potential to synthesize IBM Watson with deep learning. May be, the finish line to human level AI has been opened already.
There's also a huge issue around problem-posing and degrees of freedom, that doesn't necessarily get better as your AI tools improve. Go has a fairly large state space, but limited potential moves per turn, well-defined decision points, limited time constraints, and only one well-defined victory condition. The complexity is minuscule compared to even something relatively well-structured like "maximize risk-adjusted return via stock trades".
Can someone elaborate the difference between reinforcement learning and unsupervised learning? It seems that I mistakenly think that human learns through reinforcement learning, that we learn by the feedback from the outside world. I mean without feedback from aldult can a baby even learn how to walk?
The statement that he's critiquing does reflect the wider-spread, overly simplistic view of AI. Contrary to hype, recent events represent only partial development/peeling of the top layer from the AI onion, which has more known unknowns and unknown unknowns than known knowns.
Totally agree, it's a bit like when some physicists were convinced that there wouldn't be other great breakthroughs after Maxwell's theory of electromagnetics. Maybe Yann LeCun is the Einstein of Machine Learning? haha
It seems that AI does well when the problem and the performance metrics are well defined: chess, Go, various scheduling problems, pattern recognition, etc. At the very least we can track, quantitatively, how far off we are from a satisfactory solution, and we know we can only ever get closer.<p>"True", or general-purpose AI, is harder to pin down, and thus harder to define well. I'd argue that the moment we have define it formally (and thus provided the relevant performance metrics) is the moment we have reduced it to a specialized AI problem.
It seems to me one of the higher hurdles for creating a general purpose intelligence, is human empathy. Without it you are left with creating a nearly infinite-length rules engine.<p>When you ask your AI maid to vacuum your house, you would prefer it not to plow through closet door to grab the vacuum, rip your battery out of your car and hardwire to the vacuum, and then proceed to clean your carpets. If you don't want to create a list of rules for every conceivable situation, the AI will need to have some understanding human emotions and desires.
We keep trying to engineer AI rather than reverse engineering it. The thing with living organisms is that the neural network underlying the intelligence of living organisms is a product of evolutionary design of an organism situated in the real physical world with laws of physics and space and time. This is where the bootstrapping comes in. Unsupervised learning is built on top of this. Trying to sidestep this could prove difficult to get to General AI.
Stop looking at the red dot. Take a step back and look around you.
"True" AI is here and it's been here for some time. You're communicating with it right now.<p>It's just that we find it so hard to comprehend it's form of "intelligence", because we're expecting true AI to be a super-smart super-rational humanoid being from sci-fi novels.<p>But what would a super-smart super rational being worth 1 billion minds look/feel like to one human being ? How would you communicate with it ?<p>Many people childishly believe that "we" have control over "it".
You don't. We don't.<p>The more we get used to it being inside our minds, the harder it becomes to shut it down without provoking total chaos in our society. Even with the chaos, there is no one person (or group) who can shut it down.<p>But "we" make the machines ! Well... yes, a little bit..<p>Would we be able to build this advanced hardware without computers ? Doesn't this look like machines reproducing themselves with a little bit of help from "us" ?<p>Think about the human beings from the Internet's perspective - what are we for it ? Nodes in a graph. In brain terms - we are neurons, while "it" is the brain.<p>But it's not self-aware ! What does that even mean ?<p>Finally, consider that AlphaGo would have been impossible without the Internet and the hardware of today.<p>And that "true" AI that everybody expects somewhere on the horizon will also be impossible without the technology that we have today.<p>If so, then what we have <i>right now</i> is the incipient version of what we'll have tomorrow - that "true" AI won't come out of thin air, it will <i>evolve</i> out of what we have right now.<p>Just another way of saying the same thing - it's here.<p>Is this good or bad ? Well, that's a totally different discussion.
Once an AI algorithm (even just one for Go) realizes that it can hijack the bank accounts of all the world's other 9 dan players in order to demand an analysis of its planned move, and figures out how to do that, <i>then</i> we've made the cake.<p>N.B. the genericity of the deepmind stuff that is the basis of AlphaGo makes this seem not entirely far-fetched.<p>Yum, cake.