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Yann LeCun's comment on AlphaGo and true AI
- LERobot 11y agoTotally 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
- kzhahou 11y agoI'm surprised he'd make such an optimistic statement. I think a better analogy would be: We figured out how to make icing, but we still don't really know what a cake is.
- has2k1 11y agoHe addresses your query, not in the analogy but the additional comment. "And that's just an obstacle we know about. What about all the ones we don't know about?" With sufficient scrutiny all analogies break down, so readers must be generous with their interpretation.
- emmab 11y agoWe can describe Solomonoff-based agents like AIXI. None of them are fully sufficient for true general AI, but you could probably accomplish quite a bit with an AIXI-like agent.
- drdeca 11y agoI thought full AIXI wasn't computable though?
- emmab 11y agoYou can do time-bound AIXI and for a large enough time bound it's sufficient for all practical situations. AIXI is not tractable, but I'm responding to the parent comment saying we don't even know what a cake is.
- kzhahou 11y agoI don't consider this mathematical model of induction/prediction to be an accurate description of "intelligence" in the sense of "True AI" (which itself is open to interpretation). This is the cake: human intelligence. Right now we have pieces of it, but even the end goal isn't well defined. We know the human mind makes predictions, recognizes patterns, can formulate plans, works with both concrete and fuzzy information, and so on. But we still don't understand what human intelligence really is, overall.
- sampo 11y agoAs fas as I know, the only thing existing AIXI implementations have demonstrated, is to learn to play Pac-man at a somewhat reasonable, but not in any way stellar level.
- emmab 11y agoYes, it is not tractable. It serves as an example of a definition of an agent though.
- ronilan 11y agoThe cake is a lie. Obviously :)
- mkoryak 11y agoreddit is over there ->
- hacknat 11y agoI 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.
- jacquesm 11y ago> I know this will be controversial, psychology Why would that be controversial? It seems to make extremely good sense and even though it may be doubted in some circles I think that most people involved in AI research are painfully aware of our limited understanding of our own psyche.
- argonaut 11y agoI disagree with hacknat (and the reason I suspect it's controversial) because psychology is generally far too high-level (e.g. focused on things like traits and high-level behavior and vague "perceptions") and has far too little empirical rigor for engineers to be able to build any sort of actual system from insights gleaned from psychology. The recent replication crisis in psychology does little to help this reputation. Neuroscience does concern itself a great deal with low level biological mechanisms and architectures, and is more amenable to cross-pollination with machine learning. Though I would like to point out that thus far deep learning has taken few ideas from neuroscience.
- jacquesm 11y agoNeural networks are the big driver and arguably were taken from neuroscience. The reason why psychology is 'too high-level' is exactly what is meant with 'we don't understand it', we're approaching the psyche at the macro level of observable traits, but there is a very large gap between the 'wiring' and the 'traits', some of that gap belongs to neuroscience but quite possibly the larger parts belongs to psychology. The two will meet somewhere in the middle. A similar thing happens in biology with dna and genetics on the one side and embryology on the other.
- Geekette 11y agoThe 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.
- kailuowang 11y agoCan 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?
- sakopov 11y agoExactly what i was going to ask! Is there such a thing as unsupervised learning? A human mind is constantly fed a stream of learning data. And while some things may be predispositioned by our DNA, I think for the most part we learn directly from the rules and guidelines dictated by our parents first and our society later.
- hughperkins 11y agoSo, its like, when I learned salsa dancing, i couldnt hear the 'beat' of the music, and was always out of time. So what I did was just listen to salsa music whilst cycling and walking for a couple of weeks. Now i could hear the 'beat', and lots more structure in the music to boot.
- sakopov 11y agoAh, yes. This's a great example. I see what you're saying.
- chrisfosterelli 11y agoNot an expert, but my understanding is that humans can learn many things through reinforcement learning, but most of our intelligent decisions are a result of unsupervised learning. For instance, if the stove element was red hot and you touched it, you'd receive a feeling of pain. Reinforcement learning would suggest that you shouldn't do this again, and you might learn to not touch stove elements when they are red hot anymore. However, a human is likely to additionally realize that touching a red hot marshmallow stick would burn them as well. And at the same time, a human can also tell that a red ball is safe to touch. This sort of behaviour (internally labeling things and deciding what it is that made the stove element dangerous so you can apply that to other things) would be unsupervised learning.
- javajosh 11y agoIs 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.
- wpietri 11y agoYou might look at the work of Rodney Brooks. There was a wonderful documentary called "Fast, Cheap, and Out of Control", and I'm pretty sure it was in there that he explains his notion that true intelligence is embodied. That was years ago and I haven't kept up with the field, but perhaps it's a useful starting point for you.
- javajosh 11y agoCool, thanks. I think the idea is also important to Zoltan Torey, who believed that "consciousness" is essentially a simulation of a kind of arm in our mind. ("The Conscious Mind"). But I've never heard of anyone attempting to train an AI within a simulated world. The nice thing about doing that is that aren't limited by physics of robotics. I suspect that for basic intelligence stuff, relatively lo-fidelity simulation would be sufficient. (Although there are some very important moral issues presented by such a program!)
- wpietri 11y ago> But I've never heard of anyone attempting to train an AI within a simulated world. Ah, then you might want to follow the trail from SHRLDU: http://hci.stanford.edu/winograd/shrdlu/ http://hci.stanford.edu/winograd/shrdlu/ https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU
- javajosh 11y ago
- daxfohl 11y agoOnce 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, then we've made the cake. N.B. the genericity of the deepmind stuff that is the basis of AlphaGo makes this seem not entirely far-fetched. Yum, cake.
- fiatmoney 11y agoThere'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".
- megaman821 11y agoIt 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. 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.
- spurcell93 11y agoIf you haven't read them yet, I would suggest Isaac Asimov's Robot series, which discusses the implications of hardware-level 'laws' to which robots must abide. His take was that these laws would constrain behavior according to human judgments of harm and benefit. I wonder if there is another way of looking at it, one where AI action is not only constrained by human sensibility, but informed by it.
- viraptor 11y agoI'm not sure why you connect that behaviour to empathy. There are two simple rules here that apply to almost every possible situation, as well as the one you presented. They aren't even connected to human emotions. That's pure economy. 1. Minimise work (plugging into socket has lower cost / effort than what you described) 2. Minimise irreversible changes (or cost of reversing them) There are so many people with low empathy who are useful, I don't think this is an issue until someone needs a personal companion rather than general purpose AI.
- megaman821 11y agoThese rules cannot be applied in every situation. Then you back to writing a bunch of rules. If you told a general AI to take care of a puppy for a few days, it would end up putting a diaper on it and keeping it in a crate 24 hours a day. That would be the least amount of work to take care of the puppy, and minimize the chance of the puppy hurting itself or damaging anything else.
- pavanky 11y agoCan 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?
- rm999 11y agoThe brain needs to form structure before any sort of reinforcement learning is even possible. This structure comes from an "unsupervised" process we don't understand well.
- justicezyx 11y agoIsn't it from millions year of natural selection? The reason why a human brain can think, is just by chance.
- daveguy 11y agoPersonally I think a lot of it has to do with evolution. We certainly have different sections of the brains that are more utilized in different tasks, auditory, visual, motor, etc. I think it is likely a lot of the initial feedback is preset -- the instinctual positive and negative responses of certain stimulus.
- conceit 11y agoAs much as it doesn't help to call it intelligent design, chance is not any good either, if asking for a reason. Random chance is as good as no reason at all.
- justicezyx 11y agoMixing natural selection and intelligent design is a problem, but that's not a problem for natural selection.
- 11y ago
- juskrey 11y agoTrue life has no rules
- _snydly 11y agoI have Facebook blocked for the next week (because, you know, productivity). Can someone post LeCun's comment here?
- sound_of_basker 11y agoStatement from a Slashdot post about the AlphaGo victory: "We know now that we don't need any big new breakthroughs to get to true AI" That is completely, utterly, ridiculously wrong. As I've said in previous statements: most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don't know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI. And that's just an obstacle we know about. What about all the ones we don't know about?
- grumpy-buffalo 11y agoI 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.
- fiatjaf 11y agoStrong AI means a different thing.
- rdl 11y ago"Useful AI"?
- jlarocco 11y agoI guess it boils down to a difference in opinion as to what it means to be intelligent. IMO, AlphaGo isn't "intelligent" because all it can do is play Go. For example, it can't be taught to play chess without completely reprogramming it. Surely it's using a lot of clever algorithms, but where's the intelligence? TBH, there's not much point to this post, because the intelligence in AI has been twisted more towards your usage almost since the beginning.
- SCAQTony 11y agoI agree with you and would like to double down: Intelligence is defined as "...the ability to acquire and apply knowledge and skills." Even if Alpha-Go could play chess, manage air traffic control, and play GO the same time, it would never know it was doing that. As you mentioned above, Alpha-Go's "intelligence"is specifically pre-programed algorithms and accurate numeric inputs. If it can't create it's own algorithms or translate abstract data into numbers it can crunch on it's own, then there is no intelligence there. It just "smells" like intelligence.
- eggy 11y agoI prefer computational intelligence to AI, so maybe CI?
- flashman 11y agoIf artificial intelligence is the cake, true AI is the ability to argue about whether cake is a useful analogy.
- StanislavPetrov 11y agoThe 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.
- esfandia 11y agoIt 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. "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.
- Houshalter 11y agoThe Turing test is one such measure that is unbeatable by specialized AIs. Language understanding in general requires some degree of intelligence. I don't think the Turing test should be an actual goal of AI researchers. Turing just proposed it as a hypothetical example.
- Smerity 11y agoPreface: 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, sells well, and doesn't require the specifics. Some of the limitations preventing AlphaGo from being general: + 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). + 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. + 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) Again, amazing, interesting, stunning, but not an indication we've reached a key AI milestone. For a brilliant overview: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progress http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... John Langford also put his opinion up at: http://hunch.net/?p=3692542 http://hunch.net/?p=3692542 (note: copied from my Facebook mini-rant inspired by Langford, LeCun, and discussions with ML colleagues in recent days)
- dsharlet 11y ago> The hardware requirements were stunning (280 GPUs and 1920 CPUs for the largest variant) and were an integral part to how well AlphaGo performed Is that really true? Demis stated that the distributed version of AlphaGo beats a single machine version only 75% of the time. That's still stronger than virtually all human players, and probably would still beat Lee Sedol at least once.
- chriscappuccio 11y agoLike, duh.
- Houshalter 11y agoNo 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.
- cwyers 11y agoThere has been a lot of progress in AI research, and AlphaGo does represent some of that. But AlphaGo is not any kind of progress from the AI before it towards AGI. It's progress, but progress mostly perpendicular to the goal of "true" or "general" intelligence. It's similar to the same kind of AI that was built for chess, just for a much more difficult problem in that same class of problems. No problems useful to general intelligence were solved in making AlphaGo, just harder problems in the field of special intelligence. It's still a tremendous accomplishment, just not one that puts AGI happening within any of our lifetimes.
- sampo 11y ago> We are still a ways away from AGI, but it's now in sight. These people in 1956 also thought that AGI was in sight: https://en.wikipedia.org/wiki/Dartmouth_Conferences https://en.wikipedia.org/wiki/Dartmouth_Conferences
- Houshalter 11y agoNot most of them, and not with any certainty. The Dartmouth conference was just the very first investigation into whether it was even possible and what it would take. Of course they didn't know when AGI would be developed. The computers they had access to were smaller than my calculator! Now we have massive computers and 60 years of progress. And lots of real world successes of AI technology. Surely we are in a much better position to predict than people of 60 years ago!
- sampo 11y ago> Not most of them, and not with any certainty. This probably holds true also today.
- deegles 11y agoI 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 :) [1] http://www.popsci.com/technology/article/2009-11/neuron-computer-chips-could-overcome-power-limitations-digital http://www.popsci.com/technology/article/2009-11/neuron-comp...
- rdlecler1 11y agoWe 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.
- sytelus 11y agoIf 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.
- gracenotes 11y agoInteresting. 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 learning 'effectiveness' is a bit apples-and-oranges. Their effect together is highly collaborative. Children have to develop abstractions on their own before you can supervise them on those abstractions. It is probably fair to say that a key part of human general intelligence is creating high-level representations of low-level stimuli. It might also be fair to say that this is what the brain is doing 100% of the time. So if I may hand wave a little: while supervised learning can make a child better maximize objectives on those high-level representations (objectives they may be aware of through unsupervised observation), for the most part it does not fundamentally change the structure of those things in the child's brain. This makes unsupervised learning almost all of the cake to me. My post has the caveat that children undergo a lot of other objective-based learning besides explicit instruction from adults, and all of this maps only fuzzily to supervised vs unsupervised learning in AI, which is the issue from the submitted post.
- Animats 11y agoWhat 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".
- sago 11y agoAh, 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.
- conceit 11y agoSo 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.
- sago 11y agoI 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 will be mostly take place between well-meaning undergrads, and non-specialist pundits. (Philosophers of science have a discussion that sounds similar, to someone just perusing the literature to bolster their position, but the discussion is rather different though also not particularly relevant, in my experience.) > What do you know about it (honest question)? About recurrent NNs? Not much beyond the overview kind of level. My research was in evolutionary computation, though I did some work on evolving NN topologies, and using ecological models to guide unsupervised learning.
- conceit 11y ago
- scotty79 11y ago> 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.
- woko 11y agoSorry 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.
- scotty79 11y agoI'm not sure if supervised learning is such a narrow term but I'll take your word for it. As for recognizing faces I think we have that pretty much baked in the hardware. Faces are recognized immediately and not just by humans, also animals. I think people who lost ability to see faces, can't re-learn it.
- elementalest 11y agoThe difference between supervised and unsupervised is that the inputs are paired with known outputs for supervised. In unsupervised the agent has (initially) no knowledge of what the outputs will be, given the inputs. The baby does not know (initially) that something will cause pain, or the extremities of its joints. It must learn this over time and experience. The baby must also learn how to use the built in components, as it has no idea what outputs will occur given the inputs. As you allude to, there are built in mechanisms/configurations in the brain which provide various forms of feedback, as well as built in behaviours and responses. If there was no basic structure to the brain, I think it would be almost impossible for an unsupervised agent to develop and learn to the complexity and level of a human brain. These basic behaviours significantly speed the initial development process up.
- justsaysmthng 11y agoStop 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. 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. 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 ? Many people childishly believe that "we" have control over "it". You don't. We don't. 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. But "we" make the machines ! Well... yes, a little bit.. 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" ? 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. But it's not self-aware ! What does that even mean ? Finally, consider that AlphaGo would have been impossible without the Internet and the hardware of today. And that "true" AI that everybody expects somewhere on the horizon will also be impossible without the technology that we have today. If so, then what we have right now is the incipient version of what we'll have tomorrow - that "true" AI won't come out of thin air, it will evolve out of what we have right now. Just another way of saying the same thing - it's here. Is this good or bad ? Well, that's a totally different discussion.
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- helloeverybodys 11y agoVery Interesting
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- johanneskanybal 11y agoClick bait titles aside it's an amazing achievement.
- fallingfrog 11y agoI 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.
- emodendroket 11y agoWell, a really interesting AI could tell us things about questions where we don't even know where to start, rather than needing to be primed with tons of data.
- stormbuilder 11y agoWouldn't that be a self-supervised learning? Yes, as the brain lears, it gets rewarded or not, but not by any external organism - rather by itself (or a part of itself). The "learning instruction manual" is in there somewhere. So if you are able to code an AI so that it can run experiments over itself and gradually build this "learning instruction manual" eventually it will become able to do things you never though of in the first place.
- fallingfrog 11y agoI think that's what the AlphaGo team did - they trained their agent against itself, and it learned new moves not explicitly programmed in! With an evaluation function just saying ahead / not ahead.
- astazangasta 11y agoThat IS unsupervised learning - you are maximizing an optimization function, you are not training using a set of "truth" values.
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- maxander 11y agoIt 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.) 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.
- Sergej-Shegurin 11y ago(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. http://arxiv.org/pdf/1511.05045v2.pdf http://arxiv.org/pdf/1511.05045v2.pdf for unsupervised learning in action recognition, also http://arxiv.org/pdf/1511.06434v2.pdf http://arxiv.org/pdf/1511.06434v2.pdf and http://www.arxiv-sanity.com/search?q=unsupervised http://www.arxiv-sanity.com/search?q=unsupervised (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. Moreover, (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. (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, http://arxiv.org/pdf/1602.03218.pdf http://arxiv.org/pdf/1602.03218.pdf gives a potential to synthesize IBM Watson with deep learning. May be, the finish line to human level AI has been opened already.
- jonbarker 11y agoTo be fair AlphaGo never decided Go was a fun and worthy challenge; Lee Sedol, however, did.
- kafkaesq 11y agoA beautifully concise statement of an incredibly common misconception as to the current state of the field.