14 ms·
Artificial intelligence pioneer says we need to start over
- 52-6F-62 9y agoEverything Hinton says echoes strongly Kuhn's philosophy on the scientific paradigm. I guess it's not too much of a surprise that he thinks this is the case, but it also shows how intelligent (and shockingly humble) he is to make such a statement. I wish I'd heard of the conference, dammit!
- tw1010 9y agoI think it's great that top researchers are actually learning from Kuhn's observations. I have hope that humanity can indeed learn from its earlier historical mistakes, but it requires us to be very aware of them in order to collectively avoid them.
- 52-6F-62 9y agoI agree. Mr Hinton was somewhat vague and brief in his statements here, though. What do you think the crisis point is that he's reached exactly? (It must be sure enough for the man who introduced the paradigm in the first place to have caught on, yet the change of thought process unclear enough for him to recognize that it will be somebody newer who will have to introduce something else)
- kobeya 9y agoI think his quote is out of context. If he is saying that back propagation networks alone are insufficient for AGI, that is neither surprising nor controversial. Hinton has never been working on the AGI problem. It sounds more like the author is making use of the quotes he/she got from Hinton in the most clickbaity way possible.
- 52-6F-62 9y agoI get what you're saying, but I don't think he was speaking explicitly about AGI. To me it didn't sound like they were leaving the applied problems he has worked on. Maybe I'm reading too much between the lines, but so far there haven't been any major successes in un-supervised learning models and he's suggesting here that rather than continue with the models that have had success up to this point, it would be more worth the energy to start again at a lower level, rethinking the problem, in order to achieve similar results unsupervised. This may be where my limited knowledge is my handicap, but I didn't think you would need an AGI in order to achieve unsupervised learning. There would still be an input and an output as there is now -- but instead of massive datasets used in training the models could simple experience the problem in real time, adjusting and learning based upon new results. Of course unsupervised learning models could contribute greatly to the pursuit of AGI, but I didn't think AGI predicate unsupervised learning models (which is what I am taking from the article -- or more like article abstract -- here).
- kobeya 9y ago> Maybe I'm reading too much between the lines, but so far there haven't been any major successes in un-supervised learning models Word2vec, a variant of which is Transform, Google's new machine translation framework which beats the pants off every other approach, to name one. The system it is replacing is also unsupervised.
- LeeHwang 9y agoDo you mean the theory of scientific revolution ?
- 52-6F-62 9y agoThat's the one. I'm actually just finishing it up now, so it's fresh enough in my mind that it jumps out at me in application.
- ethnic_throw 9y agoI'd be careful reading it: Kuhn is excellent at building narratives, and what is more narrative friendly than revolution? In reality science is far more messy than he acknowledges. Unfortunately while I think many of Kuhn's observations are interesting, I'm confused what you think scientists might learn from them. After all, the work in question is science, not philosophy of science, and frankly it seems a lot of time like the latter gets in the way of work (see: Popper's radical empiricism). Poetic? Sure. Useful? Difficult to see how.... Mostly his work seems useful for bolstering the credibility of speculative Popular Science articles (or Axios in this case).
- 52-6F-62 9y agoOh well I didn't think I'd applied any subjective notion to my recognition that it sounds like the paradigmatic event that Kohn describes. And having recently gone through the book it was the first thing I noticed. My admiration for Hinton there lay separate from my remark about Kuhn. I'm not quite sure where you got that I think scientists must learn from Kuhn's observations. But I will say, that while it's kept me away from my other studies (for the obvious displacement of reading one thing and not the other), I don't see the harm in indulging in such reflection. But my background isn't engineering, mathematics, or physical sciences before my more recent work. It's in humanities and the arts where multidisciplinary studies are encouraged. I suppose I'll probably always carry that with me. If nothing else, perspective is a good thing to have -- and not all thoughts, reflections, or ideas entertained need be engaged. At any rate, the book was recommended and I try to remain open minded.
- 9y ago
- jfv 9y agoTo be fair, reinforcement learning can be done with neural networks and can be seen as a form of "unsupervised learning". The results aren't as spectacular (yet?) as what we've seen in unsupervised learning, but it has potential. I think the main problem is that we're incredibly dependent on advances in computing power to make advances in deep learning. IMHO, we've only made incremental software progress in using deep networks since the advent of convolutional networks.
- billconan 9y agothere was this tutorial on how to implement a more biological accurate neuron in javascript. https://medium.com/javascript-scene/how-to-build-a-neuron-exploring-ai-in-javascript-pt-2-2f2acb9747ed https://medium.com/javascript-scene/how-to-build-a-neuron-ex... I asked the author how to put it into use, how to train it? because clearly this biological neuron is not a smooth function. but I didn't get any response.
- Eridrus 9y agoObviously a clickbait headline, few people think current tech will lead to AGI, but while we all wait on a new breakthrough in AI, we can build much more impressive systems with what we have now than even a few years ago. I went to a talk from people who had built more scalable training algorithms for Restricted Boltzmann Mahcines (which Hinton was a pioneer in) a few months ago and asked them why they had chosen to use RBMs. Besides the waffly answers about how generative models were more interpretable than discriminative models, the real answer seems to have been that this was a research program that had started when RBMs were still in vogue, and their funding was tied to it, and once it ran out they were going to switch to investigating Generative Adversarial Networks like everyone else.
- dnautics 9y agoI don't think that's correct. There are a lot of people who should know better that think that the current ml technology will lead to agi.
- red75prime 9y agoLet me guess. You think so, because we don't know what the general intelligence is, but the current approaches of ML are surely can't be extended to produce it.
- dnautics 9y agoWe have some simple models of not even general intelligence that ml can't do. To steal from hofstadter:. Let's say I have a function that maps two strings. I'll give you an example function. "abc" -> "abd". What does "efg" map to? And how do you train current generation of ml to solve those problems in one shot like you just did? I don't want to knock machine learning. There's a very huge set of very interesting problems that it does solve that were basically intractable till now and we haven't even scratched the surface of applications for. But the existing techniques have understandable restrictions to the scope of soluble problem. I would also not be surprised if the best models included current ml strategies in some way, for example, in supervisor threads or initial processing of complicated input data into semantic-ish vector valued forms.
- bra-ket 9y agoIf you are interested in biologically plausible models of cognition check out 'vector symbolic architectures' and 'associative memory' research of the 90s.
- tw1010 9y agoBiological plausibility is just silly. What it mostly does is just put artificial constraints on the problem that doesn't need to be there. It's all mathematics. However you want to interpret or metaphorize the mathematics is up to each math-phobic field, but constraining yourself to what randomly evolved serves little good.
- 52-6F-62 9y agoWhile it's likely true that there are other ways to approach the problem, we're yet unsure of how to get there. The existing biological model has as least proven itself to work to some level, at least insofar as Descartes has lead us to believe.
- tw1010 9y agoI would bet a lot of money that engineers testing model after model in a state of flow, without any concern for biological validity, will yield a better result than any biologically inspired thing will. Time efficiency is also an important factor here. The model that will win is the one that is first to "market" (academic paper).
- 52-6F-62 9y agoCan't say I'm disagreeing with you. In fact, I'm not. To illustrate, I think it is far more likely we'll have "Jarvis" before we are building our own analogues of the human brain (or better). I do want it to happen, though. And I think it will, in spite of whatever model wins. It's just too interesting a problem. How that takes shape, I can't dream of predicting. (eg. How soon will or understanding of the use of organics in computing advance to such a level)
- 9y ago
- S_A_P 9y agoSo it says "In 1986, Geoffrey Hinton co-authored a paper that, four decades later, is central to the explosion of artificial intelligence." Is it just me that considers that 3 decades? I am curious which is the typo. Was it 76? or 3 decades?
- zwerdlds 9y agoI was trying to rationalize it this way: 1980's - first decade 1990's - second decade 2000's - third decade 2010's - fourth decade I guess, technically, the third decade of age for this paper ended in 2016, making 2017 the fourth. But this seems like silly ways to rationalize it. Genuinely seems like a typo.
- twobyfour 9y ago1986 was 31 years ago. Maybe not decades in the sense of "1990s" being a decade. But certainly 3 periods of 10 years.
- masterponomo 9y agoThey also need to start over on all that wiring behind Dr Hinton in the photo.
- iandanforth 9y agoBrief description of the issue for non-experts: Supervised learning You can judge the output of your network against ground truth. You say that's a cat? Nope, it's a dog! And then slightly adjust your network so it's less likely to give that wrong answer in the future. How exactly you adjust the network is what backpropagation describes (in combination with something called a learning rate). Unsupervised learning You need to learn without someone telling you what the answer is. Most of the time for biological intelligence there isn't an oracle describing the truth at every moment of life to judge actions/decisions against. If you don't have someone telling you you've made a mistake, how can you know when to adjust your network? And if you don't know what the truth is, exactly how to adjust the network becomes tricky. Somehow biological brains work without that oracle, and there are lots of ideas about how it does that. But right now for artificial intelligence none of those ideas has been shown to work so amazingly well that it has taken off like backprop has in the supervised learning world. Hinton wants to find that amazing algorithm.
- aaronblohowiak 9y agoI wager it has something to do with prediction and rules
- sillysaurus3 9y agoYou need to learn without someone telling you what the answer is. Most of the time for biological intelligence there isn't an oracle describing the truth at every moment of life to judge actions/decisions against. There is: Death, without reproducing. If an organism dies quickly but left children, they were biologically successful. If you didn't, then your evolutionary branch goes away. I'm not sure this can be simulated, but it might be worth trying. Biological brains are the product of a billion years of the above strategy, so hopefully it's feasible to do the same thing. Don't people already do that? Well, no. The strategy implies some sort of physics simulation with general rules, not a specific ruleset tuned to your domain.
- striking 9y ago>> Most of the time for biological intelligence there isn't an oracle describing the truth at every moment of life to judge actions/decisions against. [emphasis mine] > There is: Death, without reproducing. The problem is that it's not describing the truth at every moment of life, just the total approximate fitness of that lifeform by the end of its lifetime. It would be time consuming and cost-inefficient to simulate many possible configurations of actors until the end of their lifetimes.
- bluetwo 9y agoI agree with the article. Throwing more cycles at a problem using the existing models isn't that answer. Better, more efficient models are needed. And yes, I'm working toward that goal.
- XR0CSWV3h3kZWg 9y agoDoes anyone have a more substantive link?
- EGreg 9y agoI like this guy! Yes back-propagation can help classify and predict many things. But semantic information needs more research. For now the state of the art is Cyc.
- zebrafish 9y agoI don't know. I think backprop is probably utilized a lot in biological networks. Isn't that why we take tests in school? Obviously backprop doesn't make sense in an unsupervised setting. There's no label to backprop on. But here's an example: I see a stove eye is black when cold, then when I see it turn red, I touch it. Ow, it hurts. That's supervised learning. Don't touch things that are glowing red when they don't normally glow red. Now, I see an iron pole glowing red. It's not a spiral like the stove eye, but it's not normally glowing red and now it is. I'd better not touch it. I can deduce that these two objects are made out of a similar material since they're normally black or gray and now glow red. I can also deduce that something glowing red means it's hot. That's unsupervised learning. To me, unsupervised learning is looking at a fluffy object with four legs, eyes, a nose, tail, it moves, etc. and knowing that it's some kind of animal. Creating groups of things like k-means. Supervised learning is your mom telling you that this one example is a tiger. Unsupervised learning is understanding the delta between your one labeled example and the rest of the examples in your animal group is largest when the animal is NOT a tiger.
- 31reasons 9y agoI think Unsupervised Learning is fundamentally connected with the mission/goal of the AI. Whatever the mission is give to AI, it need to start learning information landscape by itself and make classification and use that knowledge to make predictions and act accordingly to optimized the outcome of the mission its given.
- fngl51 9y agoBut, when you revise the model based an analyzing the predictions of the previous run and adapt to the positive result, isn't that supervised learning with back-propagation?
- aeorgnoieang 9y agoBut you can't even 'give an AI a mission' without a model of the world! And, if you expect the AI to achieve its mission in a way that's acceptable to you, you better be supplying it with a big enough model for it to reasonably be able to hit that tiny target in the space of possibilities. Take self-driving cars as an example. Do you not think that any AI that will be able to do this will be essentially 'given' a huge set of knowledge before it's expected to learn anything by itself?
- Animats 9y agoMachine learning may be nearing its ceiling. The history of AI goes in cycles. Someone has a good idea which solves some problems, followed by "strong AI Real Soon Now" enthusiasm, followed by that idea hitting its ceiling. AI has been through search, backtracking, the General Problem Solver, hill-climbing, and expert systems. Each was overhyped at the time, and each hit its ceiling. The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn't the case with the previous rounds. AI used to be a dinky field - about 30-50 people each at Stanford, CMU, and MIT, plus a few tiny groups elsewhere. Now it's a huge field with big companies and big profits. That makes it self-sustaining. Hinton has a point in that we're missing something. Back-propagation is an extremely inefficient method, especially since the slower you do it, the better it seems to work. More generally, most of machine learning is "turn the problem into an optimization problem and bang on it really hard with lots of compute power". This works for a useful class of problems. But it has limits. How long until the next big idea? The last "AI winter", after expert systems, was 15 years.
- zardo 9y ago>The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn't the case with the previous rounds. My company (fortune 500) depends on expert systems, some even date back to the 80's AI boom. Doing what we do, at the scale we do it, would simply not be possible without them. Lots of companies make a lot of money with expert systems, but they have no incentive to fund AI research since expert systems have hit a clear capability ceiling.
- throwaway613834 9y ago> Lots of companies make a lot of money with expert systems, but they have no incentive to fund AI research since expert systems have hit a clear capability ceiling. I'm not sure I follow the reasoning here. Is the suggestion that AI cannot do better than expert systems?
- mistercow 9y ago
- fsavard 9y agoThat article is pretty light on details. I wonder if he pointed towards a specific form of unsupervised learning. Anyway it's pretty funny in light of an intro I remembered from one of his old papers: "It would be truly wonderful if randomly connected neural networks could turn themselves into useful computing devices by using some simple rule to modify the strength of synapses. This was the hope that lay behind the original Hebb learning rule and it is the vision that has driven neural network modelers for half a century. Initially, researchers tried simulating various rules to see what would happen. After a decade or two of messing around, researchers realized that there was a much better way to explore the space of possible learning rules: First write down an objective function [...] and then use elementary calculus to derive a learning rule that will improve the objective function." [1] ie. backprop So actually backprop was the solution to all that initial "messing around" with unsupervised rules. Though of course to be fair (if I understand correctly) those rules had very little to do with modern "unsupervised learning" methods (e.g. autoencoders, which still rely on backprop or similar optimization). [1] http://www.cs.toronto.edu/~fritz/absps/hebbdot.pdf http://www.cs.toronto.edu/~fritz/absps/hebbdot.pdf published in 2003
- meri_dian 9y agoIt's time we begin to incorporate meso to large scale structure into neural nets. The human brain is able to accomplish what it can because of the way it organizes neurons into clusters and sub organs (thalamus, Broca's area etc.)
- Nomentatus 9y agoBack in the nineteen-nineties I'd built a neural-net creator using a method other than back-propogation that was able to create nets with about a hundred neurons that played tic-tac-toe well or perfectly (depending on the net created that day.) This was on a 12 mhz 286 using what I called static point multiplication (based on shift instructions, not multiplication instructions.) So I went to Toronto to see Professor Hinton, to share. My not being a big back-propagation fan was one thing that divided us before meeting him, but otherwise I thought we'd be pretty much on the same page. It didn't go well. I happened to mention that I thought parallel programming would be a big help speeding up neural nets, and that I'd toured such a multi-processor computer in Alberta. He blew up. I never got to explain what I'd done with neural nets. He tossed me out of his office summarily and wouldn't listen to another word I had to say - somehow he'd gotten the idea in his head that I was a salesman for the company whose machine I'd seen, which machine was too weird to be considered anyway, and absolutely nothing I could say would persuade him that I wasn't a salesman. So the nets I'd created and methods other than back-propagation never got mentioned. Weirdest meeting of my life, and the end of my desire to do anything with neural nets, since other academic departments were even more violently rejecting of the idea back then.
- sjg007 9y agoWhat's the idea? Sounds interesting? Publish a paper on it? Or put up a blog post?
- deleted 9y ago[deleted]
- deleted 9y ago[deleted]
- shepardrtc 9y agoI'd like to hear more about your method. I've looked at a number of different ways of building neural nets over the years and all have their advantages and disadvantages, but if you have something completely different then I'd be thrilled to hear more. I agree with Hinton that we really need to rethink the way we're doing things.
- bryananderson 9y agoAt first glance, the obvious solution seems to be to create intelligence the same way nature did: some sort of evolution. Some sort of algorithm where multiple "networks" mutate and reproduce in some way, in response to some fitness function. Mutations that make a network more fit result in an increased reproductive rate, while other mutations decrease that rate. Evolutionary algorithms have been around for a while but haven't really taken off. Maybe the problem is that you can't get from zero to intelligence with a single fitness function. Think about it: the "fitness function" for our single-called ancestors was not how well they could thrive in a post-industrial service economy. Nor was it how well they could thrive as hunter-gatherers in the African Savannah. It was a very, very different fitness function. To get from zero to humans, nature had to evolve single-called organisms, then mitochondria, then basic multi-celled organisms, then animals that lived in the ocean, then amphibious ones, then land-dwelling ones, then mammals, then apes, then early humans, then modern humans. By the way, I left out the overwhelming majority of the steps. At each step, the evolutionary pressures on these organisms were wildly different. Each set of pressures was necessary in order for the next layer of complexity to evolve. I think that if we want to evolve an intelligent entity, we would probably have to do it like this.
- goatlover 9y agoBut nature also evolved a bunch of other organisms alongside humans that are fit in their own way. How do you evolve for just the (human level) intelligent entity and not a cockroach or rat? Point is that you're going to end up with a massive ecosystem of organisms if you could fully simulate an evolutionary history. It's not even clear that human level intelligence would evolve. It's only happened once in our planet's history. We might be flukes. You might have to run the massive simulation a thousand times and somehow be able to pinpoint the human-level intelligence when it does succeed.
- bryananderson 9y agoExcellent points! One would have to figure out which evolutionary pressures to apply in order to select for the desired traits. For example, at some point in the past, humans and rats had a common ancestor. That ancestor had two offspring. One offspring's descendants faced a series of evolutionary pressures that led to rats. The other's descendants faced a different series of pressures, leading to humans. The designer of a multi-stage evolutionary algorithm could choose which evolutionary pressures to apply, and when to apply them. The designer could thus shape the evolution of the program in the desired direction. The trick is knowing what pressures to apply in order to get the desired results. How could one do that? I have no idea! But if someone can figure it out, perhaps they could mimic what nature has blindly done. EDIT: As for the worry of ending up with a menagerie of programs, there are a couple of things that I think could help. First, the designer must be careful not to take the natural-selection analogy too far. The real world has numerous diverse environments, each with myriad niches; and in each niche, different evolutionary pressures shape organisms. This sort of algorithm need not provide all these diverse niches. It could apply a single set of pressures to all the programs in it, pushing them either in the desired direction or to extinction. Second, if the designer could identify evolutionary states that are a "dead end" and unlikely to evolve in the desired direction, they could remove the dead-end programs. Whether such dead-end states can be reliably identified is anyone's guess.
- tchitra 9y agoI should point out that OpenAI put out a blog post / paper suggesting that genetic algorithms can perform similarly to backpropagation for Atari/Q-Learning tasks: https://blog.openai.com/evolution-strategies/ https://blog.openai.com/evolution-strategies/
- tbabb 9y agoIt is fantastic to hear this from a seasoned academic about his own field-- People get trapped in orthodoxy and become unconsciously unwilling to explore bold ideas in favor to minor tweaks. The framework of thinking itself becomes precious, but big advances usually come from discarding the old framework in favor of something radically new. The kind of humility and bold thinking needed for that kind of change is rare and hard to foster (especially while still remaining grounded by empiricism).
- inetknght 9y agoI currently work in DNA analysis. I wholeheartedly agree.
- rdlecler1 9y agoWhat we lack is an AI equivalent of a theory of aerodynamics. At the moment we're just brute forcing random solution when we have perfectly good systems we should be trying to reverse engineer: biological brains. This will likely require genetic & evutionary algorithms, a developmental genotype-phenotype mapping, and competitive ecological systems for open ended evolution. Right now we have back propogration which is effectively reinforcement learning--just one piece of the puzzle. I actually applied to Hinton's lab in 2009 for a postdoct position to address these very issues. He said they didn't have any money and were not taking new postdocs.
- tehramz 9y agoI've never understood this about "AI". Why do we think that our brains or animal brains are the most effective models for intelligence? Just because we evolved to have a brain that works a certain way doesn't mean it's the most efficient or even efficient at all. Of course, I don't have much experience with AI other than what I've read.
- uoaei 9y agoDo you have much experience with brains? Are you aware of the ratio of computational capacity to energy requirements that is as yet unmatched in power? Not to mention that, as a system, the brain is highly efficient in reusing modular structures for increasingly complex tasks. I have a feeling when people mention "AI modelled after a brain" they consider a simplification of neuronal dynamics to be how to achieve that. But that is a very naïve way to do it. Looking at it from the system level is the more fruitful approach, IMHO.
- tehramz 9y agoI think it's silly to think that modeling AI after how a brain works is silly. We happened to develop the brain we have over many random iterations over billions of years. I don't understand why we think the human brain is the optimal model or method for intelligence. I guess it gives us something we kinda sorta understand to use as an example, but it seems silly to think our brain and how it works is a good model.