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The Limitations of Deep Learning

• deleted 9y ago
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• eanzenberg 9y ago
  This point is very well made: 'local generalization vs. extreme generalization.' Advanced NN's today can locally generalize quite well and there's a lot of research spent to inch their generalization further out. This will probably be done by increasing NN size or increasing the NN building-blocks complexity.
  • xamuel 9y ago
    Or maybe increasing NN size/complexity is the 21st century version of adding epicycles to make geocentrism work. http://wiki.c2.com/?AddingEpicycles http://wiki.c2.com/?AddingEpicycles
    • rhaps0dy 9y ago
      Heh, but it makes geocentrism works better! And we don't yet know how 21st century heliocentrism will look like, while adding epicycles is less daunting.
      • mojomark 9y ago
        Yay, I found the rabbit hole - technically, no cellestial body rotates purely around the other (thanks mass!). So, perhaps adding epicycles wasn't erronious after all - just a measurement from a different reference point.
    • mehh 9y ago
      Or "adding a few more rules" to a symbolic system ...
  • Cacti 9y ago
    I would really like to hear a definition of what generalization means, because I don't think we have one. Unless we're talking generalization to arbitrary distributions, which is of course unsolvable.
    • eanzenberg 9y ago
      Usually in ML context generalization means anytime a model makes a prediction on unseen (not in the training set) inputs. Usually you do CV to see how well your model generalized because you limit the training data seen, predict unseen inputs that you know the answer to, and see how far off the prediction is.
    • jd007 9y ago
      I tend to think of "generalization", in the general sense (pun not intended), to be information compression. We are simply trying to answer the question: what is the shortest description (i.e. most informationally compressed/dense version) that fits what we see in this infinite (at least to us mortals) universe of ours? Mathematically the length of such a description can be thought of as the Kolmogorov complexity. Edit: I should add, the information compression performed when generalizing can (and often is) lossy.
• reader5000 9y ago
  Recurrent models do not simply map from one vector space to another and could very much be interpreted as reasoning about their environment. Of course they are significantly more difficult to train and backprop through time seems a bit of a hack.
  • pishpash 9y ago
    Sure they do. The spaces are just augmented with timestep related dimensions.
    • unlikelymordant 9y ago
      No they aren't? RNNs have state that gets modified as time goes on. The RNN has to learn what is important to save as state, and how to modify it in response to different inputs. There is no explicit time-stamping.
      • webmaven 9y ago
        There is an implicit ordering of timesteps ("before" and "after") though, right? If you have that, you can dispense with an explicit time dimension.
        • divideby0829 9y ago
          not necessarily, depending on the usage RNN based models are sometimes trained in both directions, i.e. for every sample of say videos show it to the network in its natural time direction and then also reversed. This is motivated some say to eliminate dependence on specific order of sequences but instead to train an integrator.
          • webmaven 9y ago
            So, time's arrow can be reversed, and the model can thus extrapolate both forward and backward. Cool! However, that doesn't actually eliminate the axis/dimension. Eliminating timestamps only makes the dimension a unitless scalar (IOW 'time' tautologically increments at a 'rate' of 'one frame per frame').
• therajiv 9y ago
  As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural networks. Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains; these descriptions frequently extend to DL tutorials as well. It's important for that idea to be dispelled when people actually start applying deep models. The model's intuition doesn't work like a human's, and that can often lead to unsatisfying conclusions (e.g. the panda --> gibbon example that Francois presents). Unrelatedly, if people were more cautious about anthropomorphization, we'd probably have to deal a lot less with the irresponsible AI fearmongering that seems to dominate public opinion of the field. (I'm not trying to undermine the danger of AI models here, I just take issue with how most of the populace views the field.)
  • nerdponx 9y ago
    Well said. It's just curve fitting.
    • curiousgal 9y ago
      I hope Elon Musk understands that.
      • kinkrtyavimoodh 9y ago
        I am sure he does.
        • urethrafranklin 9y ago
          His public statements would indicate otherwise.
          • nerdponx 9y ago
• deleted 9y ago
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• toisanji 9y ago
  There is some good information in there and I agree with the limitations he states, but his conclusion is completely made up. "To lift some of these limitations and start competing with human brains, we need to move away from straightforward input-to-output mappings, and on to reasoning and abstraction." There are tens of thousands of scientists and researchers who are studying the brain from every level and we are making tiny dents into understanding it. We have no idea what the key ingredient is , nor if it is 1 or many ingredients that will take us to the next level. Look at deep learning, we had the techniques for it since the 70's, yet it is only now that we can start to exploit it. Some people think the next thing is the connectome, time, forgetting neurons, oscillations, number counting, embodied cognition,emotions,etc. No one really knows and it is very hard to test, the only "smart beings" we know of are ourselves and we can't really do experiments on humans because of laws and ethical reasons. Computer Scientists like many of us here like to theorize on how AI could work, but very little of it is tested out. I wish we had a faster way to test out more competing theories and models.
  • deleted 9y ago
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  • fchollet 9y ago
    Hardly a "made-up" conclusion -- just a teaser for the next post, which deals with how we can achieve "extreme generalization" via abstraction and reasoning, and how we can concretely implement those in machine learning models.
  • eli_gottlieb 9y ago
    >I wish we had a faster way to test out more competing theories and models. Luckily, the state of actual cognitive science and neuroscience is fairly far ahead of, "Gosh there's all these things and we just don't know." Unfortunately, MIT-style cogsci hasn't generated New Jerseyan fast-though-wrong algorithms for Silicon Valley to hype up, so the popular press keeps spreading the myth of our total ignorance. Besides which, we do know what's missing from deep learning: the ability to express anything other than a trivial Euclidean-space topological structure. We know that real data is sampled from a world subject to cause-and-effect, and that any manifold describing the data should carry the causal structure in its own topology.
• nimish 9y ago
  This is basically the Chinese Room argument though?
  • krastanov 9y ago
    Not really. Deep learning does not give you an Artificial General Intelligence (what the Chinese Room is supposed to be). The author just explains why this is so (admittedly, in a handwavy, not necessarily convincing fashion).
• msoad 9y ago
  Then people are assuming Deep Learning can be applied to a Self Driving Car System end-to-end! Can you imagine the outcome?!
  • webmaven 9y ago
    Yes. Death Race 2000.
• lordnacho 9y ago
  I'm excited to hear about how we bring about abstraction. I was wondering how a NN would go about discovering F = ma and the laws of motion. As far as I can tell, it has a lot of similarities to how humans would do it. You'd roll balls down slopes like in high school and get a lot of data. And from that you'd find there's a straight line model in there if you do some simple transformations. But how would you come to hypothesise about what factors matter, and what factors don't? And what about new models of behaviour that weren't in your original set? How would the experimental setup come about in the first place? It doesn't seem likely that people reason simply by jumbling up some models (it's a line / it's inverse distance squared / only mass matters / it matters what color it is / etc), but that may just be education getting in my way. A machine could of course test these hypotheses, but they'd have to be generated from somewhere, and I suspect there's at least a hint of something aesthetic about it. For instance you have some friction in your ball/slope experiment. The machine finds the model that contains the friction, so it's right in some sense. But the lesson we were trying to learn was a much simpler behaviour, where deviation was something that could be ignored until further study focussed on it.
  • pishpash 9y ago
    You can self train, like AlphaGo or that pong playing thing. As for the aesthetics part, there are theories on what aesthetics is, and part of it has to do with parsimony. Machines can certain constrain themselves on that.
  • thearn4 9y ago
    I've had similar thoughts when it comes to recognizing the underlying (potential) simplicity of a phenomena of interest. For example, consider a toy experiment where you take dozens of high speed sensors pointed a rig in order to study basic spring dynamics (i.e. Hooke's law). You could apply "big data analytics" or ML methods to break apart the dynamics to predict future positions past on past positions. But hopefully, somewhere along the way, you have some means of recognizing that it is a simple 1D phenomena and that most of the volume of data that you collected is fairly pointless for that goal.
    • 9y ago
• debbiedowner 9y ago
  People doing empirical experiments cannot claim to know the limits of their experimental apparatus. While the design process of deep networks remains founded in trial and error, and there are no convergence theorems and approximation guarantees, no one can be sure what deep learning can do, and what it could never do.
• pc2g4d 9y ago
  Programmers contemplating the automation of programming: "To lift some of these limitations and start competing with human brains, we need to move away from straightforward input-to-output mappings, and on to reasoning and abstraction. A likely appropriate substrate for abstract modeling of various situations and concepts is that of computer programs. We have said before (Note: in Deep Learning with Python) that machine learning models could be defined as "learnable programs"; currently we can only learn programs that belong to a very narrow and specific subset of all possible programs. But what if we could learn any program, in a modular and reusable way? Let's see in the next post what the road ahead may look like."
  • visarga 9y ago
    The author said in a Twitter conversation today that he is aware that this phrase is ignoring something essential - namely, that we have systems with memory and attention. That is something different than simple X to y mappings. With memory you can do general computation, recursivity, graphs, anything. They work well on some problems such as translation, but still need to become much better in order to match general purpose programming. But at least we're past the X->y phase.
    • divideby0829 9y ago
      considering they're the author of a python based machine learning library I would sure hope so. Still it seems like a pretty grievous oversight in writing the dang thing at all considering how at least in my fields of research memory-ful networks are increasingly popular.
      • visarga 9y ago
        It was reserved for part 2.
• denfromufa 9y ago
  If the deep learning network has enough layers, then can't it start incorporating "abstract" ideas common to any learning task? E.g. could we re-use some layers for image/speech recognition & NLP?
  • beachbum8029 9y ago
    When an implementation of AGI comes around (yes, it will come around) it will inevitably involve a number of different neural nets working together in concert as separate subsystems. That's what makes these "Neural Nets Will Never Become Conscious!" articles so hilarious. But yeah, I could see feeding the output of an array of sub-networks into a parent network. So think one NN for vision, one for hearing, etc, etc, all of those outputs feed into a parent level network that could be your abstraction network that deals with making executive level decisions.
  • unlikelymordant 9y ago
    this is exactly what happens in transfer learning. A recent paper by google ( https://research.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html https://research.googleblog.com/2017/07/revisiting-unreasona... ) shows that pre-training on a very large image database leads to improvements in state of the art for several different image problems. This is because the weights required for one image problem are not necessarily all that different from another image problem, especially in the early layers. There may not be as much common ground beteen images and e.g. NLP. Perhaps at much higher abstraction levels, but we aren't there yet.
    • webmaven 9y ago
      Transfer learning has been shown to improve training times in other modes (such using an image classification model to initialize an NLP model) over randomly initialized values.
• gallerdude 9y ago
  I'm sorry, but I don't understand why wider & deeper networks won't do the job. If it took "sufficiently large" networks and "sufficiently many" examples, I don't understand why it wouldn't just take another order of magnitude of "sufficiency." If you look at the example with the blue dots on the bottom, would it not just take many more blue dots to fill in what the neural network doesn't know? I understand that adding more blue dots isn't easy - we'll need a huge amount of training data, and huge amounts of compute to follow; but if increasing the scale is what got these to work in the first place, I don't see we shouldn't try to scale it up even more.
  • nshm 9y ago
    "sufficiently large" could be much more than number of atoms in the universe. You just do not have resources to run computation at such scale.
    • randcraw 9y ago
      This is my problem with the thesis that simply scaling deep nets to new heights will ultimately subsume all brain function. If it takes weeks to train a simple object recognizer deep net, how long would it take a grand unified deep net to learn to tie its shoelaces? Puberty?
• andreyk 9y ago
  "Here's what you should remember: the only real success of deep learning so far has been the ability to map space X to space Y using a continuous geometric transform, given large amounts of human-annotated data." This statement has a few problems - there is no real reason to interpret the transforms as geometric (they are fundamentally just processing a bunch of numbers into other numbers, in what sense is this geometric), and the focus on human-annotated data is not quite right (Deep RL and other things such as representation learning have also achieved impressive results in Deep Learning). More importantly, saying " a deep learning model is "just" a chain of simple, continuous geometric transformations " is pretty misleading; things like the Neural Turing Machine have shown that enough composed simple functions can do pretty surprisingly complex stuff. It's good to point out that most of deep learning is just fancy input->output mappings, but I feel like this post somewhat overstates the limitations.
  • deleted 9y ago
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  • emodendroket 9y ago
    Presumably that's why the word "just" is in scare quotes.
  • backpropaganda 9y ago
    Just because there's a paper on it, and the model has a name, doesn't mean it works. NTM and deep RL don't work for real problems.
  • divideby0829 9y ago
    yeah this was my main problem, I guess he is technically right because they are geometric but many of his analogies like the paper crumpling were deeply misleading as they would imply that the transformations are linear. The fact that they are not is fundamental to neural networks working.
    • tsbertalan 9y ago
      Paper-crumpling is nonlinear. Maybe your complaint is rather that paper-crumpling is "only" a topology-preserving diffeomorphism?
• ezioamf 9y ago
  This is why I don't know if it will be possible (at current limitations) to let insect like brains to fully drive our cars. It may never be good enough.
  • pishpash 9y ago
    Insects can drive themselves quite well, occasional splatters aside. This is one of the tasks that is I feel tractable. However, propose letting insects to drive and people will never accept it, but somehow they trust the SV hype men.
• eli_gottlieb 9y ago
  >But what if we could learn any program, in a modular and reusable way? Let's see in the next post what the road ahead may look like. I'm really looking forward to this. If it comes out looking like something faster and more usable than Bayesian program induction, RNNs, neural Turing Machines, or Solomonoff Induction, we'll have something really revolutionary on our hands!
• cm2187 9y ago
  I think the requirement for a large amount of data is the biggest objection to the reflex "AI will replace [insert your profession here] soon" that many techies, in particular on HN, have. There are many professions where there is very little data available to learn from. In some case (self-driving), companies will invest large amount of money to build this data, by running lots of test self-driving cars, or paying people to create the data, and it is viable given the size of the market behind. But the typical high-value intellectual profession is often a niche market with a handful of specialists in the world. Think of a trader of financial institutions bonds, or a lawyer specialized in cross-border mining acquisitions, a physician specialist of a rare disease or a salesperson for aviation parts. What data are you going to train your algorithm with? The second objection, probably equally important, also applies to "software will replace [insert your boring repetitive mindless profession here]", even after 30 years of broad adoption of computers. If you decide to automate some repetitive mundane tasks, you can spare the salary of the guys who did these tasks, but now you need to pay the salary of a full team of AI specialists / software developers. Now for many tasks (CAD, accounting, mailings, etc), the market is big enough to justify a software company making this investment. But there is a huge number of professions where you are never going to break even, and where humans are still paid to do stupid tasks that a software could easily do today (even in VBA), and will keep doing so until the cost of developing and maintaining software or AI has dropped to zero. I don't see that happening in my life. In fact I am not even sure we are training that many more computer science specialists than 10 years ago. Again, didn't happen with software for very basic things, why would it happen with AI for more complicated things.
  • kmicklas 9y ago
    > and will keep doing so until the cost of developing and maintaining software or AI has dropped to zero. I have no idea about the progress of AI, but normal software will get an order of magnitude cheaper to develop as we slowly wake up from the Unix/worse-is-better/everything-is-text mindset and abandon the dynamically typed and imperative languages, broken systems abstractions, etc. that hold us back.
    • cm2187 9y ago
• CountSessine 9y ago
  Surely we shouldn't rush to anthropomorphize neural networks, but we'd ignoring the obvious if we didn't at least note that neural networks do seem to share some structural similarities with our own brains, at least at a very low level, and that they seem to do well with a lot of pattern-recognition problems that we've traditionally considered to be co-incident with brains rather than logical systems. The article notes, "Machine learning models have no access to such experiences and thus cannot "understand" their inputs in any human-relatable way". But this ignores a lot of the subtlety in psychological models of human consciousness. In particular, I'm thinking of Dual Process Theory as typified by Kahneman's "System 1" and "System 2". System 1 is described as a tireless but largely unconscious and heavily biased pattern recognizer - subject to strange fallacies and working on heuristics and cribs, it reacts to it's environment when it believes that it recognizes stimuli, and notifies the more conscious "System 2" when it doesn't. At the very least it seems like neural networks have a lot in common with Kahneman's "System 1".
  • language 9y ago
    "Machine learning models have no access to such experiences and thus cannot "understand" their inputs in any human-relatable way" It may be that distinctions like the one you're describing here are useful to make, but I don't think this claim refutes the possibility of ML "fitting a particular piece within a larger, yet unarticulated model." I think the assertion is more that our current ways of representing elements of human experience are necessarily very lossy - or that there's some aspect of the situation that you can't describe/implement in terms of models of neural nets.
    • visarga 9y ago
      The problem with neural nets is that they have a fixed input type - tensors or sequences. For example, imagine the task is to count objects in an image and say if the number of red objects is equal to the number of green objects. You make a net that solves this situation. Then you want to change the colors, or add an extra color, and it will fail. Why - because it learns a fixed input representation. What neural nets need is to change their data format from plain tensors to object-relation graphs. The input of the network is represented as a set of objects that have relations among them, and the network has to be permute invariant to the order of presentation. An implementation is Graph Convolutional Nets. They learn to compose concepts in new ways and once they learned to count, compare, select by color, they can solve any combination of those concepts as well. That way the nets generalize better and transfer knowledge from a problem to the next. Graphs are able to reduce the complexity of learning a neural net that can perform flexible tasks. But in order to get to even better results, it is necessary to add simulation to the mix. By equipping neural nets with simulators, we can simplify the learning problem (because the net doesn't have to learn the dynamics of the environment as well, just the task at hand). Examples of simulators used in DL are AlphaGo, the Reinforcement Learning applications on Atari Games, protein/drug property prediction, generative adversarial networks (in a way). The interesting thing is that graphs are natural for simulation. They can represent objects as vertices and relations as edges, and by signal propagation the graph works like a circuit, a simulator, producing the answer. My bet is on graphs + simulators. That's how we get to the next level (abstraction and reasoning). DeepMind seems to be particularly focused on RL, games and recently, relation networks. There is also work on making dynamic routing in neural nets, in fact applying graphs implicitly inside the net, by multiple attention heads.
• AndrewKemendo 9y ago
  the only real success of deep learning so far has been the ability to map space X to space Y using a continuous geometric transform, given large amounts of human-annotated data. Yes, but that's what human's do too, only much much better from the generalized perspective. I think that fundamentally this IS the paradigm for AGI, but we are in the pre-infant days of optimization across the board (data, efficiency, tagging etc...). So I wholeheartedly agree with the post, that we shouldn't cheer yet, but we should also recognize that we are on the right track. I say all this because prior to getting into DL and more specifically Reinforcement Learning (which is WAY under studied IMO), I was working with Bayesian Expert Systems as a path to AI/AGI. RL totally transformed how I saw the problem and in my mind offers a concrete pathway to AGI.
• deepnotderp 9y ago
  I'd like to offer a somewhat contrasting viewpoint (although this might not sit well with people): deep nets aren't AGI, but they're pretty damn good. There's mounting evidence that they learn similar to how we do, at least in vision; https://arxiv.org/abs/1706.08606 https://arxiv.org/abs/1706.08606 and https://www.nature.com/articles/srep27755 https://www.nature.com/articles/srep27755 There's quite a few others but these were the most readily available papers. Are deep nets AGI? No, but they're a lot better than Mr.Chollet gives them credit for.
  • emodendroket 9y ago
    Similar, perhaps, but certainly not the same, since no human being, at whatever stage of development, would be confused by the adversarial image of a panda given as an example.
• beachbum8029 9y ago
  Pretty interesting that he says reasoning and long term planning are impossible tasks for a neural net, when those tasks are done by billions of neural nets every day. :^)
  • igammarays 9y ago
    Pretty interesting that you seem to have conclusively determined that our brain is nothing but a neural net.
• graycat 9y ago
  On the limitations of machine learning as in the OP, the OP is correct. So, right, current approaches to "machine learning* as in the OP have some serious "limitations". But this point is a small, tiny special case of something else much larger and more important: Current approaches to "machine learning" as in the OP are essentially some applied math, and applied math is commonly much more powerful than machine learning as in the OP and has much less severe limitations. Really, "machine learning" as in the OP is not learning in any significantly meaningful sense at all. Really, apparently, the whole field of "machine learning" is heavily just hype from the deceptive label "machine learning". That hype is deceptive, apparently deliberately so, and unprofessional. Broadly machine learning as in the OP is a case of old empirical curve fitting where there is a long history with a lot of approaches quite different from what is in the OP. Some of the approaches are under some circumstances much more powerful than what is in the OP. The attention to machine learning is omitting a huge body of highly polished knowledge usually much more powerful. In a cooking analogy, you are being sold a state fair corn dog, which can be good, instead of everything in Escoffier, Prosper Montagné, Larousse Gastronomique: The Encyclopedia of Food, Wine, and Cookery, ISBN 0-517-503336, Crown Publishers, New York, 1961. Essentially, for machine learning as in the OP, if (A) have a LOT of training data, (B) a lot of testing data, (C) by gradient descent or whatever build a model of some kind that fits the training data, and (D) the model also predicts well on the testing data, then (E) may have found something of value. But the test in (D) is about the only assurance of any value. And the value in (D) needs an assumption: Applications of the model will in some suitable sense, rarely made clear, be close to the training data. Such fitting goes back at least to Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone, Classification and Regression Trees, ISBN 0-534-98054-6, Wadsworth & Brooks/Cole, Pacific Grove, California, 1984. not nearly new. This work is commonly called CART, and there has long been corresponding software. And CART goes back to versions of regression analysis that go back maybe 100 years. So, sure, in regression analysis, we are given points on an X-Y coordinate system and want to fit a straight line so that as a function of points on the X axis the line does well approximating the points on the X-Y plot. Being more specific could use some mathematical notation awkward for simple typing and, really, likely not needed here. Well, to generalize, the X axis can have several dimensions, that is, accommodate several variables. The result is multiple linear regression. For more, there is a lot with a lot of guarantees. Can find those in short and easy form in Alexander M. Mood, Franklin A. Graybill, and Duane C. Boas, Introduction to the Theory of Statistics, Third Edition, McGraw-Hill, New York, 1974. with more detail but still easy form in N. R. Draper and H. Smith, Applied Regression Analysis, John Wiley and Sons, New York, 1968. with much more detail and carefully done in C. Radhakrishna Rao, Linear Statistical Inference and Its Applications: Second Edition, ISBN 0-471-70823-2, John Wiley and Sons, New York, 1967. Right, this stuff is not nearly new. So, with some assumptions, get lots of guarantees on the accuracy of the fitted model. This is all old stuff. The work in machine learning has added some details to the old issue of over fitting, but, really, the math in old regression takes that into consideration -- a case of over fitting will usually show up in larger estimates for errors. There is also spline fitting, fitting from Fourier analysis, autoregressive integrated moving average processes, David R. Brillinger, Time Series Analysis: Data Analysis and Theory, Expanded Edition, ISBN 0-8162-1150-7, Holden-Day, San Francisco, 1981. and much more. But, let's see some examples of applied math that totally knocks the socks off model fitting: (1) Early in civilization, people noticed the stars and the ones that moved in complicated paths, the planets. Well Ptolemy built some empirical models based on epi-cycles that seemed to fit the data well and have good predictive value. But much better work was from Kepler who discovered that, really, if assume that the sun stays still and the earth moves around the sun, then the paths of planets are just ellipses. Next Newton invented the second law of motion, the law of gravity, and calculus and used them to explain the ellipses. So, what Kepler and Newton did was far ahead of what Ptolemy did. Or, all Ptolemy did was just some empirical fitting, and Kepler and Newton explained what was really going on and, in particular, came up with much better predictive models. Empirical fitting lost out badly. Note that once Kepler assumed that the sun stands still and the earth moves around the sun, actually he didn't need much data to determine the ellipses. And Newton needed nearly no data at all except to check is results. Or, Kepler and Newton had some good ideas, and Ptolemy had only empirical fitting. (2) The history of physical science is just awash in models derived from scientific principles that are, then, verified by fits to data. E.g., some first principles derivations shows what the acoustic power spectrum of the 3 K background radiation should be, and the fit to the actual data from WMAP, etc. was astoundingly close. News Flash: Commonly some real science or even just real engineering principles totally knocks the socks off empirical fitting, for much less data. (3) E.g., here is a fun example I worked up while in a part time job in grad school: I got some useful predictions for an enormously complicated situation out of a little applied math and nearly no data at all. I was asked to predict what the survivability of the US SSBN fleet would be under a special scenario of global nuclear war limited to sea. Well, there was a WWII analysis by B. Koopman that showed that in search, say, of a submarine for a surface ship, an airplane for a submarine, etc. the encounter rates were approximately a Poisson process. So, for all the forces in that war at sea, for the number of forces surviving, with some simplifying assumptions, we have a continuous time, discrete state space Markov process subordinated to a Poisson process. The details of the Markov process are from a little data about detection radii and the probabilities at a detection, one dies, the other dies, both die, or neither die. That's all there was to the set up of the problem, the model. Then to evaluate the model, just use Monte Carlo to run off, say, 500 sample paths, average those, appeal to the strong law of large numbers, and presto, bingo, done. Also can easily put up some confidence intervals. The customers were happy. Try to do that analysis with big data and machine learning and will be in deep, bubbling, smelly, reeking, flaming, black and orange, toxic sticky stuff. So, a little applied math, some first principles of physical science, or some solid engineering data commonly totally knocks the socks off machine learning as in the OP.
• siliconc0w 9y ago
  A neat technique to help 'explain' models is LIME: https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime https://www.oreilly.com/learning/introduction-to-local-inter... There is a video here https://www.youtube.com/watch?v=hUnRCxnydCc https://www.youtube.com/watch?v=hUnRCxnydCc I think this has some better examples than the Panda vs Gibbon example in the OP if you want to 'see' why a model may classify a tree-frog as a tree-frog vs a billiard (for example). IMO this suggests some level of anthropomorphizing is useful for understanding and building models as the pixels the model picks up aren't really too dissimilar to what I imagine a naive, simple, mind might use. (i.e the tree-frog's goofy face) We like to look at faces for lots of reasons but one of them probably is because they're usually more distinct which is the same, rough, reason why the model likes the face. This is interesting (to me at least) even if it's just matrix multiplication (or uncrumpling high dimensional manifolds) underneath the hood,
• camperman 9y ago
  That second paragraph is the most succinct and elegant explanation of deep learning I've ever read.
• zfrenchee 9y ago
  My qualm with this article is disappointingly poorly backed up. The author makes claims, but does not justify those claims well enough to convince anyone but people who already agree with him. In that sense, this piece is an opinion piece, masquerading as a science. > This is because a deep learning model is "just" a chain of simple, continuous geometric transformations mapping one vector space into another. All it can do is map one data manifold X into another manifold Y, assuming the existence of a learnable continuous transform from X to Y, and the availability of a dense sampling of X:Y to use as training data. So even though a deep learning model can be interpreted as a kind of program, inversely most programs cannot be expressed as deep learning models [why?]—for most tasks, either there exists no corresponding practically-sized deep neural network that solves the task [why?], or even if there exists one, it may not be learnable, i.e. the corresponding geometric transform may be far too complex [???], or there may not be appropriate data available to learn it [like what?]. > Scaling up current deep learning techniques by stacking more layers and using more training data can only superficially palliate some of these issues [why?]. It will not solve the more fundamental problem that deep learning models are very limited in what they can represent, and that most of the programs that one may wish to learn cannot be expressed as a continuous geometric morphing of a data manifold. [really? why?] I tend to disagree with these opinions, but I think the authors opinions aren't unreasonable, I just wish he would explain them rather than re-iterating them.
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  • emodendroket 9y ago
    It seems unfair to level the criticism of being incomplete and not fully explaining all the points given that the lead-in to the piece says it's a book excerpt and doesn't explain a lot of stuff that a reader of the book would already have encountered.
  • ndh2 9y ago
    For one, input and output size has to be fixed. All these NNs doing image transformations or recognition only work on fixed-size images. How would you sort a set of integers of arbitrary size using a neural network? What does "solve with a NN" even mean in that context? Another problems/limitation I can think of is that in NNs you don't have state. The NN can't push something on a stack, and then iterate. How do you divide and conquer using NNs? Are NNs Turing complete? I don't see how they possibly could be.
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• latently 9y ago
  The brain is a dynamic system and (some) neural networks are also dynamic systems, and a three layer neural network can learn to approximate any function. Thus, a neural network can approximate brain function arbitrarily well given time and space. Whether that simulation is conscious is another story. The Computational Cognitive Neuroscience Lab has been studying this topic for decades and has an online textbook here: http://grey.colorado.edu/CompCogNeuro http://grey.colorado.edu/CompCogNeuro The "emergent" deep learning simulator is focused on using these kinds of models to model the brain: http://grey.colorado.edu/emergent http://grey.colorado.edu/emergent
  • geofft 9y ago
    That's about as interesting as saying that a Taylor series can approximate any analytic function arbitrarily well given time and space. Or that a lookup table can approximate any function arbitrarily well given time and space: see also the Chinese room example. The first question is whether that neural network is learnable. Sure, some configuration of neurons may exist. Is it possible given enough time and space to discover what that configuration is, given a set of inputs and outputs? The second question is whether "enough time and space" means "beyond the lifetime and resources of anyone alive," in which case it seems perfectly reasonable to me to call it a limitation. I generally want my software to work within my lifetime.
    • latently 9y ago
      I like your comment. The real question is whether they are conscious. The analogy between deep neural networks and the brain has proven to be very fruitful. Other analogies may as well. See our upcoming paper for more info. https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a/ccn_style.pdf https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a...
      • nojvek 9y ago
        I think a lot of people end up mixing being alive with being conscious. Is a tree conscious? Is a self driving car conscious? If we use the definition "Aware of its surroundings, responding and acting towards a certain goal" then a lot of things fit that definition. When an AI plays the atari games, learns from it and plays at a human level, I would call it conscious. It's not a human level conscious agent but conscious nonetheless.
• cs702 9y ago
  Yes. Here's how I've been explaining this to non-technical people lately: "We do not have intelligent machines that can reason. They don't exist yet. What we have today is machines that can learn to recognize patterns at higher levels of abstraction. For example, for imagine recognition, we have machines that can learn to recognize patterns at the level of pixels as well as at the level of textures, shapes, and objects." If anyone has a better way of explaining deep learning to non-technical people in a few short sentences, I'd love to see it. Post it here!
• kowdermeister 9y ago
  > In short, deep learning models do not have any understanding of their input, at least not in any human sense. Our own understanding of images, sounds, and language, is grounded in our sensorimotor experience as humans—as embodied earthly creatures. Well maybe we should train systems with all our sensory inputs first, like newborns leans about the world. Then make these models available open source like we release operating systems so others can build on top of that. For example we have ImageNet, but we don't have WalkNet, TasteNet, TouchNet, SmellNet, HearNet... or other extremely detailed sensory data recorded for an extended time. And these should be connected to match the experiences. At least I have no idea they are out there :)
  • reckoner2 9y ago
    This approach has always interested me. I can train an decent Cats Vs. Dogs classifier in a few minutes. But real human intelligence takes many years of continuous and varied input to develop. Are there systems out there that are taking influence from newborns being exposed to the world? An unsupervised learning system with a huge array of inputs running for years?
    • kowdermeister 9y ago
      All these current examples of AI and ML are just a very small fraction of what we mean by intelligence so I'm not surprised by the pessimistic posts that hit HN from time to time. Training systems with rich real world experiences sounds something that Open AI should be developing. It's probably not something that you can do over a weekend plus it takes serious of funding and wetware so it's probably the reason it's not there yet.
  • folksinger 9y ago
    See: Kant on a priori notions of space-time.
  • jonobelotti 9y ago
    Brooks' 'Intelligence Without Representation' (http://people.csail.mit.edu/brooks/papers/representation.pdf http://people.csail.mit.edu/brooks/papers/representation.pdf) starts with a pretty strong argument imo against the story of 'stick-together' AGI you're describing.
• erikb 9y ago
  I don't get it. If reasoning is not an option how does deep learning beat the boardgame go?
  • backpropaganda 9y ago
    Memorisation + small amounts of generalization.
    • erikb 9y ago
      Unlikely. If it's mostly memorisation it couldn't learn from playing itself. And what you describe is how AI beats chess. The problem with that is that it is a quite inhuman way to play. But AlphaGo plays quite humanly.
      • backpropaganda 9y ago
        1. Imagine infinite compute capability. Exhaustively play all possible games, and use that to figure out best moves at any state. This is essentially what Alphago did, but using translation variance to reduce the search space. 2. There is no contradiction here. We just have to accept that human-like play can emerge from memorization.
        • erikb 9y ago
          Can you explain what from your point of view is the difference between AlphaGo and a Chess AI? Because to me it sounds like the one should have resulted as an evolution from the other if it would be that simple.
          • backpropaganda 9y ago
            Yes. In Chess, it's relatively easy to judge how good a board position is, and thus people have been successful by hand-engineering the board position evaluator (also called value function in RL lingo), and then just doing tree search to take the action which improves board position the most. In Go, evaluating board position is much more difficult, and it's not possible to approximate the value function by hand-engineered code. Thus, AlphaGO approximates the value by simulating the game till win/lose from arbitrary board positions to evaluate its value. This doesn't really require neural networks. You could also do the same with table lookup. What neural networks offer here is some translation invariance generalization, and capability to compress the table into fewer parameters by identifying common input features. It's possible to achieve AlphaGo performance by just having a BIG table of state-values and using some kernel to do nearest neighbour search (such as done by deepmind here: https://arxiv.org/abs/1606.04460 https://arxiv.org/abs/1606.04460)
• LeanderK 9y ago
  the author raises some valid points, but i don't like the style it is written in. He just makes some elaborate claims about the limitation of Deep Learning, but conveys why they are limitations. I don't disagree about the fact that there are limits to Deep Learning and many may be impossible to overcome without completely new approaches. I would like to see more emphasis on why things, like generating code from descriptions, that are theoretically possible, are absolutely impossible and out of reach today and not make the intention that the tasks itself is impossible (like the halting-problem).
• meh2frdf 9y ago
  Correct me if I'm wrong but I don't see that with 'deep learning' we have answered/solved any of the philosophical problems of AI that existed 25 years ago (stopped paying attention about then). Yes we have engineered better NN implementations and have more compute power, and thus can solve a broader set of engineering problems with this tool, but is that it?
  • nv-vn 9y ago
    Yep. The whole machine learning craze is just fueled by the fact that it's now feasible to create models for handwriting/voice/image recognition that actually work reliably. But in terms of the underlying technology, we haven't had some "breakthrough" that explained how the brain works or anything even close to that.
    • landon32 9y ago
      This is totally true, but I think it's still important to note that while something like Artificial General Intelligence is still way beyond the state of the art, the state of the art still has a huge impact on the world. A tiny slice of that can be seen in autonomous vehicles and the impact that they seem poised to have.
      • brittohalloran 9y ago
        Don't underestimate the self fulfilling prophecy effect. Quite possible that the massive influx into the field right now will move the needle.
        • landon32 9y ago
          Hmm, sometimes I think that we won't get super close to AGI until we can actually model something the size of a Human Brain (in terms of neurons or Synapses). Human Brain has 1B+ Neurons, or 4Qu+ synapses. So that's 12.5 GB all at once to deal with, if you're representing neurons as either 0 or 1. However, in reality Neurons are much more complicated, and could only treat them as binary if you have a neuromorphic computer. So we would need to deal with many many times that many GB at once, even if we had really efficient ways of storing the data. That's a lot of data to deal with, especially since you need to train it, running huge computations using each neuron. I know nothing about hardware, and this is a very crude prediction/estimation of how AGI would happen, but my point is that we might be limited by Hardware for a few more years.
• thanatropism 9y ago
  This is evergreen: https://en.m.wikipedia.org/wiki/Hubert_Dreyfus%27s_views_on_artificial_intelligence https://en.m.wikipedia.org/wiki/Hubert_Dreyfus%27s_views_on_... See also, if you can, the film "Being in the world", which features Dreyfus.
• danielam 9y ago
  "This ability [...] to perform abstraction and reasoning, is arguably the defining characteristic of human cognition." He's on the right track. Of course, the general thrust goes beyond deep learning. The projection of intelligence onto computers is first and foremost wrong because computers are not able, not even in principle, to engage in abstraction, and claims to the contrary make for notoriously bad, reductionistic philosophy. Ultimately, such claims underestimate what it takes to understand and apprehend reality and overestimate what a desiccated, reductionistic account of mind and the broader world could actually accommodate vis-a-vis the apprehension and intelligibility of the world. Take your apprehension of the concept "horse". The concept is not a concrete thing in the world. We have concrete instances of things int he world that "embody" the concept, but "horse" is not itself concrete. It is abstract and irreducible. Furthermore, because it is a concept, it has meaning. Computers are devoid of semantics. They are, as Searle has said ad nauseam, purely syntactic machines. Indeed, I'd take that further and say that actual, physical computers (as opposed to abstract, formal constructions like Turing machines) aren't even syntactic machines. They do not even truly compute. They simulate computation. That being said, computers are a magnificent invention. The ability to simulate computation over formalisms -- which themselves are products of human beings who first formed abstract concepts on which those formalisms are based -- is fantastic. But it is pure science fiction to project intelligence onto them. If deep learning and AI broadly prove anything, it is that in the narrow applications where AI performs spectacularly, it is possible to substitute what amounts to a mechanical process for human intelligence.
  • ThomPete 9y ago
    The Chinese Room argument is one of the least convincing arguments against AI. Of course the man in the room isn't conscious neither is the individual neurons in your brain. It's the whole house that become conscious. The reality is that we just don't know.
• ilaksh 9y ago
  Actually there are quite a few researchers working on applying newer NN research to systems that incorporate sensorimotor input, experience, etc. and more generally, some of them are combining an AGI approach with those new NN techniques. And there has been research coming out with different types of NNs and ways to address problems like overfitting or slow learning/requiring huge datasets, etc. When he says something about abstraction and reasoning, yes that is important but it seems like something NNish may be a necessary part of that because the logical/symbolic approaches to things like reasoning have previously mainly been proven inadequate for real-world complexity and generally the expectations we have for these systems. Search for things like "Towards Deep Developmental Learning" or "Overcoming catastrophic forgetting in neural networks" or "Feynman Universal Dynamical" or "Wang Emotional NARS". No one seems to have put together everything or totally solved all of the problems but there are lots of exciting developments in the direction of animal/human-like intelligence, with advanced NNs seeming to be an important part (although not necessarily in their most common form, or the only possible approach).
• fnl 9y ago
  Put a lot simpler: Even DL is still only very complex, statistical pattern matching. While pattern matching can be applied to model the process of cognition, DL cannot really model abstractive intelligence on its own (unless we phrase it as a pattern learning problem, viz. transfer learning, on a very specific abstraction task), and much less can it model consciousness.
• deepGem 9y ago
  This is because a deep learning model is "just" a chain of simple, continuous geometric transformations mapping one vector space into another. Per my understanding - Each vector space represents the full state of that layer. Which is probably why the transformations work for such vector spaces. A sorting algorithm unfortunately cannot be modeled as a set of vector spaces each representing the full state. For instance, an intermediary state of a quick sort algorithm does not represent the full state. Even if a human was to look at that intermediary step in isolation, they will have no clue as to what that state represents. On the contrary, if you observe the visualized activations of an intermediate layer in VGG , you can understand that the layer represents some elements of an image.
• Houshalter 9y ago
  This article is a bit misleading. I believe NNs are a lot like the human brain. But just the lowest level of our brain. What psychologists might call "procedural knowledge". Example: learning to ride a bike. You have no idea how you do it. You can't explain it in words. It requires tons of trial and error. You can give a bike to a physicist that has a perfect deep understanding of the laws of physics. And they won't be any better at riding than a kid. And after you learn to ride, change the bike. Take one where the handle is inversed. And turning it right turns the wheel left. No matter how good you are at riding a normal bike, no matter how easy it seems it should be, it's very hard. Requires relearning how to ride basically from scratch. And when you are done, you will even have trouble going back to a normal bike. This sounds familiar to the problems of deep reinforcement learning, right? If you use only the parts of the brain you use to ride a bike, would you be able to do any of the tasks described in the article? E.g. learn to guide spacecraft trajectories with little training, through purely analog controls and muscle memory? Can you even sort a list in your head without the use of pencil and paper? Similarly recognizing a toothbrush as a baseball bat isn't as bizarre as you think. Most NNs get one pass over an image. Imagine you were flashed that image for just a millisecond. And given no time to process it. No time to even scan it with your eyes! You certain you wouldn't make any mistakes? But we can augment NNs with attention, with feedback to lower layers from higher layers, and other tricks that might make them more like human vision. It's just very expensive. And that's another limitation. Our largest networks are incredibly tiny compared to the human brain. It's amazing they can do anything at all. It's unrealistic to expect them to be flawless.
  • BooglyWoo 9y ago
    It's a good article in a lot of ways, and provides some warnings that many neural net evangelists should take to heart, but I agree it has some problems. It's a bit unclear whether Fchollet is asserting that (A) Deep Learning has fundamental theoretical limitations on what it can achieve, or rather (B) that we have yet to discover ways of extracting human-like performance from it. Certainly I agree with (B) that the current generation of models are little more than 'pattern matching', and the SOTA CNNs are, at best, something like small pieces of visual cortex or insect brains. But rather than deriding this limitation I'm more impressed at the range of tasks "mere" pattern matching is able to do so well - that's my takeaway. But I also disagree with the distinction he makes between "local" and "extreme" generalization, or at least would contend that it's not a hard, or particularly meaningful, epistemic distinction. It is totally unsurprising that high-level planning and abstract reasoning capabilities are lacking in neural nets because the tasks we set them are so narrowly focused in scope. A neural net doesn't have a childhood, a desire/need to sustain itself, it doesn't grapple with its identity and mortality, set life goals for itself, forge relationships with others, or ponder the cosmos. And these types of quintessentially human activities are what I believe our capacities for high-level planning, reasoning with formal logic etc. arose to service. For this reason it's not obvious to me that a deep-learning-like system (with sufficient conception of causality, scarcity of resources, sanctity of life and so forth) would ALWAYS have to expend 1000s of fruitless trials crashing the rocket into the moon. It's conceivable that a system could know to develop an internal model of celestial mechanics and use it as a kind of staging area to plan trajectories. I think there's a danger of questionable philosophy of mind assertions creeping into the discussion here (I've already read several poor or irrelevant expositions of Searle's Chinese Room in the comments). The high-level planning, and "true understanding" stuff sounds very much like what was debated for the last 25 years in philosophy of mind circles, under the rubric of "systematicity" in connectionist computational theories of mind. While I don't want to attempt a single-sentence exposition of this complicated debate, I will say that the requirement for "real understanding" (read systematicity) in AI systems, beyond mechanistic manipulation of tokens, is one that has been often criticised as ill-posed and potentially lacking even in human thought; leading to many movements of the goalposts vis-à-vis what "real understanding" actually is. It's not clear to me that "real understanding" is not, or at least cannot be legitimately conceptualized as, some kind of geometric transformation from inputs to outputs - not least because vector spaces and their morphisms are pretty general mathematical objects. EDIT: a word
• LeicaLatte 9y ago
  This is why Elon Musk is projecting. We are long ways away from AI.
• known 9y ago
  DL/ML == Wisdom of Crowds
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• sarah5 9y ago
  nice article
• x40r15x 9y ago
  I am sorry but GMO is actually bad for you.... Monsanto tried to spread gmo corn in France, they tested it on rats for a year and the rats developed multiple tumors the size of an egg.
  • x40r15x 9y ago
    Oops wrong article
  • deleted 9y ago
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• 19eightyfour 9y ago
  If this article is correct about limitations, couldn't one simply include a Turing machine model into the process to train algorithms? Some ideas: - The vectors are Turing tapes, or - Each point in a tape is a DNN, or - The "tape" is actually a "tree" each point in the tape is actually a branch point of a tree with probabilities going each way, and the DNN model can "prune this tree" to refine the set of "spanning trees" / programs. Or, hehe, maybe I'm leading people off track. I know absolutely nothing about DNN ( except I remember some classes on gradient descent and SVMs from bioinformatics ).
  • nullc 9y ago
    You can bolt all kinds of funny structures into some DNN system, but if the system doesn't have well behaved gradients (or if it isn't even differentiable) it won't train.
• pron 9y ago
  > Doing this well is a game-changer for essentially every industry, but it is still a very long way from human-level AI. We're still a long way from even insect level "intelligence" (if it could even be called that), hence the harm in calling it AI in the first place. The fact that machine learning performs some particular tasks better than humans means little. That was true of computers since their inception. The question of how much closer we are to human-level AI than to the starting point of machine learning and neural networks over 70 years ago is very much an open question. That after 70 years of research into neural networks in particular and to machine learning in general, we are still far from insect-level intelligence makes anyone suggesting a timeline for human-level AI sound foolish (although hypothetically, the leap from insect-level intelligence to human-level could be technically simple, but we really have no idea).
• randcraw 9y ago
  I really enjoyed this article. It's the first attempt I've seen to assess deep learning toward the integrated end of human level cognition or AGI. I found one point especially noteworthy: " So even though a deep learning model can be interpreted as a kind of program, inversely most programs cannot be expressed as deep learning models—for most tasks, either there exists no corresponding practically-sized deep neural network that solves the task, or even if there exists one, it may not be learnable, i.e. the corresponding geometric transform may be far too complex, or there may not be appropriate data available to learn it. Scaling up current deep learning techniques by stacking more layers and using more training data can only superficially palliate some of these issues. It will not solve the more fundamental problem that deep learning models are very limited in what they can represent, and that most of the programs that one may wish to learn cannot be expressed as a continuous geometric morphing of a data manifold. " What he seems to be suggesting is that a human level cognition built from deep nets will not be a single unified end-to-end "mind" but a conglomeration of many nets, each with different roles, i.e., a confederation or "society" of deep nets. I suspect Minsky would have agreed, and then suggested that the interesting part is how one defines, instantiates, and then interconnects the components of this society.
• plaidfuji 9y ago
  As a chemical engineer who started learning deep learning after learning regular old regression-based empirical modeling, my interpretation of deep learning is that it's just high-dimensional non-linear interpolation. If what you're trying to predict can't be represented as some combination of your existing data, it breaks immediately. Data drives everything; all models are wrong, but some are useful. (George Box)
  • plaidfuji 9y ago
    Incidentally, humans aren't very good at extrapolation, either, but our ability to generate good hypotheses differentiates us strongly from these models.