12 ms·

The deepest problem with deep learning

149 points by dsr12 8y ago

shafte 8y ago
I seem to have missed the Twitter spat that precipitated this essay, but I don't quite buy the larger argument he's making. We should judge approaches to AI based on their results, not on their conformance to a (vague, incorrect, untested) model of human cognition. Symbolic AI fell out of favor primarily because it was not delivering results in impactful problem areas. Deep learning is currently popular because we are nowhere near the limit of what results it can produce. Can this change? Of course! The history of deep learning itself proves as much. But if you want to genuinely influence the direction of the field, you have to lead by example and produce novel/interesting research results, not by kvetching in The New Yorker that your favorite approach is not getting enough attention.
- omeze 8y ago
  Its a long article but he’s advocating for a hybrid approach to deal with problems that DNNs encounter today which he believes are fundamental to DNNs (reasoning, causality). An example of such an approach taken with success is given at the end: https://arxiv.org/pdf/1711.04574.pdf https://arxiv.org/pdf/1711.04574.pdf
  - backpropaganda 8y ago
    How are you judging success here? The paper has no experiments at all, not even toy.
- antidesitter 8y ago
  > We should judge approaches to AI based on their results That's why the article discusses examples where currently popular approaches fail. > not on their conformance to a (vague, incorrect, untested) model of human cognition. What model of human cognition is "incorrect"? And is it the one presented by Marcus, or a strawman? > But if you want to genuinely influence the direction of the field, you have to lead by example and produce novel/interesting research results Are you claiming Marcus has produced no interesting research results? > not by kvetching in The New Yorker that your favorite approach is not getting enough attention Why use the term "kvetching"? I'm curious.
  - _cs2017_ 8y ago
    > Are you claiming Marcus has produced no interesting research results? Not the OP, but I'm not familiar with Marcus' contributions. What would you consider his top contributions? Are they all purely theoretical, or is there something that's already been applied in the real world?
  - shafte 8y ago
    > What model of human cognition are you claiming is "incorrect"? And is it the one presented by Marcus, or are you strawmanning? The model of human cognition I'm referring to is the hybrid connectionist-symbolic one that Marcus is well known for advocating (are YOU strawmanning? lol). I'm criticizing it for being more a theoretical model than one grounded in the physical realities of the brain, which of course no one really understands. Proposing a research program on that basis requires a high burden of proof. > Are you claiming Marcus has produced no interesting research results? Yes I am claiming that, if the benchmark for "interesting" is deep learning. There are indeed areas where deep learning is limited, and hybrid approaches could be superior. I would argue that there is not even close to enough evidence that a hybrid approach has improved generalizable power. > Why use the term "kvetching"? I'm curious. Huh? I guess it's the term my mother would use.
    - eli_gottlieb 8y ago
      >the physical realities of the brain, which of course no one really understands. No-one in machine learning, yeah, because machine learners mostly don't take neuroscience classes ;-).
    - antidesitter 8y ago
      > The model of human cognition I'm referring to is the hybrid connectionist-symbolic one that Marcus is well known for advocating > I'm criticizing it for being more a theoretical model than one grounded in the physical realities of the brain, which of course no one really understands You're contradicting yourself. On the one hand, you claim Marcus' model is "incorrect". On the other, you claim there's insufficient evidence either way. Which is it? > are YOU strawmanning? lol Do you know what the term "strawmanning" means? What could I possibly be strawmanning since I was asking for clarification? > Proposing a research program on that basis requires a high burden of proof. As opposed to...? > Yes I am claiming that, if the benchmark for "interesting" is deep learning. "Deep learning" isn't the correct benchmark since that's what Marcus is critiquing (to some extent) in the first place. > I would argue that there is not even close to enough evidence that a hybrid approach has improved generalizable power. Then you'd be wrong. Here's a good place to start your research: http://science.sciencemag.org/content/331/6022/1279 http://science.sciencemag.org/content/331/6022/1279 > Huh? I guess it's the term my mother would use. Your mother taught you to describe scientific debate as "kvetching"? That's disappointing.
- YeGoblynQueenne 8y ago
  >> Symbolic AI fell out of favor primarily because it was not delivering results in impactful problem areas. My understanding is instead that symbolic AI was working pretty damn well for its time. Expert systems routinely outperformed experts, for sure. The AI winter that killed them was brought on by political decisions taken by people who didn't really understand the field. Here's a good read on that historical period: Avoiding another AI winter, editorial in IEEE Intelligent Systems. https://www.computer.org/csdl/mags/ex/2008/02/mex2008020002.pdf https://www.computer.org/csdl/mags/ex/2008/02/mex2008020002.... Btw, "Intelligent Systems" is such a funny little expression. Basically, it was used by AI researchers during the 80's AI winter to be able to get funding for their work; because they wouldn't get any if they called it what it was, AI.
- dreamcompiler 8y ago
  Symbolic AI fell out of favor because it was overhyped. It was delivering quite impressive results--just not the promised results. Neural nets fell out of favor in the 90s for exactly the same reason. Both failures ultimately were caused by not enough computing power. Even though Deep Learning and Convolutional NNs look like major advances today, they never could have been practical before about 2005: There just wasn't enough computing power. If modern computer power were thrown at symbolic AI the same way it's been thrown at NNs, it highly likely symbolic AI would experience similarly-impressive gains.
  - hodgesrm 8y ago
    > If modern computer power were thrown at symbolic AI the same way it's been thrown at NNs, it highly likely symbolic AI would experience similarly-impressive gains. What's the basis for this conjecture? Is there a mathematical model for symbolic manipulation that would benefit from parallel execution/GPUs the way ML applications do?
    - dreamcompiler 8y ago
      My conjecture is about e.g. the Rete algorithm for rule search and the likelihood that it could be made more scalable on multicore and distributed hardware with modern functional data structures allowing easy rule updates. I don't know whether rule search or logic unification could be mapped onto GPU or TPU operations; I suspect not, but it's worth looking into.
      - jeremyjh 8y ago
        You realize someone has to write all those rules - and correctly - right? Are you saying that we literally didn't have the computing power to run all the rules we could actually write? I think you need to support this idea that symbolic approaches failed to lack of computing power.
        YeGoblynQueenne 8y ago
        Like I say in another comment, machine learning took off in part as a way to avoid having to hand-craft rules for expert systems' rule bases (although machine learning existed as a discipline from the early days of AI). So a lot of work on machine learning in the '80s and '90s went to learning rules. For instance (also in another comment) Decision Tree learners basically learn a set of If-Then-Else rules. They're one type of symbolic machine learning and there's more where they came from (e.g. Ross Quinlan's FOIL, for First-Order Inductive Learner, which is basically a first-order version of decision trees; Inductive Logic Programming which I study for my PhD; and many, many more). This work has dwindled, but it's still going. So, no, you don't have to write rules by hand, anymore than you need to set the weights of a neural net by hand. You can just learn them.
buboard 8y ago
if this is the tweet referred to: https://twitter.com/tdietterich/status/948811917593780225 https://twitter.com/tdietterich/status/948811917593780225 https://cdn-images-1.medium.com/max/800/1*W5WhToR_WP4I74Lo4uJ-1Q.png https://cdn-images-1.medium.com/max/800/1*W5WhToR_WP4I74Lo4u... Lecun didn't say he is not allowed criticize. He said that his contributions are zero but his criticisms abound. Also some of the arguments are odd: It is not possible to show the limits of deep learning because people don't know how to prove what those limits are. If there was a "limits of the universal approximation theorem" paper^, then people would use that to derive the limits of their DL systems. OTOH he proposes a number of possible implementations for symbols for which the evidence is not there, and there is no theoretical reason that necessitates their existence. Both arguments are not really falsifiable. ^ Actually there is at least one, however it seems it requires knowledge about the smoothness of the approximated function. https://www.sciencedirect.com/science/article/pii/S0888613X03000215 https://www.sciencedirect.com/science/article/pii/S0888613X0...
- deleted 8y ago
  [deleted]
paraditedc 8y ago
As someone with only basic idea of deep learning, I want to ask this question: If deep learning is mimicking how our brains work, wouldn't it be theoretically as powerful as our brains? In terms of architecture and complexity of the model.
- currymj 8y ago
  it's not really mimicking how our brains work, although that was the initial inspiration, decades ago. There are still people trying to model the actual brain but they don't overlap much with the machine learning community anymore. the idea originally was that a neuron can be roughly modeled as something that sums up inputs from other neurons and then outputs some kind of threshold/activation of those inputs. this is probably sort of true of biological neurons although as living cells they are much more complicated than that. but lots of recent and highly successful techniques in deep learning have given up on even that very weak amount of biological imitation. One simple example: lots of techniques involve multiplying together the outputs of "neurons". There is no sensible biological interpretation of this operation. Neural networks are Turing-complete (assuming all the usual pedantic assumptions you need to make that statement meaningful) so in that sense they are as powerful as our brains.
  - danmaz74 8y ago
    Being Turing complete makes them as powerful as any other digital computer, but it has little (personally I would say nothing) to do with our brain.
    - currymj 8y ago
      Why not? A human given enough time and an infinite tape can easily simulate a Turing machine. The other way is more controversial, but I don't think it's outrageous to speculate that a brain could be accurately simulated by a Turing machine.
      - danmaz74 8y ago
bra-ket 8y ago
>"And it’s where we should all be looking: gradient descent plus symbols" how about no gradient descent at all
- lkrubner 8y ago
  The problem is the exponential explosion of possibilities. Fairly amateur Python code can work through a problem in 10 minutes which, without gradient descent, would literally take a thousand years.
  - YeGoblynQueenne 8y ago
    The term is "combinatorial explosion". Like the ability to train larger, deeper, heavier neural nets, that too gets better with more computing power.
  - bra-ket 8y ago
    our brain is not doing brute force search
    - eli_gottlieb 8y ago
      Right, it's doing stochastic search through a Bayesian posterior distribution. The trouble is that established inference techniques can't really handle very general, "human-level" model classes in real time, computationally.
      - throwaway2048 8y ago
        We have no idea if that is true or not.
_cs2017_ 8y ago
I'm very disappointed with the quality of discussion between Marcus and LeCun. First Marcus responds to Bengio's interview with the arrogant and pretentious "I told you so" tweet, instead of simply saying that he agreed with Bengio. Then LeCun tweets a snarky and disrespectful response, instead of simply saying that Bengio's comments are different than Marcus's earlier critique. As a result of these tweets, I lost a huge amount of respect for both scientists.
- thaw13579 8y ago
  While I tend to agree with the authors view, I'm also disappointed by the tone. The author seems to double down on the "I told you so", saying: "I stand by that — which as far as I know (and I could be wrong) is the first place where anybody said that deep learning per se wouldn’t be a panacea, and would instead need to work in a larger context to solve a certain class of problems. Bengio was pretty much saying the same thing." which stakes out a priority claim (that deep learning is not a panacea) that appears somewhat superficial given the huge literature on causal modeling and shortcomings of the alternatives.
- evrydayhustling 8y ago
  While I agree that nobody looks great here, LeCun's response deserves to be read in context. Marcus' comment (and the linked post) isn't primarily about Bengio, it's about how he should be recognized as deep learning's earliest and most important public detractor. So, LeCun's response isn't about Marcus != Bengio, it's about the fact the Marcus' critique fundamentally hasn't deserved response or recognition this whole time, because there's not a constructive way to engage. That's a totally fair point to make, though a guy in LeCun's position probably should have said, "Gary, none of us have ever thought Deep Learning was (already) an answer for everything. The primary work of this community is to make it better."
  - woodandsteel 8y ago
    To repeat my question above: Marcus is saying that to get AGI, or at least get a lot closer to it, we need to combine ML with symbolic manipulation. Do you agree or disagree?
twtw 8y ago
I'm relatively unfamiliar with people who are actually arguing that deep learning is all that is needed for AGI - Andrew Ng can say what he wants, but do many of the researchers and engineers working with deep learning systems actually think this way? I tend to think of deep learning as adding additional sensory capabilities to machines. In this sense, is a network misclassifying a school bus as a snowplow significantly different from a GPS sensor generating garbage position data due to multipath? All existing sensors are noisy and can get confused, and yet no one says that GPS isn't useful because it can be so completely wrong in urban canyons. Of course, no one says GPS is going to become sentient - this brings us back to my first question above.
- AndrewKemendo 8y ago
  You're unfamiliar with them because there aren't any. At least none that I have seen which have any kind of history of research or applications. See my discussion with Gary here: https://twitter.com/AndrewKemendo/status/1068908423956312071 https://twitter.com/AndrewKemendo/status/1068908423956312071 Ng wasn't even really saying it in a way that is explicitly making the case - at least not enough to cause this kind of fracas. It's a bit unfortunate really, and I'm not sure what's driving the animosity or aggressiveness around this issue. While I agree with Marcus, I don't think anyone actually disagrees - largely because nobody is arguing what he is trying to address. Marcus has built a strawman and is tilting at Windmills a bit.
  - woodandsteel 8y ago
    I have a question. Marcus is saying that to get AGI, or at least get a lot closer to it, we need to combine ML with symbolic manipulation. Do you agree or disagree?
    - AndrewKemendo 8y ago
      His position is actually too poorly designed to really evaluate. However I think it's a reasonable argument to make that a General Intelligence may use some form of a symbolic system. I'd even accept the claim that General Intelligence would require some form of expert systems to cross the threshold. So in that sense, combining DL with Expert Systems, other symbolic methods or approaches like Bayesian Causal analysis is probably required.
mark_l_watson 8y ago
Great read. I have 30 years combined experience in symbolic AI and neural networks and my current day job is managing a deep learning team. I could not agree more with: “”I think it is far more likely that the two — deep learning and symbol-manipulation-will co-exist, with deep learning handling many aspects of perceptual classification, but symbol-manipulation playing a vital role in reasoning about abstract knowledge.”” My personal time projects are mostly combining symbolic AI and deep learning, but I am still trying to find a non-Python solution, including Haskell TensorFlow bindings, Armed Bear Common Lisp with DL4J, and exporting trained Keras models to a Racket environment - all plausible hacking environments but none feel ‘just right.’ If you are working on the same ideas please get in touch with me.
- peteradio 8y ago
  Can you hint the problem with a python based solution? What doesn't feel right about it.
  - eli_gottlieb 8y ago
    Pure Python is slow as a snail compared to both the underlying GPU code that has to run in deep learning applications, and the tuned, compiled code you would use for symbolic reasoning.
    - peteradio 8y ago
      Naturally, I guess I thought there was something syntax related or otherwise based on the way he said "doesn't feel right"
    - mark_l_watson 8y ago
      Python is great for short scripts and programs, but I don’t like it for large systems.
      - amelius 8y ago
        Yes, I found it is easy to run into memory or performance problems when code size becomes larger. Especially when the system is multi-threaded.
YeGoblynQueenne 8y ago
It's interesting to note here that the early years of machine learning (like the first couple of decades of it) were almost exclusively dedicated to learning symbolic rules, primarily in propositional logic. So if you pick up older machine learning papers, from around the '70s and '80s, you can see that the "models" are almost always propositional logic rules [1]. To cut a very long story short, machine learning took off in part as an attempt to automate knowledge acquisition for expert systems, the dominant AI paradigm at the time. Somehow, for reasons that are not entirely clear to me, the goal of learning the rules for a rule-based system was abandoned and since then research has focused almost exlusively on just learning. As a result of this, most machine learning work today does not consider what one can do with a trained model. And yet, just having, say, an object classifier, is not very useful on its own. For example, a robot car must be able to take decisions based on the objects its machine vision algorithms identify. Although there are learning techniques that consider this particular problem, i.e. navigation, there is little work on general inference and reasoning over the output of trained models. And this is not a symbolic vs statistical thing. I work in symbolic machine learning, and we are pretty rubbish at that, too. It's like we have lots of little pieces of a puzzle all lying around and nobody is really trying to put them together. Instead, we each carve our little (or bibger) niche and pretend nothing exists outside of it. Why? It's a mystery to me. Gary Marcus is doing good to try and shake up the field a bit. We need to move on from our successes just as we move on from our failures. And he's damn right about the how, too: symbolic AI is due for a comeback. ____________________ [1] For instance, you can see that in the foundational theoretical text for modern machine learning, Leslie Valiant's A Theory of the Learnable where the learning framework is described in terms of the propositional calculus, with a concept represented as a predicate that recognises vectors of boolean variables as members of itself or not. And of course, probably the most famous representative of propositional logic learning is the class of algorithms we probably all know as decision trees; they learn disjunctions of conjunctions - propositional logic rules.
- YeGoblynQueenne 8y ago
  >> The secondary goal of the book was to show that that was possible to build the primitives of symbol manipulation in principle using neurons as elements. Just another note here to say that the original artificial neuron, the Pitts and McCulloch neuron, was designed as a logic circuit with the ability to learn its own function. At the time -the 1930's- the propositional calculus was considered to be a good model of human intelligence.
woodandsteel 8y ago
Here's my view on this controversy (from someone who is not involved in the field). Historically there seem to be three views. One is the classic symbolic manipulation view, the second is what seems to be the ml view, and then there is the hybrid view that Marcus advocates. The first two views seem to have in common believing that their single approach alone would be sufficient for AGI. Marcus thus differs from both of them in that he thinks neither is sufficient. Assuming Marcus is taking the right approach, the question then becomes whether the hybrid approach could reach AGI. In my opinion it could not, though it probably would be able to out-think human beings in some important ways.
Invictus0 8y ago
I'm intrigued by the rotated schoolbus images. I wonder if the failure of the computer is due to the method of presentation. The schoolbus images are fundamentally two different things: an object, and a background. Humans know this intuitively: we understand the rules of 3D reality and extrapolate them unconsciously to all the objects therein. A computer is presented with just one thing: an array of pixels. Can we really expect a neural network to understand depth, scale, motion, and rotation when all it knows is two dimensions?
- mr_toad 8y ago
  > Can we really expect a neural network to understand depth, scale, motion, and rotation when all it knows is two dimensions? Frozen images at that. I’d expect better results from systems trained on videos of moving objects.
strin 8y ago
It depends on how "deep learning" is defined. If it's neural networks, the answer is likely no. but a broader definition of feature learning or "end-to-end" learning makes sense. It's likely for AGI what we need is a good, shared representation across multiple tasks, and continual learning paradigm that keeps building up new skills.
visarga 8y ago
I think the missing part - symbolic reasoning - can be handled by graph neural nets and attention mechanisms. Graph neural nets take as input a set of objects and their relations and predict information about the objects, their relations and the whole graph. They learn invariance in relation space, while being permutation invariant in the object space. This kind of invariance is what we need to improve on. We already have some promising attempts such as the GCN. This and creating environments for artificial agents - a child AI needs a place for playing and experimentation, not a mere static dataset.
sytelus 8y ago
This is your usual Gary Marcus if you have been frequenting his social media outlets. As far as I can tell, no one in the field is disputing his core thesis that deep learning is not “AI” and there is fundamental issue that deep learning doesn’t do causal reasoning and it’s rather just fancy way to do function approximation. There have been plenty of papers on this including from Bengio and there is now entire new field of adversarial deep learning which is academic way of making fun of how it miserably fails. The root cause of said Twitter firestorm was Marcus’s accusation that world’s talent and money is moving en mass in deep learning research which is likely to turn out dead end for achieving “real” AI. Even worse, many uninformed decision makers think AI is already a done deal due to all the media hype. The counter point to his accusations were that deep learning is what works now and pretty well for many real world problems so we shouldn’t be putting it down. It is likely that deep learning might become integral part of broader some Artificial General Intelligence framework in future. So no harm in keep improving it. This would be especially be important when most of the accusers don’t actually have realistic alternative with much to claim.
- YeGoblynQueenne 8y ago
  Well, the stiffling of funding to symbolic AI is a real danger. The field just keeps shrinking. And it's not just funding. Many people entering machine learning today would simply not know a predicate if it leaped up and bit them in the existential quantifiers. They also have no idea that the field they are now joining en masse actually started out with learning in the propositional calculus, neither do they understand why it then moved to statistics. In short, there is a real risk that researech funding will go to support research that doesn't know its elbow from its knee, done by people who don't understand what they are doing (because they don't understand what was done in the past). That is not a rosy situation. I get the counter point- I'm myself a bit annoyed by Marcus' insistence on how deep learning is not true AI. Very few people really expect to see "true AI" in our lifetimes. A classifier that works well is probably the best we can do and it's very reasonable that there is such excitement about the fact that we can do it. But if we spend too much effort developing classifiers, well, we'll have great classifiers. And nothing else. And then- what?
khiner 8y ago
My main takeaway: “How can deep learning be good if it’s not symbolic reasoning, you know, like scientists have proven brains work like? We think in symbols, symbols have inherent meaning, why aren’t deep learning learning symbols?! I’ve been saying exactly this for 20 years now! You know, all those deep learning hot shots think they’re so cool, but have they anticipated symbols like I have? Symbols won’t just allow us to play Atari games from raw pixel inputs, they will give us general AI. Somebody please write a paper showing how a symbol based algorithm of some kind is generally intelligent, and also that any connectionist-leaning mechanism is orthogonal to complex adaptive agent behaviors!”

149 points by dsr12 8y ago

I seem to have missed the Twitter spat that precipitated this essay, but I don't quite buy the larger argument he's making. We should judge approaches to AI based on their results, not on their conformance to a (vague, incorrect, untested) model of human cognition. Symbolic AI fell out of favor primarily because it was not delivering results in impactful problem areas. Deep learning is currently popular because we are nowhere near the limit of what results it can produce. Can this change? Of course! The history of deep learning itself proves as much. But if you want to genuinely influence the direction of the field, you have to lead by example and produce novel/interesting research results, not by kvetching in The New Yorker that your favorite approach is not getting enough attention.

omeze 8y ago
Its a long article but he’s advocating for a hybrid approach to deal with problems that DNNs encounter today which he believes are fundamental to DNNs (reasoning, causality). An example of such an approach taken with success is given at the end: https://arxiv.org/pdf/1711.04574.pdf https://arxiv.org/pdf/1711.04574.pdf
- backpropaganda 8y ago
  How are you judging success here? The paper has no experiments at all, not even toy.
antidesitter 8y ago
> We should judge approaches to AI based on their results That's why the article discusses examples where currently popular approaches fail. > not on their conformance to a (vague, incorrect, untested) model of human cognition. What model of human cognition is "incorrect"? And is it the one presented by Marcus, or a strawman? > But if you want to genuinely influence the direction of the field, you have to lead by example and produce novel/interesting research results Are you claiming Marcus has produced no interesting research results? > not by kvetching in The New Yorker that your favorite approach is not getting enough attention Why use the term "kvetching"? I'm curious.
- _cs2017_ 8y ago
  > Are you claiming Marcus has produced no interesting research results? Not the OP, but I'm not familiar with Marcus' contributions. What would you consider his top contributions? Are they all purely theoretical, or is there something that's already been applied in the real world?
- shafte 8y ago
  > What model of human cognition are you claiming is "incorrect"? And is it the one presented by Marcus, or are you strawmanning? The model of human cognition I'm referring to is the hybrid connectionist-symbolic one that Marcus is well known for advocating (are YOU strawmanning? lol). I'm criticizing it for being more a theoretical model than one grounded in the physical realities of the brain, which of course no one really understands. Proposing a research program on that basis requires a high burden of proof. > Are you claiming Marcus has produced no interesting research results? Yes I am claiming that, if the benchmark for "interesting" is deep learning. There are indeed areas where deep learning is limited, and hybrid approaches could be superior. I would argue that there is not even close to enough evidence that a hybrid approach has improved generalizable power. > Why use the term "kvetching"? I'm curious. Huh? I guess it's the term my mother would use.
  - eli_gottlieb 8y ago
    >the physical realities of the brain, which of course no one really understands. No-one in machine learning, yeah, because machine learners mostly don't take neuroscience classes ;-).
  - antidesitter 8y ago
    > The model of human cognition I'm referring to is the hybrid connectionist-symbolic one that Marcus is well known for advocating > I'm criticizing it for being more a theoretical model than one grounded in the physical realities of the brain, which of course no one really understands You're contradicting yourself. On the one hand, you claim Marcus' model is "incorrect". On the other, you claim there's insufficient evidence either way. Which is it? > are YOU strawmanning? lol Do you know what the term "strawmanning" means? What could I possibly be strawmanning since I was asking for clarification? > Proposing a research program on that basis requires a high burden of proof. As opposed to...? > Yes I am claiming that, if the benchmark for "interesting" is deep learning. "Deep learning" isn't the correct benchmark since that's what Marcus is critiquing (to some extent) in the first place. > I would argue that there is not even close to enough evidence that a hybrid approach has improved generalizable power. Then you'd be wrong. Here's a good place to start your research: http://science.sciencemag.org/content/331/6022/1279 http://science.sciencemag.org/content/331/6022/1279 > Huh? I guess it's the term my mother would use. Your mother taught you to describe scientific debate as "kvetching"? That's disappointing.
YeGoblynQueenne 8y ago
>> Symbolic AI fell out of favor primarily because it was not delivering results in impactful problem areas. My understanding is instead that symbolic AI was working pretty damn well for its time. Expert systems routinely outperformed experts, for sure. The AI winter that killed them was brought on by political decisions taken by people who didn't really understand the field. Here's a good read on that historical period: Avoiding another AI winter, editorial in IEEE Intelligent Systems. https://www.computer.org/csdl/mags/ex/2008/02/mex2008020002.pdf https://www.computer.org/csdl/mags/ex/2008/02/mex2008020002.... Btw, "Intelligent Systems" is such a funny little expression. Basically, it was used by AI researchers during the 80's AI winter to be able to get funding for their work; because they wouldn't get any if they called it what it was, AI.
dreamcompiler 8y ago
Symbolic AI fell out of favor because it was overhyped. It was delivering quite impressive results--just not the promised results. Neural nets fell out of favor in the 90s for exactly the same reason. Both failures ultimately were caused by not enough computing power. Even though Deep Learning and Convolutional NNs look like major advances today, they never could have been practical before about 2005: There just wasn't enough computing power. If modern computer power were thrown at symbolic AI the same way it's been thrown at NNs, it highly likely symbolic AI would experience similarly-impressive gains.
- hodgesrm 8y ago
  > If modern computer power were thrown at symbolic AI the same way it's been thrown at NNs, it highly likely symbolic AI would experience similarly-impressive gains. What's the basis for this conjecture? Is there a mathematical model for symbolic manipulation that would benefit from parallel execution/GPUs the way ML applications do?
  - dreamcompiler 8y ago
    My conjecture is about e.g. the Rete algorithm for rule search and the likelihood that it could be made more scalable on multicore and distributed hardware with modern functional data structures allowing easy rule updates. I don't know whether rule search or logic unification could be mapped onto GPU or TPU operations; I suspect not, but it's worth looking into.
    - jeremyjh 8y ago
      You realize someone has to write all those rules - and correctly - right? Are you saying that we literally didn't have the computing power to run all the rules we could actually write? I think you need to support this idea that symbolic approaches failed to lack of computing power.
      - YeGoblynQueenne 8y ago
        Like I say in another comment, machine learning took off in part as a way to avoid having to hand-craft rules for expert systems' rule bases (although machine learning existed as a discipline from the early days of AI). So a lot of work on machine learning in the '80s and '90s went to learning rules. For instance (also in another comment) Decision Tree learners basically learn a set of If-Then-Else rules. They're one type of symbolic machine learning and there's more where they came from (e.g. Ross Quinlan's FOIL, for First-Order Inductive Learner, which is basically a first-order version of decision trees; Inductive Logic Programming which I study for my PhD; and many, many more). This work has dwindled, but it's still going. So, no, you don't have to write rules by hand, anymore than you need to set the weights of a neural net by hand. You can just learn them.

buboard 8y ago

if this is the tweet referred to: https://twitter.com/tdietterich/status/948811917593780225 https://twitter.com/tdietterich/status/948811917593780225 https://cdn-images-1.medium.com/max/800/1*W5WhToR_WP4I74Lo4uJ-1Q.png https://cdn-images-1.medium.com/max/800/1*W5WhToR_WP4I74Lo4u... Lecun didn't say he is not allowed criticize. He said that his contributions are zero but his criticisms abound. Also some of the arguments are odd: It is not possible to show the limits of deep learning because people don't know how to prove what those limits are. If there was a "limits of the universal approximation theorem" paper^, then people would use that to derive the limits of their DL systems. OTOH he proposes a number of possible implementations for symbols for which the evidence is not there, and there is no theoretical reason that necessitates their existence. Both arguments are not really falsifiable. ^ Actually there is at least one, however it seems it requires knowledge about the smoothness of the approximated function. https://www.sciencedirect.com/science/article/pii/S0888613X03000215 https://www.sciencedirect.com/science/article/pii/S0888613X0...

deleted 8y ago
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paraditedc 8y ago

As someone with only basic idea of deep learning, I want to ask this question: If deep learning is mimicking how our brains work, wouldn't it be theoretically as powerful as our brains? In terms of architecture and complexity of the model.

currymj 8y ago
it's not really mimicking how our brains work, although that was the initial inspiration, decades ago. There are still people trying to model the actual brain but they don't overlap much with the machine learning community anymore. the idea originally was that a neuron can be roughly modeled as something that sums up inputs from other neurons and then outputs some kind of threshold/activation of those inputs. this is probably sort of true of biological neurons although as living cells they are much more complicated than that. but lots of recent and highly successful techniques in deep learning have given up on even that very weak amount of biological imitation. One simple example: lots of techniques involve multiplying together the outputs of "neurons". There is no sensible biological interpretation of this operation. Neural networks are Turing-complete (assuming all the usual pedantic assumptions you need to make that statement meaningful) so in that sense they are as powerful as our brains.
- danmaz74 8y ago
  Being Turing complete makes them as powerful as any other digital computer, but it has little (personally I would say nothing) to do with our brain.
  - currymj 8y ago
    Why not? A human given enough time and an infinite tape can easily simulate a Turing machine. The other way is more controversial, but I don't think it's outrageous to speculate that a brain could be accurately simulated by a Turing machine.
    - danmaz74 8y ago

bra-ket 8y ago

>"And it’s where we should all be looking: gradient descent plus symbols" how about no gradient descent at all

lkrubner 8y ago
The problem is the exponential explosion of possibilities. Fairly amateur Python code can work through a problem in 10 minutes which, without gradient descent, would literally take a thousand years.
- YeGoblynQueenne 8y ago
  The term is "combinatorial explosion". Like the ability to train larger, deeper, heavier neural nets, that too gets better with more computing power.
- bra-ket 8y ago
  our brain is not doing brute force search
  - eli_gottlieb 8y ago
    Right, it's doing stochastic search through a Bayesian posterior distribution. The trouble is that established inference techniques can't really handle very general, "human-level" model classes in real time, computationally.
    - throwaway2048 8y ago
      We have no idea if that is true or not.

_cs2017_ 8y ago

I'm very disappointed with the quality of discussion between Marcus and LeCun. First Marcus responds to Bengio's interview with the arrogant and pretentious "I told you so" tweet, instead of simply saying that he agreed with Bengio. Then LeCun tweets a snarky and disrespectful response, instead of simply saying that Bengio's comments are different than Marcus's earlier critique. As a result of these tweets, I lost a huge amount of respect for both scientists.

thaw13579 8y ago
While I tend to agree with the authors view, I'm also disappointed by the tone. The author seems to double down on the "I told you so", saying: "I stand by that — which as far as I know (and I could be wrong) is the first place where anybody said that deep learning per se wouldn’t be a panacea, and would instead need to work in a larger context to solve a certain class of problems. Bengio was pretty much saying the same thing." which stakes out a priority claim (that deep learning is not a panacea) that appears somewhat superficial given the huge literature on causal modeling and shortcomings of the alternatives.
evrydayhustling 8y ago
While I agree that nobody looks great here, LeCun's response deserves to be read in context. Marcus' comment (and the linked post) isn't primarily about Bengio, it's about how he should be recognized as deep learning's earliest and most important public detractor. So, LeCun's response isn't about Marcus != Bengio, it's about the fact the Marcus' critique fundamentally hasn't deserved response or recognition this whole time, because there's not a constructive way to engage. That's a totally fair point to make, though a guy in LeCun's position probably should have said, "Gary, none of us have ever thought Deep Learning was (already) an answer for everything. The primary work of this community is to make it better."
- woodandsteel 8y ago
  To repeat my question above: Marcus is saying that to get AGI, or at least get a lot closer to it, we need to combine ML with symbolic manipulation. Do you agree or disagree?

twtw 8y ago

I'm relatively unfamiliar with people who are actually arguing that deep learning is all that is needed for AGI - Andrew Ng can say what he wants, but do many of the researchers and engineers working with deep learning systems actually think this way? I tend to think of deep learning as adding additional sensory capabilities to machines. In this sense, is a network misclassifying a school bus as a snowplow significantly different from a GPS sensor generating garbage position data due to multipath? All existing sensors are noisy and can get confused, and yet no one says that GPS isn't useful because it can be so completely wrong in urban canyons. Of course, no one says GPS is going to become sentient - this brings us back to my first question above.

AndrewKemendo 8y ago
You're unfamiliar with them because there aren't any. At least none that I have seen which have any kind of history of research or applications. See my discussion with Gary here: https://twitter.com/AndrewKemendo/status/1068908423956312071 https://twitter.com/AndrewKemendo/status/1068908423956312071 Ng wasn't even really saying it in a way that is explicitly making the case - at least not enough to cause this kind of fracas. It's a bit unfortunate really, and I'm not sure what's driving the animosity or aggressiveness around this issue. While I agree with Marcus, I don't think anyone actually disagrees - largely because nobody is arguing what he is trying to address. Marcus has built a strawman and is tilting at Windmills a bit.
- woodandsteel 8y ago
  I have a question. Marcus is saying that to get AGI, or at least get a lot closer to it, we need to combine ML with symbolic manipulation. Do you agree or disagree?
  - AndrewKemendo 8y ago
    His position is actually too poorly designed to really evaluate. However I think it's a reasonable argument to make that a General Intelligence may use some form of a symbolic system. I'd even accept the claim that General Intelligence would require some form of expert systems to cross the threshold. So in that sense, combining DL with Expert Systems, other symbolic methods or approaches like Bayesian Causal analysis is probably required.

mark_l_watson 8y ago

Great read. I have 30 years combined experience in symbolic AI and neural networks and my current day job is managing a deep learning team. I could not agree more with: “”I think it is far more likely that the two — deep learning and symbol-manipulation-will co-exist, with deep learning handling many aspects of perceptual classification, but symbol-manipulation playing a vital role in reasoning about abstract knowledge.”” My personal time projects are mostly combining symbolic AI and deep learning, but I am still trying to find a non-Python solution, including Haskell TensorFlow bindings, Armed Bear Common Lisp with DL4J, and exporting trained Keras models to a Racket environment - all plausible hacking environments but none feel ‘just right.’ If you are working on the same ideas please get in touch with me.

peteradio 8y ago
Can you hint the problem with a python based solution? What doesn't feel right about it.
- eli_gottlieb 8y ago
  Pure Python is slow as a snail compared to both the underlying GPU code that has to run in deep learning applications, and the tuned, compiled code you would use for symbolic reasoning.
  - peteradio 8y ago
    Naturally, I guess I thought there was something syntax related or otherwise based on the way he said "doesn't feel right"
  - mark_l_watson 8y ago
    Python is great for short scripts and programs, but I don’t like it for large systems.
    - amelius 8y ago
      Yes, I found it is easy to run into memory or performance problems when code size becomes larger. Especially when the system is multi-threaded.

YeGoblynQueenne 8y ago

It's interesting to note here that the early years of machine learning (like the first couple of decades of it) were almost exclusively dedicated to learning symbolic rules, primarily in propositional logic. So if you pick up older machine learning papers, from around the '70s and '80s, you can see that the "models" are almost always propositional logic rules [1]. To cut a very long story short, machine learning took off in part as an attempt to automate knowledge acquisition for expert systems, the dominant AI paradigm at the time. Somehow, for reasons that are not entirely clear to me, the goal of learning the rules for a rule-based system was abandoned and since then research has focused almost exlusively on just learning. As a result of this, most machine learning work today does not consider what one can do with a trained model. And yet, just having, say, an object classifier, is not very useful on its own. For example, a robot car must be able to take decisions based on the objects its machine vision algorithms identify. Although there are learning techniques that consider this particular problem, i.e. navigation, there is little work on general inference and reasoning over the output of trained models. And this is not a symbolic vs statistical thing. I work in symbolic machine learning, and we are pretty rubbish at that, too. It's like we have lots of little pieces of a puzzle all lying around and nobody is really trying to put them together. Instead, we each carve our little (or bibger) niche and pretend nothing exists outside of it. Why? It's a mystery to me. Gary Marcus is doing good to try and shake up the field a bit. We need to move on from our successes just as we move on from our failures. And he's damn right about the how, too: symbolic AI is due for a comeback. ____________________ [1] For instance, you can see that in the foundational theoretical text for modern machine learning, Leslie Valiant's A Theory of the Learnable where the learning framework is described in terms of the propositional calculus, with a concept represented as a predicate that recognises vectors of boolean variables as members of itself or not. And of course, probably the most famous representative of propositional logic learning is the class of algorithms we probably all know as decision trees; they learn disjunctions of conjunctions - propositional logic rules.

YeGoblynQueenne 8y ago
>> The secondary goal of the book was to show that that was possible to build the primitives of symbol manipulation in principle using neurons as elements. Just another note here to say that the original artificial neuron, the Pitts and McCulloch neuron, was designed as a logic circuit with the ability to learn its own function. At the time -the 1930's- the propositional calculus was considered to be a good model of human intelligence.

woodandsteel 8y ago

Here's my view on this controversy (from someone who is not involved in the field). Historically there seem to be three views. One is the classic symbolic manipulation view, the second is what seems to be the ml view, and then there is the hybrid view that Marcus advocates. The first two views seem to have in common believing that their single approach alone would be sufficient for AGI. Marcus thus differs from both of them in that he thinks neither is sufficient. Assuming Marcus is taking the right approach, the question then becomes whether the hybrid approach could reach AGI. In my opinion it could not, though it probably would be able to out-think human beings in some important ways.

Invictus0 8y ago

I'm intrigued by the rotated schoolbus images. I wonder if the failure of the computer is due to the method of presentation. The schoolbus images are fundamentally two different things: an object, and a background. Humans know this intuitively: we understand the rules of 3D reality and extrapolate them unconsciously to all the objects therein. A computer is presented with just one thing: an array of pixels. Can we really expect a neural network to understand depth, scale, motion, and rotation when all it knows is two dimensions?

mr_toad 8y ago
> Can we really expect a neural network to understand depth, scale, motion, and rotation when all it knows is two dimensions? Frozen images at that. I’d expect better results from systems trained on videos of moving objects.

strin 8y ago

It depends on how "deep learning" is defined. If it's neural networks, the answer is likely no. but a broader definition of feature learning or "end-to-end" learning makes sense. It's likely for AGI what we need is a good, shared representation across multiple tasks, and continual learning paradigm that keeps building up new skills.

visarga 8y ago

I think the missing part - symbolic reasoning - can be handled by graph neural nets and attention mechanisms. Graph neural nets take as input a set of objects and their relations and predict information about the objects, their relations and the whole graph. They learn invariance in relation space, while being permutation invariant in the object space. This kind of invariance is what we need to improve on. We already have some promising attempts such as the GCN. This and creating environments for artificial agents - a child AI needs a place for playing and experimentation, not a mere static dataset.

sytelus 8y ago

This is your usual Gary Marcus if you have been frequenting his social media outlets. As far as I can tell, no one in the field is disputing his core thesis that deep learning is not “AI” and there is fundamental issue that deep learning doesn’t do causal reasoning and it’s rather just fancy way to do function approximation. There have been plenty of papers on this including from Bengio and there is now entire new field of adversarial deep learning which is academic way of making fun of how it miserably fails. The root cause of said Twitter firestorm was Marcus’s accusation that world’s talent and money is moving en mass in deep learning research which is likely to turn out dead end for achieving “real” AI. Even worse, many uninformed decision makers think AI is already a done deal due to all the media hype. The counter point to his accusations were that deep learning is what works now and pretty well for many real world problems so we shouldn’t be putting it down. It is likely that deep learning might become integral part of broader some Artificial General Intelligence framework in future. So no harm in keep improving it. This would be especially be important when most of the accusers don’t actually have realistic alternative with much to claim.

YeGoblynQueenne 8y ago
Well, the stiffling of funding to symbolic AI is a real danger. The field just keeps shrinking. And it's not just funding. Many people entering machine learning today would simply not know a predicate if it leaped up and bit them in the existential quantifiers. They also have no idea that the field they are now joining en masse actually started out with learning in the propositional calculus, neither do they understand why it then moved to statistics. In short, there is a real risk that researech funding will go to support research that doesn't know its elbow from its knee, done by people who don't understand what they are doing (because they don't understand what was done in the past). That is not a rosy situation. I get the counter point- I'm myself a bit annoyed by Marcus' insistence on how deep learning is not true AI. Very few people really expect to see "true AI" in our lifetimes. A classifier that works well is probably the best we can do and it's very reasonable that there is such excitement about the fact that we can do it. But if we spend too much effort developing classifiers, well, we'll have great classifiers. And nothing else. And then- what?

khiner 8y ago

My main takeaway: “How can deep learning be good if it’s not symbolic reasoning, you know, like scientists have proven brains work like? We think in symbols, symbols have inherent meaning, why aren’t deep learning learning symbols?! I’ve been saying exactly this for 20 years now! You know, all those deep learning hot shots think they’re so cool, but have they anticipated symbols like I have? Symbols won’t just allow us to play Atari games from raw pixel inputs, they will give us general AI. Somebody please write a paper showing how a symbol based algorithm of some kind is generally intelligent, and also that any connectionist-leaning mechanism is orthogonal to complex adaptive agent behaviors!”