7 ms·

MIT AGI: Conversation with Yoshua Bengio [video]

115 points by AlanTuring 8y ago

joe_the_user 8y ago
So my impression of the way that Face recognition works is that the neural-net part of the system recognizes the faces in a photo, recognizes the features on the faces and provides a map of the features to the system. The system takes these parameters and stores and recovers a given face with them. The ability to recognize individual faces comes because conveniently these parameters don't change much (but clearly it hinges on this). Of course, there are a lot of small enhancements to this process and these evolve but this is still basic outline. Which is to say that convolutional neural networks, the most common and successful neural networks, fundamentally only operate by recognizing a broad category (face, nose-on-face, mouth-on-face). This limitation seems as inherent in NNs operating with a black-box/training-set/testing-set cycle as it is in any architecture consideration. If all you can "tell" the NN is "this is X, this is not X", then it's plausible that's all it will give back. Moreover, "X/not-X" testing allows a LOT of training to happen in an unambiguous fashion, which seems necessary when what you're doing gradually pushing a giant bunch of semi-arbitrary detectors into place. Machine learning theorists can do a lot with nothing but this category-recognition tool - like having a go program recognize "good moves" and "good positions". Note, that the more elaborate "neural net within neural net" approaches involve vast levels of computing power - you're brute-forcing brute-force. AlphaGoZero probably involved the largest level of computing power ever harnessed for a single-use app. AlphaGoZero involve megaflop levels close to those estimated for the human brain though such estimates are always debatable (perhaps why we didn't see many games by it). The thing is there are plenty of ordinary computing algorithms do "more" than neural nets - perform logical operations or sort and search by vague similarity or classify on spectrum of multiple qualities (rather than the binary X or not-X of a neural network). It is just that these break down at an "industrial scale", whereas neural nets are shine at this scale. However, despite these virtues, it seems likely we'll see neural nets approaching their particular limits reasonably soon, if they haven't (in at least a sense) done so already. And it seems like any "real" extension/alternative will have to be able to use big data but use it in a more sophisticated fashion than neural nets. Oh, this relates to the video most in that when Bengio talks about how to extend neural networks, he's mostly just mentioning extension in terms of how to add abstract qualities like "causation", where one can certainly see crude, simplistic things in computation that could be called "working with causation" but you don't have that yet when you go to the level of a large-scale neural network.
- mgurlitz 8y ago
  NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. A binary "X/not-X" test often comes from applying a threshold to the NN's output. Quoting from the Universal approximation theory's Wikipedia, "neural networks can represent a wide variety of interesting functions." While they may be much better at pattern recognition, it's possible to produce almost anything with one, including Go moves, if you can devise a method to interpret the outputs.
  - iandanforth 8y ago
    >> NNs are not binary -- they're fundamentally analog That's incorrect. https://arxiv.org/abs/1602.02830 https://arxiv.org/abs/1602.02830
    - candiodari 8y ago
      You could say they're "probabilistically binary", that would at least be intuitively accurate.
  - joe_the_user 8y ago
    NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. I think that's "true and false". Yes, NNs produce a "chance of being X" but no, NNs don't naturally produce a human-intuitive "degree of being X". If the NN is looking for a "red fire engine", it's not necessarily going to produce "degree of redness" as its output. -- As to neural nets representing many functions; neural nets, Taylor series, Fourier series and so-forth can represent/approximate "any function". True but effectively irrelevant. What matters is what function a given methodology can be trained, programmed or whatever into representing.
- glass_of_water 8y ago
  > Note, that the more elaborate "neural net within neural net" approaches involve vast levels of computing power... This is one thing I worry about, that even if we were to get closer to AGI, the algorithms/system might not feasibly run with our current computing paradigm.
JabavuAdams 8y ago
Great talk! What's my best option for getting / making a transcript of this talk?
- make3 8y ago
  does this work for you ? https://ccm.net/faq/40644-how-to-get-the-transcript-of-a-youtube-video https://ccm.net/faq/40644-how-to-get-the-transcript-of-a-you...
  - JabavuAdams 8y ago
    That does! Did not know one could do that. This is super useful for my learning, thanks!
    - deleted 8y ago
      [deleted]
iandanforth 8y ago
Some of his points I want to emphasize and ones I'd like to discuss further. 1. The method of training in current use won't lead to general systems. It doesn't matter how large your image set is you'll never learn all of language and motor behavior from it. (In realistic terms) Multi-modal learning where an agent is interacting with an environment will be critical for general systems due to a common expectation that general systems will be able to explore and problem solve. The requirement to explore and problem solve needs to be built into training. I go a bit further and believe that you need a fairly high-fidelity world in which physically modeled agents are given progressively more difficult tasks if you want to achieve this generalization. As he notes at the end of the talk some variant of reinforcement learning will be key for this. 2. Disentangling representations will be useful for both generality and preventing catastrophic forgetting. Current algorithms update all parameters by default which means that all knowledge is vulnerable to loss. If you can disentangle representations (and I disagree that this can't be done in pixel space) then you can selectively prevent updates to some parameters and better prevent accidental forgetting. Concretely, mimicking the sparsity of activation seen in the brain through implementing lateral inhibition (and further exploring top-down attentional sparsification mechanisms) should be useful in this area. (some good papers on this in 2018!) 3. The ability for agents to learn from teachers and the ability to teach agents interactively will improve generality and learning speed. Agents today are largely trained from scratch. Some are fine-tuned from other agents, but relatively few are instructed in the way you would a dog or a child. Cultural transmission of information underlies a lot of what we think of as modern human intelligence so it's likely that systems that can benefit from this method of skill acquisition will have an advantage. A point not mentioned was the crucial distinction between knowledge and abilities which are innate to biological creatures (developed over evolutionary timescales) and knowledge gained during a single lifetime. Right now we try to teach agents everything and don't give them the benefit of starting with evolved bodies, reflexes, and adapted perceptive abilities. So even before we get to the cultural transmission of knowledge through teaching I think we need to (in parallel) be developing base agents which are "evolved" with solutions to lower level problems built in so that the networks don't have to re-learn how to catch yourself when falling every single time.
- yazr 8y ago

115 points by AlanTuring 8y ago

So my impression of the way that Face recognition works is that the neural-net part of the system recognizes the faces in a photo, recognizes the features on the faces and provides a map of the features to the system. The system takes these parameters and stores and recovers a given face with them. The ability to recognize individual faces comes because conveniently these parameters don't change much (but clearly it hinges on this). Of course, there are a lot of small enhancements to this process and these evolve but this is still basic outline. Which is to say that convolutional neural networks, the most common and successful neural networks, fundamentally only operate by recognizing a broad category (face, nose-on-face, mouth-on-face). This limitation seems as inherent in NNs operating with a black-box/training-set/testing-set cycle as it is in any architecture consideration. If all you can "tell" the NN is "this is X, this is not X", then it's plausible that's all it will give back. Moreover, "X/not-X" testing allows a LOT of training to happen in an unambiguous fashion, which seems necessary when what you're doing gradually pushing a giant bunch of semi-arbitrary detectors into place. Machine learning theorists can do a lot with nothing but this category-recognition tool - like having a go program recognize "good moves" and "good positions". Note, that the more elaborate "neural net within neural net" approaches involve vast levels of computing power - you're brute-forcing brute-force. AlphaGoZero probably involved the largest level of computing power ever harnessed for a single-use app. AlphaGoZero involve megaflop levels close to those estimated for the human brain though such estimates are always debatable (perhaps why we didn't see many games by it). The thing is there are plenty of ordinary computing algorithms do "more" than neural nets - perform logical operations or sort and search by vague similarity or classify on spectrum of multiple qualities (rather than the binary X or not-X of a neural network). It is just that these break down at an "industrial scale", whereas neural nets are shine at this scale. However, despite these virtues, it seems likely we'll see neural nets approaching their particular limits reasonably soon, if they haven't (in at least a sense) done so already. And it seems like any "real" extension/alternative will have to be able to use big data but use it in a more sophisticated fashion than neural nets. Oh, this relates to the video most in that when Bengio talks about how to extend neural networks, he's mostly just mentioning extension in terms of how to add abstract qualities like "causation", where one can certainly see crude, simplistic things in computation that could be called "working with causation" but you don't have that yet when you go to the level of a large-scale neural network.

mgurlitz 8y ago
NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. A binary "X/not-X" test often comes from applying a threshold to the NN's output. Quoting from the Universal approximation theory's Wikipedia, "neural networks can represent a wide variety of interesting functions." While they may be much better at pattern recognition, it's possible to produce almost anything with one, including Go moves, if you can devise a method to interpret the outputs.
- iandanforth 8y ago
  >> NNs are not binary -- they're fundamentally analog That's incorrect. https://arxiv.org/abs/1602.02830 https://arxiv.org/abs/1602.02830
  - candiodari 8y ago
    You could say they're "probabilistically binary", that would at least be intuitively accurate.
- joe_the_user 8y ago
  NNs are not binary -- they're fundamentally analog, so for classification problems, they produce probabilities that a test case is in each possible class. I think that's "true and false". Yes, NNs produce a "chance of being X" but no, NNs don't naturally produce a human-intuitive "degree of being X". If the NN is looking for a "red fire engine", it's not necessarily going to produce "degree of redness" as its output. -- As to neural nets representing many functions; neural nets, Taylor series, Fourier series and so-forth can represent/approximate "any function". True but effectively irrelevant. What matters is what function a given methodology can be trained, programmed or whatever into representing.
glass_of_water 8y ago
> Note, that the more elaborate "neural net within neural net" approaches involve vast levels of computing power... This is one thing I worry about, that even if we were to get closer to AGI, the algorithms/system might not feasibly run with our current computing paradigm.

JabavuAdams 8y ago

Great talk! What's my best option for getting / making a transcript of this talk?

make3 8y ago
does this work for you ? https://ccm.net/faq/40644-how-to-get-the-transcript-of-a-youtube-video https://ccm.net/faq/40644-how-to-get-the-transcript-of-a-you...
- JabavuAdams 8y ago
  That does! Did not know one could do that. This is super useful for my learning, thanks!
  - deleted 8y ago
    [deleted]

iandanforth 8y ago

Some of his points I want to emphasize and ones I'd like to discuss further. 1. The method of training in current use won't lead to general systems. It doesn't matter how large your image set is you'll never learn all of language and motor behavior from it. (In realistic terms) Multi-modal learning where an agent is interacting with an environment will be critical for general systems due to a common expectation that general systems will be able to explore and problem solve. The requirement to explore and problem solve needs to be built into training. I go a bit further and believe that you need a fairly high-fidelity world in which physically modeled agents are given progressively more difficult tasks if you want to achieve this generalization. As he notes at the end of the talk some variant of reinforcement learning will be key for this. 2. Disentangling representations will be useful for both generality and preventing catastrophic forgetting. Current algorithms update all parameters by default which means that all knowledge is vulnerable to loss. If you can disentangle representations (and I disagree that this can't be done in pixel space) then you can selectively prevent updates to some parameters and better prevent accidental forgetting. Concretely, mimicking the sparsity of activation seen in the brain through implementing lateral inhibition (and further exploring top-down attentional sparsification mechanisms) should be useful in this area. (some good papers on this in 2018!) 3. The ability for agents to learn from teachers and the ability to teach agents interactively will improve generality and learning speed. Agents today are largely trained from scratch. Some are fine-tuned from other agents, but relatively few are instructed in the way you would a dog or a child. Cultural transmission of information underlies a lot of what we think of as modern human intelligence so it's likely that systems that can benefit from this method of skill acquisition will have an advantage. A point not mentioned was the crucial distinction between knowledge and abilities which are innate to biological creatures (developed over evolutionary timescales) and knowledge gained during a single lifetime. Right now we try to teach agents everything and don't give them the benefit of starting with evolved bodies, reflexes, and adapted perceptive abilities. So even before we get to the cultural transmission of knowledge through teaching I think we need to (in parallel) be developing base agents which are "evolved" with solutions to lower level problems built in so that the networks don't have to re-learn how to catch yourself when falling every single time.

yazr 8y ago