9 ms·

Learning Concepts with Energy Functions

166 points by stablemap 8y ago

yters 8y ago
Every new technique is able to "quickly learn X." Something most not be so quick, otherwise why aren't these techniques turning into AGI? I think the problem is the goal is not well defined. So, increased velocity has no bearing on increased velocity towards the target. A side question, why is there no research into whether human intelligence is computable? The assumption in AI is that human intelligence is computable, but I've never seen any good argument or evidence that this is true. Seems very unscientific to exert so much energy into this research direction without validating the fundamental assumption. For example, the one instance I know of that defines AGI in a quantitative manner is Solomonoff induction (SI), but it is not computable. If SI is representative of human intelligence, then AGI is impossible.
- andbberger 8y ago
  That's just marketing. Also that assumes that the goal is some general AI. We're just getting started here
  - yters 8y ago
    I assume the goal is to achieve something useful. If AGI is impossible, then much more useful than these incremental steps is to figure out how to most effectively combine human cognition and computational systems.
    - andbberger 8y ago
      > then much more useful than these incremental steps is to figure out how to most effectively combine human cognition and computational systems. You don't know that
      - yters 8y ago
        And you don't know AGI is possible. Both are options, and we should first do some kind of basic research to determine which is a better route before dumping billions of dollars in one direction. Or, at very least we should dump money in both directions, and tune towards which is getting better real world gains.
        andbberger 8y ago
        Obviously it's possible, we're having this conversation aren't we?
        cambalache 8y ago
        In the sense that the "Artificial" part of AGI means: created by humans, we do not know if it is possible.
        andbberger 8y ago
        I mean... what's so special about biology? How about I flip a few base pairs here and there, artificial enough yet? We already routinely genetically engineer mice and other model animals such that particular bits of their brain will either glow or fire in response to laser light, we have the technology today to put make little mice helmets that we can use to steer mice around - just need to do a bit more research to find out what particular bit of the (probably) hippocampus to stimulate. Is that artificial enough? OK and how about just simulating the universe? There is a legitimate question about computability there - it does seem plausible that aspects of simulating the universe could be uncomputable. Suppose that this is case - well it's still an open question as to whether or not this spells doom for the simulation route. The uncomputable bits are going to be some quantum this or that, and it's not at all clear that such low-level bits are fundamentally required for human-level intelligence; that the high-level process of intelligence is inseparable from the underlying processes which give rise to human intelligence. Personally, I find it a highly unlikely that intelligence is inseparable/has no reduced model, for whatever my prognostication is worth. BUT even if it is inseparable, there's still a strong argument to be made that you could construct AGI through means of so-called 'embodied computation', just like biology does.
- jerf 8y ago
  Solomonoff induction is not representative of human intelligence. At least, speaking for myself, I do not exhaustively search literally every hypothesis and match it against my data. Your mileage may vary. Dunno. It's a diverse world out there, right?
  - yters 8y ago
    Solomonoff induction is a way to quantify the human ability to induce general principles from limited observations. All the computable methods are unable to achieve this ability.
    - jerf 8y ago
      With respect, no, that is not what it is. My summary is glib and phrased in non-mathematical terms, but closer. A true Solomonoff Inductor would be wildly, wildly smarter than a human being, if it could get over the problem that such a machine would also consume super-exponentially more resources than the universe has.
      - yters 8y ago
        It's not a matter of resources. Solomonoff induction is not computable, since it needs to calculate the Kolmogorov complexity of the data. If that is what is required for induction, it is surprising that humans are able to do so well at identifying concise descriptions of the data we observe. This seems inexplicable with a computational view of human cognition.
      - yters 8y ago
        Disregard, I realized my error is that the elegant program is guaranteed to halt at some point, which gives us the Kolmogorov complexity. We just will never know when that happens, even with infinite resources.
  - Xcelerate 8y ago
    > I do not exhaustively search literally every hypothesis and match it against my data Haha. My problem is that my brain attempts to do this but can’t, which just leads to analysis paralysis instead.
- pickdenis 8y ago
  > why aren't these techniques turning into AGI? What prompted you to even ask this question? Where in the article does it say that "These results are step N on the path to AGI!"? This is research: the researchers found a problem that had limited solutions before (learning concepts with limited examples) and came up with a better solution. It's not clear why you're questioning this. > [on the computability of human intelligence] This is just silly. Even if human intelligence was beyond the reach of silicon (which I and many real researchers doubt), this work is still useful even if it doesn't result in AGI. You're too fixated on AGI. It's not the end goal.
  - yters 8y ago
    From the website: "About OpenAI: OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence." At any rate, the bigger question is whether AGI is even possible. Why does no one even take a stab at answering this question? We have all these well funded research institutes that just assume AGI is possible, and we could just be throwing all the money down a hole.
    - jononor 8y ago
      Considering the commercial benefits of non-AGI machine learning, the money is not really thrown away. And many of the potential negative effects of AI can occur even without AGI, so those are worth researching too.
      - yters 8y ago
        I'd be curious to see just how large the commercial benefits of standard ML actually are. The only reason it is hyped right now is because the media is leading people to believe something close to AGI is right around the corner, because we can bruteforce Go and index a million image dataset... Anyways, all the AI/ML hype is generated not by actual commercial value, but implied AGI. So, it would behoove us to question the underlying assumption that AGI is actually possible. After all, it is the scientific thing to do.
- drdeca 8y ago
  Solomonoff induction is for just making predictions/models, AIXI is for when it needs to also take actions. Both AIXI and solomonoff induction can be approximated arbitrarily closely, given sufficient computational resources. Human intelligence is generally expected to be computable because physics is generally believed to be computable. However, don't get me wrong: I would be quite excited and probably pleased to learn that it isn't. I just am not convinced enough (or really, convinced much at all) to hang anything important on the idea that it isn't computable.
  - yters 8y ago
    Even given infinite resources Solomonoff induction cannot be computed, because we computationally cannot eliminate all non halting programs. Why believe human intelligence is limited by physics?
    - xelxebar 8y ago
      Because elan vital and phlogiston turned out to be terrible ideas? Human cognition affects human action which has physical effects. So at the least, intelligence is causally linked with known phycial processes. I'd say that's sufficient to make it amenable to physical inquiry. Pulling out drastic measures like extraphysical magic, just to hand wave at an imprecise problem seems like an act of epistemic violence or something.
      - yters 8y ago
        Certainly intelligence is causally linked with the physical world, hence our conversation. But, this does not imply intelligence is itself physical. Additionally, saying intelligence is non physical does not turn it into some kind of inscrutable magic. Could you explain why you think this is the case? For example, I wrote an article explaining how modeling the human mind as a halting oracle results in empirically meaningful results. https://am-nat.org/site/halting-oracles-as-intelligent-agents/ https://am-nat.org/site/halting-oracles-as-intelligent-agent...
        8y ago
nerdponx 8y ago
From the abstract of the article they linked: Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods. Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches. Seems like a really interesting unification of the wide variety of techniques out there in statistics and machine learning, analogous to the "everything is a computation graph, as long as it's differentiable" revolution. I like it when this kind of thing has its day. Would be interesting to see how well it works non-robotics problems.
- deleted 8y ago
  [deleted]
- BucketSort 8y ago
  See Yann's tutorial on EBM: http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf We actually ran into them when doing research in our startup. It is a really powerful perspective.
  - lucidrains 8y ago
    Thank you for this!
- eli_gottlieb 8y ago
  >Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches. Can someone explain to me what the major difference is between energy-based models and variational Bayes approximations, which throw out calculating the normalization constant and switch to maximizing the log joint probability of the data and model?
IIAOPSW 8y ago
Setting up some energy function and then finding the lowest energy state sounds a lot like adibatic quantum computing. Assuming this research lives up to the hype, quantum computers might be able to run this algorithm faster. Quantum machine learning is already a thing, but its nice to see it fit so congruently with a classical counterpart.
- discoball 8y ago
  Quantum Simulated Annealing?
arashout33 8y ago
I have no idea what's going on in this article. Is there a good resource or video for understanding this stuff?

166 points by stablemap 8y ago

Every new technique is able to "quickly learn X." Something most not be so quick, otherwise why aren't these techniques turning into AGI? I think the problem is the goal is not well defined. So, increased velocity has no bearing on increased velocity towards the target. A side question, why is there no research into whether human intelligence is computable? The assumption in AI is that human intelligence is computable, but I've never seen any good argument or evidence that this is true. Seems very unscientific to exert so much energy into this research direction without validating the fundamental assumption. For example, the one instance I know of that defines AGI in a quantitative manner is Solomonoff induction (SI), but it is not computable. If SI is representative of human intelligence, then AGI is impossible.

andbberger 8y ago
That's just marketing. Also that assumes that the goal is some general AI. We're just getting started here
- yters 8y ago
  I assume the goal is to achieve something useful. If AGI is impossible, then much more useful than these incremental steps is to figure out how to most effectively combine human cognition and computational systems.
  - andbberger 8y ago
    > then much more useful than these incremental steps is to figure out how to most effectively combine human cognition and computational systems. You don't know that
    - yters 8y ago
      And you don't know AGI is possible. Both are options, and we should first do some kind of basic research to determine which is a better route before dumping billions of dollars in one direction. Or, at very least we should dump money in both directions, and tune towards which is getting better real world gains.
      - andbberger 8y ago
        Obviously it's possible, we're having this conversation aren't we?
        cambalache 8y ago
        In the sense that the "Artificial" part of AGI means: created by humans, we do not know if it is possible.
        andbberger 8y ago
        I mean... what's so special about biology? How about I flip a few base pairs here and there, artificial enough yet? We already routinely genetically engineer mice and other model animals such that particular bits of their brain will either glow or fire in response to laser light, we have the technology today to put make little mice helmets that we can use to steer mice around - just need to do a bit more research to find out what particular bit of the (probably) hippocampus to stimulate. Is that artificial enough? OK and how about just simulating the universe? There is a legitimate question about computability there - it does seem plausible that aspects of simulating the universe could be uncomputable. Suppose that this is case - well it's still an open question as to whether or not this spells doom for the simulation route. The uncomputable bits are going to be some quantum this or that, and it's not at all clear that such low-level bits are fundamentally required for human-level intelligence; that the high-level process of intelligence is inseparable from the underlying processes which give rise to human intelligence. Personally, I find it a highly unlikely that intelligence is inseparable/has no reduced model, for whatever my prognostication is worth. BUT even if it is inseparable, there's still a strong argument to be made that you could construct AGI through means of so-called 'embodied computation', just like biology does.
jerf 8y ago
Solomonoff induction is not representative of human intelligence. At least, speaking for myself, I do not exhaustively search literally every hypothesis and match it against my data. Your mileage may vary. Dunno. It's a diverse world out there, right?
- yters 8y ago
  Solomonoff induction is a way to quantify the human ability to induce general principles from limited observations. All the computable methods are unable to achieve this ability.
  - jerf 8y ago
    With respect, no, that is not what it is. My summary is glib and phrased in non-mathematical terms, but closer. A true Solomonoff Inductor would be wildly, wildly smarter than a human being, if it could get over the problem that such a machine would also consume super-exponentially more resources than the universe has.
    - yters 8y ago
      It's not a matter of resources. Solomonoff induction is not computable, since it needs to calculate the Kolmogorov complexity of the data. If that is what is required for induction, it is surprising that humans are able to do so well at identifying concise descriptions of the data we observe. This seems inexplicable with a computational view of human cognition.
    - yters 8y ago
      Disregard, I realized my error is that the elegant program is guaranteed to halt at some point, which gives us the Kolmogorov complexity. We just will never know when that happens, even with infinite resources.
- Xcelerate 8y ago
  > I do not exhaustively search literally every hypothesis and match it against my data Haha. My problem is that my brain attempts to do this but can’t, which just leads to analysis paralysis instead.
pickdenis 8y ago
> why aren't these techniques turning into AGI? What prompted you to even ask this question? Where in the article does it say that "These results are step N on the path to AGI!"? This is research: the researchers found a problem that had limited solutions before (learning concepts with limited examples) and came up with a better solution. It's not clear why you're questioning this. > [on the computability of human intelligence] This is just silly. Even if human intelligence was beyond the reach of silicon (which I and many real researchers doubt), this work is still useful even if it doesn't result in AGI. You're too fixated on AGI. It's not the end goal.
- yters 8y ago
  From the website: "About OpenAI: OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence." At any rate, the bigger question is whether AGI is even possible. Why does no one even take a stab at answering this question? We have all these well funded research institutes that just assume AGI is possible, and we could just be throwing all the money down a hole.
  - jononor 8y ago
    Considering the commercial benefits of non-AGI machine learning, the money is not really thrown away. And many of the potential negative effects of AI can occur even without AGI, so those are worth researching too.
    - yters 8y ago
      I'd be curious to see just how large the commercial benefits of standard ML actually are. The only reason it is hyped right now is because the media is leading people to believe something close to AGI is right around the corner, because we can bruteforce Go and index a million image dataset... Anyways, all the AI/ML hype is generated not by actual commercial value, but implied AGI. So, it would behoove us to question the underlying assumption that AGI is actually possible. After all, it is the scientific thing to do.
drdeca 8y ago
Solomonoff induction is for just making predictions/models, AIXI is for when it needs to also take actions. Both AIXI and solomonoff induction can be approximated arbitrarily closely, given sufficient computational resources. Human intelligence is generally expected to be computable because physics is generally believed to be computable. However, don't get me wrong: I would be quite excited and probably pleased to learn that it isn't. I just am not convinced enough (or really, convinced much at all) to hang anything important on the idea that it isn't computable.
- yters 8y ago
  Even given infinite resources Solomonoff induction cannot be computed, because we computationally cannot eliminate all non halting programs. Why believe human intelligence is limited by physics?
  - xelxebar 8y ago
    Because elan vital and phlogiston turned out to be terrible ideas? Human cognition affects human action which has physical effects. So at the least, intelligence is causally linked with known phycial processes. I'd say that's sufficient to make it amenable to physical inquiry. Pulling out drastic measures like extraphysical magic, just to hand wave at an imprecise problem seems like an act of epistemic violence or something.
    - yters 8y ago
      Certainly intelligence is causally linked with the physical world, hence our conversation. But, this does not imply intelligence is itself physical. Additionally, saying intelligence is non physical does not turn it into some kind of inscrutable magic. Could you explain why you think this is the case? For example, I wrote an article explaining how modeling the human mind as a halting oracle results in empirically meaningful results. https://am-nat.org/site/halting-oracles-as-intelligent-agents/ https://am-nat.org/site/halting-oracles-as-intelligent-agent...
      - 8y ago

nerdponx 8y ago

From the abstract of the article they linked: Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods. Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches. Seems like a really interesting unification of the wide variety of techniques out there in statistics and machine learning, analogous to the "everything is a computation graph, as long as it's differentiable" revolution. I like it when this kind of thing has its day. Would be interesting to see how well it works non-robotics problems.

deleted 8y ago
[deleted]
BucketSort 8y ago
See Yann's tutorial on EBM: http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf We actually ran into them when doing research in our startup. It is a really powerful perspective.
- lucidrains 8y ago
  Thank you for this!
eli_gottlieb 8y ago
>Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches. Can someone explain to me what the major difference is between energy-based models and variational Bayes approximations, which throw out calculating the normalization constant and switch to maximizing the log joint probability of the data and model?

IIAOPSW 8y ago

Setting up some energy function and then finding the lowest energy state sounds a lot like adibatic quantum computing. Assuming this research lives up to the hype, quantum computers might be able to run this algorithm faster. Quantum machine learning is already a thing, but its nice to see it fit so congruently with a classical counterpart.

discoball 8y ago
Quantum Simulated Annealing?

arashout33 8y ago

I have no idea what's going on in this article. Is there a good resource or video for understanding this stuff?