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I don't see how you could arrive at that conclusion with any certainty. There is a computability argument - part of your statement implies the assumption that

I don't see how you could arrive at that conclusion with any certainty. There is a computability argument - part of your statement implies the assumption that 'AGI' is computable. I would be inclined to agree in this aspect, I think a preponderance is required before we start seriously considering that intelligence is uncomputable. After all, last time I checked the jury was still out as to whether super-Turing machines are physically realizable. So let's suppose for the sake of argument intelligence is computable. Well then in principle 'AGI' has been 'realizable' since mid-last century when we first constructed Turing machines. With the caveat that it may take the lifetime of the universe to classify an image of a cat. It is quite conceivable that, although Turing-computable, 'AGI' requires taking expectations over such large spaces that it won't really be 'realizable' until hardware improves by another few orders of magnitude.

drcode 8y ago
I think it's clear an important element of AGI simply involves traversing large search spaces: This is essentially what deep neural nets are doing with their data as they train and return classification results... and it's not unreasonable to think they can already do this with a performance on par with the human brain. The problem is that there's a lot of additional "special sauce" we're missing to turn these classification engines into AGI, and we don't have a clue if this "special sauce" is computationally intensive or not. I'm guessing the answer is "no" since the human cortex seems so uniform in structure and therefore it seems to me it is mostly involved in this rather pedestrian search part, not the "special sauce" part. (disclosure: I'm not a neuroscientist or AI researcher)
- andbberger 8y ago
  Actually I think there's good reason to think the opposite; that the "special sauce" missing in current machine learning approaches will in fact be very computationally intensive. I am thinking of two things in particular that current ML approaches lack; that are ubiquitous (well arguably for the second...) in neuroscience: feedback and modeling uncertainty A proper Bayesian approach to uncertainty will be akin to an expectation over models; that's an extra order of magnitude. Feedback is also likely to be expensive. Currently all we really know how to do is 'unroll' feedback over time and proceed as normal for feedforward networks. Also keep in mind that inference and training are pretty different things; inference is fast, but training takes a lifetime. > and it's not unreasonable to think they can already do this with a performance on par with the human brain. I disagree for the reasons stated above; we know we're missing a big piece because of the lack of feedback and modeling of uncertainty. I do happen to be a neuroscientist and an ML researcher... but I think that just means I am slightly more justified in making wild prognostications... which is totally what this is... But ultimately I'm still just some schmuck, why should you believe me? Nothing I have said in this comment can be considered scientific fact; we just don't know. But I have a feeling...
  - naasking 8y ago
    > I am thinking of two things in particular that current ML approaches lack; that are ubiquitous (well arguably for the second...) in neuroscience: feedback and modeling uncertainty You'll be interested in a recent post about a new paper for an AI physicist then: https://news.ycombinator.com/item?id=18381827 https://news.ycombinator.com/item?id=18381827
marvin 8y ago
Yes, there is of course nothing certain about my statement -- it is a purely speculative guess based on the computational power of hardware available in the world today, and that we are already having some success at using biologically-inspired techniques for classification, which constitutes part of acting intelligently in a complex environment. Sometimes, it's fun to make wild guesses. Note that there's nothing in this guess about the scale required, but I'm imagining something smaller than the Manhattan project. Maybe something on the scale of a whole AWS datacenter or something like that. An important factor is the computational power needed to emulate the essence of an intelligent entity that exists in the physical world today (i.e. the human brain & sensory system), as you're getting at in your comment about hardware capability. (That a human-equivalent or better machine for performing complex tasks in the real world is realizable with Turing-computable algorithms seems more or less guaranteed to me, unless there are physical processes happening in biological humans that are not).
- andbberger 8y ago
  > (That a human-equivalent or better machine for performing complex tasks in the real world is realizable with Turing-computable algorithms seems more or less guaranteed to me, unless there are physical processes happening in biological humans that are not) 'Guaranteed' sounds a little too strong to me. We just don't know yet. Also... > we are already having some success at using biologically-inspired techniques for classification I presume you are referring to CNNs, which in good-faith can only very _very_ loosely be characterized as biologically-inspired. People are constantly trying to draw comparisons (see the 'look the brain be like CNNs' papers that show up at NIPS every year) and I wish they would stop... certainly there are some superficial similarities but... it's more a case of convergent evolution than anything, there is no deep connection to be unraveled... I don't think there's any deep insight belying CNNs beyond 'wow building your data's symmetries into your model works great!'. Which is like a pretty obvious and fundamental thing imo...
  - marvin 8y ago
    > 'Guaranteed' sounds a little too strong to me I did say 'practically guaranteed'. Let's say (for the sake of the argument) there's less than 5% probability there's something yet undiscovered about animal brains/nervous systems, that has qualitatively different computational properties than the current theory of computation says is possible in the physical world. Does this sound excessively optimistic to you? I would be delighted to be proved wrong on this; it would be like the theory of relatively suddenly shook up Newtonian physics, in the computational domain. But I'm not aware of any evidence that our models of computability have any holes. If you know of any, please let me know! I am a curious skeptic at heart :)

drcode 8y ago

I think it's clear an important element of AGI simply involves traversing large search spaces: This is essentially what deep neural nets are doing with their data as they train and return classification results... and it's not unreasonable to think they can already do this with a performance on par with the human brain. The problem is that there's a lot of additional "special sauce" we're missing to turn these classification engines into AGI, and we don't have a clue if this "special sauce" is computationally intensive or not. I'm guessing the answer is "no" since the human cortex seems so uniform in structure and therefore it seems to me it is mostly involved in this rather pedestrian search part, not the "special sauce" part. (disclosure: I'm not a neuroscientist or AI researcher)

andbberger 8y ago
Actually I think there's good reason to think the opposite; that the "special sauce" missing in current machine learning approaches will in fact be very computationally intensive. I am thinking of two things in particular that current ML approaches lack; that are ubiquitous (well arguably for the second...) in neuroscience: feedback and modeling uncertainty A proper Bayesian approach to uncertainty will be akin to an expectation over models; that's an extra order of magnitude. Feedback is also likely to be expensive. Currently all we really know how to do is 'unroll' feedback over time and proceed as normal for feedforward networks. Also keep in mind that inference and training are pretty different things; inference is fast, but training takes a lifetime. > and it's not unreasonable to think they can already do this with a performance on par with the human brain. I disagree for the reasons stated above; we know we're missing a big piece because of the lack of feedback and modeling of uncertainty. I do happen to be a neuroscientist and an ML researcher... but I think that just means I am slightly more justified in making wild prognostications... which is totally what this is... But ultimately I'm still just some schmuck, why should you believe me? Nothing I have said in this comment can be considered scientific fact; we just don't know. But I have a feeling...
- naasking 8y ago
  > I am thinking of two things in particular that current ML approaches lack; that are ubiquitous (well arguably for the second...) in neuroscience: feedback and modeling uncertainty You'll be interested in a recent post about a new paper for an AI physicist then: https://news.ycombinator.com/item?id=18381827 https://news.ycombinator.com/item?id=18381827

marvin 8y ago

Yes, there is of course nothing certain about my statement -- it is a purely speculative guess based on the computational power of hardware available in the world today, and that we are already having some success at using biologically-inspired techniques for classification, which constitutes part of acting intelligently in a complex environment. Sometimes, it's fun to make wild guesses. Note that there's nothing in this guess about the scale required, but I'm imagining something smaller than the Manhattan project. Maybe something on the scale of a whole AWS datacenter or something like that. An important factor is the computational power needed to emulate the essence of an intelligent entity that exists in the physical world today (i.e. the human brain & sensory system), as you're getting at in your comment about hardware capability. (That a human-equivalent or better machine for performing complex tasks in the real world is realizable with Turing-computable algorithms seems more or less guaranteed to me, unless there are physical processes happening in biological humans that are not).

andbberger 8y ago
> (That a human-equivalent or better machine for performing complex tasks in the real world is realizable with Turing-computable algorithms seems more or less guaranteed to me, unless there are physical processes happening in biological humans that are not) 'Guaranteed' sounds a little too strong to me. We just don't know yet. Also... > we are already having some success at using biologically-inspired techniques for classification I presume you are referring to CNNs, which in good-faith can only very _very_ loosely be characterized as biologically-inspired. People are constantly trying to draw comparisons (see the 'look the brain be like CNNs' papers that show up at NIPS every year) and I wish they would stop... certainly there are some superficial similarities but... it's more a case of convergent evolution than anything, there is no deep connection to be unraveled... I don't think there's any deep insight belying CNNs beyond 'wow building your data's symmetries into your model works great!'. Which is like a pretty obvious and fundamental thing imo...
- marvin 8y ago
  > 'Guaranteed' sounds a little too strong to me I did say 'practically guaranteed'. Let's say (for the sake of the argument) there's less than 5% probability there's something yet undiscovered about animal brains/nervous systems, that has qualitatively different computational properties than the current theory of computation says is possible in the physical world. Does this sound excessively optimistic to you? I would be delighted to be proved wrong on this; it would be like the theory of relatively suddenly shook up Newtonian physics, in the computational domain. But I'm not aware of any evidence that our models of computability have any holes. If you know of any, please let me know! I am a curious skeptic at heart :)