6 ms·

Just for posterity, to see if I was completely bonkers in 2018: I believe that it is possible to realize AGI today, using currently available hardware, if we ju

by marvin 8y ago

Just for posterity, to see if I was completely bonkers in 2018: I believe that it is possible to realize AGI today, using currently available hardware, if we just knew enough of the principles to create the right software. Novel computational concepts have often been demonstrated on very old hardware, with the full knowledge of the tricks required to make it work. Often, more powerful hardware was required in order to pionéer the technology, and often the proof-of-concept on older hardware is too slow and clunky to have been a compelling product. But it's often been physically possible for longer than people realize. I've never made a "long game" statement like this before, so it'll be interesting to read this comment about what I thought before in 2038 or 2048, if it still exists then.

andbberger 8y ago
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 :)
Florin_Andrei 8y ago
I think we're still lacking the conceptual framework, the grand scheme if you will. AGI probably requires some kind of hierarchy of integration, and current AI is only the bottom level. We probably need to build a heck of a lot of levels on top of that, each one doing more complex integration, and likely some horizontal structure as well, with various blocks coordinating each other.
- jbattle 8y ago
  That's my intuition as well. I'd expect AGI to arise once we put together enough various tools into an integrated whole. So for classification of sensory input, you'd build in a CNN or the like. You'd build in something else (RNN?) for NLP. You'd use something else for planning (which might also work as the highest level controller determining "what sort of tool should I apply to solve goal X?"
- ilaksh 8y ago
  There are many hierarchical AI systems though. For example Hierarchical Hidden Markov models. Pretty sure Deep Mind made a RL system a bit like that. Also reminds me of Ogmai AIs technology.
  - Florin_Andrei 8y ago
    Well, a scooter and the Falcon Heavy are both "vehicles", but that doesn't mean the scooter will ever do what the rocket does.
- marvin 8y ago
  I wonder what this would look like? What would be some ways one could connect different types of AI systems in a loose and fuzzy way so that they can use each others' output in a meaningful way? I like this line of reasoning, you're basically stating that we have found effective ways of emulating some of the sensory parts of a central nervous system. Which seems intuitively right; we can classify things and use outputs from this classification in some pre-determined ways, but there's no higher-level reasoning.
Ngunyan 8y ago
I am of the same opinion..
mbrock 8y ago
I basically think software intelligence will not resemble human intelligence until it is embodied in the same body and social upbringing as humans—like, I don’t think you can actually fake it. I think you can make a software intelligence that is very intelligent in its own kind of strange alien way. But as far as involving it in human concerns, it will not really understand the human world, even if we try to make it seem like it does. A robot lawyer might be very intelligent, but it will fail Turing tests. Trying to make it pass Turing tests without giving it a human body and upbringing will amount to patching a fundamentally false system and hoping for temporary illusions. This opinion is informed by arguments made by Hubert Dreyfus stemming ultimately from a Heideggerian perspective.
- nwah1 8y ago
  But when people talk about a "superintelligence" they are not talking about creating an AI lawyer. They are talking about creating a recursively self-improving intelligent system that attempts to make use of all available resources as efficiently as possible in service of its utility function. You could envision a very intelligent but socially retarded system able to produce products more cheaply than anyone else, and thereby engage in trade and acquire resources. You could also just envision a system that ignores human morality and simply appropriates matter, space, and energy for its own purposes irrespective of what humans consider to be their property. As such, the Turing Test is fairly irrelevant.
s1dechnl 8y ago
You're not bonkers. Hardware is already at capability especially in scaled form. It largely comes down to a cohesive and broad ranging software package. This interestingly goes against the profit motivations of various groups which is why you don't hear it framed as much. Of course, when the current hype cycle is tapped, you'll start hearing the rhetoric change.
iotb 8y ago
It mainly comes down to the software and algorithmic techniques. It's not something many people want to hear as there is a lot of money and effort placed on an alternative framing. If you believe it takes massive amount of hardware/compute, it favors the current billion dollar lucrative business of cloud computing at scale. If you are made to believe it takes 100s of top ranking PhDs because its so incredibly complex, you are more likely to value the groups pursuing it higher. If the techniques and white papers are all a convoluted mess of spaghetti, one becomes of the belief that this is an unworldly pursuit. It is none of these things however. The hardware is already capable. The fundamental techniques that are popular and theories are the flawed. Essentially, you need to start over as Geoffrey Hinton said. Something no one wants to put effort into doing or fund. So, indeed AGI is here today. It's just not within the frame of the populist efforts. An capable individual with the freedom of doing deep pondering and constructions w/o any outside influence is more likely to crack this puzzle than a room of PhDs all systemically subscribed to the same fundamentally flawed approach of Optimization algorithms. As far as the compute goes, does anyone even truly spend the time to understand it anymore in this day and age of frameworks on frameworks? I mean truly understand it? And there lies to other fundamental problem. How can one make broad statements about the computational requirements of AGI when the most they know about the underlying hardware is an AWS config file? Swaths of the industry have shut themselves out from ever developing AGI. Sadly, they're the groups w/ the most funding and backing because they represent the same flawed mainstream ideology as every other AI group. It will be an outsider and the core theoretical approach is already resolved.
- andbberger 8y ago
  > Essentially, you need to start over as Geoffrey Hinton said. Oh come on, he says this every couple of years... Bengio made a meme about it... [1] [1] https://www.youtube.com/watch?v=mlXzufEk-2E https://www.youtube.com/watch?v=mlXzufEk-2E
  - iotb 8y ago
    He says it. Others have done it years ago.
- 8y ago
selestify 8y ago
> Novel computational concepts have often been demonstrated on very old hardware What are some examples of this?
- bgrayland 8y ago
  Demoscene output?
zshrdlu 8y ago
You're essentially restating the Physical Symbol System Hypothesis, formulated by the founding fathers of AI (Herbert Simon and Allen Newell). http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPapers/pssh.pdf http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPap...

by marvin 8y ago

andbberger 8y ago

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 :)

Florin_Andrei 8y ago

I think we're still lacking the conceptual framework, the grand scheme if you will. AGI probably requires some kind of hierarchy of integration, and current AI is only the bottom level. We probably need to build a heck of a lot of levels on top of that, each one doing more complex integration, and likely some horizontal structure as well, with various blocks coordinating each other.

jbattle 8y ago
That's my intuition as well. I'd expect AGI to arise once we put together enough various tools into an integrated whole. So for classification of sensory input, you'd build in a CNN or the like. You'd build in something else (RNN?) for NLP. You'd use something else for planning (which might also work as the highest level controller determining "what sort of tool should I apply to solve goal X?"
ilaksh 8y ago
There are many hierarchical AI systems though. For example Hierarchical Hidden Markov models. Pretty sure Deep Mind made a RL system a bit like that. Also reminds me of Ogmai AIs technology.
- Florin_Andrei 8y ago
  Well, a scooter and the Falcon Heavy are both "vehicles", but that doesn't mean the scooter will ever do what the rocket does.
marvin 8y ago
I wonder what this would look like? What would be some ways one could connect different types of AI systems in a loose and fuzzy way so that they can use each others' output in a meaningful way? I like this line of reasoning, you're basically stating that we have found effective ways of emulating some of the sensory parts of a central nervous system. Which seems intuitively right; we can classify things and use outputs from this classification in some pre-determined ways, but there's no higher-level reasoning.

Ngunyan 8y ago

I am of the same opinion..

mbrock 8y ago

I basically think software intelligence will not resemble human intelligence until it is embodied in the same body and social upbringing as humans—like, I don’t think you can actually fake it. I think you can make a software intelligence that is very intelligent in its own kind of strange alien way. But as far as involving it in human concerns, it will not really understand the human world, even if we try to make it seem like it does. A robot lawyer might be very intelligent, but it will fail Turing tests. Trying to make it pass Turing tests without giving it a human body and upbringing will amount to patching a fundamentally false system and hoping for temporary illusions. This opinion is informed by arguments made by Hubert Dreyfus stemming ultimately from a Heideggerian perspective.

nwah1 8y ago
But when people talk about a "superintelligence" they are not talking about creating an AI lawyer. They are talking about creating a recursively self-improving intelligent system that attempts to make use of all available resources as efficiently as possible in service of its utility function. You could envision a very intelligent but socially retarded system able to produce products more cheaply than anyone else, and thereby engage in trade and acquire resources. You could also just envision a system that ignores human morality and simply appropriates matter, space, and energy for its own purposes irrespective of what humans consider to be their property. As such, the Turing Test is fairly irrelevant.

s1dechnl 8y ago

You're not bonkers. Hardware is already at capability especially in scaled form. It largely comes down to a cohesive and broad ranging software package. This interestingly goes against the profit motivations of various groups which is why you don't hear it framed as much. Of course, when the current hype cycle is tapped, you'll start hearing the rhetoric change.

iotb 8y ago

It mainly comes down to the software and algorithmic techniques. It's not something many people want to hear as there is a lot of money and effort placed on an alternative framing. If you believe it takes massive amount of hardware/compute, it favors the current billion dollar lucrative business of cloud computing at scale. If you are made to believe it takes 100s of top ranking PhDs because its so incredibly complex, you are more likely to value the groups pursuing it higher. If the techniques and white papers are all a convoluted mess of spaghetti, one becomes of the belief that this is an unworldly pursuit. It is none of these things however. The hardware is already capable. The fundamental techniques that are popular and theories are the flawed. Essentially, you need to start over as Geoffrey Hinton said. Something no one wants to put effort into doing or fund. So, indeed AGI is here today. It's just not within the frame of the populist efforts. An capable individual with the freedom of doing deep pondering and constructions w/o any outside influence is more likely to crack this puzzle than a room of PhDs all systemically subscribed to the same fundamentally flawed approach of Optimization algorithms. As far as the compute goes, does anyone even truly spend the time to understand it anymore in this day and age of frameworks on frameworks? I mean truly understand it? And there lies to other fundamental problem. How can one make broad statements about the computational requirements of AGI when the most they know about the underlying hardware is an AWS config file? Swaths of the industry have shut themselves out from ever developing AGI. Sadly, they're the groups w/ the most funding and backing because they represent the same flawed mainstream ideology as every other AI group. It will be an outsider and the core theoretical approach is already resolved.

andbberger 8y ago
> Essentially, you need to start over as Geoffrey Hinton said. Oh come on, he says this every couple of years... Bengio made a meme about it... [1] [1] https://www.youtube.com/watch?v=mlXzufEk-2E https://www.youtube.com/watch?v=mlXzufEk-2E
- iotb 8y ago
  He says it. Others have done it years ago.
8y ago

selestify 8y ago

> Novel computational concepts have often been demonstrated on very old hardware What are some examples of this?

bgrayland 8y ago
Demoscene output?

zshrdlu 8y ago

You're essentially restating the Physical Symbol System Hypothesis, formulated by the founding fathers of AI (Herbert Simon and Allen Newell). http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPapers/pssh.pdf http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPap...