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A machine will never get the meaning of a word, unlike a very small child. I am simply amazed by the fact that a child can learn a language, catch what a questi

A machine will never get the meaning of a word, unlike a very small child. I am simply amazed by the fact that a child can learn a language, catch what a question is, offer an answer, say no (and how they like to say no), and all. As much as I wish it was possible, that much I believe it's not. The best we can do is put our knowledge of our ability to infer meaning of words into machine code.

hanspeter 11y ago
What makes the human brain not a machine?
- coldtea 11y ago
  Physical properties? What if those kind of properties of physical materials are needed in cognition? The problem is simulations of the brain are not "machines", they are algorithms, e.g. they assume everything is happening at the information processing level. To use your own example, we can design an algorithm to simulate making coffee. But the algorithm can never make coffee -- unless it's fitted and connected to a coffee making apparatus. Or take something being "wet" for example. We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc). If something depends on it, e.g. the emulation actually watering some actual flowers, then it will fail. An emulation can only water emulated flowers.
  - kylebrown 11y ago
    > The problem is simulations of the brain are not "machines", they are algorithms, Simulations are executed on concrete machines that exist in the real world. Algorithms are abstract concepts. > e.g. they assume everything is happening at the information processing level. Everything does happen at the information processing level. Any kind of physical process can be seen as a type of information processing. Information processing is not an abstract concept like an algorithm, for it to occur requires the time-evolution of concrete physical processes. > We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc). The physical property is experienced as sensory input. Machines can have sensory input. > An emulation can only water emulated flowers. You are asserting that virtual reality is different from reality, which is true. That's not the GP's question. The question is whether there is a fundamental difference between machines in the real world (with sensors and arms and so on) and the human body and brain.
    - ux-app 11y ago
      > The question is whether there is a fundamental difference between machines in the real world (with sensors and arms and so on) and the human body and brain. This is pure philosophy, as no one yet knows the answers, but what if brain-like intelligence is an emergent property of non-deterministic processes? Wouldn't it then follow that a classical computer could not be able to compute the "think function" before the heat death of the universe? personally my intuition says that strong AI cannot be encoded in silicon, or that it is a victim of the halting problem. I think we need a different substrate on which to model cognition. Or maybe not. Who knows?
      - tim333 11y ago
        It was pure philosophy but it becoming less so as we make things like cochlear implants that replace some neural circuitry with electronics.
seiji 11y ago
> A machine will never get the meaning of a word, unlike a very small child. Why not? Your brain isn't magic, just highly associative. We can do the same thing with computers real soon now.
- panic 11y ago
  We can do the same thing with computers real soon now. Haven't people been saying this for decades? AI has a long history of impressive results, but somehow none of them have actually produced "thought". Nobody even understands how the brain "thinks" at a neural level, let alone how to model that. All we can do at this point is try different models (which way or may not actually match reality) and hope we find one that works. But there's no evidence that we'll find a working model "real soon now". Impressive results that we can kinda-sorta imagine being the product of an intelligent system haven't historically been enough.
  - seiji 11y ago
    > saying this for decades? A handful of years ago I put together a computer fully loaded that gave me 1 teraflop of commuting power. Today I can put together a computer the same size that will give me 32 to 50 teraflops of programmable computing power. Many of the "AI" advances since 2007 are just running old 1970s-1990s AI algorithms on faster and faster and more parallel hardware. If you have to train a model for a few hundred trillion instructions, but your CPU only does 20 operations per second (and you have to share it with 1,000 other people), you can't iterate your science fast enough to make progress. Now we can iterate our science almost too quickly. > how the brain "thinks" at a neural level, Planes don't fly like birds. Birds don't fly like bees. True AI doesn't have to replicate mammalian (or avian or reptilian) neural topology.
    - harperlee 11y ago
      Upvote for "Birds don't fly like bees", I like the freshness of not merely pointing out that "humans can do better", but that in effect there are several paths that avoid different constraints to get to the same point!
btown 11y ago
I often wonder if the gap is that we're so caught up on training our networks on vision and text that we're ignoring the fact that living beings have a sense of time and reward as part of their input. A child knows that if it says "Mama food," it is likely to get attention, and if it gets attention, it is likely to minimize its hunger. Right now, a neural network can be trained to know that "Mama" occurs often in human dialogue, what words occur around it, even its dictionary definition and images of mothers. But it's not making the deeper connection to a strategy that minimizes hunger. When I think about this, I wonder if insights from the world of gaming "AI" would be useful in developing the training datasets for real AI. Because you can't be a mother to a billion virtual babies, but you might be able to program a set of heuristics to be a mother to a billion virtual babies. Then you have some system that trains on their life experiences...? All speculation, but very interesting stuff.
- kastnerkyle 11y ago
  There is a huge amount of research which is combining the power of deep learning for automated feature extraction with reinforcement learning for learning "natural reward signals" without label information. See any of the recent papers from Google DeepMind, such as [1] or their most recent work which is startlingly good [2] [1] http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.nature.com/nature/journal/v518/n7540/full/nature1... [2] http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-i...
  - btown 11y ago
    I'd forgotten about that! The "game" needs to get much more complex to simulate life, of course. Now I wonder if they could throw that infrastructure at Minecraft survival mode...
NathanKP 11y ago
So far scientists haven't found anything special about the human brain that can't be mimicked by a machine. Given enough neural connections, and a large enough data set, and a long enough training period there is no reason to think that a machine can't do everything a human brain can do. Put another way there is nothing magical about a child learning about the world. A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns.
- varelse 11y ago
  Not really, there are clearly epigenetic changes to neuron DNA w/r to memory formation and I don't think anyone has estimated what kind of computational firepower that represents. Second, the 3D topology of a neuron is IMO more complex than reducing it to an FP32 activation threshold (all IMO of course). Finally, I have to admit as a former biologist, I'm intrigued by microtubule activity and it seems like Dileep George and even Geoffrey Hinton are heading towards smarter but fewer neurons as opposed to just increasing the neuron count. Not surprisingly, the deep learning digerati are resisting this notion mightily just like the SVM peeps harped on neural networks until they kicked them in the keester. TLDR: It's still early, and I'm biased that there are some interesting twists and turns yet to unfold here.
  - deleted 11y ago
    [deleted]
  - alextgordon 11y ago
    Also, brain chemistry. You can't give psychoactive drugs to a circuit board...
    - zaroth 11y ago
      I'll bet in a matter of time we will find that indeed you can give something quite analogous to "psychoactive drugs" to a strong AI!
gnaritas 11y ago
> A machine will never get the meaning of a word That's an irrational and indefensible position.

hanspeter 11y ago

What makes the human brain not a machine?

coldtea 11y ago
Physical properties? What if those kind of properties of physical materials are needed in cognition? The problem is simulations of the brain are not "machines", they are algorithms, e.g. they assume everything is happening at the information processing level. To use your own example, we can design an algorithm to simulate making coffee. But the algorithm can never make coffee -- unless it's fitted and connected to a coffee making apparatus. Or take something being "wet" for example. We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc). If something depends on it, e.g. the emulation actually watering some actual flowers, then it will fail. An emulation can only water emulated flowers.
- kylebrown 11y ago
  > The problem is simulations of the brain are not "machines", they are algorithms, Simulations are executed on concrete machines that exist in the real world. Algorithms are abstract concepts. > e.g. they assume everything is happening at the information processing level. Everything does happen at the information processing level. Any kind of physical process can be seen as a type of information processing. Information processing is not an abstract concept like an algorithm, for it to occur requires the time-evolution of concrete physical processes. > We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc). The physical property is experienced as sensory input. Machines can have sensory input. > An emulation can only water emulated flowers. You are asserting that virtual reality is different from reality, which is true. That's not the GP's question. The question is whether there is a fundamental difference between machines in the real world (with sensors and arms and so on) and the human body and brain.
  - ux-app 11y ago
    > The question is whether there is a fundamental difference between machines in the real world (with sensors and arms and so on) and the human body and brain. This is pure philosophy, as no one yet knows the answers, but what if brain-like intelligence is an emergent property of non-deterministic processes? Wouldn't it then follow that a classical computer could not be able to compute the "think function" before the heat death of the universe? personally my intuition says that strong AI cannot be encoded in silicon, or that it is a victim of the halting problem. I think we need a different substrate on which to model cognition. Or maybe not. Who knows?
    - tim333 11y ago
      It was pure philosophy but it becoming less so as we make things like cochlear implants that replace some neural circuitry with electronics.

seiji 11y ago

> A machine will never get the meaning of a word, unlike a very small child. Why not? Your brain isn't magic, just highly associative. We can do the same thing with computers real soon now.

panic 11y ago
We can do the same thing with computers real soon now. Haven't people been saying this for decades? AI has a long history of impressive results, but somehow none of them have actually produced "thought". Nobody even understands how the brain "thinks" at a neural level, let alone how to model that. All we can do at this point is try different models (which way or may not actually match reality) and hope we find one that works. But there's no evidence that we'll find a working model "real soon now". Impressive results that we can kinda-sorta imagine being the product of an intelligent system haven't historically been enough.
- seiji 11y ago
  > saying this for decades? A handful of years ago I put together a computer fully loaded that gave me 1 teraflop of commuting power. Today I can put together a computer the same size that will give me 32 to 50 teraflops of programmable computing power. Many of the "AI" advances since 2007 are just running old 1970s-1990s AI algorithms on faster and faster and more parallel hardware. If you have to train a model for a few hundred trillion instructions, but your CPU only does 20 operations per second (and you have to share it with 1,000 other people), you can't iterate your science fast enough to make progress. Now we can iterate our science almost too quickly. > how the brain "thinks" at a neural level, Planes don't fly like birds. Birds don't fly like bees. True AI doesn't have to replicate mammalian (or avian or reptilian) neural topology.
  - harperlee 11y ago
    Upvote for "Birds don't fly like bees", I like the freshness of not merely pointing out that "humans can do better", but that in effect there are several paths that avoid different constraints to get to the same point!

btown 11y ago

I often wonder if the gap is that we're so caught up on training our networks on vision and text that we're ignoring the fact that living beings have a sense of time and reward as part of their input. A child knows that if it says "Mama food," it is likely to get attention, and if it gets attention, it is likely to minimize its hunger. Right now, a neural network can be trained to know that "Mama" occurs often in human dialogue, what words occur around it, even its dictionary definition and images of mothers. But it's not making the deeper connection to a strategy that minimizes hunger. When I think about this, I wonder if insights from the world of gaming "AI" would be useful in developing the training datasets for real AI. Because you can't be a mother to a billion virtual babies, but you might be able to program a set of heuristics to be a mother to a billion virtual babies. Then you have some system that trains on their life experiences...? All speculation, but very interesting stuff.

kastnerkyle 11y ago
There is a huge amount of research which is combining the power of deep learning for automated feature extraction with reinforcement learning for learning "natural reward signals" without label information. See any of the recent papers from Google DeepMind, such as [1] or their most recent work which is startlingly good [2] [1] http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.nature.com/nature/journal/v518/n7540/full/nature1... [2] http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-i...
- btown 11y ago
  I'd forgotten about that! The "game" needs to get much more complex to simulate life, of course. Now I wonder if they could throw that infrastructure at Minecraft survival mode...

NathanKP 11y ago

So far scientists haven't found anything special about the human brain that can't be mimicked by a machine. Given enough neural connections, and a large enough data set, and a long enough training period there is no reason to think that a machine can't do everything a human brain can do. Put another way there is nothing magical about a child learning about the world. A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns.

varelse 11y ago
Not really, there are clearly epigenetic changes to neuron DNA w/r to memory formation and I don't think anyone has estimated what kind of computational firepower that represents. Second, the 3D topology of a neuron is IMO more complex than reducing it to an FP32 activation threshold (all IMO of course). Finally, I have to admit as a former biologist, I'm intrigued by microtubule activity and it seems like Dileep George and even Geoffrey Hinton are heading towards smarter but fewer neurons as opposed to just increasing the neuron count. Not surprisingly, the deep learning digerati are resisting this notion mightily just like the SVM peeps harped on neural networks until they kicked them in the keester. TLDR: It's still early, and I'm biased that there are some interesting twists and turns yet to unfold here.
- deleted 11y ago
  [deleted]
- alextgordon 11y ago
  Also, brain chemistry. You can't give psychoactive drugs to a circuit board...
  - zaroth 11y ago
    I'll bet in a matter of time we will find that indeed you can give something quite analogous to "psychoactive drugs" to a strong AI!

gnaritas 11y ago

> A machine will never get the meaning of a word That's an irrational and indefensible position.