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I agree with everything but the last. Simulating a human brain is perhaps necessary for consciousness uploading, but not for super human ai. We have been able

by dwiel 11y ago

I agree with everything but the last. Simulating a human brain is perhaps necessary for consciousness uploading, but not for super human ai. We have been able to travel faster by machine than foot for a long time, in spite of not being able to build a robot that can simulate human running.

dahart 11y ago
> Simulating a human brain is perhaps necessary for consciousness uploading, but not for super human ai. I'm not suggesting otherwise, and what you said might be true, I don't know. But honest question: how can we know it's true before we can do either? How can we suggest neural nets are a good building block of super human AI, if we can't verify we can do regular human thinking, or sub-human thinking? Because the neural net is modeled after (simplified) brain neurons in the first place, it seems reasonable to suggest that if we can't simulate a simple brain, then we can't validate that the model is correct, right? It might be very productive, and it might 'work' in some sense, but we don't know whether it can truly act as the lego brick of brain building material until we can build a functional brain. Your observation is true, and worth considering. Machines can travel faster than humans, but that's different in part because we didn't start trying to simulate the human foot, right? The computational equivalent would be that machines can multiply a lot of numbers much faster than a human. That is super-human calculation, and it is mechanical thinking, of a sort, but most people wouldn't say it counts as "AI", perhaps the same way that most people wouldn't say a car counts as human running, even if it is faster. Neural nets are being used to classify images, find objects, identify people's faces in a crowd or in difficult to see situations. But, they currently can't tell you to stop classifying images because you're asking the wrong question, or interrupt you to say you're looking great today.
- argonaut 11y ago
  Before we get wildly over-optimistic, as every HN thread on AI becomes, realize that neural networks have basically nothing to do with actual biological neurons. There are a bajillion things neurons do that neural networks do not do. And there are similarly many things that are a part of neural networks, that biological neurons do not do. In addition to the fact that neuroscientists still do not have a clear understanding of biological neurons: you cannot expect to reverse-engineer something you barely understand yourself.
  - andreyk 11y ago
    This is not entirely true, as there are spiking neuron nets and neuromorphic chips (like IBM's TrueNorth) that emulate what we have so far understood about how neurons operate. But it's true that even these are simplified models and as far as I know cognitive science has not explained the higher level operation of the brain. In general I think breaking down how modern deep learning works makes it seem unlikely that it will lead to true AI without additional insights, so I am unsure why very intelligent people such as Elon Musk make it seem like an imminent threat - not to mention the idea is predicated on continued exponential increases in computing power. But the stuff going on in AI right now concerning image comprehension and question answering is fairly impressive, so I think it is feasible we will have impressive AI assistants and the like within a decade.
    - pedrosorio 11y ago
      "This is not entirely true, as there are spiking neuron nets and neuromorphic chips (like IBM's TrueNorth) that emulate what we have so far understood about how neurons operate." How is this related to the neural networks we are discussing? (deep ANNs?) As far as I know, the ANN topic in machine learning, apart from its origins, is completely unrelated to the simulation of biological neural network models: https://en.wikipedia.org/wiki/Nervous_system_network_models https://en.wikipedia.org/wiki/Nervous_system_network_models They are often confused because of the name and because ANNs did begin as extremely simplified models of biological neural networks, but the machine learning concept that is setting all the records in vision / speech recognition serves no purpose as a model of a biological neural network.
      - nl 11y ago
        the machine learning concept that is setting all the records in vision / speech recognition serves no purpose as a model of a biological neural network. I think you are overstating this. There are definite links between the two, even if ANNs end up different because of the tools we have available to us. CNNs were explicitly designed to mimic the behavior of the visual cortex. Most of Geoff Hinton's career has been built around thinking very hard about biological computation. One of the major criticisms of back-propegation in neural networks is that it is biologicaly implausible. https://en.m.wikipedia.org/wiki/Convolutional_neural_network#History https://en.m.wikipedia.org/wiki/Convolutional_neural_network...
- dwiel 11y ago
  We are making very similar points I think. My main point is that it is not a given that we must simulate a human brain to get super human AI. We may, we may not, we dont know, neural networks or otherwise.
  - dahart 11y ago
    I'm of the opinion that you're absolutely right! And as such, I don't know, but I do currently believe that we don't have the computational foundation for AI yet. My friend, who's done more AI research than I have, and who's probably smarter than me, said he thinks we probably have everything we need for the logic part, and the only thing missing is enough computation and enough data to simulate the amount of input a human gets. I was surprised by this response and pressed him on it. "It's turtles all the way down."

by dwiel 11y ago

dahart 11y ago

> Simulating a human brain is perhaps necessary for consciousness uploading, but not for super human ai. I'm not suggesting otherwise, and what you said might be true, I don't know. But honest question: how can we know it's true before we can do either? How can we suggest neural nets are a good building block of super human AI, if we can't verify we can do regular human thinking, or sub-human thinking? Because the neural net is modeled after (simplified) brain neurons in the first place, it seems reasonable to suggest that if we can't simulate a simple brain, then we can't validate that the model is correct, right? It might be very productive, and it might 'work' in some sense, but we don't know whether it can truly act as the lego brick of brain building material until we can build a functional brain. Your observation is true, and worth considering. Machines can travel faster than humans, but that's different in part because we didn't start trying to simulate the human foot, right? The computational equivalent would be that machines can multiply a lot of numbers much faster than a human. That is super-human calculation, and it is mechanical thinking, of a sort, but most people wouldn't say it counts as "AI", perhaps the same way that most people wouldn't say a car counts as human running, even if it is faster. Neural nets are being used to classify images, find objects, identify people's faces in a crowd or in difficult to see situations. But, they currently can't tell you to stop classifying images because you're asking the wrong question, or interrupt you to say you're looking great today.

argonaut 11y ago
Before we get wildly over-optimistic, as every HN thread on AI becomes, realize that neural networks have basically nothing to do with actual biological neurons. There are a bajillion things neurons do that neural networks do not do. And there are similarly many things that are a part of neural networks, that biological neurons do not do. In addition to the fact that neuroscientists still do not have a clear understanding of biological neurons: you cannot expect to reverse-engineer something you barely understand yourself.
- andreyk 11y ago
  This is not entirely true, as there are spiking neuron nets and neuromorphic chips (like IBM's TrueNorth) that emulate what we have so far understood about how neurons operate. But it's true that even these are simplified models and as far as I know cognitive science has not explained the higher level operation of the brain. In general I think breaking down how modern deep learning works makes it seem unlikely that it will lead to true AI without additional insights, so I am unsure why very intelligent people such as Elon Musk make it seem like an imminent threat - not to mention the idea is predicated on continued exponential increases in computing power. But the stuff going on in AI right now concerning image comprehension and question answering is fairly impressive, so I think it is feasible we will have impressive AI assistants and the like within a decade.
  - pedrosorio 11y ago
    "This is not entirely true, as there are spiking neuron nets and neuromorphic chips (like IBM's TrueNorth) that emulate what we have so far understood about how neurons operate." How is this related to the neural networks we are discussing? (deep ANNs?) As far as I know, the ANN topic in machine learning, apart from its origins, is completely unrelated to the simulation of biological neural network models: https://en.wikipedia.org/wiki/Nervous_system_network_models https://en.wikipedia.org/wiki/Nervous_system_network_models They are often confused because of the name and because ANNs did begin as extremely simplified models of biological neural networks, but the machine learning concept that is setting all the records in vision / speech recognition serves no purpose as a model of a biological neural network.
    - nl 11y ago
      the machine learning concept that is setting all the records in vision / speech recognition serves no purpose as a model of a biological neural network. I think you are overstating this. There are definite links between the two, even if ANNs end up different because of the tools we have available to us. CNNs were explicitly designed to mimic the behavior of the visual cortex. Most of Geoff Hinton's career has been built around thinking very hard about biological computation. One of the major criticisms of back-propegation in neural networks is that it is biologicaly implausible. https://en.m.wikipedia.org/wiki/Convolutional_neural_network#History https://en.m.wikipedia.org/wiki/Convolutional_neural_network...
dwiel 11y ago
We are making very similar points I think. My main point is that it is not a given that we must simulate a human brain to get super human AI. We may, we may not, we dont know, neural networks or otherwise.
- dahart 11y ago
  I'm of the opinion that you're absolutely right! And as such, I don't know, but I do currently believe that we don't have the computational foundation for AI yet. My friend, who's done more AI research than I have, and who's probably smarter than me, said he thinks we probably have everything we need for the logic part, and the only thing missing is enough computation and enough data to simulate the amount of input a human gets. I was surprised by this response and pressed him on it. "It's turtles all the way down."