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I think humans can do some cool things that we haven't yet figured out how to make an algorithm for. We can explore our environment, perform experiments, make h

I think humans can do some cool things that we haven't yet figured out how to make an algorithm for. We can explore our environment, perform experiments, make hypotheses, and ultimately generate theory which have predictive power. Algorithms that can do that would be valuable and would have similar properties to human intellect. One way to think of AI is what you say, highly specialized systems that have become very good at a specific task. This is very useful. It is also cool because you if you can define your problem well and come up with a fitness function then the algorithm solves your problem for you. Another way to think about AI, and this is a much more general type, is that it could possible learn new things and create new theories by performing its own experiments much like a human scientist does.

morgante 10y ago
> Algorithms that can do that would be valuable and would have similar properties to human intellect. I agree. A "scientist" algorithm would be quite cool and helpful. Where I disagree is whether that scientist algorithm should necessarily have intelligence that in any way resembles humanity. For all I care, it doesn't even need to be able to talk to be useful.
- astazangasta 10y ago
  I disagree with your disagreement. Talking - language - is intimately connected to "science", which, as a form of human knowledge, is about reducing the complexity of the universe down into semantic constructs that humans can appreciate. That is, "scientific knowledge" does not exist except in the form of human language. There is no other meaning to "science" (or "knowledge") except "making the universe intelligible to humans". A machine must also labor under these constraints, and so ultimately must produce knowledge in the form of language/semantics in order to be doing "science".
  - morgante 10y ago
    I agree that science requires some way of encoding and communicating information, but I don't agree that such a system must resemble human language. An alien system could equally do "science" even if their entire communication system was binary bits.
    - astazangasta 10y ago
      Sure, but unless they're willing to communicate their benevolent intent to us, alien science should be viewed as hostile. What is the point of a machine science that is not amenable to human examination? How could we be sure that the machine is acting in concert with human interests? A purely machine science is anathema; if the machine does science, it must participate in the community of human scientists to validate its conclusions, or else its 'knowledge', however encoded, is as obscure and terrifying as the path of an impending asteroid.
  - vintermann 10y ago
    Limiting science to knowledge expressed in human language seems unreasonable. I think that it's the degree to which a system can understand and predict the world that matters - not whether it can explain it to us. Might as well say that German mathematics isn't real mathematics, because it's all incomprehensible gobbledegook to someone who doesn't speak German.
    - kazagistar 10y ago
      Scientific knowledge already isn't expressed in normal human language; scientists build up a language of mathematical symbols, diagrams, and domain specific terms to encode and communicate their understanding in a more effective format. After all, how well do you understand a thing if you can't explain it? I suspect the real issue will be difference in mental skills... there might not be a human who can understand what the AI understands, even if it is very good at explaining.
vintermann 10y ago
Generating theories which have predictive power is exactly what deep neural nets do. Reinforcement learning systems do explore their environment and perform experiments, it's just that we give them access to a limited environment and reasonably clear objectives. Really, we cannot draw a clear line between a learning algorithm and intelligence. Any algorithm that can incorporate information from the world it is exposed to into itself and use it to predict that world is "intelligent" in a sense, and all machine learning algorithms do that.
- gambler 10y ago
  >Generating theories which have predictive power is exactly what deep neural nets do. Not really. Some symbolic/inductive systems do that. Traditional deep neural nets have a single model that is being adjusted to fit the distribution of input samples. That is not the same as creating a theory, let alone many theories. > Reinforcement learning systems do explore their environment and perform experiments Since when does trying something 1000 times with incremental changes qualifies as experiments and exploration?
  - vintermann 10y ago
    That single model is in the important respects as powerful as any model can hope to be, since it can approximate any continuous functions. Sure, it's finite, but so is my computer, and my brain. Sure, we can't guarantee that the ways we have of training this model will be able to learn these function approximations. But the same is true of my brain. So why would multiple models be better? Any set of multiple models can be aggregated and called one model, anyway. Being adjusted to fit the distribution of input samples sounds unimpressive, until you remember it can in principle do it for all distributions. And it learns to generalize from the input samples to unseen samples. Making a model to generalize from what we've seen to what we haven't yet seen is exactly the same as creating a theory, as I see it. Since when does trying something 1000 times with incremental changes qualifies as experiments and exploration? Since when doesn't it? If it's 100 times, is it experimentation and exploration then in your eye? What about 10 times? Sounds like an arbitrary distinction to me.

morgante 10y ago

> Algorithms that can do that would be valuable and would have similar properties to human intellect. I agree. A "scientist" algorithm would be quite cool and helpful. Where I disagree is whether that scientist algorithm should necessarily have intelligence that in any way resembles humanity. For all I care, it doesn't even need to be able to talk to be useful.

astazangasta 10y ago
I disagree with your disagreement. Talking - language - is intimately connected to "science", which, as a form of human knowledge, is about reducing the complexity of the universe down into semantic constructs that humans can appreciate. That is, "scientific knowledge" does not exist except in the form of human language. There is no other meaning to "science" (or "knowledge") except "making the universe intelligible to humans". A machine must also labor under these constraints, and so ultimately must produce knowledge in the form of language/semantics in order to be doing "science".
- morgante 10y ago
  I agree that science requires some way of encoding and communicating information, but I don't agree that such a system must resemble human language. An alien system could equally do "science" even if their entire communication system was binary bits.
  - astazangasta 10y ago
    Sure, but unless they're willing to communicate their benevolent intent to us, alien science should be viewed as hostile. What is the point of a machine science that is not amenable to human examination? How could we be sure that the machine is acting in concert with human interests? A purely machine science is anathema; if the machine does science, it must participate in the community of human scientists to validate its conclusions, or else its 'knowledge', however encoded, is as obscure and terrifying as the path of an impending asteroid.
- vintermann 10y ago
  Limiting science to knowledge expressed in human language seems unreasonable. I think that it's the degree to which a system can understand and predict the world that matters - not whether it can explain it to us. Might as well say that German mathematics isn't real mathematics, because it's all incomprehensible gobbledegook to someone who doesn't speak German.
  - kazagistar 10y ago
    Scientific knowledge already isn't expressed in normal human language; scientists build up a language of mathematical symbols, diagrams, and domain specific terms to encode and communicate their understanding in a more effective format. After all, how well do you understand a thing if you can't explain it? I suspect the real issue will be difference in mental skills... there might not be a human who can understand what the AI understands, even if it is very good at explaining.

vintermann 10y ago

Generating theories which have predictive power is exactly what deep neural nets do. Reinforcement learning systems do explore their environment and perform experiments, it's just that we give them access to a limited environment and reasonably clear objectives. Really, we cannot draw a clear line between a learning algorithm and intelligence. Any algorithm that can incorporate information from the world it is exposed to into itself and use it to predict that world is "intelligent" in a sense, and all machine learning algorithms do that.

gambler 10y ago
>Generating theories which have predictive power is exactly what deep neural nets do. Not really. Some symbolic/inductive systems do that. Traditional deep neural nets have a single model that is being adjusted to fit the distribution of input samples. That is not the same as creating a theory, let alone many theories. > Reinforcement learning systems do explore their environment and perform experiments Since when does trying something 1000 times with incremental changes qualifies as experiments and exploration?
- vintermann 10y ago
  That single model is in the important respects as powerful as any model can hope to be, since it can approximate any continuous functions. Sure, it's finite, but so is my computer, and my brain. Sure, we can't guarantee that the ways we have of training this model will be able to learn these function approximations. But the same is true of my brain. So why would multiple models be better? Any set of multiple models can be aggregated and called one model, anyway. Being adjusted to fit the distribution of input samples sounds unimpressive, until you remember it can in principle do it for all distributions. And it learns to generalize from the input samples to unseen samples. Making a model to generalize from what we've seen to what we haven't yet seen is exactly the same as creating a theory, as I see it. Since when does trying something 1000 times with incremental changes qualifies as experiments and exploration? Since when doesn't it? If it's 100 times, is it experimentation and exploration then in your eye? What about 10 times? Sounds like an arbitrary distinction to me.