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Yes, these are two main approaches to AI, with expert systems being the "old" AI and ML being the "modern" AI. In the old approach, you knew exactly what you wa

Yes, these are two main approaches to AI, with expert systems being the "old" AI and ML being the "modern" AI. In the old approach, you knew exactly what you wanted to achieve, and very often you had a relatively clear idea how to do it; the challenge was to construct the solution manually. In the new approach you are not so much concerned with how to do it as you train on some dataset anyway. The results are less predictable though, you might even not understand why certain things happen. The number of mistakes/false positives can be relatively high. Still, with these methods, I think we're very far from real artificial intelligence, where you can really learn something new on the basis of something old. I believe until we have a profound understanding of what it means "to understand", such an advancement in AI won't be possible.

mjburgess 9y ago
I think we do have a good-enough understanding of "understanding" to characterize what is required. However this is to be found in the textbooks of neuroscience, not in the machine manufacture manuals of a silicon lithography plant. There isn't as much mystery as there seems. When a neuroscientist asks "how does a animal perceive the world?" the answer is reasonably methodologically obvious. When a computer scientist asks, "how does a machine perceive the world?" the mystery arises only because it doesn't.
- dvfjsdhgfv 9y ago
  There is a huge difference between perception and understanding. http://news.itu.int/reality-check-not-nearly-close-strong-ai-many-believe/ http://news.itu.int/reality-check-not-nearly-close-strong-ai... > Marcus also observes “a bias in the field which is to assume that everything is learnt.” Marcus makes the argument that human beings do not learn everything by ‘trial-and-error’ – that part of our knowledge is innate, learnt over evolution – leading him to suggest that “we need more innateness if we are going to build intelligent agents … It’s not learning trial-by-trial in the way that our contemporary machines are.”
  - mjburgess 9y ago
    Well, not as much as you think. Perception is structured by concepts which are learnt. Perception is the prototypical mechanism of understanding. Insofar as we talking about "abstractions" we are just using language to model the conceptual structure of perception; and then higher order concepts to model the structure of these "perceptual" concepts. We tend to think of understanding as being equivalent to the linguistic act of accounting for a higher-order concept, when this is really only something humans can do -- and then only with tremendous difficulty needing quite significant methodological assistance from others, etc. This is the mistake Socrates makes when he asks for "definitions", as-if the dog doesn't understand the geography of where he lives only because he has no linguistic model of the concepts he's using. Or as-if the general has no knowledge of justice because he has no precise linguistic model of it. When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct. The concept "pornography" is part of seeing pornography, it is knowledge which structures your perception. The lack of perfect linguistic model for that is neither here nor there, and a bit of a blind alley for AI (and socrates). Animals, including humans, understand a great deal more than any one person can model linguistically. It is this which structures our perception and allows even trivial engagements with our environment.
    - dvfjsdhgfv 9y ago
      > When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct. Here you refer to categorization, not understanding. From the article I refer to: > “Deep learning is good at certain aspects of perception, particularly categorization, but perception is more than categorization and cognition or intelligence is more than just perception. There are many things that go into intelligence … And what we have made real progress on is perception, but the rest of it, we still haven’t made that much progress collectively in the field.” Understanding, real understanding, the "aha moment", is the very basis of new discoveries and real evolution. What we see now are systems quite good at categorizing, pattern recognition in a given set once prepared correctly and so on. There is no relation whatsoever with real intelligence, as in being able to make a new discovery based on analytical and synthetic skills. There are quite a few voices in the community saying the opposite and maintaining that mimicking intelligent action is practically the same as the intelligent action itself, but there are quite obvious limits to what can be achieved in this way.

mjburgess 9y ago

I think we do have a good-enough understanding of "understanding" to characterize what is required. However this is to be found in the textbooks of neuroscience, not in the machine manufacture manuals of a silicon lithography plant. There isn't as much mystery as there seems. When a neuroscientist asks "how does a animal perceive the world?" the answer is reasonably methodologically obvious. When a computer scientist asks, "how does a machine perceive the world?" the mystery arises only because it doesn't.

dvfjsdhgfv 9y ago
There is a huge difference between perception and understanding. http://news.itu.int/reality-check-not-nearly-close-strong-ai-many-believe/ http://news.itu.int/reality-check-not-nearly-close-strong-ai... > Marcus also observes “a bias in the field which is to assume that everything is learnt.” Marcus makes the argument that human beings do not learn everything by ‘trial-and-error’ – that part of our knowledge is innate, learnt over evolution – leading him to suggest that “we need more innateness if we are going to build intelligent agents … It’s not learning trial-by-trial in the way that our contemporary machines are.”
- mjburgess 9y ago
  Well, not as much as you think. Perception is structured by concepts which are learnt. Perception is the prototypical mechanism of understanding. Insofar as we talking about "abstractions" we are just using language to model the conceptual structure of perception; and then higher order concepts to model the structure of these "perceptual" concepts. We tend to think of understanding as being equivalent to the linguistic act of accounting for a higher-order concept, when this is really only something humans can do -- and then only with tremendous difficulty needing quite significant methodological assistance from others, etc. This is the mistake Socrates makes when he asks for "definitions", as-if the dog doesn't understand the geography of where he lives only because he has no linguistic model of the concepts he's using. Or as-if the general has no knowledge of justice because he has no precise linguistic model of it. When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct. The concept "pornography" is part of seeing pornography, it is knowledge which structures your perception. The lack of perfect linguistic model for that is neither here nor there, and a bit of a blind alley for AI (and socrates). Animals, including humans, understand a great deal more than any one person can model linguistically. It is this which structures our perception and allows even trivial engagements with our environment.
  - dvfjsdhgfv 9y ago
    > When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct. Here you refer to categorization, not understanding. From the article I refer to: > “Deep learning is good at certain aspects of perception, particularly categorization, but perception is more than categorization and cognition or intelligence is more than just perception. There are many things that go into intelligence … And what we have made real progress on is perception, but the rest of it, we still haven’t made that much progress collectively in the field.” Understanding, real understanding, the "aha moment", is the very basis of new discoveries and real evolution. What we see now are systems quite good at categorizing, pattern recognition in a given set once prepared correctly and so on. There is no relation whatsoever with real intelligence, as in being able to make a new discovery based on analytical and synthetic skills. There are quite a few voices in the community saying the opposite and maintaining that mimicking intelligent action is practically the same as the intelligent action itself, but there are quite obvious limits to what can be achieved in this way.