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What you're picking up on is that the old school vision of how AI would be achieved (Minsky, Chomsky etc) had some early success with simple games (which they t

What you're picking up on is that the old school vision of how AI would be achieved (Minsky, Chomsky etc) had some early success with simple games (which they then touted as the forerunners of AI) and then stopped. Data driven ML approaches are completely different. Physics once held that there was a substance called aether rather than vacuum, and chemistry that fire came from phlogiston, but we didn't need to rename the discipline when reasoning evolved.

tathougies 6y ago
> Physics once held that there was a substance called aether rather than vacuum, and chemistry that fire came from phlogiston, but we didn't need to rename the discipline when reasoning evolved. The key difference being that physical theories based on aether never actually worked, whereas computer programs based on graph algorithms solved many problems they set out to solve.
- angry_octet 6y ago
  In most fields classic AI was never able to achieve close to what new techniques can. Vastly over hyped. Graph algorithms are not a subset of AI.
kazinator 6y ago
The existence of the aether isn't borne out by experiments; rather, we have justification in believing in its non-existence. The algorithms of AI of decades past still work. That's a big difference. E.g. SHRDLU can be built today and you can have a conversation with it about its world of blocks. Algorithms are artifacts of mathematics. To calculate distance between two points, we still use sqrt(dx^2 + dy^2); it doesn't go out of fashion due to advancements in topology.
- angry_octet 6y ago
  Fine, plum pudding model of the atom then. It isn't perfect, but we don't throw out the name physics. I don't get this hate for new techniques being classified as AI. Just because they learn distributions instead of using classic prolog?
  - kazinator 6y ago
    The techniques aren't that new; all that is new is having the gigabytes upon gigabytes of RAM to run them, not too mention CPU power, and scads of data. I knew what a neural network was, and understood it as part of AI, when Wham! was in the Top 40 charts. The hate isn't for the "new" techniques, I think; just for the posers who claim to be AI experts because they know how to use some Python library or whatnot. You don't have AI creds if you have no background in the symbolic stuff.
    - angry_octet 6y ago
      We'll have to disagree. You can be perfectly well credentialled without having years of studying techniques that aren't very successful. It is really rewriting history to pretend that old style NN and backpropagation is the same as modern systems -- implementation techniques matter.

tathougies 6y ago

> Physics once held that there was a substance called aether rather than vacuum, and chemistry that fire came from phlogiston, but we didn't need to rename the discipline when reasoning evolved. The key difference being that physical theories based on aether never actually worked, whereas computer programs based on graph algorithms solved many problems they set out to solve.

angry_octet 6y ago
In most fields classic AI was never able to achieve close to what new techniques can. Vastly over hyped. Graph algorithms are not a subset of AI.

kazinator 6y ago

The existence of the aether isn't borne out by experiments; rather, we have justification in believing in its non-existence. The algorithms of AI of decades past still work. That's a big difference. E.g. SHRDLU can be built today and you can have a conversation with it about its world of blocks. Algorithms are artifacts of mathematics. To calculate distance between two points, we still use sqrt(dx^2 + dy^2); it doesn't go out of fashion due to advancements in topology.

angry_octet 6y ago
Fine, plum pudding model of the atom then. It isn't perfect, but we don't throw out the name physics. I don't get this hate for new techniques being classified as AI. Just because they learn distributions instead of using classic prolog?
- kazinator 6y ago
  The techniques aren't that new; all that is new is having the gigabytes upon gigabytes of RAM to run them, not too mention CPU power, and scads of data. I knew what a neural network was, and understood it as part of AI, when Wham! was in the Top 40 charts. The hate isn't for the "new" techniques, I think; just for the posers who claim to be AI experts because they know how to use some Python library or whatnot. You don't have AI creds if you have no background in the symbolic stuff.
  - angry_octet 6y ago
    We'll have to disagree. You can be perfectly well credentialled without having years of studying techniques that aren't very successful. It is really rewriting history to pretend that old style NN and backpropagation is the same as modern systems -- implementation techniques matter.