4 ms·

GOFAI was never more than a rules engine. If-then statements. Agree with you about probabilistic AI being useful in low-stakes situations, at least at first.

mistrial9 4y ago
If-then questions can lead to non-deterministic outputs with some simple feedback systems not disagreeing completely, but.. both "questions that are reasoned about", and "the code that reasons about questions".. need more careful classification in order to make use of these new data methods.. personally, I see the hype on DeepLearning to solve "find pattern in varying digital content" that is so clearly useful to the FAANG content Feudal Lords, is engaging in an investor shouting match that paves over simple use cases where DeepLearning is really not appropriate.
thwayunion 4y ago
> GOFAI was never more than a rules engine. GOFAI "aged out" of the AI label and became: compilers, databases, search algorithms, planning algorithms, programming languages, theorem proving, a million things that are still commonly used in NLP/CV/robotics, comp arch, etc. Aka, most of what makes computers actually useful. If something's over 30 years old and is still called AI, that's just shorthand for saying it's a failed idea that in the best case hasn't had its moment yet. > If-then statements. 99.999% of the software I use is just if-then statements. (Also, this is like saying that deep learning is Linear Algebra.)
cscurmudgeon 4y ago
Sorry, thats really wrong. I wouldn't call theorem provers just "if-then" statements. By that logic, everything, even large models, are if-then statements.
- bheadmaster 4y ago
  Technically, they are. There was a post on HN recently about how every model is equivalent to a decision tree.
  - dragonwriter 4y ago
    I mean, if it can be specified by a computational process, that’s trivially true, right?
    - bheadmaster 4y ago
      I'm not so sure about that. Decision trees are finite set of if-else branches, and work on finite input - much like models. I assume many of the classic arbitrary-size-input algorithms cannot be represented by a decision tree. Adding two arbitrary-sized numbers comes to mind - how would one model it as a decision tree? if (a[0] + b[0] == 0) { c[0] = 0 } else if (a[0] + b[0] == 1) { c[0] = 1 } else if ... ... } else if (a[0] + b[0] == 10) { c[0] = 0 if (a[1] + b[1] == 0) { c[1] = 1 } else ... ... } ... You can see that for arbitrary-sized input, this decision tree would have grow infinitely - which is contradictory to the finite nature of decision trees. I can't think of a proof that such a computation cannot be represented by a decision tree, and it's possible that my definitions aren't quite correct. But intuitively, I think this is the way it works. Of course, if I'm wrong, any explanation on why and how would be very welcome.

mistrial9 4y ago

If-then questions can lead to non-deterministic outputs with some simple feedback systems not disagreeing completely, but.. both "questions that are reasoned about", and "the code that reasons about questions".. need more careful classification in order to make use of these new data methods.. personally, I see the hype on DeepLearning to solve "find pattern in varying digital content" that is so clearly useful to the FAANG content Feudal Lords, is engaging in an investor shouting match that paves over simple use cases where DeepLearning is really not appropriate.

thwayunion 4y ago

> GOFAI was never more than a rules engine. GOFAI "aged out" of the AI label and became: compilers, databases, search algorithms, planning algorithms, programming languages, theorem proving, a million things that are still commonly used in NLP/CV/robotics, comp arch, etc. Aka, most of what makes computers actually useful. If something's over 30 years old and is still called AI, that's just shorthand for saying it's a failed idea that in the best case hasn't had its moment yet. > If-then statements. 99.999% of the software I use is just if-then statements. (Also, this is like saying that deep learning is Linear Algebra.)

cscurmudgeon 4y ago

Sorry, thats really wrong. I wouldn't call theorem provers just "if-then" statements. By that logic, everything, even large models, are if-then statements.

bheadmaster 4y ago
Technically, they are. There was a post on HN recently about how every model is equivalent to a decision tree.
- dragonwriter 4y ago
  I mean, if it can be specified by a computational process, that’s trivially true, right?
  - bheadmaster 4y ago
    I'm not so sure about that. Decision trees are finite set of if-else branches, and work on finite input - much like models. I assume many of the classic arbitrary-size-input algorithms cannot be represented by a decision tree. Adding two arbitrary-sized numbers comes to mind - how would one model it as a decision tree? if (a[0] + b[0] == 0) { c[0] = 0 } else if (a[0] + b[0] == 1) { c[0] = 1 } else if ... ... } else if (a[0] + b[0] == 10) { c[0] = 0 if (a[1] + b[1] == 0) { c[1] = 1 } else ... ... } ... You can see that for arbitrary-sized input, this decision tree would have grow infinitely - which is contradictory to the finite nature of decision trees. I can't think of a proof that such a computation cannot be represented by a decision tree, and it's possible that my definitions aren't quite correct. But intuitively, I think this is the way it works. Of course, if I'm wrong, any explanation on why and how would be very welcome.