6 ms·

> anything new It is nothing new. It is called (at least) "the problem of transparency". The chess context is probably not the best, because many systems allo

by mdp2021 4y ago

> anything new It is nothing new. It is called (at least) "the problem of transparency". The chess context is probably not the best, because many systems allow a lengthy complex explanation of the response (I cannot remember now the exact workings of Deep Blue - it has been a while last time I met the full info). It is a real problem in general, because we may not just want responses but we may want "to learn something", and specifically, because ANNs applied to e.g. Decision Making, or ANN based Decision Support Systems, pretty much require that natural intelligences assess the "oracular" proposals. And of course, "understanding" the engine is required to advance it. It is a key problem.

randcraw 4y ago
In 1963 Marvin Minsky called it “The Credit Assignment Problem” — which, among a multitude of variables, were most important in solving an AI task?
- mberh7 4y ago
alexpetralia 4y ago
I suppose this is why it is called "machine learning" and not "human learning"!
- mdp2021 4y ago
  We deal with intelligence to either solve problems, or to benefit from intelligence to advance our own. If you valued technical advance with a reduced concern towards civilization, ie. power over wisdom, ie. more "how" and less "why", you would get - well, for example, the present situation, that some consider "strongly suboptimal". Some said that there have been two big branches of science fiction, that revolving around power (spaceships etc.) and that revolving around information, and that we have been just lucky to have reached the latter.
- jhbadger 4y ago
  But some forms of machine learning actually are interpretable, which makes them useful in scientific investigation, which doesn't just want a black box. A good example of interpretation machine learning is decision trees -- given a series of variables and outcomes you can find out which variables are important to the outcomes and in which combinations.
FartyMcFarter 4y ago
> And of course, "understanding" the engine is required to advance it. I don't think so. People are clearly making huge advances in machine learning even though they can't explain the systems made 10 years ago, let alone more recent ones. There's a lot of trial and error / cargo-culting involved. Some researchers go as far as comparing machine learning research to Alchemy: https://www.science.org/content/article/ai-researchers-allege-machine-learning-alchemy https://www.science.org/content/article/ai-researchers-alleg... > many systems allow a lengthy complex explanation of the response All computations have this property: you can execute the code by hand for as long as you have time to. But if you do this for long enough you become a mere observer of the computer's steps, which won't give you any intuition as to why the final output is the way it is (unless the algorithm being run is very simple).
- mdp2021 4y ago
  > "understanding" the engine is required to advance it What I meant was more on the lines of "LeCun proposed Convolutional NN recognizing a promising paradigm", or "our prespective on Artificial Vision changed as we realized that the ANNs recognized textures instead of figures". > All computations And what I meant there was that in the chess systems of topic the calculations performed have been "as if symbolic", ie. reasoning on branches of consequences of the type "what could happen if I move this there" - even when adopting ANNs in some parts of the architecture -, as opposed to, say, "computing weights". That is the level "of machine code", not that "of electronics".
- randcraw 4y ago
  But an AI must “understand its reasoning” in order to revise it efficiently to learn from its mistakes. If revision is impossible, the only way to correct its reasoning is to re-train the system from scratch. But in doing that, you can only hope the problem is fixed, and worse, that new problems were not just created.
  - mdp2021 4y ago
    It is very rare, but I must congratulate. An aim is "non-deterministic virtual self-modifying code".

randcraw 4y ago

In 1963 Marvin Minsky called it “The Credit Assignment Problem” — which, among a multitude of variables, were most important in solving an AI task?

mberh7 4y ago

alexpetralia 4y ago

I suppose this is why it is called "machine learning" and not "human learning"!

mdp2021 4y ago
We deal with intelligence to either solve problems, or to benefit from intelligence to advance our own. If you valued technical advance with a reduced concern towards civilization, ie. power over wisdom, ie. more "how" and less "why", you would get - well, for example, the present situation, that some consider "strongly suboptimal". Some said that there have been two big branches of science fiction, that revolving around power (spaceships etc.) and that revolving around information, and that we have been just lucky to have reached the latter.
jhbadger 4y ago
But some forms of machine learning actually are interpretable, which makes them useful in scientific investigation, which doesn't just want a black box. A good example of interpretation machine learning is decision trees -- given a series of variables and outcomes you can find out which variables are important to the outcomes and in which combinations.

FartyMcFarter 4y ago

> And of course, "understanding" the engine is required to advance it. I don't think so. People are clearly making huge advances in machine learning even though they can't explain the systems made 10 years ago, let alone more recent ones. There's a lot of trial and error / cargo-culting involved. Some researchers go as far as comparing machine learning research to Alchemy: https://www.science.org/content/article/ai-researchers-allege-machine-learning-alchemy https://www.science.org/content/article/ai-researchers-alleg... > many systems allow a lengthy complex explanation of the response All computations have this property: you can execute the code by hand for as long as you have time to. But if you do this for long enough you become a mere observer of the computer's steps, which won't give you any intuition as to why the final output is the way it is (unless the algorithm being run is very simple).

mdp2021 4y ago
> "understanding" the engine is required to advance it What I meant was more on the lines of "LeCun proposed Convolutional NN recognizing a promising paradigm", or "our prespective on Artificial Vision changed as we realized that the ANNs recognized textures instead of figures". > All computations And what I meant there was that in the chess systems of topic the calculations performed have been "as if symbolic", ie. reasoning on branches of consequences of the type "what could happen if I move this there" - even when adopting ANNs in some parts of the architecture -, as opposed to, say, "computing weights". That is the level "of machine code", not that "of electronics".
randcraw 4y ago
But an AI must “understand its reasoning” in order to revise it efficiently to learn from its mistakes. If revision is impossible, the only way to correct its reasoning is to re-train the system from scratch. But in doing that, you can only hope the problem is fixed, and worse, that new problems were not just created.
- mdp2021 4y ago
  It is very rare, but I must congratulate. An aim is "non-deterministic virtual self-modifying code".