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> 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 thoug

> 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".
- visarga 4y ago
  There is also the problem of label noise. A label might not be necessarily correct, especially when the dataset is large. Nobody got time to check them all, and even if you put two people they might disagree.

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".
visarga 4y ago
There is also the problem of label noise. A label might not be necessarily correct, especially when the dataset is large. Nobody got time to check them all, and even if you put two people they might disagree.