Deep learning is not a simple statistical clustering algorithm.
Trial and error is a main way that people learn and it works.
Trial and error is a main way that people learn and it works.
What is it, then?
In the 60s people used backpropagation to train neural networks. NN + BP is a very simple statistical clustering algorithm. I know that when I worked on neural networks in the 90s we still used backpropagation. Are they using something different now?
Yes, why else do you think we've had such breakthroughs in the past six years?
The two (NN and SAT solvers) share little theoretical progress (and certainly no theoretical breakthrough) in the past several decades, but SAT solvers aren't marketed as "AI" in spite of their seemingly magical abilities. I know that ML researchers usually cringe at the name AI and often try to disassociate themselves from the sci-fi term, but still, the marketing is extremely aggressive and misleading.
I realize that in every generation, marketers like associating the name "AI" with some particular class of algorithms, but it's important to understand that currently, assigning that name to this class of statistical clustering algorithms (regardless of their remarkable effectiveness in some tasks) is a stretch, just as it was when the term was assigned to other algorithms.
[1]: https://en.wikipedia.org/wiki/Backpropagation
[2]: https://en.wikipedia.org/wiki/Davis%E2%80%93Putnam_algorithm
I don't deny that it works, but it isn't AI (not that statistical clustering isn't possibly a foundation for AI -- we have no idea -- but the current state-of-the-art is a far cry from the sci-fi meaning of the term).