Isn't 99.99999...% of any currently used AI good ol' statistics or (not XOR) classical operations research stuff or (not XOR) well-known and rather simple NLP?
The more specific the use case, the more that human-engineered tools/rules will dominate. E.g. Regex beats BERT 99% of the time for any specific use case of value to a real customer, but BERT does a decent job in open domains without much effort.
For one thing this ignores image recognition, which is probably the most successful field for neural network deployments.
And most modern NLP is "download all the written text on the entire internet, filter it for the good stuff (somehow), throw it into a neural network that is bigger than anything that has been successfully trained before, and wait a week before you can see if it worked". I wouldn't describe it as simple.
Uh, statistics tries to be applied probability with some assumptions, theorems, proofs, results, and applications. E.g., the assumptions for regression analysis that let us use and F-ratio and t-tests hypothesis testing and confidence intervals.
Does current AI with neural networks have such assumptions and results? Maybe with the test data could develop some such results. And AI seems to have and assumption that the training data and the test data are independent and identically distributed (i.i.d.) random variables. Uh, arguing i.i.d. for some enormous collections of data might be difficult? How 'bout some concept of approximately independent? M. Talagrand has some such in his "A New Look at Independence"?
I'd like to have some of whatever you're smoking. NLP is one of the hardest areas of science with many problems that will not be conclusively solved in my lifetime, both technical and scientific.
I didn't mean that the field is 'simple' (easy to tackle), but that most tools used on the field does not seem to me "intelligent" in any way. Which one is "intelligent" and why?
Something able to learn or to discover without relying on a 'brute force' (exhaustive exploration of the problem space) approach.
Neural networks are in a way pertinent, I reckon, however I'm not enthusiastic about the fact that using it forbids us to 'explain how it solves the problem'. It also apply, albeit somewhat to a lesser extent(?), to bayesians and such.
In Bayesian optimization, the next guess is chosen according to a non-brute force equation that takes all previous information into account.
Does it appear more intelligent than repeated slight shifts more or less toward the first order gradient?
The second seems to be more similar to how humans learn, but the former more similar to how we rationalize after learning.
Still, the first is still repeated, relatively simple applied math. I’m not sure either case jumps out as being qualitatively different in a meaningful way when it comes to intelligence.
