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Many Machine Learning Studies Don't Actually Show Anything Meaningful, But They Spread Fear, Uncertainty, and Doubt (theoutline.com)
Michael Byrne, writing for the Outline: Here's what you need to know about every way-cool and-or way-creepy machine learning study that has ever been or will ever be published: Anything that can be represented in some fashion by patterns within data -- any abstract-able thing that exists in the objective world, from online restaurant reviews to geopolitics -- can be "predicted" by machine learning models given sufficient historical data. At the heart of nearly every foaming news article starting with the words "AI knows ..." is some machine learning paper exploiting this basic realization. "AI knows if you have skin cancer." "AI beats doctors at predicting heart attacks." "AI predicts future crime." "AI knows how many calories are in that cookie." There is no real magic behind these findings. The findings themselves are often taken as profound simply for having way-cool concepts like deep learning and artificial intelligence and neural networks attached to them, rather than because they are offering some great insight or utility -- which most of the time, they are not.
This is not strong AI we are talking about here, it is weak AI. Strong AI does not exist. Weak AI cannot do anything that requires insight or understanding. It just can do statistical classification.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
"But just to give you an intuition: Weak AI cannot plan, cannot judge, cannot explore"
Some machine learning control systems are explicitly designed to form and execute plans. The ones that are designed to be trained in the real world are usually also designed to learn to do internal simulations (imagining what will happen if I do X) because feedback in the real world is slow.
Judging is pretty much what machine learning systems do.
Many reinforcement learning methods have a hyperparameter to explicitly control the amount of exploration the system does.
Are you sure you're not thinking of 1960's era tech?