You can think of DL as a model to explore huge solution spaces, is clearly not the only one but looking at things with some models sometimes makes much more sense and DL has been extremely useful. Is true than real neurons and artificial ones are different but "both are systems that perform complex tasks via the updating of weights within an astronomically large parameter space.". The brain is clearly not using the same mechanisms but it probably have some of the same problems and looking it from a DL perspective (that we understand better than the brain) could help us understand it better.
Societies are full of random stuff we learned just by trial and error and we don't even know why we do it. Can't recommend enough this book The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (https://www.goodreads.com/book/show/25761655-the-secret-of-o...)
I feel problem definition is underappreciated in current ml world and the effort is put into the ml methods. People has been able to tackle almost any problem well defined but these are usually simple in comparison to true ai so we still get "stupid" agents. What problem is the test to true ai?
> Warren Buffett, the most famous investor in the world, had this to say recently: “If a statue is ever erected to honor the person who has done the most for American investors, the hands-down choice should be Jack Bogle.”
I was not a big fan of monorepos until I work at a large company that use them. The use of a monorepo and protocol buffers make communication between systems soo much easier.
Maybe in the US but not worldwide.