cs137

Whether we are more than computers is almost a religious question (I believe yes, but I can't prove it). However, any computation that we can prove through finite language, in the sense of absolute mathematical proof, can be checked by a mechanical process or, equivalently, a Turing machine (computer).

If there's something more, we don't have the tools to prove it.

I'm convinced that everything Paul Graham has written since about 2009 is some generation of GPT. You could ask him if he's satisfied with its work.

It's actually not hard to write a program that can do what he does.

"What is wealth? Why are there unequal amounts of it? It's because wealth is what people want. If you can figure out what people want, you can generate a lot of wealth. Some people don't know this. Some people aren't cut out for startups. But people who know what people want can generate billion-dollar startups in record time by the standards of our ancestors. Unfortunately, there are people in politics and government who do not understand that wealth is what people want, and therefore cannot be regulated, because to do so is to regulate what people want, and companies that operate on this principle will find themselves unable to hire great programmers."

This stuff isn't hard to generate... I did it by hand, stream of consciousness.

Neural nets can, provably, approximate any function, modulo certain conditions about continuity (i.e., can't be discontinuous in infinitely many places, that sort of thing). Universality is trivial to prove with boolean functions (because a neural net can do NAND) but also it's also mostly true (again, subject to certain analytical demands) on R^n.

The problem is that it can require an exponential amount of time as well as data to actually get a decent network--and, of course, there's a high risk of it being overfit to idiosyncracies in the data. The standard approach to neural network training is to convert it into a gigantic calculus problem and brute-force it, and this doesn't always result in timely convergence or even a generalizable model.

This. I wonder if Bay Area people still claim to be "living in the future", as they did in the 2000s and early 2010s. They were right, but I wish they hadn't been, because the future turned out to suck.