Cal Newport

So it was just about what they literally like the data sets you're using when doing this fine tuning after you've done that big, massive pre-training where it's unsupervised.

Minimal to non-existent.

Yeah, I'm not.

Yeah.

I mean, quantum computing is really interesting.

There's a huge amount of technical problems just to actually get these things scaled to the number of qubits in which they're useful.

And there's a fallacy out there in thinking about quantum computing that it's basically like a normal computer but times a million.

Yeah.

which is just not the way these things function, right?

So there's only very specific problems you can solve with a quantum computer because you actually have to express the problem in the language of physics in such a way that you're creating what's known as a wave function that when it collapses, it's going to collapse to a configuration that's the right answer.

Therefore, like implicitly searching a large state space in sublinear time

Only certain problems allow you to do that.

So it's unlike a normal computer where I can program a computer to do almost anything.

Quantum computers, it's much more narrow what you can do with it.

Well, the big example, this was a guy who was at MIT when I was there.

Peter Shor early on was the one who figured out like, hey, one of these complicated wave function collapsing things you could do could factor prime numbers.

Q-day.

Yeah, factor numbers to see, to find the prime factors rather, find the prime factors of big numbers.

that's a really big deal because... Yeah, public key encryption.

And ironically...