Cal Newport

Now, I haven't published professional applied mathematics research.

That was my field.

I'm a distributed algorithm theorist, an applied mathematician within distributed algorithms theory.

A few years ago, I stopped doing mathematical research to help start up the Center for Digital Ethics at Georgetown and do a lot more of this public-facing technology criticism.

But I'm looking at these tools.

I'm like, man, that would have helped.

And if I return to applied math, I'll be using the hell out of those tools.

I think it would make me...

Just knowing what I know from doing this type of work, 2x more effective in terms of quality, comprehensiveness, and speed with which I produce results.

I don't think people understand how much mathematics is a combination of the creative insight plus a lot of tedious work, learning and applying results, trying different things, working out the details, getting stuck.

Where you often get stuck in mathematics, at least in applied mathematics, is you have the idea...

It's the algebra.

It's often like...

I got to bound this, like, some of these random variables.

I need to bound it at this level to show that I can apply union bound or something like that.

Or I can't apply union bound here.

And there's dependencies, but they're correlated.

They're not quite negative correlated, but we could apply like a martingale here to try to keep the discorrelation.

And I don't quite remember how that works.

And the best mathematicians, applied mathematicians, just spent more time learning results and practicing results and reading results.