Terence Tao

Science has changed in the century since.

So classically, sort of the two big paradigms for science were theory and experiment.

Then in the 20th century, numerical simulation came along.

And so you can also do computer simulations to test theories.

But then finally, in the late 20th century, we had big data.

We had the era of data analysis.

And so a lot of new progress is actually driven now by analyzing massive datasets first, collecting large datasets, and then drawing the patterns from them to deduce laws, which is a little bit different from how science used to work, where you make a few observations or you just have one

out of the blue idea.

And then you collect data to test your idea.

That's the classic scientific method.

Now it's almost reverse.

You collect big data first and then you try to get hypotheses from it.

I mean, Kepler was maybe one of the first early data scientists, but even he didn't start with Tycho's data set and analyze it.

He had some preconceived theories first.

But it seems like this is less and less the way we make progress just because the data is just so much more massive.

It's just so much more useful.

Yeah, yeah, yeah.

So the data was extremely important, but the distinction I was trying to make was that sort of traditionally you make a hypothesis and then you test it against data.

But now with machine learning and data analysis and statistics and so on, you can start with data

And through, say, statistics, work out laws that were not present before.