Jaden Schaefer

And I think the last thing that really helped was that researchers figured out some better techniques for training deep neural networks.

And this is like this is kind of where this deep learning comes in.

It's basically the idea that you stack a whole bunch of layers of neural networks to learn harder and more complex patterns.

And basically by kind of adding all of that, the data, the compute and that new strategy, everything changed.

So in the early 2010s, deep learning started to crush a lot of benchmarks.

It's also hilarious to talk about crushing benchmarks in 2010 because it's definitely different than what we have today.

But you had like image recognition that all of a sudden it actually worked.

You had speak recognition that got really good.

Translations went from being super terrible to usable.

I mean, I even remember early days of Google Translate.

You know, everyone would make fun of it.

And as time went on, it became really, really good.

So because of this, a lot of companies realized, look, this is actually scaling.

And so instead of just, you know, writing rules, you just give models more like these massive data sets and you're going to let them learn.

And so basically the more data...

you give them, the better they got.

The more compute you give them, the smarter they become.

And so I think when we kind of realized that, this kicked off basically what's known as the modern AI boom.

From there, everything got accelerated much faster.

Models got way bigger.