Jaden Schaefer

So instead of telling a computer exactly what to do, you let it learn from data.

And the idea was inspired by human brain neurons, the connections, and then kind of learning from experience.

So

Early kind of versions of neural networks existed like all the way as far back as the 1950s, but they were super, super limited.

Computers were very slow.

Data, of course, there's not a lot of data on this and the math was very hard.

So for many decades, these kind of neural networks were basically ignored.

But that all stopped after three main things happened.

So first, of course, data exploded.

You have the internet, you have smartphones, you have social media, so so much data is being created.

And suddenly we have all of this data specifically about languages and images and behavior and everything.

So all of this data.

And then second...

compute got super, super cheap and also powerful.

So the GPUs that were, you know, originally built for gaming, they turned out to be really perfect for training neural networks.

And I mean, I would even say go so far as to say, like a lot of the hardware that was built for crypto mining.

And then when the crypto winter came, that just kind of perfectly pivoted into AI.

And we had like all of this infrastructure built out that had we not been through that, we wouldn't have been able to kind of uptick training AI models as fast as we did.

So

Those that all helped.