Ramin Nakisa

Sounds simple, right?

So dog, dogs.

Pig, pigs.

But then you've got irregulars like child, children.

Where does that come from?

And then kids have to learn all this crazy stuff.

So we taught these neural networks and they'd make the same mistakes as kids.

And then we'd have to work out how the neural network did it.

So we had to work out these representation spaces.

Think of it like an embedding, yeah.

Where you represent the meaning of something.

It could be an image, it could be a sentence.

And now that's exactly what these AI models use, these embeddings and this idea of meaning.

So, you know, all of this stuff, it's all connected together.

It's just so beautiful.

And that's what I mean by filling your intellectual bag as you go along.

A lot of this stuff resurfaces.

And I just think that's fascinating.

I think the market kind of made up my mind.

I mean, I'd written a lot of tools for our community members to work out their number, like Monte Carlo simulation, where you kind of work out, given the randomness of markets, am I going to hit my target?