Stuart Russell

One by one, we figured out what the pieces needed to be, how they work together to produce the effect.

that we want.

So the best analogy I can come up with is, you know, the first cave person who left a bowl of fruit in the sun and forgot about it and then came back a few weeks later and there was sort of this big soupy thing and they drank it and got completely shit-faced.

They got drunk.

And they got this effect.

They had no idea how it worked, but they were very happy about it.

No doubt that person made a lot of money from it.

So, yeah, it is kind of bizarre, but my mental picture of these things is like a chain link fence, right?

So you've got lots of these connections, and each of those connections can be, its connection strength can be adjusted.

And then, you know, a signal comes in one end of this chain link fence and passes through all these connections and comes out the other end.

And the signal that comes out the other end is affected by your adjusting of all the connection strengths.

So what you do is you get a whole lot of training data and you adjust all those connection strengths so that the signal that comes out the other end of the network is the right answer to the question.

So if your training data is lots of photographs of animals, then all those pixels go in one end of the network.

And out the other end, you know, it activates the llama output or the dog output or the cat output or the ostrich output.

And you just keep adjusting all the connection strengths in this network until the outputs of the network are the ones you want.

So what's going on inside that network?

Well, so now you have to imagine that this network, this chain link fence, is a thousand square miles in extent.

So it's covering the whole of the San Francisco Bay Area or the whole of London inside the M25, right?

That's how big it is.

It's nighttime.