Stuart Russell

speaker

1,902 appearances 3 recordings 3 series first heard Dec 2018 last heard 24 Jun

Stuart Russell’s voice in public audio — every appearance, attributed to the second.

Trend

recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Jun 2026 with 1.

Appearances

newest first · ▶ plays the moment

The Diary Of A CEO with Steven Bartlett · The Man Who Wrote The Book On AI: 2030 Might Be The Point Of No Return! We've Been Lied To About AI! · 4 Dec 2025 podcast

I think there are some weaknesses in the framework, which I can explain, but I think that framework could have evolved to develop actually safe AI systems where we could prove mathematically that the system is going to act in our interest.

the kind of AI systems we're building now, we don't understand how they work.

So with most machines, we designed it to have a certain behavior.

So we don't need to pull it apart and see what the cogs are because we put the cogs in there in the first place, right?

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?

Showing 501–520 of 1,902 · page 26 of 96