Yoshua Bengio

speaker

2,216 appearances 5 recordings 5 series first heard Oct 2018 last heard 7 May

Yoshua Bengio’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 — 3 in all, peaking in May 2026 with 1.

Appearances

newest first · ▶ plays the moment

80,000 Hours Podcast · #243 – 'Godfather of AI' Yoshua Bengio: "I now see a path" to safe superintelligent AI · 7 May 2026 podcast

So these biases would be not something like a small number of people believe, but more like most people believe something wrong.

which induces discrimination, for example.

And there, the evidence is very clear.

The current LLMs are biased in the same general way that the population is biased right now.

And the scientists wouldn't be falling for that as easily because it would look for both what is a good explanation for what people are saying and that this explanation has to be coherent with all the other things that it knows or that it has seen.

I agree, but I also think it's very important to use the theory to guide us in making the right scrappy choices, right?

So for example, in the math for the scientist AI, we can see some requirements, for example, not using reinforcement learning to learn how to make good predictions.

In fact, stronger than that, making sure that the way it's trained

doesn't get any signal about what would be the consequences of its predictions.

So they may seem like very, algorithmically, that's very small changes to how we would train a predictor, but they give us the guarantee.

So we might as well use those particular requirements that come out of the theory.

I think that the part about being scrappy is more...

because of the cost of, you know, training large models and the engineering has to be efficient and all these things.

And we should,

be willing to cut corners on that.

And in our plan, this is why we are prioritizing the non-agentic predictor that can be used as a guardrail, which would already mitigate some of the issues and doesn't require a big overhaul of the systems that currently exist, but just is an add-on.

So that's much more likely to be adopted than something that requires a lot of investment, not just because of training the models, but because people are kind of focused on this current recipe and there's so much competition between the companies that it's very hard for the companies to allocate even attention.

It's not even money, it's attention to a slightly different way of doing things.

Yeah, I'm pretty sure that a small percentage of error is not going to make much difference.

But also, there are

Showing 601–620 of 2,216 · page 31 of 111