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.

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Recordings per month over the last 12 months — 3 in all, peaking in May 2026 with 1.

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

We're learning that joint distribution, including the latent variables, the ones that we don't observe, because of course, these are the ones we care about.

We want to ask questions about the things we don't know already the answer.

Now, there is an important element here, which is

Most of the topics that we would like the AI to make predictions over, we don't have ground truth about, for example, what people actually want or things that have to do with humans or psychology or history.

Usually the only thing we have are communication acts.

Some people said this thing, some people said something else, and often they contradict each other, right?

So there are two things here to help us deal with this kind of mismatch.

One is that the training objective for the scientist AI is basically about coming up with

So assigning probabilities to statements that are latent, that we don't observe, that are good at explaining the data we do observe.

So if we observe somebody saying the earth is flat,

First, it's going to understand it doesn't mean that the earth is flat.

It means that this person believes or says actually that the earth is flat.

And even if a lot of people were to say the earth is flat, it doesn't make the model believe that the earth is flat because there may be a better explanation

that is consistent with other sources of data, like everything we know about the planet.

And so a better explanation here is that these people form a group and they have these false beliefs like many humans have for all kinds of psychological and cultural reasons.

So that's what the scientist AI would do.

It would be trained, like its objective would be optimized when it finds good predictive explanations.

Now, another trick that is gonna help us in this process is that when we train the scientist AI and it's trying to predict a communication act, like somebody said the earth is flat,

we automatically are going to make sure that among the latent variables that are gonna be used to explain that will be whether the earth is flat or not.

So even in domains where we don't have observed truth about something, property of the world, because we basically only have communication acts, we will force the neural net to commit

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