#243 – 'Godfather of AI' Yoshua Bengio: "I now see a path" to safe superintelligent AI
episodePreviously titled “I Know How to Build Safe Superintelligence | Yoshua Bengio, the most-cited AI researcher” — renamed by the publisher on Aug 4, 2026
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What approach does Yoshua Bengio propose for building safe superintelligence?
Today, I'm speaking with Joshua Bengio. He is the scientific director at Law Zero, a Turing Award winner in 2018, the most cited computer scientist of all time, and as it happens, also the most cited scientist of any type that is still alive.
How does Scientist AI differ from traditional language models?
Thanks so much for coming on the show, Joshua. Thanks for having me. You think you found the right approach to build a safe, super-intelligent AI. What's the approach?
What changes are needed in AI training data to ensure honesty?
It's based on a simple notion that if we can bake honesty into AI, we can get safety. So then we can reduce the problem to how we train a system to be honest. And it turns out that there's a way to do that, that only requires changing the training objective and the way the data is processed. There's also another aspect, which is it's a system relying on a non-agentic foundation that is a predictor that is not trained by reinforcement learning and is going to have these honesty guarantees. But we can then use this using the same kind of math to construct a policy, construct an agent that will be trained in a way that also provides those guarantees.
So what does the new training process look like and how is it different from the models that people are familiar with?
So the main difference with the training process is that it is geared at approximating the Bayesian posterior over queries in natural language. So imagine a neural net and some extra apparatus around it, like chain of thought style, that takes questions about statements regarding properties of the world that can be true or false. given other statements and then it outputs probability. That's the core building block, we call it a predictor. We can use stochastic gradient descent on a different objective that has the property that the objective is globally minimized by the Bayesian predictor. In other words, the predictor that fits the data and has a small description length.
So you'd be building a model where you would feed in a statement, and it would basically tell you what probability it assigns to that statement being true. Yes, in context.
Can Scientist AI be developed into an agent while maintaining safety?
Yes. Hey, listeners, Rob jumping in here. Joshua is naturally pitching this in a way that's ideal for staff at frontier AI companies. And they're obviously a particularly important audience for this proposal. But I'm confident with just a few minutes of plain language explanation, everyone else will be able to follow the rest of the conversation as well. So bear with me or skip ahead about four minutes if you feel very at home with this sort of material already. As you probably know, in their first stage of training, today's large language models are taught to predict the word that's most likely to come next, at least the token that's most likely to come next. And then in a second stage, reinforcement learning trains those models to produce the kinds of responses that we're most likely to say that we like, that we want, rather than just the responses that were most probable in the full corpus of all human-generated text.
Now, Joshua's alternative is to build an AR model oriented not around predicting what a human would be likely to say or what they would prefer to hear, but around modeling what's actually true in the world by developing hypotheses and assigning probabilities to them with the goal of best explaining all of the data that it's exposed to during its training process. Joshua argues that you'd be able to train a model of this type while porting over most of the methods we use to train ordinary LLMs today, benefiting from the same neural net architectures, training techniques, scanning improvements, all of that. And you'd also be able to train it on roughly the same body of raw text that we use for all other AIs.
Why is Yoshua Bengio optimistic about AI alignment now?
But you could structure that data a bit differently, giving it what AI researchers call a different syntax. First, all of the things that people said or wrote, they get tagged as communication acts. We know someone said these things and we know where they said it, but we don't know whether they're true. And second, a small number of statements that we have strong independent grounds for, verified mathematical proofs and some scientific measurements, they get tagged as verified factual claims about the world.
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Chapters
8 chapters
1
What approach does Yoshua Bengio propose for building safe superintelligence?
0:00–0:13
2
How does Scientist AI differ from traditional language models?
0:13–0:20
3
What changes are needed in AI training data to ensure honesty?
0:20–2:26
4
Can Scientist AI be developed into an agent while maintaining safety?
2:26–3:42
5
Why is Yoshua Bengio optimistic about AI alignment now?
3:42–4:19
6
What challenges do AI companies face in achieving safety?
4:19–5:51
7
How close are we to a working prototype of Scientist AI?
5:51–6:22
8
What implications does the future of AI hold for society?
6:22–2:33:18
Speakers
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#251 – The UK's former head AI safety scientist on how to solve alignment before superintelligence arrives | Geoffrey Irving