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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Appearances
not to the truth of the underlying claim, but to the probability of that underlying claim, right?
As well as trying to find other latent variables that are good explanations to that, just like a good scientist would, right?
So a scientist or like a psychologist trying to understand
why a person said something isn't necessarily just going to believe what they say, right?
They're gonna try to understand what are the psychological factors here or the particular culture of that person that make them say those things.
So the scientists and I would do exactly the same thing.
Yes.
And, you know, in part is the way I've been communicating this, which could have been better.
I focused a lot in my presentations on the concept that we can build predictors that are non-agentic and don't have hidden goals, don't have implicit goals.
And thus we could use as safe oracles, basically.
But as you're pointing out, what the world is demanding and building are these agents that have goals.
So how does that help us, right?
In the short term, we can use a non-agentic predictor to improve the guardrails that companies are already using as monitors around existing untrusted agentic AI systems.
Because in order to prevent a bad action from happening, it's sufficient to make a non-agentic prediction about the harms, the probability of harms of various kinds that could be caused by this action.
So a non-agentic system is already something that could be useful fairly early on.
The maybe more important answer is in our research program, the next step after the guardrail is to use the same kind of principles to design an agentic scientist AI.
So an agent that has the same kind of safety guarantees.
So this is something I've been working on more recently, and I haven't talked much about.
but we can reuse the same kind of math that is used to show the safety of the non-agentic scientist AI predictor to show that you can reuse a predictor and you can train it in a modified way that will provide the same kind of guarantees.
The starting point here is that once you have this harness predictor, you can ask it agentic questions like, what is the probability that this action will lead to this maybe user goal being achieved and a safety goal being achieved in some contexts?
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