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
because it's trained to imitate the kind of variables that it sees in the data, which mostly are what people are saying, when you query it, it's going to answer in the same semantics, and that is
you know, what persona that it currently is taking given its context would answer and not necessarily what it actually believes, right?
And that, the issue, the technical like problem here is we don't have like supervised labels to teach the AI
about what it should actually believe, right?
So we can't ask it about its true beliefs.
We only get a kind of reproduction of the distribution of variables that it sees in its training data.
So in the scientist AI, this is addressed by having this clear syntactic separation between the communication acts and more like factual syntax that it can be used for latent variables and true things that we know.
So we can query it using that factual syntax.
And then the other reason why we're getting away with some of the issues with the ELK challenge is that the same language, which is like English, let's say, can be used to represent those latent variables as well as the observed statements.
And so...
basically rely on the compositional structure of language to generalize to new sentences that it has never seen.
But the meaning of those sentences is given by its understanding of language.
And this is very different from the scenario studied by those who looked into the L challenge, where we assume that the latent variables are anonymous, like they don't have a predefined meaning.
And so we don't know where to look inside the neural net, if you want, about what the beliefs are, which motivates things like mechanistic interpretability and so on.
But in the scientist AI, we bypass this problem to some extent because the latent variables are in natural language and thus are interpretable.
Now, there could still be other beliefs that are not in natural language,
that are hidden in the neural net.
But at least when we ask questions in natural language, we're going to get a honest answer.
Well, so the math that I currently have would require to get the guarantees that we actually start the training from scratch, which is expensive.
And so we would lose the guarantees if we do just using, say, the Scientist AI fine-tuning on existing models.
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