Shane Legg: belief

Transcript context

…It’s evolved a bit. When I did my thesis work around universal intelligence, I was trying to come up with an extremely universal, general, mathematically clean framework for defining and measuring intelligence. I think there were aspects of that that were successful. I think in my own mind, it clarified the nature of intelligence as being able to perform well in lots of different domains and different tasks and so on. It's about that sort of capability of performance and the breadth of performance. I found that was quite helpful and enlightening. There was always the issue of the reference machine. In the framework, you have a weighting of things according to the complexity. It's like an Occam's razor type of thing, where you weight tasks and environments which are simpler, more highly. You’ve got a countable space of semi-computable environments. And that Kolmogorov complexity measure has something built into it, which is called a reference machine. And that's a free parameter. So that means that the intelligence measure has a free parameter in it and as you change that free parameter, it changes the weighting and the distribution over the space of all the different tasks and environments. This is sort of an unresolved part of the whole problem. So what reference machine should we ideally use? There's no universal reference machine. People will usually put a Turing machine in there, but there are many kinds of different machines. Given that it's a free parameter, I think the most natural thing to do is to think about what's meaningful to us in terms of intelligence. I think human intelligence is meaningful to us in the environment that we live in. We know what human intelligence is. We are human too. We interact with other people who have human intelligence. We know that human intelligence is possible, obviously, because it exists in the world. We know that human intelligence is very, very powerful because it's affected the world profoundly in countless ways. And we know if human level intelligence was achieved, that would be economically transformative because the types of cognitive tasks people do in the economy could be done by machines then. And it would be philosophically important because this is sort of how we often think about intelligence. Historically it would be a key point. So I think that human intelligence in a human-like environment is quite a natural sort of reference point. You could imagine setting your reference machine to be such that it emphasizes the kinds of environments that we live in as opposed to some abstract mathematical environment. And so that's how I've kind of gone on this journey of — “Let's try to define a completely universal, clean, mathematical notion of intelligence” to “Well, it's got a free parameter. “ One way of thinking about it is to think more concretely about human intelligence and build machines that can match human intelligence. l notion of intelligence” to “Well, it's got a free parameter. “ One way of thinking about it is to think more concretely about human intelligence and build machines that can match human intelligence. Because we understand what that is and we know that that is a very powerful thing. It has economic, philosophical and historical importance. The other aspect of course is that, in this pure formulation of Kolmogorov complexity, it's actually not computable. I also knew that there was a limitation at the time but it was an effort to just theoretically come up with a clean definition. I think we can sort of get there, but we have this issue of a reference machine, which is unspecified. Before we move on, I do want to ask a question on the original point you made on LLMs needing episodic memory. You said that these are problems that we can solve and these are not fundamental impediments. But when you say that, do you think they will just be solved by scale or do each of these need a fine-grained specific solution that is architectural in nature?…

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