AI 不确定性数学：Zoubin Ghahramani 谈机器自我怀疑

Welcome to Google DeepMind, the podcast. Now, if you ask an AI a question, it will usually give you an absolute answer with unwavering authority, even if that answer turns out to be wrong. In fact, today's AI seems to be missing a fundamental human trait self-doubt. But long before the current wave of large language models, one academic researcher was trying to give machines a sense of their own limitations. Zoubin Ghahramani has spent the last 30 years pioneering a type of intelligence built on the mathematics of uncertainty.

Today, as a professor at Cambridge and co-lead of frontier AI at Google DeepMind, zoubin finds himself at the heart of another interesting debate. On the one side are those who are hoping that Pure Scale will be the answer to ever improving AI, and on the other are those like zoubin, who believe that true intelligence requires innovations in architecture, and that improving machine uncertainty may be one of the missing pieces.

Zubin, welcome to the podcast. Thank you. If you were to distill it all down, I mean, your central thesis is that we need to have uncertainty in. I mean, give me the top line of it. So yeah. Well if you think about intelligence, one of the most important parts of intelligence is decision making. Can't have an intelligent system that doesn't make decisions, from bacteria to animals to humans to robots decision making is really important.

And if you want to make decisions in the real world, our perception is limited. So we are always uncertain about the state of the real world. And we need to make decisions under uncertainty. We can't know everything. We don't know everything from our senses. We can't predict the future. And so fundamentally, to build an intelligent system, you need a system that can represent uncertainty, that can update its uncertainty and then can use that to make good decisions under uncertainty.

Because actually, I mean, there's two different types of uncertainty, I guess, right. There's the uncertainty of just the inherent randomness of the world. Yeah there's a pedestrian in a normal, typical street, and you just don't know which way they're going to. They're going to turn. But then there's the uncertainty of a scenario that you've never encountered before. Let me give an example from something that is becoming more and more of a reality in all our lives, which is self-driving cars.

So when you're in a self-driving car, the self-driving car has been trained on lots and lots of data. It's seen many, many scenarios. But you can imagine that there is what's called the long tail of things that could happen. For example, the car may have not been trained in many instances of hailstorms, and it may not have been trained with horses suddenly jumping in front of the car in a hailstorm. And so essentially, what you really want from an intelligent system is a certain self-awareness.

If we can use those terms, a self-awareness about its uncertainty. So it needs to be able to know the situation that it's in is something that is unusual or it hasn't seen before. And in the case of the self-driving car, for example, if it were to have a sense of its uncertainty, it would basically decide to slow down because it hasn't encountered that situation before. It's not confident that the horse isn't a bicycle or whatever it is.

There are many different kinds of uncertainty, but the beauty of it is that from a mathematical point of view, we can boil it all down to probabilities. So we can map all these different forms of uncertainty onto probabilities, and then use the rules of probability theory to manipulate uncertainty, update your state of uncertainty, et cetera. But how important is it that a machine can tell the different types of uncertainty apart.

Yeah, I think it's important insofar that the different types of uncertainty may mean different decisions. So for example, if you have what's called aleatoric uncertainty, which is the randomness of a coin flip, which is the pedestrian going to turn or which ways the pedestrian going to turn might want to decide that you're going to give up on trying to predict because it is just random, whereas in other cases, your state of belief is uncertain and you would want to collect more information to.

And in fact, that's the definition of information. So information a bit of information that we use in computer science is the reduction of your uncertainty by a factor of 2. That's what a bit is. And so collecting information is the way we reduce our uncertainty. How did these ideas of uncertainty map onto what humans are doing. So I actually studied cognitive science. I studied computer science and cognitive science as an undergraduate.

So I've always been interested in human intelligence as well as machine intelligence. And one of the really interesting things is that the field of cognitive science has really embraced these ideas of uncertainty and probabilities and so on, to try to understand both human perception and human decision making. So we go through our lives perceiving things. Perceiving the world is fundamentally an act of sensing something that is uncertain.

I can't see the back of your head, but I can infer what the back of your head might look like from you. Hope it was there. I would hope it's there. And similarly, let's say I'm hiking in Costa Rica and suddenly there's the rustling of the leaves. It may be a Jaguar. And so my sense is through evolution, my senses have developed to take in perceptual information, taken my prior beliefs, because there are Jaguars in Costa Rica, or there wouldn't be Jaguars in the middle of London.

And that process of combining information has been modeled by cognitive scientists and psychologists and neuroscientists through the language of probabilistic inference, basically. And also actually one of the interesting things is that we as humans are actually quite bad at estimating probabilities explicitly. If you ask somebody what is the probability of a certain event, they might get it wrong by orders of magnitude, for example.

And, we have these fallacies of probabilistic inference and belief. Kahneman and Tversky showed that humans are actually quite bad at representations of uncertainty at a conscious level. But in our perceptual systems, unconsciously, we tend to be quite good about these things because our survival depends on them. I'm thinking also about babies here. Toddlers and the way that they learn is a lot about their belief in what action will drive a particular outcome.

Yeah I mean, it's all implicit. Obviously babies don't know what probabilities are and they won't be able to write down any equations for you. But there are many cognitive scientists and psychologists who try to understand human learning, human perception, human decision making, using these same formalisms that we're using for AI systems. I think there are a few things here we should probably tease apart before we get really into it, because there's a difference between correctness and confidence, which both sometimes come under the umbrella of uncertainty.

Yeah, absolutely. A great example of this is we use AI systems for image classification all the time. So you give it an image and it gives you an answer of what's in the image. And we can measure correctness, but we also want it to tell us how confident it is. And more than a decade ago, people discovered that you can take an image, for example, of a school bus, modify just a few pixels in that image in an imperceptible way, so a human being would look at it and say, well, that's an image of a school bus.

You give it to the neural network and it confidently says, that's a cheetah. 99% that's a cheetah. And in fact, you could do it for any category. You could turn the school bus into a monkey or whatever in the eyes of the neural network. And so that sort of adversarial example shows us that it's not correctness that we care about alone. It's actually correctness and