The mathematics of AI uncertainty

**Hannah Fry** (0:00)
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-leader 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.
Zoubin, welcome to the podcast.

**Zoubin Ghahramani** (1:06)
Thank you.

**Hannah Fry** (1:07)
If you were to distill it all down, I mean, your central thesis is that we need to have uncertainty in AI. Just, I mean, give me the top line of it.

**Zoubin Ghahramani** (1:16)
So why? Yeah. Why? Well, you know, if you think about intelligence, one of the most important parts of intelligence is decision making. Like, you can't have an intelligence system that doesn't make decisions. You know, from bacteria to animals to humans to robots, you know, 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 intelligence 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.

**Hannah Fry** (2:10)
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.

**Zoubin Ghahramani** (2:18)
Yeah.

**Hannah Fry** (2:19)
There's a pedestrian and a normal typical streets.

**Zoubin Ghahramani** (2:22)
Yeah.

**Hannah Fry** (2:22)
And you just don't know which way they're going to turn.

**Zoubin Ghahramani** (2:25)
Yeah.

**Hannah Fry** (2:25)
But then there's the uncertainty of like a scenario that you've never encountered before.

**Zoubin Ghahramani** (2:29)
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. Like 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 intelligence 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, right? 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, etc.

**Hannah Fry** (4:05)
But how important is it that a machine can tell the different types of uncertainty apart?

**Zoubin Ghahramani** (4:10)
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 sort of randomness of a coin flip.

**Hannah Fry** (4:26)
Which way is the pedestrian going to turn?

**Zoubin Ghahramani** (4:27)
Or which way is the pedestrian going to turn? You 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, right? So information, a bit of information that we use in computer science is the reduction of your uncertainty by a factor of two. That's what a bit is. And so collecting information is the way we reduce our uncertainty.