Titans of Science: Mike Wooldridge

0:00Mike Wooldridge:Youth mental health is a complex challenge that requires comprehensive solutions. We must strengthen after-school programs. We must make digital literacy tools available in our schools.

0:11Chris Smith:We must work with mental health professionals to support children. And we must empower mentors, educators, and parents to keep kids happy. Learn more about our commitment to finding lasting solutions at empowerourfuturecoalition.com slash solutions. Paid for by the Coalition to Empower Our Future.

0:49Mike Wooldridge:Hello, welcome to the Naked Scientist podcast, the programme that brings you the biggest breakthroughs and also talks to the major movers and shakers in the worlds of science, technology and medicine. I'm Chris Smith and today we're shining the spotlight on the transformative technology that is AI and finding out from AI pioneer and this week's Titan of Science, Mike Waldridge, how it works, whether we can trust it and what the future holds.

1:22Mike Wooldridge:Oxford University's Mike Waldridge is an AI pioneer. He struck out boldly into the uncharted waters of this emerging field after a degree and then a PhD in computer science in the 1980s. He was very much an early adopter and a visionary. Ample qualifications to be our titan of science this week. And he's going to share his story and bring us up to speed on how this fast-paced field is evolving and where it's headed. Now, for reasons that will become clear later in the programme, you'll see why I haven't told you much more at this stage about Mike's career. So let's begin with how he got into computer science in the first place.

2:01Chris Smith:So I was of that generation that grew up with the Apollo space programme in the background. I can remember my parents waking me up to see one of the splashdowns from the Apollo spaceships. And that idea of space travel and technology was very, very much in the air. So I was always kind of scientifically minded. I very much wanted to be an astronaut until I discovered just how difficult it was to be an astronaut. But a big moment for me is early 1980. And I grew up in the town of Hereford, rural town. The main industry was making cider and the main leisure activity was drinking it. In this rural town, I met with some friends on a Saturday afternoon and they said, there's a shop that sells computers down the road.

2:43Chris Smith:And this seemed completely outlandish to me because I, in my head, computers were things that cost millions of pounds. The idea that you'd go into a shop in Hereford and buy one just seemed ridiculous. But we went down to this shop. It's called a Tandy store. And indeed, there in the window was a computer. And we went into the shop and we chatted to the guys behind the counter. And they literally said, play it, go ahead. They gave us some programs to type in. We had no clue what we were doing at the time. And we typed them in. And then over the next couple of weeks and months, I went back to that shop really a lot.

3:19Chris Smith:They must have regretted being so generous to me, I suspect. but I was sat in the window of that shop and I learned to program and the computer we used a TRS-80 model one was very very crude but it was the first time that an ordinary family could afford a home computer.

3:37Mike Wooldridge:Did you buy one though? Did their investment in you letting you sit there in the shop window? Did you buy one?

3:44Chris Smith:I would have loved a TRS-80. We couldn't afford one. I mean they were 400 odd pounds at the time. That was actually well out of my pocket money range. But my parents, for a birthday, bought me a Sinclair ZX80. We bought it secondhand and it was 70 pounds. And I remember being so excited when I got it home. And much of the following year or so was wasted with me learning how to program that and writing in computer games from magazines, typing them in and running them and debugging them and so on. I remember it being a very, very happy time that you could in your head think up a program and type it in and then get the machine to do something you know it would follow your instructions and you would create something and that was really really great fun but that set me then on the path for computing and I knew that that's what I wanted to do.

4:34Mike Wooldridge:Did you know that you could do computing at university though and did you know that that was the logical step?

4:41Chris Smith:Nobody in my family had a degree and only had the dimmest understanding, honestly, of what they were. Both of my parents had left school certainly by 16, but I think in my dad's case, he left at 14. I'm afraid despite my passion for computing, I wasn't a very diligent student at school or sixth form, so I didn't work terribly hard. But I did get a place on a computing degree. And that just confirmed for me that computing was what I wanted to do. You know, this was then the mid 80s. And this is when IBM PCs became a thing and all of a sudden businesses ordinary businesses could start to think about having computers and how they might use them for word processing and spreadsheets that's when all that stuff was invented and so there was a big one of the big expansions in computing at the time

5:26Mike Wooldridge:this is obviously in the pre-internet era that was 10 years away from mainstream wasn't it at that time but it still existed there were still networks because i remember my school at that time had, I mean, we used BBC microcomputers and we had Econet and all that kind of thing, but we still had dial-up modems and we would dial in to this network and share academic data among schools and universities and things. And there was a little club where we would make pages that would go up. And that was really a prelude. It's what lit my fire. I mean, were you doing that kind of thing as well?

6:01Chris Smith:Yeah. So what my story is that I did what we now call an internship, but used to be back in the day called an industrial placement for a group called the joint network team jnt who were based at rutherford appleton laboratory and they basically managed the uk branch of what became the internet it wasn't called the internet really at that time i think it was still called arpanet and the uk branch was called janet joint academic network and not every university i think at that point in the uk was even connected to it but virtually nothing outside universities, a couple of government establishments and military establishments were connected.

6:36Chris Smith:But the joint network team were busy thinking about the future of computer networks. And after that period with them, I absolutely understood that the future of computing was going to be networked. And this was not a big thing at the time, but I really, really understood this is where it's going. I mean, I can remember getting my first electronic mail message and being so excited about getting an electronic mail message sometime in would have been 87. But I say the point is at the end of that period, I really, really got it that the future of computing was going to be networked.

7:10Mike Wooldridge:It's a bit like Bill Gates coming back from that conference at Microsoft and saying, it's all about the internet now. That was a sort of epiphany for you, albeit earlier. But how does that become an interest in AI?

7:23Chris Smith:In my final year, I got to specialise and I absolutely wanted to specialize in networks, but I also found AI really, really interesting. And in my head, these two things came together. And I realized, look, if the future of computing is going to be networks, then that must be the future of AI as well. It won't just be a chatbot that's having a conversation with a human being. It will be AI programs that are talking to each other. I had that idea as an undergraduate beginning of 1989 or thereabouts, and that's what I went and did my PhD in. But it was an uphill struggle to convince people that we were going to build these AI programs that were called agents that were going to communicate with one another.

8:04Chris Smith:People were like, well, why? What's the point? And we're like, because it's inevitable. It's the future. The idea that you would have these AI programs that operate on behalf of users, that would be, you would have your agent, Chris, and I would have my agent. We want to arrange a meeting. why doesn't it just talk directly to your AI program? This is why don't the agents just communicate directly with one another? So we had this idea of AI programs operating on behalf of their users and interacting with each other. And that became this very substantial research field called multi-agent systems and what's now called agentic AI.

8:43Mike Wooldridge:I think you're pointing towards one of the big problems that the industry has, a sort of image problem in the sense that it seems to be omnipotent. AI can do everything. It's lots of different things, lots of different platforms, lots of different technologies. I'm going to ask you an almost impossible question, which is, can you try and break it down a bit for us so you can explain what these different elements of AI are and why they matter, each of them? Sure.

9:10Chris Smith:When I was an undergraduate in the 80s, the big thing at the time was what's called symbolic AI. And the question is, suppose you want to build AI that could, I don't know, translate from French to English. How are you going to do that? Well, the big idea of symbolic AI is what you need to do is you need to model the cognitive processes that a human being doing that task would carry out. In other words, the kind of the mental conversation that they would have with themselves, you would explicitly model that. It was called symbolic AI because the things that you were working with were more or less like words in a language they were symbols and symbolic AI was good for some things for mathematics but it was hopeless at anything that requires perception vision or understanding a spoken word driving a car riding a bicycle anything that involves understanding the world around you it was just really really hopeless at and it kind of ground to a halt.

10:07Chris Smith:And by the late 80s, it was very rapidly going out of fashion. There weren't, I have to say, big ideas that replaced it at the time. But lurking in the background was an idea that had been around since the 1940s called neural networks. So remember, in symbolic AI, what you're trying to do is model the mind. With neural networks, instead, what you're trying to do is kind of model the brain. We can look at a brain under a microscope and we can see what's going on there. And what we see is enormous numbers of nerve cells connected to one another in vast networks. And we know that all of human intelligence somehow reduces to the 90 odd billion neurons in a human brain, which are arranged in an enormous network.

10:53Chris Smith:So the idea of neural networks is, can we do in software what those natural neural networks do in wetware, the stuff that's in our brains? Theoretically, the answer was known a long, long time ago. Yeah, in principle, you can, but people just didn't know how to really build these things. And one of the really interesting things is all of the key scientific ideas underneath the current generation of AI, which is all based on neural networks, were invented as far back, in fact, as the 1980s. Many of your listeners, and I know you have, will have heard of Jeff Hinton, who's often described as the godfather of AI.

11:29Chris Smith:Jeff and his students and his colleague, and there was a large number of people working in this space, they invented all of the key techniques to make neural networks work. What they didn't have were computers that were powerful enough, and you need training data in order to be able to build these things.

11:45Mike Wooldridge:So that was the bottleneck then, it was the computing grunt that we just didn't have. We had the aspiration, we had the insight, we didn't have the technology to power it.

11:55Chris Smith:Yeah, and my honest belief is if Jeff and his colleagues had had the computer power that they now have available, which is, I mean, it's hard to contemplate just how much computer power is used to build AI programs at the moment, which are literally astronomical quantities of computer power. If Jeff and his colleagues had had that at their disposal in the 1980s, I really think the AI revolution that we've seen over the last 15 years or so would have happened then. But they didn't. And Jeff, I think he's pretty much on record as being still really quite bitter about this. Neural networks were unfashionable.

12:30Chris Smith:And at the turn of this century, just 25 years ago, I had colleagues that were working in this space who would have their grant proposals rejected. And the reasons for rejection would people would say, this neural network stuff's not going anywhere. It's pseudoscience. It's not proper science. We're not going to fund this and that's how unpopular neural networks were it's really remarkable to think about it now

12:51Mike Wooldridge:well what's also remarkable though don't you think mike is that you're saying that we've got computers that have extraordinary power and a massive power consumption to run them and they're mimicking what's running in your head and my head which is using about 20 watts 20 watts indeed

13:08Chris Smith:What that demonstrates to us is that current machine learning algorithms are far too power intensive and they require far, far too much training data. A human being learns to read with much less training data than an AI program requires. How many hours does a typical human driver spend on the road learning how to drive? 20 hours or something. Driverless car companies have spent thousands, almost certainly millions of hours on the road, and we don't have perfect driverless cars yet. So what's wrong?

13:40Mike Wooldridge:Is that our model is wrong? Is it that the computers are just not powerful enough yet? And where do you think the problem is?

13:46Chris Smith:No, I think it's fundamentally that the basic way that we train neural networks at the moment, which is a technique that Jeff pioneered in this space called backpropagation, is just not a very efficient algorithm. If somebody could invent an algorithm to training artificial neural networks, which required literally as little electrical energy as the energy that a human brain requires, that would be a completely transformational moment. But I don't believe anybody's seen anything yet that will do that. So that's how the space got transformed. I mean, and it started to happen around about 2005. And then it became supercharged in 2012 because people realized that if you could use GPUs, graphics processing units, for building neural networks, you got 10 times as much bang for your buck.

14:33Chris Smith:That is, you could build neural networks that were 10 times bigger or you could train them 10 times faster. But you got 10 times more neural network for your money. And that then supercharged the whole space.

14:45Mike Wooldridge:Can you explain for us, when you say that Geoff Hinton's big insight was this back propagation approach, how does it actually work? How do these networks work, albeit in a slightly inefficient way at the moment, but what's the basic premise of their function?

15:04Chris Smith:so the key point in a neural network is that you want to automatically configure the neural network so that when you present it with an input and the input maybe is a picture of chris smith and the desired output the name chris smith you want to automatically adjust the network so it's getting closer to the right output for the given input jeff and his colleagues came up with the back propagation technique, which is basically based on calculus. It's not very advanced calculus. Leibniz would have understood the calculus that's required for back propagation. It's called the chain rule for those that know about calculus.

15:41Chris Smith:But basically, it involves working backwards from the output of the network and trying to automatically adjust the network going back to the inputs. Now, the mathematics is not very sophisticated, but there's a heck of a lot of it that you have to do. That was the challenge. It's just that, you know, the amount of computing that you have to do to be able to reconfigure a neural network using that backpropagation process. And you have to do this training process repeatedly. You can't just show a neural network, one picture of Chris Smith labeled with the name, right? So you've got an input, a picture of Chris, you've got the output, the name of the person that appears there.

16:20Chris Smith:You have to train the neural network repeatedly with many pictures of you with the same name over again. And actually, you have to show it the same picture over and over again. But what you're doing throughout that process is every time you show it the input and the output, you're adjusting the network using back propagation. So it's getting closer to the desired output for the given input. And that process repeats until what's called the loss. That is, it's the errors that it's making are getting sufficiently small that you're sufficiently happy with its capabilities.

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17:48Mike Wooldridge:time is make stuff up. I mean, I've seen it for myself. We've put it to the test. We've asked it about various medical things to see what the rate of so-called confabulation is. Why does that happen? Why does it generate stuff that just doesn't exist? And what's the mechanism of that? And can we cure it?

18:06Chris Smith:Well, I think the fundamental point, it's a really important point for your listeners, is this technology is not designed to speak the truth. That's not what it's doing at all. What it's doing is exactly as you say, it's a prompt completion. Given a piece of text, it's designed to produce the likeliest next word and then the likeliest next word after that and the likeliest next word after that, given its training data. And the training data for large language models is, roughly speaking, all of the digital text that's available in the world. You scrape the whole of the World Wide Web and every bit of digital text that you can get, which is all thrown at the neural networks so that it can learn, given this phrase, what the likeliest next word to appear is.

18:46Chris Smith:And then you repeat that process. What's the likeliest next word after that and the next word after that and the next word after that. So you're appending each word that you generate to the previous ones. And that's literally as you see the words appearing on a large language model. When you give it a prompt, that's literally what it's doing. So why does it make mistakes? It's not looking things up in a database of the truth and producing the output. It's not computing the right answer for you. That's not what it's doing either. What it's doing is just based on the training data and the prompt that you give, what's the most plausible next word to appear.

19:19Chris Smith:So there's no reason that it should actually be speaking the truth. In the absence of having seen anything in the training data, intuitively, it will make its best guess about what should go there. So there's an example that you will relate to, I think, very much from my life when I was playing with GPT-3, which was really one of the breakthrough systems. It was the predecessor to chat GPT. And I asked it about me and it did a pretty good job of a few sentences coming up with known for AI and multi-agent systems. Great. This is my it's what I made my career. And then it said studied undergraduate degree at Cambridge, a marvelous institution, which I never went anywhere near.

19:56Chris Smith:I never studied there, never had a job there, was never a student there, never a postdoc or anything. Why is it doing that? Well, intuitively, my guess is, you know, for an Oxford professor studying at Oxford or Cambridge is very, very common. There will have been lots of biographies in the training data where it will have seen that. And so in the absence of any more detailed information, it just fills in the gap in a plausible way. Another way of thinking about it is that what these models are doing is compressing large amounts of data. So they've seen all of the digital data in the World Wide Web and somehow that's being compressed into the neural network.