Emerging-market stocks have been doing incredibly well lately, with the artificial intelligence boom pushing equities in Asia to record highs. But lots of people – including Pope Leo and some tech leaders – are less happy, and are calling for stricter regulation of the AI sector to protect jobs and privacy. So let’s first hear what two experts themselves think. Zoe Kleinman is the BBC’s technology editor, and Mike Wooldridge is a professor of computer science at the University of Oxford who has devoted his research career to the subject of AI. They have been speaking with Chris Smith…
Mike – AI is about getting machines to do things that currently require human or animal brains, or potentially nervous systems, or potentially human bodies. And one of the weird things about AI is that lots of things that we find very, very difficult, AI has been very good at for a long time. Like, so playing grandmaster level chess, computers have been able to do that now for 30 years, and yet most people find that a very demanding challenge. Whereas some things that we find basically trivial, like riding a bicycle, driving a car, turn out to be phenomenally hard for AI. So, AI is about pushing back the frontiers of what machines can do. And the current approach, which has turned out to work extraordinarily well, I think much better than many people expected, is that if you start with a huge amount of data, in particular, for example, textual data, just ordinary written text, just the kind of stuff that you can pull off social media or the World Wide Web, and you train an AI model on huge, huge quantities of that, it turns out to be rather good at being able to, for example, summarise text, answer questions about text, and so on. And so that’s what’s led to the current AI boom, which is around large language models like ChatGPT. And the thing that turned out to work extremely well after 70 years is to throw incredible amounts of data at the problem. And to process all of that data and build the AI, you need incredible quantities of computer power.
Chris – I was going to say incredible quantities of money as well. Would you go along with that, Zoe? What does the tech sphere say AI does very, very well?
Zoe -Traditionally, AI is basically very good at pattern spotting. And that is because, as Mike just said, it’s trained on a huge amount of data. And so it can fairly confidently predict what’s going to come next in a sequence, whether that sequence is numbers or words in a conversation or anything else that you’re throwing at it. It’s really, really good in some forms of healthcare, specifically spotting symptoms on x-rays or scans. I did a story for the BBC a couple of years ago. I went to see a tool that had been trained to spot the early signs of breast cancer in women’s routine mammograms. And this tool had identified 11 tiny, tiny changes that the human doctors had missed when they’d looked at the scans and, you know, potentially saved these women’s lives because they wouldn’t have known about it. They had no symptoms. They wouldn’t have had another scan for a few years, by which point it would have grown into potentially something, you know, more difficult to treat. It was a really humbling and lovely story to do, actually. And I interviewed one of the women who said cancer was massively in her family. She was so grateful to this tool that had potentially saved her life. But the other side of that was that there were hundreds of false alarms because we’re talking about patient data and obviously people have very strong feelings about their medical history and their medical data and the privacy that should surround that. So this tool wasn’t given anybody’s medical history. So in addition to finding these 11 cancers, it flagged hundreds and hundreds of, you know, lumps and bumps, breast tissue, I’m told is notoriously lumpy and bumpy, that had already been discredited, but it didn’t know that. And I think that kind of sums up for me, you know, where we are with AI and healthcare, we kind of really want it because it can really speed up, really help and come up with new formulas for drugs. It can churn through existing formulas of drugs at speeds we’ve never been able to manage before. But coupled up with that is this sort of, are we ready to let it loose on everybody’s data?
Chris – Downsides aside that Zoe was just referencing there, Mike, in terms of things like data concerns and so on, what about the downsides of AI? What does it not do very well that we had higher aspirations for and have been disappointed?
Mike – Well, Zoe, I think is exactly characterised what AI is good at, which is picking up on patterns. What it doesn’t do is what many people I think imagine it does. Many people imagine that when you give a problem to AI, it goes away and it computes very cleverly the solution to the problem that you’ve given it. That is exactly not what it’s doing. What it’s picking up is patterns in the problem that you’ve given and basically it’s matching those against all the examples it’s seen in its training data. So there’s a famous example that went viral a couple of years ago. Somebody tried the following prompt. How many R’s are there in the word strawberry? ChatGPT came back. There are two R’s in the word strawberry and the user said, are you sure about that? Yes, two R’s in the word strawberry. Are you absolutely sure about that? Yes, I’m absolutely sure. Would you bet a million dollars? And ChatGPT said, yes, I would bet a million dollars. We presume that isn’t a binding bet. But the point is there, the computer code to count the number of R’s in a word is one line. It’s trivial, absolutely trivial. And yet that’s not what the most sophisticated AI in the world is doing. It’s not counting the number of R’s. It’s just pattern matching and probably just picking up on the word berry rather than the word strawberry. So that’s at the nub of the problems that we have with contemporary AI. If you’ve got a problem which matches something very well in the training data that they’ve seen when they’ve been trained, then you can expect a pretty good response. But for unusual situations, situations that are outside the training data, for example, it’s not going to be very good at. And in fact, actually, the evidence is it’s very bad at. But also more fundamentally, what it’s not doing is actually computing the answer for you. It’s just pattern matching exactly as Zoe said.
Chris – It’s not putting us in a position with what it knows already, Zoe, to discover anything new. But that doesn’t mean it can’t tell us where the gaps in our knowledge are and therefore point us in the right direction, does it?
Zoe – What both excites me and terrifies me about AI is that it is at its worst right now than it will ever be. Because every single moment it’s getting better. And these tools and these systems are getting more powerful. And they are able increasingly to do a variety of different tasks better than they did the day before, the month before, the year before. I mean, you only have to look at how quickly the industry is scaling up, if you like, to see that this tech is not hanging about. By the time we’re having this conversation now, I don’t know, there might be a tool released tomorrow that will completely invalidate what I’ve just said. It’s really moving that quickly. And there has been a sense that the more machines you can throw at it and the more data you can throw at it, the better that that will be. And we have seen that. It’s kind of slowed down now. But to an extent, that’s what this kind of mad chase has been to throw as much money and data as possible at this technology to see what it can do next. And I think we’ve seen some real advances in things like coding, the whole kind of vibe coding thing, which started off not being brilliant and is now getting better. And the cybersecurity stuff is amazing. You know, you’ve got this tool that Anthropic has built called Mythos that it says is so powerful, it’s too scared to release it to the public. Now, you know, you could say that is amazing marketing hype. And it is. But everyone that’s looked at it has said, actually, it is pretty good. You know, it’s finding hidden bugs in systems. Some of them are 30 years old and nobody’s ever spotted them. And of course, this is an incredible, valuable tool potentially to a hacker, right? Because it’s the key to every hidden backdoor that there is in all of our digital systems. And if you think about the fact that everything runs digitally, our banks, our power grids, you know, everything, that’s quite a scary prospect, isn’t it? And it’s not the only one. OpenAI has one as well called ChatGPT 5.5 Cypher, I think it is, that it’s also very proud of. And people that select banks and companies that they are allowing to try it are saying, yeah, these tools are another level. It would take a human days, months, years to find these weaknesses in our systems. And this thing just finds it straight away.
Chris – And therein lies another problem, doesn’t it, Mike? Because technology, classically, when it moves incredibly fast, has a habit of leaving behind the legal system, which moves notoriously slowly and never catches up. So how do we get to grips with this? How should we regulate AI and do it in a way that it means it works everywhere for everyone? Because that’s going to be the problem otherwise, isn’t it? We’ll all slavishly follow the rules and people who don’t want to and have nefarious intent won’t.
Mike – The world’s wealthiest companies are grappling with this and by and large trying to avoid being regulated because they claim it will stifle innovation and that other countries will get there first if we start regulating and so on. So the regulatory landscape is very, very unclear. And it doesn’t look like AI regulation is going to be high on the US agenda throughout this Trump administration. So it is going to be very, very difficult, I think, to regulate on top of a moving target. I do think the UK’s historical approach to this, which is to rather than trying to legislate around the technology, that is to say, thou shalt not use neural networks for this, instead get different organisations like the medical authorities, the financial services authority and so on to think about the issues that arise there and regulate around those use cases. I think that’s probably quite a wise and sensible way forward. One big challenge, by the way, for government at the moment is they lack the talent, not just the UK government. I think governments and regulatory bodies don’t have the AI experts who are at the absolute cutting edge. So they just actually literally don’t have the expertise in-house. And our government has made a lot of strides to try to build up that expertise. But it is still a very, very, very quickly moving target.
Chris – Do you use it Zoe?
Zoe – Yeah, loads. I use it like a second brain. I use it a lot. I don’t use it for writing. I don’t use it for scripting. But I do use it, I suppose, I kind of use it on a par with the way I use Google. I fact check, but then I have to fact check the fact checking, you know, but it’s a very useful springboard for ideas. Or when you just want to know something really quickly, you know, what year did the iPhone come out? Was it 2007, 2008? Something like that. I use it to think through ideas, just kind of brainstorm stuff, I guess. I’m thinking about writing a book about something and I’ve been using it to sort of brainstorm how that might work as a structure. Because I don’t know, I’ve never written a book before, I don’t really know how that works. But I’m very, very keen to start exploring agents. But it’s quite difficult with the way things are set up for me at work, because I’m at the BBC. But I would like to explore how good it is at admin, because I’m really not very good at admin. I’m not very interested in it. I would love to be able to detail more of that off than I currently do.