‘Sitting on data is like sitting on an oilfield’: Mark Girolami, the Alan Turing Institute
Newly appointed chief scientist at the Alan Turing Institute in central London, Mark Girolami discusses why artificial intelligence has become ‘big news’ and how data-centric engineering is at its core.
“Artificial intelligence is very much an umbrella term,” says Mark Girolami. “When we say AI, what we’re really describing is a whole load of technologies that are characterised by three components at their core: data, computing and algorithms.” Girolami, who has taken up the post of the Alan Turing Institute’s first chief scientist, says that while there are plenty of people out there crossing over into philosophy and neural sciences “solving intelligence”, his approach to AI is based on these three inter-related parameters. A University of Cambridge academic, he also holds the Royal Academy of Engineering research chair in data-centric engineering. The two positions “feed off each other”, he says.
Girolami’s appointment at the Alan Turing Institute comes hot on the heels of the UK government publishing its National AI Strategy, a ten-year plan “to make the UK a global AI superpower”. What this means is that the 58-year-old British scientist has stepped into the role of the Turing’s chief scientist at a time described by the UK’s chief scientific adviser Sir Patrick Vallance as “a critical moment for UK science and technology, in particular AI and data science”. Girolami says the reason AI is “big news” now is that “there are a number of factors that have coalesced at one time”.
It’s important to place this in the context of “AI being discussed for many years”. Looking around the institute’s headquarters in London’s British Library, Girolami suggests that you can trace the term back to the mid-20th century mathematician Alan Turing, who famously raised questions such as whether machines could think. But now, there is a confluence of technologies that have taken the notion of AI out of the realms of abstract philosophy and into an area that is “actually useful”. Girolami isn’t having a swipe at philosophers: he’s drawing attention to the fact that “the lives of the average person in the street are now being affected by our ability to gather data on just about everything we do”.
He picks up his smartphone and explains that the amount of data it gathers “about me, where I go, what I do, what my interests are” is colossal. There was a time in a pre-digital landscape, he reflects, when data was in the hands of a few boffins doing weird and wonderful research “that didn’t have much to do with the real world. Now we can look at traffic flow in our cities, urban air quality. It goes on and on. We’re generating more data than ever: more than could have been imagined half a century ago. Also, our computing capabilities have shot up since Turing’s time.”
Given that AI algorithms rely on two everyday conditions of data and computing power, “AI is no longer restricted to laboratories of national importance. These two things mean that the AI algorithms of 30 years ago that plodded along without doing anything spectacular can now recognise patterns and make decisions at almost super-human performance levels. That’s why AI is big news today.” Girolami says that because AI is an umbrella term, you can include other emergent data-led phenomena under it, such as the Internet of Things (IoT), digital twins and Industry 4.0.
“They’re in the same family. They can all be traced back to the start of the internet. What did the internet do? It was the first wave in the way we changed our data capability from being able to view it on a local level to a global reach. Now with IoT, whole buildings are producing data: we can monitor energy efficiency, temperature distribution, occupancy levels and so on.” Yet the reason everyone knows about AI now, says Girolami, is that if you’ve got a smartphone, you’ve got AI in your pocket. From fitness monitoring to consumer behaviour patterns, “there’s pretty much nothing that hasn’t been impacted by the three fields of data, computing and algorithms. That all gets wrapped up in this term AI. The same goes for terms such as deep and machine learning.”
Girolami is keen to point out that today’s AI technologies transforming our lives and businesses have little to do with the horizon-scanning futurism enjoyed by middle-brow newspapers that continually inform us that robots will be taking our jobs. You can trace this back to the 1920s, says Girolami, who describes the scenario of Fritz Lang’s sci-fi movie ‘Metropolis’, in which the cybernetics take over the menial jobs. Yet that’s not what we mean by AI today (and it’s worth remembering that H G Wells thought that ‘Metropolis’ was “silly”). What we’re talking about, says Girolami, is “new technologies based on data-driven algorithms that exploit ubiquitous computing to solve real problems and to open up new markets and business opportunities”.
For all the popularity of the view that data is the bedrock of every technological megatrend in the 21st century, there is the counter argument that the amount of data that gets used productively is just the tip of the iceberg, while there’s not much evidence to suggest that organisations are using their data stock to drive their businesses forward. To judge from Girolami’s wry laugh, this is an argument he’s had to deal with on more than one occasion.
“Sitting on data is like sitting on an oilfield,” he explains. “The latent wealth is incredible. But getting to that wealth is another story entirely. And it’s exactly the same with data. There are lots of sectors that now realise they are sitting on great potential in terms of the data they’re producing. But how you get it out of the ground, how you refine it and apply it to the products that are really going to make you money… that’s the big question. Then there are all the legal, moral and ethical issues surrounding data which are very thorny.”
The big difference between fossil fuels and data though is that while reserves of oil and gas are finite and become progressively harder and more expensive to extract as resources dry up, “data is infinite. That’s one of the big challenges the Alan Turing Institute will be hoping to address: the amount of data we are producing is increasing.”