There's lots of data out there, he argues, it's just not recognised as the key to future computing needs and AI.

"And it's not just data sets about the things you're interested in, you have to have negative data too. Whatever your business is, you need data on catastrophes – catastrophes relevant to your business so you can start to make comparisons and build models of business."

Businesses applying huge data sets to AI software are transforming their activities. Shenzhen based Ping An insurance has 100 data scientists working on its underwriting business and has algorithms that can process loan approvals and claims instantaneously, instead of taking days using old technology.

Big data sets also allow traceability. If failures arise, it's possible to dig into histories of events to find the weak points. Financial-recommendation software relies on traceability, he says, in the event of poor advice.

Computers that learn for themselves are predicted to take over the workforce. But few companies are actively involving themselves in acquiring the technolgoy. Chris Ratcliffe

Mr Reeves admits there is a lot of anxiety about the impact of job losses from AI. But points out in the business of making judgements there are some decisions that an algorithm cannot make, which is why there will always be work for people.

"Evolution has made humans good at pattern recognition. It means we're good at perceiving threats. We see anomalies. We're also good at 'level shifting' – we can quickly put a problem or threat into perspective. Actually we're good at doing this with relatively little data. So it's wrong to think there is no place for humans in AI."

If anything AI should make the human side of work more effective. Even the best computer analysis is only as good as the workplace it slots into. For example, AI is now very good at assessing X-rays – better than doctors in many cases. But it's what happens after an AI diagnosis that matters. A computer assessment is often acted on by a dysfunctional health system, where human intervention is essential but subject to fallibility.


Mr Reeves argues the biggest problem in adapting AI to business is overcoming fear: fear of job losses and fear of machines running out of control.

AI is now very good at assessing X-rays - better than doctors in many cases. But it's what happens after an AI diagnosis that matters. Justin McManus

Ironically once technology becomes commonplace we accept it, he says. "You accept your phone showing you a map for how to get here. You look at recommendations Amazon has to offer you based on what its algorithm is doing. And you don't worry about that."