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Neil Lawrence - Atomic Human interview

Neil Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge where he leads the university-wide initiative on AI, and a Senior AI Fellow at the Alan Turing Institute. Previously he was Director of Machine Learning at Amazon, deploying solutions for Alexa, Prime Air and the Amazon supply chain. Co-host of the Talking Machines podcast, he's written a series for The Guardian and appeared regularly on other media. Known for his policy and societal work with the UK's AI Council, the Centre for Data Ethics and Innovation, and the OECD's Global Partnership on AI, his research focuses on improving data governance, accelerating scientific discovery, and how humans can take back control of large AI systems. His latest title is The Atomic Human.

What would you like your book to achieve?

I wanted it to speak to individuals from different backgrounds in a way that didn’t preach or tell, but told stories in a way the reader could relate to. I hope the book allows people to bring their own perspectives and combine them with a more nuanced understanding of intelligence. In a way the book is an edited version of me, one that’s more coherent and tempers my more polemic instincts. I’d like to be in a world where everyone feels confident (and informed) when having a say in how we use this technology. I hope the reader leaves the book feeling more empowered to have that say.

You emphasise how humans have to cope with very limited bandwidth compared with AIs - is there anything we can do make the best of our limitations?

I think how we deal with this varies according to our personalities. For me, it makes me realise I have to better understand someone’s culture before I understand their words. I feel arguments arise when other people’s words are combined with our premises, but to understand people you have to understand their premises. I think I’ve got better at that as I’ve got older.

I take your point that we shouldn't allow AIs to make life-changing decisions without human supervision, but can we realistically impose this?

Likely not fully, although existing legistlation already attempts to do this. In GDPR consequential decisions should be taken by humans or be rendered explainable. And I think that’s trying to get to this point. Even if we can’t enforce this precisely, I’m worried that the current debate around AI doesn’t seem to have this principle at its heart. Referring back to your first question, having this principle at the heart would be a change I hope the book can help enact. 

Given concerns about AI decision making, should autonomous vehicles be allowed on the roads (particularly narrow, windy European roads)?

I think I worry more about the busyness of the roads and the  shared usage between pedestrians, cyclists, etc that you get e.g. in city centres. Motorways would seem ideal for deployment already, but they are much more tightly regulated than other roads (no cyclists or pedestrians). This reflects what we’ve seen in previous waves of automation, humans have to adapt to computers to accommodate them. What I call the 'great AI fallacy' is the idea that this trend would be different for AI.

A colleague recently visited the US and said that one of the major challenges the Wayve vehicle he rode in had was in dropping off. It struggled to find a legal place to park near the destination. I suspect that most taxi drivers would just have parked illegally. That’s something we seem to tolerate from humans but likely won’t tolerate from machines. I think that relates to the core theme of the book, which is really about the shared jeopardy of human society. Since machines don’t share in the jeopardy I think we will never indulge them in the same way we do humans (or other animals). I think that has significant implications not just for autonomous vehicles but in a range of other societal domains where human decision making comes into contact with machines.

What’s exciting you at the moment (in AI or otherwise)?

The potential that AI could allow the computer to become a tool, not just of software engineers, but of regular people. In a similar manner to the printing press enabled writing to become accessible to regular people.

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