Machine Learning and the Future of Work: Why eventually we will all create our own AIs

I'm on my way to Russia again. I've had an amazing couple of days with a Chinese delegation from Xiamen Eye Hospital and the leading experts in retinal disease in China, who are collaborating with us on a big EPSRC project. There was a very special atmosphere: despite the language differences, we were all conscious of staring at the future of medical diagnostics where AI and humans work in partnership.

There's a lot of critical dystopian stuff about technology in society and education in the e-learning discourse at the moment. I think history will see this critical reaction more as a response to desperately nasty things going on in our universities, rather than an accurate prediction of the future. I am also subject to these institutional pathologies, but I suspect both the dystopian critiques and the institutional self-harm are symptoms of more profound changes which are going to hit us. Eventually we will rediscover a sane way of organising human thought and creativity once more, which is what our universities used to do for society.

So this is what I'm going to say the students in Vladivostok:

Machine Learning, Scientific Dialogue and the Future of Work

It is not unusual today to hear people say how the next wave of the technological revolution will be Artificial Intelligence. Sometimes this is called the "4th industrial revolution": there will be robots everywhere - robot teachers, robot doctors, robot lawyers, etc. In this imagined future, machines are envisaged to take the place of humans. But this is misleading. The future will however involve a deeper partnership between humans and intelligent machines. In order to understand this, it is important to understand how our technologies of AI work, how the processes of creating AIs and machine learning are becoming available to you and me, and how human work is likely to change in the face of technologies which have remarkable new capabilities. 

In this presentation, I will explain how it will become increasingly easy to create our own AIs. Even now, the technologies of Machine Learning are widely available, increasingly standardised and accessible to people with a bit of computer programming knowledge. The situation at the moment is very much like the early web in the 1990s, when to create a website, people needed a bit of knowledge of HTML. As with the web, creating our own AIs will become something everyone can do.  

Drawing on my work, I will explain how in a world of networked services, there is one feature about Artificial Intelligence which is largely ignored by those not informed of its technical nature: AI does not need to be centralised. A machine learning algorithm is essentially a single (and often not very large) file, which can be embedded in any individual device (this is how, for example, the facial recognition works on your phone). The world of AI will be increasingly distributed. 

Finally, I will consider what this future means for human work. One of the important distinctions between human decision-making and AI is that humans make judgements in a context; AI, however, ignores context. In other words, AI, like much information technology, actually discards information, and this has many negative consequences on the organisation of institutions, stable society and the economy. The most potentially powerful feature of AI in partnership with humans is that it can preserve information by preserving the context of human judgement. I will discuss ways in which this can be done, and why it means that those things which humans do best – empathy, criticality, creativity and conversation – will become the essence of the work we do in the future.