This foreword has been written by Professor Sir Anthony Finkelstein CBE, President, City St George’s, University of London and forms part of a publication entitled ‘Perspectives on the role of London’s higher education sector in global AI leadership: A collection of essays’ which is being published on the London Higher website one part at a time.
I cannot claim great powers of foresight, much though I would wish to.
I first encountered, what was then, ‘Artificial Intelligence’ (AI) in the late 1970s, early 1980s at the very end of the first ‘AI winter’ initiated in the UK by The Lighthill Report of 1973. I was interested, but not more than that. The programming tools, specifically Lisp (and later Prolog), did get my attention. I could see the relevance of knowledge-based approaches and so-called expert systems. I became an ‘Alvey baby’ funded to pursue post-doctoral research as part of the government programme to deliver an AI-led 5th generation of computing technologies. This gave rise to a longstanding concern with logic and symbolic reasoning that shaped a good part of my later work in software engineering.
But I was certainly never very engaged with the philosophical and other debates that swirled around AI, as they, of course, do now. Though I appreciate abstraction, I have little appetite for speculation. This is probably a personal shortcoming, but one that I am unlikely to be able to shed at this stage.
I recall my first use of GPT. I was profoundly shocked at the behaviour of the system – at what it could do. Indeed, despite the fact I ‘knew’ how it worked, in some reasonable detail, I could not comprehend it. I did not understand what ‘simply’ massive scale (amplified by some neat engineering) would yield. I had to repeat to myself that I was not seeing search but rather the results of a statistical process giving rise to predictions at the level of words and text fragments. I still do. This shock is important to recognise, and to hold onto. We have crossed a frontier.
The impacts of technology and the ways in which innovations are applied have been much studied. For advanced technologies the translation from lab to broader uptake generally takes an extended period. Though experienced by users or consumers as rapid and disruptive, technology shifts are often, when viewed at a distance and in context, relatively slow. There are, obviously, inflection points and network effects that come into play, but generally applications emerge incrementally and the gaps between the early adopters, early majority, late majority, and laggards, are lengthy.
This is not what is happening with AI. Take-up is progressing with extraordinary rapidity, productivity opportunities and applications are proliferating, the leverage that can be secured from integration with existing platforms and data resources are evidently very large and more are emerging on an almost daily basis. This is so much the case that in enterprises, impatient with even the accelerating pace of deployment, individual use for routine tasks has become commonplace. It is impossible to say with any precision where this might lead, not least in the context where the models themselves continue to develop at extraordinary speed.
So much for reflection. These changes certainly mean substantial large-scale transformation for higher education. We now have the capability to deliver highly personalised educational experiences, individual feedback, sophisticated analysis and problem solving, and more for our students. We can streamline our business processes to be more responsive and efficient. Our research will benefit from novel forms of scholarly exchange and exploitation of knowledge. This is not speculation; this is a straightforward account of what is happening. Given also the global expansion of higher education and the associated cost-pressures, reflected in the UK’s current financial sustainability crunch, using AI to deliver scaled pedagogy is simply our only available play.
As a global hub for high-growth industries including finance, tech, life sciences and the creative industries, AI is of particular importance to London and its universities. By incorporating AI into curricula, we can ensure London’s graduates remain competitive and keep global companies looking to London for talent. Through AI-driven projects, we can also strengthen ties between academia and industry and bring new investment opportunities to the capital.
I am not generally a ‘hyper’ of technology but in this case, I am content to be mistaken for one. There is no stand-back option, failure to engage with AI is straightforwardly to neglect the mission of higher education, of London and of the capital’s longstanding research excellence.
