Written by Philip Martin, Partner at Marks and Clerk
I was struck by one of the takeaways in Stanford’s recent AI Index Report: “The majority of the US AI PhD grads are from abroad—and they’re staying in the US”.
According to the report, the UK offers almost as many AI-specialised masters’ programmes, and more specialised bachelors’ programmes, as the entire EU27, and it seems likely that there is a brain drain from the UK to US industry.
As the report notes, the US has also suffered an unprecedented brain drain to industry over the past decade or two, driven by the resources and salaries available, and this has a chilling effect on AI start-ups and their success.
A transfer of knowledge to industry is a good thing: one purpose of a university is to benefit society by disseminating knowledge, and one way this can be done is by commercializing research. Universities and research institutions are a dynamo for entrepreneurship and innovation in large companies.
This is especially true of machine learning where core new techniques are continually being developed. For example, the amazing capabilities of large language models such as BERT and GPT3 have their genesis in a paper on transformers from 2017; and similar techniques are now being applied to image processing and other domains.
Not uncommonly some of the best people manage to combine academia with positions in industry, and the UK punches above its weight in AI, but to avoid a brain drain universities need to retain and train the next generation of world-class researchers.
One way to retain the best people is to pursue inspirational goals. Artificial General Intelligence (AGI) is one; and if it is difficult that makes its pursuit all the more worthwhile. Interestingly some of the biggest names in the machine learning field, including Geoffrey Hinton, Yann LeCun, Yoshua Bengio, and others, appear motivated by a desire to understand how the human brain works, and to emulate it.
Universities and research institutes are well-positioned to take a cross-disciplinary approach, collaborating with neuroscientists for cross-fertilization of both fields. Breakthroughs could emerge from more collaboration, for example providing conceptual insights and perhaps even new ways to think about the “easy problems” of consciousness, and possibly even the hard problem of consciousness. On the more applied side developing new techniques is crucial, and collaboration between universities and industry might be a way to obtain more of the compute resource that machine learning needs.
At the moment it seems that a relatively small number of institutions are contributing to many of the most significant advances. New techniques with potentially real-world applications and the groups developing the techniques are a source of people with the skills and experience to practically implement advanced new models.
Such people found spin-offs that can help solve some of the world’s greatest challenges, from medical issues to sustainable technology and agriculture. Universities and other research institutions must pursue the fundamental research that only they can advance and educate the best people for the future; the prize for AGI is unimaginable.
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