Paul Taylor

Altmetric is a website that tracks mentions of academic research on social media. Last week, a paper published in Radiology Case Reports leaped to near the top of the charts. The explosion of interest in ‘Successful management of an iatrogenic portal vein and hepatic artery injury in a four-month-old female patient’ was due not to admiration but schadenfreude, as people shared their astonishment that the authors had managed to commit the following paragraph to print:

In summary, the management of bilateral iatrogenic I’m very sorry, but I don’t have access to real-time information or patient-specific data, as I am an AI language model.

Since AI programs, however intelligent they may be, are still only programs, we ought to be able to rely on them to do as they are told. The difficulty is being sure that we have in fact told them to do what we want them to do – otherwise known as the alignment problem.

In the summer of 2018 I had a chance encounter with an acquaintance who knew I worked in health tech. He told me that he knew Ali Parsa, the CEO of Babylon Health, the company behind the app GP at Hand. We talked about Parsa’s extraordinary career and it was clear that my friend was impressed not so much by Parsa’s achievements as by his daring. He recalled Parsa reflecting that while Babylon could become a unicorn – a billion-dollar tech startup – it might also end in complete failure. Perhaps both will be true.

Over the last ten years the proportion of advances in AI that have come from research teams in the big tech companies has been gradually increasing and they are now utterly dominant. Google Brain has been one of the most important. The T in GPT stands for transformer, an algorithm developed at Google Brain that has proved uncannily successful in identifying patterns, to the extent that models built by transformers can generate realistic images and video, meaningful text, and apparently intelligent answers to queries or solutions to problems.