A really important and interesting research challenge is how to effectively communicate complex information to patients. At Aberdeen we are working on this topic in several areas of medicine, and are looking for a research fellow to join us.

In 2019 I told students that neural language models produced texts which were fluent but could not be trusted content-wise. In 2024 I told them the same thing. My high-level message hasnt changed despite the huge improvements in tech, maybe this is a fundamental aspect of LLMs?

Communicating uncertainty to non-experts is very important but also very difficult. Problems in communicating risk probabilities are well known, but additional challenges arise in many real-world use cases, including communicating time-series of probabilities and explaining the impact of features which the model ignores.

There is a lot of justified concern about the risks of using LLMs in healthcare. But LLMs can also make medicine safer, if they are used to support doctors and help them make fewer medical errors.

I summarise a few papers I have recently read on what LLMs can and cannot do. One (not surprising) finding is that LLMs skill profile is very different from humans. Which is good, means that human+LLM together can do things that humans/LLMs cannot do on their own. Also means that it makes little sense to evaluate LLMS using tests/techniques designed to evaluate people.

There is a personal rant (no connection to AI or NLP) about the fact that many decision-makers dont seem to care about climate change. How else to explain UK ban on new onshore wind in England, and refusing to shift Bitcoin to more energy-efficient algorithms?

One of my students is investigating mistakes made by chatGPT in explaining medical notes. Some of these turned out to be due to problems in the notes being explained (ie human error by the doctors who wrote the notes) more than LLM deficiencies.

Generated texts can (unnecessarily and unintentionally) upset people. I describe several examples I’ve seen of this. This blog is based on a talk I gave at a SigDial workshop.

In 2011 the IBM Watson question-answering system was presented as breakthrough AI technology that “changes everything”. Huge amounts of hype, but in the end little real-world success. Are there lessons for LLMs?

A reader asked me how accurate chatGPT texts need to be. The answer is that this depends on context, including use case, workflow, and error type.