A few years into the generative AI revolution, Andrew Porter looks at where we are now with these new tools, and what researchers need to think about before diving in…
As universities wrestle with how to address the impact of generative AI on many levels, what does it mean on an individual level for a postdoc or PhD student in a lab interested in using generative AI to support their work?
If this is you, perhaps one simple question to ask yourself is: do you want to lean into generative AI or keep your distance from it?
Benefits and limits
To lean in, you’ll want to invest your time in understanding how best to use these new tools – thinking deeply about the implications in areas such as copyright and plagiarism, confidentiality and data security, ethical practises and environmental sustainability – and figure out how the concerns and opportunities in those areas interact with your research.
Or, perhaps, now is not the time for you. And this isn’t simply a scepticism or distrust of the technology but more a weighing of the cost/benefits at this particular time, for you, in your work.
If you see an opportunity for generative AI to benefit your research – then I would suggest it’s hard to do this well without going into the deeper ethical and practical questions.
There’s no doubt that some people are benefiting from using generative AI. I’ve personally heard from people using it to help with coding and bioinformatics, as a starting point for learning about new subject areas and for cutting down large amounts of text to make it more digestible and readable. There are also people who are embedding generative AI directly into their research processes, for instance to help with analysing large data sets.
If you do see an opportunity or potential for generative AI to benefit your research – then I would suggest it’s hard to do this well without going into the deeper ethical and practical questions. If you are leaning in, it’ll be worthwhile – maybe even essential – to really understand the mechanistic basis of Generative AI and Large Language Models (LLMs), their strengths but also their limitations.
Credit: Dr Andrew Porter |CRUK | Author