Could “Artificial Intelligence (AI) have a creative role in the scientific process” was a question posed in 2023 by a group of researchers in Stockholm. AI is already being used in literature searches, to automate data collection, run statistical analyses and even for drafting some parts of industry and academic papers. Sendhil Mullainathan, an economist at the University of Chicago Booth School of Business in Illinois has suggested using AI to generate hypotheses and stated “it’s probably been the single most exhilarating kind of research I’ve ever done in my life”.
AI could help with creativity as using large language models (LLM’s) to create new text, even if it is inaccurate, it could lead to a statement such as: “here’s a kind of thing that looks true”; when you think about it, this is exactly what a hypothesis is! These “hallucinations” are sometimes likely to be something that a human would not make and could aid thinking outside of the box.
Hypotheses are on a spectrum from concrete and specific to the abstract and general, using AI in areas where fundamentals remain hidden could generate insights. For example we know there is this behaviour happening, but we do not know why, could the AI identify some rules that could possibly be applied to this situation? James Evans, a sociologist at the University of Chicago says AI systems that generate hypotheses based purely on machine learning require a lot of data. Should we be looking to build AI that goes beyond “matching pattens” but can also be guided by known laws? Rose Yu, a computer scientist at the University of California, San Diego states that it would be a “powerful way to include understanding the limits is crucial, people still need scientific knowledge into AI systems”.
Ross King a computer scientist at Chalmers University of Technology in Gothenburg is o think in a critical way. Is a coordinated campaign building robotic systems that perform experiments. Factors are being adjusted subtly in his “‘Genesis’ systems allowing these robot scientists to be more constant, unbiased. cheap, efficient and transparent than humans”.
Hypothesis generation by AI is not new, in the 1980’s Don Swanson pioneered “literature based discovery” with some software he created called “Arrowsmith” that searched for indirect connections and proposed for example that fish oil might help treat Raynaud’s syndrome, where human circulation is limited in the hands. This hypothesis when taken forward was proved to be correct in that it decreased the bloods viscosity leading to improved circulation.
Data gathering is becoming more automated and automating hypothesis generation could become an important factor as there is more data being generated than humans can handle. Scaling up “intelligent, adaptive questions” will ensure that this capacity is not wasted.
So What? This approach could lead to valid hypotheses being developed which are clear and broad in areas where the underlying principals are poorly understood. A panacea perhaps to “researchers block” to unlock blind spots? For Defence this could mean helping to avoid group think, encourage more innovation outside of the chain of command and enabling things to be done differently in an often slow to change organisation. AI could prove to be a lot more useful than performing Literature Reviews.
Full article: Nature magazine
