Developers want to free scientists to focus on discovery and innovation by helping them to draw connections from a massive body of literature.
For a researcher so focused on the past, Mushtaq Bilal spends a lot of time immersed in the technology of tomorrow.
The possibilities for these systems are enticing, especially in the context where good research assistants are in short supply; for a couple of reasons, we urge caution. 1. Such systems can hallucinate and suggest fictional references. 2. They can amplify existing biases and discrimination. 3. The method by which they are trained/their training dataset is secret. In practice, researchers should confirm their recommendations using other means, which undermines some of their appeal.
Pulling from his background as a literary scholar, Bilal has been deconstructing the process of academic writing for years, but his work has now taken a new tack. “When ChatGPT came on the scene back in November, I realized that one could automate many of the steps using different AI applications,” he says.
This new generation of search engines, powered by machine learning and large language models, is moving beyond keyword searches to pull connections from the tangled web of the scientific literature. Some programs, such as Consensus, give research-backed answers to yes-or-no questions; others, such as Semantic Scholar, Elicit and Iris, act as digital assistants — tidying up bibliographies, suggesting new papers and generating research summaries. Collectively, the platforms facilitate many of the early steps in the writing process. Critics note, however, that the programs remain relatively untested and run the risk of perpetuating existing biases in the academic publishing process
