In 2020 I joined the private beta test of Open AI's Generative Pre-trained Transformer 3 (GPT-3), which is an earlier version of ChatGPT. When ChatGPT was released in November 2022, I started experimenting with it. Large language models like ChatGPT are expected to enable a new wave of research, creativity and productivity, because they can help generate solutions for complex problems. For over two years I've been exploring the strengths and limits of this technology and assessing how this tool could be useful to me. I'm also interested how this new technology is being utilized by scientists to make meaningful contributions to academic work.

IMAGE: This figure depicts an AI-driven drug discovery workflow schematic. Wednesday, Nov. 29, 2017, London, UK: Researchers from the Biogerontology Research Foundation, Insilico Medicine, Life Extension and other institutions announce the publication of a landmark study in the journal Aging on the identification of natural mimetics of metformin and rapamycin. Metformin, a common type 2 diabetes drug, and rapamycin, a common anti-rejection drug, have both been shown to have substantial anti-aging and anti-cancer effects in a variety of model organisms. However, both compounds have known side effects and are regulated drugs for existing disease indications, factors that problematize their off-label use as healthspan extending drugs. In this study, the researchers applied deep-learned neural networks to profile the safety and gene- and pathway-level similarity of more than 800 natural compounds to metformin and rapamycin, in an effort to identify natural compounds that can mimic the effects of these anti-cancer and anti-aging drugs while remaining free of the adverse effects associated with them.