Tom B. Brown is a machine learning researcher best known as the first author of “Language Models are Few-Shot Learners,” published in May 2020 with more than thirty OpenAI co-authors. That paper introduced GPT-3, a 175-billion-parameter autoregressive language model - ten times larger than any comparable dense model before it - and showed that at sufficient scale a model can perform new tasks from a few examples given in the prompt, with no gradient updates or fine-tuning. This “few-shot” or in-context learning result reframed how the field thought about general-purpose language models.
Brown was also an author on the 2020 Scaling Laws paper. In 2021 he left OpenAI to co-found Anthropic alongside Dario and Daniela Amodei and other former OpenAI staff, where he has worked on training the Claude models.
Why business readers should care: GPT-3 was the model that made “just write a prompt” a viable way to get useful work from AI, and Brown led the paper that demonstrated it. His move to Anthropic is part of the founding story of one of the leading frontier labs.