Tim Dettmers is an Assistant Professor at Carnegie Mellon University and a Research Scientist at the Allen Institute for AI, and his research focuses on making foundation models, such as ChatGPT, accessible to researchers and practitioners by reducing their resource requirements. This involves developing novel compression and networking algorithms and building systems that allow for memory-efficient, fast, and cheap deep learning. He has won oral, spotlight, and best paper awards at conferences such as ICLR and NeurIPS. He created the bitsandbytes library for efficient deep learning, which is growing at 2.2 million installations per month, and received Google Open Source and PyTorch Foundation awards.

AI2050 Project

AI models like ChatGPT work well for general use but fail in specialized expert domains, such as the medical sciences. To make AI models work in expert domains, one must adapt them, which is costly and requires significant AI expertise. This project overcomes these cost and expertise barriers through two new approaches: (1) use AI models themselves to perform the AI model adaptation process automatically; (2) make the adaptation process cheap so it can be run on regular consumer hardware. With this solution, non-AI scientists can adapt AI models to expert domains without needing AI expertise or large computational resources.