Anshumali Shrivastava
Chief Executive Officer, ThirdAI Corp.

AI in Energy

June 6, 2:00pm
Location: Santa Clara II

Scalable, Sustainable, and Secure LLMs For All

Large Language Models (LLMs) and GPT have enormous potential to drive automation and efficiency in the future. Every enterprise is rushing toward becoming the early adopter of this novel technology. However, LLMs’ cost, energy, and privacy vulnerability are becoming significant barriers. The primary issue is that LLMs require massively specialized infrastructure and very costly training from a money and carbon perspective. In this lecture, we will look at emerging technologies that can reduce LLMs’ cost, computations, and energy footprint by several orders of magnitude. As a result, even commodity infrastructure like CPUs is sufficient to build these massively large language models with complete “air-gapped privacy”. With this technology we have the opportunity to disrupt the economics and carbon footprint of Mega-AI models.We will walk over some demos of the savings in cost and energy, including how to train 1B parameter models on your laptop without draining battery.

Anshumali Shrivastava is an associate professor in the computer science department at Rice University. He is also the Founder and CEO of ThirdAI Corp, a company that is democratizing AI to commodity hardware through software innovations. His broad research interests include probabilistic algorithms for resource-frugal deep learning. In 2018, Science news named him one of the Top-10 scientists under 40 to watch. He is a recipient of the National Science Foundation CAREER Award, a Young Investigator Award from the Air Force Office of Scientific Research, a machine learning research award from Amazon, and a Data Science Research Award from Adobe. He has won numerous paper awards, including Best Paper Award at NIPS 2014, MLSys 2022, and Most Reproducible Paper Award at SIGMOD 2019.

His work on efficient machine learning technologies on CPUs has been covered by popular press including Wall Street Journal, New York Times, TechCrunch, NDTV, Engadget, Ars technica, etc.