In this talk I'll highlight some of the key developments in artificial intelligence over the past 15 years, which serve as key ingredients in today's most advanced AI models. This will touch on development of new model architectures, advances in large-scale distributed training, ML accelerators like TPUs, and development of algorithms to improve training and serving efficiency. I'll discuss the Gemini effort at Google, both in terms of the capabilities of Gemini models but also in how we go about organizing a large-scale research and engineering effort like Gemini. Finally, I'll give a sense of the capabilities of today's models, and also highlight some areas that will advance rapidly in the next few years.
Bio: Jeff Dean is the Chief Scientist for Google Research and Google DeepMind, and currently co-leads the Gemini project. He has done research in large-scale information retrieval, operating systems, distributed systems, compilers and in the field of AI, has done work on ML accelerators, vision models, low-level software and frameworks for machine learning, sparse model architectures, algorithms like distillation and neural architecture search, large language and multimodal models, and applications of machine learning to areas like ASIC design, healthcare, and translation. He is a recipient of the ACM Prize in Computing, and the IEEE John von Neumann medal, and a Fellow of the ACM, and a member of the US National Academy of Engineering, and the American Academy of Arts and Sciences.
This talk will not be live streamed or recorded.