In This Episode

  • In this episode of Intel on AI host Amir Khosrowshahi and Luis Ceze talk about building better computer architectures, molecular biology, and synthetic DNA.

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Speaker: Luis Ceze
Co-Founder at OctoML and Venture Partner at Madrona Venture Group

Computing with DNA – Intel on AI Season 3, Episode 6

Luis Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, Co-founder and CEO at OctoML, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning, and biology. His current research focus is on approximate computing for efficient machine learning and DNA-based data storage. He co-directs the Molecular Information Systems Lab (misl.bio) and the Systems and Architectures for Machine Learning lab (sampl.ai). He has co-authored over 100 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM Research Highlights. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, Wall Street Journal, among others. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the 2013 IEEE TCCA Young Computer Architect Award, the 2020 ACM SIGARCH Maurice Wilkes Award, and UIUC Distinguished Alumni Award.

In the episode, Amir and Luis talk about DNA storage, which has the potential to be a million times denser than solid state storage today. Luis goes into detail about the process he and fellow researchers at the University of Washington along with a team from Microsoft went through in order to store the high-definition music video “This Too Shall Pass” by the band OK Go onto DNA. Luis also discusses why enzymatic synthesis of DNA might potentially be environmentally sustainable, the advancements being made in similarity searches, and his role in creating the open source Apache TVM project that aims to use machine learning to find the most efficient hardware and software combination optimizations. Amir and Luis end the episode talking about why multi-technology systems with electronics, photonics, molecular systems, and even quantum components could be the future of compute.

Academic research discussed in the podcast episode: