Breaking Into AI for Science - Alishba Imran of UC Berkeley BAIR, Arc Institute, CZ Biohub, Voltx

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Discovery Engines – with Nabil

Nabil Laoudji, YesAnd Labs LLC

12 episodes

2 days ago

Featuring the people and platforms accelerating scientific discovery. Hosted by Nabil Laoudji. More at www.discoveryengines.co

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Featuring the people and platforms accelerating scientific discovery. Hosted by Nabil Laoudji. More at www.discoveryengines.co

Science

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Breaking Into AI for Science - Alishba Imran of UC Berkeley BAIR, Arc Institute, CZ Biohub, Voltx

Discovery Engines – with Nabil

1 hour 5 minutes 38 seconds

6 months ago

Breaking Into AI for Science - Alishba Imran of UC Berkeley BAIR, Arc Institute, CZ Biohub, Voltx

Alishba Imran is a deep learning researcher at UC Berkeley's BAIR AI Lab, a Research Fellow at the Arc Institute, a former Research Intern at the Chan Zuckerberg Biohub, co-founder of battery tech startup Voltx, and co-author of "AI for Robotics: Toward Embodied and General Intelligence in the Physical World"

See below for episode links, chapters, and our socials. Hosted by Nabil Laoudji.

Support the podcast! Subscribe to our newsletter: www.discoveryengines.co. Thanks for watching 🙏🤙

Episode Links:

Chapters:

(00:00) - Preview & Introduction
(05:02) - Why AI for Science?
(10:34) - Bootstrapping Your Own Learning
(16:02) - Why Battery Testing Matters: Launching Voltx
(20:19) - Batteries, Raw Earth Minerals: Incremental vs Transformative Chemistries
(23:54) - The Business Side of Deep Tech Entrepreneurship
(29:14) - The State of Battery Tech Today
(30:50) - Working with Chan Zuckerberg Biohub
(33:37) - DynaCLR For Contrastive Learning of Cell State Dynamics
(36:21) - Why Self-Supervised + Contrastive Learning
(41:06) - Generalizing to Different Cells Contexts With Embeddings
(42:06) - Microscopy or ML Advancements for Unlocking Progress
(43:54) - Managing Terabytes of Data
(46:51) - Arc Institute: Collecting Largest Single Cell Dataset
(48:40) - Role of Tech-Founder Funded Institute To Advance Foundational Work
(50:41) - Book Launch! Merging Classical Robotics Methods With Cutting-Edge Deep Learning
(53:42) - Robotics Progress: Hype vs Reality
(57:09) - Robotics for Science
(58:54) - Future Trends: Automation, Foundational Single Cell Models, Protein and Genome Language Models, Transcriptomics
(01:00:41) - Pivoting into AI for Science Early, Mid, or Late Career
(01:02:49) - Exchanging Ideas; DynaCLR Paper; AI for Robotics Book

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