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1:29:30EN
Cognitive Revolution "How AI Changes Everything"· 2025년 10월 14일팟캐스트2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics진행자 Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.
2:02:27ENCognitive Revolution "How AI Changes Everything"· 2025년 6월 7일팟캐스트She set out to build mirror life. Then she talked the field into stopping진행자 Nathan LabenzUniversity of Minnesota geneticist Kate Adamala tells Nathan Labenz why every known organism shares one chemical alphabet — and why flipping that alphabet would be a branch of synthetic biology better left unexplored.
1:12:29ENCognitive Revolution "How AI Changes Everything"· 2025년 6월 4일팟캐스트Google's AI Co-Scientist Solved a Phage Puzzle Humans Missed for Years진행자 Nathan LabenzImperial College scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.
1:43:54ENCognitive Revolution "How AI Changes Everything"· 2025년 3월 23일팟캐스트A Year to Grow a Brain Organoid, an Hour to Simulate It진행자 Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.
1:33:01ENCognitive Revolution "How AI Changes Everything"· 2025년 1월 15일팟캐스트Rare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed진행자 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
1:13:23ENCognitive Revolution "How AI Changes Everything"· 2024년 10월 12일팟캐스트An AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.진행자 Nathan LabenzMichael Levin and Leo Pio Lopez on a multilayer embedding that predicted melanoma, a cancer with no mutations, and why biology's real interface is cognitive.
1:18:32ENCognitive Revolution "How AI Changes Everything"· 2024년 9월 20일팟캐스트A DNA Model That Can Tell Which Genes You Cannot Touch진행자 Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.
1:30:37ENCognitive Revolution "How AI Changes Everything"· 2024년 8월 30일팟캐스트64 Bytes of Random Code, and Computational Life Showed Up Anyway진행자 Nathan LabenzGoogle researchers Ettore Randazzo and Luca Versari show that self-replicators can arise from a soup of random strings with no fitness function at all.
1:59:15ENCognitive Revolution "How AI Changes Everything"· 2024년 5월 25일팟캐스트A Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI Biology진행자 Nathan LabenzThe White House set a thousand-fold lower reporting bar for biological models than for language models—and this conversation shows why that gap already looks prescient.
1:25:09ENCognitive Revolution "How AI Changes Everything"· 2024년 2월 9일팟캐스트Two-Headed Worms, Regenerated Legs, and a Sort That Learned to Wait진행자 Nathan LabenzTufts biologist Michael Levin tells Cognitive Revolution why anatomy is rewritable software, and why he stopped a paper that might have made AI more alive.
1:03:59ENCognitive Revolution "How AI Changes Everything"팟캐스트Building a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual Cells진행자 Nathan LabenzBiohub's head of science explains why today's virtual cell models fall short, how a $1.8 billion data push aims to fix it, and what a five-year information-level model of the cell could unlock.
1:45:55ENCognitive Revolution "How AI Changes Everything"팟캐스트From Static Structures to Moving Molecules: Where AI for Protein Design Stands After AlphaFold 3진행자 Nathan LabenzComputational biochemist Amelie Schreiber walks Nathan Labenz through six months of progress in structure prediction, molecular dynamics, peptide and antibody models, and early agent-driven design workflows.