絞り込み結果
12 件並び順
1:29:30EN
Cognitive Revolution "How AI Changes Everything"· 2025年10月14日ポッドキャスト2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibioticsホスト 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"· 2025年6月7日ポッドキャストShe set out to build mirror life. Then she talked the field into stoppingホスト 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"· 2025年6月4日ポッドキャストGoogle's AI Co-Scientist Solved a Phage Puzzle Humans Missed for Yearsホスト 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scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.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"· 2025年3月23日ポッドキャストA Year to Grow a Brain Organoid, an Hour to Simulate Itホスト 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"· 2025年1月15日ポッドキャストRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missedホスト 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"· 2024年10月12日ポッドキャストAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.ホスト 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"· 2024年9月20日ポッドキャストA DNA Model That Can Tell Which Genes You Cannot Touchホスト 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"· 2024年8月30日ポッドキャスト64 Bytes of Random Code, and Computational Life Showed Up Anywayホスト 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"· 2024年5月25日ポッドキャストA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI Biologyホスト 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"· 2024年2月9日ポッドキャストTwo-Headed Worms, Regenerated Legs, and a Sort That Learned to Waitホスト 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"ポッドキャストBuilding a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual Cellsホスト 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"ポッドキャストFrom Static Structures to Moving Molecules: Where AI for Protein Design Stands After AlphaFold 3ホスト 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.