{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/68470ba8d911dedd6501609c/69e058f1501ebe6715da3c65?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Episode 16 - Building AI for Life Sciences","description":"<p><br></p><p>What does it take to build AI systems that can actually help scientists? Research lead Joy Jiao and product lead Yunyun Wang discuss how OpenAI is developing models for life sciences and what responsible deployment means in a field with real biosecurity stakes. They explore how AI is already improving research workflows and where it could lead in drug discovery and more autonomous labs — including why a future with less pipetting sounds pretty good to most scientists.</p><p><br></p><p><strong>Chapters</strong></p><p><br></p><p>0:39 Introducing the Life Sciences model series</p><p>3:47 Joy’s path into life sciences</p><p>5:00 Autonomous lab with Ginkgo Bioworks</p><p>7:27 Yunyun’s path into life sciences</p><p>8:12 OpenAI’s life sciences work</p><p>9:48 Biorisk, access, and safeguards</p><p>15:43 What models can do in the lab</p><p>17:51 Building scientific infrastructure</p><p>20:14 Why compute matters for science</p><p>24:54 Where are we in 6-12 months?</p><p>29:51 Scientific adoption and skepticism</p><p>33:17 Advice for students and researchers</p><p>40:27 Where are we in 10 years?</p>","author_name":"OpenAI"}