Jassi Pannu, Assistant Professor at Johns Hopkins, explains how rapidly advancing AI is transforming biological research and raising the risk of engineered pandemics. They map today’s biosecurity landscape, from pathogen detection and DNA sequencing to vaccine development, and examine how frontier models can already troubleshoot lab work and bypass data safeguards. The conversation introduces a proposed Biosecurity Data Level framework to restrict only the most dangerous functional biological data while preserving open science. They close with a broader defense-in-depth strategy—Delay, Deter, Detect, Defend—including DNA synthesis screening, global pathogen surveillance, and practical tools like Far UV sterilization.
LINKS:
Jassi Pannu: https://x.com/JassiPannuMD
Science article that prompted this conversation: https://www.science.org/doi/10.1126/science.aeb2689
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CHAPTERS:
(00:00) About the Episode
(05:59) From outbreak to vaccine
(17:08) Threat actors and data (Part 1)
(21:23) Sponsors: VCX | Framer
(23:53) Threat actors and data (Part 2)
(31:05) Gain-of-function research risks (Part 1)
(37:39) Sponsors: Claude | Tasklet
(41:03) Gain-of-function research risks (Part 2)
(48:05) AI models in biology
(01:00:51) Dangerous AI capabilities
(01:07:59) Biosecurity data level framework
(01:18:58) Policy, governance, and infrastructure
(01:28:53) Defense in depth vision
(01:40:43) Episode Outro
(01:45:02) Outro
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AI is reshaping global power, from chip manufacturing and computing power to AI governance and US-China relations. In this episode, Ben Buchanan, Assistant Professor at The Johns Hopkins University and former White House Special Advisor for AI, explores how AI policy, geopolitics, and international cooperation intersect with AI innovation and AI safety. We discuss the strategic importance of computing power, the future of AI governance, and what it will take for democracies to lead responsibly in the age of AI.
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(0:00) Friedberg intros FDA Commissioner Marty Makary
(3:21) Inside the 42 major FDA reforms in 10 months
(7:51) The China Race: Winning back biotech leadership
(13:14) Accelerating drug approvals safely, how to cut down on clinical trial timelines
(33:13) Reshaping the failed Food Pyramid
(41:29) GLP-1s and America's Obesity Epidemic
(49:54) Rethinking vaccines from first principles
(59:40) Lowering drug prices, the power of AI for healthcare
(1:18:57) What causes autism?
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Kara sits down with Chris Urmson, CEO and co-founder of the autonomous trucking company Aurora, and Johnathon Ehsani, a professor of public health at Johns Hopkins University and leading road safety researcher, for a candid look at the future of AI-powered freight transport.
Recorded live at the Hopkins Bloomberg Center, the three discuss the rapid rise of driverless trucking, what it will take to convince a skeptical public that sharing the road with self-driving 18-wheelers will actually make driving safer, the potential for job losses, and how to regulate autonomous vehicles across state lines. It’s a deeply informed look at the promises and the trade-offs of autonomous trucking with two experts.
Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, Threads, and Bluesky @onwithkaraswisher.
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Dr. Gillian Hadfield from Johns Hopkins University and Andrew Freedman from Fathom discuss their innovative proposal to govern AI through private regulatory markets, which has been introduced as California's SB 813. Their system would separate democratic goal-setting from technical rule-making by having government bodies articulate safety outcomes while competitive private certifiers develop and enforce detailed standards, with companies receiving liability protection for compliance. The conversation explores how this market-based approach could create a "race to the top" in AI safety standards while remaining agile enough to keep pace with rapid technological development. Key challenges discussed include preventing a race to the bottom among certifiers, liability law interactions, and identifying qualified organizations to serve as effective private regulators.
Sponsors:
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PRODUCED BY:
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CHAPTERS:
(00:00) About the Episode
(04:48) Introduction and Problem Overview
(07:48) Regulatory Markets Concept Origins
(17:14) Current Governance System Failures (Part 1)
(19:28) Sponsors: Fin | Labelbox
(22:42) Current Governance System Failures (Part 2)
(25:30) Private Governance Mechanism Explained (Part 1)
(35:06) Sponsors: Oracle Cloud Infrastructure | NetSuite by Oracle
(37:38) Private Governance Mechanism Explained (Part 2)
(44:17) Liability Protection Framework
(56:39) Race to Top Dynamics
(01:07:24) Red Teaming Implementation Challenges
(01:28:47) Insurance Alternative Approaches
(01:53:51) Moving Forward Conclusions
(01:55:11) Outro
Dr. Marty Makary, a renowned surgeon and professor at Johns Hopkins, public health expert, and a two-time New York Times bestselling author, joins Scott to discuss his latest book, Blind Spots: When Medicine Gets It Wrong, and What It Means for Our Health. They go over topics including the issue of overmedication, weight loss drugs, and the food industrial complex.
Follow Marty, @MartyMakary.
Scott opens with his thoughts on the film and TV industry in Los Angeles. He then gets into the future of the search industry, specifically how Google’s stranglehold on the $300 billion search ad market is starting to weaken.
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Join host Craig Smith on episode #174 of Eye on AI as he sits down with Tianmin Shu, Assistant Professor at Johns Hopkins University in both the Computer Science and Cognitive Science departments.
In this episode, Tianmin unravels the fascinating concept of world models and their intersection with artificial and human intelligence. Discover how these models, rooted in cognitive science, offer a blueprint for understanding our environment and enhancing AI's ability to interpret, predict, and interact within it.
Explore the intricate dance between AI and cognitive science as Tianmin shares insights from his rich academic journey from UCLA to MIT, leading to his innovative work on social aspects of AI and the development of agents capable of human-level cooperation and understanding.
Dive deep into the discussion on the integration of world models with large language models, and how this synergy could revolutionize AI's predictive capabilities and reasoning, paving the way for more intuitive and interactive systems across various domains, especially in household environments.
Don't miss this riveting exploration of the next frontier in artificial intelligence research and its potential to transform our interaction with technology.
Remember to rate us on Apple Podcast and Spotify if you're intrigued by the insights shared in this episode!
This episode is sponsored by 1Password. 1Password combines industry-leading security with award-winning design to bring private, secure, and user-friendly password management to everyone. Companies lose hours every day just from employees forgetting and resetting passwords. A single data breach costs millions of dollars. 1Password secures every sign-in to save you time and money. Right now, my listeners get a free 2-week trial at: 1password.com/eyeonai
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Every year in the U.S., tens of thousands of hospital patients die of preventable causes. For many of these patients, warning signs are subtle and easy for doctors to miss. Suchi Saria is the founder and CEO of Bayesian Health, and a professor at Johns Hopkins where she runs a lab focused on machine learning and healthcare. Suchi’s problem is this: How can you use AI to detect when hospital patients are at risk of potentially deadly complications – and how can you get doctors to listen?
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Matthew Green is an Associate Professor of Computer Science at John Hopkins University, where he teaches courses, cryptography, distributed computing systems, blockchains, and cryptocurrency. He also helped create Zerocash, one of the first privacy cryptocurrency experiments, and ultimately influenced the Zcash protocol.
He recently wrote an article titled, “In Defense of Crypto(Currency)”, in response to an open letter to Congress, signed by a large group of technologists, denouncing the entire premise of cryptocurrency and urging congress to strongly regulate the space.
In this episode, Matthew talks about his article, how crypto can do better as an industry, and why to be optimistic about the future of crypto.
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------ Topics Covered:
0:00 Intro 6:34 Letter to Congress 12:27 Matthew’s Background 15:45 Why Write This Letter 19:03 Crypto Haters 24:14 What to do About Scams 28:45 Regulation 32:28 Addressing Concrete Objections 35:36 PoW Climate Issue 38:06 Blockchains Transfer Reversal 45:50 Crypto Doesn’t Scale Claim 50:42 Privacy Claims 1:02:43 Annoying Hype 1:05:50 Doing Better as an Industry 1:10:25 Matthew’s Favorite Project 1:14:58 Why Be Optimistic About Crypto 1:17:13 Closing & Disclaimers
------ Resources:
Matthew Green https://twitter.com/matthew_d_green
Letter to Congress https://concerned.tech/
In Defense of Crypto(Currency) https://blog.cryptographyengineering.com/2022/06/09/in-defense-of-cryptocurrency/
----- Not financial or tax advice. This channel is strictly educational and is not investment advice or a solicitation to buy or sell any assets or to make any financial decisions. This video is not tax advice. Talk to your accountant. Do your own research.
Today we’re joined by Suchi Saria, the founder and CEO of Bayesian Health, the John C. Malone associate professor of computer science, statistics, and health policy, and the director of the machine learning and healthcare lab at Johns Hopkins University.
Suchi shares a bit about her journey to working in the intersection of machine learning and healthcare, and how her research has spanned across both medical policy and discovery. We discuss why it has taken so long for machine learning to become accepted and adopted by the healthcare infrastructure and where exactly we stand in the adoption process, where there have been “pockets” of tangible success.
Finally, we explore the state of healthcare data, and of course, we talk about Suchi’s recently announced startup Bayesian Health and their goals in the healthcare space, and an accompanying study that looks at real-time ML inference in an EMR setting.
The complete show notes for this episode can be found at twimlai.com/go/501.
Nilay Patel interviews two experts on different sides of the bitcoin argument: a bitcoin investor and bitcoin skeptic.
The investor is Nic Carter. He’s a general partner at Castle Island Ventures, which funds startups that are building on top of the bitcoin infrastructure to make payments more accessible — basically, making sure bitcoin can function like a currency.
The skeptic is Steve Hanke. He is a professor of Applied Economics at Johns Hopkins University, senior fellow and director of the Troubled Currencies Project at the Cato Institute, a former member of President Ronald Reagan’s Council of Economic Advisers, and was the president of Toronto Trust Argentina in Buenos Aires when it was the world’s best performing mutual fund in 1995. He has also advised other countries on how to deal with hyperinflation and how to stabilize currencies.
Nilay asks them both questions about bitcoin’s place in the market and pushes them on the shakier parts of their arguments.
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Dr. Marty Makary—surgical oncologist at Johns Hopkins University School of Medicine, and health policy and innovation expert—has long been a passionate advocate for transparent pricing in the healthcare system. We don’t talk enough (or really at all) about price in healthcare, says Makary (instead, we talk about cost). But shedding a light on prices in healthcare—from not just what those prices are but how prices are set and the value we all receive as consumers of the system overall—can help us measure quality in medicine, and be a driver for real behavioral change in the healthcare system, correcting many of the unintended consequences of a fee-for-service system like surprise billing or unnecessary medical procedures.
In this conversation with a16z General Partner Julie Yoo, Makary and Yoo discuss what price transparency in the healthcare system could really do; how we can "steer" towards the good physicians who are not just highly skilled, but make the right judgment calls based on need and holistic health, not cost; how we might distinguish between high value and low value through medical appropriateness; and how we might gain clinical wisdom from other kinds of scientific discovery beyond randomized controls, especially during the wartime protocol of COVID-19.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Today we’re joined by Archana Venkataraman, John C. Malone Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University. Archana’s research at the Neural Systems Analysis Laboratory focuses on developing tools, frameworks, and algorithms to better understand, and treat neurological and psychiatric disorders, including autism, epilepsy, and others. We explore her work applying machine learning to these problems, including biomarker discovery, disorder severity prediction and mor
There's nothing better than financial crisis hindsight and earlier this month we got a big dose of it in the form of a 218-page paper by Laurence Ball, Department of Economics Chair at Johns Hopkins. In the paper, Ball makes the case that — contrary to statements by some policymakers — Lehman Brothers could have been rescued back in 2008 and the U.S. made a massive mistake in choosing not to do so. We talk to Ball about the genesis of the paper and what it means for markets today.
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