Michael Horowitz is the former deputy assistant secretary of defense for force development and emerging capabilities at the Department of Defense, and currently a professor at the University of Pennsylvania. Horowitz joins Big Technology to discuss the Anthropic–Pentagon rupture and what it signals about how the U.S. government wants to use frontier AI. Tune in to hear his inside view on how models like Claude actually get deployed in defense workflows, why a contract fight over “mass surveillance” language escalated, and what the trust breakdown says about the future of AI partnerships with the state. We also cover autonomous weapon systems vs. “fully autonomous weapons,” what today’s AI can and can’t do on the battlefield, and how AI is likely to reshape warfare over time. Hit play for a clear-eyed look at where Silicon Valley and the national security establishment collide—and what happens next.
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Dr. Zeke Emanuel is one of the country’s foremost healthcare experts. An oncologist and the former chair of the Department of Bioethics at the National Institutes of Health, he was one of the architects of the Affordable Care Act and now teaches at the University of Pennsylvania. Emanuel’s new book, “Eat Your Ice Cream: Six Simple Rules for a Long and Healthy Life,” offers practical advice on eating and living well at a time when Americans are bombarded with dubious “wellness” content everywhere they look.
Kara and Zeke talk about how nutrition advice has gotten overly complicated; why it’s OK to indulge in the occasional serving of ice cream or glass of wine; and why he mostly dismisses wearable technology as a means of measuring a healthy lifestyle. Emanuel also shares his thoughts on the Trump administration’s latest updates to the food pyramid, and his fears over the distrust the government is sowing around vaccines.
Special thanks to Politics and Prose Bookstore for hosting this live conversation.
Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, Threads, and Bluesky @onwithkaraswisher.
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Angela Duckworth, a psychologist, the co-founder of Character Lab, a professor of psychology at the University of Pennsylvania, and the author of the New York Times bestseller, “Grit: The Power of Passion and Perseverance,” joins Scott to discuss the attributes of gritty people, how to create environments for success, and ways to raise resilient kids.
Follow Angela, @angeladuckw.
Scott opens with his thoughts on Disney’s succession plan and Chick-fil-A going into the content game.
Algebra of happiness: the three rules of masculinity.
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Guest: Angela Duckworth, professor at the University of Pennsylvania and author of Grit: The Power of Passion and Perseverance
“There’s got to be a cost” when you pursue your passions, says University of Pennsylvania professor Angela Duckworth; in fact, the word “passion” comes from the Latin word for “suffering.” But that doesn’t mean that gritty people are unhappy. After the time needed for sleep, daily exercise, friends, and family, Dr. Duckworth explains, “what’s left is more than 40 hours.” Informed by her research and her own happiness, she tries to discourage her students from settling for a 9 to 5 life: “There’s so many people that exemplify a life of dedication, and hard work, and of happiness, and humor, and friends, and family, that I think we should tell young people, ‘Look, don't assume that's not possible.’”
In this episode, Angela and Joubin discuss being punctual, Danny Kahneman, AP Calculus, moving the finish line, teaching grit to children, Arthur Ashe, Diana Nyad, passion and sacrifice, hiring gritty people, “change your situation,” Marc Leder and Rodger Krouse, Invictus, ChatGPT, neural autopilot, and Steve Jobs.
In this episode, we cover:
“I have a thing with time” (01:36)
Being the GOAT (06:37)
Mr. Yom (09:27)
Chef Marc Vetri (14:15)
The Devil Wears Prada (16:03)
Talking about grit (18:12)
Satisfaction, loneliness, and happiness (20:24)
Success as a journey (28:23)
The cost of hard work (32:52)
Angela’s 70-hour work week (36:31)
Charisma and loving what you do (40:55)
Why high achievers have supportive partners (47:07)
The next book (55:25)
Pick the right market (57:45)
Therapy questions (59:53)
The Incredible Hulk vs. James Bond (01:02:45)
Automating decisions (01:05:43)
What “grit” means to Angela (01:09:39)
Links:
Connect with AngelaTwitter
LinkedIn
Additional reading:Redefining Success: Adopt the Journey Mindset to Move Forward
Buy Grit: The Power of Passion and Perseverance
Connect with JoubinTwitter
LinkedIn
Email: grit@kleinerperkins.com
Learn more about Kleiner Perkins
This episode was edited by Eric Johnson from LightningPod.fm
Today we’re joined by Michael Kearns, professor in the Department of Computer and Information Science at the University of Pennsylvania and an Amazon scholar. In our conversation with Michael, we discuss the new challenges to responsible AI brought about by the generative AI era. We explore Michael’s learnings and insights from the intersection of his real-world experience at AWS and his work in academia. We cover a diverse range of topics under this banner, including service card metrics, privacy, hallucinations, RLHF, and LLM evaluation benchmarks. We also touch on Clean Rooms ML, a secured environment that balances accessibility to private datasets through differential privacy techniques, offering a new approach for secure data handling in machine learning.
The complete show notes for this episode can be found at twimlai.com/go/662.
Today we conclude our AWS re:Invent 2022 series joined by Michael Kearns, a professor in the department of computer and information science at UPenn, as well as an Amazon Scholar. In our conversation, we briefly explore Michael’s broader research interests in responsible AI and ML governance and his role at Amazon. We then discuss the announcement of service cards, and their take on “model cards” at a holistic, system level as opposed to an individual model level. We walk through the information represented on the cards, as well as explore the decision-making process around specific information being omitted from the cards. We also get Michael’s take on the years-old debate of algorithmic bias vs dataset bias, what some of the current issues are around this topic, and what research he has seen (and hopes to see) addressing issues of “fairness” in large language models.
The complete show notes for this episode can be found at twimlai.com/go/610.
Michael Kearns, a computer scientist professor at the University of Pennsylvania and an Amazon scholar talks about differential privacy, how Amazon's research approach differs from its peers, and how AI will eventually permeate all aspects of our lives.
While the world’s temperature rises, there are scores of scientists working around the globe to study causes and solutions. One scientist in particular, David Rolnick, has stood out as a pioneer of machine-learning in the fight against climate change.
David successfully built a broader movement including others like Andrew Ng, Yoshua Bengio, Demis Hassabis, and Jennifer Chayes to champion the amazing possibilities that exist at the intersection of AI and the climate. He organized the first-ever ever AI event at the United Nations Climate Change Conference. He was named a top innovator by the MIT Technology Review -- all before the age of 30.
He sits down with Pieter to discuss his landmark paper on the applications of ML to climate change looking at use cases like weather simulations, ecological monitoring, and predicting natural resource depletion.
| SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on YouTube TheRobotBrainsPodcast, Twitter @therobotbrains, and Instagram @therobotbrains.
| Host: Pieter Abbeel | Executive Producers: Alice Patel & Henry Tobias Jones | Audio Production: Kieron Matthew Banerji | Title Music: Alejandro Del Pozo
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Steve Viscelli is a former truck driver and now an economic sociologist at University of Pennsylvania studying freight transportation, including autonomous trucks. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
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Big Rig (book): https://amzn.to/3EbaofP
Will Robotic Trucks Be “Sweatshops on Wheels?” (article): https://bit.ly/3vGGgpO
Johnny Cash – All I Do Is Drive (song): https://www.youtube.com/watch?v=DEHoagHlqrE
Steve’s Penn Gazette Interview: https://bit.ly/3nkRPyV
More Information on Automated Trucking: http://www.driverlessreport.org/
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(07:36) – Ethnography
(19:49) – Challenges of driving a truck
(38:28) – Trucking industry: State of affairs
(1:11:33) – Future of autonomous trucks
(1:37:49) – Solving the automated truck dilemma
(2:09:44) – Role of society in automated trucking
(2:36:53) – Tesla and revolutionizing the trucking industry
(2:56:33) – Hope and final thoughts
Konrad Körding joins us to discuss his work in educating the next generation in deep learning and his views on the importance of causality in deep learning research.
In this episode you will learn:
Konrad’s academic background [3:54]
Neuromatch Academy [5:23]
Artificial general intelligence [35:02]
Defining deep learning [41:24]
Symbol representation [44:12]
Konrad’s career journey [47:25]
What other skills should you develop for the future? [52:46]
What is the future of intelligence in our timeline? [56:37]
Additional materials: www.superdatascience.com/469
Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more.
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon. This episode is sponsored by Pessimists Archive podcast. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode):
00:00 – Introduction
02:45 – Influence from literature and journalism
07:39 – Are most people good?
13:05 – Ethical algorithm
24:28 – Algorithmic fairness of groups vs individuals
33:36 – Fairness tradeoffs
46:29 – Facebook, social networks, and algorithmic ethics
58:04 – Machine learning
58:05 – Machine learning
59:19 – Algorithm that determines what is fair
1:01:25 – Computer scientists should think about ethics
1:05:59 – Algorithmic privacy
1:11:50 – Differential privacy
1:19:10 – Privacy by misinformation
1:22:31 – Privacy of data in society
1:27:49 – Game theory
1:29:40 – Nash equilibrium
1:30:35 – Machine learning and game theory
1:34:52 – Mutual assured destruction
1:36:56 – Algorithmic trading
1:44:09 – Pivotal moment in graduate school
A few months ago at the recent international conference on machine learning, a workshop and research paper launched a movement to use machine learning in addressing climate change. The response was huge and has given birth to the bones of an organization climate change.ai. This week I talked to David Rolnick, a postdoc at U Penn and Priya, Donti, a Phd student at Carnegie Mellon, about how the group came together and about how the organization is developing.
Carl June is the pioneer behind CAR T-cell therapy: a groundbreaking cancer treatment that supercharges part of a patient's own immune system to attack and kill tumors. In a talk about a breakthrough, he shares how three decades of research culminated in a therapy that's eradicated cases of leukemia once thought to be incurable -- and explains how it could be used to fight other types of cancer.
Hosted on Acast. See acast.com/privacy for more information.
Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.
Every day, people are bombarded with predictions of what will happen in the future. In recent months, talk of 'inflection points' in the markets has heated up, and the possibility of the U.S. economic expansion, now the longest in history, coming to an end is being actively discussed. But how do we know if such predictions are good ones? And how can we learn to be better forecasters ourselves? On this week's episode of the Odd Lots podcast, we talk to Philip Tetlock, the Leonore Annenberg University Professor of Psychology and Management at the University of Pennsylvania, and the author of numerous books and papers on the topic of predictions.
See omnystudio.com/listener for privacy information.
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132.
