The Man Who Wrote the AI Textbook Says We're Heading For Extinction - The Story
How close are we to human extinction because of AI? Leading AI expert Professor Stuart Russell believes we’re much too close for comfort and has been raising the alarm for a few years. Ironically, Stuart himself wrote the book that laid the foundation for AI research back in the 1990s. And he was the only AI expert Elon Musk’s team called upon during their trial with OpenAI.
Stuart joins Oz to discuss what changed his mind about pursuing AI superintelligence and makes the argument that human extinction is being treated as an external liability in favor of shareholders.
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20VC: Micron Will Be More Valuable Than Meta | How Export Controls Helped Not Hurt China | Power is the Bottleneck to AI | Why Dario Has Done a Disservice to AI with his Labour Replacement Messaging with Aravind Srinivas, Founder @ Perplexity
Aravind Srinivas is the Founder and CEO of Perplexity, one of the fastest-growing AI companies in the world. Since the start of the year, Perplexity has tripled revenue to well over $500M in ARR. Aravind has raised over $1BN for the company with reported valuations reaching $20BN.
AGENDA:
05:40 – "Perplexity Changed Google More Than Any PM Ever Has"
10:15 – Why Search Is Not the Future of AI
13:05 – The Most Important Insight in AI: The Model Is NOT The Product
16:10 – Why AI Agents Will Become Bigger Than Google Search
22:00 – AI Will Design Chips, Discover Drugs & Cure Diseases
24:15 – The Secret to Building a 24/7 AI Agent
32:40 – Aravind's Wild Prediction: Micron Could Become More Valuable Than Meta
41:00 – Why Power Will Be The Biggest Bottleneck In AI For The Next Decade
45:00 – Have U.S. Export Controls Accidentally Made China Stronger?
49:00 – Why Dario Amodei's AI Doom Narrative Is Wrong
55:20 – Why Token Budgets are Total BS and Useless
58:00 – When Agent Traffic Surpasses Human Traffic, What Happens To The Internet?
01:08:00 – SpaceX, OpenAI & Anthropic IPOs: Is There Enough Capital For All Three?
01:14:00 – What Elon Musk Is Really Like Behind Closed Doors
Why audio deepfakes are so hard to spot
Voice cloning is the use of artificial intelligence to generate a clone of a real person’s voice, imitating the sound, when they pause and what words they typically emphasize. And it can be hard for people to identify voices as being AI-generated.
Research last year from UC Berkeley professor Hany Farid, an expert in digital forensics, found that people correctly identify a voice as AI-generated only 60% of the time.
Marketplace’s Stephanie Hughes spoke with Farid about the rapid sophistication of audio deepfakes, why it's so hard to tell the difference between a real voice and an AI-generated one right now, and some tips to help you spot voice clones.
#331 Sergey Levine: The Robot Revolution Nobody Is Talking About
This episode is sponsored by Modulate. Most voice AI focuses on transcription. Velma takes it further by actually understanding conversations, analyzing tone, timing, stress, and intent using its Ensemble Listening Model architecture. Explore the live preview: https://preview.modulate.ai/ What does it actually mean to build a foundation model for robots? In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.
Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.
We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.
Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.
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(00:00) Introduction: What Are Foundation Models for Robots?
(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley
(02:51) Breaking Down Foundation Models for Non-Technical People
(06:46) Why Real World Data Beats Simulation
(15:00) Building a Broad Robotics Foundation From Scratch
(24:00) The Open World Problem in Robotics
(40:00) Generalist vs Specialist Robots: Which Wins?
(47:00) Humanoid Robots: Real Innovation or Just Hype?
(55:10) The Future: Continuous Learning and the Data Flywheel
(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits
Sergey Levine - Building LLMs for the Physical World - [Invest Like the Best, EP.465]
My guest today is Sergey Levine, a professor at UC Berkeley and co-founder of Physical Intelligence. The company is building robotic foundation models designed to control any embodied system to do any task in any environment.
Sergey argues that solving robotics at full generality is the right path, and that building systems that learn across many robots, environments, and tasks may be the more scalable approach than building narrow specialists. We discuss how these models can perform new tasks without being trained on them directly, and why everyday human actions remain the hardest problems in the field.
He also reflects on how human trust and acceptance may matter as much as technical breakthroughs in determining when robots become part of daily life.
Please enjoy my conversation with Sergey Levine.
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
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Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).
Timestamps:
(00:00:00) Welcome to Invest Like the Best
(00:02:43) Intro: Sergey Levine
(00:03:29) Why Bet on Generality Over Specialization
(00:07:24) What if PI succeeds?
(00:09:05) Pros and Cons of Humanoid Robotics
(00:11:02) Timeline of Major Milestones in Robotics
(00:15:47) Sergey's Personal Journey
(00:18:22) Making General Intelligence Happen
(00:19:57) Understanding Robot Data Collection
(00:22:12) Most Surprising Discovery at Physical Intelligence
(00:24:48) The Science of Common Sense
(00:25:36) Long-Range Tasks in Robotics
(00:27:24) Why Wouldn’t We Have A Robot in Our Kitchen by 2050
(00:31:21) Other Interesting Approaches
(00:32:38) Cool vs. Useful in Robotics
(00:36:48) Form Factor Innovation
(00:38:22) Physical Intelligence Analogy
(00:39:30) Economic Transformation from Robotics
(00:40:48) Controversies in the Robotics Community
(00:42:16) Arguments Against End-to-End Learning
(00:42:34) Compositional Learning Explained
(00:43:25) Last Tasks Robots will Conquer
(00:44:30) Dark Parts of the Robotics Brain
(00:47:05) What Makes a Great Researcher
(00:50:15) Manufacturing and Scale Challenges
(00:51:17) How Companies Should Prepare for Robotics
(00:53:38) Boston Dynamics' Demos
(00:55:43) Converging Technologies Enabling Robotics
(00:56:47) How to Stay Up To Date in Robotics
(00:59:51) Near Term Objectives
(01:00:49) Confidence Level Among Researchers
(01:03:31) Google's Experimentation Culture
(01:04:24) The Kindest Thing
Four CEOs on the Future of AI: CoreWeave, Perplexity, Mistral, and IREN
(0:00) Intro live from Nvidia GTC
(0:37) CoreWeave CEO, Michael Intrator
(32:58) Perplexity CEO, Aravind Srinivas
(1:07:11) Mistral CEO, Arthur Mensch
(1:18:57) IREN CEO, Daniel Roberts
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Elon’s “Nudify” Mess: How X Supercharged Deepfakes
On Christmas Eve, Elon Musk’s X rolled out an in-app tool that lets users alter other people’s photos and post the results directly in reply. With minimal safeguards, it quickly became a pipeline for sexualized, non-consensual deepfakes, including imagery involving minors, delivered straight into victims’ notifications.
Renée DiResta, Hany Farid, and Casey Newton join Kara to dig into the scale of the harm, the failure of app stores and regulators to act quickly, and why the “free speech” rhetoric used to defend the abuse is incoherent. Kara explores what accountability could look like — and what comes next as AI tools get more powerful.
Renée DiResta is the former technical research manager at Stanford's Internet Observatory. She researched online CSAM for years and is one of the world’s leading experts on online disinformation and propaganda. She’s also the author of Invisible Rulers: The People Who Turn Lies into Reality.
Hany Farid is a professor of computer sciences and engineering at the University of California, Berkeley. He’s been described as the father of digital image forensics and has spent years developing tools to combat CSAM.
Casey Newton is the founder of the tech newsletter Platformer and the co-host of The New York Times podcast Hard Fork.
This episode was recorded on Tuesday, January 20th.
When reached for comment, a spokesperson for X referred us to a a statement post on X, which reads in part:
We remain committed to making X a safe platform for everyone and continue to have zero tolerance for any forms of child sexual exploitation, non-consensual nudity, and unwanted sexual content.
We take action to remove high-priority violative content, including Child Sexual Abuse Material (CSAM) and non-consensual nudity, taking appropriate action against accounts that violate our X Rules. We also report accounts seeking Child Sexual Exploitation materials to law enforcement authorities as necessary.
Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, Threads, and Bluesky @onwithkaraswisher.
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3D printing was supposed to disrupt prosthetic costs. It hasn’t.
Prosthetic limbs can be expensive, costing thousands to hundreds of thousands of dollars. So the industry seemed ripe for disruption when 3D printing came along. The technology requires little labor and uses economical materials. But the reality of 3D printing prosthetic limbs isn’t that straightforward, according to writer and University of California, Berkeley, lecturer Britt Young, who uses a prosthetic arm.
Marketplace’s Meghan McCarty Carino spoke with Young about why 3D printing has yet to bring down prosthesis costs.
The Man Who Wrote The Book On AI: 2030 Might Be The Point Of No Return! We've Been Lied To About AI!
AI Expert STUART RUSSELL, exposes the trillion-dollar AI race, why governments won’t regulate, how AGI could replace humans by 2030, and why only a nuclear-level AI catastrophe will wake us up
Professor Stuart Russell O.B.E. is a world-renowned AI expert and Computer Science Professor at UC Berkeley. He holds the Smith-Zadeh Chair in Engineering and directs the Center for Human-Compatible AI, and is also the bestselling author of the book “Human Compatible: AI and the Problem of Control".
He explains:
◼️What the “gorilla problem” reveals about our future under superintelligent AI
◼️How governments are outfunded by Big Tech
◼️Why current AI systems already lie and self-preserve
◼️The radical solution he’s spent a decade building to make AI safe
◼️The myth of ‘pulling the plug’ and why AI won’t be that easy to stop
[00:00] You've Been Talking About AI for a Long Time
[02:54] You Wrote the Textbook on AI
[03:29] It Will Take a Crisis to Wake People Up
[06:03] CEOs Staying in the AI Race Despite Risks
[08:04] They Know It's an Extinction-Level Risk
[10:06] What Is Artificial General Intelligence (AGI)?
[13:10] Will We Reach General Intelligence Soon?
[16:26] How Much Is Safety Really Being Implemented
[17:29] AI Safety Employees Leaving OpenAI
[18:14] The Gorilla Problem — The Most Intelligent Species Will Always Rule
[19:34] If There's an Extinction Risk, Why Don't They Stop?
[21:02] Can't We Just Pull the Plug if AI Gets Too Powerful?
[22:49] Can We Build AI That Will Act in Our Best Interests?
[24:09] Are You Troubled by the Rapid Advancement of AI?
[26:48] Do You Have Regrets About Your Involvement?
[27:35] No One Actually Understands How This AI Works
[30:36] AI Will Be Able to Train Itself
[32:24] The Fast Takeoff Is Coming
[34:20] Are We Creating Our Successor and Ending the Human Race?
[38:36] Advice to Young People in This New World
[40:52] How Do You Think AI Would Make Us Extinct?
[42:33] The Problem if No One Has to Work
[45:59] What if We Just Entertain Ourselves All Day
[48:43] Why Do We Make Robots Look Like Humans?
[56:44] What Should Young People Be Doing Professionally?
[59:56] What Is It to Be Human?
[01:03:34] The Rise of Individualism
[01:05:34] Ads
[01:06:39] Universal Basic Income
[01:08:41] Would You Press a Button to Stop AI Forever?
[01:15:13] But Won't China Win the AI Race if We Stop?
[01:18:40] Trump's Approach to AI
[01:19:06] What's Causing the Loss in Middle-Class Jobs
[01:21:02] What Will Happen if the UK Doesn't Participate in the AI Race?
[01:23:31] Amazon Replacing Their Workers
[01:29:00] Ads
[01:30:54] Experts Agree on Extinction Risk
[01:38:01] What if Aliens Were Watching Us Right Now
[01:39:35] Can We Make AI Systems That We Can Control?
[01:43:14] Are We Creating a God?
[01:47:32] Could There Have Been Advanced Civilisations Before Us?
[01:48:50] What Can We Do to Help?
[01:50:43] You Wrote the Book on AI — Does It Weigh on You?
[01:58:48] What Do You Value Most in Life?
Follow Stuart:
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How far away are we from humanoid robots doing our chores?
Robots are commonplace in factories, and increasingly in warehouses like those run by Amazon. But what about robots to help with household chores — so-called humanoids to load the dishwasher or fold the laundry?
To find out, we checked in with Ken Goldberg, professor of engineering at UC Berkeley and co-founder of the AI and robotics company Ambi Robotics. He spoke to Marketplace’s Nova Safo en route from a robotics conference in China.
Science Corner Special: David Friedberg, Cleo Abram, Alex Filippenko, and Keller Rinaudo Cliffton
(0:00) Introducing Cleo Abram
(1:12) Why YouTube has become the best platform for creators, techno-optimism at Huge If True
(8:35) Astrophysicist Alex Filippenko on the James Webb Space Telescope
(23:14) Q&A with Alex Filippenko, Cleo, and Friedberg: The universe, Fermi Paradox, black holes, NIH grants and the "war on science"
(37:37) Zipline CEO Keller Rinaudo Cliffton on the future of drone delivery
(53:29) Q&A with Keller Rinaudo Cliffton, Cleo, and Friedberg: Instant delivery explosion, competitors, impact on healthcare
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The College Crisis: Heads of Dartmouth & Berkeley Debate the Decline of US Universities
(0:00) Introducing Dartmouth President Sian Beilock and Berkeley Chancellor Rich Lyons
(1:14) The student loan burden
(8:03) DEI at US universities
(13:56) Administrative bloat
(16:12) Trump vs the Ivy League, viewpoint diversity
(21:56) Impact of K-12 education declining
(25:13) Will AI learning kill higher education?
(29:14) Rising unemployment among recent graduates
(31:35) Role of endowments
Thanks to our partners for making this happen!
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OKX - The new way to build your crypto portfolio and use it in daily life. We call it the new money app. https://www.okx.com/
Google Cloud - The next generation of unicorns is building on Google Cloud's industry-leading, fully integrated AI stack: infrastructure, platform, models, agents, and data. https://cloud.google.com/
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Fully autonomous robots are much closer than you think – Sergey Levine
Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030.
If Sergey’s right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion.
Watch on YouTube; listen on Apple Podcasts or Spotify.
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* Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: ai.studio/banana
To sponsor a future episode, visit dwarkesh.com/advertise.
Timestamps
(00:00:00) – Timeline to widely deployed autonomous robots
(00:17:25) – Why robotics will scale faster than self-driving cars
(00:27:28) – How vision-language-action models work
(00:45:37) – Changes needed for brainlike efficiency in robots
(00:57:59) – Learning from simulation
(01:09:18) – How much will robots speed up AI buildouts?
(01:18:01) – If hardware’s the bottleneck, does China win by default?
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
LIMITLESS: Aravind Srinivas: Perplexity CEO’s All-In Gamble to Take Down Google
Perplexity CEO Aravind Srinivas joins the show to explore how AI agents, personalization, and new browsing models could transform the internet experience.
From replacing mundane workflows with proactive assistance to envisioning a fully AI-native operating system, Aravind shares his long-term vision, the thinking behind Perplexity’s new Comet browser, and why curiosity will remain humanity’s most valuable skill in the AI era.
We also dive into competing with Big Tech, protecting users from AI-generated “slop,” and the architectural choices that make AI-assisted browsing faster, more reliable, and more personal.
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TIMESTAMPS
0:00 Intro
9:58 Curiosity as the Key Human Advantage
15:26 From Keywords to Questions
21:35 Personalization and Agency in Browsing
27:13 Keeping Humans in the Loop
32:30 Why People Love Perplexity
38:35 Comet - The AI Browser
47:24 The Browser vs. Agent Debate
57:16 Competing with Big Tech
1:01:26 The OS Endgame
1:08:48 Closing Thoughts
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RESOURCES
Aravind Srinivas
https://x.com/AravSrinivas
Perplexity
https://www.perplexity.ai/
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Not financial or tax advice. See our investment disclosures here:
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GPT-5 Backlash + Perplexity C.E.O. Aravind Srinivas on the Browser Wars + Hot Mess Express
OpenAI spent the week responding to outcry from users who miss the behavior of the old ChatGPT, before the latest flagship model was released.
We discuss the criticism, why it caught the company by surprise and what it indicates about the deepening emotional relationships that people are forming with chatbots.
Then, Aravind Srinivas, the chief executive of Perplexity AI, joins us to discuss his company’s new artificial intelligence-powered browser, Comet; his company’s bid to buy Google Chrome; and what the future of the internet looks like when users turn to A.I. assistants to browse the web for them. Finally, to cap it all off, we rate the craziest tech stories of the week in our game Hot Mess Express.
Guests:
Aravind Srinivas, chief executive of Perplexity AI.
Additional Reading:
Chatbots Can Go Into a Delusional Spiral. Here’s How It Happens.
Three Big Lessons From the GPT-5 Backlash
A.I. Start-Up Perplexity Offers to Buy Google’s Chrome Browser for $34.5 Billion
Elon Musk Threatens to Sue Apple Over Claims It Favors OpenAI
U.S. Government to Take Cut of Nvidia and AMD A.I. Chip Sales to China
Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app.
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Perplexity CEO on why the browser is AI's killer app
This is Alex Heath, deputy editor at The Verge. Nilay’s out on parental leave for the next few months, so I’ll be stepping in to host our Thursday episodes while he’s out. My guest today is Perplexity CEO Aravind Srinivas, who is betting that the browser is where more useful AI will get built.
Perplexity just released Comet, an AI web browser for the Mac and Windows that’s still in an invite-only beta. I’ve been using it, and it’s very interesting. In this conversation, Aravind and I also discussed the future of Perplexity, the AI talent wars, and why he thinks people will eventually pay thousands of dollars for a single AI prompt.
Read the full transcript here on The Verge.
Links:
Perplexity just launched an AI web browser | Verge
Perplexity wants to buy Chrome if Google has to sell it | Verge
The Dia browser is a big bet on the. web and AI | Verge
Perplexity’s CEO on fighting Google & the AI browser war | Command Line
Perplexity launches a $200 monthly subscription plan | Verge
Meta says it’s winning the talent war with OpenAI | Verge
Meta is trying to win the AI race with money | Verge
Meta held talks to buy Perplexity and others | Command Line
Inside Mark Zuckerberg’s AI hiring spree | Command Line
Perplexity is ready to take on Google | Command Line
Credits:
Decoder is a production of The Verge and part of the Vox Media Podcast Network.
Our producers are Kate Cox and Nick Statt. Our editor is Ursa Wright.
The Decoder music is by Breakmaster Cylinder.
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Beyond the Talk: Hany Farid on trust in the age of AI
“ How do you trust anything anymore? Who do you trust? Where do you trust?” asks technologist and digital forensic expert Hany Farid. Following his talk at TED2025, Farid sat down for a special conversation with Elise Hu, host of TED Talks Daily, to discuss the erosion of trust in American society. From TikTok algorithms to AI deepfakes, Farid argues that critical thinking education is more important than ever and why it’s therapeutic to unplug from social media and connect with nature.
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How to spot fake AI photos | Hany Farid
How do you know if that shocking photo in your feed is real, or just another AI fake? Digital forensics expert Hany Farid explains how he helps journalists, courts and governments find structural errors in AI-generated images, offering four practical tips everyday individuals can use when facing the internet’s war on reality.
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The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn
This week on No Priors, Elad speaks with Chelsea Finn, cofounder of Physical Intelligence and currently Associate Professor at Stanford, leading the Intelligence through Learning and Interaction Lab. They dive into how robots learn, the challenges of training AI models for the physical world, and the importance of diverse data in reaching generalizable intelligence. Chelsea explains the evolving landscape of open-source vs. closed-source robotics and where AI models are likely to have the biggest impact first. They also compare the development of robotics to self-driving cars, explore the future of humanoid and non-humanoid robots, and discuss what’s still missing for AI to function effectively in the real world. If you’re curious about the next phase of AI beyond the digital space, this episode is a must-listen.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ChelseaFinn
Show Notes:
0:00 Introduction
0:31 Chelsea’s background in robotics
3:10 Physical Intelligence
5:13 Defining their approach and model architecture
7:39 Reaching generalizability and diversifying robot data
9:46 Open source vs. closed source
12:32 Where will PI’s models integrate first?
14:34 Humanoid as a form factor
16:28 Embodied intelligence
17:36 Key turning points in robotics progress
20:05 Hierarchical interactive robot and decision-making
22:21 Choosing data inputs
26:25 Self driving vs robotics market
28:37 Advice to robotics founders
29:24 Observational data and data generation
31:57 Future robotic forms
The Story: Liar, Liar, Deepfakes on Fire w/ Hany Farid
Hany Farid is a professor of electrical engineering and computer sciences at the University of California, Berkeley. He's been a leading voice on digital forensics for over two decades—pioneering ways to identify if an image, audio or video has been digitally altered. Since the rise of social media, Farid has kept busy helping news organizations, government agencies and law enforcement determine what is real and what is fake online. Farid sits down with Oz to talk about his initial interest in digital forensics, the effects of misinformation on society, and whether he wants an AI likeness of himself to live on after he dies.
See omnystudio.com/listener for privacy information.
π0: A Foundation Model for Robotics with Sergey Levine - #719
Today, we're joined by Sergey Levine, associate professor at UC Berkeley and co-founder of Physical Intelligence, to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model training "recipe," emphasizing the roles of pre-training and post-training with a diverse mixture of real-world data to ensure robust and intelligent robot learning. We review the data collection approach, which uses human operators and teleoperation rigs, the potential of synthetic data and reinforcement learning in enhancing robotic capabilities, and much more. We also introduce the team’s new FAST tokenizer, which opens the door to a fully Transformer-based model and significant improvements in learning and generalization. Finally, we cover the open-sourcing of π0 and future directions for their research.
The complete show notes for this episode can be found at https://twimlai.com/go/719.
How AI will answer questions we haven't thought to ask | Aravind Srinivas
Human potential will only accelerate with AI answering questions better and faster than ever before, says Aravind Srinivas, cofounder and CEO of conversational search engine Perplexity. He examines the trends driving new AI-powered tools that nourish curiosity and creativity — and how they might usher in a new era of intellectual growth and discovery. "Knowledge does not really care about who you are, where you're from or who you have access to. Rather, what matters is the next question you're going to ask," says Srinivas.
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DeepSeek: separating fact from hype
Today, we’re bringing you a bonus episode zeroing in on DeepSeek, the Chinese AI lab that’s recently taken over the news and the app stores, beating out OpenAI's ChatGPT. Max Zeff is talking about it all with Ion Stoica, Professor of Computer Science Division at UC Berkeley and the cofounder and executive chairman of software startup Databricks.
Listen to the full episode to hear more about:
Why Stoica believes the future of AI lies in "doubling down on open source."
Microsoft's decision to host DeepSeek on Azure.
What the U.S. can do to foster accelerated innovation – with a look back at SB-1047 and a look ahead to 2025.
The controversy surrounding claims that DeepSeek used OpenAI’s models to train its own.
Equity will be back next week, so stay tuned!
Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.
Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here.
Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We’d also like to thank TechCrunch’s audience development team. Thank you so much for listening, and we'll talk to you next time.
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Databricks Founder Ion Stoica: Turning Academic Open Source into Startup Success
Berkeley professor Ion Stoica, co-founder of Databricks and Anyscale, transformed the open source projects Spark and Ray into successful AI infrastructure companies. He talks about what mattered most for Databricks' success -- the focus on making Spark win and making Databricks the best place to run Spark. He highlights the importance of striking key partnerships -- the Microsoft partnership in particular that accelerated Databricks' growth and contributed to Spark's dominance among data scientists and AI engineers. He also shares his perspective on finding new problems to work on, which holds lessons for aspiring founders and builders: 1) building systems in new areas that, if widely adopted, put you in the best position to understand the new problem space, and 2) focusing on a problem that is more important tomorrow than today.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
Mentioned in this episode:
Spark: The open source platform for data engineering that Databricks was originally based on.
Ray: Open source framework to manage, executes and optimizes compute needs across AI workloads, now productized through Anyscale
MosaicML: Generative AI startups founded by Naveen Rao that Databricks acquired in 2023.
Unity Catalog: Data and AI governance solution from Databricks.
CIB Berkeley: Multi-strategy hedge fund at UC Berkeley that commercializes research in the UC system.
Hadoop: A long-time leading platform for large scale distributed computing.
VLLM and Chatbot Arena: Two of Ion’s students’ projects that he wanted to highlight.
Ken Goldberg (roboticist)
Ken Goldberg (Why Don’t We Have Better Robots Yet?) is an award-winning artist, roboticist, and engineering professor. Ken joins the Armchair Expert to discuss being born in Nigeria, growing up in rough and tumble City of Brotherly Love, and on how that taught him how to not take things lying down. Ken and Dax address the elephant panties in the room, how a course he took in 1981 began his trajectory in robotics and AI, and the tragic archetype of Pygmalion and the hubris of falling in love with your creation. Ken explains the Czechoslovakian etymology of the word “robot,” why don’t we have better robots yet, and how he stays optimistic doing a job predicated on failure.
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#434 – Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet
Arvind Srinivas is CEO of Perplexity, a company that aims to revolutionize how we humans find answers to questions on the Internet. Please support this podcast by checking out our sponsors:
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Transcript: https://lexfridman.com/aravind-srinivas-transcript
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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
(10:52) – How Perplexity works
(18:48) – How Google works
(41:16) – Larry Page and Sergey Brin
(55:50) – Jeff Bezos
(59:18) – Elon Musk
(1:01:36) – Jensen Huang
(1:04:53) – Mark Zuckerberg
(1:06:21) – Yann LeCun
(1:13:07) – Breakthroughs in AI
(1:29:05) – Curiosity
(1:35:22) – $1 trillion dollar question
(1:50:13) – Perplexity origin story
(2:05:25) – RAG
(2:27:43) – 1 million H100 GPUs
(2:30:15) – Advice for startups
(2:42:52) – Future of search
(3:00:29) – Future of AI
20VC: Perplexity's Aravind Srinivas on Will Foundation Models Commoditise, Diminishing Returns in Model Performance, OpenAI vs Anthropic: Who Wins & Why the Next Breakthrough in Model Performance will be in Reasoning
Aravind Srinivas is the Co-Founder & CEO of Perplexity, the conversational "answer engine" that provides precise, user-focused answers to queries. Aravind co-founded the company in 2022 after working as a research scientist at OpenAI, Google, and DeepMind. To date, Perplexity has raised over $100 million from investors including Jeff Bezos, Nat Friedman, Elad Gil, and Susan Wojciki.
In Today's Episode with Aravind Srinivas We Discuss:
Biggest Lessons from DeepMind & OpenAI
What was the best career advice Sam Altman @ OpenAI gave Aravind?
What were Aravind's biggest takeaways at DeepMind?
How did DeepMind shape how Aravind built Perplexity?
What did Aravind mean by "competition is for losers?" What did he learn about talent assembly at DeepMind?
The Next AI Breakthrough: Reasoning
Does Aravind think we are experiencing diminishing returns on compute & model performance?
Does Aravind agree reasoning will be the next big breakthrough for models?
What are the reasons Aravind thinks models suck at reasoning today?
What is the timeline for reasoning improvement according to Aravind?
What does Aravind think are the biggest misconceptions about AI today?
Will Foundation Models Commoditise?
Does Aravind think foundation models will commoditise? What will the end state of foundation models look like?
Why does Aravind think the second tier models will get commoditised?
Why does Aravind think the subscription model will not work for AI models with true reasoning?
Why does Aravind think the application layer companies will benefit from foundation models commoditising?
Why does Aravind think foundation models will not verticalize?
When does Aravind think is the right time to go enterprise? What is his strategy to differentiate Perplexity from its competitors?
AI Arms Race: Who Will Win?
Who does Aravind think will be the winners of foundation models?
What do AI companies need to do to win the model arms race?
How does Aravind think startups can compete against incumbents' infinite cash flow?
What are the reasons Aravind thinks Perplexity's browsing is better than ChatGPT?
What is Aravind's biggest challenge at Perplexity today?
EP 97: How Aravind Srinivas (CEO, Perplexity) is Disrupting Google Search with AI
One of the most preeminent AI founders, Aravind Srinivas (CEO, Perplexity), believes we could see 100+ AI startups valued over $10B in our future. In the episode, we discuss why Perplexity decided to compete with Google and Aravind shares predictions for the utopian future of artificial intelligence. He also discusses the differences between working at both DeepMind and OpenAI, why personalized knowledge on demand could be a trillion-dollar opportunity and more.
(00:00) Intro
(01:34) The Evolution of Search Engines
(04:01) Perplexity: Bridging the Gap Between Google and Wikipedia
(05:04) The Philosophy Behind Perplexity and Its Unique Approach
(06:33) Redefining Search: The Shift from Links to Answers
(09:02) The Business Model Dilemma: Google's Challenge with Innovation
(13:46) The Importance of Aligning Shareholder and User Interests
(27:00) The Complexities of Building a Competitive AI Talent Pool
(33:33) Perplexity's Business Model and Subscription Strategy
(39:54) The Future of Search: Personalization and Real-Time Data
(44:21) Enhancing User Engagement with Suggested Next Questions
(48:10) Key Success Metrics and the Importance of Daily Queries
(50:21) The Challenge of Gaining User Trust
(50:48) Personalizing Products for User Engagement
(50:55) Navigating the AI Model Landscape
(51:40) The Strategic Decision Against Building Own Models
(52:22) Leveraging Open Source Models and Infrastructure Efficiency
(53:37) The Business Model and Value Proposition
(58:12) AI's Potential to Transform Work and Empowerment
(01:03:11) The Future of AI: More Compute or Breakthroughs?
(01:05:39) The Startup Ecosystem and AI's Billion-Dollar Potential
(01:07:26) AI Safety and Regulation: A Founder's Perspective
(01:08:15) Inspirations and Influences: Learning from Larry Page
(01:20:51) Building a User-Centric Product: The Perplexity Journey
(01:36:13) The Future of Perplexity and Changing User Habits
Produced: Rashad Assir & Leah Clapper
Mixed and edited: Justin Hrabovsky
Executive Producer: Josh Machiz
Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
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About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode, Logan goes behind the scenes with world-class entrepreneurs and investors. If you're interested in the real inside baseball of tech, entrepreneurship, and start-up investing, tune in every Friday for new episodes.
Executive Producer: Rashad Assir
Producer: Leah Clapper
Mixing and editing: Justin Hrabovsky
Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA
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About the Show
Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member.
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Why don't we have better robots yet? | Ken Goldberg
Why hasn't the dream of having a robot at home to do your chores become a reality yet? With three decades of research expertise in the field, roboticist Ken Goldberg sheds light on the clumsy truth about robots — and what it will take to build more dexterous machines to work in a warehouse or help out at home.
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#176 Sergey Levine: Decoding The Evolution of AI in Robotics
Join host Craig Smith on episode #176 of Eye on AI as he dives deep into the realm of robotic artificial intelligence with Sergey Levine, associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley.
In this episode, Sergey unveils the latest advancements in AI control of robots, exploring the implications of reinforcement learning and the concept of embodied AI.
Discover how Sergey's research is pushing the boundaries of AI, enabling robots to learn manipulation skills and generalize across diverse tasks, transforming the potential of home robots and beyond.
Sergey also shares insights into the RTX project, an ambitious collaboration designed to achieve remarkable generalization across different robot morphologies, enhancing robots' ability to perform language-conditioned manipulation tasks.
If you're fascinated by the intersection of AI, robotics, and the quest for creating adaptable, generalizable machines that promise to revolutionize our interaction with technology, this episode is a must-listen.
Remember to rate us on Apple Podcast and Spotify if this episode ignites your interest in the dynamic field of robotic AI and the visionary work of Sergey Levine.
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
(00:00) Preview and Introduction to Home Robots and AI in Robotics
(01:43) World Models and Language Models in Robotics
(04:01) The Challenge of Learning-Based Control and Data Utilization
(06:05) RTX Project: Generalizing Controllers Across Different Robots
(10:09) Uniformity in Model Architecture Across Labs
(13:50) Introduction of RT1 and RT2 Models for Robot Control
(16:06) The Future of Robotic Control Research and Architecture
(18:49) The Impact of Hardware Development on Robotics
(22:15) Advances in Controller and AI Model Development
(26:21) Planning and Acting with Vision Language Models
(31:38) The Proprietary vs. Open-Source Debate in Robotics
(36:23) The Future of Commercial and Open Source Robotics Applications
(40:59) The State of Robotics Research in China
