South Park Commons

The first AI persona you’ll actually want to meet face-to-face. Most AI personas stare blankly while you talk. Emma reacts while you’re still speaking. After 18 months of research at @nuance_ai, this is Emma, built on Nuplex, our full-duplex audiovisual foundation model.

Last week, @spc hosted our NYC Founder Showcase We saw the latest in AI for Science, physical AI world models, space tech, surgical robotics, sentient digital twins, chips & quantum +++ Blown away by our builders Thanks to the hundreds who came out!

Today we’re announcing Cent's $15M Series A, co-led by @BlumeVentures and @spc , and our acquisition of AI radiology platform Medsee. When you look at how healthcare works in India, almost all of it is organised around the moment someone falls ill. Hospitals, doctors and insurance are all paid to treat, and very little of the system is built to look for disease in people who feel well. It’s a big reason most cancers here are still found at stage 3 or 4. We started Cent to work on the other side of that problem. Over the last year, about 10,000 people have come to us without symptoms. 1 in 4 had something worth acting on, and 1 in 40 needed to see a doctor right away. Very few of them had any reason to suspect it. Finding disease this early, for millions of people, takes technology built for the purpose. This round lets us invest more deeply in our own hardware, AI and clinical research, and Medsee brings a strong AI reporting team into that work. Our goal is 10 million scans by 2035, and through them, helping save 1 million lives. Thank you to @BKartRed , @prateekmehta42 , Arun Sarin, @snsf , @sharathk83 , @krishna_srd and @AprameyaRadhaK for backing us, and to our team and every member who has trusted us with their health. Read more: cent.health/blog/series-a-in… @akhandelwal Arpit Garg

With open models, we can build safety tooling directly into the inference layer. Our CEO and co-founder @tuhinone sat down with @kr00ney at @cnbc to discuss Project Beacon, and our work with @GoodfireAI to make safety the default for open intelligence.

“The universe is really big, and almost none of it is inside computers.” When a problem is hard, make it bigger. @svlevine put a room full of robots in a repurposed Google conference room and had them grasp objects all day. It worked. Then it hit a wall: lots of experience, no prior knowledge. That wall became @physical_int. This conversation at @spc was awesome. I learned a lot! (00:28) From Video Games to Robotics: Sergey Levine's Journey (04:24) Founding Physical Intelligence: Why the Timing and Team Mattered (06:30) Inside Pi Zero: Building a Foundation Model for Robots (09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize (16:45) What Makes a Great Robotics Researcher? (21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops (28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots (33:24) World Models, Data Strategy, and Building a Robotics Research Culture

For years, neural networks looked inscrutable, like tangled knots that needed to be unwound. We no longer think that's the case. AI models are far more interpretable than we thought. We think this is great news for interpretability. Goodfire's @nickcammarata co-authored Zoom In, the paper that introduced polysemanticity and superposition. He spent months revisiting the vision model organisms that much of interpretability grew out of and studying modern language models up to trillions of parameters. His new essay explains what the early field, him included, got wrong, why there's reason for optimism today, and why there might be a bitter lesson for interpretability.

"The problem you're working on right now is always the hardest. But also the most interesting." @Dmitri_Dolgov has spent his career on a problem most people said would take 5 years. It's been almost 20. Stanford DARPA team. Founding member of Google's self-driving project. Co-CEO of @Waymo. When he started, self-driving was still an open question. The jump from making it work to making it safe enough for the real world felt enormous. At @SPC, he talked to me and @rsanghvi about the 2009 milestones that had nothing to do with building a product. The goal was to learn. Full conversation at SPC out now. (00:32) The First Time Waymo Felt Real (03:39) How a Fleet of Waymos Coordinates (07:47 How People Change the Way They Travel With Waymo (12:51) How Waymo Started: Understanding the Problem Before Building a Product (14:58) Inside Waymo’s AI System: Driver, Simulator, and Critic (22:00) How Waymo Adopts New AI Breakthroughs Without Breaking the Product (27:02) Why the Last 1% of Autonomous Driving Is So Hard (25:16) Where Startups Should Build in Physical AI (44:30) Can the Waymo Driver Become a General Physical AI Agent?