正在加载视频...

视频加载失败

返回首页

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners...

lightspeedvp's profile picture

Lightspeed

113,488 subscribers

144,942 次观看 • 2 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.
1:52

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

reactor

12,737,058 次观看 • 3 个月前

A Walk In The Park (part II) feat Alberto 🌎 (00:00) 👋 (01:10) What world models are (03:42) Origin story from text-to-3D to reactor (09:22) Deciding to start the company (11:22) GTA, games, and the path into programming (15:09) Building in stealth and keeping secrets (18:32) Picking investors with independent conviction (21:43) Where world models will grow first (26:08) Why low latency matters (29:34) Becoming CEO and scaling the team (32:23) Final advice
32:50

A Walk In The Park (part II) feat Alberto 🌎 (00:00) 👋 (01:10) What world models are (03:42) Origin story from text-to-3D to reactor (09:22) Deciding to start the company (11:22) GTA, games, and the path into programming (15:09) Building in stealth and keeping secrets (18:32) Picking investors with independent conviction (21:43) Where world models will grow first (26:08) Why low latency matters (29:34) Becoming CEO and scaling the team (32:23) Final advice

Nikunj Kothari

24,897 次观看 • 2 个月前

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.
1:31

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,641 次观看 • 3 个月前

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries
0:59

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 次观看 • 8 个月前

Explore where physical AI is headed next with the people shaping it. On Feb. 10 at 9 a.m. PT, join Ming‑Yu, Vice President of Research at NVIDIA, and Chubby♨️ from Superintelligence, for a conversation on how NVIDIA Cosmos reframes AI for a world of robots, AVs, and vision-driven systems. We'll dig into: ✅ Cosmos' origins and designing models for the physical world ✅ The scientific ideas and architectures behind Cosmos and its open models ✅ How open, reasoning-capable physical AI is changing how teams prototype, deploy, and iterate ✅ What a future of AI systems that understand space, time, and causality could unlock If you’re thinking into real‑world AI, this is the conversation to catch ➡️
0:39

Explore where physical AI is headed next with the people shaping it. On Feb. 10 at 9 a.m. PT, join Ming‑Yu, Vice President of Research at NVIDIA, and Chubby♨️ from Superintelligence, for a conversation on how NVIDIA Cosmos reframes AI for a world of robots, AVs, and vision-driven systems. We'll dig into: ✅ Cosmos' origins and designing models for the physical world ✅ The scientific ideas and architectures behind Cosmos and its open models ✅ How open, reasoning-capable physical AI is changing how teams prototype, deploy, and iterate ✅ What a future of AI systems that understand space, time, and causality could unlock If you’re thinking into real‑world AI, this is the conversation to catch ➡️

NVIDIA AI Developer

26,455 次观看 • 6 个月前

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George
31:52

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George

a16z

92,161 次观看 • 10 个月前

Missed the World Models talk featuring Daniel Berkovitz, Chief Product Officer, @LTXStudio and Ben Michel of Asteria at #RenderCon2026? Here's what was covered: 00:08 - Intro: Why open source video models matter for Render Network 02:13 - LTX's philosophy on open source and building tools for creative professionals 06:01 - Running LTX over a 3D viewport to get a final render 17:22 - Why generative video needs to move past the "slot machine" experience 20:27 - The case for multi-keyframe conditioning 27:53 - Why video models are rendering engines, not prompt interfaces Full talk:
43:25

Missed the World Models talk featuring Daniel Berkovitz, Chief Product Officer, @LTXStudio and Ben Michel of Asteria at #RenderCon2026? Here's what was covered: 00:08 - Intro: Why open source video models matter for Render Network 02:13 - LTX's philosophy on open source and building tools for creative professionals 06:01 - Running LTX over a 3D viewport to get a final render 17:22 - Why generative video needs to move past the "slot machine" experience 20:27 - The case for multi-keyframe conditioning 27:53 - Why video models are rendering engines, not prompt interfaces Full talk:

The Render Network

17,798 次观看 • 3 个月前

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI
57:20

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI

Matthew Berman

69,339 次观看 • 29 天前

Today’s the day🚀 We’re opening the doors to the Daydream platform - API + Playground Experience🌊 We’re the world’s first hosted StreamDiffusion platform: 1⃣ No GPU required 2⃣Sub-second latency 3⃣Advanced controls that let you experiment quickly and scale seamlessly. This is the start of an open ecosystem for real-time video and world models. Your home for open source, real-time video AI 👇🧵
0:58

Today’s the day🚀 We’re opening the doors to the Daydream platform - API + Playground Experience🌊 We’re the world’s first hosted StreamDiffusion platform: 1⃣ No GPU required 2⃣Sub-second latency 3⃣Advanced controls that let you experiment quickly and scale seamlessly. This is the start of an open ecosystem for real-time video and world models. Your home for open source, real-time video AI 👇🧵

Daydream

94,811 次观看 • 1 年前

1/ New episode – Enabling AI Models to Drive Robots with Michael Cho - Rbt/Acc from BitRobot 🦾 Hosts: Connor Lovely 🎬 EV3 Sal We discuss: - Building the world's largest robotic driving dataset - Working with researchers from Google DeepMind - Launching a memecoin ($SAM) pre-TGE Timestamps: 00:00 - Introduction 01:47 - Intersection of Robotics and AI 02:50 - Real-World Evaluation vs. Digital AI 06:00 - The Importance of Real-World Testing 07:07 - Mapping and Navigation in Robotics 08:12 - Value of Real-World Data for AI 09:38 - Differences in Data Utility 13:07 - Exploring Different Robotic Form Factors 14:04 - Cross-Embodiment in Robotics 16:35 - Introduction to BitRobot 17:30 - Transition from Frotobots to BitRobot 22:05 - Future Plans for BitRobot 24:27 - Meme Coins in Community Building 27:46 - Conclusion Watch on all platforms below ⬇️
29:16

1/ New episode – Enabling AI Models to Drive Robots with Michael Cho - Rbt/Acc from BitRobot 🦾 Hosts: Connor Lovely 🎬 EV3 Sal We discuss: - Building the world's largest robotic driving dataset - Working with researchers from Google DeepMind - Launching a memecoin ($SAM) pre-TGE Timestamps: 00:00 - Introduction 01:47 - Intersection of Robotics and AI 02:50 - Real-World Evaluation vs. Digital AI 06:00 - The Importance of Real-World Testing 07:07 - Mapping and Navigation in Robotics 08:12 - Value of Real-World Data for AI 09:38 - Differences in Data Utility 13:07 - Exploring Different Robotic Form Factors 14:04 - Cross-Embodiment in Robotics 16:35 - Introduction to BitRobot 17:30 - Transition from Frotobots to BitRobot 22:05 - Future Plans for BitRobot 24:27 - Meme Coins in Community Building 27:46 - Conclusion Watch on all platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

10,748 次观看 • 1 年前

1/ BREAKING – DePIN's Biggest New Deal: Valeo x NATIX Network with Alireza Ghods Host: Connor Lovely 🎬 We discuss: - Why a $3B, Fortune 500 company wants Natix data - What is a World Foundational Model? - Why autonomous driving needs synthetic data Timestamps: 00:00 - Introduction 01:14 - Updates on VX360 and Data Sales 02:09 - Vision Language Model and Its Applications 04:39 - Importance of Diverse Data for World Models 06:20 - Details on the Valeo Deal 08:15 - Use Cases and Future Plans for World Models 10:45 - Synthetic Data and Real-World Applications 19:37 - Future of Physical AI and Autonomous Driving 23:51 - Conclusion Watch on other platforms below ⬇️
25:05

1/ BREAKING – DePIN's Biggest New Deal: Valeo x NATIX Network with Alireza Ghods Host: Connor Lovely 🎬 We discuss: - Why a $3B, Fortune 500 company wants Natix data - What is a World Foundational Model? - Why autonomous driving needs synthetic data Timestamps: 00:00 - Introduction 01:14 - Updates on VX360 and Data Sales 02:09 - Vision Language Model and Its Applications 04:39 - Importance of Diverse Data for World Models 06:20 - Details on the Valeo Deal 08:15 - Use Cases and Future Plans for World Models 10:45 - Synthetic Data and Real-World Applications 19:37 - Future of Physical AI and Autonomous Driving 23:51 - Conclusion Watch on other platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

11,869 次观看 • 7 个月前

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
1:13:56

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI

Matt Turck

101,212 次观看 • 3 个月前

2025 has been a wild year for AI video. My conversation with Cristóbal Valenzuela, CEO of Runway: 00:00 – Meet Cris and Runway – AI Video's Wild Year 01:48 – Runway's AI Film Festival Goes From Chinatown To IMAX 04:02 – Hollywood's Shift: From Ignoring AI To Adopting It At Scale 06:38 – How Runway Saves VFX Artists' Weekends Of Work 07:31 – Inside Gen-4 And Aleph: Why These Models Are Game-Changers 08:21 – From Editing Tools To A "New Kind Of Camera" 10:00 – Beyond Film: Gaming, Architecture, E-commerce & Robotics Use Cases 10:55 – Why Advertising Is Adopting AI Video Faster Than Anyone Else 11:38 – How Creatives Adapt When Iteration Becomes Real-Time 14:12 – What Makes Someone Great At AI Video (Hint: No Preconceptions) 15:28 – The Early Days: Building Runway Before Generative AI Was "Real" 20:27 – Finding Early Product-Market Fit 21:51 – Balancing Research And Product Inside Runway 24:23 – Comparing Aleph Vs. Gen-4, And The Future Of Generalist Models 30:36 – New Input Modalities: Editing With Video + Annotations, Not Just Text 33:46 – Managing Expectations: Twitter Demos Vs. Real Creative Work 47:09 – The Future: Real-Time AI Video And Fully Explorable 3D Worlds 52:02 – Runway's Business Model: From Indie Creators To Disney & Lionsgate 57:26 – Competing With The Big Labs (Sora, Google, Etc.) 59:58 – Hyper-Personalized Content? Why It May Not Replace Film 1:01:13 – Advice To Founders: Treat Your Company Like A Model — Always Learning 1:03:06 – The Next 5 Years Of Runway: Changing Creativity Forever
1:05:04

2025 has been a wild year for AI video. My conversation with Cristóbal Valenzuela, CEO of Runway: 00:00 – Meet Cris and Runway – AI Video's Wild Year 01:48 – Runway's AI Film Festival Goes From Chinatown To IMAX 04:02 – Hollywood's Shift: From Ignoring AI To Adopting It At Scale 06:38 – How Runway Saves VFX Artists' Weekends Of Work 07:31 – Inside Gen-4 And Aleph: Why These Models Are Game-Changers 08:21 – From Editing Tools To A "New Kind Of Camera" 10:00 – Beyond Film: Gaming, Architecture, E-commerce & Robotics Use Cases 10:55 – Why Advertising Is Adopting AI Video Faster Than Anyone Else 11:38 – How Creatives Adapt When Iteration Becomes Real-Time 14:12 – What Makes Someone Great At AI Video (Hint: No Preconceptions) 15:28 – The Early Days: Building Runway Before Generative AI Was "Real" 20:27 – Finding Early Product-Market Fit 21:51 – Balancing Research And Product Inside Runway 24:23 – Comparing Aleph Vs. Gen-4, And The Future Of Generalist Models 30:36 – New Input Modalities: Editing With Video + Annotations, Not Just Text 33:46 – Managing Expectations: Twitter Demos Vs. Real Creative Work 47:09 – The Future: Real-Time AI Video And Fully Explorable 3D Worlds 52:02 – Runway's Business Model: From Indie Creators To Disney & Lionsgate 57:26 – Competing With The Big Labs (Sora, Google, Etc.) 59:58 – Hyper-Personalized Content? Why It May Not Replace Film 1:01:13 – Advice To Founders: Treat Your Company Like A Model — Always Learning 1:03:06 – The Next 5 Years Of Runway: Changing Creativity Forever

Matt Turck

18,817 次观看 • 11 个月前

Google just took the lead... Gemini 3 + Nano Banana Pro have captured the worlds attention. Let's talk about: > what these models do > best ways to use both models > how other people are using them TIME STAMPS 00:00 Introduction 01:05 Deep Dive into Gemini 3 01:50 Real World Examples of Gemini 3 04:33 Gemini 3 in Google AI Studio 06:33 Building a Landing Page with Gemini 3 09:17 Using Gemini 3 in Cursor 10:47 Interactive Water Cycle Simulation 12:30 Exploring Google Gemini's Capabilities 13:53 Introduction to Nano Banana 15:37 Creating Infographics with Nano Banana 19:08 Nano banana in Krea Nodes 23:41 Future of AI Models
25:14

Google just took the lead... Gemini 3 + Nano Banana Pro have captured the worlds attention. Let's talk about: > what these models do > best ways to use both models > how other people are using them TIME STAMPS 00:00 Introduction 01:05 Deep Dive into Gemini 3 01:50 Real World Examples of Gemini 3 04:33 Gemini 3 in Google AI Studio 06:33 Building a Landing Page with Gemini 3 09:17 Using Gemini 3 in Cursor 10:47 Interactive Water Cycle Simulation 12:30 Exploring Google Gemini's Capabilities 13:53 Introduction to Nano Banana 15:37 Creating Infographics with Nano Banana 19:08 Nano banana in Krea Nodes 23:41 Future of AI Models

Riley Brown

33,706 次观看 • 9 个月前

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
1:10

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 次观看 • 4 个月前

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History
1:04:51

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Raoul Pal

326,827 次观看 • 1 年前

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
49:06

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

66,936 次观看 • 2 个月前

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.
26:06

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.

OriginTrail

227,307 次观看 • 2 个月前

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 次观看 • 5 个月前

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia
1:01:29

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia

Activate

12,489 次观看 • 3 个月前