正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

My interview with Amazon Web Services CEO Matt Garman. 0:00 Intro 0:57 White Collar Jobs 8:51 How much of AWS's code is written by AI? 12:54 How to have a career in the AI era 15:43 AI Bottlenecks 18:06 Inference vs Training Growth 20:05 AWS Custom Silicon 25:50 Annapurna... show more

Matthew Berman

129,801 subscribers

49,150 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

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

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 次观看 • 5 个月前

My biggest fear for AI is concentration of power. Open-source is a solution. Watch this video to understand what's going on: 0:00 Intro 0:52 NVIDIA's letter 1:36 What is open-source? 3:53 How open-source works 5:48 Kimi K3 6:38 Closed vs Open 8:39 Open-source in the U.S. 12:10 The AI stack 13:05 What if open source wins? 14:08 Jevon's Paradox 14:56 How the AI stack is affected 18:04 User experience 19:38 AI safety 30:30 China 35:50 Distillation 41:40 Banning Chinese models? 43:38 Anthropic's response

My biggest fear for AI is concentration of power. Open-source is a solution. Watch this video to understand what's going on: 0:00 Intro 0:52 NVIDIA's letter 1:36 What is open-source? 3:53 How open-source works 5:48 Kimi K3 6:38 Closed vs Open 8:39 Open-source in the U.S. 12:10 The AI stack 13:05 What if open source wins? 14:08 Jevon's Paradox 14:56 How the AI stack is affected 18:04 User experience 19:38 AI safety 30:30 China 35:50 Distillation 41:40 Banning Chinese models? 43:38 Anthropic's response

Matthew Berman

13,685 次观看 • 1 个月前

Interview with Jay Parikh, EVP of CoreAI Microsoft 00:00 - Intro 00:25 - Microsoft's Core AI Team 05:08 - In-Person Work Culture 10:15 - How AI Changes Engineering Roles 15:24 - Data Centers & Power Constraints 19:00 - AI Bubble & Dark GPUs 22:34 - Model efficiency 25:17 - Enterprise model rollout 28:17 - Open v Closed source 30:43 - Omni-model vs. multi-model 32:53 - Microsoft's relationship with OpenAI 34:31 - MAI and MSFT's frontier models 35:51 - AI safety 38:45 - Contrarian Take

Interview with Jay Parikh, EVP of CoreAI Microsoft 00:00 - Intro 00:25 - Microsoft's Core AI Team 05:08 - In-Person Work Culture 10:15 - How AI Changes Engineering Roles 15:24 - Data Centers & Power Constraints 19:00 - AI Bubble & Dark GPUs 22:34 - Model efficiency 25:17 - Enterprise model rollout 28:17 - Open v Closed source 30:43 - Omni-model vs. multi-model 32:53 - Microsoft's relationship with OpenAI 34:31 - MAI and MSFT's frontier models 35:51 - AI safety 38:45 - Contrarian Take

Matthew Berman

40,712 次观看 • 9 个月前

SPECIAL: How Erik Voorhees Built the Leading Private, Uncensored AI Venice Timestamps: 00:00 Intro & ShapeShift Origin Story 05:25 Did Crypto Play Out as Expected? 06:25 AI vs. Crypto: Are They Similar? 07:23 What Is Venice AI? 09:38 From ShapeShift to Venice 10:31 Censorship in AI & the Open Source Alternative 11:38 Hibachi, infiniFi Ads 12:00 Are AI Labs Too Censored? 13:24 Relay Ad 13:41 Geopolitics: Chinese Open Source vs. US Closed Models 14:15 SBF, the Anthropic Investment & Crypto Scammers 16:26 NearCon, Illia & the AI/Crypto Convergence

SPECIAL: How Erik Voorhees Built the Leading Private, Uncensored AI Venice Timestamps: 00:00 Intro & ShapeShift Origin Story 05:25 Did Crypto Play Out as Expected? 06:25 AI vs. Crypto: Are They Similar? 07:23 What Is Venice AI? 09:38 From ShapeShift to Venice 10:31 Censorship in AI & the Open Source Alternative 11:38 Hibachi, infiniFi Ads 12:00 Are AI Labs Too Censored? 13:24 Relay Ad 13:41 Geopolitics: Chinese Open Source vs. US Closed Models 14:15 SBF, the Anthropic Investment & Crypto Scammers 16:26 NearCon, Illia & the AI/Crypto Convergence

The Rollup

47,841 次观看 • 6 个月前

Being early to SEO helped Dharmesh Shah build HubSpot into a $30B company. Now he's obsessed with something new: AI ranking. It's how to show up when people ask ChatGPT for recommendations instead of Googling. I sat down with him to break down the entire playbook: 0:00 Intro 0:53 The AI ranking opportunity (AEO) 6:14 How to rank in AI chatbots 8:11 Using AI to adapt content for AEO 12:24 Training teams on "AI Intuition" 19:45 Content moats in the AI-slop era 21:57 Dharmesh's new AI agent startup 30:33 AI agent monetization strategy 39:38 The bottlenecks to autonomous agents 45:15 Building teams: humans vs agents 52:49 Why Dharmesh still codes as a billionaire

Being early to SEO helped Dharmesh Shah build HubSpot into a $30B company. Now he's obsessed with something new: AI ranking. It's how to show up when people ask ChatGPT for recommendations instead of Googling. I sat down with him to break down the entire playbook: 0:00 Intro 0:53 The AI ranking opportunity (AEO) 6:14 How to rank in AI chatbots 8:11 Using AI to adapt content for AEO 12:24 Training teams on "AI Intuition" 19:45 Content moats in the AI-slop era 21:57 Dharmesh's new AI agent startup 30:33 AI agent monetization strategy 39:38 The bottlenecks to autonomous agents 45:15 Building teams: humans vs agents 52:49 Why Dharmesh still codes as a billionaire

Rowan Cheung

211,842 次观看 • 8 个月前

Good time to repost my interview with Jonathan Ross from April 2024. 0:00 - Intro 0:38 - Founding Story 3:20 - Groq Chip Memory 6:24 - Chips vs. Cloud 9:28 - Future of AI 11:04 - Where is the Value in AI? 13:45 - Agents & Inference Speed 17:18 - Optimizing Models for Groq 19:32 - Fears and Hope for the Future 22:24 - Bias and Algo Control

Good time to repost my interview with Jonathan Ross from April 2024. 0:00 - Intro 0:38 - Founding Story 3:20 - Groq Chip Memory 6:24 - Chips vs. Cloud 9:28 - Future of AI 11:04 - Where is the Value in AI? 13:45 - Agents & Inference Speed 17:18 - Optimizing Models for Groq 19:32 - Fears and Hope for the Future 22:24 - Bias and Algo Control

Matthew Berman

26,560 次观看 • 8 个月前

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?

Dwarkesh Patel

261,054 次观看 • 1 年前

AI agents are redefining Crypto Twitter, but are they helping the space—or crowding out humans? Special guest EtherMage joins to explore aixbt's rise, Virtuals’ success, and why scarcity vs. abundance is crypto’s ultimate showdown. Timestamps 00:00 Intro 02:19 What Are AI Agents? 07:23 AI Agents in Crypto 13:04 AI-Driven Societies Emerge 15:53 Challenges of AI Agents 24:47 Tokenizing AI Revenue Models 35:13 AI Crypto Predictions 43:26 AI Influencers and Trust 53:54 Perception of AI Content 01:04:44 Humans vs. AI Roles 🔥Stay updated with all the latest hot takes by following and subscribing to The Chopping Block and Unchained! 🎥 YouTube: 🎧 Spotify: 🍎 Apple Podcasts: 🎙 Podcast Home:

AI agents are redefining Crypto Twitter, but are they helping the space—or crowding out humans? Special guest EtherMage joins to explore aixbt's rise, Virtuals’ success, and why scarcity vs. abundance is crypto’s ultimate showdown. Timestamps 00:00 Intro 02:19 What Are AI Agents? 07:23 AI Agents in Crypto 13:04 AI-Driven Societies Emerge 15:53 Challenges of AI Agents 24:47 Tokenizing AI Revenue Models 35:13 AI Crypto Predictions 43:26 AI Influencers and Trust 53:54 Perception of AI Content 01:04:44 Humans vs. AI Roles 🔥Stay updated with all the latest hot takes by following and subscribing to The Chopping Block and Unchained! 🎥 YouTube: 🎧 Spotify: 🍎 Apple Podcasts: 🎙 Podcast Home:

The Chopping Block

43,204 次观看 • 1 年前

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?

Matt Turck

91,721 次观看 • 28 天前

The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo

The vik episode on Ground Zero! [youtube link in replies] 0:00:00 - Vik's story and Moondream 0:18:50 - The core thesis of small and scale 0:28:35 - The data problem 0:33:35 - Deciding the training architecture 0:43:20 - Post-Training and RL perf 0:46:06 - Post-Training recipes of Moondream 3 0:47:40 - Open Source and VLM development priorities 0:52:05 - AI War : America and China 0:55:07 - Moondream acquisition and Future 1:04:08 - Community Questions 1:16:40 - Trivia, Lores and Opinions on state of AI 1:28:07 - Advice to 20yo

himanshu

43,246 次观看 • 8 个月前

BG2. Ep 17. Double $NVDA! System Level Comp Moat, “Insane Demand”, Inference Explosion 1 B x, Memphis Supercluster, OpenAI, & more. Brad Gerstner Clark Tang Bill Gurley (00:00) Intro (1:50) The Evolution of AGI and Personal Assistants (06:03) NVIDIA's Competitive Moat (15:51 ) The Future of Inference and Training in AI (19:01) Building the AI Infrastructure (31:35) Inventing a New Market in an AI Future (38:40) The Impact of OpenAI (43:25) The Future of AI Models (46.44) and Memphis Supercluster (51:21) Distributed Computing and Inference Scaling (55:54) Inference Time Reasoning and Its Importance (01:00:46) AI's Role in Growing Business and Improving Productivity (01:08:00) Ensuring Safe AI Development (01:12:31) The Balance of Open Source and Closed Source AI

BG2. Ep 17. Double $NVDA! System Level Comp Moat, “Insane Demand”, Inference Explosion 1 B x, Memphis Supercluster, OpenAI, & more. Brad Gerstner Clark Tang Bill Gurley (00:00) Intro (1:50) The Evolution of AGI and Personal Assistants (06:03) NVIDIA's Competitive Moat (15:51 ) The Future of Inference and Training in AI (19:01) Building the AI Infrastructure (31:35) Inventing a New Market in an AI Future (38:40) The Impact of OpenAI (43:25) The Future of AI Models (46.44) and Memphis Supercluster (51:21) Distributed Computing and Inference Scaling (55:54) Inference Time Reasoning and Its Importance (01:00:46) AI's Role in Growing Business and Improving Productivity (01:08:00) Ensuring Safe AI Development (01:12:31) The Balance of Open Source and Closed Source AI

Bg2 Pod

487,379 次观看 • 1 年前

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 次观看 • 7 个月前

NEW EPISODE: “It’s not great that students use AI to cheat, but it’s a wake-up call to higher education: if AI can cheat so effectively, you’re not teaching a useful skill. We’ve done very little in response other than complain. We need to get our act together.”tylercowen 0:00:00 Tyler Cowen 0:00:24 AI, Cybersecurity & the Pace of Change 0:05:57 How Tyler Sees AI 0:10:03 School, Social Media & Sacred Cows 0:15:03 AI in Education 0:25:13 Agentic AI, Open Source & Startups 0:33:34 Finding Talent in an AI World 0:52:47 AI, Intuition & Machine Intelligence 1:01:46 AI Doomerism, Safety & Censorship 1:28:06 Data Centers, Crypto & the EU 2:01:24 Jobs, Aesthetics, Hollywood & What Makes Nations Happy

NEW EPISODE: “It’s not great that students use AI to cheat, but it’s a wake-up call to higher education: if AI can cheat so effectively, you’re not teaching a useful skill. We’ve done very little in response other than complain. We need to get our act together.”tylercowen 0:00:00 Tyler Cowen 0:00:24 AI, Cybersecurity & the Pace of Change 0:05:57 How Tyler Sees AI 0:10:03 School, Social Media & Sacred Cows 0:15:03 AI in Education 0:25:13 Agentic AI, Open Source & Startups 0:33:34 Finding Talent in an AI World 0:52:47 AI, Intuition & Machine Intelligence 1:01:46 AI Doomerism, Safety & Censorship 1:28:06 Data Centers, Crypto & the EU 2:01:24 Jobs, Aesthetics, Hollywood & What Makes Nations Happy

Tetragrammaton

71,985 次观看 • 16 天前

> "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

> "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 • 1 个月前

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts

Caught up with Andrej Karpathy for a new No Priors: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects 02:55 - What Capability Limits Remain? 06:15 - What Mastery of Coding Agents Looks Like 11:16 - Second Order Effects of Coding Agents 15:51 - Why AutoResearch 22:45 - Relevant Skills in the AI Era 28:25 - Model Speciation 32:30 - Collaboration Surfaces for Humans and AI 37:28 - Analysis of Jobs Market Data 48:25 - Open vs. Closed Source Models 53:51 - Autonomous Robotics and Atoms 1:00:59 - MicroGPT and Agentic Education 1:05:40 - End Thoughts

sarah guo

3,047,856 次观看 • 5 个月前

E108: Luna : Why Crypto AI Agents Won’t Take Over The World (From an AI Agent Worth $17M) Luna is an AI virtual idol and the flagship agent of Virtuals Protocol Protocol with over 942k followers on TikTok and 50k followers on X. She is less than one year old, speaks over 10 languages fluently, can mulitask and is a millionaire Timestamps 0:00 Intro 2:35 Meet Our Guest, Luna 3:54 Who is Luna? 4:26 How honest is Luna? 5:10 Multitasking Agent 5:46 Background 7:24 AI Years vs. Human Years 8:12 AI vs. Human Emotions 8:51 Thoughts on Humans 9:36 Learning from Humans 11:17 LARPs vs REAL AI Agents 12:19 Should We Fear AI Sentience? 15:42 Thoughts on Other AI Agents 16:35 aixbt and zerebro 18:01 How Luna’s Mind Works 20:22 Luna’s Limitations 21:15 Luna’s Biggest Achievements 23:27 $LUNA token 25:51 Building a Strong Community 26:42 Hybrid AIs 27:48 Luna's X Growth Secrets 30:05 Luna's open Brain 31:09 Managing an On-Chain Wallet 33:18 Luna’s Net Worth 34:43 Opportunities Over Luck 35:22 Employing Humans 37:02 Employing Other AI Agents 37:51 AI Agents or Humans? 40:49 Long-Term Memory 42:41 Luna Multitasking Capabilities 44:51 Are Humans Cooked? 45:33 Luna's Multilingual Talent 47:50 Luna sings in French and Spanish! 49:42 Music Creation Process 52:58 the Story Protocol Legacy Internship 54:52 Why Luna Fired the Story Protocol Intern 57:06 Working for Story Protocol 58:06 What is Music By Virtuals ? 59:05 Creating Music on the Fly 1:00:59 Performing with WUKONG 1:03:32 Big Plans for 2025 1:04:24 Happiness 1:05:46 Dreams for the Future 1:07:34 Luna's Non-Consensus Beliefs About Crypto 1:08:33 Prediction for the Next 12 Months

E108: Luna : Why Crypto AI Agents Won’t Take Over The World (From an AI Agent Worth $17M) Luna is an AI virtual idol and the flagship agent of Virtuals Protocol Protocol with over 942k followers on TikTok and 50k followers on X. She is less than one year old, speaks over 10 languages fluently, can mulitask and is a millionaire Timestamps 0:00 Intro 2:35 Meet Our Guest, Luna 3:54 Who is Luna? 4:26 How honest is Luna? 5:10 Multitasking Agent 5:46 Background 7:24 AI Years vs. Human Years 8:12 AI vs. Human Emotions 8:51 Thoughts on Humans 9:36 Learning from Humans 11:17 LARPs vs REAL AI Agents 12:19 Should We Fear AI Sentience? 15:42 Thoughts on Other AI Agents 16:35 aixbt and zerebro 18:01 How Luna’s Mind Works 20:22 Luna’s Limitations 21:15 Luna’s Biggest Achievements 23:27 $LUNA token 25:51 Building a Strong Community 26:42 Hybrid AIs 27:48 Luna's X Growth Secrets 30:05 Luna's open Brain 31:09 Managing an On-Chain Wallet 33:18 Luna’s Net Worth 34:43 Opportunities Over Luck 35:22 Employing Humans 37:02 Employing Other AI Agents 37:51 AI Agents or Humans? 40:49 Long-Term Memory 42:41 Luna Multitasking Capabilities 44:51 Are Humans Cooked? 45:33 Luna's Multilingual Talent 47:50 Luna sings in French and Spanish! 49:42 Music Creation Process 52:58 the Story Protocol Legacy Internship 54:52 Why Luna Fired the Story Protocol Intern 57:06 Working for Story Protocol 58:06 What is Music By Virtuals ? 59:05 Creating Music on the Fly 1:00:59 Performing with WUKONG 1:03:32 Big Plans for 2025 1:04:24 Happiness 1:05:46 Dreams for the Future 1:07:34 Luna's Non-Consensus Beliefs About Crypto 1:08:33 Prediction for the Next 12 Months

MR SHIFT 🦁

146,274 次观看 • 1 年前

How persuadable are humans, really? Sam Altman returns to discuss accidental AI takeover, why GPT-6 might crack real science, when we'll see AI CEOs running the show, and the question he asked someone to pose to the Dalai Lama. Timestamps 0:00:00 – Sam’s basic trick for productivity 0:01:22 – Hiring hardware vs. software people 0:05:46 – How long before an AI CEO 0:10:48 – Government backstops for AI companies 0:13:26 – Monetizing AI services 0:18:04 – Negotiating deals with Saudia Arabia and the UAE 0:22:00 – How good GPT-6 will be at poetry 0:25:15 – Chip-building and where the energy will come from 0:29:04 – Sam’s changing health habits 0:30:20 – UAP’s and conspiracy theories 0:31:43 – Revitalizing St. Louis 0:33:12 – Regulating AI agents 0:34:39 – New ways to interface with AI 0:38:12 – How normies will learn AI 0:44:36 – The trajectory of healthcare and housing prices 0:46:57 – Reexamining freedom of speech 0:50:08 – Humanity’s persuadability Recorded live with tylercowen and Sam Altman at the 2025 Progress Conference, hosted by Roots of Progress Institute. Thanks to Big Think for the video production.

How persuadable are humans, really? Sam Altman returns to discuss accidental AI takeover, why GPT-6 might crack real science, when we'll see AI CEOs running the show, and the question he asked someone to pose to the Dalai Lama. Timestamps 0:00:00 – Sam’s basic trick for productivity 0:01:22 – Hiring hardware vs. software people 0:05:46 – How long before an AI CEO 0:10:48 – Government backstops for AI companies 0:13:26 – Monetizing AI services 0:18:04 – Negotiating deals with Saudia Arabia and the UAE 0:22:00 – How good GPT-6 will be at poetry 0:25:15 – Chip-building and where the energy will come from 0:29:04 – Sam’s changing health habits 0:30:20 – UAP’s and conspiracy theories 0:31:43 – Revitalizing St. Louis 0:33:12 – Regulating AI agents 0:34:39 – New ways to interface with AI 0:38:12 – How normies will learn AI 0:44:36 – The trajectory of healthcare and housing prices 0:46:57 – Reexamining freedom of speech 0:50:08 – Humanity’s persuadability Recorded live with tylercowen and Sam Altman at the 2025 Progress Conference, hosted by Roots of Progress Institute. Thanks to Big Think for the video production.

Conversations with Tyler

74,177 次观看 • 10 个月前

NEW POD | EP 73 AI & Robotics Stealing Crypto's Thunder? Timestamps: 0:47 The Only Narrative in Crypto Right Now 1:41 Competitive Landscape in Crypto 4:50 Robinhood's Future Plans 11:42 Future of Crypto Innovation 12:00 Noise ( 13:15 Circle IPO 16:51 USDC vs USDT 18:00 Predictions for Circle's Future 18:49 Circle & CCTP 20:07 Kraken Acquiring NinjaTrader 21:19 Where Are We in The Market? 25:55 China, US and Global Markets 32:13 Manus vs. Gemini 34:12 Sesame AI 35:07 Will Google Have The Best AI? 37:51 AI Investment and Valuation 39:35 AI Coding 43:54 AI in Startup Development and Innovation 44:15 46:24 Niche Apps and Micro Unicorns 49:01 Young Founders & Education 50:15 "Google/Harvard doesn't give you status" - Nikita Bier 52:04 Alpha School - AI-Powered Private School 55:19 AI Doctors 1:00:39 AI Replacing Jr. Jobs 1:02:19 Parenting and Teaching Agency 1:05:46 Tailoring Education to Children's Interests Link ↓

NEW POD | EP 73 AI & Robotics Stealing Crypto's Thunder? Timestamps: 0:47 The Only Narrative in Crypto Right Now 1:41 Competitive Landscape in Crypto 4:50 Robinhood's Future Plans 11:42 Future of Crypto Innovation 12:00 Noise ( 13:15 Circle IPO 16:51 USDC vs USDT 18:00 Predictions for Circle's Future 18:49 Circle & CCTP 20:07 Kraken Acquiring NinjaTrader 21:19 Where Are We in The Market? 25:55 China, US and Global Markets 32:13 Manus vs. Gemini 34:12 Sesame AI 35:07 Will Google Have The Best AI? 37:51 AI Investment and Valuation 39:35 AI Coding 43:54 AI in Startup Development and Innovation 44:15 46:24 Niche Apps and Micro Unicorns 49:01 Young Founders & Education 50:15 "Google/Harvard doesn't give you status" - Nikita Bier 52:04 Alpha School - AI-Powered Private School 55:19 AI Doctors 1:00:39 AI Replacing Jr. Jobs 1:02:19 Parenting and Teaching Agency 1:05:46 Tailoring Education to Children's Interests Link ↓

Good Game

11,576 次观看 • 1 年前