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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

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Can AI agents automate all work? What happens to white collar jobs? I sat down with Aaron Levie (Aaron Levie), Founder and CEO of to talk about agents, deploying AI, the future of white collar jobs, the future of SaaS, and so much more. 0:00 Intro 1:02 AI at Box 2:59 New tools 5:26 Embracing AI at Box 8:13 Using AI to do more 11:09 White collar collapse? 14:51 How does demand scale? 19:29 Managing AI 25:51 Companies with too much power 29:58 Personal vs Work agents 35:37 What if AI makes a big mistake? 41:23 Who will own the agents? 43:37 SaaS is dead 48:40 Future of the web 57:23 Using AI on socials

Can AI agents automate all work? What happens to white collar jobs? I sat down with Aaron Levie (Aaron Levie), Founder and CEO of to talk about agents, deploying AI, the future of white collar jobs, the future of SaaS, and so much more. 0:00 Intro 1:02 AI at Box 2:59 New tools 5:26 Embracing AI at Box 8:13 Using AI to do more 11:09 White collar collapse? 14:51 How does demand scale? 19:29 Managing AI 25:51 Companies with too much power 29:58 Personal vs Work agents 35:37 What if AI makes a big mistake? 41:23 Who will own the agents? 43:37 SaaS is dead 48:40 Future of the web 57:23 Using AI on socials

Matthew Berman

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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

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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

40,617 Aufrufe • vor 7 Monaten

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

47,718 Aufrufe • vor 4 Monaten

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

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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 Aufrufe • vor 6 Monaten

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?

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Dwarkesh Patel

260,187 Aufrufe • vor 1 Jahr

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

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himanshu

43,246 Aufrufe • vor 7 Monaten

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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 5 Monaten

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

3,025,067 Aufrufe • vor 3 Monaten

Honored to have interviewed GitHub CEO Thomas Dohmke about everything AI and programming. Full interview below: 0:00 - Intro 0:36 - First Time Seeing GPT 1:58 - Software Development Changing 4:51 - Tab Completion UX 8:48 - Should You Learn to Code Still? 15:08 - GitHub Copilot going Open Source 20:03 - Community Contribution to GitHub 23:00 - Future of Software (AI vs. Traditional) 26:22 - AI-based Operating System 31:27 - Vibe Coding 36:11 - How to Make Vibe Coding Better 40:09 - Optimal Kind of Coding Assistant 44:20 - AI Anxiety

Honored to have interviewed GitHub CEO Thomas Dohmke about everything AI and programming. Full interview below: 0:00 - Intro 0:36 - First Time Seeing GPT 1:58 - Software Development Changing 4:51 - Tab Completion UX 8:48 - Should You Learn to Code Still? 15:08 - GitHub Copilot going Open Source 20:03 - Community Contribution to GitHub 23:00 - Future of Software (AI vs. Traditional) 26:22 - AI-based Operating System 31:27 - Vibe Coding 36:11 - How to Make Vibe Coding Better 40:09 - Optimal Kind of Coding Assistant 44:20 - AI Anxiety

Matthew Berman

18,262 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 8 Monaten

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 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

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MR SHIFT 🦁

146,188 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 1 Jahr

NEW EP: How Blockchain Fixes AI's Biggest Problem with Anand Iyer The race to control AI isn’t complete without crypto having a say. In today's episode, Andy sits down with anand iyer to explore: > Why $300B Went to Centralized AI vs $1B to Decentralized > How One Developer Could Code the Next Billion Dollar AI Company > Why Teenagers Are Using ChatGPT as Their Therapist > The Case for Staking Against AGI > Why AI Tokens Doubled While Everything Else Crashed > How Crypto's Capital Coordination Could Reshape AI Ownership Full episode links below. Timestamps: 00:00 Intro 00:58 Magic Ad 01:26 Starknet Ad 01:52 Anand’s Investing Thesis 06:29 Data Center Arms Race 13:31 AI Power Consumption 21:26 Training and Enhancing AI Models 26:33 SummerFi Ad 27:15 Mantle Ad 27:56 AI Crypto Company Challenges 35:01 Centralized AI Sentiment Change 39:05 Onchain IPO Era 45:26 New Models for AI Founders 48:43 Anand Reacts to Tweets

NEW EP: How Blockchain Fixes AI's Biggest Problem with Anand Iyer The race to control AI isn’t complete without crypto having a say. In today's episode, Andy sits down with anand iyer to explore: > Why $300B Went to Centralized AI vs $1B to Decentralized > How One Developer Could Code the Next Billion Dollar AI Company > Why Teenagers Are Using ChatGPT as Their Therapist > The Case for Staking Against AGI > Why AI Tokens Doubled While Everything Else Crashed > How Crypto's Capital Coordination Could Reshape AI Ownership Full episode links below. Timestamps: 00:00 Intro 00:58 Magic Ad 01:26 Starknet Ad 01:52 Anand’s Investing Thesis 06:29 Data Center Arms Race 13:31 AI Power Consumption 21:26 Training and Enhancing AI Models 26:33 SummerFi Ad 27:15 Mantle Ad 27:56 AI Crypto Company Challenges 35:01 Centralized AI Sentiment Change 39:05 Onchain IPO Era 45:26 New Models for AI Founders 48:43 Anand Reacts to Tweets

The Rollup

12,829 Aufrufe • vor 1 Jahr

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

32,922 Aufrufe • vor 2 Monaten

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Gaurav Sen

80,115 Aufrufe • vor 10 Monaten

Full exclusive interview with Daniele 🟧 ( Meme Quant ), discussing all things DeFAI and how Hey Anon is changing the game. 0:00 - Intro 0:50 - History Of Dani Sesta 15:31 - How Dani Leads 18:29 - Wonderland 26:05 - Focusing On AI 39:56 - HeyANON Explained 1:13:44 - Dani's Magnum Opus 1:16:01 - The Currency Of AI 1:18:12 - Solana VS Sonic 1:24:13 - Testing Out HeyANON

Full exclusive interview with Daniele 🟧 ( Meme Quant ), discussing all things DeFAI and how Hey Anon is changing the game. 0:00 - Intro 0:50 - History Of Dani Sesta 15:31 - How Dani Leads 18:29 - Wonderland 26:05 - Focusing On AI 39:56 - HeyANON Explained 1:13:44 - Dani's Magnum Opus 1:16:01 - The Currency Of AI 1:18:12 - Solana VS Sonic 1:24:13 - Testing Out HeyANON

Miles Deutscher

92,901 Aufrufe • vor 1 Jahr