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Yeah $TIG is flying under the radar An open protocol for algorithm development listening to John explain how algorithms can optimise & help you extract maximum utility from any given chip DeepMind, OpenAI, Anthropic, they're all racing to own the algorithmic layer if they win, you pay a tax...

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Jack Ai-Leung

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37,469 просмотров • 21 дней назад •via X (Twitter)

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We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow
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We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow

John Fletcher (𝔦, 𝔦)

77,692 просмотров • 1 месяц назад

This is why $TIG is so important 🔵 "We have got to acknowledge that most of the advances in AI came out of algorithm advances, not just the raw hardware" ⚫️ "The only way to really get 10x or 100x leaps is to fundamentally change the algorithm" Not just anyone's opinion.. thats from CEO of NVIDIA Jensen Huang Algorithms are where the real breakthroughs come from.. The Innovation Game (𝔦, 𝔦) is building the incentive layer to discover them.. at scale, in the open, with the best minds competing to push the frontier forward $TIG
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This is why $TIG is so important 🔵 "We have got to acknowledge that most of the advances in AI came out of algorithm advances, not just the raw hardware" ⚫️ "The only way to really get 10x or 100x leaps is to fundamentally change the algorithm" Not just anyone's opinion.. thats from CEO of NVIDIA Jensen Huang Algorithms are where the real breakthroughs come from.. The Innovation Game (𝔦, 𝔦) is building the incentive layer to discover them.. at scale, in the open, with the best minds competing to push the frontier forward $TIG

𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢

19,511 просмотров • 1 месяц назад

Shyam Sankar is trying to mobilize a country. The CTO of Palantir is ringing the alarm on the necessity to resurrect America’s industrial base, win the defense technology race against other countries, and empower workers across the nation without buying into the fears of AI doomerism. In this interview, we sat down to discuss his latest book, why he felt now was the right time to release it, and the message he hopes to communicate to the world. Thank you to Shyam Shyam Sankar for taking the time! Timestamps: 00:00 - Intro 01:02 - How Shyam got to Palantir 02:19 - Palantir's Journey over the years 07:40 - Why is now the time to mobilize 09:40 - Hardware vs Software 14:57 - The Youth and Communism 20:15 - US vs China 22:24 - AI Doomerism 26:21 - Palantir's impact on how Shyam thinks of AI 29:12 - CEOs on the AI doomer case vs optimistic case 34:50 - Rapid Fire
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Shyam Sankar is trying to mobilize a country. The CTO of Palantir is ringing the alarm on the necessity to resurrect America’s industrial base, win the defense technology race against other countries, and empower workers across the nation without buying into the fears of AI doomerism. In this interview, we sat down to discuss his latest book, why he felt now was the right time to release it, and the message he hopes to communicate to the world. Thank you to Shyam Shyam Sankar for taking the time! Timestamps: 00:00 - Intro 01:02 - How Shyam got to Palantir 02:19 - Palantir's Journey over the years 07:40 - Why is now the time to mobilize 09:40 - Hardware vs Software 14:57 - The Youth and Communism 20:15 - US vs China 22:24 - AI Doomerism 26:21 - Palantir's impact on how Shyam thinks of AI 29:12 - CEOs on the AI doomer case vs optimistic case 34:50 - Rapid Fire

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167,871 просмотров • 2 месяцев назад

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Proof of Coverage Media

56,275 просмотров • 1 год назад

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
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43,063 просмотров • 2 месяцев назад

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24,904 просмотров • 1 месяц назад

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58,538 просмотров • 1 месяц назад

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14,833 просмотров • 5 месяцев назад

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John Collison

701,401 просмотров • 2 месяцев назад

Last Friday we did an interview with Aaron van Wirdum Bitcoin Eindhoven. There were quite some nice questions and there were interesting insights to share. Hope you all enjoy it. 00:00 thank you Stijn, Jeffrey, Ramon, Roy 00:30 introduction Aaron 3:00 coin courant 8:00 how did you find bitcoin? 10:28 free Ross 11:00 why are you still interested in bitcoin? 12:00 how did you find the cypherpunks? 14:30 is there still a cypherpunk mailing list 15:30 it took you six years to write the book? 17:00 where did you write the book and where are you planning to live? 18:00 how do you see the next 15 years unfold? 23:00 opening quote of the book, Hayek 27:00 extropian movement 33:00 freezing yourself? 36:00 David chaum, democracy or dictatorship? 38:00 Hong Kong protests 40:00 tim may, government control and tax 43:00 hall finney VS Tim may, the moderate vs the radical 45:00 the goal of writing the book 47:00 Nick szabo e money and the anonymous contributer 49:00 who is Satoshi? 50:00 Satoshi and the growth of bitcoin 53:00 questions
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Last Friday we did an interview with Aaron van Wirdum Bitcoin Eindhoven. There were quite some nice questions and there were interesting insights to share. Hope you all enjoy it. 00:00 thank you Stijn, Jeffrey, Ramon, Roy 00:30 introduction Aaron 3:00 coin courant 8:00 how did you find bitcoin? 10:28 free Ross 11:00 why are you still interested in bitcoin? 12:00 how did you find the cypherpunks? 14:30 is there still a cypherpunk mailing list 15:30 it took you six years to write the book? 17:00 where did you write the book and where are you planning to live? 18:00 how do you see the next 15 years unfold? 23:00 opening quote of the book, Hayek 27:00 extropian movement 33:00 freezing yourself? 36:00 David chaum, democracy or dictatorship? 38:00 Hong Kong protests 40:00 tim may, government control and tax 43:00 hall finney VS Tim may, the moderate vs the radical 45:00 the goal of writing the book 47:00 Nick szabo e money and the anonymous contributer 49:00 who is Satoshi? 50:00 Satoshi and the growth of bitcoin 53:00 questions

₿dG

24,326 просмотров • 2 лет назад

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
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Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

64,495 просмотров • 29 дней назад

John "Tig" Tiegen reflects on the final mortar attack in Benghazi as we mark the 13th anniversary today. Watch the full now. John “TIG” Tiegen
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John "Tig" Tiegen reflects on the final mortar attack in Benghazi as we mark the 13th anniversary today. Watch the full now. John “TIG” Tiegen

Shawn Ryan Show

204,418 просмотров • 8 месяцев назад

What does it take to build AI for scientific discovery? 🧠 To celebrate 10 years of AlphaGo, Thore Graepel and Pushmeet Kohli joined Hannah Fry on our podcast to discuss how mastering games has paved the way for it to help solve more complex problems. ↓ 00:00 The AlphaGo match 02:15 Why Go? 10:58 Lee Sedol vs AlphaGo 14:58 Move 37 20:55 Move 78 24:38 Reaction from the Go community 30:45 Never before seen footage 32:05 AlphaGo to protein folding 34:00 Matrix multiplication 38:00 AlphaGo and algorithmic discovery 41:40 How to verify new discoveries 47:38 Role of mathematicians 51:43 Would we be here without AlphaGo?
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What does it take to build AI for scientific discovery? 🧠 To celebrate 10 years of AlphaGo, Thore Graepel and Pushmeet Kohli joined Hannah Fry on our podcast to discuss how mastering games has paved the way for it to help solve more complex problems. ↓ 00:00 The AlphaGo match 02:15 Why Go? 10:58 Lee Sedol vs AlphaGo 14:58 Move 37 20:55 Move 78 24:38 Reaction from the Go community 30:45 Never before seen footage 32:05 AlphaGo to protein folding 34:00 Matrix multiplication 38:00 AlphaGo and algorithmic discovery 41:40 How to verify new discoveries 47:38 Role of mathematicians 51:43 Would we be here without AlphaGo?

Google DeepMind

110,962 просмотров • 2 месяцев назад

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29
1:09:23

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29

Dan Shipper 📧

47,209 просмотров • 1 год назад

Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:
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Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

Ryan Peterman

119,407 просмотров • 2 месяцев назад

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!
1:12:15

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Nathan Lambert

26,872 просмотров • 4 месяцев назад