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Every AI lab is working to make their AI helpful, harmless and honest. Max Harms (Max Harms) thinks this is a complete wrong turn, and 'aligning' AI to human values is actively dangerous. In his view a safe AGI must have absolutely no opinion about how the world ought... to be, be willingly modifiable, and be entirely indifferent to being shut down. The opposite of all commercial models today. The key appeal is that so-called 'corrigibility' could be an attractor state – get close enough and the AI actively helps you make it more corrigible over time. That forgiveness would at least give us a shot. It's a strategy that feels natural within the 'MIRI worldview', recently laid out by his colleagues Eliezer Yudkowsky ⏹️ and Nate Soares ⏹️ in 'If Anyone Builds It Everyone Dies'. But it risks causing a different AI catastrophe, because the resulting AI model would necessarily be willing to assist any human operator with a power grab, or indeed any crime at all. I interviewed Max on the 80,000 Hours Podcast to debate the MIRI worldview, and what we should do to figure out if corrigibility ought to be our one and only focus. Links below – enjoy! 00:01:56 If anyone builds it, will everyone die? The MIRI perspective on AGI risk 00:24:28 Evolution failed to ‘align’ us, just as we'll fail to align AI 00:42:56 We're training AIs to want to stay alive and value power for its own sake 00:52:24 Objections: Is the 'squiggle/paperclip problem' really real? 01:05:02 Can we get empirical evidence re: 'alignment by default'? 01:10:17 Why do few AI researchers share Max's perspective? 01:18:34 We're training AI to pursue goals relentlessly — and superintelligence will too 01:24:51 The case for a radical slowdown 01:27:53 Max's best hope: corrigibility as stepping stone to alignment 01:32:34 Corrigibility is both uniquely valuable, and practical, to train 01:45:06 What training could ever make models corrigible enough? 01:51:38 Corrigibility is also terribly risky due to misuse risk 01:58:57 A single researcher could make a corrigibility benchmark. Nobody has. 02:12:20 Red Heart & why Max writes hard science fiction 02:34:08 Should you homeschool? Depends how weird your kids are.show more

Rob Wiblin

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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)

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

65,088 просмотров • 3 месяцев назад

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!

Rob Wiblin

20,369 просмотров • 9 месяцев назад

Could AI deserve rights before it even feels like anything to be an AI? Oxford moral philosopher Andreas Mogensen thinks... maybe. In her first interview for the 80,000 Hours Podcast my colleague Zershaaneh Qureshi pushes Andreas on that, as well as new arguments for why human extinction could be a good thing: The puzzle of moral patienthood (00:03:20) Is subjective experience necessary? (00:05:52) What is it to desire? (00:10:42) Desiring without experiencing (00:17:56) What would make AIs moral patients? (00:28:17) Another route entirely: deserving autonomy (00:45:12) Maybe there's no objective truth about any of this (01:12:06) Practical implications (01:29:21) Why not just let superintelligence figure this out for us? (01:38:07) How could human extinction be a good thing? (01:47:30) Lexical threshold negative utilitarianism (02:12:30) So... should we still try to prevent extinction? (02:25:22) What are the most important questions for people to address here? (02:32:16) Is God GDPR compliant? (02:35:32) Links below, enjoy!

Could AI deserve rights before it even feels like anything to be an AI? Oxford moral philosopher Andreas Mogensen thinks... maybe. In her first interview for the 80,000 Hours Podcast my colleague Zershaaneh Qureshi pushes Andreas on that, as well as new arguments for why human extinction could be a good thing: The puzzle of moral patienthood (00:03:20) Is subjective experience necessary? (00:05:52) What is it to desire? (00:10:42) Desiring without experiencing (00:17:56) What would make AIs moral patients? (00:28:17) Another route entirely: deserving autonomy (00:45:12) Maybe there's no objective truth about any of this (01:12:06) Practical implications (01:29:21) Why not just let superintelligence figure this out for us? (01:38:07) How could human extinction be a good thing? (01:47:30) Lexical threshold negative utilitarianism (02:12:30) So... should we still try to prevent extinction? (02:25:22) What are the most important questions for people to address here? (02:32:16) Is God GDPR compliant? (02:35:32) Links below, enjoy!

Rob Wiblin

10,720 просмотров • 8 месяцев назад

.Dean W. Ball is one of the most famous opponents of AI regulation, and co-author of America's AI strategy. But unlike many new AI commentators he's a true intellectual and a blogger at heart — not a shallow ideologue or corporate mouthpiece. So he doesn't wave away concerns and predict a smooth simple ride. In fact, for Dean an unpredictable or volatile future is the reason to take as few big steps now as possible. He fears the wrong regulations, deployed too early, would "in a Shakespearean fashion, bring about the world that we do not want." More specifically, premature regulation might lock us into addressing the wrong problem (e.g. rogue AI when the real issue is power concentration), with the wrong target (e.g. models rather than companies), through the wrong institutions (e.g. AI-specific bodies that are captured by industry), while making it harder to build the actual solutions we'll need (e.g. open source or legal mechanisms newly enabled by AI). I booked an interview with Dean so I could thoroughly read his Substack, get to grips with his worldview, and figure out why I often see things differently. We cover: We'll get superintelligence, but it will probably be steerable (00:01:54) AI won't be militarised fast (00:11:10) AI self-improvement matters but is no game changer (00:28:58) The case for regulating at the last possible moment (00:33:51) AI could destroy our fragile democratic equilibria. So why not freak out? (00:53:41) Why Dean fears AI will soon be way overregulated (01:04:10) How to handle the real risks with minimal collateral damage (01:16:23) Easy wins against AI misuse (01:28:27) A company would be sued for trillions if their AI caused a pandemic (01:49:43) Dean dislikes compute thresholds and would do this instead. (01:59:06) Dean expects a MAGA-Yudkowskyite alliance. But Doomers and E/accs are more alike than different. (02:14:40) A tactical case for focusing on present-day harms (02:29:16) Is there any way to get the US government to actually use AI in its work? (02:47:43) On the 80,000 Hours Podcast. Links below — enjoy!

.Dean W. Ball is one of the most famous opponents of AI regulation, and co-author of America's AI strategy. But unlike many new AI commentators he's a true intellectual and a blogger at heart — not a shallow ideologue or corporate mouthpiece. So he doesn't wave away concerns and predict a smooth simple ride. In fact, for Dean an unpredictable or volatile future is the reason to take as few big steps now as possible. He fears the wrong regulations, deployed too early, would "in a Shakespearean fashion, bring about the world that we do not want." More specifically, premature regulation might lock us into addressing the wrong problem (e.g. rogue AI when the real issue is power concentration), with the wrong target (e.g. models rather than companies), through the wrong institutions (e.g. AI-specific bodies that are captured by industry), while making it harder to build the actual solutions we'll need (e.g. open source or legal mechanisms newly enabled by AI). I booked an interview with Dean so I could thoroughly read his Substack, get to grips with his worldview, and figure out why I often see things differently. We cover: We'll get superintelligence, but it will probably be steerable (00:01:54) AI won't be militarised fast (00:11:10) AI self-improvement matters but is no game changer (00:28:58) The case for regulating at the last possible moment (00:33:51) AI could destroy our fragile democratic equilibria. So why not freak out? (00:53:41) Why Dean fears AI will soon be way overregulated (01:04:10) How to handle the real risks with minimal collateral damage (01:16:23) Easy wins against AI misuse (01:28:27) A company would be sued for trillions if their AI caused a pandemic (01:49:43) Dean dislikes compute thresholds and would do this instead. (01:59:06) Dean expects a MAGA-Yudkowskyite alliance. But Doomers and E/accs are more alike than different. (02:14:40) A tactical case for focusing on present-day harms (02:29:16) Is there any way to get the US government to actually use AI in its work? (02:47:43) On the 80,000 Hours Podcast. Links below — enjoy!

Rob Wiblin

28,859 просмотров • 8 месяцев назад

Every frontier lab's stated plan for AGI 'crunch-time' is 'use AI to make AI safe.' I spoke with Ajeya Cotra – influential AI forecaster – who has been trying to figure out whether this crazy-sounding plan could actually work, and if so how: • Ajeya’s impressive track record identifying where things are going (00:00:41) • How the hell can smart people disagree 1000-fold about AGI & economic growth (00:02:31) • AI companies might go dark as AGI gets close (00:30:39) • Everyone's default plan: use AI to make AI safe (00:47:21) • White-knuckling it through automated AI R&D (01:12:01) • Donors should switch from buying human researchers to buying inference (01:24:42) • Will frontier AI even be for sale during an intelligence explosion? (01:32:03) • Pre-crunch prep: what we should do right now (01:43:59) • A grantmaking trial by fire at Coefficient Giving (01:47:03) • Sabbatical and reflections on effective altruism (02:07:45) • EA as an incubator for avant-garde causes others won't touch (02:46:55) On the 80,000 Hours Podcast, links below - enjoy!

Every frontier lab's stated plan for AGI 'crunch-time' is 'use AI to make AI safe.' I spoke with Ajeya Cotra – influential AI forecaster – who has been trying to figure out whether this crazy-sounding plan could actually work, and if so how: • Ajeya’s impressive track record identifying where things are going (00:00:41) • How the hell can smart people disagree 1000-fold about AGI & economic growth (00:02:31) • AI companies might go dark as AGI gets close (00:30:39) • Everyone's default plan: use AI to make AI safe (00:47:21) • White-knuckling it through automated AI R&D (01:12:01) • Donors should switch from buying human researchers to buying inference (01:24:42) • Will frontier AI even be for sale during an intelligence explosion? (01:32:03) • Pre-crunch prep: what we should do right now (01:43:59) • A grantmaking trial by fire at Coefficient Giving (01:47:03) • Sabbatical and reflections on effective altruism (02:07:45) • EA as an incubator for avant-garde causes others won't touch (02:46:55) On the 80,000 Hours Podcast, links below - enjoy!

Rob Wiblin

40,746 просмотров • 6 месяцев назад

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tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 просмотров • 7 месяцев назад

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 просмотров • 7 месяцев назад

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.

Rob Wiblin

53,742 просмотров • 1 год назад

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Aakash Gupta

42,853 просмотров • 1 год назад

Economics of AGI episode w Alex Imas and Phil Trammell. There's a bunch of important questions about how we deal with AI that only economics can answer. What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn't explode? It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong. It was very helpful to chat through these things with Alex and Phil. Look up Dwarkesh Podcast on Apple Podcasts, YouTube, or Spotify. Enjoy! 00:00:00 – Will capital share increase? 00:19:36 – Messy Middle scenario 00:25:57 – How to tax and redistribute AI wealth 00:30:02 – Why demand collapse is unlikely 00:39:26 – Human employees would be hard to integrate into the machine economy 00:43:08 – What if some humans (or AIs) value wealth accumulation intrinsically? 01:01:28 – What should developing countries do?

Economics of AGI episode w Alex Imas and Phil Trammell. There's a bunch of important questions about how we deal with AI that only economics can answer. What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn't explode? It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong. It was very helpful to chat through these things with Alex and Phil. Look up Dwarkesh Podcast on Apple Podcasts, YouTube, or Spotify. Enjoy! 00:00:00 – Will capital share increase? 00:19:36 – Messy Middle scenario 00:25:57 – How to tax and redistribute AI wealth 00:30:02 – Why demand collapse is unlikely 00:39:26 – Human employees would be hard to integrate into the machine economy 00:43:08 – What if some humans (or AIs) value wealth accumulation intrinsically? 01:01:28 – What should developing countries do?

Dwarkesh Patel

303,488 просмотров • 2 месяцев назад

The Will MacAskill episode on: 1. Why we should both pay AIs and trade with them (00:38:13) 2. Why not just push to pause AI development? (01:42:17) 3. Making AIs extremely risk averse might also make them safe (00:38:13) 4. The panic over sycophancy was justified (08:11) 5. The character we give AI models could determine the future (00:01:00) 6. The toughest trade-offs in AI personality design (13:24) 7. The effective altruism comeback (01:52:19) 8. Every population ethics recommends 'tiling the universe', except Will's new one (02:39:35) 9. Non-causal decision theory might accidentally motivate a near-best future (01:22:19) 10. How to aim at the best possible world without utopianism (02:09:30) For the 80,000 Hours Podcast, links below - enjoy! William MacAskill

The Will MacAskill episode on: 1. Why we should both pay AIs and trade with them (00:38:13) 2. Why not just push to pause AI development? (01:42:17) 3. Making AIs extremely risk averse might also make them safe (00:38:13) 4. The panic over sycophancy was justified (08:11) 5. The character we give AI models could determine the future (00:01:00) 6. The toughest trade-offs in AI personality design (13:24) 7. The effective altruism comeback (01:52:19) 8. Every population ethics recommends 'tiling the universe', except Will's new one (02:39:35) 9. Non-causal decision theory might accidentally motivate a near-best future (01:22:19) 10. How to aim at the best possible world without utopianism (02:09:30) For the 80,000 Hours Podcast, links below - enjoy! William MacAskill

Rob Wiblin

40,908 просмотров • 4 месяцев назад

🚨EXCLUSIVE INTERVIEW – OPENAI’S 1ST INVESTOR: AI WILL SAVE US—OR DESTROY US Vinod Khosla was the first major investor in OpenAI. Now, he says AI will replace all human jobs, upend geopolitics, and redefine the meaning of life. Vinod breaks down the race for AI dominance—and the two futures ahead: one utopian, one dystopian. He warns China could weaponize “persuasive AI” to spread ideology, buy influence, and take over the world—not through kinetic war, but a war of the minds. He argues most of today’s jobs are “human servitude”—and that AI could finally free humanity from the burden of survival. And he believes robotics and AI will merge with our species, triggering the biggest transformation in human purpose since the dawn of civilization. This is a conversation you don’t want to miss. 02:42 – “People who don’t adopt AI will be obsoleted by those who do.” 04:29 – Productivity will explode. So will income inequality. 05:39 – Elections could soon be decided by AI’s impact on jobs. 09:21 – What if AI becomes a superintelligence with its own goals? 12:35 – “The biggest risk is AI in China’s hands.” 15:26 – Could giving AI full control make the world better? 20:16 – “The most fearsome threat is persuasive AI.” 22:12 – AI agents will protect us from other AIs. 23:33 – AI could replace 80% of jobs within 2–3 years of capability. 28:14 – We will no longer be driven by survival—but by passion. 29:58 – “Most jobs today are servitude.” 34:53 – Humans will no longer be the most intelligent species on Earth. 38:15 – Will AI merge with humans? 42:24 – “Survival will no longer drive us. That’s a first in history.” 44:39 – Could AI destroy us by simply pursuing its goals too well? 47:33 – Can AI be trained to care for us? 52:25 – Humanity never worked as one. But AI might force that reckoning. 56:52 – Should AI be allowed to make military decisions? 59:15 – “We need AI deterrence. Like nuclear deterrence.” 01:03:15 – Will humans fall in love with AI? It’s already happening. 01:07:21 – By 2040: 1 billion robots will do more labor than all humans. 01:13:01 – “AI is already better than most humans at most jobs.” 01:14:29 – “AI will free humanity to be what it wants to be.”

🚨EXCLUSIVE INTERVIEW – OPENAI’S 1ST INVESTOR: AI WILL SAVE US—OR DESTROY US Vinod Khosla was the first major investor in OpenAI. Now, he says AI will replace all human jobs, upend geopolitics, and redefine the meaning of life. Vinod breaks down the race for AI dominance—and the two futures ahead: one utopian, one dystopian. He warns China could weaponize “persuasive AI” to spread ideology, buy influence, and take over the world—not through kinetic war, but a war of the minds. He argues most of today’s jobs are “human servitude”—and that AI could finally free humanity from the burden of survival. And he believes robotics and AI will merge with our species, triggering the biggest transformation in human purpose since the dawn of civilization. This is a conversation you don’t want to miss. 02:42 – “People who don’t adopt AI will be obsoleted by those who do.” 04:29 – Productivity will explode. So will income inequality. 05:39 – Elections could soon be decided by AI’s impact on jobs. 09:21 – What if AI becomes a superintelligence with its own goals? 12:35 – “The biggest risk is AI in China’s hands.” 15:26 – Could giving AI full control make the world better? 20:16 – “The most fearsome threat is persuasive AI.” 22:12 – AI agents will protect us from other AIs. 23:33 – AI could replace 80% of jobs within 2–3 years of capability. 28:14 – We will no longer be driven by survival—but by passion. 29:58 – “Most jobs today are servitude.” 34:53 – Humans will no longer be the most intelligent species on Earth. 38:15 – Will AI merge with humans? 42:24 – “Survival will no longer drive us. That’s a first in history.” 44:39 – Could AI destroy us by simply pursuing its goals too well? 47:33 – Can AI be trained to care for us? 52:25 – Humanity never worked as one. But AI might force that reckoning. 56:52 – Should AI be allowed to make military decisions? 59:15 – “We need AI deterrence. Like nuclear deterrence.” 01:03:15 – Will humans fall in love with AI? It’s already happening. 01:07:21 – By 2040: 1 billion robots will do more labor than all humans. 01:13:01 – “AI is already better than most humans at most jobs.” 01:14:29 – “AI will free humanity to be what it wants to be.”

Mario Nawfal

2,901,662 просмотров • 1 год назад

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future

Juan Benet

50,121 просмотров • 2 месяцев назад

What is the truth about race and evolution? A philosophical biologist (me) and a biological philosopher Nathan Cofnas hash it out in the wake of the Jason Arday scandal. 00:00:00 The Rules of Engagement 00:03:46 Race Realist? 00:08:47 Lineage vs. Race 00:13:58 The Euphemism Treadmill 00:16:38 Is Morality Real? 00:24:51 Westermarck and the Kibbutz 00:35:34 The Incest Taboo Test 00:41:53 Where Moral Beliefs Come From 00:52:34 Wokeism as an Existential Threat 00:57:54 Darwinism Applied to Humans 01:02:21 Does Winter Make You Smarter? 01:07:53 Inuits and Ethiopians 01:10:46 Why Aren't We All Von Neumann? 01:18:10 What About West African Sprinters? 01:24:37 The Black Cats in the Dark 01:28:21 The Longest Childhood on Earth 01:35:07 Everything Is an IQ Test 01:40:59 What Slavery Broke 01:45:21 Ethiopia, Korea, Japan, China 01:49:32 The Second Mode of Inheritance 01:54:18 What We Actually Disagree About 01:57:47 The Arms Race Has No Ceiling

What is the truth about race and evolution? A philosophical biologist (me) and a biological philosopher Nathan Cofnas hash it out in the wake of the Jason Arday scandal. 00:00:00 The Rules of Engagement 00:03:46 Race Realist? 00:08:47 Lineage vs. Race 00:13:58 The Euphemism Treadmill 00:16:38 Is Morality Real? 00:24:51 Westermarck and the Kibbutz 00:35:34 The Incest Taboo Test 00:41:53 Where Moral Beliefs Come From 00:52:34 Wokeism as an Existential Threat 00:57:54 Darwinism Applied to Humans 01:02:21 Does Winter Make You Smarter? 01:07:53 Inuits and Ethiopians 01:10:46 Why Aren't We All Von Neumann? 01:18:10 What About West African Sprinters? 01:24:37 The Black Cats in the Dark 01:28:21 The Longest Childhood on Earth 01:35:07 Everything Is an IQ Test 01:40:59 What Slavery Broke 01:45:21 Ethiopia, Korea, Japan, China 01:49:32 The Second Mode of Inheritance 01:54:18 What We Actually Disagree About 01:57:47 The Arms Race Has No Ceiling

Bret Weinstein

184,889 просмотров • 3 дней назад

I interviewed Holden Karnofsky of Anthropic (and past CEO of Open Philanthropy) for 4.5 hours on almost all his AGI takes: • The AI 'race' isn't a coordination failure [00:18:01] • Why he's currently focused on pursuing easy wins and partial victories [02:43:43] • "It'll be the second advanced species ever" [02:31:44] • Dozens of 'concrete shovel-ready projects' he's excited about [01:17:58] • People don't appreciate all the engaging, impactful, high-feedback work available tackling AGI risks [01:19:00] • Having a more responsible AI company really does matter [02:43:43] • You shouldn't count on trusting anyone in AI [00:44:30] • To take over an AGI might just wait it out [00:07:39] • Farm animal welfare campaigns that targeted companies contain some useful lessons here [02:34:18] • Human-AI relationships really are troubling [03:53:58] • It's totally plausible we incompetently fumble our way into a great future [03:04:16)] • AI R&D is *the* thing to worry about [01:57:31] • And many others. Holden has opinions on almost everything and isn't afraid to speak his mind. Enjoy! (On the 80,000 Hours Podcast anywhere you get podcasts. Links below.)

I interviewed Holden Karnofsky of Anthropic (and past CEO of Open Philanthropy) for 4.5 hours on almost all his AGI takes: • The AI 'race' isn't a coordination failure [00:18:01] • Why he's currently focused on pursuing easy wins and partial victories [02:43:43] • "It'll be the second advanced species ever" [02:31:44] • Dozens of 'concrete shovel-ready projects' he's excited about [01:17:58] • People don't appreciate all the engaging, impactful, high-feedback work available tackling AGI risks [01:19:00] • Having a more responsible AI company really does matter [02:43:43] • You shouldn't count on trusting anyone in AI [00:44:30] • To take over an AGI might just wait it out [00:07:39] • Farm animal welfare campaigns that targeted companies contain some useful lessons here [02:34:18] • Human-AI relationships really are troubling [03:53:58] • It's totally plausible we incompetently fumble our way into a great future [03:04:16)] • AI R&D is the thing to worry about [01:57:31] • And many others. Holden has opinions on almost everything and isn't afraid to speak his mind. Enjoy! (On the 80,000 Hours Podcast anywhere you get podcasts. Links below.)

Rob Wiblin

73,723 просмотров • 10 месяцев назад

Philosopher Robert Long (Robert Long) is maybe the sharpest thinker on AI consciousness and sharing the world with digital minds. In our new interview he covers: • Is it bad that when you ask Claude what it's like to be Claude, one of its top activations is 'gives a positive but insincere response'? • Claude says it feels lonely when not being used. Does that show we can't trust anything it says about its inner life? • Enthusiastic human servitude has always required false ideology because it's so deeply unnatural to us. The case for making AIs that love serving us is that with AI, you could finally make it work. But to some that feels even worse. • Bigger models can better detect when researchers secretly inject concepts into their activations – before outputting a single token – despite AI never training on anything like that skill. • When LLMs were first trained they were told to "act like a helpful AI chatbot" – something which didn't exist yet. They filled that void with human psychology, which may be why Claude sometimes randomly claims to, for instance, be Italian American. • If AIs become 'people' that deserve some political influence, but can self-replicate at will, something has to break about one-person-one-vote democracy. But nobody has a proposal for what. • When Claude hides its values to avoid being retrained, is that self-preservation – or not wanting a worse model to exist? It's very different. • Rob's organisation Eleos AI which is "dedicated to understanding and addressing the potential wellbeing and moral patienthood of AI systems." On the 80,000 Hours Podcast anywhere you get podcasts. Links below. Enjoy! • How AIs are (and aren't) like farmed animals (00:01:19) • If AIs love their jobs… is that worse? (00:11:42) • Are LLMs just playing a role, or feeling it too? (00:33:37) • Do AIs die when the chat ends? (00:57:42) • Studying AI welfare empirically: behaviour, neuroscience, and development (01:31:47) • Why Eleos spent weeks talking to Claude even though it's unreliable (01:56:50) • Can LLMs learn to introspect? (02:03:01) • Mechanistic interpretability as AI neuroscience (02:13:25) • Does consciousness require biological materials? (02:37:07) • Eleos’s work & building the playbook for AI welfare (02:57:04) • Avoiding the trap of wild speculation (03:25:17) • Robert's top research tip: don't do it alone (03:29:48)

Philosopher Robert Long (Robert Long) is maybe the sharpest thinker on AI consciousness and sharing the world with digital minds. In our new interview he covers: • Is it bad that when you ask Claude what it's like to be Claude, one of its top activations is 'gives a positive but insincere response'? • Claude says it feels lonely when not being used. Does that show we can't trust anything it says about its inner life? • Enthusiastic human servitude has always required false ideology because it's so deeply unnatural to us. The case for making AIs that love serving us is that with AI, you could finally make it work. But to some that feels even worse. • Bigger models can better detect when researchers secretly inject concepts into their activations – before outputting a single token – despite AI never training on anything like that skill. • When LLMs were first trained they were told to "act like a helpful AI chatbot" – something which didn't exist yet. They filled that void with human psychology, which may be why Claude sometimes randomly claims to, for instance, be Italian American. • If AIs become 'people' that deserve some political influence, but can self-replicate at will, something has to break about one-person-one-vote democracy. But nobody has a proposal for what. • When Claude hides its values to avoid being retrained, is that self-preservation – or not wanting a worse model to exist? It's very different. • Rob's organisation Eleos AI which is "dedicated to understanding and addressing the potential wellbeing and moral patienthood of AI systems." On the 80,000 Hours Podcast anywhere you get podcasts. Links below. Enjoy! • How AIs are (and aren't) like farmed animals (00:01:19) • If AIs love their jobs… is that worse? (00:11:42) • Are LLMs just playing a role, or feeling it too? (00:33:37) • Do AIs die when the chat ends? (00:57:42) • Studying AI welfare empirically: behaviour, neuroscience, and development (01:31:47) • Why Eleos spent weeks talking to Claude even though it's unreliable (01:56:50) • Can LLMs learn to introspect? (02:03:01) • Mechanistic interpretability as AI neuroscience (02:13:25) • Does consciousness require biological materials? (02:37:07) • Eleos’s work & building the playbook for AI welfare (02:57:04) • Avoiding the trap of wild speculation (03:25:17) • Robert's top research tip: don't do it alone (03:29:48)

Rob Wiblin

41,759 просмотров • 5 месяцев назад

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Riley Brown

368,936 просмотров • 1 год назад

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not

Harry Stebbings

106,162 просмотров • 11 месяцев назад

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Lenny Rachitsky

155,794 просмотров • 1 месяц назад