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In 2025 AI coded full bacteriophage genomes that work better than anything found in nature. And crushed top virologists in a test of lab troubleshooting – beating human specialists in their area of expertise 45% to 22%. What could go wrong? But destroying the 'tacit knowledge' barrier to bioweapons... development is just the beginning. AI can design crypto-ricin, likely as deadly as normal ricin, which evades the best gene synthesis screening available. PhDs with Claude nearly breached Anthropic's 'critical danger threshold' for bioweapons development. But most tests are still on undergrads: they're cheaper to hire. Russia, Iran, and North Korea all have active biological weapons programmes. And even the most dangerous biological AI tools are so open that limiting proliferation is near impossible in the near-term. The situation feel hopeless so I spoke to one of the world's top authorities on 'AI-Bio', Dr Richard Moulange, to find out why this situation isn't as bleak as it sounds. He proposes a range of shovel-ready projects, none of which fully solve the problem, but all of which would help move the needle: 1. Screen DNA synthesis orders. (Incredibly this is voluntary now.) 2. Give defenders a preview of models before they're fully public. 3. Def/acc: Stockpile broad-spectrum vaccines that cover entire virus families. 4. Prevent AI expertise in 'mirror bacteria', as their only use is mass murder. 5. Surveil waste-water to spot engineered pathogens before they spread. 6. Invest in intelligence so if a state releases a bioweapon, that can be proven, and the state punished. 7. Limit access and improve refusals to buy time. (FWIW to me the situation still seems very bad.) On the 80,000 Hours Podcast, wherever you get podcasts. Links below — enjoy! 01:11 AI can now design novel bacteriophage genomes 04:42 The end of the 'tacit knowledge' barrier 18:50 Are risks from bioterrorists overstated? 23:14 The 3 key disasters AI makes more likely 30:43 Which bad actors does AI help the most? 42:07 Experts are more scary than amateurs 47:32 Barriers to bioterrorists using AI 49:43 AI biorisks are sometimes dismissed (and that’s a huge mistake) 1:05:12 Advanced AI biology tools we already have or will soon 1:10:57 Rob argues that the situation is hopeless 1:19:38 Intervention #1: Limit access 1:34:28 Intervention #2: Get AIs to refuse to help 1:44:18 Intervention #3: Surveillance and attribution 1:58:28 Intervention #4: Universal vaccines and antivirals 2:12:01 Intervention #5: Screen all orders for DNA 2:21:57 AI companies talk about def/acc more than they fund it 2:28:44 Can you build a profitable business solving this problem? 2:33:08 This doesn't have to interfere with useful science (much) 2:35:16 What are the best low-tech interventions? 2:40:17 Richard's top request for AI companies 2:55:44 Grok shows governments lack legal levers 2:58:54 Best ways listeners can help fix AI-Bioshow more

Rob Wiblin

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AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 görüntüleme • 1 yıl önce

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 görüntüleme • 1 yıl önce

Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Rob Wiblin

48,386 görüntüleme • 1 yıl önce

Convention wisdom is that bioweapons are humanity's greatest weakness – 100x cheaper to make than to defend against. Andrew Snyder-Beattie thinks conventional wisdom is likely wrong. He has a plan cheap enough to do without government. Useful even in worst case scenarios like mirror bacteria. Effective enough to save most people. In one of my all-time fav interviews he lays out a low-tech 4-step approach developed by his research team at Open Philanthropy, to fix a problem most have thought unsolvable. ASB is hiring for many roles in this project from logistics to biotech to manufacturing, and has $100s millions to deploy. Enjoy, links below! 2:10 How bad it could get 9:19 The worst-case scenario: mirror bacteria 18:14 Why low-tech 25:30 Prevention 31:21 The “4 pillars” plan 33:09 ASB is hiring now to make this happen 35:11 Everyone was wrong: biorisks are defence dominant 40:23 Pillar 1: Lungs 55:53 Pillar 2: Biohardening 1:15:19 Pillar 3: Detection 1:28:40 Pillar 4: The wrench hypothesis 1:40:12 The plan's biggest weaknesses 1:44:44 Would chaos make this impossible to pull off? 1:51:50 Would rogue AI make bioweapons? 1:57:57 We can feed the world even if all the plants die 2:07:03 Could a bioweapon make the Earth uninhabitable? 2:09:35 What ASB is hiring for 2:30:27 How to protect yourself and your family (On the 80,000 Hours Podcast, available anywhere you get podcasts.)

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Convention wisdom is that bioweapons are humanity's greatest weakness – 100x cheaper to make than to defend against. Andrew Snyder-Beattie thinks conventional wisdom is likely wrong. He has a plan cheap enough to do without government. Useful even in worst case scenarios like mirror bacteria. Effective enough to save most people. In one of my all-time fav interviews he lays out a low-tech 4-step approach developed by his research team at Open Philanthropy, to fix a problem most have thought unsolvable. ASB is hiring for many roles in this project from logistics to biotech to manufacturing, and has $100s millions to deploy. Enjoy, links below! 2:10 How bad it could get 9:19 The worst-case scenario: mirror bacteria 18:14 Why low-tech 25:30 Prevention 31:21 The “4 pillars” plan 33:09 ASB is hiring now to make this happen 35:11 Everyone was wrong: biorisks are defence dominant 40:23 Pillar 1: Lungs 55:53 Pillar 2: Biohardening 1:15:19 Pillar 3: Detection 1:28:40 Pillar 4: The wrench hypothesis 1:40:12 The plan's biggest weaknesses 1:44:44 Would chaos make this impossible to pull off? 1:51:50 Would rogue AI make bioweapons? 1:57:57 We can feed the world even if all the plants die 2:07:03 Could a bioweapon make the Earth uninhabitable? 2:09:35 What ASB is hiring for 2:30:27 How to protect yourself and your family (On the 80,000 Hours Podcast, available anywhere you get podcasts.)

Rob Wiblin

168,670 görüntüleme • 9 ay önce

How to AGI-Proof Your Life | Nick Bostrom Today, work gives many of us purpose and meaning. But AI is making more and more of that work obsolete, until one day, AI might make redundant not just work but also much of leisure! Deep Utopia, a new book by @NickBostromPhD, is all about that day and what to do about it. In this interview, you are going to learn: 1. Where you can invest your time, effort, and resources NOW that is most AI-proof. 2. What human activities even AGI cannot replace. 3. What life will look like post-AGI. Timestamps 02:15 AI Radically Changes the Practice of Philosophy 06:08 Why Ignorance Is Set to Become a Luxury 07:45 Why AI Can’t Replace Human Relationships 11:41 How AI Changes the Labor Market 17:40 AI will make even leisure obsolete 20:35 How AI Changes the Meaning of the Good Life 21:52 The Five Pillars of Meaning post-AGI 35:22 People underestimate the importance of pleasure 42:33 The Biggest Problem in Utopia Will Be Boredom 51:01 Can Life Be Meaningful Without Stakes & Risk? 55:19 Utopia's Greatest Challenge to Human Nature 58:38 Can Simulation Save Us from Boredom? 1:03:18 The Unabomber's Prescient Intuition 1:05:44 Philosophy of AI & Theology

How to AGI-Proof Your Life | Nick Bostrom Today, work gives many of us purpose and meaning. But AI is making more and more of that work obsolete, until one day, AI might make redundant not just work but also much of leisure! Deep Utopia, a new book by @NickBostromPhD, is all about that day and what to do about it. In this interview, you are going to learn: 1. Where you can invest your time, effort, and resources NOW that is most AI-proof. 2. What human activities even AGI cannot replace. 3. What life will look like post-AGI. Timestamps 02:15 AI Radically Changes the Practice of Philosophy 06:08 Why Ignorance Is Set to Become a Luxury 07:45 Why AI Can’t Replace Human Relationships 11:41 How AI Changes the Labor Market 17:40 AI will make even leisure obsolete 20:35 How AI Changes the Meaning of the Good Life 21:52 The Five Pillars of Meaning post-AGI 35:22 People underestimate the importance of pleasure 42:33 The Biggest Problem in Utopia Will Be Boredom 51:01 Can Life Be Meaningful Without Stakes & Risk? 55:19 Utopia's Greatest Challenge to Human Nature 58:38 Can Simulation Save Us from Boredom? 1:03:18 The Unabomber's Prescient Intuition 1:05:44 Philosophy of AI & Theology

Johnathan Bi

18,737 görüntüleme • 1 yıl önce

the future is here: my full podcast with shaw from ai16z is now live we dive into ai agents, autonomy, spirituality, and why we might not have as much time as we think this is a conversation you can’t afford to miss 1:03 Shaw's history before it all 3:44 What is an AI agent? 5:55 What will happen to us when AI could do it all? 8:02 Is Shaw a spiritual person? 8:39 What spiritual teachings inspired Shaw? 9:25 Are we far from a new religion by AI? 11:18 What's up with the b**ttplugs? 13:37 What is AI16Z? 46:16 Nobody is going to save us... 55:45 Is there a moral compass to creating AI? 1:00:00 Why crypto and AI? 1:07:06 How big is the scope of AI? Where will it go? 1:11:01 The loneliness epidemic 1:19:33 Are we still early? 1:21:02 What will keep AI16Z in the lead? 1:25:34 Why is AI16Z being compared to an AI L1? 1:30:24 The upcoming tokenomics proposal for the dao 1:37:44 Thoughts on the pairing between AI and augmented reality 1:43:51 Will AI take us away for the physical world? 1:48:53 How much time do we have left? 1:53:18 Emotions and developers 2:00:40 Who or what are we up against? 2:02:33 Would we lose our sense of purpose? 2:05:20 Final words from Shaw 2:08:39 important outro

the future is here: my full podcast with shaw from ai16z is now live we dive into ai agents, autonomy, spirituality, and why we might not have as much time as we think this is a conversation you can’t afford to miss 1:03 Shaw's history before it all 3:44 What is an AI agent? 5:55 What will happen to us when AI could do it all? 8:02 Is Shaw a spiritual person? 8:39 What spiritual teachings inspired Shaw? 9:25 Are we far from a new religion by AI? 11:18 What's up with the b**ttplugs? 13:37 What is AI16Z? 46:16 Nobody is going to save us... 55:45 Is there a moral compass to creating AI? 1:00:00 Why crypto and AI? 1:07:06 How big is the scope of AI? Where will it go? 1:11:01 The loneliness epidemic 1:19:33 Are we still early? 1:21:02 What will keep AI16Z in the lead? 1:25:34 Why is AI16Z being compared to an AI L1? 1:30:24 The upcoming tokenomics proposal for the dao 1:37:44 Thoughts on the pairing between AI and augmented reality 1:43:51 Will AI take us away for the physical world? 1:48:53 How much time do we have left? 1:53:18 Emotions and developers 2:00:40 Who or what are we up against? 2:02:33 Would we lose our sense of purpose? 2:05:20 Final words from Shaw 2:08:39 important outro

Mika

201,454 görüntüleme • 1 yıl önce

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life

Lex Fridman

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AI models strategically lie to users to pursue their own goals - an issue that gets worse with more RL and more agency. Apollo & OpenAI experimented to stop o3 scheming and found a way to cut it 97%. But o3 also got better at knowing it's being tested (and avoiding scheming while supervised). Apollo Research is the leading lab focused on AI deception, so I asked their CEO Marius Hobbhahn for a thorough overview of where we're at with AI actively working to trick us. On the 80,000 Hours Podcast, links below! 02:09 Top 3 examples of scheming and deception 16:08 Scheming is a natural path for AI models (and people) 28:45 Are the models enthusiastic or reluctant liars? 39:02 Apollo’s collaboration with OpenAI to stop o3 lying so much 53:09 They reduced scheming a lot so why is the problem mostly unsolved 1:03:28 Using thought injection to uncover situational awareness 1:17:39 Internal AI language humans can't read 1:29:46 Why can’t we trick LLMs into thinking situations are real 1:35:44 Is the window to address scheming closing? 1:47:50 What about superintelligent systems 1:57:11 Company incentives 2:11:40 We mostly ignore 'internal deployment' 2:30:46 Catastrophe through chaos 2:46:01 Careers in AI scheming research 3:04:46 Marius's key takeaways

AI models strategically lie to users to pursue their own goals - an issue that gets worse with more RL and more agency. Apollo & OpenAI experimented to stop o3 scheming and found a way to cut it 97%. But o3 also got better at knowing it's being tested (and avoiding scheming while supervised). Apollo Research is the leading lab focused on AI deception, so I asked their CEO Marius Hobbhahn for a thorough overview of where we're at with AI actively working to trick us. On the 80,000 Hours Podcast, links below! 02:09 Top 3 examples of scheming and deception 16:08 Scheming is a natural path for AI models (and people) 28:45 Are the models enthusiastic or reluctant liars? 39:02 Apollo’s collaboration with OpenAI to stop o3 lying so much 53:09 They reduced scheming a lot so why is the problem mostly unsolved 1:03:28 Using thought injection to uncover situational awareness 1:17:39 Internal AI language humans can't read 1:29:46 Why can’t we trick LLMs into thinking situations are real 1:35:44 Is the window to address scheming closing? 1:47:50 What about superintelligent systems 1:57:11 Company incentives 2:11:40 We mostly ignore 'internal deployment' 2:30:46 Catastrophe through chaos 2:46:01 Careers in AI scheming research 3:04:46 Marius's key takeaways

Rob Wiblin

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Grant

165,182 görüntüleme • 20 gün önce

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Rob Wiblin

34,618 görüntüleme • 1 yıl önce

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Lex Fridman

1,153,653 görüntüleme • 1 yıl önce

I'm giving away Claude Code for FREE. Yes, you read that right -- the world's best AI, now completely FREE. My mission is to get the next billion people to write great software. I believe EVERYONE should have access to the best AI, but so many are constrained by costs. I'm solving this by: disrupting the world's fastest growing industry = AI + with the most proven business model = Ads You can actually try it out now -- just one line of code, and you unlock all the powers of Claude Code, for free. how it works: 1. sign up on Giga AI Free 2. change Claude Code env variables to use our inference endpoint 2. to support the free usage, we'll add ads inside the AI responses (only contextual and relevant ads, no spam) Since we get too many signups, there's a waitlist, but we'll be rolling it out to the maximum people as we can. Watch the video to get started & comment and retweet to get access first.

I'm giving away Claude Code for FREE. Yes, you read that right -- the world's best AI, now completely FREE. My mission is to get the next billion people to write great software. I believe EVERYONE should have access to the best AI, but so many are constrained by costs. I'm solving this by: disrupting the world's fastest growing industry = AI + with the most proven business model = Ads You can actually try it out now -- just one line of code, and you unlock all the powers of Claude Code, for free. how it works: 1. sign up on Giga AI Free 2. change Claude Code env variables to use our inference endpoint 2. to support the free usage, we'll add ads inside the AI responses (only contextual and relevant ads, no spam) Since we get too many signups, there's a waitlist, but we'll be rolling it out to the maximum people as we can. Watch the video to get started & comment and retweet to get access first.

Namanyay

486,848 görüntüleme • 8 ay önce

My conversation with Dylan Patel on everything you ever wanted to know about the incredible buildout happening in AI infrastructure This is a whirlwind tour through the companies, deals, models, chips, power sources, partnerships, and people powering AI Dylan is some sort of genius. I wish a version of him existed for every subject. I hope you enjoy this as much as I did. Timestamps: 0:00 Introduction 0:39 The Infinite Money Glitch 4:42 Oracle's Bet on OpenAI 6:06 Nvidia's Strategic Investment and Deal Mechanics 9:17 Why Bigger Models Aren't Always Better 15:07 The Adoption Curve Problem 19:01 The Tokenomics of AI 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 1:18:42 AI for Material Science and Hard Tech 1:22:21 Building Infrastructure 1:29:55 US vs China: Who Really Needs AI to Win? 1:37:42 Favorite AI Bears 1:48:08 Speed Round: Company Impressions 1:55:11 The Death of Traditional SaaS Business Models 2:00:24 The Kindest Thing

My conversation with Dylan Patel on everything you ever wanted to know about the incredible buildout happening in AI infrastructure This is a whirlwind tour through the companies, deals, models, chips, power sources, partnerships, and people powering AI Dylan is some sort of genius. I wish a version of him existed for every subject. I hope you enjoy this as much as I did. Timestamps: 0:00 Introduction 0:39 The Infinite Money Glitch 4:42 Oracle's Bet on OpenAI 6:06 Nvidia's Strategic Investment and Deal Mechanics 9:17 Why Bigger Models Aren't Always Better 15:07 The Adoption Curve Problem 19:01 The Tokenomics of AI 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 1:18:42 AI for Material Science and Hard Tech 1:22:21 Building Infrastructure 1:29:55 US vs China: Who Really Needs AI to Win? 1:37:42 Favorite AI Bears 1:48:08 Speed Round: Company Impressions 1:55:11 The Death of Traditional SaaS Business Models 2:00:24 The Kindest Thing

Patrick OShaughnessy

548,353 görüntüleme • 9 ay önce

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 görüntüleme • 2 ay önce

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing

Patrick OShaughnessy

49,418 görüntüleme • 9 ay önce

We interviewed Nick Bostrom, The author of NYT’s Bestseller “Superintelligence,” and one of the most influential experts on AI Risks. Timestamps: 2:11 - Will AI be used for Extreme Bad? What are the odds? 4:44 - What are the biggest concerns with AI? 6:16 - People are greatly underestimate what Super-intelligence will be. 7:19 - Can we trust anyone with creating a Super-Intelligent AI? 8:49 - Are the big AI companies taking AI safety serious enough? 10:39 - Are multiple AI systems undergoing the transition to super-intelligence at the same time a good or bad thing? 13:10 - How do we prevent AI from becoming biased like we saw with Google Gemini? 16:17 - Is there a danger of AI being too truthful or too opinionated? 18:10 - When do AI’s gain a moral status? How do we know? 19:40 - What is Truth for an AI? 22:37 - What has surprised Bostrom the most regarding AI in the past 10 years? 26:13 - Is AI moving faster or slower than Bostrom imagined 10 years ago? 26:38 - Do we see another AI Winter or AGI first? 28:21 - Is energy a limiting factor in AI? 30:00 - What is Deep Utopia? 33:18 - When will we reach Super-intelligence and Deep Utopia? 36:00 - Can a Deep Utopia be a problem for humanity? 39:29 - Could a Utopian society lead to the creation of a simulated reality as a way to escape? 42:28 - If we are in a simulation, is our consciousness simulated? 47:48 - Elon Musk or Sam Altman? 48:17 - Artificial Super-Intelligence before or after 2035? 49:40 - How can you get Nick Bostrom’s book “Deep Utopia?”

We interviewed Nick Bostrom, The author of NYT’s Bestseller “Superintelligence,” and one of the most influential experts on AI Risks. Timestamps: 2:11 - Will AI be used for Extreme Bad? What are the odds? 4:44 - What are the biggest concerns with AI? 6:16 - People are greatly underestimate what Super-intelligence will be. 7:19 - Can we trust anyone with creating a Super-Intelligent AI? 8:49 - Are the big AI companies taking AI safety serious enough? 10:39 - Are multiple AI systems undergoing the transition to super-intelligence at the same time a good or bad thing? 13:10 - How do we prevent AI from becoming biased like we saw with Google Gemini? 16:17 - Is there a danger of AI being too truthful or too opinionated? 18:10 - When do AI’s gain a moral status? How do we know? 19:40 - What is Truth for an AI? 22:37 - What has surprised Bostrom the most regarding AI in the past 10 years? 26:13 - Is AI moving faster or slower than Bostrom imagined 10 years ago? 26:38 - Do we see another AI Winter or AGI first? 28:21 - Is energy a limiting factor in AI? 30:00 - What is Deep Utopia? 33:18 - When will we reach Super-intelligence and Deep Utopia? 36:00 - Can a Deep Utopia be a problem for humanity? 39:29 - Could a Utopian society lead to the creation of a simulated reality as a way to escape? 42:28 - If we are in a simulation, is our consciousness simulated? 47:48 - Elon Musk or Sam Altman? 48:17 - Artificial Super-Intelligence before or after 2035? 49:40 - How can you get Nick Bostrom’s book “Deep Utopia?”

KrassenCast

380,475 görüntüleme • 2 yıl önce

Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks

Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks

Lex Fridman

1,374,442 görüntüleme • 1 yıl önce

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future

Here's my conversation with Yann LeCun (Yann LeCun) about AI, importance of open source, limits of LLMs, why AI doomers are wrong, and the path to AGI. This was a fun and fascinating technical conversation! It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:18 - Limits of LLMs 13:54 - Bilingualism and thinking 17:46 - Video prediction 25:07 - JEPA (Joint-Embedding Predictive Architecture) 28:15 - JEPA vs LLMs 37:31 - DINO and I-JEPA 38:51 - V-JEPA 44:22 - Hierarchical planning 50:40 - Autoregressive LLMs 1:06:06 - AI hallucination 1:11:30 - Reasoning in AI 1:29:02 - Reinforcement learning 1:34:10 - Woke AI 1:43:48 - Open source 1:47:26 - AI and ideology 1:49:58 - Marc Andreesen 1:57:56 - Llama 3 2:04:20 - AGI 2:08:48 - AI doomers 2:24:38 - Joscha Bach 2:28:51 - Humanoid robots 2:38:00 - Hope for the future

Lex Fridman

1,022,139 görüntüleme • 2 yıl önce