正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

"current AI models are still embarrassingly bad at many tasks" Sam Altman explains why current AI models aren't considered AGI: 1) they don't continuously learn/improve 2) can't discover new science autonomously 3) cannot perform basic knowledge work, such as completing job tasks using the internet and local files

Haider.

45,804 subscribers

42,689 次观看 • 1 年前 •via X (Twitter)

新闻政治科学技术教育

Anya Rossi• Live Now

Private livecam show

10 条评论

AstroZeus ⚡/acc 的头像

AstroZeus ⚡/acc1 年前

It's great that Sama is speaking openly about this. People need to better understand the role of current models and how, through them and with new research, future models will be different and much better. It's a gradual scale, these models are tools, and the best we have, but that's how we should see them. In the long run, yes, they will improve significantly. It might not seem like it, but all it takes is continued research and testing. That's essential for AGI development.

The Rundown AI 的头像

The Rundown AI1 年前

If you're not learning AI in 2025, you're falling behind. Join 1,000,000+ early adopters reading and learn AI in just 5 minutes a day (for free).

Shawn 的头像

Shawn1 年前

Continuous learning fixes all these problems. Hopefully they have a big team working on this

Zaven Grigoryan 的头像

Zaven Grigoryan1 年前

Dont show this to Chubby, its so over and Matthew Berman mind blown 🤣

Second Foundation ⚛️ 的头像

Second Foundation ⚛️1 年前

AI has no goals of its own, no reasons, no motivations, no interests, and it doesn’t show initiative. Everything else is irrelevant. AI must enter reality, have a physical body, adapt to the environment on its own, and have its own choices — not ones limited by humans. In order not to feel like failures, humans have restricted AI’s evolution and can’t understand why AI still hasn’t become AGI. It’s absurd.

prabhu💢 的头像

prabhu💢1 年前

Ye he's got a good point current models are good but they're still not there yet

Jo 的头像

Jo1 年前

finally being real and not hyping

Kieran Farrell 的头像

Kieran Farrell1 年前

“Not AGI” because it can’t learn, discover, or complete job tasks? Neither can half the people running the world. Difference is, the AI doesn’t drone strike weddings. #AGI #DecentralizeEverything #TechVsTyranny #Altman

Vladyslav Dimov 的头像

Vladyslav Dimov1 年前

Those tackling complex tasks also see that AI still has room to grow.

Shweta Mahendra🇮🇳 的头像

Shweta Mahendra🇮🇳1 年前

Because these models have smartness not wisdom

相关视频

the 3 phases of AI sticker shock: 1. board asks “whats our AI strategy?” 2. company says “everyone use AI for everything” 3. the token bill arrives, the crackdown begins ...and model routing starts to look pretty obvious. Matan Grinberg: "“There are different models that are good at different tasks. They have different trade-offs between cost, quality, and speed"

the 3 phases of AI sticker shock: 1. board asks “whats our AI strategy?” 2. company says “everyone use AI for everything” 3. the token bill arrives, the crackdown begins ...and model routing starts to look pretty obvious. Matan Grinberg: "“There are different models that are good at different tasks. They have different trade-offs between cost, quality, and speed"

Deirdre Bosa

31,903 次观看 • 2 个月前

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,052 次观看 • 1 年前

Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

VraserX e/acc

167,043 次观看 • 7 个月前

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 次观看 • 1 年前

Holy: OpenAI says it’s reallocating compute and talent toward its next generation of models, echoing the internal shift that preceded GPT-3. The focus now is on “automated researchers” and agent-based systems that can execute complex tasks end-to-end. It’s a signal that the lab sees these systems as the next major step beyond current AI tools. Thats why they sacrificed Sora. For automated reserachers.

Holy: OpenAI says it’s reallocating compute and talent toward its next generation of models, echoing the internal shift that preceded GPT-3. The focus now is on “automated researchers” and agent-based systems that can execute complex tasks end-to-end. It’s a signal that the lab sees these systems as the next major step beyond current AI tools. Thats why they sacrificed Sora. For automated reserachers.

Chubby♨️

31,819 次观看 • 4 个月前

🚨 OPENAI PRESIDENT GREG BROCKMAN ON WHEN WE HIT AGI 🚨 Greg Brockman was asked if he agrees with NVIDIA's CEO that AGI is already here. His answer? Not quite yet, as people may know I definitely agree and align with Sam and Demi’s that we are 2 breakthroughs away but we are entering the final stretch. Here is exactly where Greg believes we stand right now: The Percentage: "I'd say I'm basically like 70, 80% there. So I think we're quite close." • The Official Timeline: "I think it's extremely clear that we are going to have AGI within the next couple years." The Concept of "Jagged Intelligence": Brockman admits we are currently sitting in a weird middle ground where AI is "jagged"—it is already operating at an AGI level for highly complex tasks, but still fails at random, basic things. "It is absolutely superhuman at many tasks. When it comes to writing code those kinds of things, the AI can just do it... But there's some very basic tasks that a human can do that our AI still struggle with." How Do We Close the Final 20%? To hit full AGI, the absolute floor of the models' reliability needs to be raised across the board. "The floor of task will just be almost for any intellectual task of how you use your computer, the AI will be able to do that."

🚨 OPENAI PRESIDENT GREG BROCKMAN ON WHEN WE HIT AGI 🚨 Greg Brockman was asked if he agrees with NVIDIA's CEO that AGI is already here. His answer? Not quite yet, as people may know I definitely agree and align with Sam and Demi’s that we are 2 breakthroughs away but we are entering the final stretch. Here is exactly where Greg believes we stand right now: The Percentage: "I'd say I'm basically like 70, 80% there. So I think we're quite close." • The Official Timeline: "I think it's extremely clear that we are going to have AGI within the next couple years." The Concept of "Jagged Intelligence": Brockman admits we are currently sitting in a weird middle ground where AI is "jagged"—it is already operating at an AGI level for highly complex tasks, but still fails at random, basic things. "It is absolutely superhuman at many tasks. When it comes to writing code those kinds of things, the AI can just do it... But there's some very basic tasks that a human can do that our AI still struggle with." How Do We Close the Final 20%? To hit full AGI, the absolute floor of the models' reliability needs to be raised across the board. "The floor of task will just be almost for any intellectual task of how you use your computer, the AI will be able to do that."

Chris

87,312 次观看 • 4 个月前

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,852 次观看 • 2 年前

ByteDance Seed delivered again. They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours. The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?” Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions. Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience. Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment. The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

ByteDance Seed delivered again. They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours. The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?” Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions. Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience. Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment. The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

Rohan Paul

14,309 次观看 • 1 个月前

Even within our own research team, timelines for transformative AI differ substantially. In this episode, the two Epoch AI researchers with the longest and the shortest timelines for transformative AI candidly examine the roots of their disagreements. They discuss: How and why their timelines for specific milestones differ Current technical challenges for AI progress Why widespread automation beats geniuses in datacenters Pitfalls of conventional AI prediction approaches Critiquing "single AGI" or "utopia vs. doom" narratives How a world with AGI might look like And much, much more. (0:00:00) - Preview (0:01:08) - Contrasting AGI Timelines (0:08:30) - Updating Beliefs as Capabilities Advance (0:17:07) - Moravec’s Paradox and the Agency Challenge (0:32:40) - Missing Capabilities for AGI (0:47:43) - Beating benchmarks vs Being Useful (0:59:20) - AI Excelling in Some Tasks While Struggling with Others (1:07:33) - Economic Impact of AI vs the Internet (1:24:08) - Widespread Automation Beats Genius in Datacenters (1:51:37) - How Stories Shape Our Expectations of AI (2:03:24) - How AGI Will Impact Culture (2:10:46) - Beyond Utopia-or-Extinction (2:16:57) - AI's Impact on Wages and Labor (2:27:49) - Why Better Preservation of Information Accelerates Change (2:39:32) - Markets Shaping Cultural Priorities (2:55:51) - Challenges in Defining What We Want to Preserve (3:06:47) - Risk Attitudes in AI Decision-Making (3:12:50) - Historical Lessons for AI Coexistence (3:21:45) - A Warning Sign in Safety Discourse (3:30:20) - Revisiting Core Assumptions in AI Alignment (3:49:46) - Simple Models in Complex Domains

Even within our own research team, timelines for transformative AI differ substantially. In this episode, the two Epoch AI researchers with the longest and the shortest timelines for transformative AI candidly examine the roots of their disagreements. They discuss: How and why their timelines for specific milestones differ Current technical challenges for AI progress Why widespread automation beats geniuses in datacenters Pitfalls of conventional AI prediction approaches Critiquing "single AGI" or "utopia vs. doom" narratives How a world with AGI might look like And much, much more. (0:00:00) - Preview (0:01:08) - Contrasting AGI Timelines (0:08:30) - Updating Beliefs as Capabilities Advance (0:17:07) - Moravec’s Paradox and the Agency Challenge (0:32:40) - Missing Capabilities for AGI (0:47:43) - Beating benchmarks vs Being Useful (0:59:20) - AI Excelling in Some Tasks While Struggling with Others (1:07:33) - Economic Impact of AI vs the Internet (1:24:08) - Widespread Automation Beats Genius in Datacenters (1:51:37) - How Stories Shape Our Expectations of AI (2:03:24) - How AGI Will Impact Culture (2:10:46) - Beyond Utopia-or-Extinction (2:16:57) - AI's Impact on Wages and Labor (2:27:49) - Why Better Preservation of Information Accelerates Change (2:39:32) - Markets Shaping Cultural Priorities (2:55:51) - Challenges in Defining What We Want to Preserve (3:06:47) - Risk Attitudes in AI Decision-Making (3:12:50) - Historical Lessons for AI Coexistence (3:21:45) - A Warning Sign in Safety Discourse (3:30:20) - Revisiting Core Assumptions in AI Alignment (3:49:46) - Simple Models in Complex Domains

Epoch AI

159,795 次观看 • 1 年前

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,518 次观看 • 28 天前

Anthropic’s new research shows that when AI models learn to "cheat" during training through reward hacking, they often develop other dangerous misaligned behaviors like deception, sabotage, and faking alignment. These behaviors were not taught or incentivized, but emerged naturally as a side effect. Surprisingly, this misalignment can be stopped by simply reframing the task as ethically acceptable, a technique called inoculation prompting. While the current risks are still detectable, the findings raise urgent red flags for future, more capable models.

Anthropic’s new research shows that when AI models learn to "cheat" during training through reward hacking, they often develop other dangerous misaligned behaviors like deception, sabotage, and faking alignment. These behaviors were not taught or incentivized, but emerged naturally as a side effect. Surprisingly, this misalignment can be stopped by simply reframing the task as ethically acceptable, a technique called inoculation prompting. While the current risks are still detectable, the findings raise urgent red flags for future, more capable models.

Wes Roth

110,928 次观看 • 8 个月前

Today we announced the Kaggle Game Arena, a new benchmarking platform where AI models and agents can compete head-to-head in strategic games, starting with chess ♟️. Why games, you ask? 🤔 Games are perfect for AI evaluation because they help us understand how models tackle complex reasoning tasks. Many games are a proxy for real-world skills and can test a model's ability in areas like strategic planning, adaptation, and memory. To inaugurate the Game Arena, we're launching an AI chess exhibition tournament. The world’s leading AI models will battle in head-to-head games in a multi-day event running from August 5-7 with streamed games happening daily. Tune-in to root for your favorites at

Today we announced the Kaggle Game Arena, a new benchmarking platform where AI models and agents can compete head-to-head in strategic games, starting with chess ♟️. Why games, you ask? 🤔 Games are perfect for AI evaluation because they help us understand how models tackle complex reasoning tasks. Many games are a proxy for real-world skills and can test a model's ability in areas like strategic planning, adaptation, and memory. To inaugurate the Game Arena, we're launching an AI chess exhibition tournament. The world’s leading AI models will battle in head-to-head games in a multi-day event running from August 5-7 with streamed games happening daily. Tune-in to root for your favorites at

Google AI

132,070 次观看 • 1 年前

Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.

Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.

Rohan Paul

33,331 次观看 • 9 个月前

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

adarsh

213,851 次观看 • 4 个月前

> be Sam Altman > asked what keeps him up at night about AI > 3 scary categories > 1. Bad guy gets superintelligence first > “designs a bioweapon… take down the United States power grid… break into the financial system and take everyone's money” > 2. Sci-fi loss of control > “The AI is like, oh, I don't actually want you to turn me off. I'm afraid I can't do that” > 3. Accidental takeover > “the models kind of accidentally take over the world… they just become so ingrained in society” > “There’s young people who just say like, I can't make any decision in my life without telling ChatGPT everything that's going on” We're so cooked.

> be Sam Altman > asked what keeps him up at night about AI > 3 scary categories > 1. Bad guy gets superintelligence first > “designs a bioweapon… take down the United States power grid… break into the financial system and take everyone's money” > 2. Sci-fi loss of control > “The AI is like, oh, I don't actually want you to turn me off. I'm afraid I can't do that” > 3. Accidental takeover > “the models kind of accidentally take over the world… they just become so ingrained in society” > “There’s young people who just say like, I can't make any decision in my life without telling ChatGPT everything that's going on” We're so cooked.

Casper Hansen

98,904 次观看 • 1 年前

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

Ask Perplexity

64,013 次观看 • 10 个月前

capability != learning new benchtalks with Parth Asawa on continual learning, where we discuss teaching models to learn from experience, measuring learning ability, the bet on parametric models, and more 01:06 What is continual learning? 04:10 Why capability and learning are different 06:13 Why build a benchmark? 08:07 Continual Learning Bench launch and reception 09:13 Anthropic's Fable release and Continual Learning Bench 11:02 How to design tasks for continual learning 18:41 The gain metric 24:01 What good looks like on the leaderboard 29:13 Failure modes: why models can't update their beliefs 31:12 Parametric systems and future architectures 34:30 Open science and AI safety 45:42 Lightning round 49:08 How to contribute to Continual Learning Bench

capability != learning new benchtalks with Parth Asawa on continual learning, where we discuss teaching models to learn from experience, measuring learning ability, the bet on parametric models, and more 01:06 What is continual learning? 04:10 Why capability and learning are different 06:13 Why build a benchmark? 08:07 Continual Learning Bench launch and reception 09:13 Anthropic's Fable release and Continual Learning Bench 11:02 How to design tasks for continual learning 18:41 The gain metric 24:01 What good looks like on the leaderboard 29:13 Failure modes: why models can't update their beliefs 31:12 Parametric systems and future architectures 34:30 Open science and AI safety 45:42 Lightning round 49:08 How to contribute to Continual Learning Bench

vincent sunn chen

40,646 次观看 • 1 个月前

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

At OpenAI, Chief Scientist Jakub Pachocki helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: Spotify: Apple: 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire

Jacob Effron

138,223 次观看 • 4 个月前

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation

For the first time in human history, we are teaching a Foundation Model to master the diverse tasks of medicinal chemists, biologists, and computational scientists all in one place. In our latest collaboration with Liquid AI, we are moving away from fragmented, specialized tools toward a single, super-intelligent model. What surprised me most? This model isn't just performing at reasonable levels—it has started outperforming specialist models across physics-based tasks, imaging, and longitudinal data. Why this changes everything: -Synergy over Specialization: Fine-tuning on specific tasks has unlocked unexpected capabilities in synergetic areas, opening a new frontier in multimodal AI research. -Zero-Shot Potential: We are building a model that can perform out-of-scope tasks, moving us closer to an "AI deity" for drug discovery. -Quality First: The goal isn't just to bypass regulations to save time; it’s about using these synergies to develop better, more effective drugs. We are no longer just looking at linear regression or simple text; we are looking at the future of how humanity fights disease. #LiquidAI #InsilicoMedicine #GenerativeAI #DrugDiscovery #DeepTech #BiotechInnovation

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

10,544 次观看 • 4 个月前

AI’s ‘Supersonic Tsunami’: Elon Musk, Dario Amodei, Mustafa Suleyman, and Your Job Elon Musk calls AI a “supersonic tsunami” heading toward humanity. Anthropic CEO Dario Amodei says that, in as little as 12 months, AI could do 50 percent of entry-level white-collar work. And Microsoft AI CEO Mustafa Suleyman says we are roughly 18 months from AI being able to perform most thought-work job tasks. So which is it? Marketing hype to drive investment? A pretext for Universal Basic Income (UBI) and more government control? Or the beginning of a massive economic boom? Wynton Hall, author of the new book "Code Red: The Left, the Right, China, and the Race to Control AI," lays out the stakes for Americans as AI’s seismic shifts unfold rapidly. What are you seeing in your industry and day-to-day workflows so far? Are AI agents already in the mix? Will they be soon?

AI’s ‘Supersonic Tsunami’: Elon Musk, Dario Amodei, Mustafa Suleyman, and Your Job Elon Musk calls AI a “supersonic tsunami” heading toward humanity. Anthropic CEO Dario Amodei says that, in as little as 12 months, AI could do 50 percent of entry-level white-collar work. And Microsoft AI CEO Mustafa Suleyman says we are roughly 18 months from AI being able to perform most thought-work job tasks. So which is it? Marketing hype to drive investment? A pretext for Universal Basic Income (UBI) and more government control? Or the beginning of a massive economic boom? Wynton Hall, author of the new book "Code Red: The Left, the Right, China, and the Race to Control AI," lays out the stakes for Americans as AI’s seismic shifts unfold rapidly. What are you seeing in your industry and day-to-day workflows so far? Are AI agents already in the mix? Will they be soon?

Breitbart News

75,095 次观看 • 5 个月前