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"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

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42,689 görüntüleme • 1 yıl önce •via X (Twitter)

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AstroZeus ⚡/acc profil fotoğrafı

AstroZeus ⚡/acc1 yıl önce

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 profil fotoğrafı

The Rundown AI1 yıl önce

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 profil fotoğrafı

Shawn1 yıl önce

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

Zaven Grigoryan profil fotoğrafı

Zaven Grigoryan1 yıl önce

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

Second Foundation ⚛️ profil fotoğrafı

Second Foundation ⚛️1 yıl önce

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💢 profil fotoğrafı

prabhu💢1 yıl önce

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

Jo profil fotoğrafı

Jo1 yıl önce

finally being real and not hyping

Kieran Farrell profil fotoğrafı

Kieran Farrell1 yıl önce

“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 profil fotoğrafı

Vladyslav Dimov1 yıl önce

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

Shweta Mahendra🇮🇳 profil fotoğrafı

Shweta Mahendra🇮🇳1 yıl önce

Because these models have smartness not wisdom

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VraserX e/acc

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🚨 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."

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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

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Epoch AI

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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

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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

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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

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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

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> 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.

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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 görüntüleme • 9 ay önce

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

39,598 görüntüleme • 22 gün önce

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

138,154 görüntüleme • 3 ay önce

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 görüntüleme • 3 ay önce

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

75,030 görüntüleme • 4 ay önce

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Marktechpost AI Dev News ⚡

31,551 görüntüleme • 7 ay önce

"Attention is All You Need" co‑author and NEAR Protocol founder Ilia Polosukhin reveals why centralized AI is doomed and the radical blueprint for user‑owned models. The next wave could erase your job or hand you god‑mode. In this podcast we're exploring how the cybernetic world is coming to life and what core components are needed for it. 1:47 Ilia’s journey: AI → blockchain → AI 3:58 How transformers were born at Google 9:50 Scaling laws 11:58 RL loops, new architectures, hardware limits 18:00 Creating NEAR to fix crypto‑payments for AI crowdsourcing 23:25 User‑owned AI: why decentralize models 29:46 Confidential computing & verifiable decentralized training 33:33 Agent era: AITP and multi‑agent negotiation 44:24 Enterprise adoption, SaaS disruption, economic shake‑up 52:54 Security, formal verification, AI courts 1:04:07 Jobs, status games, future of work 1:14:00 Post-AGI

"Attention is All You Need" co‑author and NEAR Protocol founder Ilia Polosukhin reveals why centralized AI is doomed and the radical blueprint for user‑owned models. The next wave could erase your job or hand you god‑mode. In this podcast we're exploring how the cybernetic world is coming to life and what core components are needed for it. 1:47 Ilia’s journey: AI → blockchain → AI 3:58 How transformers were born at Google 9:50 Scaling laws 11:58 RL loops, new architectures, hardware limits 18:00 Creating NEAR to fix crypto‑payments for AI crowdsourcing 23:25 User‑owned AI: why decentralize models 29:46 Confidential computing & verifiable decentralized training 33:33 Agent era: AITP and multi‑agent negotiation 44:24 Enterprise adoption, SaaS disruption, economic shake‑up 52:54 Security, formal verification, AI courts 1:04:07 Jobs, status games, future of work 1:14:00 Post-AGI

cyber•Fund

35,361 görüntüleme • 1 yıl önce