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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...

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31,819 görüntüleme • 6 ay önce •via X (Twitter)

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Sam Altman says OpenAI has reached another GPT-3-style moment Something important is working so well that other strong projects have to give way That is why OpenAI is focusing its compute on next-generation models and the automated researchers and companies they can power
1:09

Sam Altman says OpenAI has reached another GPT-3-style moment Something important is working so well that other strong projects have to give way That is why OpenAI is focusing its compute on next-generation models and the automated researchers and companies they can power

Haider.

59,715 görüntüleme • 6 ay önce

🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."
1:13

🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."

Chris

243,817 görüntüleme • 6 ay önce

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.
0:57

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,897 görüntüleme • 10 ay önce

Bill Gates says scaling AI systems will work for two more iterations and after that the next big frontier is meta-cognition where AI can reason about its tasks
1:56

Bill Gates says scaling AI systems will work for two more iterations and after that the next big frontier is meta-cognition where AI can reason about its tasks

Tsarathustra

160,424 görüntüleme • 2 yıl önce

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"
2:07

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"

Haider.

17,743 görüntüleme • 7 ay önce

Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.
0:31

Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.

Humanoids daily

1,444,029 görüntüleme • 11 ay önce

Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"
1:25

Jensen Huang says the industry is shifting from generative AI to agentic AI The next major step is fusing public cloud frontier models with customized open-source systems running on enterprise servers But the end goal is physical: "moving intelligence from the cloud into industrial AI, factories and robotics"

Haider.

49,466 görüntüleme • 9 ay önce

DeepMind's Mostafa Dehghani says recursive self-improvement (RSI) isn't sci-fi anymore Almost every lab now uses previous-generation models to build the next one. It's not fully automated yet "what's missing is long-horizon planning and full automation" Once that loop closes, progress is mostly limited by compute, not humans
1:58

DeepMind's Mostafa Dehghani says recursive self-improvement (RSI) isn't sci-fi anymore Almost every lab now uses previous-generation models to build the next one. It's not fully automated yet "what's missing is long-horizon planning and full automation" Once that loop closes, progress is mostly limited by compute, not humans

Haider.

18,408 görüntüleme • 6 ay önce

Introducing GPT-Realtime-2 in the API: our most intelligent voice model yet, bringing GPT-5-class reasoning to voice agents. Voice agents are now real-time collaborators that can listen, reason, and solve complex problems as conversations unfold. Now available in the API alongside streaming models GPT-Realtime-Translate and GPT-Realtime-Whisper — a new set of audio capabilities for the next generation of voice interfaces.
4:03

Introducing GPT-Realtime-2 in the API: our most intelligent voice model yet, bringing GPT-5-class reasoning to voice agents. Voice agents are now real-time collaborators that can listen, reason, and solve complex problems as conversations unfold. Now available in the API alongside streaming models GPT-Realtime-Translate and GPT-Realtime-Whisper — a new set of audio capabilities for the next generation of voice interfaces.

OpenAI

3,668,283 görüntüleme • 5 ay önce

AI video started with a simple promise: Type a prompt. Get a video. Now the interesting problem is what happens after that. A film isn’t one impressive clip. It’s dozens of connected decisions, actions, characters, camera movements, and consequences. So the next phase of AI video may be less about making prettier clips and more about building systems that can understand and control complex scenes. Rhapsody 1.0 ( Induce AI ) is an interesting step in that direction, with reasoning, story-state tracking, and verification built around the generation process. The future of AI filmmaking probably isn’t just better generation. It’s better control. You must give it a try: Mahesh KMB
0:20

AI video started with a simple promise: Type a prompt. Get a video. Now the interesting problem is what happens after that. A film isn’t one impressive clip. It’s dozens of connected decisions, actions, characters, camera movements, and consequences. So the next phase of AI video may be less about making prettier clips and more about building systems that can understand and control complex scenes. Rhapsody 1.0 ( Induce AI ) is an interesting step in that direction, with reasoning, story-state tracking, and verification built around the generation process. The future of AI filmmaking probably isn’t just better generation. It’s better control. You must give it a try: Mahesh KMB

Charles Roman

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It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!
1:46

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

elvis

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Introducing General World Models. We believe the next major advancement in AI will come from systems that understand the visual world and its dynamics, which is why we’re starting a new long-term research effort around general world models. Learn more:
3:37

Introducing General World Models. We believe the next major advancement in AI will come from systems that understand the visual world and its dynamics, which is why we’re starting a new long-term research effort around general world models. Learn more:

Runway

714,527 görüntüleme • 2 yıl önce

New course in collaboration with CrewAI, and taught by its Co-Founder and CEO, João Moura! In "Design, Develop, and Deploy Multi-Agent Systems" you'll go deep into building teams of AI agents that collaborate to handle complex, end-to-end workflows. You’ll design systems that plan, reason, and coordinate, with tools, memory, and guardrails that make them reliable and production-ready. Learn more and enroll now 👉 Featuring insights from Weaviate AI Database, Snyk, Exa, and AB InBev, whose work with CrewAI demonstrates how multi-agent systems are being applied in the field today.
0:56

New course in collaboration with CrewAI, and taught by its Co-Founder and CEO, João Moura! In "Design, Develop, and Deploy Multi-Agent Systems" you'll go deep into building teams of AI agents that collaborate to handle complex, end-to-end workflows. You’ll design systems that plan, reason, and coordinate, with tools, memory, and guardrails that make them reliable and production-ready. Learn more and enroll now 👉 Featuring insights from Weaviate AI Database, Snyk, Exa, and AB InBev, whose work with CrewAI demonstrates how multi-agent systems are being applied in the field today.

DeepLearning.AI

33,849 görüntüleme • 11 ay önce

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.
0:29

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

OptimAI Network

43,681 görüntüleme • 1 yıl önce

Frontier Notes from AGI House: Jerry Tworek Jerry Tworek on why “the era of evals is done.” 𝐅𝐮𝐥𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 ↓ Jerry Tworek spent seven years at OpenAI, where he worked on research that helped shape modern coding and reasoning models, including Codex and reinforcement learning for reasoning. He later served as VP of Research, and is now CEO of , building toward automated AI research. At AGI House, Jerry sat down with Rocky Yu for a technical conversation on Codex, HumanEval, Copilot, RL, tool use, test-time compute, and automated AI labs. We cover: › Why code became the first serious LLM vertical › What HumanEval taught the field about verifiable rewards › Why Jerry thinks “the era of evals is done” › How real-world deployment differs from static benchmarks › What GitHub Copilot taught OpenAI about product quality › How reinforcement learning shaped modern reasoning behaviors › Why tool use is “99% systems and 1% algorithms” › Whether test-time compute still has room to scale › What it takes to build automated AI research labs Watch the full interview below.
42:08

Frontier Notes from AGI House: Jerry Tworek Jerry Tworek on why “the era of evals is done.” 𝐅𝐮𝐥𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 ↓ Jerry Tworek spent seven years at OpenAI, where he worked on research that helped shape modern coding and reasoning models, including Codex and reinforcement learning for reasoning. He later served as VP of Research, and is now CEO of , building toward automated AI research. At AGI House, Jerry sat down with Rocky Yu for a technical conversation on Codex, HumanEval, Copilot, RL, tool use, test-time compute, and automated AI labs. We cover: › Why code became the first serious LLM vertical › What HumanEval taught the field about verifiable rewards › Why Jerry thinks “the era of evals is done” › How real-world deployment differs from static benchmarks › What GitHub Copilot taught OpenAI about product quality › How reinforcement learning shaped modern reasoning behaviors › Why tool use is “99% systems and 1% algorithms” › Whether test-time compute still has room to scale › What it takes to build automated AI research labs Watch the full interview below.

AGI House

12,497 görüntüleme • 2 ay önce

A military expert says ALPR systems like Flock and Axon are the same as military systems they used against foreign enemies. Pattern-of-life analysis was how they targeted and killed the enemy. And now they are pointing these killing systems at we the people. "I swore an oath to the Constitution of the United States. That oath did not end just because I took off my uniform. That is why I traveled to these chambers -because I have used a comparable system against American enemies and I know what it's capable of, and I can see the danger of pointing the system at the American people."
3:02

A military expert says ALPR systems like Flock and Axon are the same as military systems they used against foreign enemies. Pattern-of-life analysis was how they targeted and killed the enemy. And now they are pointing these killing systems at we the people. "I swore an oath to the Constitution of the United States. That oath did not end just because I took off my uniform. That is why I traveled to these chambers -because I have used a comparable system against American enemies and I know what it's capable of, and I can see the danger of pointing the system at the American people."

Orwell Day

40,822 görüntüleme • 13 gün önce

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment
1:32

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

Haider.

68,976 görüntüleme • 8 ay önce