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Most world models fall apart after a few seconds. Common failure modes include texture smearing, warped geometry, and scenes that no longer look real. LingBot-World 2.0 from Robbyant seems to hold 720p at 60 fps for a full hour of interaction. That’s impressive. Here is what makes that possible.

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I've been reading through the LingBot-World 2.0 paper today, and I think one detail deserves more attention than the hour-long demo itself. Most interactive world models can generate impressive moments, but keeping an explorable world coherent over long sessions is a much harder problem errors gradually accumulate. LingBot-World 2.0 tackles that challenge by upgrading both world prediction and interaction, sustaining hour-level generation at stable 720p and 60 fps while remaining responsive in real time. The paper argues this comes from its causal training strategy, rather than simply extending generation length. Robbyant

I've been reading through the LingBot-World 2.0 paper today, and I think one detail deserves more attention than the hour-long demo itself. Most interactive world models can generate impressive moments, but keeping an explorable world coherent over long sessions is a much harder problem errors gradually accumulate. LingBot-World 2.0 tackles that challenge by upgrading both world prediction and interaction, sustaining hour-level generation at stable 720p and 60 fps while remaining responsive in real time. The paper argues this comes from its causal training strategy, rather than simply extending generation length. Robbyant

FELIX

113,396 görüntüleme • 7 gün önce

Robbyant released LingBot-World 2.0 (Infinity), a new open-source World model capable of generating hour-long experiences. > No quality decay, stable 720p / 60 fps real-time output. > Users can interact with the world via Actions. > The world is dynamic, with built-in agents generating new events in real time. > Multiplayer is supported within the same world. Testing time 👀

Robbyant released LingBot-World 2.0 (Infinity), a new open-source World model capable of generating hour-long experiences. > No quality decay, stable 720p / 60 fps real-time output. > Users can interact with the world via Actions. > The world is dynamic, with built-in agents generating new events in real time. > Multiplayer is supported within the same world. Testing time 👀

🚨 AI News | TestingCatalog

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World models hold a lot of promise for robotics, but they're data hungry and often struggle with long horizons. We learn models from a few (< 10) human demos that enable a robot to plan in completely novel scenes! Our key idea is to model *symbols* not pixels 👇

World models hold a lot of promise for robotics, but they're data hungry and often struggle with long horizons. We learn models from a few (< 10) human demos that enable a robot to plan in completely novel scenes! Our key idea is to model symbols not pixels 👇

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I think what makes IVE popular with kids is that they are the epitome of a group that just makes you happy to look at them. And I think, as the world seems to get more chaotic, more and more adults need the pure uncomplicated happiness that is IVE.

I think what makes IVE popular with kids is that they are the epitome of a group that just makes you happy to look at them. And I think, as the world seems to get more chaotic, more and more adults need the pure uncomplicated happiness that is IVE.

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"The fact that one of the largest producers in the world is no longer part of that, really makes OPEC now a toothless tiger." Stephen Moore joins Katie to discuss the UAE's exit from the OPEC oil cartel - and breaks down what that means for energy prices here in the U.S.

"The fact that one of the largest producers in the world is no longer part of that, really makes OPEC now a toothless tiger." Stephen Moore joins Katie to discuss the UAE's exit from the OPEC oil cartel - and breaks down what that means for energy prices here in the U.S.

Katie Pavlich Tonight

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It’s officially…official. The Guinness World Record for most kickflips in one minute has been broken and verified. 38 kickflips. 60 seconds. $DEGEN. When a community comes together, this is what happens. Something that started as a meme coin bounty turned into a real-life, record-breaking moment. DEGEN isn’t just a token, it’s culture, it’s action, it’s the kind of energy that pushes boundaries and makes cool shit happen in the world. And this is just the beginning. Here’s to more moments like this. More records. More firsts. More proof that when the community rallies, anything is possible.

It’s officially…official. The Guinness World Record for most kickflips in one minute has been broken and verified. 38 kickflips. 60 seconds. $DEGEN. When a community comes together, this is what happens. Something that started as a meme coin bounty turned into a real-life, record-breaking moment. DEGEN isn’t just a token, it’s culture, it’s action, it’s the kind of energy that pushes boundaries and makes cool shit happen in the world. And this is just the beginning. Here’s to more moments like this. More records. More firsts. More proof that when the community rallies, anything is possible.

Degen 🎩

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It’s actually over. Seedance 2.0 is the most advanced video AI in the world. It can generate cinematic, Hollywood-level CGI scenes in minutes—something that used to take massive teams, months of work, and millions of dollars. What once cost a fortune now costs just a few dollars.

It’s actually over. Seedance 2.0 is the most advanced video AI in the world. It can generate cinematic, Hollywood-level CGI scenes in minutes—something that used to take massive teams, months of work, and millions of dollars. What once cost a fortune now costs just a few dollars.

nachos2d

20,877 görüntüleme • 4 ay önce

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

Rohan Paul

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Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

RoboPapers

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UK childcare is among the most expensive in the world and a growing number of mothers who pay for childcare say it no longer makes financial sense for them to work. Experts say that affordable childcare could help unlock greater economic growth

UK childcare is among the most expensive in the world and a growing number of mothers who pay for childcare say it no longer makes financial sense for them to work. Experts say that affordable childcare could help unlock greater economic growth

Reuters

56,573 görüntüleme • 3 yıl önce

Spatial understanding is important to moving around in complex environments and is a huge part of the challenge of generalizing to new scenes. Most world models, however, largely ignore this spatial dimension, focusing on 2D images. Not PointWorld, though. PointWorld is a 3D world model trained from real and simulated data which can perform a wide variety of manipulation tasks on a real robot, including grasping or handling articulated objects, all without any additional fine tuning. Wenlong Huang joins us to tell us more about what makes this work and how it’s different from other world models. Watch Episode #83 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

Spatial understanding is important to moving around in complex environments and is a huge part of the challenge of generalizing to new scenes. Most world models, however, largely ignore this spatial dimension, focusing on 2D images. Not PointWorld, though. PointWorld is a 3D world model trained from real and simulated data which can perform a wide variety of manipulation tasks on a real robot, including grasping or handling articulated objects, all without any additional fine tuning. Wenlong Huang joins us to tell us more about what makes this work and how it’s different from other world models. Watch Episode #83 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

16,197 görüntüleme • 1 ay önce

Most AI world models impress you for 20 seconds. The harder problem is making them stay coherent. That's what stood out to me in Robbyant's LingBot-World-Infinity. Instead of chasing another resolution headline, the paper focuses on reducing autoregressive drift the issue where small prediction errors compound until the world slowly falls apart. Their stress test is a single uninterrupted 60-minute rollout across 20 different scenarios with no visible quality decay. If you want to see the interactive system yourself, it's live on Reactor: 🎮 I also like that they don't pretend it's perfect. The paper openly says it doesn't have true long-term memory. Leave an area and come back later, and it regenerates something plausible instead of recalling the exact same place. That's a refreshingly honest limitation. Open weights make it even more interesting to dig into. Robbyant For anyone who wants to explore the research: 💻 GitHub: 🤗 Model Weights:

Most AI world models impress you for 20 seconds. The harder problem is making them stay coherent. That's what stood out to me in Robbyant's LingBot-World-Infinity. Instead of chasing another resolution headline, the paper focuses on reducing autoregressive drift the issue where small prediction errors compound until the world slowly falls apart. Their stress test is a single uninterrupted 60-minute rollout across 20 different scenarios with no visible quality decay. If you want to see the interactive system yourself, it's live on Reactor: 🎮 I also like that they don't pretend it's perfect. The paper openly says it doesn't have true long-term memory. Leave an area and come back later, and it regenerates something plausible instead of recalling the exact same place. That's a refreshingly honest limitation. Open weights make it even more interesting to dig into. Robbyant For anyone who wants to explore the research: 💻 GitHub: 🤗 Model Weights:

Shruti Codes

15,066 görüntüleme • 7 gün önce

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

NVIDIA Robotics

21,506 görüntüleme • 11 gün önce

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

AVALON

61,874 görüntüleme • 5 ay önce

"The promise of liberty and prosperity that makes America a light to the world—that is what that flag represents." UNC frat brothers accept the 'Award for Courage' at the Fox Nation #PatriotAwards after they defended the American flag against a mob of anti-Israel protesters earlier this year.

"The promise of liberty and prosperity that makes America a light to the world—that is what that flag represents." UNC frat brothers accept the 'Award for Courage' at the Fox Nation #PatriotAwards after they defended the American flag against a mob of anti-Israel protesters earlier this year.

Fox News

101,542 görüntüleme • 1 yıl önce

#RashmikaAndRu are finallyyyy here! A little piece of my heart, out in the world to talk about things that truly matter to me. 🥰❤ Kindness that feels intentional, Softness that feels safe, The kind of warmth that makes your heart go, “This. This is what I want.” This space is for gentle reminders, quiet joys, and stories that hold you when the world feels too loud. And I can’t wait to build it with TTT darlings. 💛

#RashmikaAndRu are finallyyyy here! A little piece of my heart, out in the world to talk about things that truly matter to me. 🥰❤ Kindness that feels intentional, Softness that feels safe, The kind of warmth that makes your heart go, “This. This is what I want.” This space is for gentle reminders, quiet joys, and stories that hold you when the world feels too loud. And I can’t wait to build it with TTT darlings. 💛

Rashmika Mandanna

84,648 görüntüleme • 1 yıl önce

Most video-action robot models are a content-creation video generator with an action module attached. LingBot-VA 2.0 from Robbyant, a video-action foundation model, throws that starting point out and trains the whole stack natively for control. And it runs closed-loop at a peak 225 Hz. It's so important because A robot cannot move responsively when its controller pauses to imagine the next few frames. LingBot-VA 2.0 predicts during execution, then corrects using each real observation. And it carries only about 13B video parameters while activating roughly 1.9B per token. Bigger robot models usually mean slower reactions, creating a direct conflict between intelligence and control. LingBot-VA 2.0 is trained from scratch for robot control rather than adapted from a video generator built for content creation. Robbyant, an embodied AI company under Ant Group, built it to learn how scenes change under actions, predict what should happen next, and turn those predictions into real-time robot movements. Most video-action systems inherit a tokenizer and video backbone trained mainly to reproduce visual appearance. LingBot-VA 2.0 rebuilds both parts around physical control. Its semantic visual-action tokenizer maps observations toward features from a frozen vision foundation model and learns compact latent actions from frame-to-frame changes using self-supervised inverse and forward dynamics. Unlabeled web video can therefore carry action-relevant training signals without robot action labels. The policy is causal from the start, so every prediction can use only past observations. Its sparse Mixture-of-Experts video backbone has about 13B total parameters, while about 1.9B are active per token, keeping the compute lower during each step. A high-level vision-language planner breaks long tasks into smaller instructions, while the low-level video-action policy handles continuous movement. Foresight Reasoning predicts future visual states while the robot is already acting, then replaces imagined states with every new real observation. Combined with few-step distillation and systems acceleration, the paper reports a peak asynchronous execution frequency of 225 Hz. The model adapts from 10–15 demonstrations, transfers across robot embodiments, and handles some new tasks zero-shot. In the paper’s own evaluations, it reaches 93.6 average on RoboTwin 2.0 and reports stronger real-world results than LingBot-VA and π0.5 across the tested tasks. 🧵 1.

Most video-action robot models are a content-creation video generator with an action module attached. LingBot-VA 2.0 from Robbyant, a video-action foundation model, throws that starting point out and trains the whole stack natively for control. And it runs closed-loop at a peak 225 Hz. It's so important because A robot cannot move responsively when its controller pauses to imagine the next few frames. LingBot-VA 2.0 predicts during execution, then corrects using each real observation. And it carries only about 13B video parameters while activating roughly 1.9B per token. Bigger robot models usually mean slower reactions, creating a direct conflict between intelligence and control. LingBot-VA 2.0 is trained from scratch for robot control rather than adapted from a video generator built for content creation. Robbyant, an embodied AI company under Ant Group, built it to learn how scenes change under actions, predict what should happen next, and turn those predictions into real-time robot movements. Most video-action systems inherit a tokenizer and video backbone trained mainly to reproduce visual appearance. LingBot-VA 2.0 rebuilds both parts around physical control. Its semantic visual-action tokenizer maps observations toward features from a frozen vision foundation model and learns compact latent actions from frame-to-frame changes using self-supervised inverse and forward dynamics. Unlabeled web video can therefore carry action-relevant training signals without robot action labels. The policy is causal from the start, so every prediction can use only past observations. Its sparse Mixture-of-Experts video backbone has about 13B total parameters, while about 1.9B are active per token, keeping the compute lower during each step. A high-level vision-language planner breaks long tasks into smaller instructions, while the low-level video-action policy handles continuous movement. Foresight Reasoning predicts future visual states while the robot is already acting, then replaces imagined states with every new real observation. Combined with few-step distillation and systems acceleration, the paper reports a peak asynchronous execution frequency of 225 Hz. The model adapts from 10–15 demonstrations, transfers across robot embodiments, and handles some new tasks zero-shot. In the paper’s own evaluations, it reaches 93.6 average on RoboTwin 2.0 and reports stronger real-world results than LingBot-VA and π0.5 across the tested tasks. 🧵 1.

Rohan Paul

10,996 görüntüleme • 4 gün önce

Elon Musk: Any country that allows unfettered mass immigration will cease to exist. “The mass immigration is insane and will lead to the destruction of any country that allows unfettered mass immigration. That country will simply cease to exist. At the end of the day, it is a numbers game, a numbers situation. If there are 8 billion people in the world, and let's say you're a country of 50 million, 60 million, for a medium-sized country, but even for a country like the United States, which is 350 million, given that there are 8 billion people in the world, it only takes a few percent of the rest of the world to move to the country to where it is no longer that country. A country is not its geography. A country is its people. This is a fundamental concept that is truly obvious. If you took the people of Italy and you teleported the people of Italy to, say, some part of the United States, it would still be Italy. But if you teleported a bunch of people from some other part of the world to Italy, where the Italian people are no longer because they've been teleported to America, then the region, the geographic region, would no longer be Italy. It would be that other country.” From: Interview with Matteo Salvini, April 5, 2025

Elon Musk: Any country that allows unfettered mass immigration will cease to exist. “The mass immigration is insane and will lead to the destruction of any country that allows unfettered mass immigration. That country will simply cease to exist. At the end of the day, it is a numbers game, a numbers situation. If there are 8 billion people in the world, and let's say you're a country of 50 million, 60 million, for a medium-sized country, but even for a country like the United States, which is 350 million, given that there are 8 billion people in the world, it only takes a few percent of the rest of the world to move to the country to where it is no longer that country. A country is not its geography. A country is its people. This is a fundamental concept that is truly obvious. If you took the people of Italy and you teleported the people of Italy to, say, some part of the United States, it would still be Italy. But if you teleported a bunch of people from some other part of the world to Italy, where the Italian people are no longer because they've been teleported to America, then the region, the geographic region, would no longer be Italy. It would be that other country.” From: Interview with Matteo Salvini, April 5, 2025

ELON CLIPS

27,107 görüntüleme • 1 yıl önce

#AusGP 🇦🇺 | Lewis Hamilton takes a moment to speak about the African continent: “I’m half African. I’ve got roots from a few different places there. Benin, Senegal, Nigeria.” “It’s something I’m really proud of, that part of the world. I think it is the most beautiful part of the world, and I don’t like that the rest of the world owns so much of it and takes so much from it and no one speaks about it.” “I’m really looking forwards, hoping that the people that are running those different countries all unite and come together and take Africa back. That’s what I want to see. Take it back from the French, take it back from the Spanish, from the Portuguese and the British.” “It’s so so important for the future, that continent. They have all the resources to be the greatest and most powerful place in the world and that’s probably why they’re being controlled the way they are.”

#AusGP 🇦🇺 | Lewis Hamilton takes a moment to speak about the African continent: “I’m half African. I’ve got roots from a few different places there. Benin, Senegal, Nigeria.” “It’s something I’m really proud of, that part of the world. I think it is the most beautiful part of the world, and I don’t like that the rest of the world owns so much of it and takes so much from it and no one speaks about it.” “I’m really looking forwards, hoping that the people that are running those different countries all unite and come together and take Africa back. That’s what I want to see. Take it back from the French, take it back from the Spanish, from the Portuguese and the British.” “It’s so so important for the future, that continent. They have all the resources to be the greatest and most powerful place in the world and that’s probably why they’re being controlled the way they are.”

deni

115,789 görüntüleme • 4 ay önce

Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?

Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?

VraserX e/acc

23,784 görüntüleme • 6 ay önce