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There are so many models that it can get confusing so today we launched an auto-router - you get the best answers from any of the top models. At the same time, we have AI experts and model enthusiasts who will enjoy access to 10 new models we just... show more

Richard Socher

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24,731 Aufrufe • vor 1 Jahr •via X (Twitter)

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We just launched newsletters on acquire.com! If you’re running a profitable newsletter, you can now list it and get acquired. If you’re a buyer, it’s one of the best models out there. Recurring revenue. Loyal audience. Simple ops. We're just getting started!

We just launched newsletters on acquire.com! If you’re running a profitable newsletter, you can now list it and get acquired. If you’re a buyer, it’s one of the best models out there. Recurring revenue. Loyal audience. Simple ops. We're just getting started!

Andrew Gazdecki

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Still on Higgsfield? See how unlimited access changes the game. Why pay $30–50/month for separate AI tools… when GlamAI just launched: 🚀 UNLIMITED ACCESS FOR EVERYONE — 70% OFF (Limited Time) Up to 10x cheaper than comparable AI stacks. Here’s what you get: ✅ Unlimited access ✅ All models included ✅ No pay-per-tool ✅ No hidden upgrades ✅ Full feature access Inside the platform: • Nano Banana Pro 2K • Seedream 4.5 4K • Kling 3.0 • + unrestricted model access

Still on Higgsfield? See how unlimited access changes the game. Why pay $30–50/month for separate AI tools… when GlamAI just launched: 🚀 UNLIMITED ACCESS FOR EVERYONE — 70% OFF (Limited Time) Up to 10x cheaper than comparable AI stacks. Here’s what you get: ✅ Unlimited access ✅ All models included ✅ No pay-per-tool ✅ No hidden upgrades ✅ Full feature access Inside the platform: • Nano Banana Pro 2K • Seedream 4.5 4K • Kling 3.0 • + unrestricted model access

Emir.

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I topped up $5 on an API aggregator ToAPIs Then I found out GPT Image 2 costs only around $0.015 per image. If you do a lot of testing or batch-generate commercial AI images, that difference adds up fast. I think I just found the secret to generating more, testing more, and spending less. And it’s not just one model. With the same key, you can access 50+ models for image, video, and text, including GPT Image 2, Gemini Omni, Seedance 2.0, Kling AI 3.0, grok-video-1.5-preview, and more. Some models are priced up to 80% lower than official platforms. Just top up and test what you need: Made on ToAPIs with GPT Image 2 + Seedance 2.0

I topped up $5 on an API aggregator ToAPIs Then I found out GPT Image 2 costs only around $0.015 per image. If you do a lot of testing or batch-generate commercial AI images, that difference adds up fast. I think I just found the secret to generating more, testing more, and spending less. And it’s not just one model. With the same key, you can access 50+ models for image, video, and text, including GPT Image 2, Gemini Omni, Seedance 2.0, Kling AI 3.0, grok-video-1.5-preview, and more. Some models are priced up to 80% lower than official platforms. Just top up and test what you need: Made on ToAPIs with GPT Image 2 + Seedance 2.0

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We released physics-intern: a simple harness for science problems! It gets models like Gemini 3.1 Pro to go from 17.7 -> 31.4, thus beating GPT 5.5 Pro. The physics-intern harness can wrap any model and via dedicated subagent boost the performance of the vanilla reasoning models. While I think more and more of these harness capability gains will be absorbed into the models (like prompting tricks disappeared over time) there is a lot to be gained right now by building good scaffolds for those models and integrating tools well. Interestingly, the exception we found that GPT 5.5 Pro actually didn't benefit from the physics-intern harness! Read more about it here: PS: I think the Harness[Model] notation is kind of nice.

We released physics-intern: a simple harness for science problems! It gets models like Gemini 3.1 Pro to go from 17.7 -> 31.4, thus beating GPT 5.5 Pro. The physics-intern harness can wrap any model and via dedicated subagent boost the performance of the vanilla reasoning models. While I think more and more of these harness capability gains will be absorbed into the models (like prompting tricks disappeared over time) there is a lot to be gained right now by building good scaffolds for those models and integrating tools well. Interestingly, the exception we found that GPT 5.5 Pro actually didn't benefit from the physics-intern harness! Read more about it here: PS: I think the Harness[Model] notation is kind of nice.

Leandro von Werra

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🌟 New mini feature today on Photo AI It now auto recognizes your model's attributes when you upload photos of yourself Like gender, age, ethnicity, eye color, etc This uses xAI's Grok because it's the least restricted and other models don't let you do any identity type stuff due to safety (???) This saves new users a few more seconds when creating new models: training a model takes only about 30 seconds now, and then it also auto generates free sample pics for you in about 5-10 seconds So in under a minute of signing up and uploading a few selfies you got your first AI photo shoot pics I learnt the faster I get new users to the classic "WOW" moment the happier they get and the less churn I get 😊

🌟 New mini feature today on Photo AI It now auto recognizes your model's attributes when you upload photos of yourself Like gender, age, ethnicity, eye color, etc This uses xAI's Grok because it's the least restricted and other models don't let you do any identity type stuff due to safety (???) This saves new users a few more seconds when creating new models: training a model takes only about 30 seconds now, and then it also auto generates free sample pics for you in about 5-10 seconds So in under a minute of signing up and uploading a few selfies you got your first AI photo shoot pics I learnt the faster I get new users to the classic "WOW" moment the happier they get and the less churn I get 😊

@levelsio

313,706 Aufrufe • vor 1 Jahr

🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute

🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute

@levelsio

485,431 Aufrufe • vor 1 Jahr

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

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NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

elvis

14,638 Aufrufe • vor 22 Stunden

I'm sure you've all noticed the 'AI is slowing down' news stories every few weeks for multiple years now - so I've pulled a tracker together to see who and when wrote these stories. There is quite a range, some are just outright wrong, others point to a reasonable limitation at the time but missing the bigger arc of progress. All of these stories were appearing as we were getting reasoning models, open source models, increasing competition from more players and skyrocketing revenue for the labs. Link to the tracker in the comments

I'm sure you've all noticed the 'AI is slowing down' news stories every few weeks for multiple years now - so I've pulled a tracker together to see who and when wrote these stories. There is quite a range, some are just outright wrong, others point to a reasonable limitation at the time but missing the bigger arc of progress. All of these stories were appearing as we were getting reasoning models, open source models, increasing competition from more players and skyrocketing revenue for the labs. Link to the tracker in the comments

Peter Gostev

37,443 Aufrufe • vor 10 Monaten

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.

Adaptyv Bio

11,484 Aufrufe • vor 10 Monaten

Robot Utility Models (RUMs) enable basic tasks – door opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇

Robot Utility Models (RUMs) enable basic tasks – door opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well zero-shot in unseen and new environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇

Mahi Shafiullah 🏠🤖

89,535 Aufrufe • vor 1 Jahr

Why import when we can make it better? One spring costs ₹10. We will use 50,000 every month → ₹60L/yr That’s $66,000 saved in foreign exchange exits from just ONE part. It also means: ✅ Jobs at factories ✅ Jobs at steel & plating plants ✅ Jobs at wire-making plants Millions of small parts like these can be made in India. Stop importing. Start making here. The benefits to our nation are incalcuable Now is the time. Kudos to Vishakh Ranotra for leading the way.

Why import when we can make it better? One spring costs ₹10. We will use 50,000 every month → ₹60L/yr That’s $66,000 saved in foreign exchange exits from just ONE part. It also means: ✅ Jobs at factories ✅ Jobs at steel & plating plants ✅ Jobs at wire-making plants Millions of small parts like these can be made in India. Stop importing. Start making here. The benefits to our nation are incalcuable Now is the time. Kudos to Vishakh Ranotra for leading the way.

Prakash Dadlani

75,040 Aufrufe • vor 10 Monaten

I think it's interesting that we're starting to see different types of specialists emerge in the field of Generative AI technology. We are seeing a new generation of world-class storytellers. Some are immaculate at vibe coding workflows and building infrastructures. Some are incredible at AI visual effects and AI cinematography. And some are just maestros at composition and world-building. And of course, many of us dip into all facets. We are witnessing the emergence of new creative and technical roles that didn't exist yesterday, and that's very exciting. And now we are starting to see the benefits of AI as an additive to existing workflows. I hope to work with many of you for the upcoming productions we have in the works at Promise.

I think it's interesting that we're starting to see different types of specialists emerge in the field of Generative AI technology. We are seeing a new generation of world-class storytellers. Some are immaculate at vibe coding workflows and building infrastructures. Some are incredible at AI visual effects and AI cinematography. And some are just maestros at composition and world-building. And of course, many of us dip into all facets. We are witnessing the emergence of new creative and technical roles that didn't exist yesterday, and that's very exciting. And now we are starting to see the benefits of AI as an additive to existing workflows. I hope to work with many of you for the upcoming productions we have in the works at Promise.

Dave Clark

13,168 Aufrufe • vor 1 Jahr

We're just going to keep growing and growing and there is nothing you can do to stop this because we have already successfully on boarded the creator first. We harnessed the power of AI by combining it with a precious metal, gold. Every single english speaker in the world knows abcdefg. By the way this is OG, bing chilling. (Lead by John Xina of China) 89S7oVB4hui8ceJqhHreWB7fcxdthfvoB7z2pfJtpump The longer you hold the more cashback you get because this is a cashback coin, but we have an auto burn mechanism. Yes, it's true, and we were first to do that also.

We're just going to keep growing and growing and there is nothing you can do to stop this because we have already successfully on boarded the creator first. We harnessed the power of AI by combining it with a precious metal, gold. Every single english speaker in the world knows abcdefg. By the way this is OG, bing chilling. (Lead by John Xina of China) 89S7oVB4hui8ceJqhHreWB7fcxdthfvoB7z2pfJtpump The longer you hold the more cashback you get because this is a cashback coin, but we have an auto burn mechanism. Yes, it's true, and we were first to do that also.

FIRSTviralonboardOGcreatorAIcoin

14,852 Aufrufe • vor 3 Monaten

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 Aufrufe • vor 1 Jahr

Today, we launched a brand-new intelligent Search box. Here's what that means: An upgrade to the Search experience with our most advanced Gemini 3.5 models, bringing with them our latest agentic capabilities You can ask across modalities (text, images, files, and videos) and Search can reason across them all We're combining AI Overviews and AI Mode into one, seamless AI Search experience. So you can ask follow-up questions, build context, and received even more tailored and personalized responses This new AI Search experience is live today across desktop and mobile, worldwide.

Google AI

44,591 Aufrufe • vor 2 Monaten

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

111,631 Aufrufe • vor 4 Tagen