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We trained a graph-native AI, then let it reason for days, forming a dynamic relational world model on its own - no pre-programming. Emergent hubs, small-world properties, modularity, & scale-free structures arose naturally. The model then exploited compositional reasoning & uncovered uncoded properties from deep synthesis: Materials with memory,... show more

Markus J. Buehler

20,650 subscribers

359,512 views • 1 year ago •via X (Twitter)

Science & Technology Education

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12 Comments

benferrum - e/jounce1 year ago

Hello sir, is there a paper/code available?

Markus J. Buehler1 year ago

Yes - here it is:

InterSystems Developers1 year ago

📺 Watch this #video to learn how #OpenSource vs. proprietary #AI models compare one to another. We explore how proprietary models dominate leaderboards like Chatbot Arena, but open-source models are quickly catching up. Plus, we discuss how businesses can start with proprietary models and transition to open-source alternatives over time 👇 Fit AI into your development strategy!

Leonidas Pitsoulis1 year ago

very interesting work, exciting to see graph reasoning as a paradigm

Markus J. Buehler1 year ago

Thanks @LPitsoulis !

Ryan Freel1 year ago

For the normies imagine you build a machine to organize your sock drawer, but instead of just sorting socks, it spends a few days thinking and suddenly figures out physics, biology, and self-repairing materials all on its own. You didn’t program it to do that. It just happened.

Ali Ihsan Nergiz1 year ago

Interesting project as always Markus, really loved the graphics around it. It kind of reminds evolutional process

Markus J. Buehler1 year ago

Thank you @aihsannergiz !

Brian Hershey1 year ago

This is next level proof of concept Markus, you kinda shifted my gears with this one 🙏

Markus J. Buehler1 year ago

Thanks 😀 we were blown away by this result also!

👾Moritz Rietschel1 year ago

what did you use grok for? the graphics?

Markus J. Buehler1 year ago

Yes, the 3D graphics - to visualize how the graphs formed over the thinking period evolve.

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22,812 views • 9 months ago

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258,178 views • 7 months ago

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367,108 views • 1 year ago

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109,369 views • 1 month ago

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AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

The Humanoid Hub

17,065 views • 5 months ago

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The most powerful AI model on earth was alive for 3 days then the US government turned it off. for everyone. on the planet. Anthropic launched Claude Fable 5 on june 9th. their best model ever. june 12th: the Commerce Department issued an export control order blocking foreign nationals Anthropic couldn’t filter foreign users in real time so they shut it down. worldwide. in hours. including for their own employees the irony: Anthropic begged for regulation. sat with the Pope. donated millions for AI rules… the government regulated them.. by killing their best product overnight the most powerful AI in the world has an off switch?? and the company doesn’t hold it first time in history a government force-killed a PUBLIC AI model it won’t be the last.

Mika

49,171 views • 1 month ago

researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.

researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.

Alex Veremeyenko

16,147 views • 8 days ago

In our research lab, we are building “real-time dreaming” - the ability to generate fully playable video worlds prompted from any text or image. Our real-time, action conditioned world model (currently running internally at 16fps at 832x480p) is trained on a combination of data, including proprietary Roblox 3D avatar/world interaction data. World models are different from multiplayer engines in that they store state and memory in video latents. Roblox is multiplayer, and we are actively researching optimal ways to simultaneously store state for thousands of players, and keep them in sync with their environment. Our world model leverages database technology which stores all user interactions on Roblox in a vector format that can be used to re-render video and interaction from any camera angle. We see several immediate uses for our Roblox world model. We will use it side-by-side text, image and video prompts as a way to launch auto-generation of immersive worlds. In Roblox Studio, a creator could walk around and use prompts to “paint” a world and then convert it into a 3D representation or direct to Roblox native as a way for many people to play simultaneously. All of this comes alive as we explore the notion of a “Dream Theater” - where one user is dreaming, while others watch and prompt them. 2/4

Roblox

4,395,373 views • 5 months ago