Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

I will defend my PhD thesis, “Physics-Constrained Generative Models for Computational Design,” on Tuesday, September 8, 2026, at PM ET. Location: MIT MIT CSAIL 32-G449 Zoom: Committee: Kaiming He, Bill Freeman, Wojciech Matusik Generative models can now propose candidate designs at high throughput, yet the physical world remains the... show more

Minghao Guo

1,332 subscribers

78,288 Aufrufe • vor 11 Tagen •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

36 Kommentare

Profilbild von Robert Scoble

Robert Scoblevor 11 Tagen

@MIT_CSAIL Your work caught my eye. Thank you and knock it out of the park!

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

Relying on downstream validation creates a fundamental trade-off between throughput and physical realizability: generating a candidate may take seconds, while validating it can take hours to months. Physical realizability is a scaling problem, not just a quality problem. (2/7)

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

Inspired by Marr’s separation of levels of analysis, we organize generative computational design into three levels: physical, computational, and generative. Because each level abstracts the one below, physical requirements can be weakened or lost at the interfaces between them. (3/7)

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

This thesis treats physical requirements as first-class citizens of the generative system. The central idea is to carry those requirements bottom-up through explicit interfaces, from the physical world through computation and into generation. We formulate computational design as constrained optimization. (4/7)

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

We realize these interfaces through three core elements: representation, constraints, and objective evaluation. At the representation interface, procedural molecular grammars, medial skeletons, and tetrahedral sphere primitives define candidate spaces that are valid by construction. At the constraint interface, differentiable projection modules enforce physical feasibility during generation. (5/7)

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

At the objective interface, unified simulators evaluate design behavior with scientific fidelity at generative throughput, spanning physical regimes from molecular dynamics to aerodynamics. Together, these contributions lay the foundation for universal precision design: general-purpose systems that tailor physically realizable designs to the specific requirements of a problem, individual, or environment, at scale. (6/7)

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

A research map of selected publications across my PhD study: #GenerativeAI #AIforScience #ComputationalDesign #ScientificMachineLearning #PhDDefense #worldmodel

Profilbild von Minghao Guo

Minghao Guovor 11 Tagen

Missing the time haha! It’s 2-3pm ET!

Profilbild von 중심

중심vor 11 Tagen

@MIT_CSAIL at what time?

Profilbild von BrenJ

BrenJvor 11 Tagen

@MIT_CSAIL @evelovesolive @beffjezos felt like this was up y'alls alley.

Profilbild von DrKnowItAll

DrKnowItAllvor 11 Tagen

@MIT_CSAIL Great work! This looks really impressive.

Profilbild von rob

robvor 11 Tagen

@MIT_CSAIL Great stuff here.

Profilbild von samir sawarkar

samir sawarkarvor 11 Tagen

@MIT_CSAIL The next leap in generative AI isn’t generating more designs. It’s generating designs that cannot violate the physics. That’s the shift from “AI can imagine it” to “AI can engineer it.”

Profilbild von Aman Chourasia

Aman Chourasiavor 11 Tagen

@MIT_CSAIL This is great!

Profilbild von Daniel Samanez

Daniel Samanezvor 11 Tagen

@MIT_CSAIL 👍 at what time is it?

Profilbild von Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.socialvor 10 Tagen

@MIT_CSAIL Congrats. It was a honor to listen to You leading us through Your work. All the best!

Profilbild von Xenonostra'

Xenonostra'vor 11 Tagen

@MIT_CSAIL I wish you the best of luck I'm sure you will do well

Profilbild von GSC

GSCvor 11 Tagen

@MIT_CSAIL

Profilbild von Zhiyuan

Zhiyuanvor 11 Tagen

@MIT_CSAIL solid work! may you good luck!

Profilbild von Yuxi Xiao

Yuxi Xiaovor 11 Tagen

@MIT_CSAIL Wow nice video for the phd thesis!!! congrats

Profilbild von Big data annotation

Big data annotationvor 11 Tagen

@MIT_CSAIL 大佬，牛逼

Profilbild von Raj Kachhadiya (KARAM)

Raj Kachhadiya (KARAM)vor 11 Tagen

@MIT_CSAIL Interesting, congratulations!

Profilbild von Anand Kumar

Anand Kumarvor 11 Tagen

@MIT_CSAIL Interesting. Looking forward to it.

Profilbild von Neil

Neilvor 8 Tagen

@MIT_CSAIL Wow this is such an interesting research direction, do you think the future of 3D is headed this way?

Profilbild von Yossi Eliaz

Yossi Eliazvor 11 Tagen

@MIT_CSAIL loved the video!

Profilbild von likhesh

likheshvor 11 Tagen

@MIT_CSAIL Amazing, what time exactly?

Profilbild von Mandar Wagh

Mandar Waghvor 10 Tagen

congrats, and that is a committee. the split i keep getting stuck on: physics constraints are mostly differentiable, so they go straight into a loss. manufacturing constraints mostly are not — tool reach, support-free overhang, demoldability, minimum wall are combinatorial, and you cannot gradient your way to a draft angle. does the thesis treat those as one kind of constraint, or as two different problems? genuinely curious which way you landed.

Profilbild von the guy

the guyvor 11 Tagen

@MIT_CSAIL 🧡

Profilbild von Jesse Jr Lim (林振燊)

Jesse Jr Lim (林振燊)vor 11 Tagen

@MIT_CSAIL wow very cool.. paused the vid alot to see whats going on

Profilbild von X.M.

X.M.vor 11 Tagen

@MIT_CSAIL all the best king

Profilbild von Volatile Markets

Volatile Marketsvor 11 Tagen

@MIT_CSAIL Love this!

Profilbild von Verdi

Verdivor 11 Tagen

@MIT_CSAIL congrats!! you didnt share a time tho? > "on Tuesday, September 8, 2026, at PM ET."

Profilbild von Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)vor 11 Tagen

@MIT_CSAIL PhaGGOT

Profilbild von Curtis

Curtisvor 10 Tagen

@MIT_CSAIL Grest work, great slides, great marketing video!

Profilbild von Black Canvas

Black Canvasvor 11 Tagen

@MIT_CSAIL Wish you the best and interesting work!

Profilbild von Supratik Bhattacharya

Supratik Bhattacharyavor 11 Tagen

@MIT_CSAIL Wow! this is super interesting. all the best!

Ähnliche Videos

My lab, at Karolinska in Stockholm, is looking for a PhD student with a computational/quantitative background to work on probabilistic/generative models of protein (structure and sequence). The research will involve methods development and applications in vaccine design.

My lab, at Karolinska in Stockholm, is looking for a PhD student with a computational/quantitative background to work on probabilistic/generative models of protein (structure and sequence). The research will involve methods development and applications in vaccine design.

Ben Murrell

21,646 Aufrufe • vor 1 Jahr

Super excited to share the last paper of my PhD: "Hallucination in World Models is Predictable and Preventable"✨ We train a 350M-param generative world model on a large dataset w/ 210 tasks and show that we can predict *when* hallucination happens and use that to fix it! 🧵1/n

Super excited to share the last paper of my PhD: "Hallucination in World Models is Predictable and Preventable"✨ We train a 350M-param generative world model on a large dataset w/ 210 tasks and show that we can predict when hallucination happens and use that to fix it! 🧵1/n

Nicklas Hansen

58,102 Aufrufe • vor 2 Monaten

Excited to show some surprising inventions on generative multiplayer games we made at Google with Stanford. We call the work MultiGen. I've always been inspired by early studios like id Software with Doom or Blizzard with Warcraft bringing networked video games to the next level. We are at the point in history where we can make strides like them, but for generative games. It's a strange feeling to be in the age of generative video games while still discovering how exactly to train the models and design the tools that make them useful. All of the tools that have been invented for classic game engines need to be redesigned for generative games. For example level and world design is not entirely possible with existing technology. We introduce editable memory to diffusion game engines that allow for design of new levels via a minimap. But we can easily imagine how this can be expanded with different creation tools. The end goal of this research direction is to allow game designers to be able to guide the generation process of their world, at the granularity that they prefer. Editable memory also allows us to add multiplayer to Generative Doom. We were amazed when we saw GameNGen some years ago, and now you can play it live with friends in real-time, on your couch or even online. Shared representations like our editable memory seem like the future for this type of experience. Models are, in some cases, expensive and approximate encoders but great interpolators and extrapolators. Leveraging their strengths lets you have completely new experiences that can be realized now and not in the distant future. This work was started at my previous team and continued in collaboration with Stanford. Congratulations to all for the discoveries.

Excited to show some surprising inventions on generative multiplayer games we made at Google with Stanford. We call the work MultiGen. I've always been inspired by early studios like id Software with Doom or Blizzard with Warcraft bringing networked video games to the next level. We are at the point in history where we can make strides like them, but for generative games. It's a strange feeling to be in the age of generative video games while still discovering how exactly to train the models and design the tools that make them useful. All of the tools that have been invented for classic game engines need to be redesigned for generative games. For example level and world design is not entirely possible with existing technology. We introduce editable memory to diffusion game engines that allow for design of new levels via a minimap. But we can easily imagine how this can be expanded with different creation tools. The end goal of this research direction is to allow game designers to be able to guide the generation process of their world, at the granularity that they prefer. Editable memory also allows us to add multiplayer to Generative Doom. We were amazed when we saw GameNGen some years ago, and now you can play it live with friends in real-time, on your couch or even online. Shared representations like our editable memory seem like the future for this type of experience. Models are, in some cases, expensive and approximate encoders but great interpolators and extrapolators. Leveraging their strengths lets you have completely new experiences that can be realized now and not in the distant future. This work was started at my previous team and continued in collaboration with Stanford. Congratulations to all for the discoveries.

Nataniel Ruiz

104,932 Aufrufe • vor 6 Monaten

Reality capture and generative AI make a powerful combination that is yet to be fully explored. Once you can perceive the world, genAI gives you the ability to reskin & augment it -- whether it's for VFX or interior design. Explore the possibilities in this compilation of my recent AI experiments. What would you create with this tech?

Reality capture and generative AI make a powerful combination that is yet to be fully explored. Once you can perceive the world, genAI gives you the ability to reskin & augment it -- whether it's for VFX or interior design. Explore the possibilities in this compilation of my recent AI experiments. What would you create with this tech?

Bilawal Sidhu

186,827 Aufrufe • vor 3 Jahren

Opus 4.5 (new model from Anthropic) can do something no model could do until now: It can close the design loop. This is (in my testing) the first model that can reliably do the visual loop of Generate Code ⇛ Render ⇛ Look at it ⇛ Improve. Step 3 (Visual critique) has been a real problem for so long. Models will be myopic or defensive when reviewing designs - it's been hard to tell if this has been a training or a vision problem. Opus 4.5 seems to have fixed it - it's not amazing, but it finally *can* do it. Claude models have historically (once again, in my testing) lagged behind in terms of visual understanding. Gemini for the last year has been the best - but there might be a new leader.

Opus 4.5 (new model from Anthropic) can do something no model could do until now: It can close the design loop. This is (in my testing) the first model that can reliably do the visual loop of Generate Code ⇛ Render ⇛ Look at it ⇛ Improve. Step 3 (Visual critique) has been a real problem for so long. Models will be myopic or defensive when reviewing designs - it's been hard to tell if this has been a training or a vision problem. Opus 4.5 seems to have fixed it - it's not amazing, but it finally can do it. Claude models have historically (once again, in my testing) lagged behind in terms of visual understanding. Gemini for the last year has been the best - but there might be a new leader.

Hrishi

58,331 Aufrufe • vor 9 Monaten

we designed + built a new website for Odyssey insane tech, they're building general purpose world models to simulate interactive worlds in real time 🤯 one of the coolest projects my team and I at The Memetic Design Company have had the chance to work on!

we designed + built a new website for Odyssey insane tech, they're building general purpose world models to simulate interactive worlds in real time 🤯 one of the coolest projects my team and I at The Memetic Design Company have had the chance to work on!

abhinav

12,938 Aufrufe • vor 6 Monaten

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.

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

1,443,875 Aufrufe • vor 10 Monaten

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

Zhou Xian

3,821,580 Aufrufe • vor 1 Jahr

This is THE moment of Physical AI! We are officially announcing Cosmos 3: Omnimodal World Models for Physical AI 🚀 - Cosmos 3 is an omnimodal world model: within a unified architecture, it can understand and generate language, images, video, audio, and actions. - It is not just a VLM, not just a video generator, not just an audio-visual generative model, and not just a physics simulator / world-action model. It can understand images and videos, generate images, videos, and audio, simulate future worlds, predict actions, and generate robot policies—enabling models to truly begin to “touch the world.” - Cosmos 3 is the #1 open-weight reasoner / T2I / I2V / robot policy across many benchmarks. Huge thanks to every teammate who fought side by side on this journey—from architecture, data, training, infra, serving, and evaluation to post-training. Every part of this project carries an incredible amount of hard work. This was my first time leading a project as Tech Lead, and I feel truly fortunate. The future of Physical AI needs models that can not only “see” and “describe” the world, but also “imagine,” “simulate,” and “act”—and eventually close the loop with the real world. I hope Cosmos 3 can become an important starting point for this direction, and I’m excited to push Physical AI into its next stage together with the open-source community. Welcome to the era of Physical AI. HuggingFace: Project Website: Code:

This is THE moment of Physical AI! We are officially announcing Cosmos 3: Omnimodal World Models for Physical AI 🚀 - Cosmos 3 is an omnimodal world model: within a unified architecture, it can understand and generate language, images, video, audio, and actions. - It is not just a VLM, not just a video generator, not just an audio-visual generative model, and not just a physics simulator / world-action model. It can understand images and videos, generate images, videos, and audio, simulate future worlds, predict actions, and generate robot policies—enabling models to truly begin to “touch the world.” - Cosmos 3 is the #1 open-weight reasoner / T2I / I2V / robot policy across many benchmarks. Huge thanks to every teammate who fought side by side on this journey—from architecture, data, training, infra, serving, and evaluation to post-training. Every part of this project carries an incredible amount of hard work. This was my first time leading a project as Tech Lead, and I feel truly fortunate. The future of Physical AI needs models that can not only “see” and “describe” the world, but also “imagine,” “simulate,” and “act”—and eventually close the loop with the real world. I hope Cosmos 3 can become an important starting point for this direction, and I’m excited to push Physical AI into its next stage together with the open-source community. Welcome to the era of Physical AI. HuggingFace: Project Website: Code:

Max Zhaoshuo Li 李赵硕

1,079,815 Aufrufe • vor 3 Monaten

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

Dylan Small

15,054 Aufrufe • vor 2 Monaten

Democrats demand release of files, Republicans agree, propose a bill, only to have a Democrat judge and Dems politicians block it. Dems does this, hoping that people will believe Trump has something to hide, while they’re the ones blocking the release of the files in the background. It’s the same old game they always play. Create a problem, draw attention to the problem, claim that somebody else is at blame for the problem and then claim you can only fix the problem if they vote for you.

Democrats demand release of files, Republicans agree, propose a bill, only to have a Democrat judge and Dems politicians block it. Dems does this, hoping that people will believe Trump has something to hide, while they’re the ones blocking the release of the files in the background. It’s the same old game they always play. Create a problem, draw attention to the problem, claim that somebody else is at blame for the problem and then claim you can only fix the problem if they vote for you.

ꪻꫝể ꪻꫝể

315,713 Aufrufe • vor 10 Monaten

Anthropic's Sholto Douglas describes the concept of a software-only singularity: "It's one where the models are far better at digital tasks than they are at physical ones. And so we see rapid change in the digital world with models, and relatively little change in the physical world. So information and software changes dramatically, and this ends up having some pretty weird effects." "It means that maybe like the drivers of what have been the last couple decades of progress in the economy turn around." "And I think we'll see that flow on into the physical world but at a delay. So you get much better at doing chip design. You get much better at training AI models. AI models get a lot faster. Chips get a lot better. The general economy gets a lot more efficient because the sort of information and message parsing that is much of the rest of the economy ends up becoming much more efficient." "But at the same time you don't yet have robots providing limitless physical abundance. Science probably progresses really fast up to the degree that you need interaction with labs or larger particle colliders or something like this. And then you go, 'Okay well I need to build the robots.'"

Anthropic's Sholto Douglas describes the concept of a software-only singularity: "It's one where the models are far better at digital tasks than they are at physical ones. And so we see rapid change in the digital world with models, and relatively little change in the physical world. So information and software changes dramatically, and this ends up having some pretty weird effects." "It means that maybe like the drivers of what have been the last couple decades of progress in the economy turn around." "And I think we'll see that flow on into the physical world but at a delay. So you get much better at doing chip design. You get much better at training AI models. AI models get a lot faster. Chips get a lot better. The general economy gets a lot more efficient because the sort of information and message parsing that is much of the rest of the economy ends up becoming much more efficient." "But at the same time you don't yet have robots providing limitless physical abundance. Science probably progresses really fast up to the degree that you need interaction with labs or larger particle colliders or something like this. And then you go, 'Okay well I need to build the robots.'"

TBPN

208,090 Aufrufe • vor 7 Monaten

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,942 Aufrufe • vor 3 Monaten

Peter Thiel on the question he asks every startup founder he invests in “Why will the 20th talented person join your company when they can get paid way more at Google, they will have to work way less hard at Google, and it will look better on their resume to go to Google?” The 20th employee won’t get the equity or prestige that someone on the founding team will get, so there needs to be another incentive if you’re going to build a truly great company and attract the best people in the world. Thiel believes the best answer is something along the lines of: “This is the only place in the world where you can work on this incredibly important problem.” He continues: “It has to—on some dimension—be a really important problem that at least some people think is the most important problem in the world. Those are the kind of businesses that are unique, and when they work, they end up being leaders in their respective markets.” If it’s a problem that a bunch of other team are working on, it will be hard to attract a truly world-class employee #20.

Peter Thiel on the question he asks every startup founder he invests in “Why will the 20th talented person join your company when they can get paid way more at Google, they will have to work way less hard at Google, and it will look better on their resume to go to Google?” The 20th employee won’t get the equity or prestige that someone on the founding team will get, so there needs to be another incentive if you’re going to build a truly great company and attract the best people in the world. Thiel believes the best answer is something along the lines of: “This is the only place in the world where you can work on this incredibly important problem.” He continues: “It has to—on some dimension—be a really important problem that at least some people think is the most important problem in the world. Those are the kind of businesses that are unique, and when they work, they end up being leaders in their respective markets.” If it’s a problem that a bunch of other team are working on, it will be hard to attract a truly world-class employee #20.

Startup Archive

316,188 Aufrufe • vor 1 Jahr

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 Aufrufe • vor 4 Monaten

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Rohan Paul

49,938 Aufrufe • vor 3 Monaten

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Tiani Pixel | Follow me on BSky!

75,960 Aufrufe • vor 2 Jahren

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Paul Mauro

23,268 Aufrufe • vor 6 Monaten

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

The Humanoid Hub

22,037 Aufrufe • vor 1 Monat

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 Aufrufe • vor 4 Monaten