Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🚨 As AI moves into the physical world, data alone is not enough. It needs real infrastructure: standards, identities, maps, sensors, positioning, connectivity and secure ways to transact. Without this foundation, isolated systems emerge that cannot work together. 1⃣ Why Open Systems Are Essential I believe it is crucial...

14,882 Aufrufe • vor 9 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,843 Aufrufe • vor 3 Monaten

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

1,078,418 Aufrufe • vor 3 Monaten

Eleven years ago we started Eclipse and bet everything on atoms. Rockets, robots, chips, factories, power systems, defense. The things civilization actually runs on. Today, we're announcing $1.3B in new capital to keep building. The physical world is overdue for transformation. Transportation runs on systems designed decades ago. The energy grid cannot keep up with demand. Healthcare depends on manual procedures that don't scale. Defense development moves at a fraction of the speed threats evolve. These are not software problems. They are full stack engineering and operations problems, and they have been underinvested in for a generation. But there has never been a better moment to solve them. The best engineers are leaving big tech to build in the physical world. AI is compressing timelines from years to quarters. Policy is aligned. And customers are not waiting — the DoD, the hyperscalers, hospitals, and the Fortune 500 are all desperate for technology that makes physical systems smarter, faster, and more resilient. Talent, capital, technology, policy, and demand are all converging at once. This is our moment 🇺🇸 We built Eclipse for this exact moment and in doing so, we launched a movement. Today that movement is 100 companies strong (and growing!). These companies supply each other, share customers, and help solve each other's hardest problems. Propulsion systems powering orbital defense. Modern supply chain infrastructure moving the worlds goods. Autonomous vehicles on three continents. Surgical robots performing procedures that used to require the world's best hands. Cloud hardware powering the AI revolution built on American soil. That's not a fund. That's an economy — an Eclipse Economy. The door is open to rebuild the physical infrastructure of the country. We intend to run through it.

Seth Winterroth 🤖

39,603 Aufrufe • vor 4 Monaten