Foundation Model
Assembly Line
X-Bow Raises More Than $70 Million Series B to Accelerate Expansion of Hypersonic and Solid Rocket Motor Technologies
X-Bow Systems Inc. (X-Bow), the nation’s leading non-traditional producer of advanced manufactured solid rocket motors (SRMs) and hypersonics technologies, announced the successful completion of the initial close of its Series B financing, raising over $70 million. This private capital will accelerate the rapid growth of its hypersonic-capable vehicles, strategic and tactical scale solid rocket motor programs, and completion of its Luling, TX gigafactory campus. The company will also be expanding its engineering and R&D facilities across New Mexico. The round was led by National Security technology-focused growth equity firm Razor’s Edge, with additional participation from Lockheed Martin Ventures, Boeing Ventures, Crosslink Capital and Balerion Space Ventures.
With this funding now secured, X-Bow will accelerate its growth trajectory as the nation’s third supplier of Solid Rocket Motors and continue in its quest to rapidly innovate and deliver agile, affordable solutions for SRMs, hypersonics and associated adjacent markets.
Skild AI Raises $300M Series A To Build A Scalable AI Foundation Model For Robotics
Skild AI, an AI robotics company building a scalable foundation model for robotics, announced it has closed a $300M Series A funding round. The round was led by Lightspeed Venture Partners, Coatue, SoftBank Group, and Jeff Bezos (through Bezos Expeditions), with participation from Felicis Ventures, Sequoia, Menlo Ventures, General Catalyst, CRV, Amazon, SV Angel, and Carnegie Mellon University. The funding brings the company to a valuation of $1.5B. The capital will be used to continue scaling the company’s model and training datasets for future commercial deployment of its technology, in addition to hiring for roles across AI, robotics, engineering, operations, and security.
Skild AI is building intelligence that is grounded in the physical world. The company is breaking the data barrier in robotics, training its model on at least 1,000X more data points than competing models. As opposed to vertically designed robots that are built for specific applications, Skild’s model serves as a shared, general-purpose brain for a diverse embodiment of robots, scenarios and tasks, including manipulation, locomotion and navigation. From resilient quadrupeds mastering adverse physical conditions, to vision-based humanoids performing dexterous manipulation of objects for complex household and industrial tasks, the company’s model will enable the use of low-cost robots across a broad range of industries and applications.
Introducing Aurora: The first large-scale foundation model of the atmosphere
A recent study by Charlton-Perez et al. (2024) underscored the challenges faced by even the most advanced AI weather-prediction models in capturing the rapid intensification and peak wind speeds of Storm Ciarán. To help address those challenges, a team of Microsoft researchers developed Aurora, a cutting-edge AI foundation model that can extract valuable insights from vast amounts of atmospheric data. Aurora presents a new approach to weather forecasting that could transform our ability to predict and mitigate the impacts of extreme events—including being able to anticipate the dramatic escalation of an event like Storm Ciarán.
Aurora’s effectiveness lies in its training on more than a million hours of diverse weather and climate simulations, which enables it to develop a comprehensive understanding of atmospheric dynamics. This allows the model to excel at a wide range of prediction tasks, even in data-sparse regions or extreme weather scenarios. By operating at a high spatial resolution of 0.1° (roughly 11 km at the equator), Aurora captures intricate details of atmospheric processes, providing more accurate operational forecasts than ever before—and at a fraction of the computational cost of traditional numerical weather-prediction systems. We estimate that the computational speed-up that Aurora can bring over the state-of-the-art numerical forecasting system Integrated Forecasting System (IFS) is ~5,000x.
Unlocking new value in industrial automation with AI
Working with the robotics team at NVIDIA, we have successfully tested NVIDIA robotics platform technologies, including NVIDIA Isaac Manipulator foundation models for robot a grasping skill with the Intrinsic platform. This prototype features an industrial application specified by one of our partners and customers, Trumpf Machine Tools. This grasping skill, trained with 100% synthetic data generated by NVIDIA Isaac Sim, can be used to build sophisticated solutions that can perform adaptive and versatile object grasping tasks in sim and real. Instead of hard-coding specific grippers to grasp specific objects in a certain way, efficient code for a particular gripper and object is auto-generated to complete the task using the foundation model and synthetic training data.
Together with Google DeepMind, we’ve demonstrated some novel and high value methods for robotic programming and orchestration — many of which have practical applications today:
- Multi-robot motion planning with machine learning
- Learning from demonstration, applied to two-handed dexterous manipulation
- Foundation model for perception by enabling a robotic system to understand the next task and the physical objects involved requires a real-time, accurate, and semantic understanding of the environment.
Archetype AI Introduces Foundation Model to Pioneer Physical AI
Archetype AI, a physical AI company helping humanity make sense of the world, announced its emergence from stealth and the introduction of Newton™, a first-of-its-kind foundation model that understands the physical world. With Newton, Archetype AI is on a mission to use the power of artificial intelligence to solve real-world problems – empowering people and organizations with an understanding of the physical environment that wasn’t previously possible.
In support of this mission, Archetype AI has raised a $13 million seed funding round led by Venrock, with participation from Amazon Industrial Innovation Fund, Hitachi Ventures, Buckley Ventures, Plug and Play Ventures and several angel investors. In conjunction with the financing, Ganesh Srinivasan, Partner at Venrock, will join the board.
With Newton, Archetype AI is introducing a first-of-its-kind physical AI foundational model that is capable of perceiving, understanding and reasoning about the world. Newton fuses multimodal temporal data – including signals from accelerometers, gyroscopes, radars, cameras, microphones, thermometers and other environmental sensors – with natural language to unlock insights about the physical world in real-time.
NVIDIA Announces Project GR00T Foundation Model for Humanoid Robots and Major Isaac Robotics Platform Update
NVIDIA announced Project GR00T, a general-purpose foundation model for humanoid robots, designed to further its work driving breakthroughs in robotics and embodied AI.
As part of the initiative, the company also unveiled a new computer, Jetson Thor, for humanoid robots based on the NVIDIA Thor system-on-a-chip (SoC), as well as significant upgrades to the NVIDIA Isaac™ robotics platform, including generative AI foundation models and tools for simulation and AI workflow infrastructure.
The SoC includes a next-generation GPU based on the NVIDIA Blackwell architecture with a transformer engine delivering 800 teraflops of 8-bit floating point AI performance to run multimodal generative AI models like GR00T. With an integrated functional safety processor, a high-performance CPU cluster and 100GB of ethernet bandwidth, it significantly simplifies design and integration efforts.
Robots powered by GR00T, which stands for Generalist Robot 00 Technology, will be designed to understand natural language and emulate movements by observing human actions — quickly learning coordination, dexterity and other skills in order to navigate, adapt and interact with the real world. In his GTC keynote, Huang demonstrated several such robots completing a variety of tasks.
Covariant Announces a Universal AI Platform for Robots
Covariant is announcing RFM-1, which the company describes as a robotics foundation model that gives robots the “human-like ability to reason.” “Foundation model” means that RFM-1 can be trained on more data to do more things—at the moment, it’s all about warehouse manipulation because that’s what it’s been trained on, but its capabilities can be expanded by feeding it more data. “Our existing system is already good enough to do very fast, very variable pick and place,” says Covariant co-founder Pieter Abbeel. “But we’re now taking it quite a bit further. Any task, any embodiment—that’s the long-term vision. Robotics foundation models powering billions of robots across the world.” From the sound of things, Covariant’s business of deploying a large fleet of warehouse automation robots was the fastest way for them to collect the tens of millions of trajectories (how a robot moves during a task) that they needed to train the 8 billion parameter RFM-1 model.
Saudi Aramco unveils industry’s first generative AI model
Aramco’s AI model is a pioneering technology in the industrial sector. It has 250 billion parameters that are adjustable during training to generate outputs or make predictions. The AI was trained using seven trillion data points, collecting more than 90 years of company history.
Amin H Nasser, CEO of Saudi Aramco, said the AI model would analyse drilling plans, geological data, historic drilling time and costs as well as recommend the most ideal well options. He added that for the company’s downstream business, “metabrain will have the capability to provide precise forecasts for refined products, including pricing trends, market dynamics and geopolitical insights”.
Aramco plans to develop a version with 1 trillion parameters by the end of this year.
Foundation Models for Materials Discovery: Our Investment in Orbital Materials
Fortunately, innovations in artificial intelligence have led to the emergence of foundation models, which are trained on vast amounts of data and leading to models that can be used across numerous applications. Those foundation models have the potential to enable inverse design, a method of material development that expedites the process by using the specific required properties as an input and generating the new material design as an output. This approach has the potential to revolutionize material development across industries, which is why we are excited to announce Toyota Ventures’ investment in Orbital Materials through our Frontier Fund.
The team has trained a 3D foundation model, named LINUS, for crystal structures and small molecules. Instead of screening millions of materials in hopes of finding one with a specific property, LINUS generates a material based on a given property in a single calculation. To do this, the team has developed a new version of the “transformer”, a model typically used for natural language processing, to allow the model to learn the relationships between the 3D structures of materials and their properties. Advanced materials that absorb and catalyze are crucial in various industries such as carbon capture, sustainable fuels, water treatments, biofeedstock upgrades, and battery recycling.
NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data
A public/private partnership involving NASA and IBM Research has led to the release of NASA’s first open-source geospatial artificial intelligence (AI) foundation model for Earth observation data. Built using NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset, the release of the HLS Geospatial Foundation Model (HLS Geospatial FM) is a milestone in the application of AI for Earth science. The model has a wide range of potential applications, including tracking changes in land use, monitoring natural disasters, and predicting crop yields. The HLS Geospatial FM is available at Hugging Face, a public repository for open-source machine learning models.
NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) played a major role in this work. Located at NASA’s Marshall Space Flight Center in Huntsville, Alabama, IMPACT is a component of NASA’s Earth Science Data Systems (ESDS) Program and is charged with expanding the use of NASA Earth observation data through innovation, partnerships, and technology, including the application of AI to these data.