Cal AI

Amazon Web Services has released an open-source toolchain aimed at physical AI development, targeting robotics and other systems where AI interacts with the real world. The toolchain is intended to help developers build, test and deploy physical AI applications more easily. Details on specific components and partner adoption have not yet been widely reported.

Zach Yadegari, the 19-year-old co-founder of the popular Cal AI calorie-tracking app, has raised $10 million for a new startup building a personal AI agent. The venture enters a competitive field, going up against existing players including Instinct, Muse and Bee. The funding and Yadegari's young age have drawn attention across tech and startup circles.

The 19-year-old founder of calorie-tracking app Cal AI has raised $10 million for a new artificial intelligence startup. The funding, reported by TechCrunch, puts the teenage entrepreneur among a wave of young founders attracting major venture backing in the AI space. Details about the new company's product and investors have not yet been widely shared, but the story is drawing attention to how quickly very young builders are securing serious capital.

Nvidia is pushing heavily into physical AI, technology that lets machines understand and act in the real world. The company says the approach could make robotaxis safer and speed up development of humanoid robots, positioning its chips and software platforms at the centre of the next wave of robotics beyond generative AI.

Amazon Web Services has released an open-source Physical AI Toolchain aimed at robotics developers. The toolchain is intended to help teams build and deploy AI-powered physical systems, and its open-source release makes the tooling freely available to the robotics community. Details on its full feature set have not yet been widely reported.

Nvidia is making a major push into what it calls physical AI, technology designed to let robots and autonomous vehicles operate safely in the real world. The company says the approach could make robotaxis more reliable and accelerate the development of humanoid robots. The announcement is drawing attention as a signal of Nvidia's ambitions beyond chips for data centers, extending into robotics and self-driving systems.

Amazon Web Services has released an open-source toolchain aimed at physical AI, giving robotics developers tools to build, train and deploy AI systems that interact with the real world. The move positions AWS against rivals courting the robotics sector and gives developers a vendor-backed, freely available alternative for embodied AI development workflows.

Microsoft is making a major push to run artificial intelligence directly on Windows PCs rather than in remote data centers, an approach the company frames as putting petaflop-level computing power on users' desks. The move ends a two-year industry debate over whether AI should run in the cloud or locally, and signals Microsoft sees the future of personal computing in on-device AI hardware.

A new report from Bioengineer.org claims that an AI tutoring system scored highly on a physics exam yet performed poorly in real classroom settings. The item highlights a gap between AI systems' ability to answer exam questions and their effectiveness at actually teaching students. Details about the system, the exam, or the classroom trials are limited to the headline.

GlobalFoundries has announced a roadmap to deliver what it calls the world's most advanced FD-SOI semiconductor platform, aimed at enabling Physical AI applications. The company positions the technology as key for chips that bridge digital intelligence with the physical world, such as robotics and sensing systems. Details on timeline and partners have not been widely reported yet.

Amazon Web Services has introduced a new physical AI toolchain aimed at smart manufacturing, giving factories tools to build AI-driven automation and robotics applications. The offering targets industrial clients looking to connect physical operations with cloud-based AI capabilities. Details beyond the launch announcement remain limited, and reaction from manufacturers or analysts has not yet been widely reported.

Physical AI systems — robots that move and manipulate objects in the real world — still lack the ability to verify their own actions, according to a new analysis. The piece argues that while embodied AI has advanced rapidly in acting on the physical environment, self-checking and quality assurance of its own output remain unsolved. The gap is drawing attention as industry pushes robots into factories, warehouses and other high-stakes settings.

Productive Robotics has introduced a collaborative robot equipped with what it calls physical AI, extending its line of cobots aimed at manufacturing and other industrial work. The announcement, covered by trade press including Today's Medical Developments, highlights the growing push to embed AI-driven perception and adaptability directly into collaborative robots. Details on pricing, availability and target applications were not included in the coverage.

Fast Company explores how technologies born in video games, such as simulations and game engines, are paving the way for physical AI, where artificial intelligence is embodied in robots that interact with the real world. The piece examines how gaming tools can train and test embodied systems, a topic drawing attention as companies invest heavily in robotics and AI beyond screens.

TwelveLabs has released Pegasus 1.6, a video understanding model aimed at physical AI applications such as robotics. The company says the model can analyze video input to help machines and robots interpret real-world visual environments, extending multimodal AI beyond screen-based tasks into embodied systems operating in physical spaces.