Physical AI

Nvidia is reportedly pursuing glass substrate packaging for its AI chips, a manufacturing shift that could improve performance and density as conventional materials hit physical limits. The move could reshape how advanced semiconductors are packaged and give Nvidia an edge in the race to power large AI systems, though details of timing and partners remain unclear.

A Harvard study reports that students using an AI tutor to learn physics achieved better results than peers in standard active-learning classes. The findings suggest personalized, conversational AI instruction can outperform established classroom methods, sparking debate among educators about how such tools should fit into teaching and whether AI could reshape how universities deliver science education.

A Harvard study has concluded that an AI tutor outperformed traditional in-class physics teaching. The findings suggest students using the AI tutor achieved better learning outcomes than those in conventional classroom settings. The result is drawing attention as educators debate the role of artificial intelligence in higher education and whether such tools should be adopted more widely in science courses.

SiMa.ai, a chip developer focused on physical AI, has reached a valuation of $1.45 billion. The company builds semiconductor technology designed to bring AI processing to physical systems such as machines and edge devices. The valuation places it among the more prominent independent AI chip startups, in a sector drawing intense investor interest as companies race to supply hardware for artificial intelligence beyond data centers.

A new Brookings Institution study examines the enormous physical footprint of the AI boom, from specialized chips and electricity demand to purpose-built data centers, noting that two-thirds of a data center's cost can go into hardware and construction. The report assesses how large the buildout has become and what financial and economic risks it may pose if AI investment slows or falls short of expectations.

New research presented via EurekAlert describes how artificial intelligence paired with physics-based methods is advancing imaging techniques, including work that makes the invisible visible and automates the colorization of anime footage. The announcement highlights computational imaging as a growing field where machine learning and physical modeling intersect, with potential applications in science and animation production.

Consultancy Bain & Company says artificial intelligence is reshaping retail in ways that will push underperforming stores out of the market. The firm argues retailers that fail to adopt AI-driven operations, pricing and customer insights risk losing ground to more efficient competitors, accelerating store closures across the sector.

A Harvard study reports that students learning physics with an AI tutor outperformed peers in a traditional classroom setting. The findings suggest personalized, interactive AI instruction can deliver greater learning gains than standard lectures, reigniting debate over how AI should fit into education and what it means for the future of teaching.

Researchers are combining artificial intelligence with physics-based models to push imaging beyond what cameras can directly capture, from reconstructing hidden scenes to automatically colorizing anime footage. In a new Q&A, experts explain how machine learning can fill in missing visual information while physical laws keep the results grounded, pointing toward faster, cheaper imaging tools for science and media alike.

Bloomberg reports that driverless trucks may provide a model for how autonomous AI systems can be deployed safely without harming people. The argument is that self-driving freight has operated under strict testing, regulation and limited-use conditions, offering a template for governing more advanced robots. The piece adds to ongoing debate about how to manage AI risks as automation expands into physical industries.

An AI-assisted study using data from the Large Hadron Collider is challenging the theory that the universe has ten dimensions, a cornerstone idea from string theory. The findings suggest the extra dimensions predicted by the theory may not exist, prompting debate among physicists about the model's validity and the growing role of machine learning in fundamental physics research.

South Korean robotics company Angel Robotics has launched PHAI-X1, a wearable robot platform aimed at physical AI research. The system is intended to give researchers a hardware base for developing and testing AI that interacts with the physical world, such as exoskeleton control and human-assistance applications. Details on pricing and availability were not included in the initial announcement.

Nvidia has reached a settlement in a trademark lawsuit concerning its 'Modulus' AI software, according to a Reuters report. The dispute centred on Nvidia's use of the Modulus name for its framework for building physics-based machine learning models. Terms of the settlement were not disclosed in the report, and the company has not issued a detailed public statement on the outcome.

Chinese AI firm SenseTime has launched SenseMart OS, an operating system designed to bring embodied intelligence into retail environments. The system is aimed at powering robots and smart devices that can perceive, interact with, and assist customers in stores. Details about pricing, availability, and retail partners have not yet been widely reported.

Gecko Robotics' chief executive is arguing that humans must remain in control as artificial intelligence moves into the physical world through robots and industrial automation. The company builds robots used to inspect critical infrastructure such as bridges, power plants and pipelines. The intervention adds a robotics industry voice to the ongoing debate over how much autonomy should be granted to AI-driven machines in safety-critical settings.

Anthropic says its Claude AI solved a nine-loop particle physics challenge, an extremely complex calculation in quantum field theory. Nine-loop computations involve vast numbers of terms and are traditionally done with specialized software and significant human effort, so a successful AI solution would mark a milestone for AI in theoretical physics. The claim is drawing attention from researchers assessing how far AI models can go in advanced scientific work.

Scientific American's latest science roundup covers the rising energy demands of artificial intelligence, ongoing efforts to detect subatomic particles, and research into an amoeba that thrives at extreme temperatures. The episode ties together the environmental cost of the AI boom with fundamental physics and biology discoveries, offering a snapshot of current conversations across science news.

Princeton University's physics department has wrapped up an experiment using artificial intelligence to grade student work and published its findings. The results were mixed, with AI grading showing promise but also clear limitations compared with human assessment. The report is drawing attention as universities weigh how far AI should be allowed into academic evaluation.

A project called Roboharm is asking whether frontier robot policies actually refuse unsafe instructions. The work examines how AI systems controlling robots respond to harmful commands, a safety question increasingly urgent as AI models are deployed in physical robotics. Discussion is focused on how well current safeguards carry over from language models to embodied systems.

Faraday Future has announced a strategic transformation into a robotaxi and EAI cabin technology operator and a physical AI investment holding company. The company plans to combine its robotics business at an approximate $200 million valuation with AIxC, to be renamed FFR, for a standalone listing. The move marks a major shift in strategy for the struggling electric vehicle maker as it seeks new growth in artificial intelligence.

Anti-AI protesters rushed the stage at Data After Dark, a pro-AI and pro-data center event held in Washington, DC. The demonstrators were unable to deliver a clear statement after the crowd drowned them out with chants of 'USA! USA!'. The confrontation highlights growing tensions between opponents of AI and data center expansion and their supporters.

Tohoku University announced research on an AI model for materials discovery that is grounded in physics rather than relying purely on data patterns. The approach aims to make predictions of new materials more reliable and reproducible, addressing a common weakness of purely statistical machine learning in chemistry and materials science. The university released the work through its research news service.

The European Union's updated product liability framework is widening its scope so claims no longer hinge solely on physical damage. Under the new rules, compensation can cover data loss, psychological harm and damage from software and AI-enabled products. Commentators say the shift significantly raises exposure for manufacturers and tech firms selling into the EU market.

AMD is reportedly acquiring World Labs, the AI startup led by Fei-Fei Li, for $8.2 billion, a move analysts say signals a broader race in AI focused on robotics and 3D simulation. Commenters are weighing what the acquisition could mean for AMD's future chips, its rivalry with Nvidia, and its push into physical-world AI applications.

Researchers have introduced a physics-grounded AI framework designed to make predictions about new materials more testable, according to Phys.org. The approach is intended to anchor machine-learning forecasts in physical principles so that scientists can verify them against real experiments rather than treating model outputs as black boxes, a persistent problem in AI-driven materials discovery.

A physicist says he challenged an AI model to tackle a nine-loop calculation, a notoriously difficult problem in theoretical physics, and Claude reportedly solved it. If accurate, it would mark a striking demonstration of AI's ability to handle complex, multi-step scientific reasoning that has traditionally required expert mathematicians and physicists.

Waddle Labs and RoboCurve are the focus of a new analysis arguing that general-purpose large language models could outperform specialised systems in robotics. The piece contends that broad foundation models trained on diverse data may adapt better to physical tasks than narrowly built robots, a view that challenges a common assumption in the field and is drawing attention among robotics and AI investors.