ai and ML
Say you’re trying to enrich Uranium and your centrifuges broke – soon it will be easy to connect an AI to figure out why
Anthropic on Thursday teased a protocol for allowing AI agents “to safely operate physical devices,” a somewhat optimistic ambition given it cannot reliably anticipate how its models behave.
The as-yet-unpublished protocol, dubbed the Model Hardware Standard (MHS), is similar in concept to the Model Context Protocol (MCP), a way for AI models to connect to data sources. Anthropic intends for MHS to allow Claude or other models to drive the hardware used in laboratories, factories, and robots.
Supopse you are an Iranian research scientist setting up a network of centrifuges to enrich uranium. You might look at how your machinery spun out of control in 2010 and think: “Maybe if we use an AI model and MHS, we could avoid that sort of mishap next time.”
MHS, however, is being battle-tested in quieter regions of the world, specifically at Howard Hughes Medical Institute’s (HHMI) Janelia Research Campus in Maryland. And now other entities with suitable laboratory and industrial equipment can apply to join the research preview.
“It typically takes a lab or manufacturing facility weeks, if not months, to set up and integrate their hardware,” Anthropic explained in its post. “Most devices don’t communicate with each other, instead requiring specialists to build bespoke integrations. MHS reduces this integration work to hours or minutes.”
Linking hardware to an AI model requires a programmable interface. Many industrial machines offer one to expose relevant controls and data — but the ecosystem of such devices is very diverse and it is not easy for developers to make a hookup.
MHS aspires to be a universal translation layer. The MHS driver software uses a limited set of primitives, such as “read” and “write,” an approach similar to the way a few simple tools like Bash can be used to power AI agents.
The driver makes connected devices discoverable in a standard format. It also supports tags that convey information about device functions, and lets users provide that data by conversing with the model during setup. The tags let the driver produce a reference file detailing device characteristics.
AI agents can use MHS to interact with devices using three control paths: MCP, the command line interface, and API code. They can carry out commands on connected instruments, monitor test results, or tweak knobs and dials.
According to Anthropic, biotechnology company Genentech has used MHS to run a drug-discovery experiment with real-time error handling. Quantum computing outfit QuEra is another user, and applied MHS to improve laser stabilization for its machines from 58 percent to 99.3 percent.
The possibilities already have partners salivating. AWS is planning to support MHS through its Strands Robots library. Automata expects to add MHS to its LINQ lab automation platform. And similar support is planned by the likes of Danaher, Doosan Robotics, MBF Bioscience, Qiagen, Tecan, and Universal Robotics.
“There’s more to learn before we open source MHS,” Anthropic said. “LLMs still lack physical intuition, having learned about the physical world from text and images. The research preview will let us build more safety evaluations and strengthen protections for using AI in the physical world.”
Let the experiments begin. ®
