Physical AI, the rapidly evolving field dedicated to controlling real-world machinery and robots, faces a crucial challenge: a fragmented landscape. At the vanguard, tech giants like OpenAI and Google are making strides with large, multimodal foundation models. Close behind, Nvidia is diligently constructing the essential platforms and tools to facilitate physical AI development.
Yet, a distinct third contingent, comprised of industrial titans such as Hitachi and Germany’s Siemens, is championing a more pragmatic approach. Their argument, gaining traction from boardroom discussions to factory floors, posits that true mastery of the physical world for AI necessitates a deep, foundational understanding of its inherent principles and mechanisms.
Hitachi recently underscored this conviction in an interview with Nikkei Asia, signaling a shift from strategic discourse to tangible implementation.
### The Imperative of Domain Expertise in Physical AI
Kosuke Yanai, deputy director at Hitachi’s Centre for Technology Innovation-Artificial Intelligence, articulates a clear distinction between theoretical and practical physical AI. “Physical AI cannot be seamlessly integrated into society without a systematic comprehension rooted in fundamental physics and a profound knowledge of industrial equipment,” he stated.
Hitachi’s proposition rests on its extensive, decades-long accumulation of such foundational knowledge through its work in railways, power infrastructure, and industrial control systems. The company boasts advanced thermal fluid simulation capabilities, modeling the complex behavior of gases and liquids, alongside signal-processing tools for equipment diagnostics. Yanai characterizes these as the engineering bedrock that underpins Hitachi’s “extensive knowledge of product design and control logic construction.”
### From Conceptualization to Real-World Deployment: Daikin and JR East Projects
While Hitachi’s comprehensive physical AI framework, the Integrated World Infrastructure Model (IWIM)—an intricate “mixture-of-experts” system that synthesizes multiple specialized models, simulators, and datasets—is still undergoing concept verification, two significant real-world deployments highlight the efficacy of its underlying methodology.
In partnership with Daikin Industries, Hitachi has implemented an AI system designed to pinpoint malfunctions in commercial air-conditioner manufacturing equipment. This system, trained on a wealth of maintenance records, procedural manuals, and design schematics, can now accurately identify the likely failing component when an anomaly is detected. This mirrors the invaluable operational intuition previously held only by seasoned engineers.
Collaborating with East Japan Railway (JR East), Hitachi has developed an AI solution that rapidly diagnoses the root causes of malfunctions within the complex control systems governing the Tokyo metropolitan area’s railway network. Crucially, it also assists operators in formulating timely response strategies. In an intricate network where operational disruptions can cascade and impact millions of daily journeys, accelerating fault diagnosis translates directly into enhanced operational resilience.
### Accelerating Development Cycles Through Advanced R&D
Hitachi’s commitment to advancing physical AI is also evident in its research and development pipeline. Findings from two key projects, presented at the prestigious ASE 2025 software engineering conference, address a persistent bottleneck in industrial AI: the considerable time and effort required for control software development and adaptation.
Within the automotive sector, Hitachi and its subsidiary, Astemo, have engineered a system leveraging retrieval-augmented generation. This technology automatically generates integration test scripts for vehicle electronic control units (ECUs) by drawing upon hardware-specific API information and frontline engineering insights. In a pilot program focused on multi-core ECU testing, this automated approach reduced integration testing man-hours by an impressive 43% compared to traditional manual methods.
For the logistics industry, the company has developed a variability management technology that modularizes robot control software into reusable components, structured around the Robot Operating System (ROS). By proactively mapping environmental variables and operational requirements for diverse warehouse settings, this system empowers operators to adapt robotic picking-and-placing workflows for new products or layouts without the need for extensive software reprogramming.
### Safety: An Integrated Design Principle
A consistent theme across Hitachi’s physical AI initiatives is the paramount importance placed on safety guardrails. These are not mere compliance add-ons but fundamental engineering constraints embedded within the system’s architecture from inception. Yanai emphasized Hitachi’s integration of control and reliability technologies derived from social infrastructure development to ensure AI outputs remain within human-defined operational parameters.
This includes rigorous input validation to filter out unsuitable training data, comprehensive output verification to guarantee that machine actions pose no risk to personnel or property, and continuous real-time monitoring of the AI model itself for any operational anomalies. This meticulous approach is critical, as physical AI systems operate in the real world, where the consequences of failure—whether in railway signaling or factory robotics—are far more consequential than those encountered in a simulated environment.
### Building the Infrastructure for Ambitious AI
On the infrastructure front, Hitachi Vantara, the group’s data and digital infrastructure arm, is positioning itself as an early adopter of NVIDIA’s RTX PRO Servers. Equipped with RTX PRO 6000 Blackwell Server Edition GPUs, these servers are designed to accelerate agentic and physical AI workloads. This advanced hardware is being integrated with Hitachi’s iQ platform to construct sophisticated digital twins—virtual replicas of physical systems—capable of simulating a wide range of scenarios, from grid fluctuations to complex robotic movements, at scale.
Concurrently, Hitachi’s IWIM concept is being developed to bridge NVIDIA’s open-source Cosmos physical AI development platform with specialized Japanese-language LLMs and visual language models. This integration is facilitated through the model context protocol (MCP), a framework designed to seamlessly interconnect the diverse models, simulation tools, and industrial datasets essential for robust physical AI systems.
The broader competitive landscape in physical AI remains dynamic and far from settled. However, Hitachi’s assertion—that domain expertise and operational data hold as much significance as model architecture—is becoming increasingly compelling, particularly as its collaborations with partners like Daikin and JR East begin to yield demonstrable, practical value.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/19152.html
