May. 2 at 2:50 AM
Industrial AI is expected to surpass language-based models by being less dependent on human languages and more grounded in universal engineering standards, according to Siemens’ global AI research head. Unlike tools like ChatGPT, which excel in text, images, and video, current models struggle with industrial data such as sensors, time series, and technical diagrams.
While “narrow AI” has long been used in industry for predictive maintenance and automation, the next step is “physical AI”—systems that interact directly with the real world through sensors and devices. This shift will require more hardware and distributed computing, moving AI inference from centralized data centers to factories, vehicles, and homes.
The transition could significantly accelerate automation, enabling tasks that were previously too complex to handle. However, it will still depend on large-scale infrastructure for training and broader adoption across industries.
$SIEGY