Industrial-Level Generative AI is Put to the Test
By: Joe McKendrick
Generative AI has been made famous for its abilities to generate marketing content, script emails, and answer queries about business trends. Now, GenAI is being put to the real test: providing support to production and other heavy-duty operations. Use cases now emerging include digital twins, robots, and multi-agent systems.
The potential use cases in heavy-duty business settings is explored by Dhakshinamoorthy Renganathan of Tata Consultancy Services in a recent article published by the Industrial IoT Consortium. “Generative AI is a major step towards achieving full general-purpose AI,” he pointed out. “Generative AI provides tools which can be leveraged to build autonomic systems. Adaptive systems are a major step towards building autonomic systems.”
Industries such as telecom, mobile phones, and automotive manufacturing stand to benefit from adaptive systems built and maintained with GenAI, Renganathan observed. Such systems “can extend the lifespan of systems and can create new business models and revenue opportunities.”
For example, generative AI adds an entirely new dimension to digital twins, he illustrates. “While traditionally digital twins have been built towards bringing together structured information in engineering, production, operations, and enterprise information, few digital twins have integrated with 3D models to improve visualization aspects of the digital twins,” he explained.
“However, a significant portion of industrial knowledge is present as documents and unstructured information. GenAI contextualizes this information very well and can be used to enrich the knowledge repository of digital twins. Such a digital twin can power many use cases.”
See also: Generative AI Poised to Unleash More Independence for Humanoid Robots
Generative AI as a go-between
Generative AI also provides natural language interfaces to robots. For example, a user can “provide high-level feedback through the generative AI large language model [LLM] while monitoring the robot’s performance,” Renganathan stated. The human can apply “prompt engineering to generate policy-based codes to adapt behavior of robots” that prov…
Continued on RTInsights.com
