Why ‘physical AI’ marks the next frontier in industrial applications

Physical AI represents the next major frontier in industrial applications of artificial intelligence. As concerns about AI spending intensify, companies are seeking tangible ways to monetize AI tools. Abu Dhabi’s state-owned oil company ADNOC recently signed a deal with AI firms G42 and OpenAI to deploy advanced AI capabilities across its energy operations, aiming to improve efficiency and tackle complex engineering challenges.

OpenAI CEO Sam Altman noted that this marks a shift where the energy industry can now benefit from AI advancements.

Physical AI, also known as AI systems that interact with the physical world, differentiates itself from today’s digital AI applications. Physical AI involves robots, camera-bots, and self-driving cars using AI to discern, comprehend, and perform complex actions in the physical world. This includes combining AI models with sensors, actuators, and control systems to enable the systems to act upon real-world environments.

Unlike digital AI applications, physical AI enables autonomous systems to perceive their environment, reason about it, and take action. For instance, AI-infused robots can gain a "common sense" understanding of the world, improving their performance over time through reinforcement learning algorithms. Nvidia CEO Jensen Huang coined the term "physical AI" and predicts a future with a billion robots, marking a new industrial revolution.

Generative AI models, such as ChatGPT or Meta’s Llama, while adept at generating language and visuals, struggle to grasp the physical world. Physical AI overcomes this limitation by integrating multimodal inputs, like images, videos, text, and real-world sensor data, to generate insights or actions that autonomous machines can execute.

The effectiveness of physical AI hinges on real-world data, which must be captured, fused, and provided to the systems for continuous improvement. Several companies have already begun implementing physical AI in real-world applications, including predictive maintenance systems, AI-driven drilling data analysis, seismic interpretation, and more.