Uncovering the Mystery Behind Physical AI
Physical AI refers to any AI system that is designed to interact with the environment, typically through specialized sensors. Photo by Matthew Modoono/Northeastern University
The term “physical AI”, coined by the CEO of NVIDIA, refers to any AI system that is designed to interact with its environment. Northeastern COE professors, through the Physical AI Research Initiative (PAIR), weigh in with their own knowledge and research on the concept, and where it may be headed.
This article originally appeared on Northeastsern Global News. It was posted by Tanner Stening.
Physical AI is already here. But what is it?
You may have seen it: that humanoid robot moonwalking across a stage to Michael Jackson’s “Billie Jean,” executing those deft slides and heel pivots before slipping several times on a set of steps and lying motionless. The video, filmed in Shenzhen, China, has taken social media by storm this week.
The scene may well augur a future in which robots imitate, perform and work alongside humans. That’s because it is an early example of what many experts are calling the next phase of the artificial intelligence boom: physical AI.
What is Physical AI?
The term “physical AI” is largely attributed to Jensen Huang, the CEO of NVIDIA, to refer to AI’s evolution away from the digital screen into the real world. In a recent company blog post, Huang suggested that the “ChatGPT moment for general robotics is just around the corner.”
Physical AI refers to any AI system that is designed to interact with the environment, typically through specialized sensors, said Yanzhi Wang, professor of electrical and computer engineering.
Examples of physical AI systems extend beyond robotics, and include medical devices, autonomous vehicles, smart manufacturing systems and AI-powered drones.
What does it mean for AI to interact with its environment?
Experts describe that interaction in terms of a system’s ability to “perceive, reason and learn” from the environment around it. These AI systems would be able to learn the laws of physics with some degree of autonomy and adaptability.
Sarah Ostadabbas, associate professor of electrical and computer engineering, said that in addition to “sensing and learning from the world,” physical AI systems should, in theory, also be able to “act independently” based on the information they take in from their surroundings.
But to bridge the gap between the simulated or virtual world and the real world, “You need to reason about what you have seen, or what you have perceived,” she added. “So this reasoning component is really important.”
Ostadabbas explained that the reasoning model relies heavily on text, or an understanding of language. Language-based systems reason from descriptions and patterns rather than through direct interaction with the physical world. “We hope that this reasoning component in these systems eventually is derived from the actual physics of the world,” she said.
At Northeastern University’s Physical AI Research Initiative, or PAIR, Ostadabbas and her colleagues are attempting to establish a framework that would help guide the development of physical AI systems.
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Photos by Matthew Modoono/Northeastern University
Read full article at Northeastern Global News