Physical AI explained: Everything you need to know

Physical AI creates systems that learn about and understand an environment directly from sensor data. Indeed, its primary input providers are sensors and actuators. Whereas generative AI requires human input, physical AI systems receive input from many tools, including cameras, microphones, temperature gauges, inertial sensors, radar and lidar.

Also, while the passage of time does not affect generative AI processes, physical AI systems require real-time perception and reasoning to understand an environment, and then react quickly and accordingly.

Physical AI needs an actuator -- robotic arms, wheels or any other device -- that moves through or interacts with an environment and enables modification or manipulation of physical objects in that environment.

Physical AI systems have greater autonomy than those in generative AI. They don't require humans to initiate actions. They make decisions based on their perception of the environment and their programming. Since learning and adaptation go hand in hand with autonomy, many physical AI systems contain learning algorithms to recognize environmental changes, adapt as needed and improve their performance over time.

Currently, most physical AI systems are limited to specific tasks or small environments with mixed results at best. Commercially, a well-known example is the Roomba autonomous floor cleaner.

Physical AI and robotics

Physical AI's interactive systems are tightly interwoven with robotics, enabling AI-powered robots to perceive, reason and act autonomously in their environments.

Traditionally, robotics relied on a variety of input devices, such as cameras, lidar, sonar and other environmental sensors. The key difference between older robotic systems and those powered by physical AI is autonomy in decision-making. Older robots are preprogrammed to make decisions or react in a certain way to hard coding.

A physical AI-powered robot relies on real-time data amid changing environmental conditions to make its decision on the spot, then learn from those decisions and adapt to similar situations. As such, these robots rely on neural networks and deep learning to analyze their experiences and improve future reactions and interactions.

Physical AI robots employ advanced actuators -- driven by AI and machine learning (ML), far surpassing current models and designs -- to interact with their environment, moving through and manipulating objects in it.

Physical AI interacts with the environment around it -- and not necessarily humans. Rather than asking a question like generative AI does, physical AI operates and adjusts within the physical world based on input from sensors and actuators.

Of course, when interacting with humans and operating autonomously, physical AI's safety and interaction protocols are paramount, ranging from human proximity alerts and collision avoidance to the ability to recognize facial expressions and even attempt to understand human intentions.

Physical AI also requires work from multiple experts in various fields, drawing from the best in robotics, computer vision, ML, control theory and mechanical engineering to develop a properly functioning system.

Finally, because of their constant interactive nature, physical AI's unique systems demand powerful processing capabilities to respond effectively during dynamic environmental changes, necessitating software algorithms and hardware that make decisions in milliseconds.

Physical AI augments human work. Computers, after all, don't tire or err, making them invaluable in a sensitive environment, such as a hospital. For instance, an AI robot ensures that a patient receives medication on time and regular monitoring of vital statistics.

Physical AI also aids in data gathering. Again, sensors and real-time analytics monitor, for example, a sensitive manufacturing process. Furthermore, physical AI is essential in any environment where constant observation, alert notification and rapid response to troublesome data are the norm.

Taken to its extreme, physical AI is useful in hazardous environments or situations where human safety is a concern. Yes, current fire departments send robots to examine areas dangerous to humans, and bomb squads deploy robots capable of containing suspected explosives, avoiding danger to human life and limb. However, physical AI adds greater intelligence and learning ability to these machines, and it potentially eliminates human participation in a dangerous task.

The future of physical AI