Physical AI
This refers to AI that moves beyond the screen and into the physical world such as robots and vehicles. It became popular as NVIDIA promoted it as a next-generation keyword.
Physical AI goes beyond AI that only answers on the screen like a chatbot, and refers to AI that is installed in machines such as robots, self-driving cars, and drones and recognizes the physical world and actually moves. It is easy to understand if you think of it as the brain of a robot that chops and cooks ingredients in the kitchen, rather than an AI that explains recipes in writing.
As generative AI achieved results in language and images, this term emerged amid predictions that the next battleground would be robots and autonomous driving to solve the labor shortage. It has become a key keyword in the industry as NVIDIA has promoted it as a next-generation growth engine along with a platform for robot development, and is used in conjunction with the investment boom in humanoid robots.
However, it is important to note that the physical world is much more difficult to predict than language, and the cost of mistakes is high, so there is a large gap between demonstration videos and actual commercialization.
✅ Why it matters
- It is attracting attention as a solution to labor shortage problems in manufacturing, logistics, and care.
- It is a key keyword for understanding the next growth stage of the AI industry.
- It is background knowledge for reading investment trends in robots, autonomous driving, and semiconductors.
⚠️ Limits and debates
- Responding to unexpected situations in the physical world is still technically difficult
- Because the machines actually move, there is a high risk of safety accidents and liability issues
- There is controversy over over-packaging due to the large difference in level between demonstration videos and mass-produced products.