Perceptions in AI

- Overview

In the context of AI, perception generally refers to the ability of a system to interpret and make sense of information from the environment. In the case of AI, perception often involves the use of sensors and data processing techniques to understand the world.

• Perception is the process of interpreting, acquiring, selecting, and organizing sensory information captured in the real world. For example, humans have sensory receptors for touch, taste, smell, sight, and hearing. As a result, messages received from these receptors are transmitted to the human brain, which organizes the data.
• Information response is achieved by interacting with the environment in order to manipulate and navigate the objects within it.
• Perception and action are key concepts in robotics. The figure below depicts the overall structure of a fully autonomous robot.
• There is an important difference between AI programs and robots. AI programs operate in computer simulations, while robots operate in the real world. In chess, for example, an AI program might be able to make moves by searching for different nodes, even though it lacks the ability to sense or touch the physical world. However, by interacting with the physical world, chess-playing robots can make moves and catch pieces.