Human intelligence is derived from the incredible duality of arriving at conclusions based on pattern perception. On the other hand, conclusions are based on highly structured and rational decision-making. Similarly, machine perception is the ability to deduce aspects of the world using input from sensors (such as cameras, microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors). Speech recognition, facial recognition, and object recognition are all examples of applications.

What is Perception in AI?

• Perception is the process by which sensory information captured in the real world is interpreted, acquired, selected, and then organized. Human beings, for example, have sensory receptors for touch, taste, smell, sight, and hearing. As a result, the information received from these receptors is transmitted to the human brain, which organizes the data.
• Response to information is taken by interacting with the environment in order to manipulate and navigate the objects that are in it.
• Perception and action are critical concepts in robotics. The following figures depict the fully autonomous robot in its entirety.
• There is a critical distinction to be made between an AI programme and a robot. The AI programme operates in a computer-simulated environment, whereas the robot operates in the real world. For instance, in chess, an AI programme may be able to make a move by searching through different nodes despite the fact that it lacks the ability to sense or touch the physical world. However, by interacting with the physical world, the chess-playing robot can make a move and grasp the pieces.

What is Machine Perception?

Machine perception enables the computer to use sensory input in addition to conventional computational methods to gather information more accurately and present it in a more comfortable manner for the user. Computer vision, machine hearing, machine touch, and machine smelling are all examples of these. Furthermore, making machines see, feel, and perceive the world in the same way humans do is the ultimate goal of machine perception. This means that machines will be able to make decisions, warn us when they don't, and most importantly, explain why they didn't work out.

Our Brain in a Simplistic Model

The brain model would be composed of two components:

• Right: On the basis of perception
• Left: Logic-based

Our senses—taste, sight, touch, smell, and hearing—provide patterns to the appropriate part of our brain in order for it to generate perceptions. whereas all of our logical interpretations have an effect on the left part and result in a structured and rational understanding of a situation or problem.

In all situations, both hemispheres of the brain are active concurrently. While the right part of the brain is making sense of things based on patterns, the rational part of the brain is making sense of things based on some logical structure and making sense of things in the same way.

What do perceptions have to do with AI?

Today, the majority of AI systems are based on deep learning, which involves exposing the AI system to tens of thousands of illustrative examples. The Deep Learning method is about putting small details and subtle nuances from images, videos, or sounds into the parameters of the AI system's neural network. Following training, the AI system is capable of perceiving input data based on patterns in images, faces, objects, motions, or sounds fed into the system. The AI system makes decisions based on how it sees patterns in the data it receives. The AI system acts like the right side of the brain, which is good at seeing patterns.

Additionally, the left side is concerned with comprehending and dealing with the situation's logic. This is more akin to the standard computing that we are accustomed to with a personal computer (PC) or a smartphone. This is about structuring situations with rules that can be expressed in "IF-THEN-ELSE" logic. In our simplified model, it is comparable to the left side of the brain.

Conclusion

Deep learning is concerned with recognising patterns in input data and deriving perceptions based on deep learning about a particular subject. These perceptions are expressed as a "level of confidence" in the situation-specific decisions to be made. AI is, in effect, artificial perception. An AI machine simulates the human brain's perception ability. Additionally, today's AI neural networks are typically focused on a single domain of expertise. Connecting 100s or 1000s of specialized neural networks could lead to a broader general-purpose intelligence, like how our brain's different parts are linked together.

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