top of page
  • Writer's pictureAvner Farkash

The walking apple

Perception is how we interpret sensory information from the world around us. Humans and machines perceive the world in different ways, with each having its advantages and limitations.

Our experiences, expectations, and emotions shape human perception. We use our senses to gather information about the world and then interpret that information based on our past experiences and knowledge. For example, if we see a red apple, we recognize it as an apple because we have seen and eaten apples before. Our expectations and emotions also influence our perception. If we are hungry, the sight and smell of an apple may be particularly appealing. In contrast, if we have a negative experience associated with apples, such as getting sick after eating one, our perception of the apple may be harmful. Machines, on the other hand, perceive the world through sensors and algorithms. They do not have past experiences or emotions to influence their perception, and they cannot interpret sensory information as humans do. Instead, machines use algorithms to analyze sensor data and make decisions based on that analysis. For example, a self-driving car might use sensors to detect the distance between it and other vehicles on the road and then use an algorithm to decide when to brake or accelerate. While machines have some advantages over humans regarding perception, they also have limitations. For example, machines can only perceive what they have been programmed to perceive. They cannot recognize objects or situations outside of their programming and may not be able to interpret sensory information as humans do. Additionally, machines do not have emotions or intuition, which can be necessary in some situations. In conclusion, the perception of humans and machines is different, each with advantages and limitations. While humans rely on their experiences, emotions, and expectations to perceive the world, AI engines use sensors and algorithms. By understanding these differences, we can appreciate each approach to perception's unique strengths and weaknesses and work to develop systems that combine the best of both worlds.


bottom of page