The Dunning-Kruger Effect is a bias where people think that they are smarter than they actually are. A person with low ability is more likely to overestimate themselves. This is due to their lack of perception of the task at hand and lack of previous experience. Both leading them to mis-calibrate there judgement. If a person has tried to shoot a basket a 1,000 times, they are more likely to judge their own skills as compared to someone who has tried 10 times.
Neural networks during learning also exhibit some similar behaviour. When a neural network is undergoing training by use a dataset, it optimize it’s own parameters to create a best guess for the problem at hand. However, if there are not enough data to learn from the network will more often fail to generalize the problem at hand. This would mean that it has failed to get an “average” of the problem at hand forcing it to misread real world data.
Everyone is susceptible to this.
Study conducted. Tests on group of students. People who scored less overestimated while competent people underestimated. This is common when we set out to learn something new. Initially with little knowledge we seem to believe that we know a lot more than what we actually do. But as we progress and see the big picture as to how hard it is or how vast the topic is we tend to doubt ourselves. Psychologists call this the valley of despair.
This slope is similar to the hype cycle. Even on a large scale, especially in the realms of new technology adoption, a large group of people tend to follow these steps.