People tend to have different opinions when it comes to machine learning. Some have the ideology that machine learning may mark the end of the human race; or a catastrophic end to privacy. Human beings have the idea that machine learning will give rise to superhuman intelligence, but this is not usually the case. On the contrary, it is quite apparent that computers do not have common sense, and there is no way in this world that they can overtake the thinking capacity of human beings.
At times, people believe that machine learning is purely meant for data summarization, but that’s not the case. The reality of the matter is that machine learning tends to predict the future by checking on your history. A good example of future prediction is the type of movies one has watched. In such a situation, the machine will be able to suggest similar films based on your past preferences.
Learning algorithms tend to discover more profound knowledge and not just correlations between events as people tend to assume. According to Pedro Domingos, a mole can be used to explain the rule of learning algorithms. For example, if a mole grows with an irregular shape and color, there is a high possibility that it might have skin cancer.
Another myth is that machine learning does not discover causal relationships but correlations only. Machine learning performs different activities and observes whether the result of the occurrence is due to the other event, or if the outcome means that there is a causal relationship between two different events. A computer can quickly discover the causal relationship by just looking at the past data, without necessarily having to experiment.
Machine learning is also believed to cause hallucinating patterns due to the intense provision of data. However, this is not the case as machine learning experts tend to keep this information at minimum levels. This is achieved by gathering more information which has the same set of attributes.
Many are the times when people assume machine learning ignores preexisting knowledge. In actual sense learning, algorithms use data to refine a pre existing body of knowledge, which is detailed as long as it is fed in a computer in simple terms. Lastly, the idea that machine learning cannot predict hidden events that have occurred previously is purely a misconception. Machine learning predicts rare events with high accuracy through comparison.