The rapid development of the technology industry is assisting people in opening up entirely new pages of knowledge; however, people are misunderstanding and increasingly exaggerating the real uses and features of it. Machine Learning has always been a contentious topic, particularly in the field of AI, because some people have misconceptions about the nature of Machine Learning.
Join American Code Lab to learn 5 basic misconceptions in Machine Learning you won’t want to make.
- Are Machine Learning and AI the same?
This is a common mistake made by the majority of people who are in the early stages of learning about Information Technology. In fact, Machine Learning is a part of AI, Artificial Intelligence allows Machine Learning to imitate intelligent human behavior and Machine Learning is a tool of AI that allows systems to automatically learn and improve experiences for optimizing our work.
- Machine Learning predicts the future
According to some studies, Machine Learning predicts the future inadvertently, which causes many people to misunderstand the nature of this. Machine Learning can predict the future and is accurate only when certain conditions are met. Given the way Machine Learning works, problems with data that is similar to the past will foretell the future. That necessitates constant information updating, and microcomputers must process data in a high volume of work, requiring programmers to build complex algorithms with absolute accuracy, which not everyone can do.
- Machine Learning technology can solve any problem
This is completely impossible, while Machine Learning has made breakthroughs in solving complex problems, it is still far from creating the thinking machines like our imagination. Algorithms applied in Machine Learning are all destined to solve specific problems and depend on many factors, especially human assistance. Google and Facebook have adopted this tool in optimizing the user experience and shared that through monitoring they realized that Machine Learning will not be able to completely replace humans in the future because it relies on large amounts of available data or requires constant updating of information to process and produce results.
- The more data there is, the better Machine Learning will perform.
In fact, not every data is useful for Machine Learning. All data needs to be sorted and paste into different features. Currently, based on transfer learning, Machine Learning systems can be trained to use data with a smaller data set.
- Machine Learning always serves good deeds
In addition to its other functions, Machine Learning is used in anti-virus tools, where programmers will systematize data based on appropriate algorithms, transforming Machine Learning into a solid line of defense, capable of monitoring every movement of intrusion steps before cyber attacks to warn users. However, at present, many bad actors take advantage of the optimization in data analysis and statistics, turning Machine Learning into a dangerous tool when relying on it to aggregate user information and conduct widespread fraud.
The fact that Machine Learning participates in most human life activities aids in improving labor productivity, but Machine Learning still has limitations that need to be overcome in order to improve. Instead of deifying this toolkit, we should conduct an accurate, scientific evaluation that ensures Machine Learning objectivity.