What Is Machine Learning? Types And Examples

Machine learning, like most technologies, comes with significant challenges. Some of these impact the day-to-day lives of people, while others have a more tangible effect on the world of cybersecurity.

Many people are concerned that machine-learning may do such a good job doing what humans are supposed to that machines will ultimately supplant humans in several job sectors. In some ways, this has already happened although the effect has been relatively limited. 

For example, the car industry has robots on assembly lines that use machine learning to properly assemble components. In some cases, these robots perform things that humans can do if given the opportunity. However, the fallibility of human decisions and physical movement makes machine-learning-guided robots a better and safer alternative.

Also, a machine-learning model does not have to sleep or take lunch breaks. It also will not call in sick or get into disputes with others. Some manufacturers have capitalized on this to replace humans with machine learning algorithms.

However, the fear may be somewhat overblown. While machine-learning can do things humans cannot, it also does jobs that humans would rather not do. The same human resources that machine learning “replaced” can, in many cases, be used to accomplish other tasks—tasks that machines cannot do. These include making managerial decisions on the fly, and serving as mentors, teachers, artists, and other jobs where human discretion is paramount.

Technological singularity refers to the concept that machines may eventually learn to outperform humans in the vast majority of thinking-dependent tasks, including those involving scientific discovery and creative thinking. This is the premise behind cinematic inventions such as “Skynet” in the Terminator movies.

However, not only is this possibility a long way off, but it may also be slowed by the ways in which people limit the use of machine learning technologies. The ability to create situation-sensitive decisions that factor in human emotions, imagination, and social skills is still not on the horizon. Further, as machine learning takes center stage in some day-to-day activities such as driving, people are constantly looking for ways to limit the amount of “freedom” given to machines.

Because these debates happen not only in people’s kitchens but also on legislative floors and within courtrooms, it is unlikely that machines will be given free rein even when it comes to certain autonomous vehicles. If cars that completely drove themselves—even without a human inside—become commonplace, machine-learning technology would still be many years away from organizing revolts against humans, overthrowing governments, or attacking important societal institutions.

Since machine learning can analyze objects and people’s faces, it is possible for human privacy to be invaded by the machines that collect and store their data, including those that pertain to their belongings and objects within their homes. 

For example, if machine learning is used to find a criminal through facial recognition technology, the faces of other people may be scanned and their data logged in a data center without their knowledge. In most cases, because the person is not guilty of wrongdoing, nothing comes of this type of scanning. However, if a government or police force abuses this technology, they can use it to find and arrest people simply by locating them through publicly positioned cameras. For many, this kind of privacy invasion is unacceptable.

On the other hand, machine learning can also help protect people's privacy, particularly their personal data. It can, for instance, help companies stay in compliance with standards such as the General Data Protection Regulation (GDPR), which safeguards the data of people in the European Union. Machine learning can analyze the data entered into a system it oversees and instantly decide how it should be categorized, sending it to storage servers protected with the appropriate kinds of cybersecurity.

Because machine-learning models recognize patterns, they are as susceptible to forming biases as humans are. For example, a machine-learning algorithm studies the social media accounts of millions of people and comes to the conclusion that a certain race or ethnicity is more likely to vote for a politician. This politician then caters their campaign—as well as their services after they are elected—to that specific group. In this way, the other groups will have been effectively marginalized by the machine-learning algorithm.

Similarly, bias and discrimination arising from the application of machine learning can inadvertently limit the success of a company’s products. If the algorithm studies the usage habits of people in a certain city and reveals that they are more likely to take advantage of a product’s features, the company may choose to target that particular market. However, a group of people in a completely different area may use the product as much, if not more, than those in that city. They just have not experienced anything like it and are therefore unlikely to be identified by the algorithm as individuals attracted to its features.

The future of machine learning lies in hybrid AI, which combines symbolic AI and machine learning. Symbolic AI is a rule-based methodology for the processing of data, and it defines semantic relationships between different things to better grasp higher-level concepts. This enables an AI system to comprehend language instead of merely reading data.