Bergur Thormundsson
Research expert covering technology in the Nordics, AI and smart cities
Get in touch with us nowWhat is the purpose of machine learning?
Interesting as its title might be, machine learning is the subset of artificial intelligence (AI) that does the least learning. Nonetheless, it is one of the most ubiquitous types of AI. Machine learning is a type of software that aims to automate and simplify processes with simple programs. It is ever present in the modern world, being used in nearly all industries. Automatic responses to queries, automated stock trading, computer vision, recommendation engines, and customer service are some examples of the utilities of machine learning. Machine learning is a large market, encompassing the majority of AI software and projects. In line with this, the machine learning market is also the largest segment of the AI market. This market is expected to grow from around 140 billion U.S. dollars to nearly two trillion U.S. dollars by 2030.What kind of AI is machine learning
Of the forms of AI used, machine learning is the simplest. Other AI subsects include deep learning, neural networks, and natural language processing. Machine learning uses datasets to categorize incoming information and provides solutions based on those narrow categorizations. The amount of data required to run machine learning programs is vast but nearly all are categorized by programmers, making it more reliant on human involvement.Machine learning is divided into three types of learning systems: supervised; unsupervised; and semi-supervised. Supervised machine learning uses labeled datasets to easily classify incoming information in order to be more comprehensible for human consumption. An example of this is the automatic categorization of emails into spam or relevant emails. Unsupervised machine learning uses unlabeled datasets to identify similarities or differences between datasets. An example of this is separating customers based on interests. Meanwhile, semi-supervised machine learning is a hybridization of the two and allows specifically labeled datasets to categorize unlabeled data.
Challenges to machine learning
The main challenges to the increased use of machine learning are ethical. Prime among these is privacy, as the datasets retain personal data for further categorization purposes. Data that is available on servers is also at risk of being hacked. Another concern is automation in workplaces, leading to a reduced number of jobs and relocations. Technical superiority will make a difference between societies, with more developed economies reaping a greater benefit from the increased automation. How companies and industries respond to these challenges will have a significant impact on their capability in the field.
