Evolution has taught us that humans excel at learning. We have spent centuries gathering and processing information so we have enough input to decide what to do next. We also have the advantage of collective knowledge and memory so we can learn from historical patterns. We are at an age in our technological development that artificial intelligence is also capable of learning. This process, called machine learning, allows AI systems to gather and analyze information just like humans, even though the method of input may be different. Here are some ways in which machines mimic human learning.
Supervised learning is a method of learning in which both the input and output variables are known, and an algorithm is used to find the mapping function that gets you from the input to the output. Machines can make predictions about the mapping functions, which can be corrected by a “teacher” until an acceptable level of performance is reached.
Unsupervised learning is just as it sounds: learning without direct guidance. A system is given information that is not classified, and the machine uses observation to group information by patterns and structures. Unsupervised learning is classified by two types of algorithms, clustering and association. Clustering works well when you are looking for an inherent pattern in groups, and association is a problem in which you want to discover rules that describe large portions of data.
Reinforcement is used when you want to increase the accuracy of your system’s predictions. Reinforcement is unlike supervised learning in that there is no answer and the system is tasked with making its own decision based on experience. Machine learning tools can learn from previous data and make more accurate and efficient decisions based on past behavior.
The benefits of machine learning are numerous. Computers are capable of processing vast amounts of data much more quickly than humans, and tend to make decisions that are more accurate. It is also an inexpensive way to process large amounts of information. Many companies use machine learning software to help make predictions about how consumers will behave and recommend specific products and services accordingly.
As artificial intelligence continues to improve, the capabilities of machine learning also grow. We can teach computers how to interpret data and adapt to new information by relating it to past experience through supervised and unsupervised methods. Machine learning is a tremendous opportunity to increase the efficiency of information processing.