Differences between Data Mining, Machine Learning and Artificial Intelligence
With so many terms dominating the technology landscape, it can be easy to get overwhelmed or lose track of what means what. Because so many of them have similar characteristics with cross-functional purposes, sometimes the lines become blurred, making it difficult to distinguish one from the other.
For instance, while data mining, machine learning and artificial intelligence all have something in common, they also have distinct applications that make them unique. Below are some of their major differences.
Data Mining is the process of discovering previously unseen patterns and trends in a large dataset. It’s a tool used by organizations to extract valuable insights and discover new information that can help them make better business decisions.
An example of data mining is a bank “mining” data to find out which loans are most likely to default or which customers will be most likely to accept new product offers. In general, data mining is used across industries to identify a sales trend or buying pattern, improve a production process, predict the adoption of a new product, etc.
One of data mining’s defining characteristics is that it typically uses batched information to reveal insights at a certain point in time rather than an on-going basis.
Fun fact: data mining is also known as “knowledge discovery in databases” (KDD), which was coined in 1989 by Gregory Piatestsky-Shapiro. The term “data mining” appeared in the database community in 1990.
Simply put, machine learning is the ability of a computer to learn from mined datasets. As the name implies, it’s all about the machine automatically learning algorithms in order to improve its performance of a particular task.
Unlike data mining which requires humans to interpret the insights and apply them to business decisions, machine learning eliminates the human element by making the machines do the work and learn through experience over time.
In other words, computers learn how to determine probabilities and make predictions based on their own data analysis. The more a computer program "learns" about a data set, the better it predicts the outcome of a new set of data.
For example, virtual assistants like Siri, Alex and Google use machine learning to improve their responses to questions. Another example is healthcare technology using machine learning to analyze medical imaging to identify any anomalies.
Perhaps the most obvious example of machine learning is in social media; machine learning algorithms use all the information they’ve captured on your social media account – your likes, engagements, behavior, etc. – to customize your social media feeds to your particular preferences.
In general, machine learning is incredibly valuable for ongoing processes, marketing campaigns, customer service improvements, etc.
Fun fact: the term “machine learning” was coined in 1959 by Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence. He stated that machine learning “gives computers the ability to learn without being explicitly programmed.”
Artificial Intelligence (AI) is a term used to categorize the development of intelligent machines designed to mimic the problem-solving and decision-making capabilities of humans. In other words, AI is any system that seems human-like and can imitate-human behavior.
For instance, many companies today use chatbots that are powered by AI to answer customers’ questions. These AI chatbots, however, use machine learning to improve their understanding of customers’ responses and answers in order to create a more “real” experience.
AI is playing an increasingly large role in everyday life, powering search engines, product recommendations, voice recognition systems, etc.
Fun fact: the idea behind “artificial intelligence” dates back to 1950 when Alan Turing, known as the “father of computer science,” explored whether machines could think for themselves in his work, “Computing Machinery and Intelligence.” He came to develop what is now famously known as the “Turing Test,” where a human interrogator tries to distinguish between a computer and human text response.
When it comes to understanding the differences between data mining, machine learning and artificial intelligence, you can think about it like this: data mining is the technique of diving deep into data to extract useful information, whereas machine learning is a method of improving complex algorithms to make machines more useful. Accordingly, machine learning is a subfield of artificial intelligence, as AI uses machine learning algorithms for its intelligent behavior.