As Artificial Intelligence (AI) becomes more prominent in everyday life and all areas of tech, especially generative AI, it can be easy to mistake Machine Learning (ML) capabilities as using AI instead, especially if a company is relabeling their ML features as AI to keep up with the hype. And in case you’d like to check our credibility, while we do list our ML and intelligent features on our AI webpage, we’re very clear about which capabilities are powered by AI and which are simply intelligent. The differences between AI and ML are important to note as we navigate the ever-evolving tech world.
“Machine Learning” is a term coined by Arthur Samuel, a programmer who worked for IBM. He created a program that calculated which side of a game of checkers would win depending on the layout of the board. When ML was officially invented is a little up in the air, as Samuel created his program in 1952 but didn’t officially coin the term until his paper explaining the program in 1959. In the early 60s, ML was developed further as a “learning machine” to evaluate speech patterns, sonar signals, and more.
ML is a subset of AI that helps AI learn and become more intelligent. It uses intelligent algorithms to recognize patterns, analyze data, and learn without being instructed by humans. The goal is for the computer to be able to learn on its own using models of data and evolving based on its experience.
The phrase “Artificial Intelligence” was coined in 1956 (which was before Machine Learning was officially coined). John McCarthy came up with it during the Dartmouth workshop, which was the event that marked the formal beginning of AI as a discipline. While AI as we know it has only been around since ‘56, the concept of machines thinking for themselves dates back to Ancient Greek mythology in automatons created by the Hephaistos, the most famous being Talos, the bronze protector of Crete.
However, today, AI is the ability of a computer to mimic the cognitive abilities of people, from learning, to problem-solving, to reasoning. AI can react to learning new information, which can be provided by people or ML training. AI suddenly became a huge part of everyday life with the release of ChatGPT. Even if you don’t use ChatGPT itself, most (if not all) GenAI features use ChatGPT securely to generate results.
Nowadays, AI systems are built using ML to help them learn how to interpret the data fed to them by the ML models. The ML models are refined and optimized by data scientists, which is why building responsible AI without letting personal biases leak into the models is so crucial, and often difficult. Some ML algorithms require supervision, and the data scientists provide both the input data and exactly what the output should be, while others use only the input data and learn from it on their own.
Simply put, ML learns so AI can reason. Using our own platform as an example, the bots that work on Intelligent Object Identification are simply intelligent - no ML or AI. The ML comes in at self-healing. ML trains our self-healing algorithms (that also learn from which self-healing actions our users accept and reject) to handle dynamic data and complex applications and be able to roll with the punches of changing data instead of breaking. We have several applications of AI, like our test data generation that generates reusable test data in-app, so no more copy/pasting from a spreadsheet. If you’re ready to see all the ML and AI in action, then get started with a free trial of Virtuoso!