Tech buzzwords are everywhere. From scholarly articles to Twitter, as technology continues to advance, the vocab continues to evolve and overlap. Digital transformation, machine learning, artificial intelligence, robotic process automation, business process automation… are all of these terms really different? Or are they just different versions of the same thing?
We looked at a few of the biggest tech buzzwords – Artificial intelligence, machine learning and robotics – and figured out what makes them different, and how they can work together.
Artificial intelligence (AI) performs tasks using human intelligence and cognition. It has the ability to recognize patterns and understand natural language, allowing the technology to learn and solve problems. AI spans from “narrow,” or being able to perform one specific task extremely well – like searching a database– or “general,” which means that it can perform every task it is intended to perform equal to or better than a human. One of the biggest differentiators of AI is that it can learn without being programmed. Occasionally, AI uses machine learning to kick-start its decision-making abilities.
Machine learning is a form of AI, as well as a way of achieving AI. According to Arthur Samuel who coined the term “machine learning” in 1959, “The ability to learn without being explicitly programmed.” Machine learning uses an algorithm to learn, or to be trained to do something, but is not hardcoded with specific directions. Unlike AI, machine learning uses an algorithm to learn and make decisions, continuously using the data it pulls in and statistical analysis to make better-informed decisions.
Robots are programmable machines that carry out tasks autonomously, or independently. They’re not computers, but also not completely artificially intelligent. Robots are physical objects and vary in their ability to learn. Some robots do not learn at all. Instead, they perform a task repetitively (think surgery robots and assembly line robots). These robots are not artificially intelligent. However, some robots can self-learn and have machine learning capabilities built in. These robots are considered AI and perform more complex tasks.
Where does RPA fit in?
Simply put, RPA fits into all of those categories, as well as none of those categories. Ok, maybe that’s not so simple. Machine learning and AI engage in “thinking,” while RPA is more concerned with “doing.” That is, unless you implement AI or ML technology into your RPA solution. In this case, your RPA solution will do more than just follow the built-in workflow; it learns certain procedures, engages in trial-and-error, adjusts based on the results, and it makes cognitive decisions and predictions like a human would. Oftentimes, RPA solutions incorporate optical character recognition (OCR), which is considered AI, in order to pull information and make decisions based on that information. When this happens, RPA is no longer solely responsible for handling tedious, repetitive tasks, but for making intelligent decisions. More to come in the future!