A.I. and ML are often used interchangeably, while many people consider one to be a subset of the other (which one is the bigger set depends on who you ask). However, things may not be as clear-cut as they may seem, since the communities of these two fields are not all the related, while there is a sort of rivalry among the hard-core members of each one of them. Why is that though if A.I. and ML are so similar to each other, enough to confuse even data scientists?
First of all, let’s start with some definitions. A.I. is the group of methods, algorithms, and processes, that bring about computer systems that emulate human intelligence, even if the intelligence they usually exhibit is quite different to our own. Also, these systems often take the form of self-sufficient machines, such as robots, as well as agent programs that roam the Internet or cyber space in general. ML on the other hand is the group of methods, algorithms, and processes that bring about computer systems that solve some data analytics problem in an efficient manner, through some training procedure (the learning part of machine learning). The latter can be with the help of some specific outcomes (aka targets) or without. Also, the training can take the form of feedback on the system’s predictions, which is like on-the-job training of sorts.
Clearly, there is a close link between ML and data science, since ML systems are designed for this sort of problems. A.I. systems on the other hand, may tackle different kinds of problems too (e.g. finding the optimal route given some restrictions). So, there is a part of A.I. that is leveraged in data science and a part of A.I. that has nothing to do with our craft. That part of A.I. that is used in data science has a large intersect with ML, mainly through network-based systems, such as ANNs. Lately, Deep Learning networks, which are specialized and more sophisticated kinds of ANNs, have become quite popular and are also part of that intersect between A.I. and ML.
Many people who work in A.I. consider it more of a science than ML and they are right in a way. Most of ML methods are heuristics based and don’t have much theory behind them, while the ones that are tied to Stats (Statistical and ML hybrids) are heavily restrained by the assumptions that the Stats theory has. A.I. methods are generally data-driven though, but also related to processes found in nature, so they are not out of the blue.
Nevertheless, a data scientist who is being professional and pragmatic doesn’t put too much emphasis on the differences between A.I. and ML methods, since he cares more about how they can be applied to solve the problems at hand. So, even if these two families of methods are not the same, nor is one a subset of the other, they are both very useful, if not essential, in practical data science.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.