More engineers and other professionals are getting started with machine learning – they're doing the early research and building initial systems, to start exploring how this field of artificial intelligence can open up doors for individuals and companies.
However, throughout the process, there's quite a bit of confusion. What is machine learning, anyway?
The basic idea is that new technologies enable machines to “think” and “learn” in ways that are more similar to the ways that the human brain works.
That said, there are more than a few ways to describe this process. For a little more, let's go to StackOverflow, a mainstay for programmers and other IT professionals looking for definitions and real explanations of technical issues. A StackOverflow thread describes machine learning as “the process of teaching computers to create results based on input data.”
Another writer describes machine learning as “a field of computer science, probability theory, and optimization theory which allows complex tasks to be solved for which a logical, procedural approach would not be possible or feasible.”
This latter definition hits close to a major point on what machine learning is – and isn’t.
When the writer says “a logical, procedural approach would not be possible or feasible,” that points to the real “magic” and value of machine learning. Simply speaking, it’s “post-logic” – machine learning goes beyond what tradition, linear and sequential codebase programming can do!
Taking a step back, we can look at the basic building blocks of machine learning to better understand how.
First, there's training data – the training data gives the program inputs to work from.
Along with the training data, there are algorithms that crunch that data and interpret it in various ways. Experts describe the essential work of machine learning as “pattern recognition” – and you'll see this in the StackOverflow page, too – but again, that only partly describes how machine learning works.