What Is Machine Learning?

After stints at MIT and Bell Telephone Laboratories, Arthur L. Samuel joined IBM in 1949 at the Poughkeepsie Laboratory. His efforts helped boost the computing power of the company’s machines, such as with the development of the 701 (this was IBM’s first commercialized computer system).

But he also programmed applications. And there was one that would make history—that is, his computer checkers game. It was the first example of a machine learning system (Samuel published an influential paper on this in 1959⁶). IBM CEO Thomas J. Watson, Sr., said that the innovation would add 15 points to the stock price!⁷

Then why was Samuel’s paper so consequential? By looking at checkers, he showed how machine learning works—in other words, a computer could learn and improve by processing data without having to be explicitly programmed. This was possible by leveraging advanced concepts of statistics, especially with probability analysis. Thus, a computer could be trained to make accurate predictions.

This was revolutionary as software development, at this time, was mostly about a list of commands that followed a workflow of logic.

To get a sense of how machine learning works, let’s use an example from the HBO TV comedy show Silicon Valley. Engineer Jian-Yang was supposed to create a Shazam for food. To train the app, he had to provide a massive dataset of food pictures. Unfortunately, because of time constraints, the app only learned how to identify…hot dogs. In other words, if you used the app, it would only respond with “hot dog” and “not hot dog.”

While humorous, the episode did a pretty good job of demonstrating machine learning. In essence, it is a process of taking in labeled data and finding relationships. If you train the system with hot dogs—such as thousands of images—it will get better and better at recognizing them.

Yes, even TV shows can teach valuable lessons about AI!

But of course, you still need much more. In the next section of the chapter, we’ll take a deeper look at the core statistics you need to know about machine learning. This includes the standard deviation, the normal distribution, Bayes’ theorem, correlation, and feature extraction.

Then we’ll cover topics like the use cases for machine learning, the general process, and the common algorithms.