What Everyone Should Know about Machine Learning Posted on : Jun 27 - 2017

Over the last few months I’ve had the opportunity to talk to a lot of decision-makers about artificial intelligence in general and machine learning in particular. Several of these executives had been asked by their investors about their machine learning (ML) strategies and where they have already implemented ML. So how did this technical subject all of a sudden become a topic of discussion in company boardrooms?

Computers are supposed to solve tasks for humans. The traditional approach is to “program” the desired procedure; in other words, we teach the computer a suitable problem-solving algorithm. The algorithm is a detailed description of a procedure, similar to a recipe. There are many tasks that can be described effectively by an algorithm. For example, in elementary school, we all learned the algorithms used to add numbers. When it comes to carrying out algorithms of this kind quickly and flawlessly, computers are far superior to humans.

However, this procedure has its limitations. How do we recognize a photo of a cat? This apparently easy task is difficult to structure as an algorithm. Let’s pause for a moment and think about it. Even simple instructions such as “has four legs” or “has two eyes” have their drawbacks, because these features may be hidden, or the photo might only show part of the cat. Then we encounter the next task of recognizing a leg or an eye, which is just as difficult as identifying a cat.

This is exactly where the strength of machine learning lies. Rather than having to develop an algorithm to solve the problem, the computer uses examples to learn the algorithm for itself. We train the computer on the basis of samples. Using our cat example, this could mean that we train the system using a large number of photos, with those depicting a cat labeled accordingly (supervised learning). In this way, an algorithm evolves and matures that is eventually capable of recognizing cats on unfamiliar pictures. View More