The scientific field of machine learning (ML) is a branch of artificial intelligence, as defined by Computer Scientist and machine learning pioneer Tom M. Mitchell:

“Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience“

An algorithm can be thought of as a set of rules/instructions that a computer programmer specifies, which a computer is able to process. Simply put, machine learning algorithms learn by experience, similar to how humans do. For example, after having seen multiple examples of an object, a compute-employing machine learning algorithm can become able to recognize that object in new, previously unseen scenarios.

How does machine learning work?

In the video above [3], Head of Facebook AI Research, Yann LeCun simply explains how machine learning works with easy to follow examples. Machine learning utilizes a variety of techniques to intelligently handle large and complex amounts of information to make decisions and/or predictions.

In practice, the patterns that a computer (machine learning system) learns can be very complicated and difficult to explain. Consider searching for dog images on Google search — as seen in the image below, Google is incredibly good at bringing relevant results, yet how does Google search achieve this task? In simple terms, Google search first gets a large number of examples (image dataset) of photos labeled “dog” — then the computer (machine learning system) looks for patterns of pixels and patterns of colors that help it guess (predict) if the image queried it is indeed a dog.

At first, Google’s computer makesa random guess of what patterns are reasonable to identify an image of a dog. If it makes a mistake, then a set of adjustments are made in order for the computer to get it right. In the end, such collection of patterns learned by a large computer system modeled after the human brain (deep neural network), that once is trained can correctly identify and bring accurate results of dog images on Google search, along with anything else that you could possibly think of — such process is called the training phase of a machine learning system.

Imagine that you were in charge of building a machine learning prediction system to try and identify images between dogs and cats. The first step, as we explained above, would be to gather a large number of labeled images with “dog” for dogs and “cat” for cats. Second, we would train the computer to look for patterns on the images to identify dogs and cats, respectively.

Once the machine learning model has been trained [7], we can throw at it (input) different images to see if it can correctly identify dogs and cats. As seen in the image above, a trained machine learning model can (most of the time) correctly identify such queries.

Why is machine learning important?

Machine learning its incredibly important nowadays. First, because it can solve complicated real-world problems in a scalable way, second, because it has disrupted a variety of industries within the past decade [9], and continues to do so in the future, as more and more industry leaders and researchers are specializing in machine learning, along taking what they have learned in order to continue with their research and/or develop machine learning tools to impact their own fields positively. Third, artificial intelligence has the potential to incrementally add 16% or around $ 13 trillion to the US economy by 2030 .

Acknowledgments:

Post Credit: Anthony Platanios, Doctoral Researcher with the Machine Learning Department at Carnegie Mellon University.