What is machine learning?

Machine learning is often mentioned together with terms such as "big data" and "artificial intelligence", or A.I. for short, but it is quite different from both. In order to understand what machine learning is and why it is useful, it is important to understand what big data is and how machine learning applies to it. Big data is a term used to describe huge data sets that are created as the result of large increases in data gathered and stored, for example, through cameras, sensors, or Internet social sites. It is estimated that Google alone processes over 20 petabytes of information per day and this number is only going to increase. IBM estimated (http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html) that every day, 2.5 quintillion bytes are created and that 90% of all the data in the world has been created in the last two years.

Clearly, humans alone are unable to grasp, let alone analyze, such a huge amount of data, and machine learning techniques are used to make sense of these very large data sets. Machine learning is the tool used for large-scale data processing and is well suited for complex datasets with huge numbers of variables and features. One of the strengths of many machine learning techniques, and deep learning in particular, is that it performs best when it can be used on large data sets improving its analytic and predictive power. In other words, machine learning techniques, and especially deep learning neural networks, "learn" best when they can access large data sets in order to discover patterns and regularities hidden in the data.

On the other hand, machine learning's predictive ability can be well adapted to artificial intelligence systems. Machine learning can be thought of as "the brain" of an artificial intelligence system. Artificial intelligence can be defined (though this definition may not be unique) as a system that can interact with its environment: artificial intelligence machines are endowed with sensors that allow them to know about the environment they are in and tools with which they can relate back. Machine learning is therefore the brain that allows the machine to analyze the data ingested through its sensors to formulate an appropriate answer. A simple example is Siri on an iPhone. Siri hears the command through its microphone and outputs an answer through its speakers or through its display, but in order to do so it needs to "understand" what it is being said to formulate the correct answer. Similarly, driverless cars will be equipped with cameras, GPS systems, sonars and lidars, but all this information needs to be processed in order to provide a correct answer, that is, whether to accelerate, brake, turn, and so on. The information processing that leads to the answer represents what machine learning is.