Machine Learning

What is it?
Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge. This capacity to learn from experience, analytical observation, and other means, results in a system that can continuously self-improve and thereby offer increased efficiency and effectiveness.

The revolution?
The field of automated learning and discovery--often called data mining, machine learning, or advanced data analysis--is currently undergoing a revolution. The progressing computerization of professional and private life, paired with a sharp increase in memory, processing and networking capabilities of today's computers, makes it now more than ever possible to gather and analyze vast amounts of data. For the first time ever, the people all around the world are connected to each other electronically through the Internet, making available huge amounts of online data at an astonishingly increasing pace.

How is data mining involved in the revolution?
Sparked by these innovations, we are currently witnessing a rapid growth of a new industry, called the data mining industry. Companies and governments have begun to realize the power of computer-automated tools for systematically gathering and analyzing data. For example, medical institutions have begun to utilize data-driven decision tools for diagnostic and prognostic purposes; various financial companies have begun to analyze their customers' behavior in order to maximize the effectiveness of marketing efforts; the Government now routinely applies data mining techniques to discover national threats and patterns of illegal activities in intelligence databases; and an increasing number of factories apply automatic learning methods to optimize process control.

How is machine learning involved in the revolution?
There is an increase in research activities on issues related to automated learning and discovery. Recent research has led to revolutionary progress, both in the type methods that are available, and in the understanding of their characteristics.

What are the other branches of science involved?
While the broad topic of automated learning and discovery is inherently cross-disciplinary in nature--it falls right into the intersection of disciplines like statistics, computer science, cognitive psychology, robotics, social sciences, and public policy--these fields have mostly studied this topic in isolation. So where is the field, and where is it going? What are the most promising research directions? What are opportunities of cross-cutting research, and what is worth pursuing?