Why Is Machine Learning an Important 21st Century Concept?

JR Ahn currently works with Sutter Hill Ventures in Palo Alto, California, as an executive-in-residence. In that capacity, he reviews investment proposals from emerging companies and offers experience in setting strategic priorities. JR Ahn’s areas of particular funding expertise include machine learning and the structure and functioning of neural networks.

Machine learning in the field of artificial intelligence refers to the ability of computer systems and neural networks to acquire new information and capabilities without being explicitly programmed to acquire them. When they encounter new data, these systems are able to build on that knowledge to self-modify and improve their capacities based on it. Researchers and students of the topic typically investigate how machines can learn to recognize objects and language, how they might differentiate among a set of images, or how they identify or reproduce patterns.

Rather than mining data in order to help human beings better understand it, machine learning makes use of data in order to find patterns and make commensurate, and cumulative, system adjustments. An algorithm used in machine learning can be supervised — meaning that it can leverage past data-focused intelligence to better understand new data — or it can be unsupervised, in which case it can make inferences based on past intelligence and experiences.

Proponents of machine learning point to its potential to vastly expand human understanding of how the world works on multiple levels. Additionally, its ability to enhance performance in critical industries such as finance, transportation, and military logistics further speaks to its potential.

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