Machine Learning: Definition & Opportunity

Infinia ML CEO Robbie Allen defines machine learning in Adobe’s “Glass Tank” at The AI Conference in New York City, May 1, 2018.

Before defining machine learning, it helps to define artificial intelligence (AI).

AI is a field of computer science with numerous branches, including machine learning, natural language processing, computer vision, and robotics.

Machine learning (ML) means that software has the ability to learn patterns in data without being explicitly programmed to understand those patterns. Give it some data to learn from, and over time it can make new predictions or classify data based on what it learned in the past, without a person configuring specific logic.

We are early in the the development of machine learning. While some algorithms may be mature, other surrounding ML elements, like data preparation and deployment  – are immature.

At Infinia ML, we believe that we could stop innovating around machine learning today and there would still be a ten-year backlog of opportunities to deploy ML in the enterprise.

Of course, innovation is not stopping – in fact, we’re innovating at a break-neck pace.

But while there is massive opportunity to deploy machine learning inside the enterprise, but there are not enough qualified people who understand how to do it. And that’s the problem we’re helping to solve at Infinia ML.


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