Making Machine Learning Accessible: 3 Ways Entrepreneurs Can Apply It Today Posted on : Jul 19 - 2017

Only by learning the basics today and seeing past the hype can entrepreneurs make machine learning their next revenue machine.

Machine learning isn't new to the enterprise, but technological advances and accelerating investments have made it available to the average entrepreneur.

In fact, according to a recent Forrester survey, machine-learning investments are increasing 300 percent this year compared to last year. Already, machine learning has made its mark in areas like self-driving cars, personalized-content recommendations and even face-recognition filters.

Clearly, though, machine learning can do much more than suggest content and steer cars. In fact, a category of it called "deep learning" promises to completely change how we market, produce and sell products.

Unlike traditional models, which require specific rules and feature sets to extract meaning from data, deep learning models autonomously draw conclusions and create their own classification rules from unstructured data.

That last phrase -- unstructured data -- is more important than it appears. In contrast to structured data, such as organized charts and tables, unstructured data includes "everyday" data, such as pictures and sounds, which is much more difficult for computers to analyze. For artificial intelligence, tackling unstructured information is a big breakthrough.

Digging into deep learning

If you're struggling to see how innovative deep learning is, you're not alone. To understand it, imagine simultaneously teaching a baby and a computer to recognize cats in photographs.

With traditional machine learning, the computer has to be told which cat features -- whiskers, paws and tails -- to look for in the images. These hand-engineered models then make predictions based on those features. If an image doesn't follow the rules, the machine can't adapt. If a cat's tail is out of the frame, for example, the computer might not even know it's a cat.

A baby, on the other hand, needs no such guidance. After viewing enough images, the baby will build a mental framework to distinguish what is or isn't a cat. Deep learning, like the baby, takes unstructured input without guidance and determines for itself, while considering all pixel values, which among the images contains a cat. Given enough time and data, deep learning models can make sense of virtually any unstructured data set.

So, how has such an incredible tool flown under entrepreneurs' radar? Well, deep learning has only become commercially viable in the past decade. Conceived not long after the dawn of AI in the 1950s, early neural networks could simulate just a few neurons at once. Although deep learning was revived several times throughout the '70s and '80s, we simply didn't have the data or processing power to make it work until the mid-2000s. View More