Deep learning for executives: how will it change your business?
Chances are, your company is awash in a tsunami of data these days. And you’ve thrown everything from open-source databases to machine learning algorithms — as well as an army of data scientists — at the problem. But I bet if you asked your data scientists how effective those tools are, they’d say they can still barely keep afloat. This is exactly why businesses today need to kick the tires on deep learning — there is simply too much data, and too much variety, for smart people to feature engineer their way to the right solutions.
I will explain. But before we dive deeper, you may want to read my primer on deep learning, “Deep Learning for Executives: What Exactly is it Again?”
Previously, data science staff had less issues keeping up with a company’s data. One, there was less of it. But two, it was mostly homogenous. Companies were keeping tabs on simple business indicators, like tracking clicks on a checkout button or looking at sensor data streams to predict repairs. Now we are living in a world where Google and Facebook collect14 types of dataabout their customers alone. And that variety of data will only increase exponentially as we continue to index the physical world in the form of pictures, videos, audio and sensor data.
To tackle this evolution, companies need to look beyond traditional machine learning, where algorithms easily grasp numerical information that is computational by nature. Traditional machine learning requires feature engineering. This is where humans provide context for data so a machine can make better predictions. In short, feature engineering is a human-in-the-loop on most machine learning projects, where data scientists use domain knowledge of the data to make machine algorithms more accurate.
There is finesse to feature engineering, but it somewhat defeats the purpose of machine learning — the idea of feeding any data into a machine and getting insights back, without much human mediation. It stands to reason, for many emerging use cases, feature engineering must be, at least partially, automated. This is where machine learning applications morph into deep learning applications, which simplifies feature engineering in many ways, putting more of the work on machines, and ever more complex, self-learning models.
Take video or images. Traditional machine learning models can’t make heads-or-tails of complex images, but deep learning can, relatively easily, teach itself the difference between cats and dogs. Vision and image detection are great deep learning applications. With it, businesses can track sentiment from pictures on Instagram.
