From Uber using past trips to predict its customers’ future habits to Facebook automatically tagging a picture you upload of your family, data is everywhere these days, and smart companies are using it to inform a better experience for their customers. Could the same be true for your company?
When it comes to making sense of big data, enterprises initially invested in machine learning. Simply put, machine learning uses algorithms to find patterns in data fed to it by humans. (There are resources out there for executives that want a high-level overview of this approach.) Typically, machine learning deals with data that is simpler. This low-dimensional data can be analyzed in light of a handful of factors. But eventually, some companies started amassing so much highly complex data — things like images, which are complex, since ordering their bits of data and dimensions of primary colors are important — that it was time for something more sophisticated — enter deep learning.
“Wait,” you may be thinking. “I’ve been using those terms interchangeably. Aren’t they the same thing?” That is a pretty widespread assumption, but deep learning is exactly as it sounds — deeper.
Deep learning uses a layered approach to make better decisions by constantly curating the data is it fed. Think of it like this: Machine learning is like when you would cram for a test in college by re-reading your notes. Deep learning is when a child is continually presented with the letters of the alphabet and slowly learns the trillions of ways to sequence those letters into words. In one example, previously identified data is interpreted. In the second, the interpreter realizes the potential of the data they are given.
Why is this important for a business? It can give your business data insights at a broader scope and higher level of fidelity for more complex use cases.
Traditional analytics only takes a business so far. But as companies amass more and more data, past algorithmic methods, like decision trees and linear regressions, may not up for the task, depending, again, on the use case. Think of it like trying to predict what color the next car driving down a street will be by keeping tabs on the ones that pass by before it. Decision trees could do so on a two-lane road with moderate traffic, but deep learning can do the same on a six-lane highway with cars going Autobahn speeds.
As deep learning progresses, its sophistication is getting more and more human-like. By teaching machines to optimize input based on reward — the AI equivalent of giving a dog a treat for sitting — Google trained up its program DeepMind to beat humans at the game Go
. This was once an unthinkable bar for deep learning to pass, with the 19-by-19 square board offering much more complexity
than the computer-human chess matches from the
IBM Deep Blue days.
While deep learning progresses to higher and higher accuracies, it is getting really good at things like identifying symbols, words and letters. Take a spin through some of Google’s A.I. Experiments
, like the often hilarious “Quick, Draw!” and see for yourself. The same technology that lets Google know you just drew a horse lets its self-driving cars know what a cyclist looks like. And while deep learning is still lagging in areas like natural language recognition, all it’s going to take is more practice for the machine to get it right.
So how do you know if deep learning is right for your business? For starters, you need a lot of data for it to work, otherwise it’s a lot like taking a rocketship to the grocery store. It’s a powerful tool, but you need a really complex problem to effectively use it.
So, is deep learning necessary for your business? Can it help drive revenue and profitability? Come back next time where I’ll review why adopting deep learning is important for your business.
For more on deep learning, check out this blog