This Technology Is About To Make Us All Way More Productive

4 minute read

The future has not only arrived — it’s been here for years. You’ve just probably been too bogged down in the digital drudgery of your life to notice. But it’s not too late to get onboard with the coming times. In fact, machine learning will get you there before you know it.

Machine learning lets computers learn tasks they weren’t specifically programmed to do. And over the past few years, it’s been driving some of the biggest changes to computing that we’ve ever seen.

One example of how you might have already used machine learning may come at the end of the workday, when Google Now flashes up on your smartphone screen saying there’s traffic on your usual route home. What makes this such a remarkable leap is that Google Now hasn’t just connected the dots between where you are and where you live, it’s also added and interpreted several other data points in between—like that you typically go someplace around 5 p.m., that place is your home, and that you tend to get there by traveling on certain roads.

Machine learning lets a computer continually adapt itself to your inputs so it can keep improving its results. Another excellent example of this is found in Apple’s new iPhone operating system. Engineered with what Apple bills as more “proactive” intelligence, iOS 9 pushes apps that you often use in certain situations to your lock screen for easy access. So if you tend to listen to podcasts on your commute to work, it might suggest you open Stitcher every morning around the time you leave home.

These shortcuts may seem like small ones, but they just scratch the surface of how machine learning can help you be more efficient. “We’re at the early stages of applying machine learning to productivity,” says Tim Porter, founder of Gluru, a startup building a smart personal assistant for people’s daily workflow.

Born of Porter’s personal frustration with the huge amount of data we all seem to be drowning in, Gluru is one of many companies that are developing machine learning solutions for the masses. But it’s a great example of the technology because it shows how, through the power of cloud computing, this artificial intelligence revolution won’t just be tied to the Apples, Googles, and Microsofts of the world.

Gluru essentially wants to lift people above the endless waves of data trying to pull us under. Just think of all your personal emails, the files on your computer, and things you’ve stuffed away in online storage accounts. Now pile your professional data on top of that — and don’t forget that random cloud service a colleague made you you sign up for to download his presentation last month. All these disparate data sources are hard to keep track of and impossible to easily search. Gluru connects these data points and scours your email and calendar for meetings, bringing related files to your screen before your appointments begins.

“Our reason for being is to try and save you time, and ultimately that saves, not only you, but maybe your company, money in being more efficient,” says Porter.

Privacy and security advocates might take exception to sprinkling all this data around the web, but Porter says that Gluru uses bank-grade security in its product. While that’s encouraging, it’s also beside the point—machine learning, if you’re using a smart phone, a virtual assistant, or even many computers, is already here. Just last week, in introducing Siri’s interface on the new Apple TV, Apple didn’t say it explicitly, but the fact remains: machine learning will soon be operating your television.

“When it comes to machine learning, the more you use it, the better it gets, essentially,” says Porter. “As it serves up files, and you interact with them, it gets better and better, in terms of the accuracy of what it provides you—that’s something that is really powerful.”

And in connecting all these digital dots, the technology frees us from having to do it ourselves, and giving us more time to do the things that machines can’t, like write a delightful novel, visit an ailing friend, or volunteer in a third-world country. In other words: living, not working.

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