It’s not every day you can witness an entire class of software making the transition from specialized, expensive-to-develop code to a general-purpose technology. But that’s exactly what’s happening with machine learning.
Chances are, you’re already hip-deep in machine-learning applications. It’s how Google Photo organizes those pictures from your vacation in Spain. It’s how Facebook suggests tags for the pictures you took at last week’s soccer match. It’s how the cars of nearly every major automaker can help you avoid unsafe lane changes.
And it’s also the start of something even bigger.
Machine learning – which enables a computer to learn without new programming – is exploding in its ability to handle highly complex tasks. It can make houses and buildings not just smart, but actively intelligent. It can take e-commerce from a one-size-fits-all experience to something personalized. It might even find your next date.
Driving this surge of machine-learning development is a wave of data generated by mobile phones, sensors, and video cameras. It’s a wave whose scope, scale, and projected growth are unprecedented.
Every minute of every day, YouTube gains 300 hours of video, Apple users download 51,000 apps, and 347,222 100,000 Tweets make their way into the world. Those stats come from the good folks at Domo, who call the time we’re living in “an era where data never sleeps.”
Until now, the hot topic of conversation has been how to analyze information and take action based on the results. But the volume of data has become so great, and its trajectory so steep, that we need to automate many of those actions. Now.
As a result, we expect machine learning will become the next great commodity. In the short term, we expect the cost of advanced algorithms to plummet – especially given multiple open-source initiatives – and to spur new areas of specialization. Longer term, we expect these kinds of algorithms to make their way into standard microprocessors.
Marc Andreessen once said software is eating the world. In the case of machine learning, it will have a very large appetite.
To understand the potential of machine learning as a commodity, Linux is a good place to start. Released as a free, open-source operating system in 1991, it now powers nearly all the world’s supercomputers, most of the servers behind the Internet, and the majority of financial trades worldwide – not to mention tens of millions of Android mobile phones and consumer devices.
Like Linux, machine learning is well down the open-source path. In the last few months, Baidu, Facebook, and Google have released sets of open-source machine-learning algorithms. Another group of high-tech heavyweights, including Sam Altman, Elon Musk, and Peter Thiel, have launched the OpenAI initiative. And universities and tech communities are adding new tools to the mix.
In the short-to-medium term, we see three outcomes from this activity.
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