Even though heat and sound are both forms of energy, when you were a kid, you probably didn’t need to be told not to speak in thermal convection. And each time your children come across a stray animal, they likely don’t have to self-consciously rehearse a subroutine of zoological attributes to decide whether it’s a cat or a dog. Human beings come pre-loaded with the cognitive gear to simply perceive these distinctions. The differences appear so obvious, and knowing the differences comes so naturally to us, that we refer to it as common sense. Computers, in contrast, need step-by-step handholding—in the form of deterministic algorithms—to render even the most basic of judgments. Despite decades of unbroken gains in speed and processing capacity, machines can’t do what the average toddler does without even trying. That is—until now.
Over the last half-dozen years, deep learning, a branch of artificial intelligence inspired by the structure of the human brain, has made enormous strides in giving machines the ability to intuit the physical world. At Facebook’s AI lab, they’ve built a deep learning system capable of answering simple questions to which it had never previously been exposed. The Echo, Amazon’s smart speaker, uses deep learning techniques. Three years ago, Microsoft’s chief research officer impressed attendees at a lecture in China with a demonstration of deep learning speech software that translated his spoken English into Chinese, then instantly delivered the translation using a simulation of his voice speaking Mandarin—with an error rate of just 7%. It now uses the technology to improve voice search on Windows mobile and Bing.
The most powerful tech companies in the world have been quietly deploying deep learning to improve their products and services, and none has invested more than Google. It has “bet the company” on AI, says the New York Times, committing huge resources and scooping up many of the leading researchers in the field. And its efforts have borne fruit. A few years ago, a Google deep learning network was shown 10 million unlabeled images from YouTube, and proved to be nearly twice as accurate at identifying the objects in the images (cats, human faces, flowers, various species of fish, and thousands of others) as any previous method. When Google deployed deep learning on its Android voice search, errors dropped by 25% overnight. At the beginning of this year, another Google deep learning system defeated one of the best players of Go—the world’s most complex board game.
This is only the beginning. I believe that over the next few years start-ups and the usual big tech suspects will use deep learning to upgrade a wide suite of existing applications, and to create new products and services. Entirely new business lines and markets will spring up, which will, in turn, give rise to still more innovation. Deep learning systems will become easier to use and more widely available. And I predict that deep learning will change the way people interact with technology as radically as operating systems transformed ordinary people’s access to computers.
Historically, computers performed tasks by being programmed with deterministic algorithms, which detailed every step that had to be taken. This worked well in many situations, from performing elaborate calculations to defeating chess grandmasters. But it hasn’t worked as well in situations where providing an explicit algorithm wasn’t possible—such as recognizing faces or emotions, or answering novel questions.
Trying to approach those challenges by hand-coding the myriad attributes of a face or phoneme was too labor-intensive, and left machines unable to process data that didn’t fit within the explicit parameters provided by the programmers. Think of the difference between modern voice-assistants like Siri or Alexa, which allow you to ask for things in various ways using natural language, vs. automated phone menu systems, which only perform if you use the specific set of non-negotiable words that they were programmed to understand. By contrast, deep learning-bas