ning 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-based systems make sense of data for themselves, without the need of an explicit algorithm. Loosely inspired by the human brain, these machines learn, in a very real sense, from their experience. And some are now about as good at object and speech recognition as people.
So how does deep learning work?
Deep learning systems are modeled after the neural networks in the neocortex of the human brain, where higher-level cognition occurs. In the brain, a neuron is a cell that transmits electrical or chemical information. When connected with other neurons, it forms a neural network. In machines, the neurons are virtual—basically bits of code running statistical regressions. String enough of these virtual neurons together and you get a virtual neural network. Think of every neuron in the network below as a simple statistical model: it takes in some inputs, and it passes along some output.
For a neural network to be useful, though, it requires training. To train a neural network, a set of virtual neurons are mapped out and assigned a random numerical “weight,” which determines how the neurons respond to new data (digitized objects or sounds). Like in any statistical or machine learning, the machine initially gets to see the correct answers, too. So if the network doesn’t accurately identify the input – doesn’t see a face in an image, for example — then the system adjusts the weights—i.e., how much attention each neuron paid to the data—in order to produce the right answer. Eventually, after sufficient training, the neural network will consistently recognize the correct patterns in speech or images.
The idea of artificial neurons has been around for at least 60 years, when, in the 1950s, Frank Rosenblatt built a “perceptron” made of motors, dials, and light detectors, which he successfully trained to tell the difference between basic shapes. But early neural networks were extremely limited in the number of neurons they could simulate, which meant they couldn’t recognize complex patterns. Three developments in the last decade 