Cloud-computing services deliver AI to the rest of us
Facebook’s deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google’s AI has learned to anticipate what you’ll be searching for. But there’s no need to feel left out, even if your company’s computers haven’t learned much lately.
A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning—the high-powered version du jour of machine learning—to their specific business needs.
“Deep-learning algorithms dominate other machine-learning methods when data sets are large,” says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM. “Thus any company or application that has well-formed prediction problems—such as forecasting demand or translating between languages—could benefit from deep learning.”
With cloud-based deep learning, companies can simply select a cloud service and browse its online offerings of application programming interfaces for software tasks such as recognizing images of corgi dogs or automatically translating a restaurant menu. Some services will even tailor their machine-learning tools to the data and needs of individual companies.
According to Lipton, the rise of cloud services for machine learning hinges on at least two factors: first, a continued rise in the demand for machine learning as the technology has matured in its ability to solve a wide variety of problems with economic value; and second, the relative scarcity of machine-learning talent, which makes it tough for every company to build its own machine-learning team. Competition for talent has become even tougher with startups trying to compete with tech giants like Microsoft and IBM, which can afford to vacuum up the best and brightest.
Most commercial applications of machine learning rely on supervised learning. This involves algorithms that can observe correctly labeled examples and learn to perform certain tasks through imitation. Artificial neural networks are currently the most popular and successful algorithms for supervised machine learning on large data sets. They learn by passing information through an interconnected network of multiple nodes (also known as neurons). The connections between these nodes each have adjustable weights that influence the flow of information through the graph. Nodes are generally arranged in layers. But historically it was feasible to train networks with only one hidden layer of neurons in addition to the input and output layers.
Deep learning takes these methods to the next level by filtering the data through multiple layers of neurons, Lipton explains. At each layer, the network can learn successively more abstract representations of relationships between data points. With enough layers and enough nodes, deep neural networks can perform a host of functions.
The challenge in building a neural network is training it for specific tasks. Starting from a random setting of the weights, examples from the data set are presented to the neural network one after the next. Each time, the neural network’s weights are tuned slightly to bring the network’s output closer to the correct output.
A number of startups seem primarily interested in demonstrating their deep-learning research in order to draw the attention of larger companies that might acquire them, Lipton notes. Salesforce.com and Twitter have acquired startups, including MetaMind and Whetlab, respectively. Some of these acquisitions have been done to swell the ranks of these tech giants’ own deep-learning teams....MORE