Deep Learning Catches On in New Industries, from Fashion to Finance
The machine-learning technique known as deep learning, which has shown impressive results in voice and image recognition, is finding new applications.
Computers that can perform tasks that currently require humans could open up new business opportunities, but also displace workers.
A machine-learning technique that has already given computers an eerie ability to recognize speech and categorize images is now creeping into industries ranging from computer security to stock trading. If the technique works in those areas, it could create new opportunities but also displace some workers.
Deep learning, as the technique is known, involves applying layers of calculations to data, such as sound or images, to recognize key features and similarities. It offers a powerful way for machines to recognize similarities that would normally be abstruse to a computer: the same face seen from different angles, for instance, or a word spoken in different accents (see “10 Breakthrough Technologies 2013: Deep Learning”). The mathematical principles that underlie deep learning are relatively simple, but when combined with huge quantities of training data and computer systems capable of powerful parallel computations, the technique has resulted in dramatic progress in recent years, especially in voice and image recognition.
For example, Google uses deep learning for voice recognition on Android phones, while Facebook uses the technology to identify friends in users’ photographs (see “Facebook Creates Software That Matches Faces Almost as Well as You Do”).
Other tech companies are following. At an event in Boston last week, two researchers from eBay described how the company is using deep learning to categorize products in images posted by sellers. By studying images that have already been tagged, the system can tell the difference, for example, between a pair of flip-flops and a pair of flats. This is helping to improve eBay’s search engine, especially for products that haven’t been tagged very well.
People in other fields and industries are starting to show an interest in deep learning. At the Boston event, researchers, engineers, and entrepreneurs discussed progress in the field and its potential application in advertising, finance, and medicine. One attendee who had previously applied machine learning techniques to hedge funds had founded a startup to use deep learning to predict market shifts like a sudden plunge in a currency’s value. Another attendee, from a major U.S. insurance company, was looking into using deep learning to identify fraudulent claims.
Andrew Ng, a leading figure in the field, and both an associate professor at Stanford and chief scientist at the Chinese company Baidu, said at the conference that deep learning has already proven useful. “One of the things Baidu did well early on was to create an internal deep learning platform,” Ng said. “An engineer in our systems group decided to apply it to decide a day in advance when a hard disk is about fail. We use deep learning to detect when there might’ve been an intrusion. Many people are now learning about deep learning and trying to apply it to so many problems.”
Deep learning is being tested by researchers to glean insights from medical imagery. Emmanuel Rios Velazquez, a postdoctoral researcher at the Dana-Farber Cancer Institute in Boston, is exploring whether deep learning could help to more accurately predict a patient’s outcome from images of his or her cancer.
Drug discovery is another promising area. Olexandr Isayev, a research scientist from the University of North Carolina at Chapel Hill, has shown that deep learning algorithms can help train computers to pick out potentially useful drug molecules from hundreds of millions of candidates. Isayev fed data from hundreds of thousands of experiments into his computer systems, and then had his system predict how a molecule might bind to a particular group of proteins. “A typical machine-learning algorithm does one objective function,” he said. “[With deep learning] you can do multiple optimizations. For example, you might want to maximize binding with this protein but minimize binding with some other protein.”
Deep learning doesn’t work best for everything, as Isayev’s work demonstrates. He says the improvements it offered for computerized drug discovery were modest compared to what it could do for computerized image recognition.
Even so, the potential for deep learning to be applied more broadly can be seen with the emergence of some well-funded startups. Palo Alto-based MetaMind, which has developed a deep learning platform, was founded by Richard Socher, who studied under Ng at Stanford. “We have people classifying fashion, cars, houses, satellite images, and each of these are already gigantic industries,” Socher says. “The beauty of deep learning is that, from the raw input to the final output, it’s all learned.”