“Alexa features more than 50,000 skills built by third party developers,” Prasad said. “We are helping democratize AI through our Alexa Skills Kit
.” At the same time, Prasad said, over the past 12 months, the Alexa team has reduced Alexa’s error rate.
“Because we have had this massive growth in skills,” Prasad said, “just maintaining the accuracy would be great. But the team has gone further and even reduced the error rates in every location and every language Alexa has launched in.”
One of the techniques that enabled that improvement, Prasad explained, is active learning, in which automated systems sort through training data to extract those examples that are likely to yield the most significant improvements in accuracy. Alexa researchers have found that active learning can reduce the amount of data necessary to train a machine learning system by up to 97 percent, Prasad said, enabling much more rapid improvement of Alexa’s natural-language-understanding systems.
Alexa researchers have also made what Prasad described as a “breakthrough” in the rapid development of new deep-learning networks, machine learning systems that consist of thousands or even millions of densely connected (virtual) processing units. This breakthrough combines deep learning for natural-language understanding with transfer learning, in which a network trained to perform a task for which a large set of training data is available is then retrained on a related task, with little available data.
“What this will do is give 15 percent relative improvement in accuracy for custom skills with no additional work from the third-party developer,” Prasad said. “We are rolling this out in the coming months to all skills.”