The recent breakthroughs in deep learning techniques have led to solving classical, hard AI problems that are emulating a lot of what people can do—things like vision and speech and reading. "That's really broken open recently, and that's very exciting," Platt said.
Although the techniques go back to the early '90s, the problems were not solved because computers were very slow then. "And there was not much data. And a lot of these techniques about neural networks were put on the shelf because they were just too slow or not effective enough," Platt said. "But people have revisited them in the last two to three years. And it's possible now because we have much more compute, particularly with parallel computing. And we have much more data that we've gathered, and also many more labels. And now that we have all these ingredients, we're getting these spectacular breakthroughs."
Danny Sabbah, CTO and general manager of Next Generation Platform at IBM, told eWEEK the very same thing. Indeed, Sabbah said IBM had the wherewithal to produce its Watson deep learning cognitive computing system more than a decade ago, but the computer technology was not readily available to make it feasible.
Platt said he believes the trend of making AI consumable through APIs—which IBM is doing by opening up Watson to developers—is an important one because "machine learning is tricky. Not all developers can use machine learning," he said. "They have to learn a little bit before they can start using it effectively. There are many libraries you can use to write machine learning code. There are even a few deep learning libraries developers can use. But the deep learning itself is difficult. Some people call it black magic to try to get it to work."
So, he added, a lot of developers will not be using deep learning directly, but will be consuming Web services that were built on top of deep learning. And companies that are not in IT will be able to build line of business apps that will call into Web services built on deep learning. They could use something like a speech recognition API based on deep learning and then wrap some of their own business code around it and add the vocabulary from their business or industry.
"So anything where you say, 'I want my computers to see, hear and understand in a way that might be specialized to my business,' then you can imagine calling into an API," Platt said. However, "A lot of this is future," he added.