Perhaps you thought as I did that Watson was all about deep learning. That’s not quite right. As Will Knight describes in his recent article IBM Pushes Deep Learning with a Watson Upgrade, Watson was originally about natural language understanding and statistical analysis of unstructured text designed specifically to find cryptic Jeopardy clues.
This highlights a growing problem in AI research. The field is becoming increasingly fragmented.
“A key challenge for modern AI is putting back together a field that has almost splintered among these methodologies,” says James Hendler, director of the Rensselaer Polytechnic Institute for Data Exploration and Applications in Troy, New York. RPI has access to an early version of Watson donated to the university by IBM, and Hendler teaches courses based on the technology. “The key thing about Watson,” he says, “is that it’s inherently about taking many different solutions to things and integrating them to reach a decision.”
So to upgrade Watson and eventually mimic real intelligence IBM is combining different AI techniques, including deep learning, in the commercial version of Watson.
In this new upgrade IBM aims to add deep learning to the commercial version of Watson. The move could make the platform considerably smarter and more useful, and points to a promising future direction for AI research.
In its effort to commercialize Watson, IBM has made some of the features developed for the Jeopardy! challenge, as well as some new ones, available to developers via a cloud application programming interface (API). It has now added three deep-learning-based features to this Watson API: translation, speech-to-text, and text-to-speech. These could be used to build, for example, apps or websites that offer translation or transcription services. But developers could also connect them to other Watson services that parse questions and search for answers in large amounts of text. This could lead to an app that makes it possible to search large numbers of documents with naturally spoken queries.
You can read the additional details in Will Knight’s original blog IBM Pushes Deep Learning with a Watson Upgrade.