You know what I mean? Watson does.

Share this post:

Watson knows the definition of “brake” – the device that stops something; or the act of stopping something. But what about the hundreds of ways that the car-driving public describes problems with their brakes to a mechanic? The brakes might “squeal,” or “judder.” Or maybe they’re “soft.” All of those descriptors could indicate a different issue that aren’t understood by a computer. Until now. Thanks to IBM Research’s Dan Gruhl, Watson can help the mechanic and the car manufacturer pinpoint the problem, based on common, even slang descriptions.

Dan Gruhl at 2015 Cognitive Colloquim

Watson Concept Expansion, on demonstration at the Cognitive Computing Colloquium in San Francisco, expanded the cognitive system’s dictionary of related words, concepts, euphemisms, and colloquialisms to better-understand context across any industry or field. Dan’s team feeds Watson unstructured data from blogs and news articles, to Twitter’s firehose, among other sources.

Then, because this is a human-machine partnership, meant to augment one another’s intelligence, the human expert begins to tell Watson what is relevant (squeaky brakes), and not (I need a break). While Watson gathers and organizes by relevance online documents and discussions about how to fix various kinds of brake problems. 

“It’s a game of patterns. If I start with ‘apple’ and ‘blackberry’ in the tool, it needs to interact with me a few times before it realizes I am talking about fruits and not cell phone manufacturers.” Dan said. 

That context from such a wide and diverse source that only Watson could gather and comprehend (in seconds) gives domain experts a new way to understand their industry, brand, or product. Try it out on Bluemix, now.

 

More stories

A new supercomputing-powered weather model may ready us for Exascale

In the U.S. alone, extreme weather caused some 297 deaths and $53.5 billion in economic damage in 2016. Globally, natural disasters caused $175 billion in damage. It’s essential for governments, business and people to receive advance warning of wild weather in order to minimize its impact, yet today the information we get is limited. Current […]

Continue reading

DREAM Challenge results: Can machine learning help improve accuracy in breast cancer screening?

        Breast Cancer is the most common cancer in women. It is estimated that one out of eight women will be diagnosed with breast cancer in their lifetime. The good news is that 99 percent of women whose breast cancer was detected early (stage 1 or 0) survive beyond five years after […]

Continue reading

Computational Neuroscience

New Issue of the IBM Journal of Research and Development   Understanding the brain’s dynamics is of central importance to neuroscience. Our ability to observe, model, and infer from neuroscientific data the principles and mechanisms of brain dynamics determines our ability to understand the brain’s unusual cognitive and behavioral capabilities. Our guest editors, James Kozloski, […]

Continue reading