|According to IBM's Michael Barborak, it’s not human versus machine that will represent how artificial intelligence evolves, but human plus machine taking on challenges together and achieving more than either could do on its own.|
When IBM’s Watson defeated two grand-champions on the TV quiz show, Jeopardy!, the world’s smartest computer was matched up against two really smart humans. The quiz-show win captured peoples’ attention, but, these days, as IBM develops new practical uses for Watson it’s becoming clear that these technologies will be used primarily to augment human intelligence, not compete with people or replace us.
According to Michael Barborak, Manager, Unstructured Language Engineering at IBM Research it’s not human versus machine, but human plus machine taking on challenges together and achieving more than either could do on its own.
Barborak manages the Natural Language Engineering and Frameworks department of the Watson project. This group is tasked with improving the DeepQA system through principled and thoughtful software engineering. Some of our projects include a Matching Framework to represent alignment between texts, a Term Matching Framework to support ecosystems of term matchers, and the TeachWatson service which supports synchronous and asynchronous opportunities for improving Watson through human interaction.
Nowhere is this powerful new one-two punch clearer than in the world of medicine and healthcare. Cognitive machines have the potential to help physicians diagnose diseases and assess the best treatments for individual patients. But, to make the most of this opportunity, machines will have to be designed and trained to interact with doctors in ways that are most natural to them.
Barborak joined IBM Research from a digital marketing agency a few months before the Jeopardy! contest was aired. He was hired to help develop real-world applications for Watson. Now, he is the Watson team’s manager of natural language engineering.
It was clear when I joined that the Watson technology would have to be adapted to be useful in healthcare, banking, retailing, education and other spheres of business and life. Watson was designed to form precise answers to precise questions on Jeopardy!, but that’s not the way the world works. To be useful in real life, the system must be able to understand complex, real-world scenarios so it can help people deal with them. So we had to train Watson to use its question and answer capabilities like a pick to chip away at a complex scenario and break it down into comprehensible pieces. The system had to be able to discover salient facts, form hypotheses, test them, and arrive at conclusions. So we developed a technology we call the Watson inference chaining system, or WICS, to achieve this.At Cleveland Clinic, we found a perfect match for our inference-chaining technology—which helped us evolve it into the application IBM calls WatsonPaths. The Clinic’s Lerner College of Medicine uses problem-based-learning methods to teach students how to think like doctors. Using medical scenarios, they walk step by step through the process a physician goes through to evaluate a patient’s condition and determine the best treatment.
|Image Source: IBM Research|
"When I look ahead into the era of cognitive computing, I see a revolution unfolding before my eyes. For the first time, computers will adapt to the way we want to do things, rather than vice versa. That will be a remarkable change," says Barborak.
SOURCE A Smarter Planet
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