LAST YEAR the US media got very excited as IBM Watson beat every human competitor on the popular TV game show Jeopardy. Like all game shows, the basis is simple: a host asks questions and the fastest competitor to press a button and give the most correct answers gains the highest number of points and wins.
For the first time a machine overtook the best-of-the-best human players with all the aplomb of HAL9000 (the ship computer from the movie 2001: A Space Odyssey). And while building such a machine is more complex than the chess playing Deep Blue lineage, it is essentially a sophisticated search engine with an element of common sense built in.
While Deep Blue was purely algorithmic, augmented by thousands of past game plans, and an element of learning, IBM Watson does leave it somewhat in the dust of history. A non-specific data set addressed by natural language subject to regional differences, slang and other nuances involves more than mere word recognition, semantics, parsing, search and find. We are talking the subtlety of language, context, and accounting for such elements as irony, riddles, sarcasm and obscure relationships.
Until Jeopardy, only humans could cope with all the dimensions of natural language and general data sets including local, regional and global variations, colloquialisms and hidden meaning. Six years ago this problem was deemed beyond our technology and knowledge. But just as “we” really understand that the world of data is getting way beyond human comprehension, our technology steps up to the plate with the abilities we desperately need.
Watson is not just a deep Q&A machine; it also deals in confidence levels for all information and the replies it gives. This one element alone is something humans are inherently very bad at and is much needed in all walks of life from medicine, news, business, politics, science and technology.
For Watson to win against human opponents it needed access to vast amounts of data, be trained in natural language, and be fast to search, sort, compose, assess the probability of being right, and even faster at “pressing a button”.
How did the IBM team solve all the problems simultaneously? They used multi-algorithm solutions, with endless testing, tuning and real environment training and refinement. And it worked. Watson is very impressive.
So what next? Imagine this ability in the hands of your doctor. Just make available all that medical data, go search, look at all the case histories – symptoms, diagnoses, medication, treatment and outcomes, and then see a confidence figure for your diagnosis and prognosis. Now how powerful would that be? Well, just a year on and it is happening.
Thinking more broadly, this technology could revolutionise the call centre business, not to mention all search and find functions plus general Q&A situations. So when will it be on your laptop? Never. But it will be in the cloud soon and we – yes, all of us – will be contributing to its ability as we ask the questions and feed in our data and experience.
Watson’s abilities will be refined and expanded by our questions, inputs, discoveries and technology refinements. In turn, our abilities will be enhanced and refined by “Watson inputs” and collaboration.
As we all face a bigger and bigger mountain of data day-on-day, this technology could turn out to be a godsend. And if we could see it combined with business, financial and legal models, then it might just become the ultimate manager-machine partnership. And believe me, our abilities and our progress will be turned up another notch as the number of erroneous/bad decisions reduce and the waste of all resources is driven down. ?
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