The excitement about IBM’s computer, Watson, and its appearance on the Jeopardy! game show rose to a feverish pitch as the man versus machine drama played out on television earlier this week. The machine won–by no means a foregone conclusion. The episode proved that a small team of highly-motivated geniuses backed by an ambitious, deep-pocketed corporation can create a machine capable of beating the most expert of humans at a sophisticated mental game. It is truly a remarkable moment in the history of computer science and innovation.
Yet in Stephen Baker’s book about the contest, Final Jeopardy: Man vs. Machine and the Quest to Know Everything, the most interesting questions are not about machines but about humans. This is intentional. On page 18, Baker writes that, whether the computer won or lost, it is his hope that it “might lead millions of spectators to reflect on the nature, and probe the potential, of their own humanity.”
By Stephen Baker
Picture the smart, unassuming person at a meeting, who says, “Well, I’m no expert, but once I saw this case where….” That person is doing something that until recently was uniquely human: Soft-pedaling an idea.
Humans beings can soft-pedal because we know what we know (or at least think we do). We also know what we don’t know. And then there’s this entire domain of knowledge in which we know a thing or two. That gray area in the middle is important, because that’s where we can dabble. We can come up with insights and discuss them with people who are better informed. This process widens a discussion beyond the cloistered world of experts. It can lead to insights, the generation of hypotheses, and innovation.
One of the very special aspects of Watson, IBM’s Jeopardy computer, is that it “knows” what it doesn’t know–or, more precisely, simulates this knowledge through statistical analysis. Looking beyond Jeopardy, to Watson’s career in business, this gauge of its confidence is one of its most valuable features.
Say Watson is working in a hospital emergency room. A person comes in with a combination of symptoms that no one has seen before. Someone lists them for Watson. (The machine doesn’t have voice-recognition now, but that could be engineered in a matter of days.) Watson scours its base of documents and research papers, finds various combinations of these symptoms, and lists possible diseases or disorders that the person might have. Each one is accompanied by a confidence gauge. It turns out in this case Watson has only 14% confidence in, say, lupus.
That 14% amounts to a big shrug of Watson’s electronic shoulders. It does not know and is admitting as much. And maybe the doctors have done tests and know that it’s not lupus. But maybe below Lupus, with only an 8% confidence rank, is some other disease that they hadn’t considered. It may be wrong. It may be idiotic. But it may also lead to a thought, a connection. After all, that 8% came from some combination of the symptoms that Watson found in its research. In effect, Watson–even in its ignorance–has come up with a list of hypotheses (along with pointers to its sources). If even one of these hypotheses nudges doctors toward a correct diagnosis, the machine has provided a service–even without “knowing” the answer.
Editor’s note: The following is a guest post from Dr. David Ferrucci, Principal Investigator, DeepQA/Watson, IBM
Just as IBM set its sights on defeating a chess Grandmaster with Deep Blue in 1997, the company’s scientists have developed a Natural Language Processing, Question Answer machine, named Watson (after company founder Thomas J. Watson, Sr.), to challenge two of the world’s trivia grand masters, to be aired on U.S. television from February 14-16, 2011.
Win or lose on national television, Watson will answer the immediate questions, “does it answer questions accurately?” and “does it answer questions quickly?” with a resounding “yes.”
Beyond excitement for the match itself, the team of IBM scientists is motivated by the possibilities that Watson’s breakthrough computing capabilities hold for building a smarter planet and helping people in their business tasks and personal lives. Watson’s ability to understand the meaning and context of human language, and rapidly process information to find precise answers to complex questions, holds enormous potential to transform how computers help people accomplish tasks in business and their personal lives.
Watson will enable people to rapidly find specific answers to complex questions. The technology could be applied in areas such as healthcare, for accurately diagnosing patients, to improve online self-service help desks, to provide tourists and citizens with specific information regarding cities, prompt customer support via phone, and much more.
Like Deep Blue, Watson represents a major leap in the capacity of information technology systems to identify patterns, gain critical insight and enhance decision-making despite daunting complexity. But while Deep Blue was an amazing achievement in the application of compute power to a computationally well-defined and well-bounded game, Watson faces a challenge that is open-ended and defies the well-bounded mathematical formulation of a game like Chess. Watson has to operate in the near limitless, ambiguous and highly contextual domain of human language and knowledge.
Watson’s technology furthers IBM’s leadership in analytics solutions, which help organizations use the vast amount of information they collect to improve their business operations and service to their customers. Additionally, Watson harnesses IBM’s commercial POWER7 system, showcasing how IBM workload-optimized systems provide unmatched capabilities for processing thousands of simultaneous tasks at rapid speeds, once the realm of only scientific supercomputers.
Read more about the technology behind Watson at ibmwatson.com.