By Steve Canepa, General Manager, Global Media & Entertainment Industry

February seems to be a month of excitement for all movie, television and sports enthusiasts. It’s that time of year – Super Bowl madness and Oscar Buzz – frenzy so electric that it transcends worlds – into the social media world. Think about it, how long does it take for you to see a Tweet or Facebook post once you hear the winner for Best Motion Picture or following the first touch-down? Seconds?

Information flows so quickly that Twitter alone is handling approximately 35MB of data a second, every second. The majority of this social media data represents public ‘streams of consciousness’, data that approximates human thought and speech, what we in the business call unstructured data.  But, as anyone who has filled in a tax form, booked a flight or applied for a loan knows: computers prefer data with structure, data fields that have entries in strictly controlled formats.

The good news is change is coming. Computers are becoming smarter about unstructured data (unstructured data isn’t just natural language … it’s photos, videos, emails, tweets, audio, sensor data, mobile device data).  For example, using advanced analytics technologies and natural language processing we can now begin to understand the patterns behind human expression. Not just ‘key words’ that have been identified and indexed, but all words, as we type them or say them.  We may have spent most of the computing age training humans to communicate with computers, using methods optimized for the machines, but today the reverse is happening. We’re now training computers to communicate with us and understand us in our own language. It is not easy. It is as the IBM Research team behind Watson declared, a Grand Challenge. But it’s a challenge that can lead to some very important and far-reaching results.

Watson represents a pinnacle achievement in Deep QA and natural language processing but there are many routes to the top and plenty of room for additional exploration and discovery. The team of researchers, students and faculty at the University of Southern California (USC) Annenberg Innovation Lab are taking a slightly different approach to the Grand Challenge. Rather than using the Answer Question formulation of Jeopardy!, they are applying IBM analytics software, and some very smart coding and modeling, to train computers to understand and analyze Tweets. The project is part of an ongoing collaboration between the lab and IBM to explore how technology can be used by organizations from news outlets and journalists to movie studios, broadcasters and retailers to better understand, respond, and predict public sentiment. To date, the model has been applied to film forecasting, the World Series and fashion retailing trends, in an effort to identify social media trends and better understand public opinions. For example, just last week IBM and USC analyzed millions of public tweets to determine the fans’ sentimental Super Bowl Quarterback favorite – Tom Brady or Eli Manning. Just like the game, Eli Manning in a late game-changing move, overtook Tom Brady as the Social Media MVP with 66% positive sentiment vs. Brady’s 61%.

But why stop at the World Series and Super Bowl? AIL and IBM are now collaborating with the Los Angeles Times to measure moviegoer sentiment toward the upcoming Academy Awards race.  Dubbed a ‘Senti Meter’, we’re analyzing Oscar- related positive and negative opinions shared via millions of tweets to determine who will win “The People’s Oscars”. The project has been profiled by the Los Angeles Times and we can all follow the evolving sentiment for Best Actor, Best Actress and Best Picture categories over the next two weeks by visiting http://graphics.latimes.com/senti-meter/.

This project is much more than just analyzing which best picture or movie star fans are rooting for – it’s an example of how movie studios can better understand their audience preferences and use social media to improve their marketing programs and in turn improve box office results.   There is no doubt that the Twitterverse and other social media platforms are changing communication as we know it. Tweets, Facebook and blog posts are becoming a vital resource for many organizations including the media industry to identify trends, inform reporting and understand as well as connect with their audience.

Think of how much change in the last year has been driven or expressed or reported in social media. Think how much social value could have been derived if we’d had the ability to understand and react to these social media conversations and sentiments – in context and in real time. We can now analyze the vast river of public data that streams from Twitter in its unstructured complexity, and apply a level of sentiment to the commentary. In other words the computer can now determine, with the certainty level of a non-native speaker, that the tweet it just analyzed expressed a positive or negative sentiment and how strongly that sentiment was stated – all in real-time. We can then apply this analysis to deliver business value – the effectiveness of marketing activities, customer responses to services, products and promotions, the impact of advertising, or the reaction to real world events…   the list is limitless.

This new capability will eventually deliver solutions founded on semantic analysis of Big Data that are only just now being imagined. And it will happen faster than we expect. Stay tuned, there is more on the way….

Learn more about the work IBM and USC are doing on social media sentiment http://www.ibm.com/press/us/en/pressrelease/36720.wss

Technorati Tags: Academy Awards, Analytics, Big Data, business analytics, Education, Entertainment, entrepreneur, IBM, IBM Research, Jeopardy, Media, nfl, Oscars, picture stories, predictive analytics, retail, smarter commerce, Smarter Planet, social business, social media, storytelling, twitter, Watson

IBM's Watson computing system

When we think of the systems that make up a smarter planet, what typically comes to mind are industries like manufacturing, transportation, energy, or banking.  But there is another ‘industry’ that needs to become smarter.  We might call it the humanitarian industry.  That is, the system that creates a safety net to support society and is made up of philanthropies, social services, education organizations, NGOs and government agencies.

In many ways, this is the most human of all systems.  So it is ironic to consider how Watson, a computing system, could help us solve civic, social and cultural challenges and make smarter humanitarian decisions. But Watson’s deep QA technology presents new possibilities to do just that.  Through private sector collaboration with nonprofits, Watson can become the next innovation to be used as a force for societal good.

IBM has a strong track record in leveraging technology to help solve society’s challenges.  We’ve used grid technology to build World Community Grid, DNA analysis to develop the Genographic Project, and speech recognition technology to create Reading Companion.  What we’ve learned through these and other corporate citizenship efforts is that innovation can lead to outcomes with greater societal impact than simple corporate philanthropy alone (i.e. check writing).  We’ve also learned that getting the most from innovations like Watson requires collaboration and partnerships.

That’s why IBM recently invited about 100 leaders from the humanitarian sector to learn about the technology behind Watson and to discuss how we might work together to apply that technology toward achieving societal good.  Ideas ranged from how Watson could help us cut error rates of emergency systems (like 911 and 311) to how Watson might be used as an aid to transform how teachers teach and children learn.  You can learn more about the event by reading this article in Fast Company.

At the end of the event, we encouraged attendees to share their best ideas about how Watson’s technology might be applied to humanitarian endeavors.  I invite you to do so as well.  Please visit the citizen IBM blog to learn more and share your ideas.

Technorati Tags: citizenIBM, citizenship, humanitarian, nonprofit, philanthropy, Watson

From the IBM Watson Research Team: During Watson’s participation in Jeopardy! last week, we received a large number of questions (especially on reddit!) about Watson, how it was developed and how IBM plans to use it in the future. Below are answers to some of the most popular questions (you can also read the responses on the reddit blog). As background, here’s who’s on the team.

1. Could you give an example of a question (or question style) that Watson always struggled with? (Chumpesque)

Any questions that require the resolution of very opaque references especially to everyday knowledge that no one might have written about in an explicit way. For example, “If you’re standing, it’s the direction you should look to check out the wainscoting.”

Or questions that require a resolving and linking opaque and remote reference, for example “A relative of this inventor described him as a boy staring at the tea kettle for an hour watching it boil.”

The answer is James Watt, but he might have many relatives and there may be very many ways in which one of them described him as studying tea boil. So first, find every possible inventor (and there may be 10,000′s of inventors), then find each relative, then what they said about the inventor (which should express that he stared at boiling tea). Watson attempts to do exactly this kind of thing but there are many possible places to fail to build confident evidence in just a few seconds.

2. What was the biggest technological hurdle you had to overcome in the development of Watson? (this_is_not_the_cia)

Accelerating the innovation process – making it easy to combine, weigh evaluate and evolve many different independently developed algorithms that analyze language form different perspectives.

Watson is a leap in computers being able to understand natural language, which will help humans be able to find the answers they need from the vast amounts of information they deal with everyday. Think of Watson as a technology that will enable people to have the exact information they need at their fingertips.

3. Can you walk us through the logic Watson would go through to answer a question such as, “The antagonist of Stevenson’s Treasure Island.” (Who is Long John Silver?) (elmuchoprez)

Step One: Parses sentence to get some logical structure describing the answer

X is the answer.

antagonist(X).

antagonist_of(X, Stevenson’s Treasure Island).

modifies_possesive(Stevenson, Treasure Island).

modifies(Treasure, Island)

Step Two: Generates Semantic Assumptions

island(Treasure Island)

location(Treasure Island)

resort(Treasure Island)

book(Treasure Island)

movie(Treasure Island)

person(Stevenson)

organization(Stevenson)

company(Stevenson)

author(Stevenson)

director(Stevenson)

person(antagonist)

person(X)

Step Three: Builds different semantic queries based on phrases, keywords and semantic assumptions.

Step Four: Generates 100s of answers based on passage, documents and facts returned

from 3. Hopefully Long-John Silver is one of them.

Step Five: For each answer formulates new searches to find evidence in support or

refutation of answer — score the evidence.

Positive Examples:

Long-John Silver the main character in Treasure Island…..

The antagonist in Treasure Island is Long-John Silver

Treasure Island, by Stevenson was a great book.

One of the great antagonists of all time was Long-John Silver

Richard Lewis Stevenson’s book, Treasure Island features many great

characters, the greatest of which was Long-John Silver.

Step Six:  Generate, get evidence and score new assumptions

Positive Examples: (negative examples would support other characters,

people, books, etc associated with any Stevenson, Treasure or Island)

Stevenson = Richard Lewis Stevenson

“by Stevenson” –> Stevenson’s

main character –> antagonist

Step Seven: Combine all the evidence and their scores

Based on analysis of evidence for all possible answer compute a final

confidence and link back to the evidence.

Watson’s correctness will depend on evidence collection, analysis and

scoring algorithms and the machine learning used to weight and combine the

scores.

4. What is Watson’s strategy for seeking out Daily Doubles, and how did it compute how much to wager on the Daily Doubles and the final clue? (AstroCreep5000)

Watson’s strategy for seeking out Daily Doubles is the same as humans — Watson hunts around the part of the grid where they typically occur.

In order to compute how much to wager, Watson uses input like its general confidence, the current state of the game (how much ahead or behind), its confidence in the category and prior clues, what is at risk and known human betting behaviors. We ran Watson through many, many simulations to learn the optimal bet for increasing chances of winning.

5. It seems like Watson had an unfair advantage with the buzzer. How did Jeopardy! and IBM try to level the playing field? (Raldi)

Jeopardy! and IBM tried to ensure that both humans and machines had equivalent interfaces to the game. For example, they both had to press down on the same physical buzzer.  IBM had to develop a mechanical device that grips and physically pushes the button. Any given player however has different strengths and weakness relative to his/her/its competitors. Ken had a fast hand relative to his competitors and dominated many games because he had the right combination of language understanding, knowledge, confidence, strategy and speed. Everyone knows you need ALL these elements to be a Jeopardy! champion.

Both machine and human got the same clues at the same time — they read differently, they think differently, they play differently, they buzz differently but no player had an unfair advantage over the other in terms of how they interfaced with the game. If anything the human players could hear the clue being read and could anticipate when the buzzer would enable. This allowed them the ability to buzz in almost instantly and considerably faster than Watson’s fastest buzz. By timing the buzz just right like this, humans could beat Watson’s fastest reaction. At the same time, one of Watson’s strengths was its consistently fast buzz — only effective of course if it could understand the question in time, compute the answer and confidence and decide to buzz in before it was too late.

The clues are in English — Brad and Ken’s native language; not Watson’s. Watson analyzes the clue in natural language to understand what the clue is asking for. Once it has done that, it must sift through the equivalent of one million books to calculate an accurate response in 2-3 seconds and determine if it’s confident enough to buzz in, because in Jeopardy! you lose money if you buzz in and respond incorrectly. This is a huge challenge, especially because humans tend to know what they know and know what they don’t know. Watson has to do thousands of calculations before it knows what it knows and what it doesn’t.  The calculating of confidence based on evidence is a new technological capability that is going to be very significant in helping people in business and their personal lives, as it means a computer will be able to not only provide humans with suggested answers, but also provide an explanation of where the answers came from and why they seem correct.

6. What operating system does Watson use? What language is he written in? (RatherDashing)

Watson is powered by 10 racks of IBM Power 750 servers running Linux, and uses 15 terabytes of RAM, 2,880 processor cores and is capable of operating at 80 teraflops. Watson was written in mostly Java but also significant chunks of code are written C++ and Prolog, all components are deployed and integrated using UIMA.

Watson contains state-of-the-art parallel processing capabilities that allow it to run multiple hypotheses – around one million calculations – at the same time. Watson is running on 2,880 processor cores simultaneously, while your laptop likely contains four cores, of which perhaps two are used concurrently. Processing natural language is scientifically very difficult because there are many different ways the same information can be expressed. That means that Watson has to look at the data from scores of perspectives and combine and contrast the results. The parallel processing power provided by IBM Power 750 systems allows Watson to do thousands of analytical tasks simultaneously to come up with the best answer in under three seconds.

7. Are you pleased with Watson’s performance on Jeopardy!? Is it what you were expecting? (eustis)

We are pleased with Watson’s performance on Jeopardy!  While at times, Watson did provide the wrong response to the clues, such as its Toronto response, it is still a giant leap in a computer’s understanding of natural human language; in its ability to understand what the Jeopardy! clue was asking for and respond with the correct response the majority of the time.

8. Will Watson ever be available public [sic] on the Internet? (i4ybrid)

We envision Watson-like cloud services being offered by companies to consumers, and we are working to create a cloud version of Watson’s natural language processing. However, IBM is focused on creating technologies that help businesses make sense of data in order to enable companies to provide the best service to the consumer.

So, we are first focused on providing this technology to companies so that those companies can then provide improved services to consumers. The first industry we will provide the Watson technology to is the healthcare industry, to help physicians improve patient care.

Consider these numbers:

• Primary care physicians spend an average of only 10.7 – 18.7 minutes face-to-face with each patient per visit.
• Approximately 81% average 5 hours or less per month – or just over an hour a week — reading medical journals.
• An estimated 15% of diagnoses are inaccurate or incomplete.

In today’s healthcare environment, where physicians are often working with limited information and little time, the results can be fragmented care and errors that raise costs and threaten quality. What doctors need is an assistant who can quickly read and understand massive amounts of information and then provide useful suggestions.

In terms of other applications we’re exploring, here are a few examples of how Watson might some day be used:

• Watson technology offered through energy companies could teach us about our own energy consumption.  People querying Watson on how they might improve their energy management would draw on extensive knowledge of detailed smart meter data, weather and historical information.
• Watson technology offered through insurance companies would allow us to get the best recommendations from insurance agents and help us understand our policies more easily. For our questions about insurance coverage, the question answering system would access the text for that person’s actual policy, the other policies that they might have purchased, and any exclusions, endorsements, and riders.
• Watson technology offered through travel agents would more easily allow us to plan our vacations based on our interests, budget, desired temperature, and more. Instead of having to do lots of searching, Watson-like technology could help us quickly get the answers we need among all of the information that is out there on the Internet about hotels, destinations, events, typical weather, etc, to plan our travel faster.

9. How raw is your source data? I am sure that you distilled down whatever source materials you were using into something quick to query, but I noticed that on some of the possible answers Watson had, it looked like you weren’t sanitizing your sources too much; for example, some words were in all caps, or phrases included extraneous and unrelated bits. Did such inconsistencies not cause you any problems? Couldn’t Watson trip up an answer as a result? (knorby)

Some of the source data was very messy and we did several things to clean it up.  It was relatively rare, less than 1% of the time that this issue overtly surfaced in a confident answer. Evidentiary passages might have been weighed differently if they were cleaner, however. We did not measure how much of problem messy data effected evidence assessment.

10. I’m interested in how Watson is able to (sometimes) use object-specific questions like “Who is –” or “Where is –”. In the training/testing materials I saw, it seemed to be limited to “What is–” regardless of what is being talked about (“What is Shakespeare?”), which made me think that words were only words and Watson had no way of telling if a word was a person, place, or thing. Then in the Jeopardy challenge, there was plenty of “Who is–.” Was there a last-minute change to enable this, or was it there all along and I just never happened to catch it? I think that would help me understand the way that Watson stores and relates data. (wierdaaron)

Watson does distinguish between people, things, dates, events, etc. certainly for answering questions. It does not do it perfectly of course, there are many ambiguous cases where it struggles to resolve.  When formulating a response, however, since “What is….” was acceptable regardless, early on in the project, we did not make the effort to classify the answer for the response. Later in the project, we brought more of the algorithms used in determining the answer to help formulate the more accurate response phrase. So yes, there was a change in that we applied those algorithms, or the results there-of, to formulate the “who”/”what” response.

11. Now that both Deep Blue and Watson have proven to be successful, what is IBM’s next “great challenge”? (xeones)

We don’t assign grand challenges, grand challenges arrive based on our scientists’ insights and inspiration. One of the great things about working for IBM Research is that we have so much talent that we have ambitious projects going on in a wide variety of areas today. For example:

• We are working to make computing systems 1,000 times more powerful than they are today from the petascale to the exascale.
• We are working to make nanoelectronic devices 1,000 times smaller than they are today, moving us from an era of nanodevices to nanosystems. One of those systems we are working on is a DNA transistor, which could decode a human genome for under $1000, to help enable personalized medicine to become reality.
• We are working on technologies that move from an era of wireless connectivity — which we all enjoy today — to the Internet of Things and people, where all sorts of unexpected things can be connected to the Internet.

12. Can we have Watson itself / himself do an AMA? If you give him traditional questions, ie not phrased in the form they are on jeopardy, how well will he perform- how tailored is he to those questions, and how easy would it be to change that? Would it be unfeasible to hook him up to a website and let people run queries?

At this point, all Watson can do is play Jeopardy and provide responses in the Jeopardy format. However, we are collaborating with Nuance, Columbia University Medical Center and the University of Maryland School of Medicine to apply Watson technology to healthcare. You can read more about that here: http://www-03.ibm.com/press/us/en/pressrelease/33726.wss

13. After seeing the description of how Watson works, I found myself wondering whether what it does is really natural language processing, or something more akin to word association. That is to say, does Watson really need to understand syntax and meaning to just search its database for words and phrases associated with the words and phrases in the clue? How did Waston’s approach differ from simple phrase association (with some advanced knowledge of how Jeopardy clues work, such as using the word “this” to mean “blank”), and what would the benefit/drawback have been to taking that approach?

Watson performs deep parsing on questions and on background content to extract the syntactic structure of sentences (e.g., grammatical and logical structure) and then assign semantics (e.g., people, places, time, organization, actions, relationship etc).  Watson does this analysis on the Jeopardy! clue, but also on hundreds of millions of sentences from which it abstracts propositional knowledge about how different things relate to one another. This is necessary to generate plausible answers or to relate an evidentiary passage to a question even if they are expressed with different words or structures. Consider more complex clues like: “A relative of this inventor described him as a boy staring at the tea kettle for an hour watching it boil.”

Sometimes, of course, Jeopardy questions are best answered based on the weight of a simple word associations.  For example, “Got ___ !” – well if “Milk” occurs mostly frequently in association with this phrase in everything Watson processed, then Watson should answer “Milk”.  It’s a very quick and direct association based on the frequency of exposure to that context.

Other questions require a much deeper analysis. Watson has to try many different techniques, some deeper than others, for almost all questions and all at the same time to learn which produces the most compelling evidence. That is how it gets its confidence scores for its best answer. So even the ones that might have been answered based on word-association evidence, Watson also tried to answer other ways requiring much deeper analysis. If word association evidence produced strong evidence (high confidence scores) then that is what Watson goes with. We imagine this is to the way a person might quickly peruse many different paths toward an answer simultaneously but then will provide the answer they are most confident in being correct.

14. In the time it takes a human to even know they are hearing something (about .2 seconds) Watson has already read the question and done several million computations. It’s got a huge head start. Do you agree or disagree with that assessment?

The clues are in English — Brad and Ken’s native language; not Watson’s. Watson must calculate its response in 2-3 seconds and determine if it’s confident enough to buzz in, because as you know, you lose money if you buzz in and respond incorrectly. This is a huge challenge, especially because humans tend to know what they know and know what they don’t know. Watson has to do thousands of calculations before it knows what it knows and what it doesn’t.  The calculating of confidence based on evidence is a new technological capability that is going to be very significant in helping people in business and their personal lives, as it means a computer will be able to not only provide humans with suggested answers, but also provide an explanation of where the answers came from and why they seem correct. This will further human ability to make decisions.

Technorati Tags: Watson

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.”

Baker’s book was released on Feb. 17, the day after Jeopardy! aired the third and final “Watson” show. I consumed it digitally, reading the first chapters in ebook form before the contest was broadcast and gulping down the last one when it was published immediately afterward. It’s a new day for publishing.

Final Jeopardy makes for a captivating read. Baker, who I must divulge is a friend of mine, has a delightful writing and thinking style. He does an artful job of describing complicated technologies and concepts, including artificial intelligence and game theory, and of revealing the strong and colorful personality of the main human protagonist of the drama, Watson project leader David Ferrucci. He has also mastered the art of the put down of smart machines. For example, on page 30, he quips: “In answering these questions, the computer, for all its processing power and memory, resembled nothing so much as a student with serious brain damage.”

As Baker follows the development of “Watson” by IBM scientists step by step, he explains that from the beginning this project was not an attempt to create a machine that mimics the workings of the human brain. Rather, it’s an effort to create a tool that’s expert at doing a few difficult things very well. It figures out what’s being asked in a question, quickly searches its vast database of information for potential answers, evaluates its confidence level in the answer it comes up with and activates a mechanism to signal that it’s prepared to answer the question. It also knows when it doesn’t know the answer.

Even with such a limited agenda, though, the machine’s success forces us human’s to reconsider our role in the future of the planet. Machines don’t just store information and automate tasks these days. They’re taking over more and more of our thinking tasks. As this happens, what will be left for humans to do?

Baker’s conclusion, on the final page of Final Jeopardy: “The solution, from a purely practical point of view, is to fine-tune the mind for the jobs and skills in which the Watsons of the world still struggle: the generation of ideas, concepts, art, and humor.”

That’s true. But executing on this strategy will be a struggle for many people. College professors, business consultants, software architects, pop stars and stand-up comedians have little to fear from computers. But a vast swath of society makes a living doing the kind of knowledge work that smart computers will master. This means many millions of people will have to reinvent themselves—and quickly. This transition will be faster than the agrarian and industrial revolutions.

It’s a challenging time for humans. Globalization has turned the world into one large talent pool where everybody competes with everybody else. Broadband communications networks spread information in milliseconds, making it a commodity. Smart machines force people to be smarter. There’s no place to hide—except, for some, in a sort of willful ignorance. Yet that refuge will only be temporary.

But it’s also a great time, as Baker’s book illustrates. For people like Ferrucci and his team, the frontiers of science are more welcoming than ever before. And, because the systems of the world are is increasingly instrumented, interconnected and intelligent, there are abundant opportunities to use science and innovation to improve the way the world works. So our species has the potential to reinvent not only ourselves but our planet. Fortunately, as machines get smarter, so can we.

Technorati Tags: Final Jeopardy, IBM, Stephen Baker, Watson

How could the machine have been so wrong? David Ferrucci, the manager of the Watson project at IBM Research, explained during a  viewing of the show on Monday morning that several things probably confused Watson. First, the category names on Jeopardy! are tricky. The answers often do not exactly fit the category. Watson, in his training phase,  learned that categories only weakly suggest the kind of answer that is expected, and, therefore, the machine downgrades their significance.  The way the language was parsed provided an advantage for the humans and a disadvantage for Watson, as well. “What US city” wasn’t in the question. If it had been, Watson would have given US cities much more weight as it searched for the answer. Adding to the confusion for Watson, there are cities named Toronto in the United States and the Toronto in Canada has an American League baseball team. It probably picked up those facts from the written material it has digested. Also, the machine didn’t find much evidence to connect either city’s airport to World War II. (Chicago was a very close second on Watson’s list of possible answers.) So this is just one of those situations that’s a snap for a reasonably knowledgeable human but a true brain teaser for the machine.

The mistake actually encouraged Ferrucci. “It’s goodness,” he said. Watson knew it did not know that right answer with any confidence. Its confidence level was about 30%. So it was right about that. Moreover, Watson has learned how the categories work in Jeopardy! It understands some of the subtleties of the game, and it doesn’t make simplistic assumptions. Think about how Watson could be used in medicine, as a diagnostic aid. A patient may describe to a doctor a certain symptom or a high level of pain, which, on the surface, may seem to be an important clue to the cause of the ailment. But Watson may know from looking at a lot of data that that symptom or pain isn’t the key piece of evidence, and could alert the doctor to be aware of other factors.

(By the way, there are many fields where Watson could help out. IBM general counsel Robert Weber describes how Watson might be used in the legal profession in a guest blog posting on The National Law Journal Web site. Anne K. Altman, general manager, Global Public Sector, talks about how Watson could be helpful to government in a posting on Government Technology magazine’s blog.)

Another encouraging sign: Watson bet intelligently, just $947, so it still won the game by a wide margin. “That’s smart,” Ferrucci said. “You’re in the middle of the contest. Hold onto your money. Why take a risk?”

Watson may not have much of a sense of humor, but Ferrucci sure does. He wore a Toronto Blue Jays jacket to the Jeopardy! viewing.

–

Here are some explanations of how Watson plays J

Here’s some info on how Watson sees, hears and speaks from Dave Gondek:

http://ibmresearchnews.blogspot.com/2010/12/how-watson-sees-hears-and-speaks-to.html

Here and here are a couple of essays by Stephen Baker, author of Final Jeopardy, on matters of machine intelligence.

Technorati Tags: David Ferrucci, IBM, Jeopardy, Watson

At the beginning of the game, Watson got off to a fast start, crisply answering questions about Beatles songs and literary characters. He got the first Daily Double with his answer of  “Hyde”–identifying the evil character in the Robert Louis Stevenson book The Strange Case of Dr Jekyll and Mr Hyde.

The humans came back in the second segment, beating Watson to the buzzer on a couple of questions that the machine knew the answers to but wasn’t quick enough to get a shot at answering. Watson showed some panache with his valiant attempt to pronounce the name of the character Jean Valjean, from Victor Hugo’s Les Miserables, with a French accent.

Those of you who watched the contest: What did you think?

One of  Watson’s misses highlighted how difficult it is for a machine to play Jeopardy! The category was Olympic Oddities:  “It was the anatomical oddity of US gymnist George Eyser who won a gold medal on the parallel bars in 1904.” Ken Jennings said “arm” and was wrong. Watson said “leg,” but host Alex Trebek ruled him incorrect because he didn’t say the gymnast’s leg was missing.

During a viewing of the tape of the show earlier today, David Ferrucci, the IBM manager who heads up the Watson project, explained that Jeopardy! is an enormously broad domain of knowledge, and some of the classifications are pretty vague. In this case, the computer very likely didn’t understand what an “oddity” is. “The computer wouldn’t know that a missing leg is odder than anything else,” said Ferrucci. Still, over time, by reading more material and playing more games, Watson could come to understand. (Would he be able to understand that he is an oddity, I wonder.)

All in all, Ferrucci was pleased with the outcome of the first day. “I had a good feeling at the end of the first show,” he said. “I thought: Everybody will realize the computer is competitive. It can play with champions. It answered some tough questions.”

Technorati Tags: David Ferrucci, IBM, Jeopardy, Watson

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.

Steve Baker

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.

When Watson plays Jeopardy on Feb. 14, 15, and 16, many will focus on the machine’s speed and precision on the clues it gets right. But its hidden strength is its knowledge of its own knowledge–including its own limitations. Like us, it’s able to hazard a guess and, equally important, label it as such. That’s one area where Watson stands out.

Stephen Baker is author of Final Jeopardy–Man vs Machine and the Quest to Know Everything.

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The clue: Two of the greatest human Jeopardy! television game show players, ever.  The correct response:  Ken Jennings. Brad Rutter.

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.

Click here to view the embedded video.

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.

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