Posted by
Steve Hamm in
IBM, MIT Sloan School of Management and Harvard Business School today are sponsoring a symposium at the the two universities. The morning topic: How advances in information technology can help improve productivity, and improve incomes and create jobs for the 99%. It’s being followed this afternoon by a mock Jeopardy! match between Watson, IBM’s very smart computer, and teams from MIT and HBS.
Update:
Teams of three students from MIT/Sloan and HBS take on IBM’s Watson. (This is only the second contest matching Watson against collegians. In the previous contest, Watson beat teams from Carnegie Mellon University and the University of Pittsburgh. Pitt came in second, much to the chagrin of rival CMU!)
Harvard wins the first question, with “What is Belize?” Answering: countries in central America, ending with “e”
But then Watson takes over, running the category.
The machine picks “Who’s Your Daddy Company?” as the next category, eliciting a huge hook of laughter from the audience.
They finished the Jeopardy! round, with Watson, $8600; Harvard, $5200 ; and MIT, $-200 .
(I got disconnected from HBS’s Wi-Fi at a crucial moment, destroying the coverage of the second round. Grrrrr)
Final Jeopardy!
Clue: Finding the spot for this memorial caused its creator to say “Americans will march across that skyline.”
The question: Mt. Rushmore.
Harvard and Watson answer correctly. MIT does not.
Final score: Watson, $53,601; Harvard, $42,399; MIT, $100.
!!!!! Continue Reading »
Posted by
Steve Hamm in
We asked on the People for a Smarter Planet Facebook page what IBM’s next grand challenge should be–now that a team at IBM Research accomplished the previous grand-challenge goal of creating a computer that could beat past champions at TV’s Jeopardy! quiz show. More than 750 people responded with ideas and votes. And the winner, with 303 votes, is: “create a working quantum computer.”
This quest would be plenty challenging. Computer Scientists have been developing theories about quantum computing ever since physicist Richard Feynman first proposed the concept of computing based on quantum mechanical phenomena in 1982. Nearly 30 years later, there are no quantum computers.
Another proposition came in a close second, with 277 votes: “fight global warming.” (This one got my vote.)
Other suggestions ranged from the earnest, such as “take healthcare to the next level,” with 18 votes; to the ridiculous, “time travel,” with 97 votes.
We’ll pass along the top suggestions to the folks at IBM Research.
To read what it’s all about, see two previous posts, this one by IBM researcher Dario Gil about the effort to create learning systems, and this one, the live blogging stream from IBM Research’s colloquium, the Frontiers of IT.
Posted by
Steve Hamm in
We asked students and faculty members at the ACM International Collegiate Programming Contest what they think Watson should do next, now that it has vanquished the former champions of Jeopardy! Here’s what they had to say.
On several occasions during his decades as an IBM executive, former CEO Thomas J. Watson Jr. went out of his way to differentiate between human brains and computers. The goal of IBM, he said, was to make machines that could serve as tools for humans, not replace them. By taking on many of the more routine tasks that people were saddled with, he said, machines would free humans up to do more fulfilling creative work.
Today’s computers are “smarter” than those of the mid-20th century, when Watson was busy trying to calm people’s fears about the onslaught of electronic brains. But even with the achievements and potential of Watson, the Jeopardy-playing computer, IBM is pushing the borders of machine capability with the goal of augmenting human thought, not replacing human brains with machines.
Still it’s intriguing to compare the two, and for scientists, nature offers models for next-generation computer design. At the conference IBM Research Colloquia – Zurich last week, Prof. Karlheinz Meier of Heidelberg University spoke about neuromorphic computers during his presentation on Brain Inspired Computing. A key aspect of neuromorphic computing is understanding how the brain works and attempting to mimic some of its functions in silicon.
For me, the most intriguing moment during Meier’s presentation was when an audience member asked him a question that got to the heart of the man vs. machine debate. Essentially, he asked: Why copy nature? Why not aim to do better?
People were impressed when IBM’s Watson question-and-answer computer beat the two former champions at the Jeopardy! TV quiz show. Now they’re asking: what else can it do?
The company’s researchers and business leaders have been busy searching commercial uses for the technology, and they’re making progress. David Ferrucci, the IBM Fellow who heads up the Watson project, today told a group of journalists and analysts at a briefing on Big Data at IBM Research that the healthcare field is especially promising. IBM is developing applications in collaboration with physicians and researchers at Columbia University and the University of Maryland. Meanwhile, it took one researcher just three months to adapt the Watson Jeopardy! database to the medical field. Presumably, adaptations to other domains will be relatively easy, as well.
But a big issue is affordability. Watson is “embarrassingly parallel,” in computer science parlance—meaning the machine uses thousands of high-performance microprocessors. Embarrassing parallelism is expensive.
During a break form the briefing, I asked Ferrucci and other IBM colleagues about the affordability issue. Ferrucci’s response was that the cost of computing is dropping rapidly, and that the more industries Watson can serve and the more applications that are running on the platform, the more affordable it will be for each individual client.
Rod Smith, another IBM Fellow who heads up the Emerging Internet Technologies group at IBM Research, told me that another key will be the simplicity of the user interface. If IBM can develop user-friendly interfaces, clients will get rapid adoption of the technology and rapid returns on their investments. So, even if the service is relatively expensive, it will be worth the price.
Another key to affordability will be setting up Watson services for specific industries as shared services offered from the computing cloud. That way, many clients can use the same application and the same computing resources, making the services highly efficient.
It has been just three months since the Jeopardy! contest, but, already, it’s clear that the Watson machine has a life after Jeopardy! In the months ahead we’ll find out whether it can play a major role in making the world work better.
Posted by
Lia P Davis in
Dr. Eric Brown from IBM Research preps four members of congress - Jared Polis (D-Colo.), Bill Cassidy (R-La.), Jim Himes (D-Conn.), Rush Holt (D-N.J.) - for an exhibition game against IBM's Watson on Monday, Feb. 28, 2011. The match fostered a conversation among government leaders about the importance of IT to U.S. global competitiveness and encouraged greater focus on math and science education. Final score: Watson $40,300, Congressional Members $30,000. (Photo credit: Tom Briglia/Feature Photo Service for IBM)
Coming off the heels of its recent Jeopardy! win, IBM’s Watson computing system faced off against Congress last night for an exhibition match in Washington with five U.S.Congress members.
The bipartisan group put politics aside to test their trivia knowledge and foster conversations about the importance of IT to U.S. global competitiveness and encourage greater focus on math and science education. Continue Reading »
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.
Posted by
Guest in
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. Continue Reading »
8:40
Posted by
Steve Hamm in
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.”
Posted by
Steve Hamm in
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.
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.
