IBM's Watson system beat two former Jeopardy! game show champions on television February 14-16, 2011. Details of the Match in the NY Times story Computer Wins on Jeopardy!: Trivial, It's Not. (Feb. 17, 2011).
IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show, Jeopardy. The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After three years of intense research and development by a core team of about 20 researchers, Watson is performing at human expert levels in terms of precision, confidence, and speed at the Jeopardy quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that can be used as a foundation for combining, deploying, evaluating, and advancing a wide range of algorithmic techniques to rapidly advance the field of question answering (QA).
Almost every word has more than one meaning. Modern search engines solve this problem using knowledge bases. Yago was one of the first knowledge bases, developed by scientists at the Max Planck Institute for Informatics in Saarbrücken and the Télécom ParisTech in Paris. Last week, the researchers received an award for their work on Yago from the most important scientific journal in the field of artificial intelligence. Today, they are releasing Yago's source code.
Since his appearance on the game show in 2011, IBM has expanded Watson's talents, building on the algorithms that allow him to read and derive meaning from natural language. Toronto Western, part of the University Health Network, is the first hospital in Canada to use Watson for research in Parkinson's, a neurological disorder. The centre has a track record of running clinical trials for off-label drug use, which means taking a drug approved for treatment of one condition and repurposing it for another. Visanji, 39, is a scientist at the hospital's Morton and Gloria Shulman Movement Disorders Centre, the country's biggest Parkinson's clinic.
In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. I can look through these patterns much more efficiently because machines are a lot faster, and I can generalize those patterns using machine learning techniques. So, machines got a lot faster, huge volumes of data became available, and machine learning techniques allowed me to discover patterns in that language data more rapidly and more effectively than ever before. Watson analyzed how words occur together in questions and passages, and it came up with an approximation: this phrase might mean that phrase, and if I see this phrase as part of the question and this phrase as part of a potential answer passage, since they might mean the same thing, then such connections might help formulate an answer.
It takes in several key areas, including image, facial, text and speech recognition, and hopes to implement the technology into its computer operating systems and smartphone software. Now, IBM is partnered with over 300 firms from all fields, including Twitter, Wellpoint (Medical Insurance) and Chatterbox (Children's Technology), to use Watson's Natural Language Processing capabilities for their own ends. Produced by Baidu, Google's Chinese equivalent, Minwa is their landmark project, and mirrors the IBM Watson model, with over 72 processors and 144 graphics processors. Not unlike Watson, Minwa's Natural Language Processing capabilities are some of the most impressive in the world, but the whole project was shrouded in disrepute after the most recent Image Classification Challenge, in which Minwa posted a 4.58% error rate, better than its competitors from Google and Microsoft, and better than the average human rate of 5%.
The number of jobs where it's possible for human labor to add value is shrinking, and new jobs that require human labor are becoming more and more rare. Is it the responsibility of companies that develop or deploy automated systems to retrain the workers they replace? This isn't simply automation; it's a new life form, which raises civil rights issues and the brand new problem of dealing with alien life forms (and they will be alien - completely different priorities from any previous known life form, and the only life form we know of that didn't result from the same kind of evolutionary process we did). All of the questions around strong AI are a lot bigger and have a lot bigger consequences than whether we'll have to retrain humans to do different jobs.
Earlier this month, the nation watched as Watson, a computer system designed by IBM, drubbed the two all time champions of Jeopardy. It was a much more difficult challenge than, say, beating a grandmaster at chess. To win, Watson had to navigate the vagaries of human speech, the idioms, the puns, the cultural references -- all the things, in short, that make language delightful and deeply machine unfriendly. Journalist Stephen Baker spent a year behind the scenes, as the team of IBM engineers struggled to design and build Watson in time for the show. He tells the story of project Watson, and what it means for the future, in his new book, "Final Jeopardy: Man vs. Machine and the Quest to Know Everything."
They say it's the tallest tower in the world; looked over the ledge and lost my lunch." This is the quintessential sort of clue you hear on the TV game show "Jeopardy!" It's witty (the clue's category is "Postcards From the Edge"), demands a large store of trivia and requires contestants to make confident, split-second decisions. This particular clue appeared in a mock version of the game in December, held in Hawthorne, N.Y. at one of I.B.M.'s research labs. Two contestants -- Dorothy Gilmartin, a health teacher with her hair tied back in a ponytail, and Alison Kolani, a copy editor -- furrowed their brows in concentration.
Developers of artificial intelligence (A.I.) now have an added incentive to pursue their work: $5 million dollars. The prize money was announced at the annual TED conference Wednesday, in a joint initiative between tech giant IBM and X Prize, the company behind the world's first private space race to reach the moon. Motivating the backers of this competition is, among other things, a desire to demonstrate the potential benefits to mankind of advances in A.I., but many skeptics have yet to be convinced. "Personally, I am sick and tired of the dystopian conversation around artificial intelligence," said X Prize founder Peter Diamandis when unveiling the prize. The competition challenges teams to "develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges," according to an X Prize statement.