IBM Watson & Artificial Intelligence

Technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage including the full text of Wikipedia.

Watson is a question answering (QA) computing system that IBM built to apply advanced Processing,information retrieval,knowledge,representation,

automated reasoning, and machine learning technologies to the field of open domain question answering.

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).

The goals of IBM Research are to advance computer science by exploring new ways for computer technology to affect science, business, and society. Roughly three years ago, IBM Research was looking for a major research challenge to rival the scientific and popular interest of Deep Blue, the computer chess-playing champion (Hsu 2002), that also would have clear relevance to IBM business interests.

THE WORKING -IBM WATSON

The Watson supercomputer processes at a rate of 80 Teraflops 

(trillion floating-point operations per   second).
To replicate (or surpass) a   high-functioning human’s ability to answer questions, Watson accesses 90 servers with a combined data store of over 200 million pages of information, which it processes against six million logic rules.

Intelligence Possessed  

Many say IBM Watson is just an Algorithm , many say IBM WATSON is a future of Artificial Intelligence and few say it can change the world we see Today , but actually what is the fact . What if the IBM WATSON keeps on learning and developing its own intelligence level as it has defeated some of the brilliant minds in the world in a QUIZ but still the question remains "HOW IS IT HELPFUL" ?
IBM is currently attempting to merge artificial intelligence and the blockchain into a single, powerful prototype. With blockchain tech's promise of near-frictionless value exchange and artificial intelligence’s ability to accelerate the analysis of massive amounts of data, the joining of the two could mark the beginning of an entirely new paradigm.

WATSON DEFEATED HUMANS AT JEOPARDY 

In an American's favourite game show, will air a special edition where the all-time greatest human champions, Ken Jennings and Brad Rutter, competed against IBM's Watson cluster computer.During trial sessions, Watson beat the humans—just barely. It finally defeated in the the real contest  aired on Feb. 14, 15 and 16. 

"Watson is the result of a long commitment to human language technology, large data analytics, and supercomputing, all coming together," said?John Kelly, director of IBM Research. 

Finally, the Jeopardy Challenge represents a unique and compelling AI question similar to the one underlying DeepBlue (Hsu 2002) — can a computer system be designed to compete against the best humans at a task thought to require high levels of human intelligence, and if so, what kind of technology, algorithms, and engineering is required? 
While we believe the Jeopardy Challenge is an extraordinarily demanding task that will greatly advance the field, we appreciate that this challenge alone does not address all aspects of QA and does not by any means close the book on the QA challenge the way that Deep Blue may have for playing chess.

SUMMARY

The Jeopardy Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researcherss, 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 may be used as a foundation for combining, deploying, evaluating, and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

The architecture and methodology developed as part of this project has highlighted the need to take a systems-level approach to research in QA, and we believe this applies to research in the broader field of AI. We have developed many different algorithms for addressing different kinds of problems in QA and plan to publish many of them in more detail in the future. However, no one algorithm solves challenge problems like this. End-to-end systems tend to involve many complex and often overlapping interactions. A system design and methodology that facilitated the efficient integration and ablation studies of many probabilistic components was essential for our success to date. The impact of any one algorithm on end-to-end performance changed over time as other techniques were added and had overlapping effects. Our commitment to regularly evaluate the effects of specific techniques on end-to-end performance, and to let that shape our research investment, was necessary for our rapid progress.

Rapid experimentation was another critical ingredient to our success. The team conducted more than 5500 independent experiments in 3 years — each averaging about 2000 CPU hours and generating more than 10 GB of error-analysis data. Without DeepQA’s massively parallel architecture and a dedicated high-performance computing infrastructure, we would not have been able to perform these experiments, and likely would not have even conceived of many of them.Tuned for the Jeopardy Challenge, Watson has begun to compete against former Jeopardy players in a series of “sparring” games. It is holding its own, winning 64 percent of the games, but has to be improved and sped up to compete favorably against the very best.

We have leveraged our collaboration with CMU and with our other university partnerships in getting this far and hope to continue our collaborative work to drive Watson to its final goal, and help openly advance QA research.