IBM chose to host the first Watson university symposium in Pittsburgh because of Carnegie Mellon's key contributions to the development of Watson - led by Eric Nyberg, professor, Language Technologies Institute, CMU School of Computer Science - and the university's role as a leading centre for computer science research and education. In addition, the University of Pittsburgh has a long and productive partnership with IBM in research projects such as Cloud computing, carbon nanotubes, and smarter health care research around pandemic disease outbreaks and tissue regeneration.
By bringing this technology to the university community, IBM aims to inspire the next generation of innovators and entrepreneurs to think about how technologies such as Watson can benefit society. The event also discussed the skills students need to drive future innovation.
"This is the first time we're bringing together Watson, IBM scientists, faculty, and students to prepare for the next evolution in computing", stated Bernie Meyerson, vice president of innovation and university programmes for IBM. "Watson will transform how technology is applied to assist doctors, business people and more. Our hope is that seeing Watson first hand will spark innovation from the leaders of tomorrow so that together we can continue to build a smarter planet."
"Machines that think have been Carnegie Mellon's stock in trade since the first artificial intelligence programme was invented here more than 50 years ago", stated Jared L. Cohon, president of Carnegie Mellon University. "IBM and Carnegie Mellon have been frequent collaborators during that period and, over the past decade, have enjoyed particular success in building question-answering machines. The recent triumph of Watson has been gratifying for the faculty and students involved, and we are pleased that our student body today will be the first to see this technological breakthrough in person."
"The Deep Question Answering technology that underlies IBM Watson's ability to extract, organize, analyze, and assess massive quantities of information at record speeds has far-reaching implications across a wide range of sectors, among them education, business, law, and medicine", stated University of Pittsburgh Chancellor Mark A. Nordenberg. "The real-world applications of this cutting-edge technology - such as assisting health care professionals in evaluating complex and multiple diagnostic and patient treatment options - have the extraordinary potential to enhance the human condition and transform lives. Pitt is delighted to play an important role in this symposium and to once again join forces with our academic partner Carnegie Mellon University and industry leader IBM as it celebrates its landmark centennial anniversary."
Watson, named after IBM founder Thomas J. Watson, was built by a team of IBM scientists who set out to accomplish a grand challenge - build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. The Jeopardy! format provides the ultimate challenge because the game's clues involve analyzing subtle meaning, irony, riddles, and other language complexities in which humans excel and computers traditionally do not.
Watson represents a major leap forward for computer science. With its combination of sheer data processing power, natural language recognition and machine learning, the system demonstrates that technology has the potential to help humans improve the performance of many endeavours - everything from medicine to education, law and environmental protection. The technology itself was developed in collaboration between IBM's Watson Research team and the academic community including CMU.
A team of researchers from CMU, led by Professor Nyberg, assisted IBM in the development of the Open Advancement of Question-Answering Initiative (OAQA) methodology for Watson. CMU also made two direct contributions to Watson: a source expansion algorithm which identifies the best text resources for answering questions about a given topic, and an answer-scoring algorithm which improves Watson's ability to recognize when a candidate answer is likely to be correct.