Building Watson -- A Brief Overview of DeepQA and the Jeopardy! Challenge
We will give an overview of the building of Watson a computer system
that was able to defeat human grand champions in the game of Jeopardy
(a popular quiz show in the United States)
A computer system that can directly and precisely answer natural
language questions over an open and broad range of knowledge has been envisioned by scientists and writers since the advent of computers themselves. While current computers can store and deliver a wealth of digital content created by humans, they are unable to operate over it in human terms. The quest for building a computer system that can do open-domain Question Answering is ultimately driven by a broader vision that sees computers operating more effectively in human terms rather than strictly computer terms. They should function in ways that understand complex information requirements, as people would express them, for example, in natural language questions or interactive dialogs. Computers should deliver precise, meaningful responses, and synthesize, integrate, and rapidly reason over the breadth of human knowledge as it is most rapidly and naturally produced – in natural language text.
The DeepQA project at IBM shapes a grand challenge in Computer Science that aims to illustrate how the wide and growing accessibility of natural language content and the integration and advancement of
Natural Language Processing, Information Retrieval, Machine Learning,
Knowledge Representation and Reasoning, and massively parallel
computation can drive open-domain automatic Question Answering
technology to a point where it clearly and consistently rivals the
best human performance. A first stop along the way was the Jeopardy!
Challenge, where a computer system beat human grand champions in the game of Jeopardy!. In this talk, we will give an overview of the
DeepQA project and the Jeopardy! Challenge.
Karthik Visweswariah is a Senior Technical Staff Member at IBM
Research, India. His primary interests are in statistical modelling
applied to text and speech. Prior to joining IBM Research, India in
2008 he spent 8 years at IBM’s T.J. Watson Research Centre in the
Human Language Technologies group working on improving speech
recognition technologies in IBM’s products. His current focus is on
improving machine translation for translating between Indian languages and English. He has published over 60 papers in these areas in various refereed journals and conferences. He obtained his Ph.D from Princeton University in 1999 and B.Tech from Indian Institute of Technology, Madras.