David Gondek (IBM )
Making Community · Keynote
Tuesday, 09:00, 1 hour | Ballrooms ABCD
Computer systems that can directly and accurately answer peoples' questions over a broad domain of human knowledge have been envisioned by scientists and writers since the advent of computers themselves. Open domain question answering holds tremendous promise for facilitating informed decision making over vast volumes of natural language content. Applications in business intelligence, healthcare, customer support, enterprise knowledge management, social computing, science and government would all benefit from deep language processing.
The DeepQA project is aimed at exploring how advancing and integrating Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning (ML), massively parallel computation and Knowledge Representation and Reasoning (KR&R) can greatly advance open-domain automatic Question Answering.
An exciting proof-point in this challenge is to develop a computer system that can successfully compete against top human players at the Jeopardy! quiz show (www.jeopardy.com). Attaining champion-level performance Jeopardy! requires a computer system to rapidly and accurately answer rich open-domain questions, and to predict its own performance on any given category/question. The system must deliver high degrees of precision and confidence over a very broad range of knowledge and natural language content with a 3-second response time. The need for speed and high precision demands a massively parallel computing platform capable of generating, evaluating and combing 1000's of hypotheses and their associated evidence.
In this talk I will introduce the audience to the Jeopardy! Challenge and how we tackled it using DeepQA.
David Gondek is a Research Scientist on the DeepQA/Watson Project. His primary role is developing DeepQA's machine learning algorithms and infrastructure, which are used for ranking and estimating confidence in possible answers. He also leads Watson's strategy team. David has a background in Machine Learning.