Google AI researchers find strange new reason to play Jeopardy!

ZDNet

When IBM's Watson computer beat two world champions at the game show Jeopardy! in 2011, it was a moment to marvel at how a machine could take comprehend the language of a question and could mine its vast memory for an appropriate response. Google scientists have found another use for Jeopardy! And this week, they've made that work an open-source software tool available on GitHub to anyone using Google's TensorFlow framework for machine learning. "Active Question Answering," or Active QA, as the TensorFlow package is called, will reformulate a given English-language question into multiple different re-wordings, and find the variant that does best at retrieving an answer from a database. The system was developed by feeding Jeopardy!


Building Watson: An Overview of the DeepQA Project

AI Magazine

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).


Building Watson: An Overview of the DeepQA Project

AI Magazine

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 3 years of intense research and development by a core team of about 20 researches, 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 AI Behind Watson -- The Technical Article

#artificialintelligence

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.


Training Reinforcement Learning Agents to Ask the Right Questions

#artificialintelligence

That paradigm assumes that the target knowledge is already embedded in the dataset and doesn't require any further clarifications but that rarely resembles how humans learn. When presented with a new subject, we are constantly forced to ask questions and clarifications about it. What if we could build the same skill into artificial intelligence(AI) models. The ability of formulate questions is a fundamental element of the human cognition process. The cornerstone of human's dialogs relies on our ability to express questions in a myriad of ways in order to obtain a specific answer.