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构建Watson:DeepQA概述为危险! 挑战

Building Watson: An Overview of DeepQA for the Jeopardy! Challenge
课程网址: http://videolectures.net/aaai2011_ferrucci_building/  
主讲教师: David Ferrucci
开课单位: IBM公司
开课时间: 2011-09-28
课程语种: 英语
中文简介:
能够直接、准确地回答人们问题的计算机系统;自从计算机出现以来,科学家和作家就设想了有关人类知识的广泛领域的问题。开放领域问题的回答对于促进对大量自然语言内容的明智决策有着巨大的前景。商业智能、医疗保健、客户支持、企业知识管理、社会计算、科学和政府等领域的应用都将受益于深度语言处理。DeepQA项目旨在探索如何推进和集成自然语言处理、信息检索、机器学习、大规模并行计算、知识表示和推理,从而极大地促进开放域自动回答问题。在这个挑战中一个令人兴奋的证明点是开发一个计算机系统,它可以成功地在危险边缘与顶级人类选手竞争!智力竞赛节目。获得冠军级别的表现危险!要求计算机系统能够快速、准确地回答丰富的开放领域问题,并预测其在任何给定类别/问题上的性能。该系统必须在非常广泛的知识和自然语言内容范围内,以3秒的响应时间提供高精确度和方便性。为了做这个DeepQA证据和评估许多相互竞争的假设。成功的关键是自动学习,并在一系列复杂算法和不同的证据维度上结合精确的二次曲线。要知道什么时候开会,需要精确的会议记录;和你的竞争对手较量,赌多少钱。高精度和精确的conidence计算对于在业务环境中提供真正的价值是至关重要的,在业务环境中,帮助用户更快、更方便地关注正确的内容可以带来很大的不同。高速和高精度的需要需要一个大规模并行计算平台,能够生成、评估和梳理1000个假设及其相关证据。在这次演讲中,我将向听众介绍危险!挑战和我们如何使用DeepQA解决它。
课程简介: 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 beneit from deep language processing. The DeepQA project is aimed at exploring how advancing and integrating natural language processing, information retrieval, machine learning, massively parallel computation, and knowledge representation and reasoning 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. 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 conidence over a very broad range of knowledge and natural language content with a 3-second response time. To do this DeepQA evidences and evaluates many competing hypotheses. A key to success is automatically learning and combining accurate conidences across an array of complex algorithms and over different dimensions of evidence. Accurate conidences are needed to know when to “buzz in” against your competitors and how much to bet. High precision and accurate conidence computations are just as critical for providing real value in business settings where helping users focus on the right content sooner and with greater conidence can make all the difference. The need for speed and high precision demands a massively parallel computing platform capable of generating, evaluating and combing 1000s 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.
关 键 词: 计算机系统; 开放式域名问题; 明智决策
课程来源: 视频讲座网
最后编审: 2020-04-30:chenxin
阅读次数: 54