德勤:2025年以后.pdf
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1、! ! AI INDEX, NOVEMBER 2017!1! ! AI INDEX, NOVEMBER 2017 STEERING COMMITTEE Yoav Shoham (chair) Stanford University Raymond Perrault SRI International Erik Brynjolfsson MIT Jack Clark OpenAI PROJECT MANAGER Calvin LeGassick !2! ! AI INDEX, NOVEMBER 2017 TABLE OF CONTENTS Introduction to the AI Index
2、 2017 Annual Report 5 Overview 7 Volume of Activity 9 Academia 9 Published Papers 9 Course Enrollment 11 Conference Attendance 14 Industry 16 AI-Related Startups 16 AI-Related Startup Funding 17 Job Openings 18 Robot Imports 21 Open Source Software 23 GitHub Project Statistics 23 Public Interest 25
3、Sentiment of Media Coverage 25 Technical Performance 26 Vision 26 Object Detection 26 Visual Question Answering 27 Natural Language Understanding 28 Parsing 28 Machine Translation 29 Question Answering 30 Speech Recognition 31 !3! ! AI INDEX, NOVEMBER 2017 Theorem Proving 32 SAT Solving 33 Derivativ
4、e Measures 34 Towards Human-Level Performance? 37 Whats Missing? 41 Expert Forum 44 Get Involved! 68 Acknowledgements 70 Appendix A: Data Description for example, how well computers can understand images and prove mathematical theorems. The methodology used to collect each data set is detailed in th
5、e appendix. These first two sets of data confirm what is already well recognized: all graphs are “up and to the right,” reflecting the increased activity in AI e#orts as well as the progress of the technology. In the Derivative Measures section we investigate the relationship between trends. We also
6、 introduce an exploratory measure, the AI Vibrancy Index, that combines trends across academia and industry to quantify the liveliness of AI as a field. When measuring the performance of AI systems, it is natural to look for comparisons to human performance. In the Towards Human-Level Performance se
7、ction we outline a short list of notable areas where AI systems have made significant progress towards !7! ! AI INDEX, NOVEMBER 2017 matching or exceeding human performance. We also discuss the di#iculties of such comparisons and introduce the appropriate caveats. Discussion Sections Following the d
8、isplay of the collected data, we include some discussion of the trends this report highlights and important areas this report entirely omits. Part of this discussion centers on the limitations of the report. This report is biased towards US-centric data sources and may overestimate progress in techn
9、ical areas by only tracking well-defined benchmarks. It also lacks demographic breakdowns of data and contains no information about AI Research & Development investments by governments and corporations. These areas are deeply important and we intend to tackle them in future reports. We further discu
10、ss these limitations and others in the Whats Missing section of the report. As the reports limitations illustrate, the AI Index will always paint a partial picture. For this reason, we include subjective commentary from a cross-section of AI experts. This Expert Forum helps animate the story behind
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