全球化研究报告-PDF版

IHS Markit：智能互融： 借助5G、人工智能和云技术释放机遇白皮书（英文版）（24页）.pdf

Intelligent Connectivity Unleashing opportunities with the power of 5G, AI and cloud 10 December 201.

2020-07-31 24页 5星级

Daxue Consulting：2020年中国人工智能白皮书（英文版）（140页）.pdf

在中国调查Al和冠状病毒之间的关系时，我们发现大数据(Al)在遏制疫情方面取得了前所未有的技术进步。事实上，根据世界卫生组织的一份报告，该报告列出了2020年2月16日至24日派往中国的专家调查组的调.

2020-07-02 140页 5星级

A4AI：2019年度可负担能力报告（英文版）(42页)(42页).pdf

Affordability Report www.a4ai.org A global coalition working to make broadband affordable for all 20.

2019-12-01 42页 5星级

斯坦福：2019人工智能报告（英文版）(291页).pdf

2019 annual report ar intelligence index Raymond Perrault (report coordinator) SRI International Yoa.

2019-12-01 291页 5星级

AI_Now研究所：2018年度报告(英文版)(62页).pdf

AI Now Report 2018 Meredith Whittaker , AI Now Institute, New York University, Google Open Research Kate Crawford , AI Now Institute, New York University, Microsoft Research Roel Dobbe , AI Now Institute, New York University Genevieve Fried , AI Now Institute, New York University Elizabeth Kaziunas , AI Now Institute, New York University Varoon Mathur , AI Now Institute, New York University Sarah Myers West , AI Now Institute, New York University Rashida Richardson , AI Now Institute, New York University Jason Schultz , AI Now Institute, New York University School of Law Oscar Schwartz , AI Now Institute, New York University With research assistance from Alex Campolo and Gretchen Krueger (AI Now Institute, New York University) DECEMBER 2018 CONTENTS ABOUT THE AI NOW INSTITUTE3 RECOMMENDATIONS4 EXECUTIVE SUMMARY7 INTRODUCTION10 1. THE INTENSIFYING PROBLEM SPACE12 1.1 AI is Amplifying Widespread Surveillance12 The faulty science and dangerous history of affect recognition13 Facial recognition amplifi es civil rights concerns15 1.2 The Risks of Automated Decision Systems in Government18 1.3 Experimenting on Society: Who Bears the Burden?22 2. EMERGING SOLUTIONS IN 201824 2.1 Bias Busting and Formulas for Fairness: the Limits of Technological “Fixes”24 Broader approaches27 2.2 Industry Applications: Toolkits and System Tweaks28 2.3 Why Ethics is Not Enough29 3. WHAT IS NEEDED NEXT32 3.1 From Fairness to Justice32 3.2 Infrastructural Thinking33 3.3 Accounting for Hidden Labor in AI Systems34 3.4 Deeper Interdisciplinarity36 3.5 Race, Gender and Power in AI37 3.6 Strategic Litigation and Policy Interventions39 3.7 Research and Organizing: An Emergent Coalition40 CONCLUSION42 ENDNOTES44 This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License 2 ABOUT THE AI NOW INSTITUTE The AI Now Institute at New York University is an interdisciplinary research institute dedicated to understanding the social implications of AI technologies. It is the fi rst university research center focused specifi cally on AIs social signifi cance. Founded and led by Kate Crawford and Meredith Whittaker, AI Now is one of the few women-led AI institutes in the world. AI Now works with a broad coalition of stakeholders, including academic researchers, industry, civil society, policy makers, and affected communities, to identify and address issues raised by the rapid introduction of AI across core social domains. AI Now produces interdisciplinary research to help ensure that AI systems are accountable to the communities and contexts they are meant to serve, and that they are applied in ways that promote justice and equity. The Institutes current research agenda focuses on four core areas: bias and inclusion, rights and liberties, labor and automation, and safety and critical infrastructure. Our most recent publications include: Litigating Algorithms , a major report assessing recent court cases focused on government use of algorithms Anatomy of an AI System , a large-scale map and longform essay produced in partnership with SHARE Lab , which investigates the human labor, data, and planetary resources required to operate an Amazon Echo Algorithmic Impact Assessment (AIA) Report , which helps affected communities and stakeholders assess the use of AI and algorithmic decision-making in public agencies Algorithmic Accountability Policy Toolkit , which is geared toward advocates interested in understanding government use of algorithmic systems We also host expert workshops and public events on a wide range of topics. Our workshop on Immigration, Data, and Automation in the Trump Era , co-hosted with the Brennan Center for Justice and the Center for Privacy and Technology at Georgetown Law, focused on the Trump Administrations use of data harvesting, predictive analytics, and machine learning to target immigrant communities. The Data Genesis Working Group convenes experts from across industry and academia to examine the mechanics of dataset provenance and maintenance. Our roundtable on Machine Learning, Inequality and Bias , co-hosted in Berlin with the Robert Bosch Academy, gathered researchers and policymakers from across Europe to address issues of bias, discrimination, and fairness in machine learning and related technologies. Our annual public symposium convenes leaders from academia, industry, government, and civil society to examine the biggest challenges we face as AI moves into our everyday lives. The AI Now 2018 Symposium addressed the intersection of AI ethics, organizing, and accountability, examining the landmark events of the past year. Over 1,000 people registered for the event, which was free and open to the public. Recordings of the program are available on our website . More information is available at www.ainowinstitute.org . 3 RECOMMENDATIONS 1. Governments need to regulate AI by expanding the powers of sector-specifi c agencies to oversee, audit, and monitor these technologies by domain. The implementation of AI systems is expanding rapidly, without adequate governance, oversight, or accountability regimes. Domains like health, education, criminal justice, and welfare all have their own histories, regulatory frameworks, and hazards. However, a national AI safety body or general AI standards and certifi cation model will struggle to meet the sectoral expertise requirements needed for nuanced regulation. We need a sector-specifi c approach that does not prioritize the technology, but focuses on its application within a given domain. Useful examples of sector-specifi c approaches include the United States Federal Aviation Administration and the National Highway Traffi c Safety Administration. 2.Facial recognition and affect recognition need stringent regulation to protect the public interest. Such regulation should include national laws that require strong oversight, clear limitations, and public transparency. Communities should have the right to reject the application of these technologies in both public and private contexts. Mere public notice of their use is not suffi cient, and there should be a high threshold for any consent, given the dangers of oppressive and continual mass surveillance. Affect recognition deserves particular attention. Affect recognition is a subclass of facial recognition that claims to detect things such as personality, inner feelings, mental health, and “worker engagement” based on images or video of faces. These claims are not backed by robust scientifi c evidence, and are being applied in unethical and irresponsible ways that often recall the pseudosciences of phrenology and physiognomy. Linking affect recognition to hiring, access to insurance, education, and policing creates deeply concerning risks, at both an individual and societal level. 3.The AI industry urgently needs new approaches to governance. As this report demonstrates, internal governance structures at most technology companies are failing to ensure accountability for AI systems. Government regulation is an important component, but leading companies in the AI industry also need internal accountability structures that go beyond ethics guidelines. This should include rank-and-fi le employee representation on the board of directors, external ethics advisory boards, and the implementation of independent monitoring and transparency efforts. Third party experts should be able to audit and publish about key systems, and companies need to ensure that their AI infrastructures can be understood from “nose to tail,” including their ultimate application and use. 4.AI companies should waive trade secrecy and other legal claims that stand in the way of accountability in the public sector. Vendors and developers who create AI and automated decision systems for use in government should agree to waive any trade secrecy or other legal claim that inhibits full auditing and understanding of their software. Corporate secrecy 4 laws are a barrier to due process: they contribute to the “black box effect” rendering systems opaque and unaccountable, making it hard to assess bias, contest decisions, or remedy errors. Anyone procuring these technologies for use in the public sector should demand that vendors waive these claims before entering into any agreements. 5.Technology companies should provide protections for conscientious objectors, employee organizing, and ethical whistleblowers. Organizing and resistance by technology workers has emerged as a force for accountability and ethical decision making. Technology companies need to protect workers ability to organize, whistleblow, and make ethical choices about what projects they work on. This should include clear policies accommodating and protecting conscientious objectors, ensuring workers the right to know what they are working on, and the ability to abstain from such work without retaliation or retribution. Workers raising ethical concerns must also be protected, as should whistleblowing in the public interest. 6.Consumer protection agencies should apply “truth-in-advertising” laws to AI products and services. The hype around AI is only growing, leading to widening gaps between marketing promises and actual product performance. With these gaps come increasing risks to both individuals and commercial customers, often with grave consequences. Much like other products and services that have the potential to seriously impact or exploit populations, AI vendors should be held to high standards for what they can promise, especially when the scientifi c evidence to back these promises is inadequate and the longer-term consequences are unknown. 7.Technology companies must go beyond the “pipeline model” and commit to addressing the practices of exclusion and discrimination in their workplaces. Technology companies and the AI fi eld as a whole have focused on the “pipeline model,” looking to train and hire more diverse employees. While this is important, it overlooks what happens once people are hired into workplaces that exclude, harass, or systemically undervalue people on the basis of gender, race, sexuality, or disability. Companies need to examine the deeper issues in their workplaces, and the relationship between exclusionary cultures and the products they build, which can produce tools that perpetuate bias and discrimination. This change in focus needs to be accompanied by practical action, including a commitment to end pay and opportunity inequity, along with transparency measures about hiring and retention. 8.Fairness, accountability, and transparency in AI require a detailed account of the “full stack supply chain.” For meaningful accountability, we need to better understand and track the component parts of an AI system and the full supply chain on which it relies: that means accounting for the origins and use of training data, test data, models, application program interfaces (APIs), and other infrastructural components over a product life cycle. We call this accounting for the “full stack supply chain” of AI systems, and it is a necessary condition for a 5 more responsible form of auditing. The full stack supply chain also includes understanding the true environmental and labor costs of AI systems. This incorporates energy use, the use of labor in the developing world for content moderation and training data creation, and the reliance on clickworkers to develop and maintain AI systems. 9.More funding and support are needed for litigation, labor organizing, and community participation on AI accountability issues. The people most at risk of harm from AI systems are often those least able to contest the outcomes. We need increased support for robust mechanisms of legal redress and civic participation. This includes supporting public advocates who represent those cut off from social services due to algorithmic decision making, civil society organizations and labor organizers that support groups that are at risk of job loss and exploitation, and community-based infrastructures that enable public participation. 10. University AI programs should expand beyond computer science and engineering disciplines. AI began as an interdisciplinary fi eld, but over the decades has narrowed to become a technical discipline. With the increasing application of AI systems to social domains, it needs to expand its disciplinary orientation. That means centering forms of expertise from the social and humanistic disciplines. AI efforts that genuinely wish to address social implications cannot stay solely within computer science and engineering departments, where faculty and students are not trained to research the social world. Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations. 6 EXECUTIVE SUMMARY At the core of the cascading scandals around AI in 2018 are questions of accountability: who is responsible when AI systems harm us? How do we understand these harms, and how do we remedy them? Where are the points of intervention, and what additional research and regulation is needed to ensure those interventions are effective? Currently there are few answers to these questions, and the frameworks presently governing AI are not capable of ensuring accountability. As the pervasiveness, complexity, and scale of these systems grow, the lack of meaningful accountability and oversight including basic safeguards of responsibility, liability, and due process is an increasingly urgent concern. Building on our 2016 and 2017 reports, the AI Now 2018 Report contends with this central problem and addresses the following key issues: 1.The growing accountability gap in AI, which favors those who create and deploy these technologies at the expense of those most affected 2.The use of AI to maximize and amplify surveillance, especially in conjunction with facial and affect recognition, increasing the potential for centralized control and oppression 3.Increasing government use of automated decision systems that directly impact individuals and communities without established accountability structures 4.Unregulated and unmonitored forms of AI experimentation on human populations 5.The limits of technological solutions to problems of fairness, bias, and discrimination Within each topic, we identify emerging challenges and new research, and provide recommendations regarding AI development, deployment, and regulation. We offer practical pathways informed by research so that policymakers, the public, and technologists can better understand and mitigate risks. Given that the AI Now Institutes location and regional expertise is concentrated in the U.S., this report will focus primarily on the U.S. context, which is also where several of the worlds largest AI companies are based. The AI accountability gap is growing: The techno

2018-12-01 62页 5星级

Edison：智能音频报告（2018冬季）(英文版)(11页).pdf

21%of people in the U.S. 18 own a Smart Speaker, or around 53 million people Google Home is a trademark of Google Inc. 14 million new smart speaker owners in 2018 Early Adopters 13.5% Early Majority 34% Late Majority 34% Laggards Innovators 2.5% JAN 2017 DEC 2017 New Product Adoption Curve: Smart Speakers DEC 2018 How many smart speakers do you own? 62% 48% 21% 22% 17% 30% December 2017 December 2018 OneTwoThree or more Base: Own a smart speaker and expressing an opinion December 2018December 2017 Number of Smart Speakers in U.S. households grows by 78% in one year 66.7 Million 118.5 Million Number of smart speakers in U.S. households Very Likely 7% Somewhat Likely 23% Not At All Likely 69% Dont know 1% How likely are you to purchase another smart speaker within the next six months? Base: Own a smart speaker Several times a day 29% Nearly every day 24% At least once per week 18% At least once per month 8% Less than once per month 5% Never 16% How often do you use your smart speaker? Base: Own a smart speaker and expressing an opinion 77 75 62 December 2018December 2017January 2016 % aware of any smart speaker device Awareness of Smart Speakers Source: January 2016 data from Infinite Dial from Edison Research and Triton Digital; December 2017 and 2018 data from The Smart Audio Report from NPR and Edison Research Methodology 1002 person telephone survey Adults age 18 and older National study conducted 12/26/2018 12/30/2018 Tracking from study conducted 12/26/2017 12/30/2018 npr.org/smartaudio

2018-12-01 11页 5星级

斯坦福：2018年人工智能指数“AI Index”报告（英文版）(94页).pdf

1 2 Steering Committee Yoav Shoham (Chair) Stanford University Raymond Perrault SRI International Erik Brynjolfsson MIT Jack Clark OpenAI James Manyika McKinsey Global Institute Juan Carlos Niebles Stanford University Terah Lyons Partnership On AI John Etchemendy Stanford University Barbara Grosz Harvard University Project Manager Zoe Bauer AI INDEX 2018 3 How to cite this Report: Yoav Shoham, Raymond Perrault, Erik Brynjolfsson, Jack Clark, James Manyika, Juan Carlos Niebles, Terah Lyons, John Etchemendy, Barbara Grosz and Zoe Bauer, The AI Index 2018 Annual Report”, AI Index Steering Committee, Human-Centered AI Initiative, Stanford University, Stanford, CA, December 2018. AI INDEX 2018 Our Mission is to ground the conversation about AI in data. The AI Index is an effort to track, collate, distill, and visualize data relating to artificial intelligence. It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Welcome to the AI Index 2018 Report (c) 2018 by Stanford University, “The AI Index 2018 Annual Report” is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International) https:/creativecommons.org/licenses/by-nd/4.0/legalcode 4 Table of contents AI INDEX 2018 Introduction to the AI Index 2018 Report Overview Volume of Activity Research Published Papers Course Enrollment Participation Robot Software Industry Startups / Investment Jobs Patents AI Adoption Survey Earnings Calls Robot Installations Open Source Software GitHub Project Statistics Public Interest Sentiment of Media Coverage Government mentions Technical Performance Vision Natural Language Understanding Other Measures Derivative Measures Government Initiatives Human-Level Performance Milestones Whats Missing? Acknowledgements Appendix 5 6 8 9 21 26 29 31 33 35 36 38 41 42 43 44 47 50 55 57 59 63 66 69 5 We are pleased to introduce the AI Index 2018 Annual Report. This years report accomplishes two objectives. First, it refreshes last years metrics. Second, it provides global context whenever possible. The former is critical to the Indexs mission grounding the AI conversation means tracking volumetric and technical progress on an ongoing basis. But the latter is also essential. There is no AI story without global perspective. The 2017 report was heavily skewed towards North American activities. This reflected a limited number of global partnerships established by the project, not an intrinsic bias. This year, we begin to close the global gap. We recognize that there is a long journey ahead one that involves further collaboration and outside participation to make this report truly comprehensive. Still, we can assert that AI is global. 83 percent of 2017 AI papers on Scopus originate outside the U.S. 28 percent of these papers originate in Europe the largest percentage of any region. University course enrollment in artificial intelligence (AI) and machine learning (ML) is increasing all over the world, most notably at Tsinghua in China, whose combined AI ML 2017 course enrollment was 16x larger than it was in 2010. And there is progress beyond just the United States, China, and Europe. South Korea and Japan were the 2nd and 3rd largest producers of AI patents in 2014, after the U.S. Additionally, South Africa hosted the second Deep Learning Indaba conference, one of the worlds largest ML teaching events, which drew over 500 participants from 20 African countries. AIs diversity is not just geographic. Today, over 50% of the Partnership on AIs members are nonprofits including the ACLU, Oxfords Future of Humanity Institute, and the United Nations Development Programme. Also, there is heightened awareness of gender and racial diversitys importance to progress in AI. For example, we see increased participation in organizations like AI4ALL and Women in Machine Learning (WiML), which encourage involvement by underrepresented groups. Introduction to the AI Index 2018 Annual Report AI INDEX 2018 6 The report has four sections: 1.Data: Volume of Activity and Technical Performance 2.Other measures: Recent Government Initiatives, Derivative measures, and Human-Level Performance 3.Discussion: Whats Missing? 4.Appendix DATA The Volume of Activity metrics capture engagement in AI activities by academics, corporations, entrepreneurs, and the general public. Volumetric data ranges from the number of undergraduates studying AI, to the percent of female applicants for AI jobs, to the growth in venture capital funding of AI startups. The Technical Performance metrics capture changes in AI performance over time. For example, we measure the quality of question answering and the speed at which computers can be trained to detect objects. The 2018 AI Index adds additional country-level granularity to many of last years metrics, such as robot installations and AI conference attendance. Additionally, we have added several new metrics and areas of study, such as patents, robot operating system downloads, the GLUE metric, and the COCO leaderboard. Overall, we see a continuation of last years main takeaway: AI activity is increasing nearly everywhere and technological performance is improving across the board. Still, there were certain takeaways this year that were particularly interesting. These include the considerable improvement in natural language and the limited gender diversity in the classroom. OTHER MEASURES Like last year, the Derivative Measures section investigates relationships between trends. We also show an exploratory measure, the AI Vibrancy Index, which combines trends across academia and industry to quantify the liveliness of AI as a field. We introduce a new qualitative metric this year: Recent Government Initiatives. This is a simplified overview of recent government investments in artificial intelligence. We include initiatives from the U.S., China, and Europe. The AI Index looks forward to including more government data and analysis in future reports by collaborating with additional organizations. The Human-Level Performance Milestones section of the report builds on our timeline of instances where AI shows human and superhuman abilities. We include four new achievements from 2018. AI Index Report Overview AI INDEX 2018 7 Finally, to start a conversation in the AI community, the Whats Missing? section presents suggestions from a few experts in the field, who offer ideas about how the AI Index could be made more comprehensive and representative. APPENDIX The Appendix supplies readers with a fully transparent description of sources, methodologies, and nuances. Our appendix also houses underlying data for nearly every graph in the report. We hope that each member of the AI community interacts with the data most relevant to their work and interests. SYMBOLS We earmark pages with the globe symbol below when discussing AIs universality. This includes country comparisons, deep dives into regions outside of the U.S., and data on diversity in the AI community. AI Index Report Overview (continued) AI INDEX 2018 8 AI INDEX 2018 VOLUME OF ACTIVITY The graph below shows growth in annual publishing rates of academic papers, relative to their rates in 1996. The graph compares the growth of papers across All fields, Computer Science (CS), and Artificial Intelligence (AI). The growth of annually published papers in AI continues to outpace that of annually published papers in CS, suggesting that growth in AI publishing is driven by more than a heightened interest in computer science. See Appendix 1 for data and methodology. AI outpaces CS AI papers on Scopus have increased 8x since 1996. CS papers increased 6x during the same timeframe. VOLUME OF ACTIVITY RESEARCH Published Papers: Papers by topic 9 Note: This visual uses the Scopus query search term “Artificial Intelligence,” not the Elsevier keyword approach. See more details in the appendix. Growth of annually published papers by topic (19962017) Source: Scopus AI PapersCS PapersAll Papers Growth in papers (relative to 1996) 2000200520152010 1x 3x 5x 7x 9x The graph below shows the number of AI papers published annually by region. Europe has consistently been the largest publisher of AI papers 28% of AI papers on Scopus in 2017 originated in Europe. Meanwhile, the number of papers published in China increased 150% between 2007 and 2017. This is despite the spike and drop in Chinese papers around 2008. See Appendix 2 for data and methodology. Europe is the largest publisher of AI papers In 2017, 28% of AI papers on Scopus were affiliated with European authors, followed by China (25%) and the U.S. (17%). VOLUME OF ACTIVITY RESEARCH Published Papers: AI papers by region 10 Note: We speculate that the increase in AI papers in China around 2008 is a result of The National Medium- and Long-Term Program for Science and Technology Development (2006 2020), and other government programs that provide funding and a range of incentive policies for AI research. Similarly, FP7 (20072013) and other science and technology research programs in Europe may have contributed to the small uptick in papers around 20082010. Annually published AI papers on Scopus by region (19982017) Source: Elsevier China Number of papers United StatesEuropeRest of World 2000200520152010 0 5,000 10,000 15,000 20,000 The graph below shows the number of AI papers on Scopus, by subcategory. Categories are not mutually exclusive. 56 percent of papers fell into the Machine Learning and Probabilistic Reasoning category in 2017, compared to 28% in 2010. For most categories below, papers were published at a faster rate during the period 20142017 than in the period 20102014. Most notably, Neural Networks had a compound annual growth rate (CAGR) of 3% from 20102014, followed by a CAGR of 37% from 20142017. See Appendix 2 for data and methodology. VOLUME OF ACTIVITY RESEARCH Published Papers: AI papers by subcategory 11 The number of Scopus papers on Neural Networks had a CAGR of 37% from 2014 to 2017 Number of AI papers on Scopus by subcategory (19982017) Source: Elsevier Number of papers Machine Learning and Probabilistic Reasoning Search and Optimization NLP and Knowledge Representation Computer Vision Fuzzy Systems Planning and Decision Making Neural Networks Total 60,000 40,000 20,000 2000200520102015 0 The graph below shows the number of AI papers on arXiv, by each papers primary subcategory. The right axis refers the sum of all AI papers on arXiv (indicated by the grey dashed line). The number of AI papers on arXiv is increasing overall and in a number of subcategories. This points to AI authors tendency to disseminate their research, regardless of whether it is peer reviewed or has been accepted into AI conferences. This also points to the fields competitive nature. Computer Vision (CV) and Pattern Recognition has been the largest AI subcategory on arXiv since 2014; prior to 2014, growth in this category closely tracked Artificial Intelligence and Machine Learning. In addition to showing a growing interest in Computer Vision (and its general applied applications), this also indicates the growth in other AI application areas, such as Computation and Language and Robotics. See Appendix 3 for data and methodology. VOLUME OF ACTIVITY RESEARCH Published Papers: AI papers on arXiv “.Aside from the increase in publications, its important to note the adoption of arXiv by these communities for disseminating results. Weve seen many times how establishing some critical mass then catalyzes ever higher levels of participation within a community.” Paul Ginsparg, Cornell 12 Number of AI papers on arXiv by subcategory (20102017) Source: arXiv Artificial IntelligenceComputation about half of all companies had embedded AI into a corporate business process. However, its still early; most had not yet adopted the complementary practices necessary to capture value from AI at scale.” -Michael Chui, McKinsey North America: N = 479; Developing markets (incl. China): N = 189 (China N = 35); Europe: N = 803 North America Developing markets (incl. China) Europe Robotic process automation Machine learning Conversational interfaces Computer vision NL text understanding NL speech understanding NL generation Physical robotics Autonomous vehicles Percent of respondents Capabilities embedded in at least one company function (2018) Source: McKinsey AsiaPacific: N = 263; India: N = 197; Middle East and North Africa: N = 77; Latin America: N = 127 India Middle East and North Africa Latin America Robotic process automation Machine learning Conversational interfaces Computer vision NL text understanding NL speech understanding NL generation Physical robotics Autonomous vehicles Asia Pacific Percent of respondents Capabilities embedded in at least one company function (2018) Source: McKinsey Telecom: N = 77; High tech: N = 215; Financial services: N = 306; Professional services: N = 221; Electric power and natural gas: N = 54; Healthcare systems and services: N = 67; Automotive and assembly: N = 120; Retail: N = 46; Travel, transport, and logistics: N = 55; Pharma and medical products: N = 65. Telecom High tech Financial services Professional services Power Telecom: N = 77; High tech: N = 215; Financial services: N = 306; Professional services: N = 221; Electric power and natural gas: N = 54; Healthcare systems and services: N = 67; Automotive and assembly: N = 120; Retail: N = 46; Travel, transport, and logistics: N = 55; Pharma and medical products: N = 65. See description and data from Service operations, Product / service development, and Marketing / sales on the previous page. Organizations adopt AI in business functions that provide the most value within their industry This implies that the rate of AI progress for specific applications will likely correlate to uptake in industries where that specialization is particularly important. Supply-chain management ManufacturingRisk Telecom High tech Financial services Professional services Power see appendix Accuracy ImageNet competition test set accuracyImageNet 2012 validation set accuracy Human performance ImageNet competition ends in 2017. 2012201420162018 70% 2010 80% 90% 100% 48 TECHNICAL PERFORMANCE VISION Object detection: ImageNet training time The graph below shows the amount of time it takes to train a network to

2018-12-01 94页 5星级

Edison：2017年美国智能音箱用户调查报告(英文版)(31页)（31页）.pdf

65%say that they wouldnt want to go back to life without their Smart Speaker Methodology 1,010 person telephone survey Adults age 18 and older National study conducted 12/26/2017 12/30/2017 806 person online survey Adults age 18 and older National study conducted 11/17/2017 11/22/2017 All respondents reported owning an Amazon or Google Smart Speaker 16% of Americans 18 own a Smart Speaker, or around 39 million people Google Home is a trademark of Google Inc. Google Home is a trademark of Google Inc. AMAZON ALEXAGOOGLE HOME 4% % owning Smart Speaker Smart Speaker Ownership Total Population 18 11% In the 2017 Holiday Season: 7% of Americans got a Smart Speaker 4% of Americans got their first Smart Speaker Smart Speakers are changing behaviors and forming new habits 39 34 30 27 26 23 17 Traditional AM/FM radio Smartphone Television Tablet Computer Printed publications Is the time you spend using your Smart Speaker replacing any time you used to spend with? % saying yes Sonos or other whole-house audio system Base: Smart Speaker owners 30%of Smart Speaker owners say their speaker is replacing time spent with TV “Compared to the first month of owning a Smart Speaker, are you using it? More often 51% About the same amount 33% Less often 16% Base: Had a Smart Speaker more than one month and gave a response 23% who are listening to more audio said they are listening to more news/talk 28% who are listening to more audio said they are listening to more podcasts Base: Own a Smart Speaker 71% are listening to more audio since getting a Smart Speaker Top three (3) indexing activities by day part: 5am 9am9am 3pm5pm 7pm7pm 9pm 9pm Midnight #1Traffic Drop in to an Alexa device in home Find restaurants/ businesses Games Control smart home devices #2WeatherAdd to to-do list Recipes/cooking requests Send messages to other devices Audiobooks #3NewsAdd to shopping listOrder foodChildren storiesTimer/alarms 64%of Smart Speaker owners are interested in having Smart Speaker technology in their car Base: Driven or rode in a car in the past month (95%) How interested would you be in having the Smart Speaker technology? 39 31 24 17 16 25 29 29 18 14 20 23 24 24 26 7 7 8 8 12 9 10 15 33 32 In your car/vehicle* On your phone On your television At your workplace At places other than your home/work/car Very Interested (5) Not at all Interested (1) Base: Smart Speaker Owners. *Driven or rode in a car in the past month. The Communal Experience How often do you use the Smart Speaker with others in your household? Most of the time 53% Occasionally 39% Rarely 6% Never 2% Base: Smart Speaker owners 66%of Smart Speaker owners use their speaker to entertain friends and family 60 30 28 18 13 13 13 12 12 11 11 Play music Answer a general question Get the weather Tell a joke Listen to music AM/FM radio Get the news Set a timer/alarm Control household devices Check the time Get a sports score or update Play a game Top tasks requested while spending time with friends and family: % requesting item The Smart Home Where is your Smart Speaker typically located? Living room/family room/den 52% Kitchen 21% Master bedroom 19% Other bedroom 4% Home office 2% Somewhere else 2% Base: Own only one Smart Speaker and giving a response 31%of Smart Speakers owners have controlled household devices with a Smart Speaker in the last week Where in the last week did you request your Smart Speaker to control household devices? % saying yes 61 38 36 14 12 6 3 Living room Kitchen Master bedroom Other Bedroom Bathroom Home office Other room Base: Have controlled household devices with Smart Speaker in the last week 6 46 42 44 34 37 46 Midnight-5am9pm-midnight 7pm-9pm 5pm-7pm 3pm-5pm 9am-3pm 5am-9am At what time in the last week did you request your Smart Speaker to control household devices? Base: Have controlled household device with Smart Speaker in the last week (31%) % saying they controlled in that time period 38%of owners plan to buy additional smart speakers to control smart home devices Purchasing behaviors and connecting with brands Through your Smart Speaker, have you? % saying yes Re-ordered an item you have previously purchased Ordered a new product you have not previously purchased Added an item to your cart so you could review it later for purchase Researched an item you might want to purchase 31)% 58 51 48 45 42 28 22 Household supplies Electronics Health and beauty Pet food/supplies Groceries Home and garden Baby products Which of these have you purchased using your Smart Speaker? % saying yes Base: Have placed an order with Smart Speaker in the last week (13%) 2 15 33 32 30 17 13 Midnight-5am9pm-midnight 7pm-9pm 5pm-7pm 3pm-5pm 9am-3pm 5am-9am At what time in the last week did you order an item with your Smart Speaker? Base: Have placed an order with Smart Speaker in the last week (13%) % saying they ordered in that time period 43%of Smart Speaker owners would be interested in using skills from companies or brands they follow on social media Base: Follow any companies or brands on social networking sites npr.org/smartaudio

2017-12-01 31页 5星级

ResearchInChina：2017-2021年全球和中国机器视觉产业报告（简版）（英文版）（17页）.pdf

报告目标本报告已与市场主管等进行了调查。其他来源还包括相关杂志、学术机构和咨询公司。建立有关市场规模、竞争模式、市场细分、市场主要参与者的目标和策略、回顾和预测的全面、实际、每年更新和具成本效益的资料.

2017-12-01 17页 5星级

BelferCenter-2017年人工智能与国家安全报告英文-2017.7-132页（132页）.pdf

STUDY JULY 2017 Artificial Intelligence and National Security Greg Allen Taniel Chan A study on behalf of Dr. Jason Matheny, Director of the U.S. Intelligence Advanced Research Projects Activity (IARPA) BE L F E R CENTER STUDY Belfer Center for Science and International Affairs Harvard Kennedy School 79 JFK Street Cambridge, MA 02138 www.belfercenter.org Statements and views expressed in this report are solely those of the authors and do not imply endorsement by Harvard University, Harvard Kennedy School, the Belfer Center for Science and International Affairs, or IARPA. Design attacks can be defended In 1945, fighter aircraft were roughly 50 times as expensive as a new civilian car By WW2, only sophisticated orgs. could match state of the art in aero- space tech. One of the first passenger airlines used reconfigured WW1 bombers Factories appear similar to other industry and can be concealed Cyber Cyber can damage physical infrastruc- ture and steal key info. but less assured Even terrorists and criminals can afford quite useful capabilities Low-end attacks require minimal expertise; high-end reserved for states Commercial IT sys- tems can be used for attacks; similar skills in demand for civil/military Even sensitive national security systems are routinely infiltrated without detection Biotech Natural pandemics have killed tens of millions; bioweapons could also Equipment is cheap, though expertise can be expensive Though different now, at first relatively few people had needed expertise Biopharma and medical industries need similar equip- ment and expertise as bioweapons Weaponization facilities difficult to distinguish from commercial LowModerateHigh 44 Artificial Intelligence and National Security Government Technology Management Approach In what is admittedly (and necessarily) a partial oversimplification, we have classified the U.S. governments management paradigm for each of the four technologies. Our goal here is to clarify how government viewed the nature of the challengeespecially in its early decadesand characterize what approach they ultimately took to meet it. A more detailed justification of our analysis is provided in the Appendix. The four approaches are summa- rized in Table 2: Table 2: Government Technology Management Approach Nuclear All-out effort, government-led development and utilization Extraordinary levels of spending and dedication of national resources to nuclear technology continued for many decades after development From 1940 to 1996, 11% of total federal government spend- ing was related to nuclear weapons, even with arms control and voluntary restrictions Initially, nuclear technology was treated as classified regard- less of origin. Illegal to hold patents on nuclear. Aerospace Government-led public private partnership Heavy government involvement in the aerospace sector with research and development support, acting as an anchor customer, and major regulation Tech. superiority seen as key to national power; govt. restricted access to aerospace tech. using classification and export restrictions Despite predominant government role, the U.S. Aircraft industry remained within the Amerian economic model of capitalism and free enterprise Cyber Government seeding and harvesting Govt. heavily involved in supportin R U.S. repeatedly ignores need for safety upgrades/investment Aerospace Success Aside from brief periods during WW1 and WW2, U.S. was and is undisputed leader in developing and using military aerospace tech. Success After WW2, the U.S. emerged as the clear winner in building commercial aircraft for the rapidly growing market in air transportation Success Main risks are accidental crashes and attacks from superior air forces, both of which the U.S. has responded to effectively Cyber Success Though cyber domain is not as amenable to dominance as aero- space, the U.S. clearly has leading tech and capabilities in both cyber and defense Partial Success U.S. commercial industry leads the world in computing and internet sectors, but U.S. govt. left commer- cial too vulnerable to criminal and nation-state cyber attacks Partial Failure While the U.S. developed offensive cyber superiority, the govt. failed for decades to address the asymmetric vulnerability it faced in espionage and attack Biotech N/A U.S. voluntarily disbanded bioweapons program, saying deterrent from nukes was suffi- cient. USSR bioweapons program continued, however. Success U.S. has largest biotech industry worldwide and the R Favorable government support of R most risky research was delayed until risks better understood, BWC helpful but had key failures (USSR) 46 Artificial Intelligence and National Security AI Technology Profile: A Worst-case Scenario? Comparing the technology profile of AI with the prior technology cases, we find that it has the potential to be a worst-case scenario. Proper pre- cautions might alter this profile in the future, but current trends suggest a uniquely difficult challenge. Destructive Potential: High At a minimum, AI will dramatically augment autonomous weapons and espionage capabilities and will represent a key aspect of future military power. Speculative but plausible hypotheses suggest that General AI and especially superintelligence systems pose a potentially existential threat to humanity.87 O Cost Profile: Diverse, but potentially low Developing cutting-edge capabilities in machine learning and AI can be expensive: many firms are spending billions or hundreds of millions of dollars on R leaked copies of AI software might be virtually free. Complexity Profile: Diverse, but potentially low Advancing the state of the art in AI basic research requires world- class talent, of which there is a very limited pool. O Nick Bostrom, Elon Musk, Bill Gates, Stephen Hawking, and many others have expressed concern regarding this scenario. 47 Belfer Center for Science and International Affairs | Harvard Kennedy School However, applying existing AI research to specific problems can sometimes be relatively straightforward and accomplished with less elite talent. Technical expertise required for converting commercially available AI capabilities into military systems is currently high, but this may decline in the future as AI improves. Military/Civil Dual-Use Potential: High Militaries and commercial businesses are competing for essentially the exact same talent pool and using highly similar hardware infrastructure. Some military applications (e.g. autonomous weapons) require additional access to non-AI related expertise to deliver capability. Difficulty of Espionage and Monitoring: High Overlap between commercial and military technology makes it difficult to distinguish which AI activities are potentially hostile. Few if any physical markers of AI development exist. Total number of actors developing and fielding advanced AI sys- tems will be significantly higher than nuclear or even aerospace. Monitors will find it difficult to assess AI aspects of any autono- mous weapon system without direct access. 48 Artificial Intelligence and National Security Lessons Learned Having provided our observations of previous cases, we will now attempt to summarize lessons learned. We recognize that there are vast differences of time, technology, and context between these cases and AI. This is our effort to characterize some lessons which endure nevertheless. Lesson #1: Radical technology change begets radical government policy ideas The transformative implications of nuclear weapons technology, com- bined with the Cold War context, led the U.S. government to consider some extraordinary policy measures, including but not limited to the following: EnactedGiving one individual sole authority to start nuclear war: The United States President, as head of government and commander in chief of the military, was invested with supreme authority regarding nuclear weapons88 ConsideredInternationalizing control of nuclear weapons under the exclusive authority of the United Nations in a collective security arrangement P 89 EnactedVoluntarily sharing atomic weapons technology with allies (which occurred) and adversaries including the Soviet Union (which did not)90 ConsideredAtomic annihilation: Pre-emptive and/or retaliatory atomic annihilation of adversaries, which could have resulted in mil- lions or even billions of deathsQ P This was the so-called Baruch Plan, which the U.S. proposed at the United Nations but abandoned shortly thereafter. To this day there is significant debate over whether the United States offered the Baruch Plan in sincerity. Q Senior U.S. military officials, including Lieutenant General Leslie Groves, the director of the Manhattan Project, and General Orvil Anderson, commander of the Air University, publicly argued that the United States should strike the Soviet Union with nuclear weapons to prevent them from acquiring nuclear technology. Respected foreigners including Winston Churchill, John Von Neumann, and Bertrand Russell all advised the United States to do the same. How seriously the United States senior leadership considered this first strike advice is difficult to say with certainty. Retaliatory nuclear strikes and mutually assured destruction remain the official policy of the United States. 49 Belfer Center for Science and International Affairs | Harvard Kennedy School EnactedVoluntarily restricting development in arms control frameworks to ban certain classes of nuclear weapons and certain classes of nuclear tests The world has lived with some of these policies for seven decades, so the true extent of their radicalism (at the time they were first considered) is hard to convey. The first example is perhaps the easiest, because it required passage of the Presidential Succession Act of 1947, which laid the founda- tion for the 25th Amendment to the United States Constitution. Though there were other proximate causes for the 25th Amendment, such as the assassination of President Kennedy, it is only a mild stretch to say that the invention of nuclear weapons was so significant that it led to a change in the United States Constitution. Though nuclear weapons clearly resulted in the most radical policy pro- posals, the other cases also led to significant changes. For instance, the Department of Defense ultimately created a full armed service to make use of aerospace technology, the organization now called the U.S. Air Force. Cyber challenges led to the creation of U.S. Cyber Command. These were significant changes, though time has made them familiar. It remains unclear what the full impact of AI technology on national security will be, and how fast it will arrive. So far, we have argued that it is highly likely to be a transformative military technology. Some, such as Nick Bos- trom, believe that the recursive improvement property of AI has the potential to create a superintelligence that might lead to the extinction of the entire human species.91 If continued rapid progress in AI leads some governments to share Bostroms view, they may consider policies as truly radical as those considered in the early decades of nuclear weapons. The bigger and more visible the impacts of AI become (and we argue the impacts are likely to be increasingly large and obvious over time) the more policymakers will feel justified in making extreme departures from existing policy. Lesson #2: Arms races are sometimes unavoidable, but they can be managed 50 Artificial Intelligence and National Security Fears of aerial bombing led to an international treaty banning the use of weaponized aircraft, but voluntary restraint was quickly abandoned and did not stop air war in WWI. In 1899, diplomats from the worlds leading military powers convened in The Hague for a peace conference. One of the more interesting outcomes of the conference was a five-year moratorium on all offensive military uses of aircraft.R Though the intention was to later make the ban permanent, it was abandoned at the second Hague conference of 1907 once countries saw the irresistible potential of aerial warfare. Accordingly, all the great powers began constructing and planning for the use of aircraft bombers.92 In 1910, the combined military air fleets of the European great powers contained 50 airplanes. By 1914, the number reached 700.93 When World War I broke out, the only real limitation on the use of military air power was technology: the primitive airplanes had limited range and bomb-carrying capacity. Still, every European belligerents capital, save Rome, was bombed from the air.94 The applications of AI to warfare and espionage are likely to be as irresistible as aircraft. Preventing expanded military use of AI is likely impossible. Aerospace technology ultimately became nearly synonymous with military power, and it seems likely that applications of AI will ultimately go the same route. Just as businesses are choosing machine learning because competitively they have no choice, so too will militaries and intelligence agencies feel pressure to expand the use of military AI applications. Michael Rogers, head of the United States National Security Agency and Cyber Command, agrees: “It is not the if. Its only the when to me. This is coming.”95 That sense of inevitability derives not only from how useful AI is already proving to be, but also from the belief that current applications have only scratched the surface of what capabilities are likely to come. Though outright bans of AI applications in the national security sector are unrealistic, the more modest goal of safe and effective technology management must be pursued. R At the time, diplomats were primarily concerned with aerial bombardment from motor-driven balloons, but the treaty language was sufficiently broad that it applied to fixed-wing aircraft upon their invention. 51 Belfer Center for Science and International Affairs | Harvard Kennedy School The ban of aircraft fell apart, but the United States, its allies, and even its adversaries did develop a framework that sought to limit the risks of aerospace technology. Though many details will remain unclear until the technology is more mature, eventually the United States and other actors will have to develop a regime that limits the risk of military AI technology proliferation. Lesson #3: Government must both promote and restrain commercial activity Failure to recognize the inherent dual-use nature of technology can cost lives, as the example of the Rolls-Royce Nene jet engine shows. After World War II, the United States recognized that facilitating economic growth of the commercial aerospace industry and maintaining military secrecy were often at odds. For instance, the United Kingdom had superior jet engine technology at the end of World War II but faced significant financial challenges. The British engine manufacturers, seeking export rev- enues, sold 25 of their “commercial” Rolls-Royce Nene Jet Engines to the Soviet

2017-12-01 132页 5星级

AARPResearch：2017年美国中老年人智能手机使用态度报告(英文版)(29页)（29页）.pdf

AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Technology Use and Attitudes among Mid-Life and Older Americans December 2017 G. Oscar Anderson, Senior Research Communications Advisor AARP Research https:/doi.org/10.26419/res.00210.001 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Methodology This study was fielded from November 16-27, 2017 using GfKs KnowledgePanel, a probability based web panel designed to be representative of the adult US population. Respondents needed to be age 50 or older to complete the survey. Completion rate was 59.9% and resulted in a total sample of 1,520. The data are weighted by age within gender, education, race/ethnicity, household income, language preferences, and Census division to reflect US adults age 50 or older. Margin of error at the 95% confidence level 2.71% for Total Adults 50 N=1520 2 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Executive Summary Mobile and traditional computing devices are the primary tech for Americans 50 . Over nine in ten own a computer or laptop, seven in ten adults 50 own a smartphone, and over four in ten own a tablet. Adults 70 are more likely to have older technology such as desktops and feature phones than those age 50-69. Technology such as wearables and home assistants have been adopted by only a very small percentage of the 50 market. Younger adults are more likely to own a wearable than those over 70. Traditional activities dominate computer use for adults over 50, but a sizeable minority are using their device to manage medical care or learn online. Among those who own such devices, top activities include surfing the internet, making purchases, getting news, and banking. Adults 70 do fewer activities on their computers than those under 70, with a couple exceptions such as gaming (over half play games on their computer) and email. Surfing the web is the top activity for tablet users and older adults who have both are more likely to use their tablet for playing games and watching video than on a computer. 3 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Executive Summary Top smartphone activities for adults 50 focus on staying connected and being on the go. Nearly nine in ten smartphone owners say they use their device to send IMs/texts or emails, and over three quarters say they use it to get directions or traffic information. Other top activities include purchasing apps, surfing the internet, getting news, and accessing social media. Among those who own all three devices, each device has different uses: computers are used for more practical tasks, tablets for entertainment, and smartphones for social and on the go activities. Across all devices, over seven in ten adults 50 are on social media. Adults 50 are equally likely to use social on their computers and mobile devices. Over half of smartphone owners use a social app weekly. Privacy and security is still an issue for most older adults, but many dont take proactive steps to protect themselves online. Just 18% of those 50 are extremely/very confident that their data are kept private online. With the exception of financial institutions, most adults 50 do not completely trust companies to keep their data secure. They are most likely to trust banks and healthcare organizations and least likely to trust the media, social media sites, and membership organizations. Nonetheless, many adults 50 fail to take proactive steps to secure their data. A little over half use a passcode on their phones or tablets, and only a third use two-factor authentication. 4 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Executive Summary Nine in ten (91%) of those with devices say they use technology to stay in touch with friends and family. Among those under 70, text messaging has overtaken email as the tool most used to stay connected, though most use three channels (email, texts, and social media). Few older adults have used virtual reality and many are unfamiliar with augmented reality. Most older adults have heard of virtual reality devices but few have tried them. Adults age 50- 59 are the most likely to have tried or own a device, but adoption is still small. Over six in ten adults have never heard of augmented reality and very few have tried it. Adults in their 50s are more likely to have heard of AR than those over 60. 5 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVEDAARP RESEARCH | AARP.ORG/RESEARCH | 2016 AARP. ALL RIGHTS RESERVED Main Findings 6 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Mobile and traditional computers are primary tech devices for Americans 50 70% 62% 61% 43% 21% 21% 13% 7% Smartphone Laptop Desktop Tablet Regular phone E-Reader Wearables Home assistant Device Adoption among 50 7 78% 66% 58% 46% 14% 20% 15% 7% 73% 63% 59% 40% 19% 20% 14% 8% 55% 56% 66% 40% 34% 23% 8% 6% SmartphoneLaptopDesktopTabletRegular phone E-ReaderWearablesHome assistant Device Adoption by Age 50-5960-6970 Younger adults are significantly more likely to own smartphones, laptops and wearable devices (e.g., smart watches, fitness trackers, etc.) than those 70 . Adults over 70 are more likely to own desktops and feature phones than their younger counterparts. a ab c c c c c Letters represent a statistically significant difference between indicated age groups at the 95% confidence level. abc Q.Tech1 (Base: All respondents. N=1520). Which of the following items do you have? AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Those already invested in the technology are more likely to purchase it again in the future Q.Tech3A (Base: All respondents. N=1520). Do you plan to purchase a within the next year? 8 Yes 6% No 94% Plan to Purchase a Wearable Device Yes 19% No 81% Plan to Purchase a Smartphone Yes 12% No 88% Plan to Purchase a Tablet Owns deviceDoes not own device 50-59 a 60-69 b 70 c Smartphone23%*10$01% Tablet14%*102% Wearable12%*5%8%6%5% Percentage Who Plan to Purchase in the Next Year * or letter represent a statistically significant difference between indicated groups at the 95% confidence level. Those who have already purchased a device are more likely to say they will purchase another one in the next year, particularly for smartphones. Likewise, adults under 70 are more likely to plan to purchase a phone or tablet in the next year compared to their 70 counterparts. AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Traditional activities dominate computer use for adults over 50, but a sizeable minority are using their device to manage medical care or learn online Surfing the web, making purchases, getting news, and doing banking transactions dominate how older adults use their computers. A third say they do online learning on their computer (34%) and manage or receive medical care (32%). 81% 74% 69% 65% 63% 58% 55% 45% 45% 45% 42% 34% 32% 31% 17% 8% 7% Visit websites or surf the internet Make a purchase Get news and other info Perform banking or financial transactions Send or receive IMs or Emails Access a social networking site Comparative shop for discounts and deals Play a game Get directions or traffic info Watch videos or shows Get health and fitness info Take classes, webinars, or read/watch how-to tutorials Manage or receive medical care Post your own reviews, ratings, or comments online Download or purchase an app Track health or fitness via apps or website Use a voice activated assistant Activities Performed on Desktop/Laptop (among those who own device) Q.Tech2 (Base: Those who own computer/laptop. N=1402). Please indicate whether you do each activity for each device you own. 9 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Computer users in their 50s and 60s are more likely to use their devices for a wider variety of activities, but adults 70 are more likely to game. Adults in their 50s and 60s are more likely to surf the internet, bank, comparison shop, and watch video on their computers than those over 70. However, computer users 70 are equally likely to look for news and send emails as their younger counterparts, and are more likely to play games on the computer. 85% 77% 70% 67% 58X% 60% 40% 46% 52% 84% 74% 70% 67% 65% 62% 55% 46% 47% 48% 73% 70% 67% 59% 66% 54% 47% 52% 43% 30% Visit websites or surf the internet Make a purchase Get news and other info Perform banking or financial transactions Send or receive IMs or Emails Access a social networking site Comparative shop for discounts and deals Play a game Get directions or traffic info Watch videos or shows Activities Performed on Desktop/Laptop by Age (Top 10) (Among those who own desktop/laptop computers) 50-5960-6970 10 Q.Tech2 (Base: Those who own computer/laptop. N=1402). Please indicate whether you do each activity for each device you own. abc a aa c c c cc c c c Letters represent a statistically significant difference between indicated age groups at the 95% confidence level. AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Adults in their 50s and 60s are more engaged in online learning activities on their computers than those 70 . Adults in their 50s and 60s are more likely to use their computers to engage in online learning activities and posting ratings and reviews than those age 70 . 41% 38% 29% 35% 18% 9% 6% 43% 37% 34% 32% 19% 10% 8% 41% 24% 34% 24% 15% 6% 5% Get health and fitness info Take classes, webinars, or read/watch how-to tutorials Manage or receive medical care Post your own reviews, ratings, or comments online Download or purchase an app Track health or fitness via apps or website Use a voice activated assistant Activities Performed on Desktop/Laptop by Age (Bottom 7) (Among those who own desktop/laptop computers) 50-5960-6970 11 Q.Tech2 (Base: Those who own computer/laptop. N=1402). Please indicate whether you do each activity for each device you own. abc c c c c Letters represent a statistically significant difference between indicated age groups at the 95% confidence level. AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Surfing the web is the top activity for tablet users and about half are likely to use their device for entertainment such as playing games and watching video. Over half of tablet owners use their tablets for surfing the web, getting news, downloading apps, messaging and email, and playing games. Nearly half (48%) watch video on their tablets and another four in ten use it for shopping (42% make purchases; 40% comparison shop). 71% 58% 54% 54% 53% 53% 48% 42% 40% 33% 31% 29% 24% 21% 19% 17% 8% Visit websites or surf the internet Get news and other info Download or purchase an app Send or receive IMs or Emails Play a game Access a social networking site Watch videos or shows Make a purchase Comparative shop for discounts and deals Get health and fitness info Get directions or traffic info Perform banking or financial transactions Take classes, webinars, or read/watch how-to tutorials Post your own reviews, ratings, or comments online Use a voice activated assistant Manage or receive medical care Track health or fitness via apps or website Activities Performed on Tablet (among those who own device) Q.Tech2 (Base: Those who own tablet; n=647). Please indicate whether you do each activity for each device you own. 12 AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Adults under 70 do a larger variety of activities on their tablets than those 70 , but those over 70 are about equally likely to play games, use social media and send email on their devices. Adults in their 50s and 60s are more likely to surf the internet, download apps, watch video, shop and make purchases on their tablets than those over 70. Those in their 50s are also more likely than those 70 to use their tablets to get news and other info. 78% 63% 59% 54% 50% 55% 59% 46E% 35% 77% 58% 59% 57X% 56% 50% 46% 44% 36% 56% 50% 39% 49% 53% 47% 28% 30% 26&% Visit websites or surf the internet Get news and other info Download or purchase an app Send or receive IMs or Emails Play a game Access a social networking site Watch videos or shows Make a purchase Comparative shop for discounts and deals Get health and fitness info Activities Performed on Tablet by Age (Top 10) (Among those who own a tablet) 50-5960-6970 13 Q.Tech2 (Base: Those who own tablets. n=647). Please indicate whether you do each activity for each device you own. abc c c c c c c c cc c c Letters represent a statistically significant difference between indicated age groups at the 95% confidence level. AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Among less common activities, adults in their sixties are more likely to use their tablets to manage medical care and post reviews than those in their 50s or 70s. Tablet users age 60-69 are more likely than those 70 to manage medical care and post reviews than those 70 . They are slightly (but not significantly) more likely to learn online and use voice assistants than those 70 as well. Those in their 50s are more likely to use their tablets to perform online banking transactions than those 70 . 334% 26% 21% 16% 9% 34% 30% 27% 26% 24% 22% 9% 23% 22% 17% 16% 17% 12% 5% Get directions or traffic info Perform banking or financial transactions Take classes, webinars, or read/watch how-to tutorials Post your own reviews, ratings, or comments online Use a voice activated assistant Manage or receive medical care Track health or fitness via apps or website Activities Performed on Tablet by Age (Bottom 7) (Among those who own a tablet) 50-5960-6970 14 Q.Tech2 (Base: Those who own tablets. n=647). Please indicate whether you do each activity for each device you own. abc c c c Letters represent a statistically significant difference between indicated age groups at the 95% confidence level. AARP RESEARCH | AARP.ORG/RESEARCH | 2017 AARP. ALL RIGHTS RESERVED Messaging, directions, and surfing the internet top list of activities people do on their smartphones. Nearly 90% of smartphone owners say they use their device to send IMs or emails, and over three quarters (77%) say they use it to get directions or traffic information. Other top activities include purchasing apps, surfing the internet, getting news, and accessing social media. Adults 50 are much more likely to use voice assistants on their smartphone than they are on any other device. 88% 77% 69% 64% 62% 60% 45% 42% 37% 35% 35% 34% 29% 28% 24% 19% 13% Send or receive IMs or Emails Get directions or traffic info Download or purchase an app Visit websites or surf the internet Get news and other info Access a social networking site Use a voice activated assistant Play a game Comparative shop for discounts and deals Make a purchase Perform banking or financial transactions Watch videos or shows Get health and fitness info Manag

2017-12-01 29页 5星级

Edison：2017年美国智能音箱用户调查报告(英文版)(31页) (3).pdf

65%say that they wouldnt want to go back to life without their Smart Speaker Methodology 1,010 person telephone survey Adults age 18 and older National study conducted 12/26/2017 12/30/2017 806 person online survey Adults age 18 and older National study conducted 11/17/2017 11/22/2017 All respondents reported owning an Amazon or Google Smart Speaker 16% of Americans 18 own a Smart Speaker, or around 39 million people Google Home is a trademark of Google Inc. Google Home is a trademark of Google Inc. AMAZON ALEXAGOOGLE HOME 4% % owning Smart Speaker Smart Speaker Ownership Total Population 18 11% In the 2017 Holiday Season: 7% of Americans got a Smart Speaker 4% of Americans got their first Smart Speaker Smart Speakers are changing behaviors and forming new habits 39 34 30 27 26 23 17 Traditional AM/FM radio Smartphone Television Tablet Computer Printed publications Is the time you spend using your Smart Speaker replacing any time you used to spend with? % saying yes Sonos or other whole-house audio system Base: Smart Speaker owners 30%of Smart Speaker owners say their speaker is replacing time spent with TV “Compared to the first month of owning a Smart Speaker, are you using it? More often 51% About the same amount 33% Less often 16% Base: Had a Smart Speaker more than one month and gave a response 23% who are listening to more audio said they are listening to more news/talk 28% who are listening to more audio said they are listening to more podcasts Base: Own a Smart Speaker 71% are listening to more audio since getting a Smart Speaker Top three (3) indexing activities by day part: 5am 9am9am 3pm5pm 7pm7pm 9pm 9pm Midnight #1Traffic Drop in to an Alexa device in home Find restaurants/ businesses Games Control smart home devices #2WeatherAdd to to-do list Recipes/cooking requests Send messages to other devices Audiobooks #3NewsAdd to shopping listOrder foodChildren storiesTimer/alarms 64%of Smart Speaker owners are interested in having Smart Speaker technology in their car Base: Driven or rode in a car in the past month (95%) How interested would you be in having the Smart Speaker technology? 39 31 24 17 16 25 29 29 18 14 20 23 24 24 26 7 7 8 8 12 9 10 15 33 32 In your car/vehicle* On your phone On your television At your workplace At places other than your home/work/car Very Interested (5) Not at all Interested (1) Base: Smart Speaker Owners. *Driven or rode in a car in the past month. The Communal Experience How often do you use the Smart Speaker with others in your household? Most of the time 53% Occasionally 39% Rarely 6% Never 2% Base: Smart Speaker owners 66%of Smart Speaker owners use their speaker to entertain friends and family 60 30 28 18 13 13 13 12 12 11 11 Play music Answer a general question Get the weather Tell a joke Listen to music AM/FM radio Get the news Set a timer/alarm Control household devices Check the time Get a sports score or update Play a game Top tasks requested while spending time with friends and family: % requesting item The Smart Home Where is your Smart Speaker typically located? Living room/family room/den 52% Kitchen 21% Master bedroom 19% Other bedroom 4% Home office 2% Somewhere else 2% Base: Own only one Smart Speaker and giving a response 31%of Smart Speakers owners have controlled household devices with a Smart Speaker in the last week Where in the last week did you request your Smart Speaker to control household devices? % saying yes 61 38 36 14 12 6 3 Living room Kitchen Master bedroom Other Bedroom Bathroom Home office Other room Base: Have controlled household devices with Smart Speaker in the last week 6 46 42 44 34 37 46 Midnight-5am9pm-midnight 7pm-9pm 5pm-7pm 3pm-5pm 9am-3pm 5am-9am At what time in the last week did you request your Smart Speaker to control household devices? Base: Have controlled household device with Smart Speaker in the last week (31%) % saying they controlled in that time period 38%of owners plan to buy additional smart speakers to control smart home devices Purchasing behaviors and connecting with brands Through your Smart Speaker, have you? % saying yes Re-ordered an item you have previously purchased Ordered a new product you have not previously purchased Added an item to your cart so you could review it later for purchase Researched an item you might want to purchase 31)% 58 51 48 45 42 28 22 Household supplies Electronics Health and beauty Pet food/supplies Groceries Home and garden Baby products Which of these have you purchased using your Smart Speaker? % saying yes Base: Have placed an order with Smart Speaker in the last week (13%) 2 15 33 32 30 17 13 Midnight-5am9pm-midnight 7pm-9pm 5pm-7pm 3pm-5pm 9am-3pm 5am-9am At what time in the last week did you order an item with your Smart Speaker? Base: Have placed an order with Smart Speaker in the last week (13%) % saying they ordered in that time period 43%of Smart Speaker owners would be interested in using skills from companies or brands they follow on social media Base: Follow any companies or brands on social networking sites npr.org/smartaudio

2017-12-01 31页 5星级