Artificial Intelligence Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025, According to Tractica – Yahoo Finance

More than 330 AI Use Cases Will Contribute to Market Growth Across 28 Industries with the Strongest Enterprise AI Opportunity in Automotive, Consumer, Financial Services, Telecommunications and Retail Sectors

Artificial intelligence (AI) within the consumer, enterprise, government, and defense sectors is migrating from a conceptual "nice to have" to an essential technology driving improvements in quality, efficiency, and speed. According to a new report from Tractica, the top industry sectors where AI is likely to bring major transformation remain those in which there is a clear business case for incorporating AI, rather than pie-in-the-sky use cases that may not generate return on investment for many years.

"The global AI market is entering a new phase in 2020 where the narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," says principal analyst Keith Kirkpatrick. According to the market intelligence company, AI is likely to thrive in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. Not surprisingly, the consumer sector has demonstrated its ability to capture AI, thanks to the combination of three key factors large data sets, high-performance hardware and state-of-the-art algorithms. Tractica estimates that many of the top enterprise AI verticals will follow and replicate a strategy similar to the consumer Internet companies. Annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025.

Tracticas report, "Artificial Intelligence Market Forecasts," provides a quantitative assessment of the market opportunity for AI across the consumer, enterprise, government, and defense sectors. The study includes market sizing, segmentation, and forecasts for 333 AI use cases, including more than 200 unique use cases. Tractica has added use cases spread across multiple industries, including energy, manufacturing, retail, consumer, transportation, public sector, media and entertainment, telecommunications, and financial services. Global market forecasts, segmented by use case, technology, geography, revenue type, and meta category, extend through 2025. An Executive Summary of the report is available for free download on the firms website.

About Tractica

Tractica, an Informa business, is a market intelligence firm that focuses on emerging technologies. Tracticas global market research and consulting services combine qualitative and quantitative research methodologies to provide a comprehensive view of the emerging market opportunities surrounding Artificial Intelligence, Robotics, User Interface Technologies, Advanced Computing and Connected & Autonomous Vehicles. For more information, visit http://www.tractica.com or call +1.303.248.3000.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200106005317/en/

Contacts

Sherril Hanson+1.303.248.3338press@tractica.com

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Artificial Intelligence Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025, According to Tractica - Yahoo Finance

Artificial Intelligence(AI) Applications In Higher Education Business – CIO Applications

If too many applicants accept offers it will be a problem for Facilities Management to fit the class and then there can be a potential resource constraint issue. Financial aid/scholarship is offered to many applicants to entice them to accept the offer and usually, there is a fixed budget for financial aid. As seen above, the admission decision making has to include several moving parts and multiple constraints. This creates a great opportunity to use data science to solve many of these problems. Data science models can be used to predict who should be offered admissions, what is the chance of the admitted person to accept the offer and how much financial aid should be awarded for each potential offer to matriculate.

Universities get several data points for each applicant/ student from beginning of the application cycle, during several years of the program until career placement. These data comprise of academic scores, demographic, academic interaction, performance, placement and many more aspects. All these data can be used comprehensively to review, monitor and advise each student for better academic outcome during the course of the program and also in the future.

Data science models can be used to predict who should be offered admissions, what is the chance of the admitted person to accept the offer and how much financial aid should be awarded for each potential offer to matriculate

Last but not the least; AI application can be used to streamline university financial operations. Machine Learning can be used to facilitate accounting in terms of coding, reconciliation and effective reporting.

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Artificial Intelligence(AI) Applications In Higher Education Business - CIO Applications

Trustworthy artificial intelligence in healthcare: It’s time to deliver – Open Access Government

In Europe, chronic diseases account for 86% of deaths and 77% of disease burden, thereby creating a tremendous challenge on societies. At the same time, digitalisation is bringing huge technological and cultural opportunities. In healthcare, the usage of data-driven forecasts on individual and population health by the integration of artificial intelligence (AI)-enabled algorithms has the potential to revolutionise health protection and chronic care provision, while securing the sustainability of healthcare systems.

European start-ups, small and medium-sized enterprises (SMEs) and large corporations offer smart AI-enabled digital solutions, backed by medical evidence. They could help to achieve the WHO Sustainable Development Goals and reduce premature mortality from major chronic diseases by 25% up to 2025.1 Some, but too few of the solutions are available in the markets. A paradigm example is the increasing availability and reimbursement of closed-loop metabolic control (artificial pancreas) systems for persons with diabetes.2

There is no excuse for inaction as we have evidence-based solutions. This statement was made by the co-chairs of the WHO Independent High-Level Commission on Noncommunicable Diseases in 2018 paired with the appeal on global governments to start from the top in taking action against the global disease burden.3

In April 2019, a high-level expert group on AI set up by the European Commission (EC) published Ethics Guidelines for Trustworthy AI.4 The guidelines lay down criteria for trustworthy AI with emphasis on ethical, legal and social issues and follows the goal to promote trustworthy AI.

First, trustworthy AI should respect all applicable laws and regulations.

Second, trustworthy AI should adhere to the ethical principles of respect for human autonomy (e.g. people should keep full and effective self-determination); prevention of harm (with paying particular attention to vulnerable persons); fairness (e.g. ensuring that people are free from discrimination and stigmatisation and can seek effective redress against AI-enabled decisions); and explicability (e.g. a full traceability, auditability and transparent communication on system capabilities particularly in case of black-box algorithms).

And third, trustworthy AI should be robust and ensure that, even with good intentions, no unintentional harm can occur.

They were inspired by the ethical principles and should be met through developers, deployers and end-users.

First, human agency & oversight (e.g. decision autonomy, governance); second, technical robustness and safety (e.g. security & resilience to attack, accuracy & reproducibility of outcomes); third, privacy & data governance (e.g. data integrity & protection, governance of data access); fourth, transparency (data, systems, business models); fifth, diversity, non-discrimination and fairness (e.g. avoidance of bias, co-creation, user-centrism); sixth, societal and environmental well-being (e.g. promotion of democracy, ecological sustainability); and seventh, accountability (e.g. forecast quality, auditability, report of negative impacts).

Most recently, the guidelines underwent an early reality check through EIT Health a public-private partnership of about 150 best-in-class health innovators backed by the European Union (EU) and collaborating across borders to deliver new solutions that can enable European citizens to live longer, healthier lives.5

A survey among start-ups and entrepreneurs, as well as EIT Health partners from industry, academia and research organisations, indicated a currently low (22% of respondents) have awareness of the guidelines. More than 60% of respondents were aware that their AI application will need regulatory approval.

Among the seven requirements on trustworthy AI, the highest priority was given to privacy & data governance, technical robustness & safety, followed by traceability and human agency & oversight.

Lower ranked, though still relevant, were the ethics of diversity, non-discrimination & fairness (respondents are working on it following e.g. an iterative approach to improving data sets and removing biases), accountability (currently traditional auditing, post-market surveillance and procedures for redress appear to be relied on) and societal and environmental well-being (the former appears self-evident for health solutions, consciousness in the context of health solutions for the latter is possibly not yet well established).

Clearly, there is a conflicting interdependence between a comprehensive resolution of every conceivable ethical, legal & social issue, the imperative to eventually break down the longstanding barriers to personalised and preventative healthcare (which would save millions of lives) and the requirement for European societies to tackle global competition for a worldwide market penetration of trustworthy AI.

We agree with the recent TIME TO DELIVER appeal from the World Health Organization (WHO).3 In collaboration with vital communities, such as EIT Health, European governments should go ahead in establishing a productive balance between promoting innovation, welcoming global competition and defining healthcare-specific ethical, legal and social requirements for trustworthy AI.

We welcome the idea of establishing world reference testing facilities for AI recently contributed by Roberto Viola (Director General of DG Connect at the EC).6 EIT Health should be in a privileged position to orchestrate such testing facilities for AI, through providing secured validation environments applying the high ethical and regulatory standards of clinical contract research.

Here partners from innovation, education and business should collaborate on concrete AI-enabled solutions for an effective assessment of real risks and opportunities, followed by the provision of a solution-specific ELSI7 dossier in order then to join forces to launch the trustworthy AI-enabled solution to the markets and scale-up the business model.

That way, European societies could be impactful in breaking down innovation barriers and eventually providing thoroughly validated solutions globally to the persons who need them most.

References

1 As proclaimed by the WHO in 2012 (Gulland, A. 2013, BMJ 346:f3483; http://www.who.int/nmh/en/)

2 Schliess, F. et al. 2019, J. Diabetes Sci. Technol. 13(2):261-267, 2019; https://www.eithealth.eu/en_US/close; https://www.eithealth.eu/diabeloop-for-children; https://www.eithealth.eu/en_US/d4teens. close, diabeloop-for-children and d4teens are innovation projects dedicated to closing the loop in diabetes care. They are supported by EIT Health, a network of best-inclass health innovators that collaborates across borders and delivers solutions to enable European citizens to live longer, healthier lives. EIT Health is supported by the EIT, a body of the European Union.

3 https://www.who.int/ncds/management/time-to-deliver/en/

4 https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

5 https://www.eithealth.eu/-/eit-health-holds-panel-on-ai-and-ethics-at-the-world-health-summit

6 https://www.eithealth.eu/ai-and-health

7 ELSI, ethical, legal and social issues.

Please note: This is a commercial profile

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Trustworthy artificial intelligence in healthcare: It's time to deliver - Open Access Government

Failure to Scale Artificial Intelligence Could Put 80% of Indian Organizations Out of Business: Study – Analytics India Magazine

Nearly 80% of C-level executives in India believe if they dont move beyond experimentation to aggressively deploy artificial intelligence (AI) across their organizations, they could risk of going out of business by 2025, according to a newly released study from Accenture.

The research, titled AI: Built to Scale and produced by Accenture Strategy and Accenture Applied Intelligence, found that while 79% of C-level executives in India believe they wont achieve their business strategy without scaling AI, only a few have made the shift from mere experimentation to creating an organization powered by robust AI capabilities. As a result, this small group of top performers is achieving nearly three times the return from AI investments as their lower-performing counterparts.

Over the years, the use of AI has permeated various functions demonstrating its true transformative potential. Most companies now also recognize that they need to scale AI for growth and relevance, yet they are unable to do so. Those who push through the barriers to embedding AI more deeply in their organization, are seeing a return on their AI investment of 70% or more, said Anindya Basu, geographic unit and country senior managing director, Accenture in India.

He further added, Indian businesses need to step on the pedal and learn from the leaders. They need to make strategic investments to scale AI as thats the only way to realize its true business value.

The report reveals that the secret to success for top performers centres around three key elements:

These companies demonstrate their deep commitment by scaling AI at

a much higher rate conducting nearly twice as many pilots than other companies. However, this commitment to AI does not necessarily translate to higher spend, with top performers reporting lower investment levels on their AI implementations pilots and full-scale deployments than lower performers.

According to the report, nearly all global respondents, approximately 95% agreed on the importance of data as the foundation to scaling AI, but the top performers are more intentional and focused on ensuring that the right, relevant data assets are in place to underpin their AI efforts. They are more adept at structuring and managing data, with 61% wielding a large, accurate data set and more than two-thirds, i.e. 67%, effectively integrating both internal and external data sets.

A key barrier to the successful scaling of AI is the lack of the right people strategy. Companies need to ensure their employees understand both what AI is and how it applies to their day-to-day role. While the top leadership team can serve as the champions responsible for scaling AI initiatives, embedding teams with AI across the entire organization is not only a powerful signal about the strategic intent of the effort but will also enable faster culture and behavioural changes, said Saurabh Kumar Sahu, managing director and lead for Applied Intelligence, Accenture in India.

This strategic approach is further bolstered by another key characteristic of top performers assembling the right talent to drive results. Instead of relying on a single AI champion, 92% have strategically embedded multi-disciplinary teams throughout their organizations. This cross-functional approach also helps ensure diversity of thinking which, in addition to having tangible benefits for considerations like Responsible AI that can also maximize the value an organization sees from their AI deployments.

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Failure to Scale Artificial Intelligence Could Put 80% of Indian Organizations Out of Business: Study - Analytics India Magazine

MIT School of Engineering and Takeda join to advance research in artificial intelligence and health – MIT News

MITs School of Engineering and Takeda Pharmaceuticals Company Limited today announced the MIT-Takeda Program to fuel the development and application of artificial intelligence (AI) capabilities to benefit human health and drug development. Centered within the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), the new program will leverage the combined expertise of both organizations, and is supported by Takedas three-year investment (with the potential for a two-year extension).

This new collaboration will provide MIT with extraordinary access to pharmaceutical infrastructure and expertise, and will help to focus work on challenges with lasting, practical impact. A new educational program offered through J-Clinic will provide Takeda with the ability to learn from and engage with some of MIT's sharpest and most curious minds, and offer insight into the advances that will help shape the health care industry of tomorrow.

We are thrilled to create this collaboration with Takeda, says Anantha Chandrakasan, dean of MITs School of Engineering. The MIT-Takeda Program will build a community dedicated to the next generation of AI and system-level breakthroughs that aim to advance healthcare around the globe.

The MIT-Takeda Program will support MIT faculty, students, researchers, and staff across the Institute who are working at the intersection of AI and human health, ensuring that they can devote their energies to expanding the limits of knowledge and imagination. The new program will coalesce disparate disciplines, merge theory and practical implementation, combine algorithm and hardware innovations, and create multidimensional collaborations between academia and industry.

We share with MIT a vision where next-generation intelligent technologies can be better developed and applied across the entire health care ecosystem, says Anne Heatherington, senior vice president and head of Data Sciences Institute (DSI) at Takeda. Together, we are creating an incredible opportunity to support research, enhance the drug development process, and build a better future for patients.

Established within J-Clinic, a nexus of AI and health care at MIT, the MIT-Takeda Program will focus on the following offerings:

James Collins will serve as faculty lead for the MIT-Takeda Program. Collins is the Termeer Professor of Medical Engineering and Science in MITs Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, J-Clinic faculty co-lead, and a member of the Harvard-MIT Health Sciences and Technology faculty. He is also a core founding faculty member of the Wyss Institute for Biologically Inspired Engineering at Harvard University and an Institute Member of the Broad Institute of MIT and Harvard.

A joint steering committee co-chaired by Anantha Chandrakasan and Anne Heatherington will oversee the MIT-Takeda Program.

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MIT School of Engineering and Takeda join to advance research in artificial intelligence and health - MIT News

Use of artificial intelligence in different forms can help achieve $5 trillion economy: Piyush Goyal – The New Indian Express

By PTI

NEW DELHI: Use of artificial intelligence (AI) in different forms can help achieve the target of making India a USD 5 trillion economy in the coming years, Commerce and Industry Minister Piyush Goyal said on Monday.

He said various departments are working to see how AI, space technology and other modern tools can be used to push economic growth of the country.

"We in the government believe that AI can, in different forms, help us achieve the USD 5 trillion benchmark, which we have set for over (next) five years," he said here at a function.

The minister added that AI can also help expand in a more cost-effective and outcome-oriented manner.

Goyal, who also has the railways portfolio, said in railways, a team is focusing to see how "we could benefit from AI" as the potential is humongous.

"AI can help in every sector to do our job better," he said adding it can hep improve ease of living and ease of doing business.

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Use of artificial intelligence in different forms can help achieve $5 trillion economy: Piyush Goyal - The New Indian Express

Artificial Intelligence in Aviation Market 2020 Size, Share Metrics, Growth Trends and Forecast to 2026 – Food & Beverage Herald

New Jersey, United States, Verified Market Research indicates that the Artificial Intelligence in Aviation Market is expected to surge at a steady rate in the coming years, as economies flourish. The research report, titled [Global Artificial Intelligence in Aviation Market Research Report 2020], provides a comprehensive review of the global market. Analysts have identified the key drivers and restraints in the overall market. They have studied the historical milestones achieved by the Global Artificial Intelligence in Aviation Market and emerging trends. A comparison of the two has enabled the analysts to draw a potential trajectory of the Global Artificial Intelligence in Aviation Market for the forecast period.

Global Artificial Intelligence in Aviation Market was valued at USD 0.11 Billion in 2017 and is projected to reach USD 1.8 Billion by 2025, growing at a CAGR of 45.3% from 2018 to 2025.

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Top 10 Companies in the Global Artificial Intelligence in Aviation Market Research Report:

Global Artificial Intelligence in Aviation Market: Competitive Landscape

Competitive landscape of a market explains strategies incorporated by key players of the market. Key developments and shift in management in the recent years by players has been explained through company profiling. This helps readers to understand the trends that will accelerate the growth of market. It also includes investment strategies, marketing strategies, and product development plans adopted by major players of the market. The market forecast will help readers make better investments.

Global Artificial Intelligence in Aviation Market: Drivers and Restrains

This section of the report discusses various drivers and restrains that have shaped the global market. The detailed study of numerous drivers of the market enable readers to get a clear perspective of the market, which includes market environment, government policies, product innovations, breakthroughs, and market risks.

The research report also points out the myriad opportunities, challenges, and market barriers present in the Global Artificial Intelligence in Aviation Market. The comprehensive nature of the information will help the reader determine and plan strategies to benefit from. Restrains, challenges, and market barriers also help the reader to understand how the company can prevent itself from facing downfall.

Global Artificial Intelligence in Aviation Market: Segment Analysis

This section of the report includes segmentation such as application, product type, and end user. These segmentations aid in determining parts of market that will progress more than others. The segmentation analysis provides information about the key elements that are thriving the specific segments better than others. It helps readers to understand strategies to make sound investments. The Global Artificial Intelligence in Aviation Market is segmented on the basis of product type, applications, and its end users.

Global Artificial Intelligence in Aviation Market: Regional Analysis

This part of the report includes detailed information of the market in different regions. Each region offers different scope to the market as each region has different government policy and other factors. The regions included in the report are North America, South America, Europe, Asia Pacific, and the Middle East. Information about different region helps the reader to understand global market better.

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Table of Content

1 Introduction of Artificial Intelligence in Aviation Market

1.1 Overview of the Market 1.2 Scope of Report 1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining 3.2 Validation 3.3 Primary Interviews 3.4 List of Data Sources

4 Artificial Intelligence in Aviation Market Outlook

4.1 Overview 4.2 Market Dynamics 4.2.1 Drivers 4.2.2 Restraints 4.2.3 Opportunities 4.3 Porters Five Force Model 4.4 Value Chain Analysis

5 Artificial Intelligence in Aviation Market, By Deployment Model

5.1 Overview

6 Artificial Intelligence in Aviation Market, By Solution

6.1 Overview

7 Artificial Intelligence in Aviation Market, By Vertical

7.1 Overview

8 Artificial Intelligence in Aviation Market, By Geography

8.1 Overview 8.2 North America 8.2.1 U.S. 8.2.2 Canada 8.2.3 Mexico 8.3 Europe 8.3.1 Germany 8.3.2 U.K. 8.3.3 France 8.3.4 Rest of Europe 8.4 Asia Pacific 8.4.1 China 8.4.2 Japan 8.4.3 India 8.4.4 Rest of Asia Pacific 8.5 Rest of the World 8.5.1 Latin America 8.5.2 Middle East

9 Artificial Intelligence in Aviation Market Competitive Landscape

9.1 Overview 9.2 Company Market Ranking 9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview 10.1.2 Financial Performance 10.1.3 Product Outlook 10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Artificial Intelligence in Aviation Market 2020 Size, Share Metrics, Growth Trends and Forecast to 2026 - Food & Beverage Herald

With support from Guardian readers, we can make the 2020s the decade of hope – The Guardian

If a week is a long time in politics, then the past decade has been a lifetime in the media world literally in some cases.

The 2010s will be remembered for many things: protest, austerity, populism, mass migration, Brexit. But perhaps one of the most dangerous developments in this most difficult of decades has been the revolution in the way we produce, share and disseminate information.

Our media how it is produced, financed and distributed has been turned upside down. And as we enter the 2020s, the implications of this are clear for all to see: competing versions of the truth; liars and confabulators winning high office across the world; polarisation and antagonism; deep fakes, rumours, confusion; the evaporation of trust.

Ten years ago, the traditional media ecosystem was still just about intact. Several rapid technological developments atomised it. The proliferation of smartphones ate away at the print model we had always used, and many newspapers local and national were forced to shut up shop. People stopped paying for news. Meanwhile, the seemingly unstoppable rise of social media produced rival platforms that would quickly suck advertising billions away from news providers.

The resultant financial penury meant many titles turned to billionaires, sheikhs or oligarchs for a lifeline. Social medias growing power also meant that those with resources and reach could shape their own message, however dishonest, rather than rely on traditional media as a channel.

It was this perfect storm that you, our growing community of 1 million-plus supporters, helped us weather. We saw that factual, honest reporting had never been so in jeopardy, or so essential. We knew it would be hard but we chose a different approach to sustainability.

We remain determined to retain our editorial independence and keep our journalism open to everyone, regardless of who they are or what they can afford. We knew that so many of our readers shared this same value. So we asked you to contribute voluntarily, for the benefit of those who cannot. Remarkably, it worked.

Thanks to you, our supporters in 180 countries, we have been able to retain proper editorial independence at a time when the world urgently needs unbiased, trustworthy sources of information.

Thanks to you, we have been able to produce groundbreaking journalism that challenges those in authority, and gives voice to those who arent. In the past decade, we have exposed the mistreatment of the Windrush generation, helped fight global corruption with our Panama Papers investigation, won a Pulitzer prize for our work with the whistleblower Edward Snowden into the actions of the NSA, and revealed the way election campaigns are skewed in the digital age with the Cambridge Analytica files. Just last month, our climate pledge demonstrated our determination to show leadership in environmental journalism and commit to steps we will take organisationally to become greener and more ethical.

Thanks to you, we have succeeded in positioning ourselves as a leading voice on the most critical issues facing the world today: the environment, nativism, fairness, social justice, inequality. With your support, we can continue to produce the journalism we know means so much and makes such a huge difference in the world. We rely on your support for our future.

In 2019, we announced that after years of financial uncertainty in this most challenging of media climates, the Guardian broke even. It was a tremendous moment for all those who have worked on these stories, and for our supporters around the world who played a key part in making our journalism possible. Thank you so much. There is a good chance that, together, we can dare to hope for a better world.

As 2020 unfolds, we ask for your ongoing support. If you are able to, please consider supporting us today with a contribution of any size. Each and every one makes a big difference to our future.

Happy new year, from all of us at the Guardian.

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With support from Guardian readers, we can make the 2020s the decade of hope - The Guardian

‘I want this book to be politically useful: the explosive memoir exposing Silicon Valley – The Guardian

In a leafy cafe courtyard in San Francisco, Anna Wiener is cradling a cup of tea while eavesdropping on the next table. Theres a man wearing shiny pants, holding forth on artificial intelligence and the Chinese hegemon, she says, eyes glimmering with amusement.

It will come as no surprise to readers of her debut, Uncanny Valley, that Wiener is as quick witted in person as she appears on the page. All writing is a sort of performance, she says. In the book Wiener condenses four years of working at tech startups in Silicon Valley into a neat narrative about outsized male egos, dramatic wealth disparities and the psychological toll on young female employees.

Despite its unsavoury and troubling contents unregulated surveillance technology, ruthless bosses, casual sexual harassment the book is a delight. Deftly drawn characters are granted pseudonyms and companies are unnamed; instead they are identified by cutting descriptions. Facebook is the social network everyone hated and Edward Snowden is the NSA whistleblower who was back in media. Microsoft is the highly litigious Seattle-based software conglomerate. Essentially, she says: Its important to remember that Google is an ad platform and that Facebook is a surveillance platform.

I envied their sense of entitlement to the future. There were no crises in their vision only opportunities.

Wiener interviewed former colleagues and friends, engaging in what she calls a dance around everyone elses NDA. She also scoured her iMessage chats and email archive for granular details, such as the humourless office flag that read In Meritocracy We Trust or the CEO in his 20s who instructed employees to BCC his mother on the companys customer support communications. My Gmail is an incredible corpus of mid-20s work anxiety, she sighs. And an archive of what in hindsight are very obvious ways to navigate work situations that were overly complex, because I didnt know how to be a person.

The book first took shape in 2015 as a lightly fictionalised essay for the Brooklyn-based literary magazine n+1. Wieners piece went viral but the surge of attention came as a shock. I thought that no one in literary n+1 world would care about Silicon Valley startups, and that no one in Silicon Valley reads n+1. For years, she had documented the cultish work rituals and peculiar cultural norms of the industry, but it wasnt until Dayna Tortorici, editor of n+1, visited San Francisco that she considered synthesising those observations into a cohesive narrative. Dayna has a theory that people in tech arent used to being seen because everything is mediated, Wiener says. I wrote the piece to entertain her.

As an editor, Tortorici had noticed an unnerving pattern: when writing about their workplaces, female contributors were often threatened for violating NDAs, whereas rarely, if ever were NDAs used against male writers. (Despite the industrys fervent defences of freedom of speech, most tech companies also enforce strict policies that ensure former employees stay silent about work conditions, including sexual harassment allegations.)

While she didnt face NDA-related constraints, Wieners first drafts were fairly restrained. Tortorici encouraged her to reveal more about her former bosses without worrying about reprisals. By 2018, Wiener had left her job at the software development platform GitHub after a seven-way auction to expand the original n+1 essay into a book. (And in January 2018, Universal Pictures optioned film rights; the screenplay is now in its initial development stages and Wiener is executive producer.) I very deliberately wrote this book as non-fiction and memoir, she says, because if I wrote it as fiction, it could be mistaken as satire. And I dont know how politically useful satire about the tech industry is in 2019. I want this book to be politically useful.

Wiener, now 32, grew up in Brooklyn, New York. Her mother is a writer and gun-control activist co-founder of the nonprofit New Yorkers Against Gun Violence and her father a business journalist; she was exposed to feminist ideas and progressive politics at a young age. In person, Weiner is self-possessed. Apart from bitten fingernails and frank asides about therapy there are few signs of neuroses, although she says: I have very bad anxiety. Her worries around the book centre on her use of creative non-fiction techniques: the story is based on real events, but the specific timeline and characters are compressed for clarity and cohesion. She is concerned about whether people in the tech industry will understand the conventions of this approach and about possible backlash from tech executives whose public images can directly affect their companies stock value. I sometimes have these daytime nightmares about testifying in court about creative non-fiction, where Im like, I would like to summon Vivian Gornick to the stand to explain compression, or Id like to bring in John DAgata to discuss composite characters.

Just as New York City is a core character in Ben Lerners auto-fictitious 10:04 and various areas of California play leading roles in Joan Didions seminal essay collection The White Album, both influential texts for Wiener, San Francisco and its immersive digital world are central characters in Uncanny Valley. The digital landscape is textured with what is referred to as God mode an employees unbridled access to her companys database, from which she can intensively track users and glean their personal information. In the book, Wiener describes her workplaces blase, apolitical attitude to God mode: We didnt think of ourselves as participating in the surveillance economy. We certainly werent thinking about our role in facilitating and normalising the creation of unregulated, privately held databases on human behaviour. She continues: Users might not know they were being tracked, but that was between them and our customer companies.

While writing, Wiener studiously avoided reading books about the tech industry, and instead focused on office novels and compact memoirs, including Renata Adlers groundbreaking novel Speedboat, which centred on a New York journalist in the 70s; and Ana Castillos The Guardians, a novel about a Mexican-American woman living along the US/Mexico border. I tried to reread Ellen Ullmans Close to the Machine a little bit stoned in a hot bath an ostensibly relaxing situation and almost had a panic attack. Its essentially a perfect memoir and sets the standard, she explains. (Ullman is an engineer who documented her experience developing software at the forefront of the male-dominated technology boom in the 90s.)

For a moment, Wiener is distracted by the cafes playlist as Nirvana thunder over the speakers. I feel like Im 16 and burning incense in my bedroom and telling my mom Im a vegetarian, she deadpans.

We return to unpacking Silicon Valleys accountability problem and popular modes of aggrandisement. The way that people spoke in San Francisco was so strange to me, like all of the acronyms, jargon and weird things that people do to the English language. To inspire us in a meeting, one CEO said, Were at war! And like, Learnings. Why? Its lesson, she says. I find the naming scheme of the last 15 years in tech companies to be very funny, these names are just obscene. She rattles off AppLovin and Verbling as two of her favourites. You can practically throw a spitball and hit a badly named company.

Throughout the memoir, there are moments when Wiener acquiesces to male characters demands only to correct her course with a renewed sense of agency. In a memorable scene, she goes out to a Japanese bar with her mostly male co-workers to celebrate their bosss birthday, conceiving of herself as the babysitter, fifth wheel, chaperone, little sister, ball and chain, and concubine. She explains: I was always trying to be someones girlfriend, sister or mother. (Uncanny Valley takes place in the years leading up to the #MeToo movement; details about a sexual assault incident were withheld from the book to protect her former colleagues anonymity.)

The 2016 election result strikes at the end of the book with cataclysmic force, no doubt a reflection of the way the author herself experienced the event. The major failure of the media in the years leading up to the election was to not take tech companies and their ambitions very seriously, Wiener says. The media engaged with the industry on the industrys terms. It lapped up the mythology.

In reference to the founders of an ebook startup, Wieners first tech job, she writes: I envied their sense of entitlement to the future. There were no crises in their vision only opportunities. In California, this techno-optimist outlook is often associated with anti-union, libertarian politics. By contrast, she says: Ive always had a hard time picturing a future, which one could credit to having witnessed a major terrorist attack as a teenager. She pauses. In my head I was like, dont mention 9/11. She continues: My hope for the future is that we start to move slower and at a smaller scale.

At the end of Uncanny Valley, after the 2016 election, Wiener writes that she felt that the industry was in for a reckoning, that it was the beginning of the end, that what [she] had experienced in San Francisco was the final stage of a prelapsarian era, the end of our generational gold rush, an unsustainable age of excess. A freewheeling culture of misinformation, offensive memes and trolling unfettered by regulation or oversight only proliferated. Wiener says: The city and the industry, bound by the ecosystem, continued to cycle and churn.

Of course, conflicting views of the future also reflect a greater schism in industry. Just as the publishing world shrinks, tech companies bloat with capital. If we continue on the track that were on, were going to move into an era of even greater privatisation, Wiener says, shifting uncomfortably in her chair. The future will be increasingly homogeneous, divisive and private. To illustrate this point she highlights public goods or services that are increasingly privatised, like for-profit coding boot camps, which are marketed as an investment or a substitute for a four-year university degree. The tech industry is trying to provide solutions to crises that they didnt necessarily create, but that they are now exacerbating.

Everyone deserves better. Especially employees and consumers. But I dont know that change is going to come from within the industry because the incentives of venture capital encourage speed and rapid growth, which inspire a certain thoughtlessness or recklessness. She pauses. There is currently very little accountability.

Uncanny Valley: a Memoir is published by HarperCollins (RRP 16.99). To order a copy go to guardianbookshop.com. Free UK p&p over 15.

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'I want this book to be politically useful: the explosive memoir exposing Silicon Valley - The Guardian

Edward Snowden The Twitter Master (2020-01-05) – Global Real News

Hello! Today we did a major analysis of Edward Snowdens Twitter activity. Lets jump right into it. First, the primary metrics: as of 2020-01-05, Edward Snowden (@Snowden) has 4210614 Twitter followers, is following 1 people, has tweeted 4638 times, has liked 489 tweets, has uploaded 377 photos and videos and has been on Twitter since December 2014.

Going from top to bottom, their latest tweet, at the time of writing, has 476 replies, 2,823 retweets and 7,909 likes, their second latest tweet has 71 replies, 318 reweets and 1,435 likes, their third latest tweet has 149 replies, 610 retweets and 5,327 likes, their fourth latest tweet has 73 replies, 747 retweets and 1,814 likes and their fifth latest tweet has 76 replies, 1,402 retweets and 3,595 likes. (We could keep going, but we think you get the idea )

MOST POPULAR:

Going through Edward Snowdens last couple pages of tweets (including retweets, BTW), the one we consider the most popular, having incited a whopping 1017 direct replies at the time of writing, is this:

That really seems to have caused quite a bit of discussion, having also had 11046 retweets and 64013 likes.

LEAST POPULAR:

Now what about Edward Snowdens least popular tweet as of late (again, including retweets)? We reckon its this one:

That only had 2 direct replies, 71 retweets and 186 likes.

THE VERDICT:

We did a huge amount of of research into Edward Snowdens Twitter activity, looking through what people were saying in response to them, their likes/retweet numbers compared to the past, the amount of positive/negative responses and so on. We wont go into that any more, so our verdict is this: we say the online sentiment for Edward Snowden on Twitter right now is totally fine.

Well leave it there for today. Thanks for reading, and write a comment if you disagree with me. However, we wont publish anything overly rude.

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Edward Snowden The Twitter Master (2020-01-05) - Global Real News