Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the precarious nature of the future of work (long after the coronavirus pandemic ends), the continuing mixed attitudes of consumers about data privacy, and the possible resilience of this year's investments in AI. The IT department's need for AI talent has tripled between 2015 and 2019, but the number of AI jobs posted by IT is still less than half of that stemming from other business units; departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. By 2025, at least two of the top 10 global retailers will establish robot resource organizations to manage nonhuman workers; 77% of retailers plan to deploy AI by 2021, with the deployment of robotics for warehouse picking as the No. 1 use case [Gartner] By 2024, AI, virtual personal assistants, and chatbots will replace almost 69% of the manager's workload [Gartner] "Supervised machine learning doesn't live up to the hype. It isn't actual artificial intelligence akin to C-3PO, it's a sophisticated pattern-matching tool… Rather than seeing exponential improvements in the quality of AI performance (a la Moore's Law), we're instead seeing exponential increases in the cost to improve AI systems"--Stefan Seltz-Axmacher, founder, Starsky Robotics "…why are we holding our hands behind our back trying to build AI without mechanisms that infants have?"--Gary "We haven't really gone to great depth with deep learning yet. We've had a limited amount of training data so far. We've had limited structures with limited compute power. But the key point is that deep learning learns the concept, it learns the features. "…such capabilities [as "deepfake" transformation of the human face] were called image processing 15 years ago, but are routinely termed AI today.

"We build models and machines in AI that are more complicated than we can understand" Only 13% of finance organizations are using artificial intelligence, analytics, and automation to transform multiple processes across their enterprises; when it comes to meeting business expectations to generate actionable insights from company data, not a single survey respondent states that their function can do so significantly; none of the executives say they have a structured way to generate predictive insights to meet businesses' changing and varied demands; no one is using AI, analytics, and automation to fundamentally reimagine their finance function [Genpact survey of 500 CFOs and senior finance executives] The Indiana Donor Network can immediately spot anomalies in its donor network and boost contributions. The machine learning-based system from Sisense Inc. can spot, for example, when a hospital that regularly produces organ donations doesn't deliver any in a particular week. After an anomaly is spotted, the organization can arrange a meeting the next day with the hospital to review protocols. Before, a problem in the system would have come to light 30 to 45 days after the fact. I want to emphasize, though, that we haven't solved language understanding yet in any satisfying way"--Sam Bowman "This is a new period in the world's history--we build models and machines in AI that are more complicated than we can understand"--Jason Yosinski, co-founder of Uber AI Labs

Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the growing respect for data and its uses by businesses everywhere and the increasingly positive--but still mixed--attitudes towards AI by US consumers. The second wave of AI, right now, is soon going to fail because too much trickery and even self-trickery is used"--Simone Teufel, University of Cambridge "What's happening right now is not'AI.' That was an intellectual aspiration and that's still alive today as an aspiration… the dreams and aspirations are five hundred years from now--that's like the Greeks sitting there and saying it would be neat to get to the moon someday. We have no clue how the brain does computation"--Michael I. Jordan, University of California, Berkeley

Only 22% of CRM users say AI meaningfully helps them a lot at work and only 12% actually use a specific AI-based tool; 11% say AI in their CRM allows them to focus on high-value customers and 36% CRM think AI is a strong value for the money spent on it [Dynata and Freshworks survey of 501 U.S. CRM users] Stanford's Michael Webb has developed a new methodology to estimate the impact of AI on jobs: Matching patent applications with job descriptions, he found that "AI exposure is highest for high-skilled occupations, suggesting that AI will affect very different people than software and robots," concluding that "high-wage occupations are relatively more exposed to AI than low-wage occupations" [The Impact of Artificial Intelligence on the Labor Market] More than 60% of Americans believe the government and companies collect data about them daily; more than 80% feel they have little control over the data collected about them by the government and companies and believe the risks of this data collection outweigh the benefits; 59% say they have little or no understanding regarding what is done with the data collected about them by companies and 78% have similar lack of understanding about the government's data collection; 70% say their personal data is less secure than 5 years ago [Pew Research Center] Over 74% of consumers are still confused about how their data is handled; over 49% still don't trust the privacy policies that businesses have shared; 32% of consumers are "Privacy Actives"--they care about privacy, are willing to act to protect it, and have already acted by switching companies or providers based on their data-sharing practices. European startups pursuing some kind of AI-related product or service are on track to raise $4.9 billion in 2019, up from $3.2 billion in 2018. Funding this year for robotics will reach $1.4 billion and for deep learning $320 million [Venturebeat.com] The market for virtual digital assistants (VDAs) will grow from $1.3 billion in 2018 to more than $8.9 billion in 2025 [Tractica]

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the role of augmented intelligence, combining human intelligence with artificial intelligence to produce better results in cybersecurity defense and in getting more business value from the use of IoT data. Two thirds of UK financial institutions report they use machine learning (ML) in some form; ML is most commonly used in anti-money laundering (AML) and fraud detection as well as in customer-facing applications (e.g. Analysis of about 150 Federal departments and agencies identified 171 different uses of machine learning. Two of the leading agencies were the Securities and Exchange Commission and the Social Security Administration. The SEC uses machine learning to help identify scammers who may engage in insider trading.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted workers' positive attitudes toward AI and robots, challenges in implementing enterprise AI, the perceived benefits of AI in financial services, and the impact of AI on the business of Big Tech. Google's Natural Language Processing (NLP) model BERT was added to its set of search algorithms and will help it better understand one in 10 searches in the U.S. in English ("With the latest advancements from our research team in the science of language understanding--made possible by machine learning--we're making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search") [Google Keyword] Across more than 18,500 tasks, for each occupation, on average, workers were asked to perform 3.7 fewer tasks in 2017 than seven years earlier. When looking at the impact of AI and machine learning on tasks across seven years, the data show that among tasks that are more suitable for machine learning (e.g., scheduling, credential validation), workers, by occupation, were asked to perform 4.3 fewer tasks. Conversely, among tasks that are less suitable for machine learning (e.g., design, industry knowledge), workers, by occupation, were asked to perform 2.9 fewer tasks. This reflects a 46% larger decline in demand for tasks that are more likely to be suitable for machine learning, compared to those that are less likely.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the rapidly increasing expectations regarding the business benefits of AI and the low incidence of business gains so far; the increasing adoption of AI by businesses worldwide and the challenges in its implementation and integration with exiting processes; and how companies respond to AI by both reducing and training their workforce. The report estimated the combined AI spending from large-capitalization financial institutions at more than $150 billion annually. In the past two years, BB&T Corp. has embraced a digital-first approach to plugging in artificial intelligence and robotics into its back-office, customer-service and compliance operations. That should eclipse the 1,281 companies that raised $16.8 billion in all of 2018, according to the 3Q 2019 PitchBook-NVCA Venture Monitor [VentureBeat] "The values of AI designers or the purchasing administrators are not necessarily the values of the bedside clinician or patient. Those value collisions and tensions are going to be sites of significant ethical conflict"--Danton Char, assistant professor of anesthesiology, perioperative and pain medicine at Stanford University Medical Center "I don't yet fully subscribe to the view that the machine is completely autonomous and operates without human intervention. At least as of today, and probably the foreseeable future, the AI machine is just another tool"--Andrei Iancu, director of the U.S. Patent and Trademark Office, speaking about recognizing AI systems that develop new products as inventors "If leaders think about AI like a balance sheet, then they're missing the point. You need to get emotional attachment to the disruptive nature that it can bring"--Werner Boeing, CIO, Roche Diagnostics "The major upside for us is driving more engagement….Right behind that is the ability to monetize this and generate incremental revenue for us and for our clubs….This data's going to be hugely valuable"--Dave Lehanski, NHL senior vice president of business development and global partnerships

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the growth in consumers' acceptance of chatbots, especially for help with routine tasks; questions about medical AI algorithms missing the worst patient outcomes; and new predictions for 2020 and beyond about the future of AI and work. Manpower France collects 1.3 million invoices from 80,000 companies annually. After nine months of testing Sidetrade's Aimie, a traditional machine learning-based tool, Manpower found that its collections increased 12% [Fortune] The first eight IT teams at Fannie Mae to receive Moogsoft's AIOps tool have seen a 35% reduction in IT incidents over the past 12 months; the teams using the AIOps tool have cut the time needed to resolve problems by between 25% and 75%, depending on the issue; Fannie expects that when it deploys the AI system to all business units and the system gets better at pinpointing root causes, monthly incidents will decline by 50% to 60% over the next year [WSJ] Familial hypercholesterolaemia or FH is a common genetic disorder that carries a 20-times higher risk for life-threatening cardiovascular disease, but today less than 10% percent of the 1.3 million Americans born with FH are diagnosed. The FIND FH screening algorithm was trained on data from 939 clinically diagnosed individuals and 83,136 individuals presumed free of FH. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174,000 individuals).

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the rapidly increasing expectations regarding the business benefits of AI and the low incidence of business gains so far; the increasing adoption of AI by businesses worldwide and the challenges in its implementation and integration with exiting processes; and how companies respond to AI by both reducing and training their workforce. The report estimated the combined AI spending from large-capitalization financial institutions at more than $150 billion annually. In the past two years, BB&T Corp. has embraced a digital-first approach to plugging in artificial intelligence and robotics into its back-office, customer-service and compliance operations. That should eclipse the 1,281 companies that raised $16.8 billion in all of 2018, according to the 3Q 2019 PitchBook-NVCA Venture Monitor [VentureBeat] "The values of AI designers or the purchasing administrators are not necessarily the values of the bedside clinician or patient. Those value collisions and tensions are going to be sites of significant ethical conflict"--Danton Char, assistant professor of anesthesiology, perioperative and pain medicine at Stanford University Medical Center "I don't yet fully subscribe to the view that the machine is completely autonomous and operates without human intervention. At least as of today, and probably the foreseeable future, the AI machine is just another tool"--Andrei Iancu, director of the U.S. Patent and Trademark Office, speaking about recognizing AI systems that develop new products as inventors "If leaders think about AI like a balance sheet, then they're missing the point. You need to get emotional attachment to the disruptive nature that it can bring"--Werner Boeing, CIO, Roche Diagnostics "The major upside for us is driving more engagement....Right behind that is the ability to monetize this and generate incremental revenue for us and for our clubs....This data's going to be hugely valuable"--Dave Lehanski, NHL senior vice president of business development and global partnerships

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted US consumers' interest in AI working alongside their physicians, the current porn-heavy state of deepfakes, the potential business benefits of Robotic Process Automation (RPA), and the impact of automation on incomes and employment. The internet is home to at least 14,678 deepfakes, according to a new report by DeepTrace. Funds run by computers that follow rules set by humans account for 35% of America's stockmarket, 60% of institutional equity assets and 60% of trading activity. Exchange-traded funds (etfs) and mutual funds automatically track indices of shares and bonds--in September 2019 these vehicles had $4.3trn invested in American equities, exceeding the sums actively run by humans for the first time [The Economist] Only 16% expect to reduce training budgets; 52% of the U.S. employees surveyed believe they have the necessary skills to be successful in an AI-enabled workplace, 20% saying they do not possess the right skills, and 28% reporting they aren't sure; 25% of employers and 20% of employees see a definite gap in workers' skills; 63% of U.S. employees expressed willingness to use a virtual or digital assistant to help them self-manage tasks and deadlines [Genesys surveys of 303 employers and 1,001 employees] Only 29% of finance departments that have deployed Robotic Process Automation (RPA) have utilized the technology for financial reporting; the average amount of avoidable rework in accounting departments can take up to 30% of a full-time employee's overall time. The first comprehensive review of studies published since 2012 comparing analysis of medical images by healthcare professionals and deep learning systems found humans and machines are on a par.