Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the number of manufacturing jobs eliminated by robots; why robots could replace financial analysts; the very small number of organizations not evaluating or using AI today; and the debate over the usefulness of Covid-19 contact-tracing.
AI and automation
From 1990 to 2007, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2%—each additional robot added in manufacturing replaced about 3.3 workers nationally, on average; the increased use of robots in the workplace also lowered wages by roughly 0.4% [MIT]
The number of industrial robots worldwide rose by about 65% to 2.4 million units from 2013 to 2018; in the same period, employment in the US automotive industry – the largest adopter of robots in the US – increased by 22% from 824,400 to 1,005,000 jobs [International Federation of Robotics]
59.4% of Americans believe the Covid-19 pandemic will lead to an acceleration of automated workplace technologies within the next year; the 35-44 age group most believes this, followed closely by the 65+ age group [SYKES]
Indiana University researchers found that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several dimensions; Robo-Analysts collectively produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts, consistent with them being less subject to behavioral biases and conflicts of interest; consistent with automation facilitating a greater scale of research production, Robo-Analysts revise their recommendations more frequently than human analysts and also adopt different production processes, relying less on earnings announcements, and more on the large volumes of data released in firms’ annual reports; Robo-Analysts’ recommendation revisions exhibit weaker short-window return reactions, suggesting that investors do not trade on their signals; portfolios formed based on the buy recommendations of Robo-Analysts appear to outperform those of human analysts, suggesting that their buy calls are more profitable; the results suggest that Robo-Analysts are a valuable, alternative information intermediary to traditional sell-side analysts for investment advice [SSRN]
Decision makers in the US credit 3 success factors for Robotic Process Automation (RPA) initiatives with a strong understanding of business processes automated as the top factor (70%), followed by advanced planning (63%), and having a simple workflow to automate (50%); the two leading factors causing RPA project failures are the complexity of projects (57%) and not fully understanding the intended automated process (39%); 40% believe RPA will be most valuable for improving the customer experience, followed by financial planning and decision making (38%) [ABBYY and Opinium Research survey of 400 senior decision makers in the US, UK, France and Germany]
AI enterprise adoption
33% of organizations worldwide were still evaluating AI in December 2019, down from 54% in 2018; 15% are not doing anything with AI, down from 20% in 2018; top functions in which AI is used are Research and development (48%), IT (34%), customer service (29%); top barriers to AI adoption are company’s culture (22%), identifying business use cases (20%), lack of skills/qualified people (17%), lack of data/quality issues (16%) [O’Reilly]
AI employment
The key centers employing AI professionals in the US are San Francisco (27%), New York (13%), Seattle (9%), and Los Angeles, Boston, and Washington-Baltimore (roughly 5% each) [CSET]
The Life of Data, the fuel for AI
Total citizen archivist transcribed pages rose from 196 the week of March 9, 2020, to 6,659 the week of May 4, 2020; the number of physical records in the National Archives has increased since 1991 by a factor of 3 and the number of electronic records has increased by a factor of 1,654 [National Archives]
66% of organizations are accelerating their migration of analytics to the cloud due to Covid-19; 63.9% are investing more in their data platform and analytics due to Covid-19 [Yellowbrick survey of over 1,000 enterprise IT managers and executives]
From April 6 to April 13, Google saw more than 18 million daily malware and phishing emails related to Covid-19 scams, in addition to the 240 million daily spam messages it sees related to coronavirus [The Verge]
Check Point researchers detected in April a huge increase in the number of coronavirus-related cyber attacks to an average of 14,000 a day, which is six times the average number of daily attacks [Check Point]
Israel’s Security Agency was responsible for the early discovery of around a third of all the verified Covid-19 cases in the country; some 80,072 text messages informing people that they had been in contact with a carrier were sent out [Calcalist]
The PM told Australians in April the contact tracing app was key to getting back to normal but just one person has been identified using its data [The Guardian]
Of the 50% of Americans who said they’d use a coronavirus contact-tracing app, only 17% said they would definitely use it compared to 32% who said they’d probably use it. Of the 50% who said they wouldn’t use the app, 20% were certain they wouldn’t and 30% thought they probably wouldn’t use it; 43% said they trust the tech companies responsible for creating these contact tracing tools – specifically Apple and Google; 47% trust health insurance companies and 57% trust public health agencies [Washington Post and the University of Maryland]
AI funding
Nearly 500 AI startups across 42 countries raised over $8.4 billion in Q1’20 [CB Insights]
San Francisco nets a majority of AI industry funding within the US, with 52% of a total $80.4 billion; New York follows with $9.1 billion, or approximately 11% [CSET]
AI markets
The AI market worldwide will grow from $42.8 billion in 2019 to $152.9 billion in 2023 [Analytics Insight]
The AI market worldwide will grow from $20.67 billion in 2018 to $202.57 billion in 2026 [Fortune Business Insights]
AI quotable quotes
“Soon, as medical science and computer science further converge, we will move into an era of fully autonomous AI when we may expect people to choose wearables, biosensors, and smart home detectors to keep them safe and informed. And as data quality and diversity increase from the wearables and other internet-of-things devices, a virtuous cycle of improvements will kick in. In this world a novel coronavirus could be tracked, traced, intercepted, and cut off before it got going”—Kai-Fu Lee