Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the strong state of AI surveillance worldwide, the lack of adherence to common privacy principles in companies’ data privacy statement, the growing adoption of AI by global businesses, and the perception of AI as a major risk by institutional investors.
AI surveillance and the state of data privacy
At least 75 out of 176 countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms (56 countries), facial recognition systems (64 countries), and smart policing (52 countries); technology linked to Chinese companies—particularly Huawei, Hikvision, Dahua, and ZTE—supply AI surveillance technology in 63 countries and U.S. firms’ technology—from IBM, Palantir, and Cisco—is present in 32 countries; 51% of advanced democracies deploy AI surveillance systems [Carnegie Endowment for International Peace AI Global Surveillance (AIGS) Index]
An analysis of 29 variables in 1,200 privacy statements against common themes in three major privacy regulations (the EU’s GDPR, California’s CCPA, and Canada’s PIPEDA) found that many organizations’ privacy statements fail to meet common privacy principles; less than 1% of organizations had language stating which types of third parties could access user data; only 2% of organizations had explicit language about data retention; only 32% of organizations had “readable” statements based on OTA standards [Internet’s Society’s Online Trust Alliance]
AI and the future of work
57% of technology companies do not expect technological advances will displace any of their workers in the next five years; 29% of respondents expect job displacement and 68% plan to retain workers by offering reskilling programs; software development (63%), data analytics (54%), engineering (52%), and AI/machine learning (48%) are the tech skills in highest demand [Consumer Technology Association survey of 252 tech business leaders]
Business adoption of AI
17% of 30 Global 500 companies have reported the use of AI/machine learning at scale and 30% reported selective use in specific business functions; in 3 years, 50% expect to be using AI/machine learning at scale; 26% have deployed RPA at scale across the enterprise or major functions; 65% say their use of RPA today is selective and siloed by individual groups or functions; in 3 years, 83% expect to have RPA deployed at scale; companies investing in AI report achieving on average 15% productivity improvements for the projects they are undertaking; most companies reported that their investments in AI-related talent and supporting infrastructure will increase approximately 50% to 100% in the next three years [KPMG 2019 Enterprise AI Adoption Study based on in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]
85% of organizations surveyed have a data strategy and 77% have implemented some AI-related technologies in the workplace, with 31% already seeing major business value from their AI efforts; top business functions for gaining most value from AI are sales (35%) and marketing (32%) and top technologies are machine learning (34%), chatbots (34%), and robotics (28%) [Mindtree survey of 650 IT leaders in the US and UK]
Expected business impact of AI
Top AI priorities for the next 3 to 5 years: customer and market insights that will refine personalization, driving sales and retention; back office and shared services automation to remove repetitive human tasks; finance and accounting streamlined to improve efficiency and compliance; analysis of unstructured voice and text data for specific functional use cases [KPMG 2019 Enterprise AI Adoption Study based in in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]
85% of institutional investors view AI as an investment risk that could potentially provoke societal backlash as well as geopolitical tension; 52% of the investors surveyed, who stated AI was a risk, also regarded it an opportunity, whereas 33% saw it as only a risk and 7% regard it as an opportunity only [BNY Mellon Investment Management and CREATE-Research in-depth, structured interviews with 45 CIOs, investment strategists and portfolio managers among pension plans, asset managers and pension consultants in 16 countries and a literature survey of about 400 widely respected research studies]
AI research successes
A deep learning algorithm, trained on non-imaging and sequential medical records, predicted the development of non-melanoma skin cancer in an Asian population with 89% accuracy [JAMA Dermatology]
Researchers at MIT developed a machine learning model that can estimate a patient’s risk of cardiovascular death. Using just the first fifteen minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories. Patients in the top quartile were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. By comparison, patients identified as high risk by the most common existing risk metrics were only three times more likely to suffer an adverse event compared to their low-risk counterparts [MIT CSAIL]
AI venture capital investments
U.S. AI and machine learning startups raised $6.62 billion so far in 2019, and international startups raised $6.79 in the same period. The global total for all of 2018 was $19.5 billion [Crunchbase News]
AI market forecasts
The North America AI chip market is estimated to reach $30.62 billion in 2027, up from $2.5 billion in 2018 [ResearchAndMarkets]
The Asia Pacific AI chip market is estimated to reach $22.27 billion in 2027, up from $1.03 billion in 2018 [ResearchAndMarkets]
AI quotes of the week
“An AI-equipped surveillance camera would be not a mere recording device, but could be made into something closer to an automated police officer”—Edward Snowden
“When you get into the millions, you can really start to generate the levels at which humans stop understanding the correlations, and the machines start to understand the correlations”—Ricky Knox, co-founder and CEO, Tandem Bank
“As AI gets better at performing the routine tasks traditionally done by humans, only the hardest ones will be left for us to do. But wrestling with only difficult decisions all day long is stressful and unpleasant”—Fred Benenson, former vice president of data, Kickstarter
“AI can do things previously unimaginable with the volume, velocity, variety and veracity of big data. It can deliver an edge given the information intensity of all of the processes in asset management”—Amin Rajan, CEO, Create-Research
“By 2025, a quarter of all miles driven will be driven by on-demand services”—Amy Wyron, vice president of business solutions, Gett
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