Moderation Team
Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI found that
- 70% of Americans would not trust an autonomous vehicle but 72% of Chinese consumers would trust one;
- Only 4% of U.S. executives plan to deploy AI enterprise-wide in 2020, down from 20% last year;
- Only 26% of U.S. executives say they have put measures into place to mitigate potential AI bias;
- Hiring in the US for “Artificial Intelligence Specialist” has grown 74% annually in the past 4 years;
- Global private AI investment was over $70 billion in 2019.
AI consumer adoption
70% of Americans would not trust an autonomous vehicle but 72% of Chinese consumers would trust one; even if the car could drive itself, 88% of American consumers still want their own exclusive car, rather than access to a pool of ‘robotaxis’ (82% of German consumers, 76% of French consumers); “I would like to be one of the first to try an autonomous vehicle”—China 28%, France 15%, UK 9%, Germany 11%, US 13%; “I would be very unlikely to use an autonomous car”—USA 40%, Germany 44%, UK 33%, France 29%, China 4% [OC&C survey of 10,029 consumers in 5 countries]
AI business adoption
Only 4% of U.S. executives plan to deploy AI enterprise-wide in 2020, down from 20% last year; 42% investigating use, 23% report pilots within discrete areas, 18% already implemented in multiple areas, 13% plan to deploy in multiple areas; over 90% of executives surveyed believe that AI offers more opportunities than risks, and nearly half are expecting AI to disrupt either their geographical markets, the sectors in which they operate, or both [PwC survey of 1,062 US business executive]
58% of large companies surveyed report adopting AI in at least one function or business unit in 2019, up from 47% in 2018; only 19% of large companies surveyed say their organizations are taking steps to mitigate risks associated with explainability of their algorithms, and only 13% are mitigating risks to equity and fairness, such as algorithmic bias and discrimination [2019 AI Index]
60% of business executives do not feel that their organizations are fully aligned on how they should develop and use AI; more than 70% of executives say that their companies have adopted AI in the last three years; only 26% said they have put measures into place to mitigate potential AI bias; only 25% said they disclose the data AI collects and what is done with it; only 16% said they have a dedicated committee within their organizations to oversee AI use; only 13% said they identify intelligent agents or chatbots as non-human entities [GLG survey of 160 C-suite executives in financial services, healthcare, and consulting]
22% of business decision-makers said their companies have been in production with machine learning for a year; 50% spend between 8 and 90 days deploying a single machine learning model; pain points included scale (33%), version control and model reproducibility (32%), and getting executive buy-in (26%) [Algorithmia survey of 750 business decision-makers]
54% of UK senior decision makers report their business currently uses AI (i.e., chatbots, virtual assistants, Natural Language Processing, facial recognition) for customer service departments, compared to 97% in the Netherlands, 86% in France and 81% in Germany; across all countries surveyed, chatbots (37%), NLP (34%) and Robotic Process Automation (31%) were the most popular AI technologies for businesses to be adopting to improve their customer service [Freshworks survey of over 800 senior decision makers in customer service departments]
22% of U.S. healthcare organizations use a software platform that provides AI capability, an eight-point increase from 2017; 31% said they plan to have AI capability within the next three years [HealthLeaders Media]
AI (39%) and big data (23%) are expected to disrupt and transform the pharma sector the most over the next two years and these technologies will continue to dominate as investment targets in the near future [Global Data worldwide survey of Pharma business executives]
The Future of Work
Hiring in the US for “Artificial Intelligence Specialist” has grown 74% annually in the past 4 years; #2 has been “robotics engineer” (40%) and #3—”Data Scientist” (37%) [Third Annual LinkedIn U.S. Emerging Jobs Report]
In the US, the share of AI jobs grew from 0.3% in 2012 to 0.8% of total jobs posted in 2019; Singapore, Brazil, Australia, Canada and India experienced the fastest growth in AI hiring from 2015 to 2019; in 2018, over 60% of AI PhD graduates went to industry, up from 20% in 2004; in 2018, over twice as many AI PhD graduates went to industry as took academic jobs in the US; AI faculty leaving academia for industry in the US continues to accelerate, with over 40 departures in 2018, up from 15 in 2012 and none in 2004 [2019 AI Index]
The “under-appreciated” workforce—experienced workers with long tenures at their companies, aged 50 and above—are estimated to have contributed $7.6 trillion to U.S. economic activity in 2015, set to jump to over $13.5 trillion by 2032. Yet, those employees also face the threat of having their work replaced by machines, with older workers in the U.S. doing jobs that are on average 52% automatable. However, a rapidly aging population and falling birthrate means retraining this workforce is vital for the success of many companies [Mercer, Oliver Wyman, Marsh & McLennan Advantage]
AI Research
Between 1998 and 2018, the volume of peer-reviewed AI papers has grown by more than 300%; in 2018, over 21% of graduating Computer Science PhDs specialize in Artificial Intelligence/Machine Learning; progress on some broad sets of natural-language processing classification tasks, as captured in the SuperGLUE and SQuAD2.0 benchmarks, has been remarkably rapid; performance is still lower on some NLP tasks requiring reasoning, such as the AI2 Reasoning Challenge, or human-level concept learning task, such as the Omniglot Challenge; time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July 2019 and the cost to train such a system has fallen similarly; prior to 2012, AI results closely tracked Moore’s Law, with compute doubling every two years; post-2012, compute has been doubling every 3.4 months [2019 AI Index]
In a field experiment, researchers tried to assess who could generate a higher profit for a business-to-business company that sells aluminum—humans or machines? While in most cases, the prices recommended by an AI system led to higher profits, salespersons did better when pricing for quotes or clients with unique or complex characteristics [Yael Karlinksy-Shichor and Oded Netzer]
AI funding
In 2019, global private AI investment was over $70 billion, with AI-related startup investments over $37 billion, M&A $34 billion, IPOs $5 billion, and Minority Stake valued around $2 billion; investment in AI startups worldwide continues its steady ascent—from a total of $1.3 billion raised in 2010 to over $40.4 billion in 2018 (with $37.4 billion in 2019 as of November 4th); funding has increased at an average annual growth rate of over 48%; Autonomous Vehicles (AVs) received the largest share of global investment over the last year with $7.7 billion (9.9% of the total), followed by Drug, Cancer and Therapy ($4.7 billion, 6.1%), Facial Recognition ($4.7 billion, 6.0%), Video Content ($3.6 billion, 4.5%), and Fraud Detection and Finance ($3.1 billion, 3.9%) [2019 AI Index]
AI in healthcare startups raised almost $1.6 billion across 103 financing rounds in the third quarter of 2019 (including $550 million for Babylon Health), making it the top-funded AI sub-sector [CB Insights]
The Life of Data, the fuel for AI
America’s most cyber insecure cities: Las Vegas, Houston, New York, Miami-Fort Lauderdale, Harrisburg-Lancaster-Lebanon-York; America’s least vulnerable cities: Salt Lake City, St. Louis, Seattle-Tacoma, Austin, Albuquerque-Santa Fe [Coronet analysis of data from over 93 million security events over 12 months in the top 50 U.S. metropolitan regions]
31% of enterprises had experienced a data breach within the past two years; 27% either do not currently comply with national or global mobile device protection regulations or are completely unaware if they do; personal data collection and misuse is a major concern: 41% in the US, 69% in Canada, 70% in the UK, 72% in France, and 78% in Germany [SOTI, IQPC, and Enterprise Mobility Exchange]
There was a 29% increase in suspected online retail fraud during the start of the 2019 holiday shopping season compared to the same period in 2018, and a 60% increase in suspected e-commerce fraud during the same period from 2017 to 2019 [iovation analysis of online retail transactions between Thanksgiving and Cyber Monday over the last three years]
Data is Eating the World
In Q3 2019, mobile data traffic grew 68% annually. The high growth rate continues to be influenced by the increased number of smartphone subscriptions in India and increased data traffic per smartphone per month in China. In general, traffic growth is being driven by both the rising number of smartphone subscriptions and an increasing average data volume per subscription, fueled primarily by more viewing of video content [Ericsson Mobility Report]
AI market forecasts
The AI market in China will reach $11.9 billion by 2023, up from $4.25 billion in 2020 [IDC and QbitAI]
The enterprise Virtual Digital Assistant software market will reach $8.9 billion in 2025, up from $1.3 billion in 2018 [Tractica]
The AI in agriculture market will reach $2,015.7 million by 2024, up from $578 million in 2019 [BIS Research]
AI quotable quotes
“The number of parameters in a neural network model is actually increasing on the order of 10x year on year. This is an exponential that I’ve never seen before and it’s something that is incredibly fast and outpaces basically every technology transition I’ve ever seen”—Naveen Rao, Intel
“Humans are one more reason why the Waze map and app is so accurate, compared to any other navigational app on the market. It is the human factor, and you would truly not get that with AI”—Chad Richey, volunteer Waze map editor
“We should also keep in mind that explainability has limits. After all, human decisions aren’t always explainable either”—Andrew Ng
“I think many people in the lab, including Yann [LeCun], believe that the concept of ‘AGI’ is not really interesting and doesn’t really mean much. On the one hand, you have people who assume that AGI is human intelligence. But I think it’s a bit disingenuous because if you really think of human intelligence, it is not very general. Then other people project onto AGI the idea of the singularity—that if you had an AGI, then you will have an intelligence that can make itself better, and keep improving. But there’s no real model for that. Humans can’t make themselves more intelligent. I think people are kind of throwing it out there to pursue a certain agenda”—Jerome Pesenti, VP of Artificial Intelligence, Facebook
“…we can’t predict the future. That should be common sense. But we seem to have decided to suspend common sense when AI is involved”—Arvind Narayanan, Princeton University
“In the past decade, we’ve figured out how to build artificial neural nets that can achieve superhuman performance at almost any task for which we can define a loss function and gather or create a sufficiently large dataset. While this is unlocking a wealth of valuable applications, it has not created anything resembling a ‘who’”—Blaise Aguera y Arcas, Google
Source: www.forbes.com
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