The year 2020 may have been one of turmoil and uncertainty across the globe, but artificial intelligence remained on a steady course of growth and further exploration — perhaps because of the Covid-19 crisis. Healthcare was a big area for AI investment, and concerns about diversity and ethics grew — but little action has been taken. Most surprisingly of all, while AI job growth accelerated across the world, it flattened in the US.
These are among the key metrics of AI tracked in the latest release of the AI Index, an annual data update from Stanford University’s Human-Centered Artificial Intelligence Institute. The index tracks AI growth across a range of metrics, from degree programs to industry adoption.
Here are some key measures extracted from the 222-page index:
AI investments rising: The report cites a McKinsey survey that shows the Covid-19 crisis had no effect on their investment in AI, while 27% actually reported increasing their investment. Less than a fourth of businesses decreased their investment in AI.
AI jobs grow worldwide, flatten in the US: Another key metric is the amount of AI-related jobs opening up. Surprisingly, the US recorded a decrease in its share of AI job postings from 2019 to 2020-the first drop in six years. The total number of AI jobs posted in the US also decreased by 8.2% from 2019 to 2020, from 325,724 in 2019 to 300,999 jobs in 2020. This may be attributable to the mature market in the US, the report’s authors surmise. Globally, however, demand for AI skills is on the rise, and has grown significantly in the last seven years. On average, the share of AI job postings among all job postings in 2020 is more than five times larger than in 2013. In 2020, industries focused on information (2.8%); professional, scientific, and technical services (2.5%); and agriculture, forestry, fishing, and hunting (2.1%) had the highest share of AI job postings among all job postings in the US.
AI investment in healthcare increased significantly: The product category of “drugs, cancer, molecular, drug discovery” received the greatest amount of private AI investment in 2020, with more than $13.8 billion, 4.5 times higher than 2019, the report states. “The landscape of the healthcare and biology industries has evolved substantially with the adoption of machine learning,” the report’s authors state. “DeepMind’s AlphaFold applied deep learning technique to make a significant breakthrough in the decades-long biology challenge of protein folding. Scientists use ML models to learn representations of chemical molecules for more effective chemical synthesis planning. PostEra, an AI startup used ML-based techniques to accelerate COVID-related drug discovery during the pandemic.”
Generative everything: “AI systems can now compose text, audio, and images to a sufficiently high standard that humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained applications of the technology. That promises to generate a tremendous range of downstream applications of AI for both socially useful and less-useful purposes.”
AI has a diversity and ethics challenge: In 2019, 45% new U.S. resident AI PhD graduates were white — by comparison, 2.4% were African American and 3.2% were Hispanic, the report states. Plus, “despite growing calls to address ethical concerns associated with using AI, efforts to address these concerns in the industry are limited. For example, issues such as equity and fairness in AI continue to receive comparatively little attention from companies. Moreover, fewer companies in 2020 view personal or individual privacy risks as relevant, compared with in 2019, and there was no change in the percentage of respondents whose companies are taking steps to mitigate these particular risks.”
Computer vision has become industrialized: “Companies are investing increasingly large amounts of computational resources to train computer vision systems at a faster rate than ever before. Meanwhile, technologies for use in deployed systems-like object-detection frameworks for analysis of still frames from videos-are maturing rapidly, indicating further AI deployment.”
AI conference attendance up, virtually: An important metric of AI adoption is conference attendance. “That’s way up. If anything, Covid-19 may have led to a higher number of people participating in AI research conferences, as the pandemic forced conferences to shift to virtual formats, which in turn led to significant spikes in attendance,” the survey’s authors contend.
More and more information and research is available: The number of AI journal publications grew by 34.5% from 2019 to 2020 — a much higher percentage growth than from 2018 to 2019 (19.6%), the report’s authors state. “In just the last six years, the number of AI-related publications on arXiv grew by more than six-fold, from 5,478 in 2015 to 34,736 in 2020. AI publications represented 3.8% of all peer-reviewed scientific publications worldwide in 2019, up from 1.3% in 2011.”