Recently, McKinsey released a global survey report on the state of AI, which is the third consecutive year the report has been released. Interviews with executives and surveys of practitioners found that the gap between companies that apply AI and those that do not may widen.
The survey report shows that the adoption of AI is more prevalent in technology and telecommunications than in other industries, followed by automotive and manufacturing. More than two-thirds of respondents said the adoption of AI has increased revenue, but less than a quarter saw a significant impact.
The McKinsey State of AI report, along with questions about AI adoption and implementation, looked at companies whose AI adoption led to a 20% or more increase in EBIT (earnings before interest and taxes) in 2019. These companies are likely to be efficient in rating senior executives and more likely to hire data scientists than others.
High-performing companies are also more likely to have a strategic vision and AI roadmap than other companies with ratios that differ by 20 to 30 percent or more, deploying AI models using AI or needing to use synthetic data when they encounter a century of insufficient data volumes. These results appear to be in line with an Altimeter Group survey conducted by Microsoft in early 2019, which found that half of high-growth businesses plan to implement AI within the next year.
If there’s anything surprising in the report, it’s that only 16 percent of respondents said their companies have moved deep learning projects beyond the experimental stage. (This is the first year McKinsey has focused on the deployment of deep learning.)
Equally surprising, the report shows, companies have made little progress in addressing the risks associated with AI deployments. Compared with responses submitted last year, companies that took steps to mitigate such risks saw an average 3% increase in addressing 10 different risks, from national security and physical safety to compliance and fairness.
Cybersecurity is the only risk that most respondents say their companies are grappling with. There are several categories that believe AI risks associated with companies are declining, including the equity and equity areas, which fell from 26% in 2019 to 24% in 2020.
“While some risks such as personal safety apply only to specific industries, it is difficult to understand why a high percentage of respondents do not recognize general risks.” McKinsey partner Roger Burkhardt “It is especially surprising to see little mitigation or improvement in this risk, given concerns about racial bias and other examples of discriminatory treatment, such as age-based targeting in job advertisements on social media,” said the report.
Not surprisingly, the survey found that certain industries have seen increased levels of automation during the pandemic. This is true in industries such as agriculture, construction, meatpacking and shipping, VentureBeat found.
“Most high-performing respondents said their organizations have increased investment in AI in every major business function in response to the pandemic, while less than 30% of other respondents said the same,” the report reads. .”
McKinsey’s 2020 State of AI Global Survey, conducted online from June 9 to June 19, drew nearly 2,400 results, with 48% of respondents saying their companies use some form of AI. A 2019 McKinsey survey of about the same number of business leaders found that while nearly two-thirds of companies reported increased revenue due to their use of AI, many were struggling to expand their use .
Another state of AI
A month before McKinsey released its business survey report, Air Street Capital released its State of Artificial Intelligence report, which is now in its third year. The London-based venture capital firm found that the AI industry is popular when it comes to corporate funding, but its report said the concentration and computing of AI talent was “a huge problem”.
Other serious issues identified by Air Street Capital include the ongoing brain drain from academia to industry, and problems with the reproducibility of models created by smaller firms. A team of 40 Google researchers also recently found that insufficient specification is a major obstacle to machine learning.
Many of the findings in the report, along with a new analysis of AI research papers, found that the increasing concentration of deep learning activity among big tech companies, industry leaders and elite universities is fueling inequality. The team behind the analysis said the widening “computing divide” could be partly addressed by implementing a national research cloud.
As the year ends, we can expect more reports on the state of machine learning. The State of AI report, released over the past two months, shows various challenges, but says AI can help businesses save costs, generate revenue and follow proven best practices to succeed. At the same time, researchers are looking to address the huge variety of opportunities associated with deploying AI.
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