In this week’s roundup, we look at how the COVID-19 pandemic is increasing the speed of adoption for artificial intelligence (AI) and machine learning (ML). We dive into the impacts of AI on financial technology, including improvements in stock market predictions. And, lastly, we gain a better understanding of how to build trust in AI throughout an organization.
By Eric Knorr, Editor in Chief at CIO.com
A month before the coronavirus pandemic hit the U.S., 18% of IT leaders said their companies were using AI/ML technologies. Just five short months later, almost half of those surveyed said their organizations were more likely to consider implementing AI and ML as a way to decrease costs and/or increase productivity due to economic changes. While the AI revolution has long been expected, COVID-19 has undoubtedly increased the speed of it.
By Ved Raj, contributing writer for CustomerThink.com
Fintech—or financial technology—is one of the industries likely to benefit from AI and ML across all functions. Because financial institutions have troves of data dating back decades, they are well-positioned to put that data to work and make better credit and financing predictions and improve the speed and accuracy of financial transactions.
By Tommy Peterson, contributing writer for EdTechMagazine.com
Before the COVID-19 pandemic, many higher education institutions were in the process of testing AI technologies in a range of ways across campuses. However, the pandemic caused a sudden switch to remote learning and upended the way classes are taught. Even when the pandemic ends, universities are expected to adopt a blended approach to in-person and online learning—the prospect of which is making leaders reconsider the role of AI in higher education.
By Melanie Lefkowitz, contributing writer to the Cornell Chronicle
Anticipating moves in the stock market—both short-term and long-term—has historically been a tricky pursuit. And while stock analysts commonly use large datasets to inform their predictions, machine learning is exponentially increasing their ability to make more accurate and faster forecasts. This has deep implications for who has access to and will benefit from ML technologies.
By Nallen Sriamen, contributing writer for Forbes.com
One of the hurdles leaders face in implementing AI in their organizations is getting buy-in from all areas of the company, and mistrust of the data is a common reason. A key solution to this problem includes implementing a “data lake” of information from a variety of trusted sources—and not just those that confirm what executives want to believe. It is also important that the data and AI algorithms are accessible across the company and are as transparent as possible.
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