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The Future of Artificial Intelligence in a Post-COVID-19 World

In this special guest feature, Betsy Hilliard, Principal Scientist at Valkyrie, offers three emerging trends showing how AI will play a major role in a post-COVID world and shape the business landscape moving forward. Valkyrie is a science-driven consulting firm that aims to solve organizational and global challenges through AI and machine learning. Betsy’s professional expertise spans across several domains including financial services, online ad auction markets, government policy analysis and optimization. Previously, she worked as a Data Scientist at USAA Bank and RIIPL (a Rhode Island policy research lab), and interned at Google, Yahoo! and Oak Ridge National Laboratory. Betsy has a Master’s in Computer Science from Brown University where she was a research assistant and published work on multi-agent learning in economic markets and Reinforcement Learning for human-agent collaboration. She studied Computer Science and Economics at Brandies University.

Over the past several years, artificial intelligence (AI) has frequently been cited as an emerging technology that improves efficiency across a multitude of industries. Crucially, AI relies on large amounts of historical data from the systems it was built to improve. So, what happens when those industries and everyday life are dramatically transformed in a matter of weeks and suddenly nobody behaves the same way they used to?

COVID-19 has undeniably altered billions of lives across the globe. While many are longing for a “return to normal,” many aspects of society will never quite return to the way things were and the shock has permanently altered the economic landscape. Communities, schools, businesses and global infrastructures will still have to adapt to a post-COVID world once a vaccine is widely available and administered.

What does this mean for the future of AI in industry?

Below are three emerging trends showing how AI will play a major role in a post-COVID world and shape the business landscape moving forward.

A need for planning under uncertainty

Before the pandemic, industries could rely on history to help them plan for the future. More advanced companies could benefit from the efficiencies gained by using machine learning (ML) techniques to more accurately predict their future, but most industries did not require advanced analytics to make many business decisions. As the world and society has changed, industries have found that they can no longer assume that what worked last month will work this month. Artificial Intelligence can be used to detect and account for behavior changes and predict the effects of policies. The same techniques being used to figure out when to reopen schools can also be used to predict and manage spread at large events, in restaurants and more.

Growth of AI and data science in old-guard industries

Some analog-heavy industries such as mining, energy, manufacturing and food production (chicken processing, meat companies) will embrace AI tools such as computer vision and robotics out of pure necessity, but other industries may find that they suddenly have the opportunity to implement AI as well. As society has heavily leaned on technology to continue day-to-day functionality, many companies have digitized both their internal processes as well as the products and services they produce. Companies that make the digital leap will be more likely to survive the turmoil of a global pandemic as people look for non- and low-contact ways to interact with businesses. Many old-guard businesses have had difficulty implementing AI because some part of their processes or product delivery isn’t digitized. These companies and industries may realize an unexpected benefit of digitizing: the opportunity to implement Analytics and AI.

Democratization of AI and machine learning

The growing infrastructure and accessibility of AI and ML will allow for disruption in a wide variety of industries, by companies of all sizes. The democratization of AI and ML and accessibility of tools is particularly beneficial for smaller companies that are trying to innovate and disrupt their industry. As the techniques and technical infrastructure required to implement machine learning become more standardized, the effort and expertise needed to get a working ML system running will be more focused on identifying the most valuable challenge to solve, understanding the domain and data, and analyzing system performance.

AI’s inherent trait is to optimize efficiency through the collection of data. As the economic shock of the pandemic forces industries to move to digital solutions and also account for more uncertainty there will be an abundance of data and challenges for AI to solve. While we may never return to what normal used to look like, AI is primed to welcome the world to a new perspective.

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