From autonomous vehicles, predictive analytics applications, and facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI span dozens of industries. Regardless of the AI application, though, these use cases all have a common aspect. After implementing thousands of AI projects, experts have come to realize that despite all of the diversity in applications, AI use cases fall into one or more of seven common patterns. One of them—the pattern-matching pattern—has allowed machines to digest large amounts of data to identify patterns, anomalies, and outliers in the data, so organizations can unearth previously undiscovered insights in their datasets.

In this article, you’ll learn how pattern-matching is being put to use in today’s organizations to prevent fraud, find the best job candidates, manage inventories in times of crisis, and empower data scientists with new perspectives on how to improve critical processes.

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One of the main challenges that modern researchers encounter centers on the size of their datasets. As research projects become increasingly more digital, the vast volumes of data that research generates demands enormous raw computing power, for both storage and computational capacity, which most organizations simply didn’t have access to. Cloud technology and artificial intelligence (AI) address this need, allowing researchers to free themselves from the constraints of their physical hardware. Cloud-based approaches can also boost the speed and efficiency of publishing, increasing the impact of research publications.

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This video showcases how Allstate is transforming customer support with artificial intelligence. With Azure Cognitive Services, Allstate is capturing insights from customer calls to enable a more seamless customer experience.

For the 2018 racing season, NASCAR imposed new regulations on competitors, including limiting the number of team members allowed on the track during races. For legendary race team Hendrick Motorsports, this meant that many of the race-day crew would now need to perform their critical duties remotely from the team headquarters in Concord, NC. Hendrick Motorsports adjusted to the new regulations with technology, by making Microsoft Teams their new hub for all race communications and decision making.

Thanks to cloud technology, researchers can take advantage of faster, more advanced and more scalable resources that enable them to perform tasks that they can’t manage using in-house resources. The cloud also enables scientists to quickly run tests across a variety of platforms and virtual machines with different specifications, which can aid rapid prototyping, interoperability testing, and performance testing. A cloud solution can also provide researchers with ways to augment their existing resources by handling peaks in demand and reducing competition for local resources, when multiple teams are working toward an upcoming deadline. By reducing dependence on on-site computing, research projects can increase efficiency and scalability, and deliver greater results faster.

Watch this video to learn how Australian National University (ANU) is using Microsoft Azure to help decode more than 7 billion base pairs of DNA in the human genome and take its research further than ever before.

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