The AI Revolution Is Here – A Podcast And Interview With Nate Yohannes

Artificial Intelligence (AI) is the catalyst for the fourth industrial revolution, the most significant technological advancement thus far. AI has the potential to solve incredible challenges for all of humanity, including climate change, education, design, customer experiences, governance, and food. That said, among the biggest concerns regarding AI is the potential for unexpected and unintended consequences in building and deploying AI products. According to Nate Yohannes, Principal Program Manager for Mixed Reality and AI Engineering at Microsoft, having appropriate representation at decision-making tables when building AI products is critical to help prevent these unfortunate scenarios. From his perspective, representation will go a long way toward preventing the unexpected consequences of AI products, and it will also guarantee that the products that are created will have the widest consumer base possible: everyone.

Listen to this podcast for an inspiring conversation about how to make the possible a reality by approaching AI with a new mindset that takes into consideration its full potential for improving the lives of everyone.

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3 reasons to modernize your data estate ​

Organizations that modernize their data estate can reap major benefits, including enhanced security and compliance, significant cost savings, and the ability to empower employees via business

insights and advanced analytics capabilities. Organizations that don’t, on the other hand, risk putting themselves behind the curve by missing out on game-changing cost benefits, leaving their businesses open to potential vulnerabilities, and by not taking advantage of modern analytics capabilities.

Read this infographic to learn how businesses are taking advantage of the latest technologies by modernizing their datasets to create transformational benefits throughout their organizations, and discover how they’re doing so by leveraging Microsoft Azure.

View: 3 reasons to modernize your data estate ​

Mercy Housing keeps residents safe at home using Microsoft Teams


When the pandemic ushered in a new reality of social distancing, Mercy Housing was able to continue supporting the residents and communities it serves while keeping everyone safe using Microsoft Teams. This customer story video outlines when COVID-19 created a tremendous need for a tool that can maintain personal connection, Teams has allowed the company to maintain reaching residents and serving its communities, growing the business while working remotely, all while supporting highly secure environments for remote work.

Systems Imagination uses SQL Server 2019 Big Data Clusters to deliver cutting-edge medical insights


Systems Imagination uses big data to discover new tiers of medical insights unobtainable through conventional means. In order to gain the distributed computing power it needed, the company adopted Microsoft SQL Server 2019 Big Data Clusters. Now, it delivers results based on petabytes of data to its customers within hours while simultaneously keeping operating costs low.

ANU: Collaborative research and data sharing


Data-sharing is an immense enabler of scientific advancements. When researchers choose to make their data publicly available, they are allowing their work to contribute far beyond the original findings. Making research data more accessible also fosters transparency and trust in their work, enables other researchers to reproduce and validate their findings, and ultimately, contributes to the pace of scientific discovery by allowing others to reuse and build on top of the data. Fortunately, cloud technology has made it easier than ever to share data across major research institutions. Empowered by tools that include Microsoft Azure, researchers are relying more and more on digital collaboration and open source software to drive scientific breakthroughs.

Watch this video to learn how researchers at Australian National University (ANU) are using Azure to build on each other’s findings and avoid a siloed approach to data that leads to slower research pipelines.