This article was written by DataRobot and originally appeared on the DataRobot Blog here: https://www.datarobot.com/blog/the-true-impact-of-ai-can-be-found-on-a-more-intelligent-tomorrow/
If you’ve gone shopping, done some online banking, or had an online health consultation recently, chances are that you have come into contact with or have been aided by AI. Not only is AI becoming more common in our homes and everyday lives, companies are also trying to understand what AI means for their businesses and their customers and how they can use their data to become AI-driven. As more enterprises adopt AI, however, there is more noise around this burgeoning technology.
To cut the hype and explore the true impact of AI on the world, we launched the More Intelligent Tomorrow podcast, hosted by Ben Taylor, Chief AI Evangelist at DataRobot, and Ari Kaplan, Director of AI Evangelism and Strategy at DataRobot. The podcast features candid conversations with industry innovators to explore the potential impact of AI on the world around us. It covers what the future may look like, technology trends, lessons learned, organizational transformation experiences, best AI use cases, career advice, and more.
In our Quarterly AI Thought Leadership Digest, we will share highlights from some industry thought leaders who were guests on the podcast in this past quarter. In these episodes, we covered a wide variety of exciting and timely topics, such as ethical and safety guidelines for AI and data, the democratization of data, the pitfalls of shiny-new-toy syndrome, and the effects of AI on healthcare and other industries.
The Quarterly Digest features almost a dozen industry thought leaders from global enterprises, governments, and DataRobot, as well as their ideas on what it takes to create successful AI projects in 2021 and beyond. Many highlight the respective arts of transparent storytelling and explainable AI to implement successful AI programs. They also note that a lot of use cases are not automation. They’re augmentation, which means that the end result is not the model but the model’s output and the story that it helps tell.
Transparency helps people in your organization trust the model’s output and use it to make strategic and often life-altering decisions. After establishing transparency, you can start figuring out how you’re going to ensure that models are performing well, that they’re continuously updated as new data comes in, and that the relevant people on your teams can own the fundamental pieces of this process. For all this to fall into place, you need an MLOps strategy and infrastructure.
Refreshingly, the More Intelligent Tomorrow podcast isn’t just about models, algorithms, and the latest advancements in AI. Quite often, our guests emphasize the importance of strong family ties and a healthy routine. Chris Lynch put it best: “Vacation is an investment in future performance.”
To hear more about AI ethics, data safety and democratization, the perils of shiny-new-toy syndrome, AI and healthcare, and how to be successful and maintain a sustainable work-life balance, check out Datarobot.com/podcast or http://datarobot.buzzsprout.com/. You can also listen everywhere you already enjoy podcasts, including Apple, Spotify, Stitcher, and Google.