Investing is one of the most quantitatively intensive fields there is. Still, it is cluttered with old-school models that are simple and heuristic-based. The new-age millennial investors recognize the power of artificial intelligence. They are increasingly looking to utilize the power of AI to democratize the world of investing and get access to tools to invest, like professional Wall Street investors. Artificial Intelligence has had an enormous impact and has surpassed humans in many fields, from gaming to computer vision to self-driving cars.

If data starts at the Edge, why can't we do as much as possible right there from an AI point of view? The explosive growth in Edge devices and applications requires new thinking as to where and how data is analyzed, and insights are derived. New Edge computing options, coupled with more demanding speed-to-insight requirements in many use cases, are driving up the use of artificial intelligence (AI) and machine learning (ML) in Edge applications. Where AI and ML are applied (at the Edge or in a data center or cloud facility) is a complex matter. To get some insights into current strategies and best practices, we recently sat down with Said Tabet, Chief Architect, AI/ML & Edge; and Calvin Smith, CTO, Emerging Technology Solutions; both in the Office of the Global CTO at Dell Technologies. We discussed the growing need for AI and ML to bring sense to the large amount of Edge data that is generated today, the compute requirements for AI/ML in Edge applications, and whether such computations should be done at the Edge or in a data center or cloud facility. RTInsights: What are today's emerging trends, and how do AI and ML fit into the Edge discussion?