This episode is the narration of the Virtualitics blog post Get started with network graphs—what they are and why they matter.

To see the visuals referenced in the podcast, go to https://blog.virtualitics.com/

When we think of AI we tend to think of predictive models and supervised learning. But AI can be applied to exploration and understanding or data right from the start, leading to better business value. Co-head of AI, Aakash Indurkhya sits down with author and data scientist, Tobias Zwingmann to talk about how hypothesis-driven exploration isn’t getting the job done and is leaving valuable insight on the table, and setting you up for underwhelming AI. Join us as we discuss how using AI to explore data (Intelligent Exploration):

• Discovers high-impact insight that you may have overlooked
• Clarifies where you should direct your AI resources by uncovering good use cases and assessing feasibility
• Helps remove the bias from your exploration

Author Tobias Zwingmann helps BI professionals, business analysts and data analysts understand high-impact areas of artificial intelligence. You'll learn how to leverage popular AI-as-a- service and AutoML platforms to ship enterprise-grade proofs of concept without the help of software engineers or data scientists.

Learn more about Virtualitics at virtualitics.com and see the slides with transcript here - https://blog.virtualitics.com/how-to-overcome-your-biases-and-get-better-results-from-data-explorati0

The future is no-code or low-code, but as more and more companies implement AI solutions and enable non-technical users,  how do you maintain accuracy and responsible use?

The answer: explainable AI paired with multi-dimensional visualizations. These combined provide a more complete picture of the story playing out in a predictive system and are a must for ensuring responsible AI applications. 

Join Caitlin Bigsby, Head of Product Marketing, as she sits down with Virtualitics’ Co-Heads of AI, Sarthak Sahu, and Aakash Indurkhya to discuss ways to incorporate visualizations into the ML lifecycle and key considerations.