[Note: I originally posted this to troyangrignon.com and LinkedIn. This series expanded rapidly and it made sense to move it into its own blog and podcast.]

This is the beginning of a new series of articles that I'm writing on Applied Artificial Intelligence (AI) and Machine Learning (ML). My goal is to help people better understand what AI really is (and isn't), where we are now and what might happen next, and then to help answer questions about how it can be applied in the near to mid-term in their organizations. The end-goal is that hopefully this will demystify AI/ML and also let people understand where it can be applied in the real world to solve real problems.

In this series, I hope to cover some of the following, although I'm sure this outline will change over time. Think of this like a Table of contents to articles and podcast episodes as they get added over time.

  • Introduction to the series (this post)
  • Definition of AI and terms (narrow, general, super)
  • A brief history and near-term future of Artificial Intelligence (AI)
  • Where can you learn more about AI?
  • Comparing the Big 3 US-based AI as a service platforms
  • The Robotics / RPA / AI / ML / Data / IaaS Stack
  • Understanding the relationship between AI, Expert Systems, Machine Learning, Deep Learning, Algorithms, Models & Capabilities
  • A simplified view of the AI/ML stack
  • Vertical specific applications and use cases across finance, healthcare, transportation, retail, utilities, public sector, manufacturing, security, autonomous vehicles, and many other domains.
  • Functional Impact of AI on sales, marketing, customer service, back-office, etc.
  • Issues facing AI including making it "explainable", removing bias in training, and making it safe (usually referred to as AI Alignment/safety)

I hope you'll join me for the series, and the blog, and the podcast!