Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “…

Summary:  If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources.  Read the research, then consult with your own data scientists for a better evaluation of risk. It’s likely not as bad as you think. 

 …

Summary:  Too many solutions.  We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production.  The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.

 …

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

 …

If many of your clients don’t understand the difference between artificial intelligence (AI) and intelligent systems, you’re not alone. There’s a deeply rooted misconception about AI that isn’t going to clear up anytime soon.

AI has become a marketing buzzword and is being used interchangeably with computer algorithms that analyze data and produce a…