Exploring the Intersection of Machine Learning and Analytics Sisense

When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play the Logical Song by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?', This isn't OLAP, it is a learning algorithm that grows smarter and more efficient over time and as more unique queries accumulate. It learns to identify the reusable chunks within each query, and to use these as a knowledge base for future reference".