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Ahmed Elsamadisi, Founder & CEO of Narrator.ai – Interview Series


Ahmed Elsamadisi is the Founder & CEO of Narrator.ai,  a data intelligence company that equips decision makers with personalized actionable insights.

Ahmed started his career at Cornell’s Autonomous Systems Laboratory focusing on human-robot interaction and Bayesian data fusion as well as building algorithms for autonomous cars.

What initially attracted you to AI and data science?

I fell in love with how people make decisions.  Starting with psychology, to social engineering, and finally to how we reason about uncertainty.  This led me to dive into Bayesian mathematics and the world started making more sense.  I decided to embark on a journey to replicate how we make decisions.

You’ve had a phenomenal career including having worked at Cornell’s Autonomous Systems Laboratory, could you share some highlights from this time period?

Cornell’s ASL was a lot of fun!  From our Autonomous Car to a fleet of mobile robots, I got to experience building, programming and testing algorithms and hardware in real settings.  My favorite moment was a project I led to see if we can play a game of 20 Questions with all the students at Cornell.  The game was simple, a robot is looking for an object and it can ask Cornell students yes/no questions to help it find the item.  One tiny twist, humans can lie.

In this situation there is no real information, nothing that is absolutely true.  I worked on an algorithm that could fuse information from people and sensors to make better decisions.  This project later got picked up by Business Insider and got known as “the robot who can tell lies.”

These moments where data and algorithms can do something that you could not easily imagine, is what makes these projects phenomenal.

The idea for Narrator originated from your frustration of working with data at your previous employer WeWork. What were the issues you were facing with star schema modelling?

Every company uses a star schema for their data models.  It makes sense!  You build tables that represent a set of questions that you want to answer and then you give it to people to plot it. The challenge is that questions are constantly growing and changing and thus the series of tables you build are never enough to answer all possible questions.  The only solution is to build more tables, which causes a deviation from the source of truth.

I always google “air flow data modeling” to show people what the best case scenario of a modeling layer is and it is complex.  Hundreds of tables depending on each other.

That being said, that was the only option.  At WeWork, I talked to many unicorns and saw the same situation that we were facing.  We spent millions of dollars on data tools.  We built hundreds of models.  Data was still losing trust and failing to answer questions in a timely manner. Then every 1-2 years we would rebuild the system using the newest tools but the same approach.

If every company that implemented a star schema ended up needing to rebuild their system then there is an issue with the framework.

How did you come across a better solution to work with data?

Initially, I took inspiration from data blogs.  In a blog, a company can tell a story of a customer and an analysis without ever showing us their data model.  They use customers, doing actions in time to explain any analysis of algorithms. Effectively these are all concepts that everyone understands (a user viewed the website, then booked a meeting).  I wondered why that structure wasn’t used in data if it seems to have the potential to represent anything.  The short answer is this data structure is really not queryable by BI tools (there is nothing to JOIN on).

Our data model, which we call an activity schema, seemed to have the potential to really change the world.  It could allow every company to have 1 single data model that can answer any question. Every company could have the same data model — thus analysis and algorithms could be shared across…



Read More: Ahmed Elsamadisi, Founder & CEO of Narrator.ai – Interview Series

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