How to win an AI Hackathon, without using AI

Once “big data” is thrown into the mix, the AI solution is all but certain. But is AI always needed?

How to win an AI Hackathon, without using AI

January 30, 2020

Anyone reading a newspaper or listening to the news is led to believe that AI is the solution to all problems. From self-driving cars to detecting disease to catching fraud, there doesn’t seem to be a situation that AI can’t tackle. Once “big data” is thrown into the mix, the AI solution is all but certain. But is AI always needed? Over the last eighteen months, Jumping Rivers has entered (and won) four Hackathons. All Hackathons were characterised with “big data” and the need to improve prediction. All Hackathons were won without using AI (or any sort of machine learning). This talk will focus on one particular competition around reducing leakage at Northumbrian Water. Using a combination of R, Shiny, and Tidyverse (and a few other tricks), we were able to demonstrate within the short Hackathon time frame that clear presentation of data to the front line engineers was more likely to reduce leakage, than simply providing vague estimates of a potential future leak

About the speaker

Colin Gillespie

Colin is a Senior Statistics lecturer at Newcastle University and is a co-founder of Jumping Rivers. He has used R for over twenty years and has been teaching R for the past fifteen years. He recently co-authored the O’Reilly book on Efficient R Programming.