Colin Gillespie | How to win an AI Hackathon, without using AI | RStudio (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
rstudio
Shiny
tidyverse
Rstudio::conf(2020)
Colin Gillespie
RStudio
Data Science
Machine Learning
Python
Stats
Tidyverse
Data Visualization
Data Viz
Ggplot
Technology
Coding
Connect
Server Pro
Shiny
Rmarkdown
Package Manager
CRAN
Interoperability
Serious Data Science
Dplyr
Forcats
Ggplot2
Tibble
Readr
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Tidyr
Purrr
Github
Data Wrangling
Tidy Data
Odbc
Rayshader
Plumber
Blogdown
Gt
Lazy Evaluation
Tidymodels
Statistics
Debugging
Programming Education
Rstats
Open Source
Oss
Reticulate