More Than Meets the AI

Terms like AI, Machine Learning or Data Science are often used interchangeably in newspapers, businesses, and job advertisements. This creates false expectations about what these algorithms can really do.

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It started a few years ago with Data Science, which only meant performing traditional data analysis on an increasingly larger dataset.

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Most industries already had a Data Science team back then, but the tools they were using pertained more to the field of statistics than computer science. Soon, the attention shifted more towards computer science, mostly due to the technical challenge of analysing large datasets. Nevertheless, predictive modelling in Data Science, which uses statistics to predict outcomes, remained mostly similar to the one used in traditional statistics, like principal component analysis, regression, K-means, random forests, and Xgboost. Since these algorithms learn a set of “rules” from data, they have been dubbed as Machine Learning algorithms to emphasise that these rules are not hard coded by the programmer.