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Gobbledygook Galore! Ep. 0 – Introduction

TL;DR: Keep in touch with the reality of machine learning and artificial intelligence – without the fuss and hype. Read what’s real and righteous and get things done efficiently.

Regular and prolonged exposure to the technical press is sure to make one’s head spin in a fit of futurism: Crime-predicting artificial intelligences, tasty McMachine Learning menusAI-powered robotic surgeons and daily reports alike spark the imagination and almost suggest that utopia must be within close reach. Evidently, the stark contrast with our everyday lives is rather humbling and begs the question of what the technological landscape of machine learning looks like in reality, across all industries and in consideration of all sizes of businesses.

You may have developed the impression that while the degree to which you personally are working with technologies related to machine learning is negligible or downright zero, seemingly everybody else must be using it in their profession. We argue that this impression does not reflect reality but is shared by many due to the circumstance that, by its nature of being driven to sensationalism and neomania, the technical media manufactures a biased view of the contemporary state of artificial intelligence and related technologies, skewed towards the grand and illustrious while in chronic neglect of representing the daily and commonplace, the latter being where the majority of humans spend the majority of their lives.

Given this background, we aim this series of blogs to augment and clarify where we find publicized information to be deficient and to provide a perspective where the big picture is missing. The aim is not to belittle the recent achievements and current developments of machine learning but, on the contrary, to provide a sober picture of them as they are interesting and exciting by themselves without need for exaggeration of any kind.

In Ep. 1 we’ll start out by a clarification of the essential nomenclature related to machine learning: What is machine learning anyway, what is meant by artificial intelligence, how do these buzzwords relate to one another and where do things like neural networks and deep learning come in? Stay tuned.