Are you committed to putting your data scientist skills to work on a project with a positive impact on the environment, health and safety? Our client is a dynamic spin-off of UCLouvain developing innovative gas micro-sensors for the industry and the environment. The company, created in 2019, has just raised 2,5 Mk€ to industrialize and market its first micro-sensors by 2022/early 2023, and at the same time continue the R&D towards novel disruptive products. Its technology can indeed be applied in many sectors of activity, from the (petro-)chemical to the healthcare industry, through agri-food, recycling, smart cities and smart buildings markets. The company currently consists of 12 enthusiastic, dynamic and friendly engineers.

The word on the street is if you don't invest in ML as a company or become an ML specialist, the industry will leave you behind. The hype has caught on at all levels, catching everyone from undergrads to VCs. Words like "revolutionary," "innovative," "disruptive," and "lucrative" are frequently used to describe ML. Allow me to share some perspective from my experiences that will hopefully temper this enthusiasm, at least a tiny bit. This essay materialized from having the same conversation several times over with interlocutors who hope ML can unlock a bright future for them. I'm here to convince you that investing in an ML department or ML specialists might not be in your best interest. That is not always true, of course, so read this with a critical eye. The names invoke a sense of extraordinary success, and for a good reason. Yet, these companies dominated their industries before Andrew Ng's launched his first ML lectures on Coursera. The difference between "good enough" and "state-of-the-art" machine learning is significant in academic publications but not in the real world. About once or twice a year, something pops into my newsfeed, informing me that someone improved the top 1 ImageNet accuracy from 86 to 87 or so. Our community enshrines state-of-the-art with almost religious significance, so this score's systematic improvement creates an impression that our field is racing towards unlocking the singularity. No-one outside of academia cares if you can distinguish between a guitar and a ukulele 1% better. Sit back and think for a minute.