As we come to the end 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey.

To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies which evolve at the pace of science, and social technologies which evolve at the pace at which humans can change — much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines (‘physical technology’) mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust (‘social technology’).

Source: towardsdatascience.com
Author: Jason T Widjaja