10 predictions for data science and AI in 2020
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10 predictions for data science and AI in 2020
As we come to the end of 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.
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To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that 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').
With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just 'cool things' for data science teams.
1. Data science and AI