Aaron delivered this talk at!!Con, which is hands-down our favorite programming conference. Joel Grus's Livecoding Madness - Let's Build a Deep Learning Library was dazzling. N.K. Jemisin completed her Broken Earth trilogy with The Stone Sky, and it was the best sci-fi we read this year. While not directly related to AI, it explores consequences of a society shaped by technology inherited from the past. Autonomous by Annalee Newitz is an interesting look at bio-hacking and robot relationships. Kyle McDonald's twitter account is not just about visualization (e.g., his dispatches from NIPS were fascinating), but we particularly love it when he shares his work looking at data.

Machine learning is exciting, but the work is complex and difficult. It typically involves a lot of manual lifting -- assembling workflows and pipelines, setting up data sources, and shunting back and forth between on-prem and cloud-deployed resources. The more tools you have in your belt to ease that job, the better. Thankfully, Python is a giant tool belt of a language that's widely used in big data and machine learning. Here are five Python libraries that help relieve the heavy lifting for those trades.