[Links of the Day] 11/01/2018 : Two machine learning conference NIPS 2017 & Robot Learning CoRL 2017 , CS Paper ML detector can still be fooled too easily

• Nips : This conference is considered one of the biggest events in ML\DNN Research community. Here are two sets of notes from the conference by ‎Olga Liakhovich and by David Abel. These are two fairly long article but worth a read. Looks like fairness and bias is one of the big topics of the moment. Also, I like how ML is compared to alchemy. The current approach is extremely fragile, tailor-made and not fully understood. Too often machine learning tools are considered black box where you shove in data at one end and get a result on the other. 
• Conference on Robot Learning (CoRL) : robot and machine learning are converging at an aggressive pace. It is rather impressive how all these different aspects of computer science are clicking together and with each small improvement in each domain lead to an overall jump in robotic capability. 
• Adversarial Examples that Fool Detectors : last but not least, common machine learning classifiers are still way too fragile and can be easily fooled. With the boom in use of ML technique everywhere. This can become really quickly a problem in the near future.