AI Weekly 2 June 2018


Hi! New AI Weekly is here! Enjoy your weekend reading AI news and don’t forget to share it with your friends 😉




Why thousands of AI researchers are boycotting the new Nature journal – Academics share machine-learning research freely. Taxpayers should not have to pay twice to read their findings


Why you need to improve your training data, and how to do it – Academic papers are almost entirely focused on new and improved models, with datasets usually chosen from a small set of public archives, but is it really an efficient way to build ML system?


Leaked Emails Show Google Expected Lucrative Military Drone AI Work to Grow Exponentially – in March that Google had secretly signed an agreement with the Pentagon to provide cutting edge artificial intelligence technology for drone warfare, the company faced an internal revolt. About a dozen Google employees have resigned in protest and thousands have signed a petition calling for an end to the contract. The endeavor, code-named Project Maven by the military, is designed to help drone operators recognize images captured on the battlefield.




Comprehensive Introduction to Monte Carlo Methods – Monte Carlo methods look at the problem in a completely novel way compared to dynamic programming. It asks the question: How many samples do I need to take from our environment to discern a good policy from a bad policy?


Introducing Machine Learning Practica – This hands-on practicum contains video, documentation, and interactive programming exercises, illustrating how Google developed the state-of-the-art image classification model powering search in Google Photos. To date, more than 10,000 Googlers have used this practicum to train their own image classifiers to identify cats and dogs in photos.




Prediction Machines: The Simple Economics of Artificial Intelligence




How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning – An end-to-end example of how to build a system that can search objects semantically.




Observe and Look Further: Achieving Consistent Performance on Atari – Despite significant advances in the field of deep Reinforcement Learning (RL), today’s algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. Authors identify three key challenges that any algorithm needs to master in order to perform well on all games: processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently. In this paper, they propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.




TrackML Particle Tracking Challenge –  in this competition, you’re challenged to build an algorithm that quickly reconstructs particle tracks from 3D points left in the silicon detectors.




UC Berkeley Open-Sources 100k Driving Video Database – UC Berkeley’s Artificial Intelligence Research Lab (BAIR) has open-sourced their newest driving database, BDD100K, which contains over 100k videos of driving experience, each running 40 seconds at 30 frames per second.
BDD100K’s total image count is 800 times larger than Baidu ApolloScape (released this March), 4,800 times larger than Mapillary and 8,000 times larger than KITTI.

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