AI Weekly 29 June 2018

Graphic: James Gathany/Smithsonian.com

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

GENERAL

Facial recognition software is not ready for use by law enforcement – recent news of Amazon’s engagement with law enforcement to provide facial recognition surveillance (branded “Rekognition”), along with the almost unbelievable news of China’s use of the technology, means that the technology industry needs to address the darker, more offensive side of some of its more spectacular advancements. Clearly, facial recognition-powered government surveillance is an extraordinary invasion of the privacy of all citizens — and a slippery slope to losing control of our identities altogether.

OpenAI Five – OpenAI team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today they play with restrictions, they aim to beat a team of top professionals at The International in August subject only to a limited set of heroes. Dota 2 is one of the most popular and complex esports games in the world, with creative and motivated professionals who train year-round to earn part of Dota’s annual $40M prize pool (the largest of any esports game).

Ways to think about machine learning – We’re now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it. It’s not just that startups are forming every day or that the big tech platform companies are rebuilding themselves around it – everyone outside tech has read the Economist or BusinessWeek cover story, and many big companies have some projects underway. We know this is a Next Big Thing.

AI Can Smell Illnesses in Human Breath – Researchers from Loughborough University, Western General Hospital, the University of Edinburgh, and the Edinburgh Cancer Centre in the United Kingdom, recently developed a deep learning-based method that can analyze compounds in the human breath and detect illnesses, including cancer, with better than-human average performance.

VIDEOS

CognitionX 2018 – all conference videos

LEARNING

From 2D to 3D Photo Editing

Backpropagation algorithm – The backpropagation algorithm is essential for training large neural networks quickly. This article explains how the algorithm works.

PROGRAMMING

Papers with Code

Keras or PyTorch as your first deep learning framework – so, you want to learn deep learning? Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills – picking the right deep learning framework to learn is the essential first step towards reaching your goal.

Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures – TensorFlow implementation

PAPERS

Self-Supervised Tracking via Video Colorization – Tracking objects in video is a fundamental problem in computer vision, essential to applications such as activity recognition, object interaction, or video stylization. However, teaching a machine to visually track objects is challenging partly because it requires large, labeled tracking datasets for training, which are impractical to annotate at scale. In “Tracking Emerges by Colorizing Videos”, Google team introduce a convolutional network that colorizes grayscale videos, but is constrained to copy colors from a single reference frame. In doing so, the network learns to visually track objects automatically without supervision. Importantly, although the model was never trained explicitly for tracking, it can follow multiple objects, track through occlusions, and remain robust over deformations without requiring any labeled training data.

Scalable Deep Reinforcement Learning for Robotic Manipulation – How can robots acquire skills that generalize effectively to diverse, real-world objects and situations? While designing robotic systems that effectively perform repetitive tasks in controlled environments, like building products on an assembly line, is fairly routine, designing robots that can observe their surroundings and decide the best course of action while reacting to unexpected outcomes is exceptionally difficult. However, there are two tools that can help robots acquire such skills from experience: deep learning, which is excellent at handling unstructured real-world scenarios, and reinforcement learning, which enables longer-term reasoning while exhibiting more complex and robust sequential decision making. Combining these two techniques has the potential to enable robots to learn continuously from their experience, allowing them to master basic sensorimotor skills using data rather than manual engineering.

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