AI Weekly 16 July 2018

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Software beats animal tests at predicting toxicity of chemicals – machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals — and sometimes outperforms — expensive animal studies, researchers report.
Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits’ eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses.

Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras – with millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people. It wants to assemble a vast and unprecedented national surveillance system, with crucial help from its thriving technology industry.

How should we evaluate progress in AI? – The evaluation question is inseparable from questions about what sort of thing AI is—and both are inseparable from questions about how best to do it.


Reinforcement learning’s foundational flaw – In this essay, authors are going to address the limitations of one of the core fields of AI.

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution – Uber uses convolutional neural networks in many domains that could potentially involve coordinate transforms, from designing self-driving vehicles to automating street sign detection to build maps and maximizing the efficiency of spatial movements in the Uber Marketplace.

Do Bayesians Overfit?


A Project Based Introduction to TensorFlow.js – in this post author introduce how to use TensorFlow.js by demonstrating how it was used in the simple project Neural Titanic. he shows how to visualize the evolution of the predictions of a single layer neural network as it is being trained on the tabular Titanic Dataset for the task of binary classification of passenger survival.

Collection of Interactive Machine Learning Examples


Facebook Research at ICML 2018 – Machine learning experts from around the world are gathering in Stockholm, Sweden this week for the 35th International Conference on Machine Learning (ICML) to present the latest advances in machine learning understanding. Research from Facebook will be presented in oral paper and poster sessions. Facebook researchers and engineers will also be organizing and participating in workshops throughout the week.

Glow: Better Reversible Generative Models – OpenAI introduced Glow, a reversible generative model which uses invertible 1×1 convolutions. It extends previous work on reversible generative models and simplifies the architecture. This model can generate realistic high resolution images, supports efficient sampling, and discovers features that can be used to manipulate attributes of data. We’re releasing code for the model and an online visualization tool so people can explore and build on these results.

Soccer On Your Tabletop – authors present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of their paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games.

Troubling Trends in Machine Learning Scholarship – collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible.

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