AI Weekly 6 April 2018

Hi! New AI Weekly is here! This week was really great for AI industry, at first it’s definitely worth to watch all videos from TensorFlow Dev Summit 2018. From general news, Apple has hired Google’s chief of search and artificial intelligence, John Giannandrea and Google employees have signed a letter protesting the company’s involvement in a Pentagon program. There are also 2 interesting papers, which were published, especially the one about The Tsetlin Machine, which seems to be interesting alternative for neural networks. If you are developer i strongly recommend taking part in OpenAI Retro Contest, which will be really fun, of course that’s not all… just enjoy your weekend reading other AI news and don’t forget to share it with your friends 😉


Google Workers Urge C.E.O. to Pull Out of Pentagon A.I. Project – Thousands of Google employees, including dozens of senior engineers, have signed a letter protesting the company’s involvement in a Pentagon program that uses artificial intelligence to interpret video imagery and could be used to improve the targeting of drone strikes. The letter, which is circulating inside Google and has garnered more than 3,100 signatures, reflects a culture clash between Silicon Valley and the federal government that is likely to intensify as cutting-edge artificial intelligence is increasingly employed for military purposes.

Apple Hires Google’s A.I. Chief – Apple has hired Google’s chief of search and artificial intelligence, John Giannandrea, a major coup in its bid to catch up to the artificial intelligence technology of its rivals. Apple said on Tuesday that Mr. Giannandrea will run Apple’s “machine learning and A.I. strategy,” and become one of 16 executives who report directly to Apple’s chief executive, Timothy D. Cook. The hire is a victory for Apple, which many Silicon Valley executives and analysts view as lagging its peers in artificial intelligence, an increasingly crucial technology for companies that enable computers to handle more complex tasks, like understanding voice commands or identifying people in images.


Course Project Reports for 2017 CS224n: Natural Language Processing with Deep Learning


TensorFlow Dev Summit 2018 videos

An introduction to Reinforcement Learning

MIT AGI: Boston Dynamics (Marc Raibert, CEO)


MobileNetV2 – the next generation of on-device models that push the state of the art for mobile visual recognition including classification, object detection and semantic segmentation.

CometML wants to do for machine learning what GitHub did for code – allows data scientists and developers to easily monitor, compare and optimize their machine learning models. The service provides you with a dashboard that brings together the code of your machine learning (ML) experiments and their results. In addition, the service also allows you to optimize your models by tweaking the hyperparameters of your experiments. As you train your model, Comet tracks the results and provides you with a graph of your results, but it also tracks your code changes and imports them so that you can later compare all the different aspects of the various versions of your experiments.


The Tsetlin Machine – Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Unknown to many, there exists an arguably even simpler and more versatile learning mechanism, namely, the Tsetlin Automaton. Merely by means of a single integer as memory, it learns the optimal action in stochastic environments. In this paper, author introduces the Tsetlin Machine, which solves complex pattern recognition problems with easy-to-interpret propositional formulas, composed by a collective of Tsetlin Automata.

Learning to navigate in cities without a map – in this paper DeepMind present an interactive navigation environment that uses first-person perspective photographs from Google Street View and gamify that environment to train an AI. As standard with Street View images, faces and license plates have been blurred and are unrecognisable. They build a neural network-based artificial agent that learns to navigate multiple cities using visual information (pixels from a Street View image).


OpenAI Retro Contest – April 5 to June 5, 2018 – transfer-learning contest using the Sonic The Hedgehog™ series of games for SEGA Genesis. In this contest, participants try to create the best agent for playing custom levels of the Sonic games — without have access to those levels during development.


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