Towards Federated Learning at Scale — research paper from Google on a distributed machine learning approach which enables training on a large corpus of decentralized data residing on devices like mobile phones. They’re working on it for Android; first app is the keyboard: Our system enables one to train a deep neural network, using TensorFlow, on data stored on the phone which will never leave the device. The weights are combined in the cloud with Federated Averaging, constructing a global model which is pushed back to phones for inference. An implementation of Secure Aggregation ensures that on a global level, individual updates from phones are uninspectable. The system has been applied in large-scale applications, for instance in the realm of a phone keyboard.
Mozilla’s Clever-Commit — By combining data from the bug-tracking system and the version-control system (aka, changes in the code base), Clever-Commit uses artificial intelligence to detect patterns of programming mistakes based on the history of the development of the software. This allows us to address bugs at a stage when fixing a bug is a lot cheaper and less time consuming than upon release.Video.
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SaaS Web Design Trends — everything from where the logo is to what action is being called for to the rise of custom illustrations (versus photographs).
The Role of Social Context for Fake News Detection — In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world data sets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection. (via Paper a Day)