We investigate some aspects of the history of antisemitism in France, one of the cradles of modern antisemitism, using diachronic word embeddings. We constructed a large corpus of French books and periodicals issues that contain a keyword related to Jews and performed a diachronic word embedding over the 1789-1914 period. We studied the changes over time in the semantic spaces of 4 target words and performed embedding projections over 6 streams of antisemitic discourse. This allowed us to track the evolution of antisemitic bias in the religious, economic, socio-politic, racial, ethic and conspiratorial domains. Projections show a trend of growing antisemitism, especially in the years starting in the mid-80s and culminating in the Dreyfus affair. Our analysis also allows us to highlight the peculiar adverse bias towards Judaism in the broader context of other religions.

We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data source on its own captures all the information available to the analyst. In this paper, we propose a flexible plug-in empirical Bayes estimator that synthesizes both sources of information and may leverage any black-box predictive model. We show that our approach is within a constant factor of minimax for a simple data-generating model. Furthermore, we establish robust convergence guarantees for our method that hold under considerable generality, and exhibit promising empirical performance on both real and simulated data.

Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms. By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales that time-domain models have yet to achieve. We apply our model to a variety of audio generation tasks, including unconditional speech generation, music generation, and text-to-speech synthesis---showing improvements over previous approaches in both density estimates and human judgments.

Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to understand the story. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.

What if this is the wrong question? When it comes to automating customer conversations with chatbots and AI-driven virtual agents, the real value of that automation may well be in generating revenue and growth, not in cutting headcount. Already, there are a few notable examples of chatbots generating significant revenue. For example, the casual dining chain TGI Fridays decided to reach out to millennials with an irreverent, brand-appropriate chatbot available through Facebook Messenger and Twitter . As Fridays' Chief Experience Officer Sherif Mityas explained, "This is an opportunity to think differently about how we engage with guests who talk to us." It has worked spectacularly well.

Television news coverage brings to mind images of newsreaders in studios, reporters in the field, previously recorded footage and rapid-fire barrages of vivid advertising imagery. This raises the question of just how long a typical "shot" lasts and whether there are substantial differences between television news stations. Using the "Shot Change" detection feature of Google's Video AI platform to analyze a week of television news, what new insights could we learn about the speed at which television news narratives move? Google's Video AI API brings the company's image analysis algorithms to the world of video. While in the past videos had to be split into frames and analyzed as still images, the Video AI API enables videos to be analyzed natively, enabling time-based analysis like detecting shot changes.

An image of a Syrian refugee using virtual reality to help researchers design a shelter has been chosen as the winner of the 2019 national science photography competition organised by the Engineering and Physical Sciences Research Council.

Don DeSanto, director of strategic partnerships for the BAE Systems Intelligence & Security sector, said of the project: "RPAs fuel machine learning tools by feeding them high volumes of structured data necessary for it to begin learning and improving automatically, without being programmed. Human-machine teaming is the future of technology, and RPAs serve as workforce multipliers that can be designed to automate many common tasks performed in organizations every day." According to information from BAE Systems, the tools allow human analysts to shift their attention to managing more critical challenges.; RPAs are capable of searching, sorting, and in some cases, processing large data sets to complete work that currently takes employees hours to complete.

Deep learning has revolutionized the machine understanding of imagery. Yet today's image recognition models are still limited by the availability of large annotated training datasets upon which to build their libraries of recognized objects and activities. To address this, Google's Vision AI API expands its native catalog of around 10,000 visually recognized objects and activities with the ability to perform the equivalent of a reverse Google Images search across the open Web and tally up the top topics used to caption the given image everywhere it has previously appeared, lending unprecedentedly rich context and understanding, even yielding unique labels for breaking news events. What might this process yield for a week of television news? Google's Vision AI API represents a unique hybrid between traditional deep learning-based image labeling based on a library of previously trained models and the ability to leverage the open Web to annotate images based on the most common topics visually similar images are captioned with. Using its Web Entities feature, the Vision AI API performs what amounts to a reverse Google Images search over the open Web, identifying images across the entire Web that look most similar to the given image.

Lionbridge, the world's most trusted globalization partner, is pleased to announce the launch of Lionbridge AI. Marrying the market-leading human-annotated AI training data services and linguistic capabilities of Lionbridge, formerly known as Machine Intelligence, with the data training platform and marketplace of recently-acquired Gengo, Lionbridge AI provides a suite of capabilities and services that meets the end-to-end needs of companies building the next generation of machine learning and artificial intelligence (AI) systems. "This is an incredible opportunity to bring together our services, technology platform and voice capabilities into a single offering," said Lionbridge CEO John Fennelly. "We are confident that Lionbridge AI will help our customers deliver improved, more engaging, and increasingly human-like experiences to their artificial intelligence initiatives." As nearly every company contemplates how to use AI to build smarter products and services, while determining how to derive greater predictive capabilities to strengthen the customer experience, Lionbridge AI is perfectly positioned to support the ever-expanding array of uses for artificial intelligence.