None of these people exist. These images were generated using deepfake technology. Last month during ESPN's hit documentary series The Last Dance, State Farm debuted a TV commercial that has become one of the most widely discussed ads in recent memory. It appeared to show footage from 1998 of an ESPN analyst making shockingly accurate predictions about the year 2020. As it turned out, the clip was not genuine: it was generated using cutting-edge AI.

Artificial intelligence, as a revolutionary force in restructuring production and consumption patterns, has long been on the agenda of policy-makers.

The Chinese military believes it is losing a high-stakes competition with the United States and Russia to lead the world in artificial intelligence (AI).

The outbreak of COVID- 19 has made jobless a lot of workers, now Artificial Intelligence has started taking over people's jobs. Several industries have already been affected in several sectors by the intrusion of artificial intelligence, and journalism turns out not to be an exception. Microsoft has recently fired dozens of journalists, who were responsible for arranging and editing news stories to be replaced with automatic systems. At a time when Indian news media companies are sacking employees due to COVID related economic downfall, the possibility of robots is now a major threat to our job security. Artificial Intelligence is still in its early stages, but it will be interesting to see how it will change the fourth pillar of democracy, in the era of web journalism and shouting debates on Prime Time TV news.

Many unsupervised representation learning methods belong to the class of similarity learning models. While various modality-specific approaches exist for different types of data, a core property of many methods is that representations of similar inputs are close under some similarity function. We propose EMDE (Efficient Manifold Density Estimator) - a framework utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds. Our approximate representation has the desirable properties of being fixed-size and having simple additive compositionality, thus being especially amenable to treatment with neural networks - both as input and output format, producing efficient conditional estimators. We generalize and reformulate the problem of multi-modal recommendations as conditional, weighted density estimation on manifolds. Our approach allows for trivial inclusion of multiple interaction types, modalities of data as well as interaction strengths for any recommendation setting. Applying EMDE to both top-k and session-based recommendation settings, we establish new state-of-the-art results on multiple open datasets in both uni-modal and multi-modal settings. We release the source code and our own real-world dataset of e-commerce product purchases, with special focus on modeling of the item cold-start problem.

Several custom web scrapers were created for retrieving news articles from various online news organizations. All web scrapers were run every two hours to retrieve articles from the following five news sites: the Atlantic, the British Broadcasting Corporation (BBC) News, Fox News, the New York Times and Slate Magazine. Web scrapers continue to run every two hours in perpetuity, scraping additional news articles. Collectively, the web scrapers used each news organization's RSS feed as input, storing the scraped output into a custom database. Article URLs were used for disambiguation; where two scraped articles shared a URL, the most recently retrieved article replaced previous versions of articles. As of November 2019, we collected a total of 105,000 news articles from five media organizations. Figure 2 depicts the number of cumulative articles scraped for each news organization over time. Even though articles from Fox News were regularly scraped four months later than other news sources, the number of articles scraped rose quickly, and now constitutes the news organization with the most scraped articles. Given the news scrapers run at regularly scheduled two-hour intervals for all news organization, this suggests that Fox News updates its RSS feed with new articles far more often than others, and the Atlantic updates its RSS feed far less frequently than others.

Recommender Systems are giving increasing importance to the beyond accuracy quality measures, and reliability is one of the most important in the Collaborative Filtering context. This paper proposes Bernoulli Matrix Factorization (BeMF), a matrix factorization model to provide both prediction values and reliability ones. This is a very innovative approach from several perspectives: a) it acts on the model-based Collaborative Filtering, rather than in the memory-based one, b) it does not use external methods or extended architectures for providing reliability such as the existing solutions, c) it is based on a classification-based model, instead of the usual regression-based ones, and d) the matrix factorization formalism is supported by the Bernoulli distribution, to exploit the binary nature of the designed classification model. As expected, results show that the more reliable a prediction is, the less liable to be wrong: recommendation quality has been improved by selecting the most reliable predictions. State-of-the-Art quality measures for reliability have been tested, showing improved results compared to the baseline methods and models.

The Guardian is running a story about how Microsoft has laid off 27 journalists tasked with writing MSN News, outsourced to PA Media, and replaced them with a series of machine learning algorithms. Journalism doesn't seem like a profession that's about to undergo automation. But MSN News, like many other news portals, is pretty much a rehash of agency stories or licensed items from other media written in a neutral way, avoiding any kind of editorial line, since doing so would mean losing a part of the potential audience. The job consists of providing a panorama of what's making the news, and from there, summarizing, extracting, and writing in a particularly dispassionate manner. MSN News is simply a news aggregator.

Sonos first debuted the Playbar in 2013. Since then, the company introduced the Playbase (2017) and compact Beam soundbar (2018). But for those who wanted a more robust arrangement with either a soundbar on its own or one paired with a sub and/or satellite speakers, the Playbar was still your best bet. However, that device is seven years old, so it's hard to justify investing in one. Enter the Arc ($799): a completely redesigned soundbar that offers all that we've come to expect from recent Sonos products, plus Dolby Atmos audio, and one potentially big headache.