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.

Facebook's new feature to transfer photos from your profile to a Google Photos backup is now available globally, after previously only being accessible in the US and Canada. The tool was later rolled out to parts of Africa, Asia Pacific, and Latin America in February 2020, European countries in March 2020, but can now be accessed by all users across the world. The tool lets you make copies of all the photos and videos on your account, and move them to another platform more easily than having to mass download, and then reupload, the content. Going to "Your Facebook Information" in your Facebook Settings Selecting "Transfer a Copy of Your Photos or Videos and entering your Facebook password Choosing Google Photos – with the company stating that more options will be available over time Clicking the "Confirm Transfer" button It is currently unclear what other options will be available, but Facebook has previously said that if companies join the Data Transfer Project then they would be able to transfer content from Facebook to other platforms. The project was established in 2018 to "create an open-source, service-to-service data portability platform so that all individuals across the web could easily move their data between online service providers whenever they want," according to its website.

The world is in the midst of an energy transition. This massive shift aims to move away from reliance on fuels that are destructive to the climate, the environment, and people's well-being. The goal established by the UN is to "ensure access to affordable, reliable, sustainable and modern energy for all" by 2030. While governments, energy companies, and activists dominate the headlines, the progress with infrastructure and technology won't be sufficient. A successful energy transition for the good of all humanity depends on the action of individuals.

The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research. This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results.

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.

Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.

Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions both to an individual's initial features and to intermediate outcomes and features at each subsequent stage, which are affected by decisions in prior stages. Examples include personalized first- and second-line treatments of chronic conditions like diabetes, cancer, and depression, which adapt to patient response to first-line treatment, disease progression, and individual characteristics. While existing literature mostly focuses on estimating the optimal DTR from offline data such as from sequentially randomized trials, we study the problem of developing the optimal DTR in an online manner, where the interaction with each individual affect both our cumulative reward and our data collection for future learning. We term this the DTR bandit problem. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear. We demonstrate our algorithm and its benefits both in synthetic experiments and in a case study of adaptive treatment of major depressive disorder using real-world data.

This Friday, Artificial Intelligence fashion startup Bigthinx, in partnership with Fashinnovation, will live stream the first fully digital 3D Virtual Fashion Show (including digitised human models) since the coronavirus pandemic forced the fashion industry online. The'virtual' aspect is that the models and clothes are being created using 3D digital design, rendering, and animation, based on technical data (including garment measurements) and photographs of the models and clothes. This will be the first time many fashion professionals have seen virtual fashion since the industry-wide discussions about implementing it ramped up, following the coronavirus-induced lockdown. The realization that digital fashion will be a critical long-term solution rather than a temporary measure is evident in industry announcements from Helsinki Fashion Week, the first to declare they will show 3D virtual fashion shows for the upcoming season and beyond, before Covid-19 forced Milan, New York and others to follow suit. In creating this 3D virtual show, with opportunity comes numerous challenges, especially for a technology company known for its'body scan' avatar solution based on just two photos and a selfie from a smartphone.

Elon Musk says he is quitting Twitter "for a while". The SpaceX and Tesla founder is one of the site's most high-profile users, and regularly sends tweets making major announcements about himself or his companies. But his Twitter account has brought controversy, too, with Mr Musk regularly causing controversy with his posts. "Off Twitter for a while," Mr Musk wrote in the post. It comes just days after SpaceX launched Nasa astronauts into space in a historic mission.

Drought is a natural creeping threat with numerous damaging effects in various aspects of human life. Accurate drought prediction is a promising step in helping policy makers to set drought risk management strategies. To fulfill this purpose, choosing appropriate models plays an important role in predicting approach. In this study, different models of Artificial Neural Network (ANN) are employed to predict short and long-term of droughts by using Standardized Precipitation Index (SPI) at different time scales, including 3, 6, 12, 24 and 48 months in Tabriz city, Iran. To this end, different combination of calculated SPI and time series of various hydro-meteorological variables, such as precipitation, wind velocity, relative humidity and sunshine hours for years 1992 to 2010 are used to train the ANN models. In order to compare the models performances, some well-known measures, namely RMSE, Mean Absolute Error (MAE) and Correlation Coefficient (CC) are utilized in the present study. The results illustrate that the application of all hydro-meteorological variables significantly improves the prediction of SPI at different time scales.