Misinformation such as fake news has drawn a lot of attention in recent years. It has serious consequences on society, politics and economy. This has lead to a rise of manually fact-checking websites such as Snopes and Politifact. However, the scale of misinformation limits their ability for verification. In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims. Behind the scenes BRENDA uses a tested deep neural network architecture to automatically identify fact check worthy claims and classifies as well as presents the result along with evidence to the user. Since BRENDA is a browser extension, it facilities fast automated fact checking for the end user without having to leave the Webpage.

This week HBO makes its assault on streaming with the new HBO Max service. At launch it will include tons of archival content like the back catalog of Friends, Rick & Morty and 2001: A Space Odyssey, as well as a fleet of new original shows, movies and specials hoping to get your attention on Wednesday. I'm looking forward to the new Looney Tunes Cartoons, as well as the comedy/drama series Love Life with Anna Kendrick, but there's a lot more to choose from. Of course, Netflix is the current giant in streaming and it's not laying down for the newcomer. This week it already added Uncut Gems for viewers in the US, and later this week it will premiere a Jeffrey Epstein documentary series and the new show Space Force starring Steve Carell.

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.

But 2019 was the year the earth burned. In Australia, the world watched in horror as bushfires destroyed 10.3 million hectares, marking the continent's most intense and destructive fire season in over 40 years. Earlier that fall, California saw more than 101,000 hectares destroyed, with damages upward of $80 billion. Alaska saw nearly a million. Record-breaking fires also hit Indonesia, Russia, Lebanon -- but nowhere saw the sheer mass of media coverage as the fires that tore through the Amazon nearly all last summer. By year's end, thousands of global media outlets had reported that Brazil's largest rainforest played host to more than 80,000 individual forest fires in 2019, resulting in an estimated 906,000 square hectares of environmental destruction. At the time, Brazil's National Institute for Space Research reported it was the fastest rate of burning since record keeping began in 2013. But amid the charred ruins of one of the largest oxygen-producing environments on the planet, a secret lies buried beneath the soil.

Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies. Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process.

The traditional method of training AI models involves setting up servers where models are trained on data, often through the use of a cloud-based computing platform. However, over the past few years an alternative form of model creation has arisen, called federated learning. Federated learning brings machine learning models to the data source, rather than bringing the data to the model. Federated learning links together multiple computational devices into a decentralized system that allows the individual devices that collect data to assist in training the model. In a federated learning system, the various devices that are part of the learning network each have a copy of the model on the device.

A New York Times, Wall Street Journal, and USA Today Bestseller! Featured on CBS 60 Minutes Kai-Fu Lee named a Wired Icon, as part of Wired Magazine's 25th Anniversary Feature Publishers Weekly Fall 2018 Top 10 in Business & Economics Featured in the New York Times, the Wall Street Journal, the Washington Post, Wired, Financial Times, Bloomberg Businessweek, Business Insider, Forbes, and more. "After thirty years of pioneering work in artificial intelligence at Google China, Microsoft, Apple and other companies, Lee says he's figured out the blueprint for humans to thrive in the coming decade of massive technological disruption: 'Let us choose to let machines be machines, and let humans be humans.'"--Forbes "Kai-Fu Lee believes China will be the next tech-innovation superpower and in his new (and first) book, AI Superpowers: China, Silicon Valley, and the New World Order, he explains why. Taiwan-born Lee is perfectly positioned for the task."--New Times "AI Superpowers: China, Silicon Valley, and the New World Order, by Kai-Fu Lee, about the ways that artificial intelligence is reshaping the world and the economic upheaval new technology will generate. We need to start thinking now about how to address these gigantic changes."--Senator

A survey has been compiled on the topic of "Universal Adversarial Perturbations", entirely by the student members of Vision and Language Group, IIT Roorkee. It is a compilation and analysis of the latest advancements in the field of universal adversarial perturbation, which is basically a small noise that can be added to any image in a dataset to fool a neural network. The arXiv preprint for the same can be found here: https://arxiv.org/abs/2005.08087 Hope you will find it useful and any constructive feedback is welcome!!

Relying on its self-developed full-stack end-to-end AI technology, Xmov has opened the process of intelligent characterization from intelligent modeling to AI performance animation technology which drives facial expressions, eyes, body and finger movements, and generating short videos or real-time broadcasts to realize interaction and commercialization of virtual intelligent property (IP). Ling's team will generate content around such characteristics as the national essence of Peking Opera and the combination of classic and modern fashion on social platforms like Weibo, Instagram, and TikTok, Xmov said in a statement. With commercial programming, the 3D virtual figure will engage in business endorsements, live broadcasts, and participate in online and offline activities. CEO and Xmov founder Chai Jinxiang said virtual IP value will be released at the maximum level via livestreaming and short videos. The emerging virtual idol sector, backed by China's younger generation, has been on the fast development track in recent years, with more domestic internet firms increasing investment in this nascent sector.

There is a big step from machine learning to Deep Learning (DL) in that DL requires much more data than ML.