REUTERS: Alphabet Inc's Google signed its biggest cloud computing customer in healthcare yet, according to an announcement on Monday (Nov 11), gaining with the deal datasets that could help it tune potentially lucrative artificial intelligence tools. The Wall Street Journal earlier reported Google teaming up with Ascension to collect personal health-related information of millions of Americans across 21 states. The partnership will also explore artificial intelligence and machine learning applications to help improve clinical effectiveness as well as patient safety, Ascension said in a statement. Google Cloud Chief Executive Officer Thomas Kurian has made it a priority in his first year on the job to aggressively chase business from leaders in six industries, including healthcare. The company previously had touted smaller healthcare clients, such as the Colorado Center for Personalized Medicine.

As of now, most are aware that Artificial Intelligence (AI) is rapidly becoming a present part of our daily lives. However, a lot of us would be rather surprised to learn of some of the tasks that AI already knows how to undertake. Here are 14 incredible things that AI can do right now. Be it news articles, books, weblinks and more, SummarizeBot can make automatic text summarizations using AI and machine learning that not just reads communications but also report back the most essential information. As of now, SummarizeBot can be used in Facebook Messenger or Slack and it makes use of natural language processing, machine learning, artificial intelligence, and blockchain technologies.

During this project period, the automated system was extended to detect fractures (i.e., structural defects that determine the remaining service life of …

More than a decade ago leading UK investigative journalist Nick Davies published Flat Earth News, an exposé of how the mass media had abdicated its responsibility to the truth. Newsroom pressure to publish more stories, faster than their competitors had, Davies argued, led to journalists becoming mere "churnalists", rushing out articles so fast that they could never check on the truth of what they were reporting. Shocking as Davies' revelations seemed back in 2008, they seem pretty tame by today's standards. We now live in a post-truth world of Fake News and'alternative facts'; where activists don't just seek to manipulate the news agenda with PR but now use advanced technology to fake images and footage. A particularly troubling aspect of these'deepfake' videos is their use of artificial intelligence to fabricate people saying or doing things with almost undetectable accuracy.

The resulting images contain all the objects with perfect masks and bounding box labels, over some arbitrary backgrounds. However, the generated training data still looks fairly different from real images. I do, however, have a large dataset of unlabeled real images with the real objects in them. Would anyone be aware of a method for tuning a generated image to look more similar to the images in the real dataset? I would want to preserve spatial information so as to not invalidate generated labels, but also add noise / shadows / pixel artifacts in a meaningful way that resembles those found in my real dataset. My first thought was to look for papers using something like auto-encoders, but I was flooded with papers about VAEs and end-to-end generation. Is anyone aware of research for this specific problem?

When it comes to managing content, Netflix understood the difficulties involved. The company handles enormous amounts of data each day and is under pressure to provide the proper content to each user. Educational publishers face similar issues. Those publishers can learn valuable lessons from the way Netflix addressed those issues. Netflix made a significant investment in artificial intelligence to develop and implement its popular personalized recommendations feature.

As artificial intelligence (AI) becomes more commonplace in newsrooms, Cardiff University will be introducing the technology into its syllabus to produce'industry-ready journalists'. As of September 2020, students on the MA International Journalism course will learn to use programmes like Dataminr, Chartbeat, TweetDeck and Google Analytics - the inclusion of Dataminr being a first for UK universities. It features on a new'emerging journalism' module where students will use AI-powered tools to identify and report on a breaking news story, as this is becoming standard practice in local newsrooms, like Reach plc. As well as these practical skills, the module also offers the theory of using this technology for problem-solving. He stressed that aspiring journalists need to come to terms with AI, or risk being left behind by its advancements.

Scientists have built simulations to help explain behavior in the real world, including modeling for disease transmission and prevention, autonomous vehicles, climate science, and in the search for the fundamental secrets of the universe. But how to interpret vast volumes of experimental data in terms of these detailed simulations remains a key challenge. Probabilistic programming offers a solution--essentially reverse-engineering the simulation--but this technique has long been limited due to the need to rewrite the simulation in custom computer languages, plus the intense computing power required. To address this challenge, a multinational collaboration of researchers using computing resources at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) has developed the first probabilistic programming framework capable of controlling existing simulators and running at large-scale on HPC platforms. The system, called Etalumis ("simulate" spelled backwards), was developed by a group of scientists from the University of Oxford, University of British Columbia (UBC), Intel, New York University, CERN, and NERSC as part of a Big Data Center project.

The deluge of fake news was first called out in the wake of the 2016 election when shady websites run by foreign interests spread misinformation, much of which gained a foothold on Facebook. OpenAI worried releasing a bot that could pump out fake news in large quantities would be dangerous for society. Although, some AI researchers felt the firm was just looking for attention. This technology or something like it would be available eventually, they said, so why not release the bot so other teams could develop ways to detect its output.

This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural networks for performing automatic music composition. Although high accuracies are obtained in single-step prediction scenarios, most models fail to generate accurate multi-step chord predictions. In this paper, we postulate that this comes from the multi-scale structure of musical information and propose new architectures based on an iterative temporal aggregation of input labels. Specifically, the input and ground truth labels are merged into increasingly large temporal bags, on which we train a family of encoder-decoder networks for each temporal scale. In a second step, we use these pre-trained encoder bottleneck features at each scale in order to train a final encoder-decoder network. Furthermore, we rely on different reductions of the initial chord alphabet into three adapted chord alphabets. We perform evaluations against several state-of-the-art models and show that our multi-scale architecture outperforms existing methods in terms of accuracy and perplexity, while requiring relatively few parameters. We analyze musical properties of the results, showing the influence of downbeat position within the analysis window on accuracy, and evaluate errors using a musically-informed distance metric.