Intel hosts its 2019 Intel AI Summit on Tuesday, Nov. 12, 2019, in San Francisco. Tune in to the livestream to hear from Naveen Rao, Intel corporate vice president and general manager of the Intel Artificial Intelligence Products Group, as he shares significant product updates across the Intel artificial intelligence portfolio in addition to Intel's vision for the future of AI hardware and software.

Artificial Intelligence and Machine Learning enable Retailers to boost their bottom lines, in part by showing when it's not necessary to offer a discount.

Create a new Text Classifier Model Project in Create ML and add the training folder. You can choose any of the techniques to train your model and go have a cup of coffee while the training and validation happens. It took me 4 hours to get a Transfer learning model trained. Here's an illustration that compares the model metrics across the two techniques in Text Classification: The transfer learning based model extrapolates better. Though you can try achieving better accuracy by increasing the dataset size(TL model training took four hours on a dataset of 15000 texts for me).

A think tank has released two deepfake videos which appear to show election rivals Boris Johnson and Jeremy Corbyn advocating each other for Britain's top role. The clips are produced by Future Advocacy and intend to show that people can no longer necessarily trust what they see in videos, not just to question what they read and hear. Boris Johnson has a message for you.#GE2019 Jeremy Corbyn has a message for you.#GE2019 In the era of fake news, people are becoming increasingly aware not to believe everything they read.

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.