Wouldn't it be fabulous to know for sure that an article you read online is authentic and contains trusted sources? If everyone used AI to fact check, fake news and data could be eliminated permanently from online news sites. Menlo Park, CA-based AI startup, Diffbot has announced an official partnership with the European Journalism Centre to combat fake news. The company is the only other US company aside from Microsoft and Google to crawl and index the entire web to create its Knowledge Graph. Journalists can access the DKG through the Data Journalism platform created by the European Journalism Centre to provide resources, materials, online courses and community forums for data journalists all over the world.

Some people say that publishing has already been disrupted, that this current state is the new model. But I don't think the disruption has even started yet. As Jeff Bezos says, "it's always Day One." In the last ten years, we've seen the rise of digital publishing, print on demand, and the independent author movement, as well as the growth of streaming audio and the use of internet marketing tools like Facebook and Amazon Ads to sell more books. In this episode, I'll talk about some of the possible disruptions to come for authors and the publishing industry due to the rise of Artificial Intelligence (AI) in the next 10 years. This episode is sponsored by my Patrons, authors who are passionate about the future of publishing and help support my time in producing episodes like this. Ten years ago, when I started self-publishing, I was over-the-top excited about the potential of ebooks (see my embarrassing video here!). I could see the incredible possibilities as a creator to reach the whole world with my words. Since starting out in 2008, I have built a multi-six-figure business as an author-entrepreneur, taking action on that feeling of optimism and learning everything I needed to know to write, publish, market, and make a living with my writing.

We are happy to announce the SpeechBrain project, that aims to develop an open-source and all-in-one toolkit based on PyTorch. The goal is to develop a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others. The project will be led by Mila (Montréal) and is sponsored by Samsung, Nvidia, and Dolby. SpeechBrain will also benefit from the collaboration and expertise of other partners such as Avignon Université, Facebook/PyTorch, IBM Research, and Fluent.ai. Reddit is an awesome place to discuss, so please, let us know what you would like to see implemented for the speech community!

Christoph Lutge, director of the Institute for Ethics in Artificial Intelligence at Germany's Technical University of Munich, said there is "a chance that these AI systems might be fairer eventually, but they will need guidelines." In January, the Institute for Ethics in Artificial Intelligence was established at Germany's Technical University of Munich (TUM), with initial funding from a five-year, $7.5-million grant from Facebook. The Institute has issued its first call for proposals, and an advisory board was recently appointed. The Institute's director, Christoph Lütge, holds the Peter Löscher Chair in Business Ethics at TUM. Lütge recently spoke about ethics in artificial intelligence (AI) generally, and the new Institute specifically. Can you give an example of the type of ethical question in AI that the Center might be dealing with?

The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments. Enterprise AI adoption has more than doubled in the last four years1, and organizations continue to invest significant time and resources in building machine learning and deep learning models for a wide range of AI use cases such as fraud detection, personalized medicine, and predictive customer analytics. However, the biggest challenge faced by technical professionals is operationalizing ML, also known as the "last mile," to successfully deploy and manage these models, and unlock business value. According to Gartner, by 2021, at least 50 percent of machine learning projects will not be fully deployed due to lack of operationalization.2

Macmillan Cancer Support is using artificial intelligence (AI) to encourage people to take part in its World's Biggest Coffee Morning event later this …

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According to a study, over 76 percent of business leaders believe that Artificial Intelligence (AI) is fundamental to their organization's success.

New research found Abbott's algorithm created through machine learning could give doctors a more individualized calculation, leveraging factors such …

In view of the attention it has received of late, it is easy to think artificial intelligence (AI) is a new discovery. In fact, the concept appeared in the mid-1950s. Because it was ahead of the technology then available, it languished on the shelf of "interesting ideas" for years. Today, artificial intelligence is commonplace. Navigation systems in cars, fitness apps, Alexa and Siri, Amazon, Netflix, weather forecasting, and high-speed stock trading are among current must-have AI applications.