Television news coverage is typically thought of as a visual medium, yet most of the narrative we consume from television comes in the form of spoken narration. Watching a news show with the audio muted and closed captioning off reinforces that the visual elements of television act more as enrichment than primary information conveyor. This means that quantifying this spoken narrative is imperative to understanding what television news is paying attention to and how it is framing and covering those events. Using Google's Cloud Speech-to-Text API to transcribe a week of television news coverage and annotating it with Google's Natural Language API, what might we learn about how television news covers the world? In the United States, most television stations provide closed captioning for their news programming, meaning they already come with a textual human-produced transcript.

Aerospace & Defense Industry to See Greatest Impact from Artificial Intelligence Compared to Other Key Emerging Technologies, Accenture Report Finds Study underscores the need for reskilling in the sector for future competitiveness NEW YORK; June 13, 2019 – The aerospace and defense (A&D) industry will be more affected by artificial intelligence (AI) than by any other major emerging technology over the next three years, according to Aerospace & Defense Technology Vision 2019, the annual report from Accenture (NYSE: ACN) that predicts key technology trends likely to redefine business. The study also underscores the growing importance of reskilling programs as a competitive lever. AI, comprising technologies that range from machine learning to natural language processing, enables machines to sense, comprehend, act and learn in order to extend human capabilities. One-third (33%) of A&D executives surveyed cited AI as the technology that will have the greatest impact on their organization over the next three years -- more than quantum computing, distributed ledger or extended reality. In fact, two-thirds (67%) of A&D executives said they have either adopted AI within their business or are piloting the technology.

Graph Neural Network has become the new fashion in many graph-based learning problems. Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (e.g. As the team behind this library, we want to share with you the new release of DGL (v0.3) that is much faster (up to 19x faster) and more scalable for training GNNs on large graphs (up to 8x larger). For whom have never heard of DGL or Graph Neural Network, maybe it is worth to take a look at this new trend of geometric deep learning. Checkout this 10-minute tutorial about how to use Graph Neural Network to predict community membership (https://docs.dgl.ai/tutorials/basics/1_first.html).

Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb tackle the issue of how artificial intelligence technologies can have differing effects on jobs in …

We know content should be valuable, comprehensive, new, relevant, and accurate. Even more fundamentally important than all of those things is that it needs to be authentic. People trust factual information presented with sincere intentions. This era of "fake news" has ushered in a lot of fear for this very reason; we have had to fight to build the authority of our pages and domains to signal that we are worthy of trust. But our industry has yet to face its biggest challenge.

Has AI raised the floor but not the ceiling with marketing? Have we over-indexed on having content at scale? And is there a way for marketers to understand when hyper-personalization will cross the line into creepiness? In this episode of thinkPod, we are joined by Kate Bradley Chernis (Founder & CEO of Lately) and Joey Camire (principal & founding team of Sylvain Labs). We talk to Kate and Joey about whether AI will replace human marketers, where we are currently with AI and marketing, the difficulty of getting marketers to write, and how AI can bring delight to consumers. We also get into the hot debate around a company's responsibility with user data and imagine a future where each cup of yogurt is tracked. "AI as it relates to marketing is raising the floor. It doesn't totally feel like it's currently raising the ceiling." "I'm here to tell you that when marketing, it'll never replace humans altogether because it just doesn't work. "AI is not at the place right now where it's saying like, well, based on my understanding of supply and demand economics, you should be changing your price model. What you choose to do with the understanding that the system is providing you is still going to land on someone's lap. So your ability to be creative, your ability to write, your ability to wrangle concept and insight. What do you do with the information that you're being provided from a system that is finding things that you might not otherwise be able to find." –Joey Camire "How can we consistently use that [hyper-personalization] in our messaging without compromising our brand? And so the way that we succeed in doing that is really being super emotional and human.

Driven by the rise of transformative digital technologies and the proliferation of data, human storytelling is rapidly evolving in ways that challenge and expand our very understanding of narrative. Transmedia -- where stories and data operate across multiple platforms and social transformations -- and its wide range of theoretical, philosophical, and creative perspectives, needs shared critique around making and understanding. MIT's School of Architecture and Planning (SA P), working closely with faculty in the MIT School of Humanities, Arts, and Social Sciences (SHASS) and others across the Institute, has launched the Transmedia Storytelling Initiative under the direction of Professor Caroline Jones, an art historian, critic, and curator in the History, Theory, Criticism section of SA P's Department of Architecture. The initiative will build on MIT's bold tradition of art education, research, production, and innovation in media-based storytelling, from film through augmented reality. Supported by a foundational gift from David and Nina Fialkow, this initiative will create an influential hub for pedagogy and research in time-based media.

In early 2018 a video that appeared to feature former President Obama discussing the dangers of fake news went viral. The clip, created by comedian Jordan Peele, foreshadowed challenges that have now become all too real. These days, tech firms, media companies and consumers are all routinely forced to make determinations about whether content is authentic or fake -- and it's increasingly hard to tell the difference. Deepfakes are videos and images that have been digitally manipulated to depict people saying and doing things that never happened. Most deepfakes use artificial intelligence to alter video and to generate authentic-sounding audio.

Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage. A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

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