Philip Sheppard has recorded solo cello albums, composed more than 60 soundtracks, and adapted 206 national anthems for Olympic Games medal ceremonies. He knows every corner of the famed Abbey Road Studios. His work is the process of creating music, but sometimes he just wants to listen, preferably while walking in the woods. During one of those walks, in 2016, he didn't want to be bothered choosing what music to play. Instead he imagined some sort of magical accompaniment piped into his earphones that would dynamically reflect his surroundings and his mood, a literal soundtrack for his saunter.

Yesterday, Microsoft's Build 2020 developer conference kicked off (remotely), and we saw the first results of Microsoft's billion-dollar investment in OpenAI, a company co-founded by Elon Musk. Microsoft announced it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research focuses on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

If you want to understand how people are thinking (and feeling) about new technologies, it's important to understand how media outlets are thinking (and writing) about new technologies. A recent analysis of how journalists have dealt with the ethics of artificial intelligence (AI) suggests that reporters are doing a good job of grappling with a complex set of questions--but there's room for improvement. To learn more about the work, why they did it, and why it's important, we talked to the researchers who did the work: Veljko Dubljević, corresponding author of the paper and an assistant professor of philosophy at NC State; Leila Ouchchy, first author of the paper and a former undergrad at NC State; and Allen Coin, co-author of the paper and a graduate student at NC State. The paper, "AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media," was published in the journal AI & Society on March 29. The Abstract: This paper focuses, in part, on ethical issues related to AI technologies that people would use in their daily lives.

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The advertising landscape has transformed dramatically in the last two years. A huge part of that transformation is related to developments in artificial intelligence (AI) and machine learning. This IAB guide is designed to help brand marketers and their agencies identify the opportunities that artificial intelligence and machine learning present, the range of options available, and some recent best practices for applying AI to marketing and advertising. Developed by the IAB AI Working Group which was formed to help marketing and technology executives navigate the impact AI and machine learning will have on the world of digital advertising, this is the first guide of its kind to offer a full picture of the benefits of AI in marketing, real-world use cases, best practices, and key takeaways for marketers looking to leverage AI to better engage with customers at scale. When IAB put out the call to its members to form an AI working group, the response was overwhelming: 115 members raised their hands to contribute to our collective industry understanding of this nascent and essential topic. Their responses informed the basis of this IAB guide, focusing on the areas of greatest importance to the marketing industry at this time.

The e-commerce giant has a foothold in audiobooks, fresh groceries, Netflix-style video streaming and oh-so-much-more. It's no surprise, therefore, that the company wants to widen its influence in the video game industry. The Jeff Bezos empire already owns Twitch, the biggest game live-streaming service, and supports developers with its CryEngine-based Lumberyard platform and AWS server infrastructure. But it's never been a heavyweight game publisher. Multiplayer brawler Breakaway was canceled and The Grand Tour Game was a forgettable TV show tie-in. A lot is riding on Crucible, then.

Sony Corp. and Microsoft Corp. have partnered to embed artificial intelligence capabilities into the Japanese company's latest imaging chip, a big boost for a camera product the electronics giant describes as a world first for commercial customers. The new module's big advantage is that it has its own processor and memory built in, which allows it to analyze video using AI tech like Microsoft's Azure, but in a self-contained system that's faster, simpler and more secure to operate than existing methods. The two companies are appealing to retail and logistics businesses with potential uses like optimizing warehouse and factory automation, quantifying the flow of customers through stores and making cars smarter about their drivers and environment. At a time of increasing public surveillance to help rein in the spread of the coronavirus, this new smart camera also has the potential to offer more privacy-conscious monitoring. And should its technology be adapted for personal devices, it even holds promise for advancing mobile photography. Instead of generating actual images, Sony's AI chip can analyze the video it sees and provide just metadata about what's in front of it -- saying instead of showing what's in its frame of vision.

Despite the significant progress in automatic speech recognition (ASR), distant ASR remains challenging due to noise and reverberation. A common approach to mitigate this issue consists of equipping the recording devices with multiple microphones that capture the acoustic scene from different perspectives. These multi-channel audio recordings contain specific internal relations between each signal. In this paper, we propose to capture these inter- and intra- structural dependencies with quaternion neural networks, which can jointly process multiple signals as whole quaternion entities. The quaternion algebra replaces the standard dot product with the Hamilton one, thus offering a simple and elegant way to model dependencies between elements. The quaternion layers are then coupled with a recurrent neural network, which can learn long-term dependencies in the time domain. We show that a quaternion long-short term memory neural network (QLSTM), trained on the concatenated multi-channel speech signals, outperforms equivalent real-valued LSTM on two different tasks of multi-channel distant speech recognition.

Particularly in Natural Language Processing (NLP), the necessity of a good representation of the words that achieves some implicit context understanding is important [Turian et al., 2010]. A typical representation to feed in a machine learning model is the binary one-hot vector, in this case, an array with as many positions as words in the vocabulary is created, and the words are represented by a version of the array containing a one digit in the position corresponding to the word. For example, the sentence "I like eating bread and eating cheese", would have as vocabulary the set "I", "like", "eating", "bread", "and", "cheese", thus the representation of this words would be 6-dimensional binary one-hot vectors like "I" 100000, "like" 010000, cheese 000001. As you can imagine, this representation has no context understanding at all, since all words are completely independent, "bread" and "cheese" are as different between them as "I" and "like", which for a human is not the case.

In July 1848, L'illustration, a French weekly, printed the first photo to appear alongside a story. It depicted Parisian barricades set up during the city's June Days uprising. Nearly two centuries later, photojournalism has bestowed libraries with legions of archival pictures that tell stories of our past. But without a methodical approach to curate them, these historical images could get lost in endless mounds of data. That's why the Library of Congress in Washington, D.C. is undergoing an experiment. Researchers are using specialized algorithms to extract historic images from newspapers.