Most television stations still rely on human transcription to generate the closed captioning for their live broadcasts. Yet even with the benefit of human fluency, this captioning can vary wildly in quality, even within the same broadcast, from a nearly flawless rendition to near-gibberish. At the same time, automatic speech recognition has historically struggled to achieve sufficient accuracy to entirely replace human transcription. Using a week of television news from the Internet Archive's Television News Archive, how does the station-provided primarily human-created closed captioning compare with machine-generated transcripts generated by Google's Cloud Speech-to-Text API? Automated high-quality captioning of live video represents one of the holy grails of machine speech recognition. While machine captioning systems have improved dramatically over the years, there has still been a substantial gap holding them back from fully matching human accuracy.

Analogy is a key solution to automated music generation, featured by its ability to generate both natural and creative pieces based on only a few examples. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level representations and their relationships, from one piece to another; however, this procedure requires disentangling music representations, which takes little effort for musicians but is non-trivial for computers. Three sub-problems arise: extracting latent representations from the observation, disentangling the representations so that each part has a unique semantic interpretation, and mapping the latent representations back to actual music. An explicitly-constrained conditional variational auto-encoder (EC2-VAE) is proposed as a unified solution to all three sub-problems. In this study, we focus on disentangling the pitch and rhythm representations of 8-beat music clips conditioned on chords. In producing music analogies, this model helps us to realize the imaginary situation of "what if" a piece is composed using a different pitch contour, rhythm pattern, chord progression etc., by borrowing the representations from other pieces. Finally, we validate the proposed disentanglement method using objective measurements and evaluate the analogy examples by a subjective study.

Over the Top (OTT) content distribution has changed the way video or audio content is consumed and it seems to stay for a while. Consumption of home entertainment via Internet-connected devices has increasingly become the trend for most people. Even though linear TV today continues to offer traditional TV packages along with OTT, viewing habits have shifted towards OTT-only content over the years. Studies suggest that if your platform is easily discoverable, if you have great content, if you offer an intuitive user experience, and if it is reasonably priced, people will subscribe and get hooked onto your OTT service. OTT customer acquisition and retention is quite a challenge, especially in a market with many players that offer original content, that are attractively priced, and that offer consistent user experience.

Earlier in 2017, Facebook was working on a new highly-intelligent AI chat-bot that could talk to and negotiate with humans in a realistic manner. When one Facebook engineer had the bright idea to take two of these AI bots and let them talk to each other, that's when something unexpected and terrifying happened. The two AIs invented their own language that us humans couldn't understand and began using it to talk to each other. The Facebook engineers had no idea what they where talking about, but it was very clear that the AIs did communicate. They had invented their own secret code to converse using the power of artificial neural networks.

Deep-learning techniques have made it easier and easier for anyone to forge convincing misinformation. Two researchers at the United Nations decided to find out. In a new paper, they used only open-source tools and data to show how quickly they could get a fake UN speech generator up and running. They used a readily available language model that had been trained on text from Wikipedia and fine-tuned it on all the speeches given by political leaders at the UN General Assembly from 1970 to 2015. Thirteen hours and $7.80 later (spent on cloud computing resources), their model was spitting out realistic speeches on a wide variety of sensitive and high-stakes topics from nuclear disarmament to refugees.

Once upon a time, media content had a price. To consume it, you had to pay for it. Years passed, and with advances in technology along came digital piracy. The problem grew significantly with Internet making its way into everyone's home and then expanded exponentially when the same technology was made accessible from everyone's hands. Some media domains, such as live sports, only later began being exploited by modern day pirates.

Almost half of HR leaders say predictive analytics and machine learning would improve their HR function, according to a Censuswide survey commissioned by MHR Analytics. Only one in ten (11%) of respondents currently use advanced analytics techniques, but the research indicates a clear desire among HR professionals to implement predictive analytics to upgrade and enhance almost all aspects of their role. Laura Timms, MHR Analytics' Product Strategy Manager said: "The results mirror the conversations we are having with our 750 customers – there is a real step-change in how HR professionals see their roles evolving and becoming more sophisticated. "While most organizations still use spreadsheet-based data, they are aware that harnessing predictive analytics and machine learning tools will enable them to spend much less time on admin and manual tasks and become valuable and efficient business strategists." "As we approach the next decade, it's clear that organizations in general, as well as the HR experts employed within them, are thinking about how they can climb the data maturity scale to remain competitive, using HR analytics, workforce planning systems, and even AI."

NEW YORK - Google on Thursday released new details about its cloud streaming video game service Stadia, which will be available in 14 countries starting in November. For the launch, Google will sell its "founders edition bundle" hardware pack for $129, with a monthly subscription price of $9.99. In Europe, the price will be €129 and €9.99 per month. The new gaming platform aims for a Netflix-style subscription that enables players to access games on any device, powered by the cloud. That could disrupt the huge gaming industry by allowing users to avoid consoles and game software on disc or download.

Actors who flub dialogue may become less of a bane to producers thanks to an AI that allows such to be edited just by retyping a transcript of their lines. The software works by combining existing clips with digital face models to create new footage that is lip-synced to match the desired edits. The technology could have the potential to be abused to create more creepy deepfake videos that make people appear to say things that they never did. However, the researchers say that these risks are more than outweighed by the benefits of the program, which could also cheaply localise or translate content. At present, the technique only works on videos with a forward-facing interview that are of a particular length - 40 minutes or over.

We are excited to announce that, to help us get there, we have acquired Fabula AI (Fabula), a London-based start-up, with a world-class team of machine learning researchers who employ graph deep learning to detect network manipulation. Graph deep learning is a novel method for applying powerful ML techniques to network-structured data. The result is the ability to analyze very large and complex datasets describing relations and interactions, and to extract signals in ways that traditional ML techniques are not capable of doing. Twitter has been criticized for the amount of fake news and misinformation that easily spreads on its platform. Though the company has taken steps to combat such misinformation in recent years, fake news is still a major problem for the social network.