Regardless of which piece of visual media or literature first instilled thoughts of artificial intelligence in our minds, it had quite the effect on us. Despite AI being very much a part of our current society, many of us don't realize it's here. Instead we're fixed on dystopian futures and malevolent machines. In reality, AI is disrupting dozens of sectors. From healthcare and transportation to fintech and telecommunications -- more than 154,000 AI patents have been filed since 2010 alone.

Giant technology companies might cause significant disruption to the world's financial system, the head of the International Monetary Fund has warned. Christine Lagarde said just a few firms with big data access and artificial intelligence could run the global payment and settlement arrangements. Her warning came as the G20 finance ministers met in Japan. The summit is also discussing the need to close tax loopholes for internet giants like Facebook and Google. One of the options being considered is to tax such companies where they make their profits - rather than where they base their headquarters.

Tech giants like Facebook and governments around the world are struggling to deal with disinformation, from misleading posts about vaccines to incitement of sectarian violence. As artificial intelligence becomes more powerful, experts worry that disinformation generated by A.I. could make an already complex problem bigger and even more difficult to solve. In recent months, two prominent labs -- OpenAI in San Francisco and the Allen Institute for Artificial Intelligence in Seattle -- have built particularly powerful examples of this technology. Both have warned that it could become increasingly dangerous. Alec Radford, a researcher at OpenAI, argued that this technology could help governments, companies and other organizations spread disinformation far more efficiently: Rather than hire human workers to write and distribute propaganda, these organizations could lean on machines to compose believable and varied content at tremendous scale.

The Report Titled on "Global Artificial Intelligence (AI) in Social Media Market – Segmented By Technology (Machine Learning and Deep Learning, …

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.