An Australian team has won a competition to write a hit Eurovision song using artificial intelligence. An editor for Dutch broadcaster VPRO had the idea, after the Netherlands won last year's Eurovision Song Contest. And it grew into an international effort after this year's contest was cancelled because of the coronavirus pandemic. The winning song, Beautiful the World, was inspired by nature's recovery from the bushfires earlier this year. A total of 13 teams took part, from the Netherlands, Australia, Sweden, Belgium, the UK, France, Germany and Switzerland.

The future is now people. Not only do we have pandemic-proof rave suits being designed, we also now might be on the precipice of having music released made with Artificial Intelligence thanks to the latest development from OpenAI. Aptly titled'Jukebox', the new model is now able to generate genre-specific music. According to OpenAI's website, Jukebox is "a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles." Using over 1.6million songs as their dataset, Jukebox is able to use a song provided as input, and generate a sample produced from scratch in specific genres as output.

Even in the year 2020, it's not very hard to be led astray on Facebook. Click a few misleading links and you can find yourself at the bottom of an ethnonationalist rabbit hole facing a flurry of hate speech and medical misinformation. But with the help of AI and machine learning systems, the social media platform is accelerating its efforts to keep this content from spreading. It's bad enough that we're having to deal with the COVID-19 pandemic without being bombarded on Facebook with ads for sham cures and conspiracy theories passed off as the gospel truth. The company is already partnering with 60 fact checking organizations to fight this disinformation and has issued a temporary ban to halt the sale of PPE, hand sanitizers, and cleaning supplies on the platform since the start of the outbreak in March.

This is the AI Song Contest, where thirteen teams from Europe and Australia compete for the title of the best song created using artificial intelligence. The teams have been working overtime lately, hunched over their computers to create the ultimate Eurovision hit with the help of artificial intelligence. The best track as chosen by the audience and AI experts will be unveiled in this festive live stream. Who will scoop up douze points and win the AI Song Contest 2020? The AI Song Contest researches the creative possibilities of artificial intelligence and is organised by Dutch public broadcaster VPRO in collaboration with NPO 3FM and NPO Innovation.

Thomas Roszak was working as a maintenance technician at Ocado's giant warehouse in Hatfield when he received a very unusual assignment. His regular job involved repairing and maintaining the online supermarket's automated sorting and packing system, which puts together grocery orders from customers. It can be physically demanding work, manipulating heavy panels and working with other pieces of bulky machinery. In a project designed to ease that burden, Ocado Technology, had been developing a robot that can recognise when a technician might need help and step in with either the right tool or help with lifting. "I grew up on movies like The Terminator, so when I saw that robot I was actually impressed with what it looked like. You can actually imagine it looks like a man."

The economic fallout from the COVID-19 pandemic has caused an unprecedented crisis in journalism that could decimate media organizations around the world. The future of journalism -- and its survival -- could lie in artificial intelligence (AI). AI refers "to intelligent machines that learn from experience and perform tasks like humans," according to Francesco Marconi, a professor of journalism at Columbia University in New York, who has just published a book on the subject: Newsmakers, Artificial Intelligence and the Future of Journalism. Marconi was head of the media lab at the Wall Street Journal and the Associated Press, one of the largest news organizations in the world. His thesis is clear and incontrovertible: the journalism world is not keeping pace with the evolution of new technologies.

Fake news has become a huge issue in our digitally-connected world and it is no longer limited to little squabbles -- fake news spreads like wildfire and is impacting millions of people every day. How do you deal with such a sensitive issue? Countless articles are being churned out every day on the internet -- how do you tell real from fake? It's not as easy as turning to a simple fact-checker which is typically built on a story-by-story basis. In this series, we will see two approaches to predict if a given article is fake or not.

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.

We introduce GACELA, a generative adversarial network (GAN) designed to restore missing musical audio data with a duration ranging between hundreds of milliseconds to a few seconds, i.e., to perform long-gap audio inpainting. While previous work either addressed shorter gaps or relied on exemplars by copying available information from other signal parts, GACELA addresses the inpainting of long gaps in two aspects. First, it considers various time scales of audio information by relying on five parallel discriminators with increasing resolution of receptive fields. Second, it is conditioned not only on the available information surrounding the gap, i.e., the context, but also on the latent variable of the conditional GAN. This addresses the inherent multi-modality of audio inpainting at such long gaps and provides the option of user-defined inpainting. GACELA was tested in listening tests on music signals of varying complexity and gap durations ranging from 375~ms to 1500~ms. While our subjects were often able to detect the inpaintings, the severity of the artifacts decreased from unacceptable to mildly disturbing. GACELA represents a framework capable to integrate future improvements such as processing of more auditory-related features or more explicit musical features.

More than three-quarters (78%) of consumers worldwide say companies must address bias in artificial intelligence (AI) and new research from Genpact (NYSE: G), a global professional services firm focused on delivering digital transformation, finds that they will reward businesses that take action. The study, now in its third year, underscores how AI continues to present opportunities for growth, but businesses still have work to do to address customers' concerns about bias and workers' concerns about equity in re-skilling opportunities. Empathising deeply with customer concerns is what will separate the winners from losers. Genpact's study, AI 360: Hold, fold, or double down?, shows that while 69% of UK consumers worry about AI discriminating against them, and 64% fear that AI will make decisions that affect them without their knowledge, companies that understand these issues and act accordingly can succeed. The study analyses perceptions of three distinct audiences that are critical to AI's widespread adoption in business: senior executives, workers, and consumers.