A team of researchers who have built an artificially-intelligent writer say they are withholding the technology as it might be used for "malicious" purposes. OpenAI, based in San Francisco, is a research institute backed by Silicon Valley luminaries including Elon Musk and Peter Thiel. It shared some new research on using machine learning to create a system capable of producing natural language, but in doing so the team expressed concern the tool could be used to mass-produce convincing fake news. Which, to put it another way, is of course also an admission that what its system puts out there is unreliable, made-up rubbish. Still, when it works well, the results are impressively realistic in tone - which is why I've shared a sample of it below.

The creators of a revolutionary AI system that can write news stories and works of fiction – dubbed "deepfakes for text" – have taken the unusual step of not releasing their research publicly, for fear of potential misuse. OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough. At its core, GPT2 is a text generator. The AI system is fed text, anything from a few words to a whole page, and asked to write the next few sentences based on its predictions of what should come next. The system is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses.

As journalists face increased layoffs despite the growing appetite for up-to-the-minute, timely news, a new trend has quietly been disrupting the news industry. News organizations are increasingly turning toward artificial intelligence (AI) for production, using a variety of new automated systems to pump out content with minimal need for direct human input. According to a report by The New York Times, Bloomberg News relies on a system called Cyborg to produce about a third of its articles. Most of Cyborg's output takes the form of company earnings reports that are rife with percentages, charts, and other financial data that can be crunched down into a news story quickly and accurately. Increasingly, major news agency like Reuters and Associated Press, along with a number of newspapers such as Washington Post and Los Angeles Times, are using algorithms to crunch out news on everything from local minor league sports games to earthquakes.

Sam's mum Jude told BBC Radio 5 Live: "Perhaps most importantly for Sam, it's knowing what's going on during school without getting any germs. And just not feeling left out. Feeling like he is still part of the class and that people haven't forgotten him."

Can we talk about this first? Artificial intelligence is going to change the world profoundly, although exactly how is still unclear. The CEO of one AI company recently declared that "working for a living will become obsolete" as smart robots begin providing everything we need from self-driving cars to health care. That's a little hard to believe. But business leaders think AI could soon reduce the human workforce by as much as 99 percent in certain sectors.

Abstract--The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages inanalyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network, they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network. Then, an existing NRL method is used to learn node embeddings for each compressed network. Finally, the node embeddings of the input network are obtained by concatenating the node embeddings from all compressed networks. Empirical studies for link prediction on five real-world datasets demonstrate the advantages of HSRL over state-of-the-art methods. I. INTRODUCTION The science of networks has been widely used to understand thebehaviours of complex systems.

Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series. Currently, most of multiscale RNNs use fixed scales, which do not comply with the nature of dynamical temporal patterns among sequences. In this paper, we propose Adaptively Scaled Recurrent Neural Networks (ASRNN), a simple but efficient way to handle this problem. Instead of using predefined scales, ASRNNs are able to learn and adjust scales based on different temporal contexts, making them more flexible in modeling multiscale patterns. Compared with other multiscale RNNs, ASRNNs are bestowed upon dynamical scaling capabilities with much simpler structures, and are easy to be integrated with various RNN cells. The experiments on multiple sequence modeling tasks indicate ASRNNs can efficiently adapt scales based on different sequence contexts and yield better performances than baselines without dynamical scaling abilities.

The new effects-driven science-fiction thriller "Alita: Battle Angel" stages a behind-the-scenes tussle for the ages: it is a collaboration between Robert Rodriguez, a filmmaker known for such neo-pulp action films as "From Dusk till Dawn" and "Sin City," and James Cameron, a filmmaker whose technological sophistication is matched by a simplistic emotionalism. Here they are thrown together in a virtual video ring and try to collaborate. And, however sincere and earnest their alliance may be, the movie itself tells a different tale: Cameron's sensibility wins, hands down. Not only does Rodriguez give up most of the fun, but Cameron also runs away with the substance. And that's all the more unfortunate, as the two are evenly matched early on in the film and the outcome of their efforts appears, at first, promising.

AI is getting better and better at writing convincing material, and that's leading its creators to wonder whether they should release the technology in the first place. Elon Musk's OpenAI has developed an algorithm that can generate plausible-looking fake news stories on any topic using just a handful of words as a starting point. It was originally designed as a generalized language AI that could answer questions, summarizing stories and translating text, but researchers soon realized that it could be used for far more sinister purposes, like pumping out disinformation in large volumes. As a result, the team only plans to make a "simplified version" of its AI available to the public, according to MIT Technology Review. The technology thankfully has some rough edges at the moment. It frequently writes stories that are either plagiarized or are only cohesive on the surface, and only occasionally hits the jackpot.

When Netflix said that David Fincher and Tim Miller's Love, Death and Robots was an animated series for mature audiences, it wasn't kidding around. The streaming giant has posted the trailer for the 18-story anthology, and you definitely won't be watching this with younger viewers. The title is not only apt, but can sometimes describe one scene -- there are multiple displays of robot sexuality, for starters. The trailer doesn't show enough to indicate whether these will be thought-provoking tales or simply a bit risqué, but it's certainly enough to raise eyebrows (and ears, given the thumping industrial soundtrack). And even if you don't care for it, look at it this way: it might open the door for more adult-oriented animation on Netflix.