An artificial intelligence project backed by SpaceX founder Elon Musk has been so successful its developers are not releasing it to the public for fear it will be misused. Research group Open AI developed a'large-scale unsupervised language model' that is able to generate news stories from a simple headline. But the group insists it will not be releasing details of the programme and instead has unveiled a much smaller version for research purposes. Its developers claim the technology is poised to rapidly advance in the coming years and the full specification and details of the project will only be released when the negative applications have been discussed by researchers. Elon Musk's AI research group Open AI announced in a paper yesterday that it has generated'a large-scale unsupervised language model' that can write news stories from little more than a headline.

I'm going to go ahead and spoil Alita: Battle Angel for you. Not because I'm a dick, but because revealing the ending tells you nothing about the plot and will ruin absolutely nothing about the film. It ends--drum roll, please--with Alita (Rosa Salazar), sword in hand, staring down her foe, her Big Bad. Then it cuts to black and the credits play. The whole movie is a setup for a punch line that never comes.

Recently I uploaded a experiment: can a Perceptron play a simple game? I did this to prove that you can do interesting things with simple code. Now I just finished my MLP, so I can do more robust things. Anyways, here's the link in case you want to see the code.

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.