Human susceptibility to epidemics of misinformation and disease has grown manyfold as the world we inhabit keeps getting smaller due to increased access to online information and soaring global mobility. Social media platforms have changed the way we consume information [Schmidt et al., 2017], and more and more people find their news through social media [Newman et al., 2015]. Following the 2016 presidential election in the United States, there have been investigations into the spread of false stories, or "fake news" on social media, and based on web browsing data, archives of fact-checking websites, and results from an online survey, a recent study found that social media were an important source of election news [Allcott and Gentzkow, 2017]. While ascertainment of social network structures is generally difficult using traditional survey-based approaches, such as name generators, which are survey questions designed to solicit information about friends and acquaintances of a subject, online platforms readily capture the structure of large-scale social networks, therefore making them well suited to study spread of information whether accurate or not. Further, although the transmission mechanisms are very different, the spread of information in online systems has many similarities to the spread of infectious diseases among hosts in a population. From a mathematical and statistical point of view, one can therefore investigate the spread of pathogens and the spread of information in the same framework as long as the network structure accurately captures the transmission pathways and the spreading process is parametrized appropriately. In this paper, we consider a simple susceptible-infected (SI) process and a more complex spreading process on a fixed and known network structure. This spreading process may be conceptualized as propagating either a pathogen or a piece of information. We focus on addressing two distinct questions that are relevant in both settings: (1) How to infer the unknown parameters associated with the spreading process?

Duncan Jones' next movie won't be coming to theaters -- it's going straight to streaming. The Moon and Warcraft director has revealed that his long-in-the-making sci-fi film noire, Mute, will premiere on Netflix February 23rd. The movie is set in a future Berlin where a mute bartender (played by Alexander Skarsgård) has to trust a pair of American surgeons (led by Paul Rudd) as he tracks down a disappeared woman. There's no trailer yet, but in many ways the effort taken to release the movie is the hook -- Netflix is giving Jones a chance that might not have come up through conventional formats. As Jones noted, Mute is his "Don Quixote." It was supposed to be his first movie (he had a first draft in 2003), but it got pushed back for a number of reasons.

What happens when you take a perfectly good neural network and, figuratively, stick a screwdriver in its brain? You get melancholy glitch-art music videos that turn talking heads into digital puppets. A machine learning developer named Jeff Zito made a series of music videos using a deep learning network based on Face2Face. Originally developed to generate stunningly realistic image transfers, like controlling a digital Obama in real-time using your own facial movements, this project takes it in a different direction. When it comes to art, for example, computations and algorithms often don't matter as much as chaos and noise do.

The company says in future its text-to-image technology could act as a sketch assistant to painters and interior designers. With more computing power, Mr He imagines the technology could generate animated films based on screenplays. He added this would'augment the work that animated filmmakers do' by removing some of the manual labour involved.

Almost every year at CES, potentially the greatest gathering of visionaries and ideas that push technology forward, one would see a robot walking, rolling or sliding down an aisle. Occasionally, one of these robots would even pop up during a company's presentation, as was in the case of LG's Cloi during this year's show. While these robots are always interesting to look at, they rarely deliver on the promises of their maker -- whether it be a startup or a veteran like LG. Take a look again at Cloi's embarrassing refusal to answer the questions of LG's head of marketing in the U.S., David VanderWaal: LG's Cloi was one among the many robots that graced the CES this year. The most useful among them, as Wilson Rothman noted in The Wall Street Journal, were not the humanoid looking ones.

The former executive director of LSL Property Services, David Newnes, is one of the key backers of a new property management platform powered by artificial intelligence. Former TV Dragon James Caan and PropTech guru Faisal Butt are amongst other investors in AskPorter, which has now secured investment of over £500,000. AskPorter is a platform with an AI digital assistant that serves as a personal assistant for property managers and as a concierge for customers and tenants. The digital assistant'Porter' proactively carries out planned duties, guides customers through enquiries and reporting issues, and delivers concierge services, only reverting to a human manager or approved service provider when necessary. "This new technology has useful, practical applications that will add to and significantly improve the service that managing agents are able to offer landlords and tenants - both in AST and block management" says Newnes.

Imagine being able to generate high-quality photos just be describing them to a computer. This sci-fi scenario is now a reality, thanks to Microsoft's new AI tool. Drawing Bot created the above image simply from the description of "a bird with a yellow body, black wings and a short beak," using a new technique where the AI pays close attention to individual words when generating images from caption-like text descriptions, resulting in a 3-fold boost in image quality compared to other text-to-image generation techniques. The bot can do more than just birds, being able to draw everything from ordinary pastoral scenes, such as grazing livestock, to the absurd, such as a floating double-decker bus. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," said Xiaodong He, a principal researcher and research manager in the Deep Learning Technology Center at Microsoft's research lab in Redmond, Washington.

Check out the "Media, entertainment, and advertising" sessions at the Strata Data Conference in San Jose, March 5-8, 2018. Hurry--early price ends January 19. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify.

Anybody who has tried Google Photos would agree that this free photo storage and management service from Google is smart. It packs in various smart features like advanced search, ability to categorize your pictures by locations and dates, automatically create albums and videos based on similarities, and walk you down the memory lane by showing you photos of the same day several years ago. There are many things Google Photos can do that several years ago would be machine-ly impossible. Google Photos is one of the many "smart" services from Google that uses a machine learning technology called TensorFlow. The word learning indicates that the technology will get smarter by time to the point that our current knowledge cannot imagine.

Microsoft today is unveiling new artificial intelligence technology that's something of an artist – a "drawing bot." The bot is capable of creating images from text descriptions of an object, but it also adds details to those images that weren't included the text, indicating that the AI has a little imagination of its own, says Microsoft. "If you go to Bing and you search for a bird, you get a bird picture. But here, the pictures are created by the computer, pixel by pixel, from scratch," explained Xiaodong He, a principal researcher and research manager in the Deep Learning Technology Center at Microsoft's research lab in Redmond, Washington, in Microsoft's announcement. "These birds may not exist in the real world -- they are just an aspect of our computer's imagination of birds."