A developer and YouTuber named Denis Shiryaev recently used "several neural networks" to update a famous French short film from 1895 to make it appear as though it were shot on a modern phone. Called "L'arrivée d'un train en gare de La Ciotat," the 50-second French short documentary shows a train arriving at Ciotat train station. The film is famous for allegedly causing theater goers at the time to panic out of fear the train would burst through the screen and crash into them. While this tale is largely dismissed as little more than legend, it's easy to imagine the impact such a visual would have had on someone who'd never seen a motion picture before. Shiryaev apparently felt the classic masterpiece needed a modern update so they employed what they refer to on Reddit as "several neural networks" to upscale the film to 4K resolution at 60 fps. The video's also had sound added, which makes it all the more eerily… normal.

What exactly can AI currently do for HR professionals, and will it ever replace them? Find out the answer to this, and more questions regarding AI, in this article. AI is touted as the most innovative technology that is already changing everything about our lives. But while journalists praise robotic companions and helpers, some of us are still waiting for the huge changes that are supposed to be happening all around us. Because let's face it, the fact that Netflix or YouTube have machine learning algorithms which handpick content for us isn't really a huge change in our lives.

Underwater photography is hard to get right. Special filters, artificial lights, and top-of-the-line underwater cameras can help, but there's still a lot of water between the camera and the object in the photo. We've become accustomed to the blue-green tint of underwater photography. How would the ocean look without water? What are the true colors of a coral reef?

Clearview AI CEO Hoan Ton-That tells CBS correspondent Errol Barnett that the First Amendment allows his company to scrape the internet for people's photos. Google and YouTube have sent a cease-and-desist letter to Clearview AI, the facial recognition company that has been scraping billions of photos off the internet and using it to help more than 600 police departments identify people within seconds. That follows a similar action by Twitter, which sent Clearview AI a cease-and-desist letter for its data scraping in January. The letter from Google-owned YouTube was first seen by CBS News. (Note: CBS News and CNET share the same parent company, ViacomCBS.) The CEO of Clearview AI, a controversial and secretive facial recognition startup, is defending his company's massive database of searchable faces, saying in an interview on CBS This Morning Wednesday that it's his First Amendment right to collect public photos.

Artificial intelligence (AI) has been used to create a stunning 4K version of the classic black and white film L'Arrivée d'un train en gare de La Ciotat. The 45-second clip is famed for the urban legend -- which is now thought to be false -- that cinema-goers fled the theatre when they saw the film for the first time for fear the train would burst through the screen. YouTuber Denis Shiryaev posted the remastered video on his site which is now in 4K quality at 60 frames per second. The upscaled and source videos look very different when compared side-by-side (pictured). This'upscaled' footage was created using neural network-powered algorithms such as Topaz Labs' Gigapixel AI and DAIN.

While AI-generated writing has yet to produce a movie script of any worth, Guardian film critic Steve Rose says start-ups are still angling to forge a tool that will. ScriptBook, for example, is developing an AI-generated writing tool that will suggest scenes for sci-fi movies when screenwriters get stuck. In the meantime, Hollywood is already using AI analytics to help predict which combinations of ideas, movie scripts and actors will translate into box office gold, according to Rose. Not surprisingly, Rose wonders where this is all going. "Could it be that by embracing AI, Hollywood is unwittingly building an equivalent of the Terminator movies' Skynet?" Rose observes.

AI is widely misunderstood and still too rudimentary for us to be worrying. But it's not too soon to contemplate the ethical implications of intelligent machines and systems. An AI system is only as good as the data it receives. It is able to interpret them only within the narrow confines of the supplied context. It can't distinguish causation from correlation.

And he's right to be worried, because AI is being used for sinister purposes by The Consortium, an ultra-secret group of ruthless trillionaires ... CONSORTIUM is an Artificial Intelligence black comedy by English science-fiction novelist Simon Fox.

The explosive growth of fake news has eroded the credibility of medias and governments. Fake news detection has become an urgent task. News articles along with other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short). In this paper, we focus on studying the HIN- based fake news detection problem. We propose a novel fake news detection framework, namely Hierarchical Graph Attention Network (HGAT) which employs a novel hierarchical attention mechanism to detect fake news by classifying news article nodes in the HIN. This method can effectively learn information from different types of related nodes through node-level and schema-level attention. Experiments with real-world fake news data show that our model can outperform text-based models and other network-based models. Besides, the experiments also demonstrate the expandability and potential of HGAT for heterogeneous graphs representation learning in the future.

Automatic music generation is an interdisciplinary research topic that combines computational creativity and semantic analysis of music to create automatic machine improvisations. An important property of such a system is allowing the user to specify conditions and desired properties of the generated music. In this paper we designed a model for composing melodies given a user specified symbolic scenario combined with a previous music context. We add manual labeled vectors denoting external music quality in terms of chord function that provides a low dimensional representation of the harmonic tension and resolution. Our model is capable of generating long melodies by regarding 8-beat note sequences as basic units, and shares consistent rhythm pattern structure with another specific song. The model contains two stages and requires separate training where the first stage adopts a Conditional Variational Autoencoder (C-VAE) to build a bijection between note sequences and their latent representations, and the second stage adopts long short-term memory networks (LSTM) with structural conditions to continue writing future melodies. We further exploit the disentanglement technique via C-VAE to allow melody generation based on pitch contour information separately from conditioning on rhythm patterns. Finally, we evaluate the proposed model using quantitative analysis of rhythm and the subjective listening study. Results show that the music generated by our model tends to have salient repetition structures, rich motives, and stable rhythm patterns. The ability to generate longer and more structural phrases from disentangled representations combined with semantic scenario specification conditions shows a broad application of our model.