UK startup Fabula AI reckons it's devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals. Even Facebook's Mark Zuckerberg has sounded a cautious note about AI technology's capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side. "It will take many years to fully develop these systems," the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. "This is technically difficult as it requires building AI that can read and understand news." But what if AI doesn't need to read and understand news in order to detect whether it's true or false? Step forward Fabula, which has patented what it dubs a "new class" of machine learning algorithms to detect "fake news" -- in the emergent field of "Geometric Deep Learning"; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this'non-Euclidean' space. The startup says its deep learning algorithms are, by contrast, capable of learning patterns on complex, distributed data sets like social networks.

The Harvard College Observatory (now the Center for Astrophysics) in Cambridge, Massachusetts has long been a bastion of astronomical research, its history stretching back to the center's founding in 1839. But for the first forty years of its existence, the HCO was quite literally an old boys club. While amateur female astronomers helped fund and even construct the observatory's telescopes, "it wasn't really seen as proper to allow them out on the roof, in the night, on their own, to actually use instruments," Daina Bouquin, Head Librarian of the Wolbach Library at the Center for Astrophysics and lead of the PHaEDRA project, told Engadget. "The beginning of the whole capacity to do that starts like photography, with people putting together these all-sky surveys," she continued. "And the first group of people to do that, to put together a full survey of the entire visible universe at the time was the Harvard Computers."

I have heard a lot about rising popularity of machine learning'plug and play' program suits such as IBM Watson and Aspentech Mtell. I am used to building my own models and identifying the best candidate fit for each new dataset, these developers claim they can do all this automatically. Do any of you have any hands-on experience with the performance of these models and the strengths and weaknesses compared to building your own?

From virtual assistants like Siri and Alexa, to recommendations from a music or movie streaming service, to self-driving cars, Artificial Intelligence is all around us. And yet, few technologies are as polarizing and misunderstood. The notion of Artificial Intelligence often generates either awe or fear in most people, but whether you believe it will save the world or enslave the world, the first thing you need to know is that there is more than one type of AI. General Artificial Intelligence (also known as "Artificial General Intelligence" or "AGI") is what most people are thinking of when they talk about AI: a computer that's as "smart" as a person--one that exhibits reason and common sense, makes decisions, perceives the world around it and can learn new things on its own. Such a system would require immense computational power that, when combined with advanced robotics, would be able to carry out any task a person could do, as well as many others we could not.

Artificial intelligence scares the heck out of some people. Others are excited about the rise of the new technology. Either way, AI will be a greater part of our lives in the future as the line between science fiction and science fact dissolves. Are we ready for a new kind of relationship with technology? A new book that explores the popular HBO series "Westworld" might give us an idea.

This article is written by Jonty Sinai and he is explaining the fundementals of neural ode and how its different from current artificial neural network.

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After 14 long years, Kingdom Hearts III has finally graced our consoles. Stuck in development limbo for 14 years, fans of the series were worried this game might not ever see the light of day, or worse, that it would be rushed out unfinished. Now that it's here, does it live up to the hype? From the first few hours of the game, it's clear to see it's anything but rushed. When you arrive in the first Disney-based world you're immediately drawn in by an overpowering sense of nostalgia and the realisation of how much you've been missing Kingdom Hearts.

Online food ordering and delivery app Swiggy on Monday announced it has acqui-hired Kint.io, an Artificial Intelligence (AI) start-up that applies deep learning and computer vision for object recognition in video, for an undisclosed sum. Founded in 2014, the Bengaluru-based start-up would assist Swiggy in boosting its computer-vision technology and consumer experience. "This acqui-hire is part of our strategy to scale our tech prowess by bringing in entrepreneurial teams that can solve unique customer problems, while leveraging the network and resources at Swiggy," said Dale Vaz, Head of Engineering and Data Sciences, Swiggy. Kint.io is the first technology-led acqui-hire for Swiggy as it makes investments in its long-term strategy of building AI-first platforms. "AI research has leap-frogged this past year but lack of data, cultural biases and inability to adapt to our diversity has somehow always pulled us back when it comes to applying AI to India-based problems. This is where Swiggy left us stumped," said Jawahar and Veeraraghavan.

New work from MIT researchers peers under the hood of an automated fake-news detection system, revealing how machine-learning models catch subtle but consistent differences in the language of factual and false stories. The research also underscores how fake-news detectors should undergo more rigorous testing to be effective for real-world applications. Popularized as a concept in the United States during the 2016 presidential election, fake news is a form of propaganda created to mislead readers, in order to generate views on websites or steer public opinion. Almost as quickly as the issue became mainstream, researchers began developing automated fake news detectors -- so-called neural networks that "learn" from scores of data to recognize linguistic cues indicative of false articles. Given new articles to assess, these networks can, with fairly high accuracy, separate fact from fiction, in controlled settings.