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Robotics startup Canvas emerges from stealth with $19M and union partnership

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A union operator steers the machine — which slightly resembles an … and perfecting the automated, machine learning technology that powers the …


How machine learning was used to decode an ancient Chinese cave – Times of India

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'Hidden' Sanskrit text and machine learning helped date an ancient cave along the ancient Silk Road.



AI shows where cities need trees the most

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The company's Tree Canopy Lab uses aerial imagery, 3D digital surface models and artificial intelligence to estimate and map the density of tree canopy …


Beware, AI is Learning All Our Worst Bias Impulses

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Artificial Intelligence (AI) is starting to be more than just a technology that exists to help humans. It is slowly invading people's lives and is even making changes in the routine. Even though when AI looks like a futuristic technology that could do only good to humans, there are concerns on its bias. The science fiction movies have given us a vague outlook on AI technology. The movie directors have portrayed AI robots either as a humble creature that falls in love or a vicious character that takes over humanity.


Here's how we're using AI to help detect misinformation

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Artificial Intelligence is a critical tool to help protect people from harmful content. It helps us scale the work of human experts, and proactively take action, before a problematic post or comment has a chance to harm people. Facebook has implemented a range of policies and products to deal with misinformation on our platform. These include adding warnings and more context to content rated by third-party fact-checkers, reducing their distribution, and removing misinformation that may contribute to imminent harm. But to scale these efforts, we need to quickly spot new posts that may contain false claims and send them to independent fact-checkers -- and then work to automatically catch new iterations, so fact-checkers can focus their time and expertise fact-checking new content.


Video SemNet: Memory-Augmented Video Semantic Network

arXiv.org Artificial Intelligence

Stories are a very compelling medium to convey ideas, experiences, social and cultural values. Narrative is a specific manifestation of the story that turns it into knowledge for the audience. In this paper, we propose a machine learning approach to capture the narrative elements in movies by bridging the gap between the low-level data representations and semantic aspects of the visual medium. We present a Memory-Augmented Video Semantic Network, called Video SemNet, to encode the semantic descriptors and learn an embedding for the video. The model employs two main components: (i) a neural semantic learner that learns latent embeddings of semantic descriptors and (ii) a memory module that retains and memorizes specific semantic patterns from the video. We evaluate the video representations obtained from variants of our model on two tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that our model is able to predict genres and IMDB ratings with a weighted F-1 score of 0.72 and 0.63 respectively. The results are indicative of the representational power of our model and the ability of such representations to measure audience engagement.


Why Aren't There More Sci-Fi Movies About Dreams?

WIRED

In the recent movie Coma, everyone who falls into a coma finds themselves inhabiting the same surreal landscape. Science fiction author Anthony Ha enjoyed the film's premise, and is surprised there aren't more science fiction movies about dreaming. "There isn't quite as much as I would have expected," Ha says in Episode 441 of the Geek's Guide to the Galaxy podcast. "There's so much dream fantasy fiction--and certainly there are a number of science fiction examples too--but it seems a lot less common." The best-known science fiction dream movies, such as Inception and The Cell, are at least a decade old, and the best-known novels on the subject were published in the 1960s and '70s.


How role-playing a dragon can teach an AI to manipulate and persuade

MIT Technology Review

An AI that completes quests in a text-based adventure game by talking to the characters has learned not only how to do things, but how to get others to do things. The system is a step toward machines that can use language as a way to achieve their goals. Pointless prose: Language models like GPT-3 are brilliant at mimicking human-written sentences, churning out stories, fake blogs, and Reddit posts. But there is little point to this prolific output beyond the production of the text itself. When people use language, it is wielded like a tool: our words convince, command, and manipulate; they make people laugh and make people cry.


Dada Group Receives Grants of Funds by 2020 Shanghai Artificial Intelligence Development …

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Dada Group will further enhance the digital transformation capability of empowering retailers based on artificial intelligence and big data, and promote …