By Gary Pretty, Technical Strategist, Mando Group
It seems like bots are everywhere these days, with more and more popping up every day. From bots that help us tag people on Facebook to simple Twitter bots that respond to our tweets.
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What generates voltage when you warm it up, push on it, or blow on it?
Get your mind out of the gutter. The correct answer is polyvinylidene fluoride, a material NASA researchers have refined for use in morphing aircraft that shapeshift in response to their environment. But wait! There’s more: It can also kickstart the human body’s healing process.
Because of its potential to heal the world and make it a better place, the polymer’s inventors, Mia Siochi and Lisa Scott Carnell, have now turned it over to the public through NASA’s Technology Transfer Program. Through that process, companies license NASA technology for cheap and turn it into products to sell to non-astronauts. But transforming space stuff into Earth stuff isn’t always smooth. Turned-over technology can get lost inside the catalog, stall out in the bowels of a company, or become part of a product the original inventors wouldn’t approve of.
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A Marinha dos Estados Unidos fez uma parceria com cientistas do Massachusetts Institute of Technology (MIT) para criar uma rede de sensores barato, portátil que monitora o consumo de energia de cada um dos seus dispositivos domésticos.
O sistema consiste de cinco sensores de tamanho selo-postais colocados acima ou perto de linhas elétricas que entram na casa, que podem identificar cada luminária ou aparelho com base em seu uso de energia. Ele canaliza os dados para um aplicativo em tempo real, permitindo que as pessoas possa ver quando sua geladeira consome para um ciclo de degelo, por exemplo.
“Já existem maneiras de monitorar o uso de energia doméstico, mas elas envolvem a contratação de um eletricista licenciado ou cortando linhas de energia ou tubos caro para anexar, equipamento especializado,” professor de engenharia do MIT e chefe do projeto Stephen Leeb disse em um comunicado de imprensa. “Com o nosso sistema, é possível instalar sensores sem contato usando laços zip ou mesmo velcro, e usar o processamento de sinal para medir o consumo de energia. Também poderia servir como uma maneira de dizer quando o equipamento precisa de manutenção ou substituição.”
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What might life be like once autonomous vehicles populate the roads? With the help of colleague Timothy Bonds, RAND’s Nidhi Kalra described what may occur when autonomous vehicles “democratize transportation.” Read our recap from #PoliticsAside: r.rand.org/326y
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What if a simple algorithm were all it took to program tomorrow’s artificial intelligence to think like humans?
According to a paper published in the journal Frontiers in Systems Neuroscience, it may be that easy — or difficult. Are you a glass-half-full or half-empty kind of person?
Researchers behind the theory presented experimental evidence for the Theory of Connectivity — the theory that all of the brains processes are interconnected (massive oversimplification alert) — “that a simple mathematical logic underlies brain computation.” Simply put, an algorithm could map how the brain processes information. The painfully-long research paper describes groups of similar neurons forming multiple attachments meant to handle basic ideas or information. These groupings form what researchers call “functional connectivity motifs” (FCM), which are responsible for every possible combination of ideas.
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In experiments with magnetic atoms conducted at extremely low temperatures, scientists have demonstrated a unique phase of matter: the atoms form a new type of quantum liquid or quantum droplet state. These so called quantum droplets may preserve their form in absence of external confinement because of quantum effects. The joint team of experimental physicists from Innsbruck and theoretical physicists from Hannover report on their findings in the journal Physical Review X.$$!ad_code_content_spilt_video_ad!$$” Our Quantum droplets are in the gas phase but they still drop like a rock,” explains experimental physicist Francesca Ferlaino when talking about the fascinating experiment. In the laboratory, her team observed how macrodroplets formed in a quantum gas.
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When you see a photo of a dog bounding across the lawn, it’s pretty easy for us humans to imagine how the following moments played out. Well, scientists at MIT have just trained machines to do the same thing, with artificial intelligence software that can take a single image and use it to to create a short video of the seconds that followed. The technology is still bare-bones, but could one day make for smarter self-driving cars that are better prepared for the unexpected, among other applications.
The software uses a deep-learning algorithm that was trained on two million unlabeled videos amounting to a year’s worth of screen time. It actually consists of two separate neural networks that compete with one another. The first has been taught to separate the foreground and the background and to identify the object in the image, which allows the model to then determine what is moving and what isn’t.
According to the scientists, this approach improves on other computer vision technologies under development that can also create video of the future. These involve taking the information available in existing videos and stretching them out with computer-generated vision, by building each frame one at a time. The new software is claimed to be more accurate, by producing up to 32 frames per second and building out entire scenes in one go.
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