A Japanese robot has begun probing the radioactive water at Fukushima's nuclear reactor. The marine robot, nicknamed the'little sunfish', is on a mission to study structural damage and find fuel inside the three reactors of the devastated plant. Experts said remote-controlled bots are key to finding fuel at the dangerous site, which has likely melted and been submerged by highly radioactive water. A Japanese robot has begun probing the radioactive water at Fukushima's nuclear reactor. An underwater robot has captured images and other data inside Japan's crippled Fukushima nuclear plant on its first day of work.

Auto-Encoders are unsupervised models that aim to learn patterns from observed data by minimizing a reconstruction cost. The useful representations learned are often found to be sparse and distributed. On the other hand, compressed sensing and sparse coding assume a data generating process, where the observed data is generated from some true latent signal source, and try to recover the corresponding signal from measurements. Looking at auto-encoders from this \textit{signal recovery perspective} enables us to have a more coherent view of these techniques. In this paper, in particular, we show that the \textit{true} hidden representation can be approximately recovered if the weight matrices are highly incoherent with unit $ \ell^{2} $ row length and the bias vectors takes the value (approximately) equal to the negative of the data mean. The recovery also becomes more and more accurate as the sparsity in hidden signals increases. Additionally, we empirically demonstrate that auto-encoders are capable of recovering the data generating dictionary when only data samples are given.

What if a machine helped you make a dinner reservation? In some ways, most of us already do that when we book a dinner reservation with OpenTable. The app makes it seem like it isn't machine learning making it all possible, although we likely get a hint of cognitive power behind how it all works. Really, we're just hoping to meet for business at a seafood restaurant in Mill Valley north of the Golden Gate Bridge. We might not care if there is an AI involved; what we care about is whether there is a table available.

Prepare to be "teleported" from Southern California to the Bay Area. That's the idea behind a new bus equipped with sleeping cabins that leaves Santa Monica at night and delivers your well-rested self to San Francisco the next morning. Snooze your way north in what Tom Currier, CEO and co-founder of the new service called Cabin, calls "a hotel that moves from one place to the other." He thinks travelers will enjoy making the journey using their sleep time as travel time. "I'm not spending six hours driving, I'm on a memory foam bed, and I'm more refreshed when I get into the city," Currier said.

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Considered the first electrical speech synthesizer, VODER (Voice Operation DEmonstratoR) was developed by Homer Dudley at Bell Labs and demonstrated at both the 1939 New York World's Fair and the 1939 Golden Gate International Exposition. Difficult to use and difficult to operate, VODER nonetheless paved the way for future machine-generated speech.

At biotech startup Zymergen, robotic fingers are poised to pick microbe colonies in an AI-controlled quest for strains that crank out more chemicals. EMERYVILLE, CALIFORNIA--If this is the biology laboratory of the future, it doesn't look so different from today's. Scientists in white lab coats walk by with boxes of frozen tubes. The chemicals on the shelves--bottles of pure alcohol, bins of sugar, protein, and salts--are standard issue for growing microbes and manipulating their genes. You don't even notice the robots until you hear them: They sound like crickets singing to each other amid the low roar of fans.

The SpaceX Dragon capsule that made history by becoming the first recycled spacecraft to fly two missions was recovered on Tuesday. The capsule took supplies to the International Space Station and splashed down as planned in the Pacific Ocean on Monday. SpaceX announced on Twitter that the Dragon hit the water off the California coast shortly after 5am Monday. After being released by the space station's robotic arm, the capsule completed a 5½-hour journey back to Earth, part of which was captured by NASA astronaut Jack Fischer. NASA astronaut Jack Fischer photographed the SpaceX Dragon capsule as it reentered Earth's atmosphere before splashing down in the Pacific Ocean west of Baja California at 8:12 a.m. EDT, July 3, 2017.

Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

Scientists are using artificial intelligence (AI) to help predict which embryos will result in IVF success. In a new study, AI was found to be more accurate than embryologists at pinpointing which embryos had the potential to result in the birth of a healthy baby. Experts from Sao Paulo State University in Brazil have teamed up with Boston Place Clinic in London to develop the technology in collaboration with Dr Cristina Hickman, scientific adviser to the British Fertility Society. They believe the inexpensive technique has the potential to transform care for patients and help women achieve pregnancy sooner. During the process, AI was "trained" in what a good embryo looks like from a series of images.