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Payloads on the flight will collect valuable data to improve technologies for future exploration missions. This flight will be specifically be used to study how dust disperses in microgravity. Understanding dust dynamics can help abate the damage that is caused by particles contaminating hardware and habitats. Swoop in: https://go.nasa.gov/2Gr79YT

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It’s a problem that has many academics here worried. As India becomes increasingly polarised, coordinated efforts to popularise pseudoscientific theories, and to aggrandise the nation’s own scientific past, have begun to gain ground, they say. It’s a worrying mash-up of nationalism, religion, and scientific bunkum that appears to be an increasingly easy sell—and one that leaves the population both misinformed and perennially at odds with itself. “That is why our leaders and scientists talk about how evolution is wrong,” said Aniket Sule, an astrophysicist and colleague of Karandikar at HBCSE, “or how Indians were first to invent plane or atomic theory, or how cow worship is scientific.”


A wave of superstitions is being promoted as legitimate science.

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Buried beneath 4,000 feet of Antarctic ice lies Lake Mercer, a subglacial body of water that formed thousands of years ago and has been long separated from the rest of the world. A project to explore this lake—and its mysterious contents—is finally set to begin later this month.

Called Subglacial Antarctic Lakes Scientific Access, or SALSA for short, the project aims to uncover new knowledge about Antarctica’s subglacial lakes, of which over 400 are known to exist. Over the next two months, SALSA scientists will explore one of the largest subglacial lakes in West Antarctica, a body of water known as Lake Mercer. The team will bore through some 4,000 feet (1,200 meters) of ice using a 60-centimeter-wide drill capped with hot water. In addition to extracting water and mud samples, the researchers will deploy a remotely operated vehicle—a scientific first for a subglacial lake.

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STOREY COUNTY — The need for more infrastructure and housing is constraining Tesla Motors’ desire to grow its footprint in Northern Nevada, Tesla CEO Elon Musk said Tuesday.

Musk’s comment came during a technology and innovation summit hosted by Gov. Brian Sandoval at Tesla’s Gigafactory 1 located just outside of Sparks.

Musk said he envisions growing the number of employees at the Nevada factory from roughly 7,000 currently to upward of 20,000 in the future, and more than doubling the physical footprint of the 5.8 million-square-foot lithium-ion battery factory.

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Tesla launched a new ‘Enhanced Anti-theft’ system as an aftermarket product last month and we now take a first look at one of the very first installations on a Model X.

There have been strings of Tesla thefts earlier this year that prompt the automaker to offer several new security features.

We reported on several Tesla vehicle thefts through relay attacks in Europe earlier this year.

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Interest in artificial neural networks has skyrocketed over the years as companies like Google and Facebook have invested heavily in machines that can think like humans. Today, an AI can recognize objects in photos or help generate realistic computer speech, but Nvidia has successfully built a neural network that can create an entire virtual world with the help of a game engine. The researchers speculate this “hybrid” approach could one day make AI-generated games a reality.

The system build by Nvidia engineers uses many of the same parts as other AI experiments, but they’re arranged in a slightly different way. To goal of the project was to create a simple driving simulator, but without using any humans to design the environment.

Like all neural networks, the system needed training data. Luckily, work on self-driving cars has ensured there’s plenty of training footage of a vehicle driving around city streets. The team used a segmentation network to recognize different object categories like trees, cars, sky, buildings, and so on. The segmented data is what Nvidia fed into its model, which used a generative adversarial network to improve the accuracy of the final output. Essentially, one network created rendered scenes, and a second network would pass or fail them. Over time, the network is tuned to only create believable data.

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