MIT tech lets self-driving cars “see” under surface of road

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In poor visibility, your car could look for landmarks — under the surface of the road.

 MIT is working on self-driving technology that allows cars to “see” through the ground up to a depth of ten feet below the surface of the road. The idea is to allow self-driving cars to figure out exactly where they are — especially when snow, heavy fog, or other bad weather obscures road markings.

Current-generation self-driving cars typically rely on cameras and light detection sensors (LIDAR) to position themselves on roadways. But once the snow starts falling and covers up lane markers, it can get tricky for the car to tell where it is — and that could spell disaster, especially at highway speeds.

A team at MIT’s Computer Science and Artificial Intelligence Lab have come up with a new system they call “Localizing Ground Penetrating Radar” (LGPR) that can create a real-time map of the ground below the road’s surface.

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Elon Musk says all advanced AI development should be regulated, including at Tesla

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Tesla and SpaceX CEO Elon Musk is once again sounding a warning note regarding the development of artificial intelligence. The executive and founder tweeted on Monday evening that “all org[anizations] developing advance AI should be regulated, including Tesla.”

Musk was responding to a new MIT Technology Review profile of OpenAI, an organization founded in 2015 by Musk, along with Sam Altman, Ilya Sutskever, Greg Brockman, Wojciech Zaremba and John Schulman. At first, OpenAI was formed as a non-profit backed by $1 billion in funding from its pooled initial investors, with the aim of pursuing open research into advanced AI with a focus on ensuring it was pursued in the interest of benefiting society, rather than leaving its development in the hands of a small and narrowly-interested few (i.e., for-profit technology companies).

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How cheap must batteries get for renewables to compete with fossil fuels?

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Production of lithium batteries for environmentally friendly electric cars future of energy

 While solar and wind power are rapidly becoming cost-competitive with fossil fuels in areas with lots of sun and wind, they still can’t provide the 24/7 power we’ve become used to. At present, that’s not big a problem because the grid still features plenty of fossil fuel plants that can provide constant baseload or ramp up to meet surges in demand.

But there’s broad agreement that we need to dramatically decarbonize our energy supplies if we’re going to avoid irreversible damage to the climate. That will mean getting rid of the bulk of on-demand, carbon-intensive power plants we currently rely on to manage our grid.

Alternatives include expanding transmission infrastructure to shuttle power from areas where the wind is blowing to areas where it isn’t, or managing demand using financial incentive to get people to use less energy during peak hours. But most promising is pairing renewable energy with energy storage to build up reserves for when the sun stops shining.

The approach is less complicated than trying to redesign the grid, say the authors of a new paper in <emJoule, but also makes it possible to shift much more power around than demand management. A key question that hasn’t been comprehensively dealt with, though, is how cheap energy storage needs to get to make this feasible.

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MIT scientists develop a way to recover details from blurry images

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A group of MIT researchers have developed a way to recover lost details from images and create clear copies of motion-blurred parts in videos. Their creation, an algorithm called a “visual deprojection model,” is based on a convolutional neural network. They trained that network by feeding it pairs of low-quality images and their high-quality counterparts, so it could learn how the latter can produce blurry, barely visible footage.

When the model is used to process previously unseen low-quality images with blurred elements, it analyzes them to figure out what in the video could’ve produced the blur. It then synthesizes new images that combine data from both the clearer and blurry parts of a video. Say, you have footage of your yard with something moving on screen — the technology can create a version of that video that clearly shows the movement’s sources.

During the team’s tests, the model was able to recreate 24 frames of a video showing a particular person’s gait, their size and the position of their legs. Before you get excited and think that it could one day make CSI’s zoom and enhance a reality, the researchers are more focused on refining the technology for medical use. They believe it could be used to convert 2D images like X-rays into 3D images with more information like CT scans at no additional cost — 3D scans are a lot more expensive — making it especially valuable for developing nations.

“If we can convert X-rays to CT scans, that would be somewhat game-changing. You could just take an X-ray and push it through our algorithm and see all the lost information.”

Via Engadget.com

 

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The world’s most advanced nanotube computer may keep Moore’s Law alive

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Up close photograph of nanotube

 MIT researchers have found new ways to cure headaches in manufacturing carbon nanotube processors, which are faster and less power hungry than silicon chips.

A team of academics at MIT has unveiled the world’s most advanced chip yet that’s made from carbon nanotubes—cylinders with walls the width of a single carbon atom. The new microprocessor, which is capable of running a conventional software program, could be an important milestone on the road to finding silicon alternatives.

The electronics industry is struggling with a slowdown in Moore’s Law, which holds that the number of transistors that can be packed on a silicon processor doubles roughly every couple of years. This trend is facing its physical limits: as the sizes of the devices shrink to a few atoms, electrical current is starting to leak from the metallic channels that shuttle it through transistors. The heat that’s released saps semiconductors’ energy efficiency—and may even cause them to fail.

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Sorry, scooters aren’t so climate-friendly after all

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A look at the full lifetime emissions of the vehicles call into question the ecological assumptions around “micromobility.”

Bird boasts that its dockless electric scooters allow customers to “cruise past traffic and cut back on CO2 emissions—one ride at a time.”

Its rival Lime claims the vehicles “reduce dependence on personal automobiles for short distance transportation and leave future generations with a cleaner, healthier planet.”

But the mere fact that battery-powered scooters don’t belch pollution out of a tailpipe doesn’t mean they’re “emissions free,” or as “eco-friendly” as some have assumed. The actual climate impact of the vehicles depends heavily on how they’re made, what they’re replacing, and how long they last.

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Machine learning has been used to automatically translate long-lost languages

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Some languages that have never been deciphered could be the next ones to get the machine translation treatment.

In 1886, the British archaeologist Arthur Evans came across an ancient stone bearing a curious set of inscriptions in an unknown language. The stone came from the Mediterranean island of Crete, and Evans immediately traveled there to hunt for more evidence. He quickly found numerous stones and tablets bearing similar scripts and dated them from around 1400 BCE.

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MIT AI model is ‘significantly’ better at predicting breast cancer

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The model can find breast cancer earlier and eliminates racial disparities in screening.

MIT researchers have invented a new AI-driven way of looking at mammograms that can help detect breast cancer in women up to five years in advance. A deep learning model created by a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital can predict — based on just a mammogram — whether a woman will develop breast cancer in the future. And unlike older methods, it works just as well on black patients as it does on white patients.

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A robot has figured out how to use tools

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In a startling demonstration, the machine drew on experimentation, data, and observation of humans to learn how simple implements could help it achieve a task.

Learning to use tools played a crucial role in the evolution of human intelligence. It may yet prove vital to the emergence of smarter, more capable robots, too.

New research shows that robots can figure out at least the rudiments of tool use, through a combination of experimenting and observing people.

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Sorry, graphene—borophene is the new wonder material that’s got everyone excited

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Stronger and more flexible than graphene, a single-atom layer of boron could revolutionize sensors, batteries, and catalytic chemistry.

Not so long ago, graphene was the great new wonder material. A super-strong, atom-thick sheet of carbon “chicken wire,” it can form tubes, balls, and other curious shapes. And because it conducts electricity, materials scientists raised the prospect of a new era of graphene-based computer processing and a lucrative graphene chip industry to boot. The European Union invested €1 billion to kick-start a graphene industry.

This brave new graphene-based world has yet to materialize. But it has triggered an interest in other two-dimensional materials. And the most exciting of all is borophene: a single layer of boron atoms that form various crystalline structures.

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