This new tool uses “semantic segmentation” to bring a whole new level of power to image editing.
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- In 2016 alone, 58,320 people in the U.S. died from leukemia. This new development could increase a patients’ chance of survival
- Current treatment methods can increase a patient’s risk of secondary cancer after remission. This Engineered T-Cell approach has not shown to be carcinogenic
Wouldn’t it be wonderful if the need for chemotherapy no longer existed? In some cases, these treatments aren’t even effective enough to send patients into remission, but for many people, there are few other options.
What if there was an easier and more effective way to tackle cancer? Thanks to one recent case, there is.
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Arranging employees in an office is like creating a 13-dimensional matrix that triangulates human wants, corporate needs, and the cold hard laws of physics: Joe needs to be near Jane but Jane needs natural light, and Jim is sensitive to smells and can’t be near the kitchen but also needs to work with the product ideation and customer happiness team—oh, and Jane hates fans. Enter Autodesk’s Project Discover. Not only does the software apply the principles of generative design to a workspace, using algorithms to determine all possible paths to your #officegoals, but it was also the architect (so to speak) behind the firm’s newly opened space in Toronto.
That project, overseen by design firm The Living, first surveyed the 300 employees who would be moving in. What departments would you like to sit near? Are you a head-down worker or an interactive one? Project Discover generated 10,000 designs, exploring different combinations of high- and low-traffic areas, communal and private zones, and natural-light levels. Then it matched as many of the 300 workers as possible with their specific preferences, all while taking into account the constraints of the space itself. “Typically this kind of fine-resolution evaluation doesn’t make it into the design of an office space,” says Living founder David Benjamin. OK, humans—you got what you wanted. Now don’t screw it up.
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COLORADO SPRINGS, Colo. – The seats in Blue Origin’s suborbital spaceship are like a dentist’s chair that’s fully extended, with a big difference. You can float out of this one when weightlessness sets in.
Of course, we couldn’t get the zero-G experience when we tried out the seats in a mock-up of the New Shepard crew capsule, on display here at the 33rd Space Symposium. But we did get a condensed version of the 11-minute flight scenario, from launch to landing.
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Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. During that time it was also clear that, given the existing hardware, if people did voice searches for three minutes per day or dictated to their phone for short periods, Google would have to double the number of datacenters just to run machine learning models.
The need for a new architectural approach was clear, Google distinguished hardware engineer, Norman Jouppi, tells The Next Platform, but it required some radical thinking. As it turns out, that’s exactly what he is known for. One of the chief architects of the MIPS processor, Jouppi has pioneered new technologies in memory systems and is one of the most recognized names in microprocessor design. When he joined Google over three years ago, there were several options on the table for an inference chip to churn services out from models trained on Google’s CPU and GPU hybrid machines for deep learning but ultimately Jouppi says he never excepted to return back to what is essentially a CISC device.
We are, of course, talking about Google’s Tensor Processing Unit (TPU), which has not been described in much detail or benchmarked thoroughly until this week. Today, Google released an exhaustive comparison of the TPU’s performance and efficiencies compared with Haswell CPUs and Nvidia Tesla K80 GPUs. We will cover that in more detail in a separate article so we can devote time to an in-depth exploration of just what’s inside the Google TPU to give it such a leg up on other hardware for deep learning inference. You can take a look at the full paper, which was just released, and read on for what we were able to glean from Jouppi that the paper doesn’t reveal.
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Big Dog marches again.
The US Marine Corps is preparing to resume testing on its four-legged robot, “Spot.”
A project of the Corps’ Warfighting Lab, the dog-sized device is slated to re-enter developmental testing in the fall.
Capt. Mike Malandra, who heads the Warfighting Lab’s science and technology branch, said that Spot’s hydraulic legs may make it more maneuverable than the small, unmanned Modular Advanced Armed Robotic System, which features treads similar to a tank rather than limbs.
The solitary mountain on the dwarf planet Ceres may be slowly disappearing, following in the footsteps of earlier peaks.
New research suggests that the outer layer of the icy world may be slowly shifting over time, allowing the peak to gradually stretch out and sink into the crust. Similar mountains may have peppered the planet in the past and flattened out over time.
“It’s sort of like if you spill some syrup or honey on a plate and you watch it spread out over time, not instantaneously like water does but a little more slowly, it eventually gets to a flatter, broader shape; it’s the same process,” Michael Sori, a planetary scientist at the University of Arizona, told Space.com. [NASA Probe Snaps Stunning New Pics of Dwarf Planet Ceres].
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