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Category: robotics/AI

Circa 2017

This bubbly concept car protects more than the driver; its next-generation rubber exterior can save pedestrians, too.

Traditional metal panels are replaced with soft rubber, which absorbs the impact of a collision. The car is also a shapeshifter, meaning that the rubber panels move and flex, forming a more aerodynamic shape.

The futuristic concept was recently showcased at the Tokyo Motor Show, which also featured artificially intelligent cars and electric vehicles. But none as adorable as this rubbery car.

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“With the help of advanced sensors, AI, and communication technologies, it will be possible to replicate physical entities, including people, devices, objects, systems, and even places, in a virtual world,” the white paper states.

Sharing the full story, not just the headlines.

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A new gadget called the OpenCV AI Kit, or OAK, looks to replicate the success of Raspberry Pi and other minimal computing solutions, but for the growing fields of computer vision and 3D perception. Its new multi-camera PCBs pack a lot of capability into a small, open-source unit and are now seeking funding on Kickstarter.

The OAK devices use their cameras and onboard AI chip to perform a number of computer vision tasks, like identifying objects, counting people, finding distances to and between things in frame and more. This info is sent out in polished, ready-to-use form.

Having a reliable, low-cost, low-power-draw computer vision unit like this is a great boon for anyone looking to build a smart device or robot that might have otherwise required several and discrete cameras and other chips (not to mention quite a bit of fiddling with software).

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I will post a bunch of links to things people can do at home while under lockdown. This is one of my favorite sites. Feel free to check it out and post from it as well.

Calculus is the key to fully understanding how neural networks function. Go beyond a surface understanding of this mathematics discipline with these free course materials from MIT.

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The Indian army is keen to procure RQ-11 UAV Raven – unmanned aerial vehicle from the US besides Israeli Spike Firefly “loitering” munition to support its ground soldiers, reports the HindustanTimes.

Rafale jets dodge all radars, air defence systems; bombs Turkish facilities in Libya

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Robotic landers and rovers have been touching down on Mars since the 1970s, but when will humanity finally set foot on the Red Planet?

Experts believe the technical challenges are nearly resolved, but political considerations make the future of any crewed mission uncertain.

NASA’s human lunar exploration program, Artemis, envisions sending people back to the Moon by 2024 and using the experience gained there to prepare for Mars.

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Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints.

According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations and a constraint satisfaction solver to classify valid configurations for the particular platform, where the neural network model will run efficiently.

The Reason Behind the Patent.

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The 400 kilogram wheeled system moves about the lab guided by LIDAR laser scanners and has an industrial robotic arm made by German firm Kuka that it uses to carry out tasks like weighing out solids, dispensing liquids, removing air from the vessel, and interacting with other pieces of equipment.

In a paper in Nature, the team describes how they put the device to work trying to find catalysts that speed up reactions that use light to split water into hydrogen and oxygen. To do this, the robot used a search algorithm to decide how to combine a variety of different chemicals and updated its plans based on the results of previous experiments.

The robot carried out 688 experiments over 8 days, working for 172 out of 192 hours, and at the end it had found a catalyst that produced hydrogen 6 times faster than the one it started out with.

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Researchers from the Max Delbrück Center for Molecular Medicine have developed a new tool that makes it easier to maximize the power of deep learning for studying genomics. They describe the new approach, Janggu, in the journal Nature Communications.

Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You’d spend way more time on preparation, than actually cooking. For computational biologists, it’s been a similar time-consuming process for analyzing . Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models.

To streamline this process, researchers from MDC developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models. “Before, you ended up wasting a lot of time on the technical aspect, rather than focusing on the biological question you were trying to answer,” says Dr. Wolfgang Kopp, a scientist in the Bioinformatics and Omics Data Science research group at MDC’s Berlin Institute of Medical Systems Biology (BIMSB), and first author of the paper. “With Janggu, we are aiming to relieve some of that technical burden and make it accessible to as many people as possible.”

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