China appears to be a leader in maglev technology and continues to find innovative ways to use it.
A car equipped with magnetic levitation (maglev) technology has been successfully tested on a highway in East China’s Jiangsu province, according to an article by China Daily.
Provincial transport authorities are testing a new highway lane for maglev cars. The experiment saw a 2.8-tonne car float 35 millimeters above the road and run smoothly on a highway without crashing or veering.
A high-tech sponge can absorb water vapor from the air and convert it to pure hydrogen for use in hydrogen fuel cell vehicles and machines. This…
“We think that each car needs to sound for itself.”
It was roughly six years ago when Audi started designing bold soundtracks for its growing line of hybrids and EVs. Why did the 111-year-old carmaker need custom sounds for its forward-looking product line? It all comes down to one thing: electric vehicles are practically silent, even when traveling at high speeds.
Audi.
The idea of silent cars might seem exciting if you spend your days walking beside noisy urban streets, but quiet cars have a couple of drawbacks. For one thing, they’re dangerous to pedestrians and other drivers. That’s why most countries have a series of regulations that set acceptable ranges for the volume and pitch of the noises that EVs have made. Another down of noiseless EVs is the driving experience. A full-bodied roar makes driving more fun.
Visit https://brilliant.org/Veritasium/ to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription. Digital computers have served us well for decades, but the rise of artificial intelligence demands a totally new kind of computer: analog.
Thanks to Mike Henry and everyone at Mythic for the analog computing tour! https://www.mythic-ai.com/
Thanks to Dr. Bernd Ulmann, who created The Analog Thing and taught us how to use it. https://the-analog-thing.org.
Moore’s Law was filmed at the Computer History Museum in Mountain View, CA.
Welch Labs’ ALVINN video: https://www.youtube.com/watch?v=H0igiP6Hg1k.
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References:
Crevier, D. (1993). AI: The Tumultuous History Of The Search For Artificial Intelligence. Basic Books. – https://ve42.co/Crevier1993
Valiant, L. (2013). Probably Approximately Correct. HarperCollins. – https://ve42.co/Valiant2013
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65, 386–408. – https://ve42.co/Rosenblatt1958
NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser (1958). The New York Times, p. 25. – https://ve42.co/NYT1958
Mason, H., Stewart, D., and Gill, B. (1958). Rival. The New Yorker, p. 45. – https://ve42.co/Mason1958
Alvinn driving NavLab footage – https://ve42.co/NavLab.
Pomerleau, D. (1989). ALVINN: An Autonomous Land Vehicle In a Neural Network. NeurIPS, 1305-313. – https://ve42.co/Pomerleau1989
ImageNet website – https://ve42.co/ImageNet.
Russakovsky, O., Deng, J. et al. (2015). ImageNet Large Scale Visual Recognition Challenge. – https://ve42.co/ImageNetChallenge.
AlexNet Paper: Krizhevsky, A., Sutskever, I., Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS, (25)1, 1097–1105. – https://ve42.co/AlexNet.
Karpathy, A. (2014). Blog post: What I learned from competing against a ConvNet on ImageNet. – https://ve42.co/Karpathy2014
Fick, D. (2018). Blog post: Mythic @ Hot Chips 2018. – https://ve42.co/MythicBlog.
Jin, Y. & Lee, B. (2019). 2.2 Basic operations of flash memory. Advances in Computers, 114, 1–69. – https://ve42.co/Jin2019
Demler, M. (2018). Mythic Multiplies in a Flash. The Microprocessor Report. – https://ve42.co/Demler2018
Aspinity (2021). Blog post: 5 Myths About AnalogML. – https://ve42.co/Aspinity.
Wright, L. et al. (2022). Deep physical neural networks trained with backpropagation. Nature, 601, 49–555. – https://ve42.co/Wright2022
Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530144–147. – https://ve42.co/Waldrop2016
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Written by Derek Muller, Stephen Welch, and Emily Zhang.
Filmed by Derek Muller, Petr Lebedev, and Emily Zhang.
Animation by Iván Tello, Mike Radjabov, and Stephen Welch.
Edited by Derek Muller.
Additional video/photos supplied by Getty Images and Pond5
Music from Epidemic Sound.
Produced by Derek Muller, Petr Lebedev, and Emily Zhang.
How can mobile robots perceive and understand the environment correctly, even if parts of the environment are occluded by other objects? This is a key question that must be solved for self-driving vehicles to safely navigate in large crowded cities. While humans can imagine complete physical structures of objects even when they are partially occluded, existing artificial intelligence (AI) algorithms that enable robots and self-driving vehicles to perceive their environment do not have this capability.
Robots with AI can already find their way around and navigate on their own once they have learned what their environment looks like. However, perceiving the entire structure of objects when they are partially hidden, such as people in crowds or vehicles in traffic jams, has been a significant challenge. A major step towards solving this problem has now been taken by Freiburg robotics researchers Prof. Dr. Abhinav Valada and Ph.D. student Rohit Mohan from the Robot Learning Lab at the University of Freiburg, which they have presented in two joint publications.
The two Freiburg scientists have developed the amodal panoptic segmentation task and demonstrated its feasibility using novel AI approaches. Until now, self-driving vehicles have used panoptic segmentation to understand their surroundings.
Designer Oscar Vinals introduced his concept for HSP Magnavem in 2018, but it’s getting attention again because of the developments in the industry.
In the Providentia++ project, researchers at the Technical University of Munich (TUM) have worked with industry partners to develop a technology to complement the vehicle perspective based on onboard sensor input with a bird’s-eye view of traffic conditions. This improves road safety, including for autonomous driving.
The expectations for autonomous driving are clear: “Cars have to travel safely not only at low speeds, but also in fast-moving traffic,” says Jörg Schrepfer, the head of Driving Advanced Research Germany at Valeo. For example, when objects fall off a truck, the “egocentric” perspective of a car will often be unable to detect the hazardous debris in time. “In these cases, it will be difficult to execute smooth evasive action,” says Schrepfer.
Researchers in the Providentia++ project have developed a system to transmit an additional view of the traffic situation into vehicles. “Using sensors on overhead sign bridges and masts, we have created a reliable, real-time digital twin of the traffic situation on our test route that functions around the clock,” says Prof. Alois Knoll, project lead manager TUM. “With this system, we can now complement the vehicle’s view with an external perspective—a bird’s-eye view—and incorporate the behavior of other road users into decisions.”
Move over, Elon Musk and Richard Branson: A Canadian company wants to join the fight for better high-speed train travel.
Toronto-based TransPod recently unveiled plans for a “FluxJet,” a fully-electric transportation system that’s “a hybrid between an aircraft and a train.” The project, currently in the conceptual stage, would involve 82-foot-long, magnetically levitated trains that would carry passengers at roughly 621 miles per hour.
That’s faster than a commercial jet, and roughly three times the speed of most high-speed trains — with zero emissions, no less. The FluxJet would rely on “contactless power transmission,” where the train would pull power from the existing electric grid through magnetic fields, the company says.
UNSW engineers have built a new high-speed motor which has the potential to increase the range of electric vehicles.
The design of the prototype IPMSM type motor was inspired by the shape of the longest railroad bridge in South Korea and has achieved speeds of 100,000 revolutions per minute.
The maximum power and speed achieved by this novel motor have successfully exceeded and doubled the existing high-speed record of laminated IPMSMs (Interior Permanent Magnet Synchronous Motor), making it the world’s fastest IPMSM ever built with commercialized lamination materials.
