The loss of life would be equivalent to six planes, each carrying 200 passengers, killing everyone on board, every year.
Reducing air pollution from road transport will save thousands of lives and improve the health.
In our published research we evaluated the costs and benefits of a rapid transition. In one scenario, Australia matches the pace of transition of world leaders such as Norway. The modeling estimates this would save around 24,000 lives by 2042. Over time, the resulting greenhouse emission reductions would almost equal Australia’s current total annual emissions from all sources.
We also calculated the total costs and benefits through to 2042. Australia would be about 148 billion Australian dollars better off overall with a rapid transition.
A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.
“The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”
Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.
This is also the fastest IPMSM built with commercialized lamination materials.
Researchers at the University of New South Wales Sydney have developed a new electric motor that can clock 100,000 revolutions per minute. The high power density achieved as a result of this new design could help reduce the weight of electric vehicles (EVs) and thereby increase their range, a university press release said.
EV makers around the world have been looking for ways to address the range anxiety of their battery-powered vehicles. One of the options is to increase the size of the battery pack, which also increases the weight of the vehicle, creating more problems to solve.
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.
“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.
▀▀▀ 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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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.
