Electric motors have a lot of power and it is instant on-demand.
When Tesla unveiled its new Plaid Model S earlier this month, it took the world by storm, fueling dreams of speed, racing, and fun. In the sense of a grand finale to Tesla’s event, another event was talked about in the weeks following. That event was the annual race to the summit of Pikes Peak. Also known as “The Race to the Clouds,” the entire track winds through 156 turns over 12.42 miles as it reaches the summit of Pikes Peak.
There’s no motorcycle on the planet like this one. British company White Motorcycle Concepts (WMC) has put land speed record holders on notice with a 2WD, hydraulically hub-steered electric motorcycle, designed around a giant hole. The company says the WMC250EV should be capable of more than 250 mph (402 km/h) thanks to a massive 69 percent reduction in drag.
Rob White has paid his dues in the racing world, working on numerous Formula One, Le Mans Prototype, V8 supercar and World Endurance Championship race teams over the last 25-odd years. And his approach to motorcycle design is clearly influenced by the world of high-end cars.
Going super fast ends up being much more about aerodynamics than horsepower; the air becomes a ferocious adversary as you move past two or three times highway speed. Motorcycles are aerodynamically ugly without big, streamlined fairings, chiefly because of the big, funny-shaped human on the back.
Researchers at Google Brain announced a deep-learning computer vision (CV) model containing two billion parameters. The model was trained on three billion images and achieved 90.45% top-1 accuracy on ImageNet, setting a new state-of-the-art record.
The team described the model and experiments in a paper published on arXiv. The model, dubbed ViT-G/14, is based on Google’s recent work on Vision Transformers (ViT). ViT-G/14 outperformed previous state-of-the-art solutions on several benchmarks, including ImageNet, ImageNet-v2, and VTAB-1k. On the few-shot image recognition task, the accuracy improvement was more than five percentage-points. The researchers also trained several smaller versions of the model to investigate a scaling law for the architecture, noting that the performance follows a power-law function, similar to Transformer models used for natural language processing (NLP) tasks.
First described by Google researchers in 2017, the Transformer architecture has become the leading design for NLP deep-learning models, with OpenAI’s GPT-3 being one of the most famous. Last year, OpenAI published a paper describing scaling laws for these models. By training many similar models of different sizes and varying the amount of training data and computing power, OpenAI determined a power-law function for estimating a model’s accuracy. In addition, OpenAI found that not only do large models perform better, they are also more compute-efficient.
The AIR program was run by a company called Persistent Surveillance Systems with funding from two Texas billionaires. The city police department admitted to using planes to surveil Baltimore residents in 2016 but approved a six-month pilot program in 2020, which was active until October 31st.
The city of Baltimore’s spy plane program was unconstitutional, violating the Fourth Amendment protection against illegal search, and law enforcement in the city cannot use any of the data it gathered, a court ruled Thursday. The Aerial Investigation Research (or AIR) program, which used airplanes and high-resolution cameras to record what was happening in a 32-square-mile part of the city, was canceled by the city in February.
Local Black activist groups, with support from the ACLU, sued to prevent Baltimore law enforcement from using any of the data it had collected in the time the program was up and running. The city tried to argue the case was moot since the program had been canceled. That didn’t sit well with civil liberties activists. “Government agencies have a history of secretly using similar technology for other purposes — including to surveil Black Lives Matter protests in Baltimore in recent years,” the ACLU said in a statement Thursday.
In an en banc ruling, the US Court of Appeals for the Fourth Circuit found that “because the AIR program enables police to deduce from the whole of individuals’ movements, we hold that accessing its data is a search and its warrantless operation violates the Fourth Amendment.” Chief Judge Roger Gregory wrote that the AIR program “is like a 21st century general search, enabling the police to collect all movements,” and that “allowing the police to wield this power unchecked is anathema to the values enshrined in our Fourth Amendment.”
Without GPS, autonomous systems get lost easily. Now a new algorithm developed at Caltech allows autonomous systems to recognize where they are simply by looking at the terrain around them—and for the first time, the technology works regardless of seasonal changes to that terrain.
Details about the process were published on June 23 in the journal Science Robotics.
The general process, known as visual terrain-relative navigation (VTRN), was first developed in the 1960s. By comparing nearby terrain to high-resolution satellite images, autonomous systems can locate themselves.
If you walk down the street shouting out the names of every object you see — garbage truck! bicyclist! sycamore tree! — most people would not conclude you are smart. But if you go through an obstacle course, and you show them how to navigate a series of challenges to get to the end unscathed, they would.
Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning — is strategic. It learns how to take a series of actions in order to reach a goal. That’s powerful and smart — and it’s going to change a lot of industries.
Two industries on the cusp of AI transformations are manufacturing and supply chain. The ways we make and ship stuff are heavily dependent on groups of machines working together, and the efficiency and resiliency of those machines are the foundation of our economy and society. Without them, we can’t buy the basics we need to live and work.
The airless tire isn’t a new concept.
Michelin first introduced its idea for one called.
the Tweel over decade ago, and it started selling.
production versions for small lawn and construction equipment a few years back. But what.
Is new about the tech is its use for actual production cars, and that’s where this new Michelin Uptis tire comes in. The Uptis is designed to handle not just the weight of a real car like the old Tweel, but also be able to provide proper grip and durability at highway speeds, too.
Though the design is now more capable, the Uptis airless tire still uses the same basic idea as the Tweel. Sandwiched between the outer tread and the inner aluminum wheel are a bunch of spokes or ribs that substitute air pressure. These spokes are made of a combination of rubber and fiberglass reinforced resin.
I think there is actually a company that makes something similar to this.
The self-balancing bike is a reminder of the incredibly creative projects that students and young recently graduated engineers can come up with — another recent example is an all-electric monowheel built by a group of Duke University students.
In principle, Zhi Jui Jun’s self-balancing bike should work with someone riding it as well, though no one is shown riding it in Jui Jun’s video — the bicycle steering and keeping balance with the added top-heavy weight of a person would be a sight to behold. Stay posted for updates on any “piloted” tests in the future.
A global semiconductor shortage is causing havoc, delaying car production and affecting operations at large consumer electronics manufacturers.
