Data privacy protections are almost nonexistent when it comes to automobiles.
Mozilla looked at 25 car brands and found that all of them collected too much personal data, and from multiple sources—monitoring not just which buttons you push or what you do in any of the infotainment system’s apps but also data from other sources like satellite… More.
Today, the Mozilla Foundation published its analysis of how well automakers handle the privacy of data collected by their connected cars, and the results will be unlikely to surprise any regular reader of Ars Technica. The researchers were horrified by their findings, stating that “cars are the worst product category we have ever reviewed for privacy.”
A team of scientists from Ames National Laboratory has developed a new machine learning model for discovering critical-element-free permanent magnet materials. The model predicts the Curie temperature of new material combinations. It is an important first step in using artificial intelligence to predict new permanent magnet materials. This model adds to the team’s recently developed capability for discovering thermodynamically stable rare earth materials. The work is published in Chemistry of Materials.
High performance magnets are essential for technologies such as wind energy, data storage, electric vehicles, and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements like neodymium and dysprosium. These materials are in high demand but have limited availability. This situation is motivating researchers to find ways to design new magnetic materials with reduced critical materials.
Machine learning (ML) is a form of artificial intelligence. It is driven by computer algorithms that use data and trial-and-error algorithms to continually improve its predictions. The team used experimental data on Curie temperatures and theoretical modeling to train the ML algorithm. Curie temperature is the maximum temperature at which a material maintains its magnetism.
While regulators move to create the rules and regulations for personal air vehicles, eVTOL (electric vertical takeoff and landing) crafts and flying taxis, the ecosystem of managing all aspects of those flying vehicles on the ground is getting underway.
The flying vehicles, despite not needing a traditional runway since they take off straight up and land vertically, still need a place to do that and be serviced and maintained.
Since most are electric, the flying vehicles need battery charging or changing between flights. They also need a facility for passengers to get on and off and a place to leave their car or have Uber drop them off or pick them up.
“Operating and navigating in home environments is very challenging for robots. Every home is unique, with a different combination of objects in distinct configurations that change over time. To address the diversity a robot faces in a home environment, we teach the robot to perform arbitrary tasks with a variety of objects, rather than program the robot to perform specific predefined tasks with specific objects. In this way, the robot learns to link what it sees with the actions it is taught. When the robot sees a specific object or scenario again, even if the scene has changed slightly, it knows what actions it can take with respect to what it sees.
We teach the robot using an immersive telepresence system, in which there is a model of the robot, mirroring what the robot is doing. The teacher sees what the robot is seeing live, in 3D, from the robot’s sensors. The teacher can select different behaviors to instruct and then annotate the 3D scene, such as associating parts of the scene to a behavior, specifying how to grasp a handle, or drawing the line that defines the axis of rotation of a cabinet door. When teaching a task, a person can try different approaches, making use of their creativity to use the robot’s hands and tools to perform the task. This makes leveraging and using different tools easy, allowing humans to quickly transfer their knowledge to the robot for specific situations.
Historically, robots, like most automated cars, continuously perceive their surroundings, predict a safe path, then compute a plan of motions based on this understanding. At the other end of the spectrum, new deep learning methods compute low-level motor actions directly from visual inputs, which requires a significant amount of data from the robot performing the task. We take a middle ground. Our teaching system only needs to understand things around it that are relevant to the behavior being performed. Instead of linking low-level motor actions to what it sees, it uses higher-level behaviors. As a result, our system does not need prior object models or maps. It can be taught to associate a given set of behaviors to arbitrary scenes, objects, and voice commands from a single demonstration of the behavior. This also makes the system easy to understand and makes failure conditions easy to diagnose and reproduce.”
Electric vehicles use lithium ion batteries with small amounts of nickel, manganese and cobalt. How do they work and what chemistry affects their properties?
Toyota, renowned as the world’s largest car company, has often been perceived as an anti-EV automaker due to its cautious approach and reluctance to embrace the EV revolution.
Instead of succumbing to the hype surrounding these vehicles, Toyota has consistently maintained its stance, emphasizing the need for battery technology to reach a certain stage before committing to the electric path.
The Koenigsegg CC850 that debuted during August’s 2022 Monterey Car Week is a re-imagination of the Swedish firm’s first production car, and one of the highlights is a transmission like no other.
The CC850 uses a version of the 9-speed Light Speed Transmission from the Koenigsegg Jesko, but with a new Engage Shifter System (ESS) that makes the automatic transmission transform into a 6-speed manual (it still maintains the full automatic mode with nine gears, though). This Engineering Explained video provides a deep dive into Koenigsegg’s reinvented manual.
Koenigsegg’s latest hypercar features an automatic transmission that can transform into a manual complete with a clutch pedal.
Don’t always follow what is popular, as what is good becomes popular, like the iPhone vs Nokia phones. How many people has Tiktok poached from Facebook? WeChat is inspiring Elon Musk. Yeah China has made fake stuff in the past, but this isn’t as well as China’s electric car market. I don’t discriminate, and I even want to buy my parents a Nigerian car that runs on LNG and CNG. Eric I hope if they make this concept, you buy Gen this Honor V Purse, or even buy both of you the Honor Magic V2.
The HONOR Magic V2 features a 6.2 Cover Display, a 7.92-inch Main Foldable Display, 120Hz Refresh Rate, has Triple Rear Cameras, 50MP Primary, 50 Ultrawide and 20MP Telephoto, Dual 16MP Selfie Camera, 5000mAh Battery, Powered by the latest Qualcomm Snapdragon 8 Gen 2 with up to 16GB RAM, Pen Support and Price is from 8,999 Yuan.
X’s new privacy policy, which is due to come into effect on September 29, states that the company “may use the information we collect and publicly available information to help train our machine learning or artificial intelligence models for the purposes outlined in this policy.” This policy is not included in its previous terms, which are still posted online.
Musk responded to a post about this change on X, saying that it would only use publicly available information to train the AI and would not use “DMs or anything private.”
During a live audio session on X – formerly Twitter – in July, Elon Musk said that his AI startup, xAI, would use public data from his social media platform to train its AI models. Insider reached out to X for comment but didn’t immediately hear back. It is not clear how it will use the information from X and which AI models this relates to.
