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Teslas are among the most susceptible vehicles to be hacked due to their Bluetooth locks, cybersecurity firm NCC Group said. The cars can be remotely unlocked and controlled by hackers that can exploit a vulnerability in the Bluetooth system’s security, the group said.

NCC Group researcher Sultan Qasim Khan was shown in a video opening, then driving a Tesla using a small relay device attached to a laptop. The device bridged a large gap between the Tesla and the Tesla owner’s phone, Reuters said.

“This proves that any product relying on a trusted BLE connection is vulnerable to attacks even from the other side of the world,” NCC said in a statement. BLE means Bluetooth Low Energy, and is a technology utilized in vehicles and Bluetooth locks that will automatically unlock or unlatch when an authorized device is nearby. While it is a convenience feature, it is not immune to attacks, which was the point of NCC’s experiment.

No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.

What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed.

In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.

Tesla is again rumored to be near a deal for a major new battery venture in Indonesia after a new round of talks with officials and even CEO Elon Musk meeting with the Indonesian President Joko “Jokowi” Widodo.

It’s not the first time that talks of Tesla making a major investment in Indonesia have emerged.

In 2020, we reported on Tesla allegedly being in talks with the Indonesian government to build a new nickel venture in the country, which has a strong nickel reserve.

A research group from Politecnico di Milano has developed a new computing circuit that can execute advanced operations, typical of neural networks for artificial intelligence, in one single operation.

The circuit performance in terms of speed and paves the way for a new generation of computing accelerators that are more energy efficient and more sustainable on a global scale. The study has been recently published in the prestigious Science Advances.

Recognizing a face or an object, or correctly interpreting a word or a musical tune are operations that are today possible on the most common electronic gadgets, such as smartphones and tablets, thanks to artificial intelligence. For this to happen, complicated neural networks needs to be appropriately trained, which is so energetically demanding that, according to some studies, the that derives from the training of a complex can equal the emission of 5 cars throughout their whole life cycle.

The steering wheel, gas, and brakes that safety drivers will use if they need to take over are separate from the system the buses use to navigate autonomously. During the initial two-week testing period, buses will run without passengers, but the companies involved are aiming to have riders on board by summer.

The self-driving software made by Fusion Processing, called CAVstar for “connected and autonomous vehicles,” isn’t limited to radar, lidar, or cameras, but rather integrates all three. The buses are clearly marked as autonomous so nearby drivers are aware that a computer’s running the show. The question is, how much will this impact drivers’ behavior and relevant driving decisions? Would you feel less rude cutting off a driverless bus? More obliged to let it pass you? Or just sort of confused by the whole situation?

Each bus can carry 36 passengers, and the number of planned trips per day mean the autonomous buses could move up to 10,000 passengers a week. The project’s leaders anticipate the self-driving buses reducing average trip time and improving schedule reliability of the route. This sounds like it’ll mostly be a good thing, but what will happen when, say, an elderly or disabled passenger needs some extra time to get on or off the bus?

While many other companies in the industry are focusing on designing and developing hydrogen-fueled aircraft from scratch, ZeroAvia is gearing up to test its retrofitted Dornier 228 this summer.

The company has partnered with its strategic investor, Shell, to design and build two commercial-scale mobile hydrogen refuelers for use at ZeroAvia’s research and development site in Hollister, California. At ZeroAvia’s test facility in Hollister, Shell will also provide compressed, low-carbon hydrogen supply to the facility and other locations in the Western U.S.

This strategic collaboration will support the development of ZeroAvia’s flight testing program in the U.S. following the arrival of its second Dornier 228 at Hollister last month and will advance the company’s Hydrogen Airport Refueling Ecosystem (HARE) on a larger scale.

NVIDIA has published the source code of its Linux kernel modules for the R515 driver, allowing developers to provide greater integration, stability, and security for Linux distributions.

The source code has been published to NVIDIA’s GitHub repository under a dual licensing model that combines the GPL and MIT licenses, making the modules legally re-distributable.

The products supported by these drivers include all models built on the Turing and Ampere architecture, released after 2018, including the GeForce 30 and GeForce 20 series, the GTX 1,650 and 1,660, and data center-grade A series, Tesla, and Quadro RTX.

Deep reinforcement learning.

The system is so efficient because it uses deep reinforcement learning, meaning it actually adapts its processes when it is not doing well and continues improving when it makes progress.

“We have set this up as a traffic control game. The program gets a ‘reward’ when it gets a car through a junction. Every time a car has to wait or there’s a jam, there’s a negative reward. There’s actually no input from us; we simply control the reward system,” said Dr. Maria Chli, a reader in Computer Science at Aston University.