Lifeboat Foundation

German company Nanoflow Cell unveiled a sleek looking fully electric Quant 48Volt at the Geneva Motor Show this year with the goal of the company bringing the first production car in the world to be powered by saltwater. The Quant 48Volt has two tanks of liquid with dissolved metallic salt which gives them opposite charges. The liquid is separated by a membrane where positively charged ions lose an electron generating electricity.

One fill up of the tanks are good for 621 miles (1,000 km) which astonishingly is greater distance our gasoline vehicles can take us. However, to fill up the tank which has the 3x capacity of large SUV will take quite a bit of time but certainly not hours.

The salt water powered vehicle generates 560kW (760HP) and goes 0-60mph in 2.4 seconds.

Note: This article is for people who already have a basic idea about working with Unity and are interested in Neural Networks and Reinforcement Learning. No prior experience with neural networks and/or PhD required! This article provides all needed to obtain the following…

Technicians attached the first-of-its-kind Mars Helicopter to Perseverance’s belly on Monday (April 6) at NASA’s Kennedy Space Center in Florida, where the rover is being prepped for its planned July 17 launch.

Elon Musk has revealed the reason behind Tesla’s cabin-facing camera that has been in the Model 3 for years without being used.

When Tesla launched the Model 3, it equipped the vehicle with a standard cabin-facing camera located in the rearview mirror.

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Nowadays, artificial neural networks have an impact on many areas of our day-to-day lives. They are used for a wide variety of complex tasks, such as driving cars, performing speech recognition (for example, Siri, Cortana, Alexa), suggesting shopping items and trends, or improving visual effects in movies (e.g., animated characters such as Thanos from the movie Infinity War by Marvel).

Traditionally, algorithms are handcrafted to solve complex tasks. This requires experts to spend a significant amount of time to identify the optimal strategies for various situations. Artificial neural networks — inspired by interconnected neurons in the brain — can automatically learn from data a close-to-optimal solution for the given objective. Often, the automated learning or “training” required to obtain these solutions is “supervised” through the use of supplementary information provided by an expert. Other approaches are “unsupervised” and can identify patterns in the data. The mathematical theory behind artificial neural networks has evolved over several decades, yet only recently have we developed our understanding of how to train them efficiently. The required calculations are very similar to those performed by standard video graphics cards (that contain a graphics processing unit or GPU) when rendering three-dimensional scenes in video games.

The Tesla CEO is aiming to enable his company’s cars to drive without human interaction.

A new iOS game from NASA asks players to identify the coral in 3D images of the ocean floor. Doing so will help train AI to do it automatically.

In nuclear physics, like much of science, detailed theories alone aren’t always enough to unlock solid predictions. There are often too many pieces, interacting in complex ways, for researchers to follow the logic of a theory through to its end. It’s one reason there are still so many mysteries in nature, including how the universe’s basic building blocks coalesce and form stars and galaxies. The same is true in high-energy experiments, in which particles like protons smash together at incredible speeds to create extreme conditions similar to those just after the Big Bang.

Fortunately, scientists can often wield simulations to cut through the intricacies. A simulation represents the important aspects of one system—such as a plane, a town’s traffic flow or an atom—as part of another, more accessible system (like a computer program or a scale model). Researchers have used their creativity to make simulations cheaper, quicker or easier to work with than the formidable subjects they investigate—like proton collisions or black holes.

Simulations go beyond a matter of convenience; they are essential for tackling cases that are both too difficult to directly observe in experiments and too complex for scientists to tease out every logical conclusion from basic principles. Diverse research breakthroughs—from modeling the complex interactions of the molecules behind life to predicting the experimental signatures that ultimately allowed the identification of the Higgs boson—have resulted from the ingenious use of simulations.

In what seems to be the automotive equivalent of a brazen act intended to show dominance, Elon Musk has announced on Twitter that Tesla will be releasing a “Plaid” version of the Cybertruck. Musk did not provide further details in his tweet, though there is little doubt that the feature will make the already daunting Tri-Motor pickup into something downright scary for its fossil fuel-powered rivals like the Ford F-150 Raptor.

Elon Musk’s Plaid Cybertruck revelation was shared on Twitter late Wednesday. While responding to Tesla owner-enthusiast and Third Row Podcast member Sofiaan Fraval, Musk stated that he would be driving a Plaid version of the all-electric pickup truck for personal use. This came as a pleasant surprise to the EV community, especially since this is the first time that such a version of the Cybertruck has been mentioned.

Elon Musk has mentioned in the past that the Tesla Cybertruck will have the handling and performance of a sports car, and this was highlighted during the vehicle’s unveiling event. Apart from showcasing its strength by having the hulking all-electric pickup pull a Ford F-150 like a rag doll, Tesla also featured the Cybertruck drag racing a Porsche 911, and crushing the iconic sports car in the process. These are bold demonstrations, and each was met with equal parts excitement and skepticism from the auto community.