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We got a glimpse at what a new cross between a helicopter and a jet aircraft might look like after Bell released a new image. It’s of a model used in wind tunnel tests of its entry in DARPA’s Speed and Runway Independent Technology (SPRINT) program.

Rotorcraft like helicopters have the advantage of vertical takeoffs and landings in rough country but haven’t much in the way of speed. Jet planes have lots of speed but need runways and even the STOVL variety need a properly flat surface to land on. It was long accepted that these were two very different classes of aircraft without much in the way of overlap.

That is, until DARPA initiated its SPRINT program aimed at making the twain meet in an aircraft that could take off, land, and hover like a rotorcraft and then transition into a jet when in vertical flight.

Batteries made from waste and methane offer lower CO2 emissions than current technologies.


It’s also being claimed that the technology has the potential to improve fast-charging speed by up to 50%, making EV ownership even more convenient. Lithium-sulfur batteries are expected to cost less than half the price per kWh of current lithium-ion batteries, according to Stellantis.

The batteries will be produced using waste materials and methane, with significantly lower CO2 emissions than any existing battery technology. Zeta Energy battery technology is intended to be manufacturable within existing gigafactory technology and would leverage a short, entirely domestic supply chain in Europe or North America, according to a press release.

Ned Curic, Stellantis’s Chief Engineering and Technology Officer, stated that the collaboration with Zeta Energy is another step in helping advance the company’s electrification strategy as they work to deliver clean, safe, and affordable vehicles.

Scientists at the University of California, Irvine have uncovered the atomic-scale mechanics that enhance superconductivity in an iron-based material, a finding published recently in Nature.

Using advanced spectroscopy instruments housed in the UC Irvine Materials Research Institute, the researchers were able to image atom vibrations and thereby observe new phonons—quasiparticles that carry thermal energy—at the interface of an iron selenide (FeSe) ultrathin film layered on a (STO) substrate.

“Primarily emerging from the out-of-plane vibrations of oxygen atoms at the interface and in apical oxygens in STO, these phonons couple with electrons due to the spatial overlap of electron and phonon wave functions at the interface,” said lead author Xiaoqing Pan, UC Irvine Distinguished Professor of materials science and engineering, Henry Samueli Endowed Chair in Engineering and IMRI director.

MIT engineers have released DrivAerNet++, an open-source dataset of over 8,000 car designs, to accelerate automotive innovation using AI. This dataset, featuring detailed aerodynamic data, aims to enhance fuel efficiency and electric vehicle range, promoting sustainable car design advancements.


Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a . While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

Companies currently rely heavily on simulations to ensure that new versions meet a wide range of requirements. AV 2.0 systems are more sensitive to differences between real-world data and simulated data, so simulations need to be as realistic as possible. Instead of using hand-built 3D environments and pre-programmed vehicle behaviors, future testing will need to use advanced machine learning techniques to create highly realistic and scalable simulations.

It’s crucial for car manufacturers and the wider vehicle industry to adopt AI technologies in their development processes and products. There’s enormous potential for improved autonomous driving capabilities, better interaction between humans and machines and increased productivity for developers.

Just as software revolutionized many industries, AI is set to do the same—but even faster. Companies that quickly embrace these technologies may have a first-mover advantage and the chance to set industry standards. Those that delay may quickly fall behind, as their products will lack features compared to competitors.

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Tesla hasn’t unveiled its next generation human robot in the form of the app named GEN-3 Teslabot, bringing with it significant advancements in the field of humanoid robotics, merging state-of-the-art engineering with a design inspired by human anatomy. This next-generation robot demonstrates exceptional dexterity and precision, setting a new benchmark for what humanoid robots can accomplish. From catching a tennis ball mid-air to envisioning tasks like threading a needle, the Teslabot is poised to reshape how robots interact with and adapt to the world around them.

Wouldn’t it be great if robots didn’t just assemble cars or vacuum your living room but perform tasks requiring the finesse of human hands—threading a needle, playing a piano, or even catching a tennis ball mid-air. It sounds like science fiction, doesn’t it? Yet, Tesla’s latest innovation, the GEN-3 Teslabot, is bringing us closer to that reality. With its human-inspired design and new engineering, this robot is redefining what we thought machines could do.

But what makes the Teslabot so extraordinary? It’s not just the flashy demonstrations or its sleek design. It’s the way Tesla has managed to replicate human dexterity and precision in a machine, giving it the potential to tackle tasks we once thought only humans could handle. From its 22 degrees of freedom in the hand to its vision-driven precision, it’s a glimpse of what’s to come. Let’s dive into the details of Tesla’s GEN-3 Teslabot and explore how it’s pushing the boundaries of what’s possible.

The Matrix is a groundbreaking AI model capable of generating infinite, high-quality video worlds in real time, offering unmatched interactivity and adaptability. Developed using advanced techniques like the Video Diffusion Transformer and Swin-DPM, it enables seamless, frame-level precision for creating dynamic, responsive simulations. This innovation surpasses traditional systems, making it a game-changer for gaming, autonomous vehicle testing, and virtual environments.

🔍 Key Topics Covered:
The Matrix AI model and its ability to generate infinite, interactive video worlds.
Real-time applications in gaming, autonomous simulations, and dynamic virtual environments.
Revolutionary AI techniques like Video Diffusion Transformer, Swin-DPM, and Interactive Modules.

🎥 What You’ll Learn:
How The Matrix AI redefines video generation with infinite-length, high-quality simulations.
The transformative impact of real-time interactivity and domain generalization in AI-driven worlds.
Why this breakthrough is a game-changer for industries like gaming, VR, and autonomous systems.

📊 Why This Matters:

However, the IMO warned that lava may still continue to flow beneath this solidified crust toward the protective barriers near the Blue Lagoon, though its advance has significantly slowed.

Over the last week, the constant flow of the lava has gradually engulfed the tourist destination’s car park and continues to grow in size.

The Blue Lagoon was evacuated ahead of the lava’s arrival, as were 50 homes in the town of Grindavík, which is home to 3,800 residents.