OpenAI has released Sora Turbo, an AI video generator, to the public. It offers faster, high-definition video creation up to 22 seconds. Available to ChatGPT Plus and Pro subscribers, it includes safeguards against misuse. Currently, it is not available in European and British markets.

OpenAI on Monday released the latest version of its highly anticipated Sora video generator to the public, stepping into an increasingly crowded field of AI tools that has raised concerns about disruption to creative industries.

The company behind ChatGPT said its latest version, dubbed Sora Turbo, offers significant speed improvements over the February preview model and can create high-definition videos lasting up to 22 seconds.

While tech giants Google and Meta have also announced similar video tools, none have yet met the huge expectations set for AI since the launch of ChatGPT two years ago.

Google has unveiled a quantum computing chip, “Willow,” capable of performing tasks in minutes that would take supercomputers 10 septillion years. This breakthrough in error correction marks a significant step towards practical quantum computing, with potential applications in drug discovery, fusion energy, and climate change solutions.

Google on Monday showed off a new quantum computing chip that it said was a major breakthrough that could bring practical quantum computing closer to reality.

A custom chip called “Willow” does in minutes what it would take leading supercomputers 10 septillion years to complete, according to Google Quantum AI founder Hartmut Neven.

“Written out, there is a 1 with 25 zeros,” Neven said of the time span while briefing journalists. “A mind-boggling number.”

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 novel design. 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.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. The study is published on the arXiv preprint server.

A team of roboticists at École Polytechnique Fédérale de Lausanne, working with a colleague from the University of California, has designed, built and demonstrated a bird-like robot that can launch itself into flight using spring-like legs.

The group describes their robot in a paper published in the journal Nature. Aimy Wissa, an aerospace engineer at Princeton University, has published a News & Views piece in the same journal issue suggesting possible ways the innovation could be used in real-world applications.

Some types of drones, such as those with rotors, can rise straight up off the ground—others that are powered with forward-facing rotors or engines that push exhaust out the back must either race along a runway or catapult to get airborne. For this new project, the research team developed a new design for getting such craft into the air—jumping using spring-like legs.

Transistors based on two-dimensional (2D) semiconductors, such as molybdenum disulfide (MoS₂) and tungsten diselenide (WSe₂), could outperform conventional silicon-based transistors, while also being easier to reduce in size. To perform well, these transistors need to be based on high-quality dielectric materials, which can be difficult to prepare.

Researchers at Nanyang Technological University, Nanjing University of Aeronautics and Astronautics recently introduced a new promising strategy to prepare the dielectric materials for these transistors. Their approach, outlined in a paper published in Nature Electronics, was successfully used to deposit an ultrathin and uniform native oxide of gallium Ga₂O₃ on the surface of MoS₂.

“Traditional methods of preparing dielectric layer, such as atomic layer deposition (ALD), encounter quality problems because of the high-quality surface of 2D semiconductors without sufficient nucleation points, especially at thin thicknesses down to a few nanometers,” Kongyang Yi, first author of the paper, told Tech Xplore.

El Capitan is now the most powerful supercomputer in existence, as proven by TOP500’s list.

MIT CSAIL researchers have developed a generative AI system, LucidSim, to train robots in virtual environments for real-world navigation. Using ChatGPT and physics simulators, robots learn to traverse complex terrains. This method outperforms traditional training, suggesting a new direction for robotic training.

A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.

The group has published a paper describing their work and possible uses for it on the arXiv preprint server. They also presented their ideas at the recent Conference on Robot Learning (CORL 2024), held in Munich Nov. 6–9.

Getting robots to navigate in the real world at some point involves teaching them to learn on the fly, or by training them with videos of similar robots in a real-world environment. While such training has proven to be effective in limited environments, it tends to fail when a robot encounters something novel. In this new effort, the team at MIT developed virtual training that better translates to the real world.

Waymo has expanded its robotaxi service to the general public in Los Angeles, allowing anyone with the Waymo One app to request a ride. This marks a significant step in autonomous vehicle technology, as Waymo continues to lead the industry with over 50,000 weekly passengers and a strong safety record.

Waymo on Tuesday opened its robotaxi service to anyone who wants a ride around Los Angeles, marking another milestone in the evolution of self-driving car technology since the company began as a secret project at Google 15 years ago.

The expansion comes eight months after Waymo began offering rides in Los Angeles to a limited group of passengers chosen from a waiting list that had ballooned to more than 300,000 people. Now, anyone with the Waymo One smartphone app will be able to request a ride around an 80-square-mile (129-square-kilometer) territory spanning the second largest U.S. city.

After Waymo received approval from California regulators to charge for rides 15 months ago, the company initially chose to launch its operations in San Francisco before offering a limited service in Los Angeles.

Researchers at Penn Engineering have developed PanoRadar, a system that uses radio waves and AI to provide robots with detailed 3D environmental views, even in challenging conditions like smoke and fog. This innovation offers a cost-effective alternative to LiDAR, enhancing robotic navigation and perception capabilities.

In the race to develop robust perception systems for robots, one persistent challenge has been operating in bad weather and harsh conditions. For example, traditional, light-based vision sensors such as cameras or LiDAR (Light Detection And Ranging) fail in heavy smoke and fog.

However, nature has shown that vision doesn’t have to be constrained by light’s limitations—many organisms have evolved ways to perceive their environment without relying on light. Bats navigate using the echoes of sound waves, while sharks hunt by sensing electrical fields from their prey’s movements.

Radio waves, whose wavelengths are orders of magnitude longer than light waves, can better penetrate smoke and fog, and can even see through certain materials—all capabilities beyond human vision. Yet robots have traditionally relied on a limited toolbox: They either use cameras and LiDAR, which provide detailed images but fail in challenging conditions, or traditional radar, which can see through walls and other occlusions but produces crude, low-resolution images.

Scientists have developed an AI-based authentication scheme to enhance vehicle security in the Internet of Vehicles (IoV).

Scientists claim to have developed an artificial intelligence tool to consolidate the privacy of vehicles and their drivers.

How to preserve the privacy of the so-called Internet of Vehicles (IoV) has emerged as a major challenge due to geographical mobility of vehicles and insufficient resources, the scientists say.

The problem has been aggravated, according to the scientists, due to the “limited resources of onboard units (OBUs)” and the shortcomings of embedded sensors installed in vehicles, which “lure the adversaries to launch various types of attacks.”