Lifeboat Foundation

Google Deepmind will soon begin researching autonomous language agents such as Auto-GPT, potentially boosting the viable applications of LLMs such as Gemini.

Google DeepMind is looking for researchers and engineers to help build increasingly autonomous language agents, Edward Grefenstette, director of research at Google DeepMind, announced at X.

Such AI agents already exist in early stages, with Auto-GPT being one of the earliest examples. The basic idea is to create a system that autonomously achieves a given goal using a mix of prompt engineering, self-prompting, memory, and other system parts. While such agents are already showing promising results, they are still far from being able to achieve good results on their own and usually require human feedback and decision-making.

Active particles can form two-dimensional solids that are different from those formed by nonmotile particles, showing long-range crystalline order accompanied by giant spontaneous deformations.

If you compress a liquid slowly enough at low temperatures, it will freeze into an ordered solid: a crystal. Or at least that’s what we’re used to seeing in three dimensions. If you instead consider particles confined to a two-dimensional (2D) plane, the outcome is quite different. For equilibrium systems, a 2D solid stabilizes into a structure that lacks long-range order—it becomes less ordered further away from a central lattice site. The behavior of systems far from equilibrium, such as self-propelled particles, remains, however, an open question. In a numerical study of bacteria-like particles, Xia-qing Shi of Soochow University in China and his colleagues now show that active crystals follow a slightly different set of rules than their nonmotile counterparts [1]. Like 2D equilibrium crystals, 2D active systems stabilize into an ordered solid-like phase but with extremely large particle fluctuations around the configuration of a perfect crystal lattice.

The German automaker’s next-generation advanced driver assistance system, or ADAS, will feature in its Neue Klasse range of electric cars that the company revealed on Saturday — that will launch in 2025.

It comes as traditional automakers look to boost the technology in their cars, with a particular focus on ADAS, to compete with Tesla.

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.”

Continue reading “Connected cars are a ‘privacy nightmare,’ Mozilla Foundation says” »

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.

Continue reading “Toyota Robots That Do Housework!” »

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.

Toyota maintained its course to focus on alternative options or rather specifically saying hydrogen path for its automobility future.

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.