The one advantage combat pilots had over artificial intelligence was unpredictability. Now a new study has put that in jeopardy.
Category: robotics/AI – Page 65
Chinese firm Xpeng announced its plans to mass-produce flying cars and humanoid robots by next year.
He Xiaopeng, XPeng Motors’ chairman and CEO, stated that if the project remains on track, XPeng could be the first company to mass-produce flying cars globally, reports a Chinese online daily.
The company’s Iron humanoid robot is now in use at the EV maker’s Guangzhou factory, and it plans to start mass-production. By 2026, humanoid robots with entry-level Level 3 capabilities in the country are expected to enter moderate-scale commercial production, Xiapeng added.
Chinese EV maker XPeng aims to mass-produce flying cars and humanoid robots, with Level 3 robots set for commercial production by 2026.
Empa researchers are working on artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic, yet powerful structures using 3D printing. One day, these could be used in medicine or robotics – and anywhere else where things need to move at the touch of a button.
A team of researchers from Empa’s Laboratory for Functional Polymers is working on actuators made of soft materials. Now, for the first time, they have developed a method for producing such complex components using a 3D printer. The so-called dielectric elastic actuators (DEA) consist of two different silicone-based materials: a conductive electrode material and a non-conductive dielectric. These materials interlock in layers. “It’s a bit like interlacing your fingers,” explains Empa researcher Patrick Danner. If an electrical voltage is applied to the electrodes, the actuator contracts like a muscle. When the voltage is switched off, it relaxes to its original position.
3D printing such a structure is not trivial, Danner knows. Despite their very different electrical properties, the two soft materials should behave very similarly during the printing process. They should not mix but must still hold together in the finished actuator. The printed “muscles” must be as soft as possible so that an electrical stimulus can cause the required deformation. Added to this are the requirements that all 3D printable materials must fulfill: They must liquefy under pressure so that they can be extruded out of the printer nozzle. Immediately thereafter, however, they should be viscous enough to retain the printed shape. “These properties are often in direct contradiction,” says Danner. “If you optimize one of them, three others change … usually for the worse.”
Modern communication networks rely on optical signals to transfer vast amounts of data. But just like a weak radio signal, these optical signals need to be amplified to travel long distances without losing information.
The most common amplifiers, erbium-doped fiber amplifiers (EDFAs), have served this purpose for decades, enabling longer transmission distances without the need for frequent signal regeneration. However, they operate within a limited spectral bandwidth, restricting the expansion of optical networks.
To meet the growing demand for high-speed data transmission, researchers have been seeking ways to develop more powerful, flexible, and compact amplifiers. Even though AI accelerators, data centers, and high-performance computing systems handle ever-increasing amounts of data, the limitations of existing optical amplifiers are becoming more evident.
AI models are growing ever-more capable, accurate and impressive. The question of if they represent “general intelligence” is increasingly moot.
An AI model can scan your brain with non-invasive equipment and convert your thoughts into typed sentences — with no implants required.
Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just mimicking patterns? A new study from the University of Amsterdam and the Santa Fe Institute reveals that while GPT models perform well on some analogy tasks, they fall short when the problems are altered, highlighting key weaknesses in AI’s reasoning capabilities. The work is published in Transactions on Machine Learning Research.
Analogical reasoning is the ability to draw a comparison between two different things based on their similarities in certain aspects. It is one of the most common methods by which human beings try to understand the world and make decisions. An example of analogical reasoning: cup is to coffee as soup is to??? (the answer being: bowl)
Large language models (LLMs) like GPT-4 perform well on various tests, including those requiring analogical reasoning. But can AI models truly engage in general, robust reasoning or do they over-rely on patterns from their training data? This study by language and AI experts Martha Lewis (Institute for Logic, Language and Computation at the University of Amsterdam) and Melanie Mitchell (Santa Fe Institute) examined whether GPT models are as flexible and robust as humans in making analogies.
A little over a year after releasing two open Gemma AI models built from the same technology behind its Gemini AI, Google is updating the family with Gemma 3.
According to the blog post, these models are intended for use by developers creating AI applications capable of running wherever they’re needed, on anything from a phone to a workstation with support for over 35 languages, as well as the ability to analyze text, images, and short videos.
The company claims that it’s the world’s best single-accelerator model, outperforming competition from Facebook’s Llama, DeepSeek, and OpenAI for performance on a host with a single GPU, as well as optimized capabilities for running on Nvidia’s GPUs and dedicated AI hardware.
Gemma 3’s vision encoder is also upgraded, with support for high-res and non-square images, while the new ShieldGemma 2 image safety classifier is available for use to filter both image input and output for content classified as sexually explicit, dangerous, or violent.
To go deeper into those claims, you can check out the 26-page technical report.
Last year it was unclear how much interest there would be in a model like Gemma, however, the popularity of DeepSeek and others shows there is interest in AI tech with lower hardware requirements.
A team from Princeton University has successfully used artificial intelligence (AI) to solve equations that control the quantum behavior of individual atoms and molecules to replicate the early stages of ice formation. The simulation shows how water molecules transition into solid ice with quantum accuracy.
Roberto Car, Princeton’s Ralph W. *31 Dornte Professor in Chemistry, who co-pioneered the approach of simulating molecular behaviors based on the underlying quantum laws more than 35 years ago, said, “In a sense, this is like a dream come true. Our hope then was that eventually, we would be able to study systems like this one. Still, it was impossible without further conceptual development, and that development came via a completely different field, that of artificial intelligence and data science.”
Modeling the early stages of freezing water, the ice nucleation process could increase the precision of climate and weather modeling and other processes like flash-freezing food. The new approach could help track the activity of hundreds of thousands of atoms over thousands of times longer periods, albeit still just fractions of a second, than in early studies.
Shanghai’s robotics revolution is here! At a cutting-edge startup, humanoid robots are being trained to navigate the real world-learning tasks from sorting objects to taking coffee. But how does Al collect and refine the data that powers these machines? We got access to a 2,000-square-meter data factory, where robots are trained through motion capture, human guidance, and real-world simulations. With China’s tech and supply chain advantages, could these humanoids become part of our daily lives sooner than we think? #HumanoidRobots #Al #FutureTech.
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