Aurora consists of four photonically interconnected modular and independent server racks, containing 35 photonic chips and 13km of fiber optics. The system operates at room temperature and is fully automated, which Xanadu says makes it capable of running “for hours without any human intervention.”
The company added that in principle, Aurora could be scaled up to “thousands of server racks and millions of qubits today, realizing the ultimate goal of a quantum data center.” In a blog post detailing Aurora, Xanadu CTO Zachary Vernon said the machine represents the “very first time [Xanadu] – or anyone else for that matter – have combined all the subsystems necessary to implement universal and fault-tolerant quantum computation in a photonic architecture.”
Description: Sam Altman admitted OpenAI might have been wrong about keeping its AI models private and acknowledged DeepSeek’s open-source approach is making waves in the industry. Meanwhile, DeepSeek claims to have built an AI model as powerful as OpenAI’s GPT-o1 for a fraction of the cost, raising concerns about potential data theft and U.S. chip restrictions. At the same time, Altman is pushing a $500 billion AI data center project called “Stargate” while facing a personal lawsuit, as Google quietly adjusts its AI strategy and Microsoft investigates DeepSeek’s rapid rise.
*Key Topics:* - *Sam Altman’s shocking admission* about OpenAI’s past mistakes and DeepSeek’s rising influence. - How *DeepSeek claims to rival OpenAI’s GPT-o1* at a fraction of the cost, raising legal concerns. - The *AI arms race escalates* as OpenAI, DeepSeek, Microsoft, and Google battle for dominance.
*What You’ll Learn:* - Why *OpenAI might change its stance on open-source AI* after DeepSeek’s disruptive impact. - How *Microsoft is investigating DeepSeek* over alleged unauthorized use of OpenAI’s data. - The *$500 billion “Stargate” project* and why experts doubt Altman’s ambitious AI infrastructure plans.
*Why It Matters:* This video explores the *intensifying AI war, where **DeepSeek’s bold claims* challenge industry giants, forcing OpenAI, Google, and Microsoft to rethink their strategies while massive investments reshape the future of artificial intelligence.
*DISCLAIMER:* This video analyzes the latest AI developments, including *OpenAI’s internal struggles, DeepSeek’s rapid rise, and the shifting landscape of AI innovation and competition*.
Unitree, a Chinese robotics company competing with outfits like Boston Dynamics, Tesla, Agility Robotics and others, has unveiled a new video of its humanoid G1 and H1 robots, showing off some new moves.
The smaller, more affordable G1 robot is shown running, navigating uneven terrain and walking in a more natural way. Unitree told us that because the robots were operating in environments it hadn’t mapped with LIDAR, these demos were remote controlled.
Unitree’s taller H1 humanoid robot also showed off some new moves at a Spring Festival Gala. The robots performed a preset routine learned from data produced by human dancers. The company says “whole body AI motion control” kept the robots in sync and allowed the robots to respond to any unplanned changes or events.
At the heart of language neuroscience lies a fundamental question: How does the human brain process the rich variety of languages? Recent developments in Natural Language Processing, particularly in multilingual neural network language models, offer a promising avenue to answer this question by providing a theory-agnostic way of representing linguistic content across languages. Our study leverages these advances to ask how the brains of native speakers of 21 languages respond to linguistic stimuli, and to what extent linguistic representations are similar across languages. We combined existing (12 languages across 4 language families; n=24 participants) and newly collected fMRI data (9 languages across 4 language families; n=27 participants) to evaluate a series of encoding models predicting brain activity in the language network based on representations from diverse multilingual language models (20 models across 8 model classes). We found evidence of cross-lingual robustness in the alignment between language representations in artificial and biological neural networks. Critically, we showed that the encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for brain responses in a held-out language, even across language families. These results imply a shared component in the processing of different languages, plausibly related to a shared meaning space.
When it comes to AI research, the company leading the way is undoubtedly OpenAI. Having successfully launched ChatGPT, the San Fransisco-based organisation has bigger targets in mind now.
In December 2024, it launched its latest version, o3, which has shown significant progress when it comes to Artificial General Intelligence (AGI). In other words, it has launched an AI system that can understand, learn and apply knowledge across a wide variety of tasks just like a human being.
But now, OpenAI CEO Sam Altman has revealed in his latest blog that the focus has shifted towards Superintelligence.
Pipistrel Aircraft has announced the successful completion of the first hover flight for its Nuuva V300, a hybrid-electric vertical takeoff and landing (VTOL) unmanned aircraft designed for long-range logistics and specialized defense operations.
The milestone brings the company closer to deploying its autonomous cargo drone, which promises to revolutionize aerial deliveries with a 600-pound payload capacity and a 300-nautical-mile range.
The Nuuva V300 represents a leap forward in hybrid-electric propulsion, combining eight battery-powered electric motors for vertical takeoff with an internal combustion engine for forward flight. This dual-power system enhances fuel efficiency, minimizes maintenance costs, and provides greater operational flexibility. The aircraft’s design allows it to carry up to three Euro pallets (EPAL) through a nose-loading fuselage, offering a streamlined solution for cargo logistics, humanitarian aid, and defense applications.
A class of synthetic soft materials called liquid crystal elastomers (LCEs) can change shape in response to heat, similar to how muscles contract and relax in response to signals from the nervous system. 3D printing these materials opens new avenues to applications, ranging from soft robots and prosthetics to compression textiles.
Controlling the material’s properties requires squeezing this elastomer-forming ink through the nozzle of a 3D printer, which induces changes to the ink’s internal structure and aligns rigid building blocks known as mesogens at the molecular scale. However, achieving specific, targeted alignment, and resulting properties, in these shape-morphing materials has required extensive trial and error to fully optimize printing conditions. Until now.
In a new study, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Princeton University, Lawrence Livermore National Laboratory, and Brookhaven National Laboratory worked together to write a playbook for printing liquid crystal elastomers with predictable, controllable alignment, and hence properties, every time.
An Oregon State University researcher has helped create a new 3D printing approach for shape-changing materials that are likened to muscles, opening the door for improved applications in robotics as well as biomedical and energy devices.
The liquid crystalline elastomer structures printed by Devin Roach of the OSU College of Engineering and collaborators can crawl, fold and snap directly after printing. The study is published in the journal Advanced Materials.
“LCEs are basically soft motors,” said Roach, assistant professor of mechanical engineering. “Since they’re soft, unlike regular motors, they work great with our inherently soft bodies. So they can be used as implantable medical devices, for example, to deliver drugs at targeted locations, as stents for procedures in target areas, or as urethral implants that help with incontinence.”
A biomaterial that can mimic certain behaviors within biological tissues could advance regenerative medicine, disease modeling, soft robotics and more, according to researchers at Penn State.
Materials created up to this point to mimic tissues and extracellular matrices (ECMs)—the body’s biological scaffolding of proteins and molecules that surrounds and supports tissues and cells—have all had limitations that hamper their practical applications, according to the team. To overcome some of those limitations, the researchers developed a bio-based, “living” material that encompasses self-healing properties and mimics the biological response of ECMs to mechanical stress.
They published their results in Materials Horizons, where the research was also featured on the cover of the journal.
