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It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain. It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article, they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields — also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science. And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important, but there is more going on in the brain above and beyond that,” says senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

A Cornell-led research team has developed an artificial intelligence-powered ring equipped with micro-sonar technology that can continuously—and in real time—track fingerspelling in American Sign Language (ASL).

In its current form, SpellRing could be used to enter text into computers or smartphones via fingerspelling, which is used in ASL to spell out words without corresponding signs, such as proper nouns, names and technical terms. With further development, the device—believed to be the first of its kind—could revolutionize ASL translation by continuously tracking entire signed words and sentences.

The research is published on the arXiv preprint server.

01:13 How Does Tesla Bot Gen 3 Handle Real-World Tasks?
06:12 How much does the Tesla Bot Gen 3 truly cost?
10:36 How is Tesla planning to sell the Bot Gen 3?
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New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! Recently, Elon Musk confidently announced that the Tesla Bot Optimus can navigate independently in 95% of complex environments and react in just 20 milliseconds!
With a plan to produce 10,000 Tesla Optimus Gen 3 units in 2025, Tesla is leveraging its AI infrastructure, manufacturing capabilities, and real-world testing across more than 1,000 practical tasks to prepare for mass production this year.

New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! In today’s episode, we have compiled evidence from official announcements, technical demonstrations to validate the feasibility of this plan and pinpoint the final timeline and pricing for the 2025 production model.
But before we dive into price analysis in Part 2 and exactly launching time in Part 3 of this episode, you should first understand what we expect from this Tesla humanoid robot—and more importantly, whether it’s truly worth the price.
How Does Tesla Bot Gen 3 Handle Real-World Tasks?

New UPDATE! Elon Musk LEAKED Tesla Bot Gen 3 10K Mass Production & All Real-Life Tasks Testing! John Kennedy, nearly seventy, lay motionless on the floor, pain radiating from his hip and spine. His phone was just a few steps away—close, yet out of reach. Then, everything went dark.
A humanoid robot detected his fall. It gently lifted him up, scanned his injuries, and instantly sent an alert to his doctor.
Then came the doctor’s words, they wanted to send him to an assisted living facility.

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#888999evs #teslacarworld #teslacar #888999 #teslabot #teslaoptimus #teslabotgen2 #teslabotgen3
subcribe: https://bit.ly/3i7gILj

Does autoimmunity underlie minimal change disease?

Tobias B. Huber, Nicola M. Tomas & team report a direct pathogenic role of anti-nephrin autoantibodies in the development of podocytopathy with a minimal change disease phenotype:

The electron microscopy image shows moderate podocyte foot process effacement (without electron-dense deposits) in the anti-nephrin rabbit.


Address correspondence to: Tobias B. Huber or Nicola M. Tomas, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany. Phone: 49.40.7410.53908; Email: [email protected] (TBH); [email protected] (NMT).

Battery waste has become an increasing problem in recent years due to the massive demand for consumer electronics like smartphones and laptops, as well as the electrification of the automotive industry.

A recent report from Stanford University in the US, published in the journal Nature Communications, found that recycling lithium-ion batteries is far more environmentally friendly than mining for new materials.

Researchers have advanced a decades-old challenge in the field of organic semiconductors, opening new possibilities for the future of electronics. The researchers, led by the University of Cambridge and the Eindhoven University of Technology, have created an organic semiconductor that forces electrons to move in a spiral pattern, which could improve the efficiency of OLED displays in television and smartphone screens, or power next-generation computing technologies such as spintronics and quantum computing.

The semiconductor they developed emits circularly polarized light—meaning the light carries information about the ‘handedness’ of electrons. The internal structure of most inorganic semiconductors, like silicon, is symmetrical, meaning electrons move through them without any preferred direction.

However, in nature, molecules often have a chiral (left-or right-handed) structure: like human hands, are mirror images of one another. Chirality plays an important role in like DNA formation, but it is a difficult phenomenon to harness and control in electronics.

A major breakthrough in organic semiconductors.

Semiconductors are materials with electrical conductivity that falls between conductors and insulators, making them essential for modern electronics. They are typically crystalline solids, the most common of which is silicon, used extensively in the production of electronic components such as transistors and diodes. Semiconductors are unique because their conductivity can be altered and controlled through doping—adding impurities to the material to change its electrical properties. This property allows them to serve as the foundation for integrated circuits and microchips, powering everything from computers and smartphones to advanced medical devices and renewable energy technologies. The behavior of semiconductors is also crucial in the development of various electronic, photonic, and quantum devices.

A team of researchers led by Colorado State University graduate student Luke Wernert and Associate Professor Hua Chen has discovered a new kind of Hall effect that could enable more energy-efficient electronic devices.

Their findings, published in Physical Review Letters in collaboration with graduate student Bastián Pradenas and Professor Oleg Tchernyshyov at Johns Hopkins University, reveal a previously unknown Hall mass in complex magnets called noncollinear antiferromagnets.

The Hall effect—first discovered by Edwin Hall at Johns Hopkins in 1879—usually refers to electric current flowing sideways when exposed to an external magnetic field, creating a measurable voltage. This sideways flow underpins everything from vehicle speed sensors to phone motion detectors. But in the CSU team’s work, electrons’ spin (a tiny, intrinsic form of angular momentum) takes center stage instead of .

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 research team led by Prof. Jiang Changlong from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed an innovative dual-mode sensing platform using upconversion nanoparticles (UCNPs). This platform integrates fluorescence and colorimetric methods, offering a highly sensitive and low-detection-limit solution for bilirubin detection in complex biological samples.

The findings, published in Analytical Chemistry, offer a new technological approach for the early diagnosis of jaundice.

Jaundice is a critical health issue in neonates, affecting 60% of newborns and contributing to early neonatal mortality. Elevated free bilirubin levels indicate jaundice, with healthy levels ranging from 1.7 μM to 10.2 μM in healthy individuals. Concentrations below 32 μM typically don’t show classic symptoms. Rapid and accurate detection of bilirubin in neonates is critical.