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The DOJ Is Demanding Apple And Google Identify Over 100,000 Users Of This Car App

In the letter, EZ Lynk lawyers wrote that Apple and Google are planning to fight the subpoenas. Walmart declined to comment. None of the other companies subpoenaed responded to a comment request.

“These requests for potentially hundreds of thousands of people’s PII go well beyond the needs of this case and create serious privacy concerns,” wrote EZ Lynk’s lawyers in the letter. “Investigating this claim does not require identifying each person who has used the product.”

The government said in the letter its request for data was fair and appropriate, and it had “consistently sought customer information” because its lawyers want to interview witnesses about their use of EZ Lynk’s technology. It has already presented evidence to the court of people using the company’s tools to remove emissions controls on their cars, including Facebook and EZ Lynk forum posts outlining that use of the product.

Brighter red micro-LEDs could help solve full-color display stability challenge

Researchers at The University of Osaka, in collaboration with Ritsumeikan University, have demonstrated that growing europium-doped gallium nitride (Eu-doped GaN) on a semipolar crystal plane dramatically improves red light emission. The team found that this approach selectively promotes the formation of highly efficient Eu luminescent centers, resulting in red emission intensity more than 3.6 times higher than that of conventionally grown polar-plane material.

The study is published in the journal Applied Physics Letters.

Red emitters based on Eu-doped GaN are attracting attention as promising light sources for next-generation micro-LED displays because they can provide narrow-linewidth, wavelength-stable red emission based on intra-4f-shell transitions of Eu ions. This is particularly important for full-color monolithic integration with blue and green InGaN LEDs, where wavelength stability under device operation is essential.

Godfather of AI: How To Make Safe Superintelligent AI

The co-inventor of modern AI and the most cited living scientist believes he’s figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio – Turing Award Winner and founder of LawZero – is disturbed by the many unintended drives and goals present in today’s AIs, their ability to tell when they’re being tested, and demonstrated willingness to lie. AI companies are trying to stamp these out in a ‘cat-and-mouse game’ that Yoshua fears they’re losing.

But Yoshua is optimistic: he believes the companies can win this battle decisively with a single rearrangement to how AI models are trained, and has been developing mathematical proofs to back up the claim. The core idea is that instead of training AI to predict what a human would say, or to produce responses we’d rate highly, we should train it to model what’s actually true.

Learn more & full transcript: https://80k.info/bengio.

Yoshua argues this new architecture, which he calls “Scientist AI,” is a small enough change that we could keep almost all the techniques and data we use to train frontier AIs like Claude and ChatGPT. And that the new architecture need not cost more, could be built iteratively, and might be more capable as well as more honest.

Until recently, the biggest practical objection to Scientist AI was simple: the world wants agents, and Scientist AI isn’t one. But in new research, Yoshua has extended the design and believes the same honest predictor can be turned into a capable agent without losing its \.

Photonics advance could enable compact, high-performance lidar sensors

Lidar systems use pulses of infrared light to measure distance and map a 3D scene with high resolution, allowing autonomous vehicles to rapidly react to obstacles that appear in their path. But traditional lidar sensors are expensive, bulky systems with many moving parts that degrade over time, limiting how the sensors can be deployed.

A new study from MIT researchers could help to enable next-generation lidar sensors that are compact, durable, and have no moving parts. The key advance is a novel design for a silicon-photonics chip, which is a semiconductor device that manipulates light rather than electricity.

Typically, such silicon-photonics chip-based systems have a restricted field of view, so a silicon-photonics-based lidar would not be able to scan angles in the periphery. Existing workarounds to this problem increase noise and hamper precision.

Magnetic checkerboard separates microparticles by size and sends them along different paths

A team of researchers from the Universities of Tübingen, Bayreuth, and Kassel, and the Polish Academy of Sciences has developed a method for precisely controlling the movement of magnetic microparticles based on their size. These suspended particles, known as colloidal particles, range in size from a few tens of nanometers to several micrometers. Controlling them is important for applications such as drug delivery, medical laboratory tests, and the synthesis of new materials. The team’s study has now been published in Physical Review Letters.

The new method involves positioning microparticles above a magnetic layer that is patterned like a chessboard. In previous studies, magnetic transportation of the colloidal particles was limited to a specific height. At this distance, although the magnetic forces appear to balance each other out, the particles move regardless of their size. Therefore, it was not possible to control the particles specifically based on their size.

Researchers combine five metals to build a better nanocrystal

A nanocrystal is an extraordinarily tiny piece of material—composed of anywhere from a few to a few thousand atoms—in which atoms are arranged in a precise, ordered structure. Think of it like taking a piece of gold and shrinking it down to the size of a few hundred atoms. It’s still gold, still crystalline, just almost incomprehensibly small.

Nanocrystals are in the transistors inside computers and smartphones, in smartphone displays and TV screens, in the gold-nanoparticle sensors that power COVID and pregnancy tests, and in the pipes of your car exhaust system, among countless other innovations.

Their small size gives them a dramatically higher ratio of surface area to volume, making them especially useful as catalysts—materials that speed up chemical reactions without being consumed in the process.

A three-dimensional micro-instrumented neural network device

A three-dimensional soft electronic sensor and stimulator array that is integrated with a three-dimensional cultured neural network can be used to record action potential from multiple planes over a period of 6 months, monitor evolving connectivity maps and pharmacological responses, as well as construct a reservoir neural network for biocomputing.

Artificial Brain Controlled Robot

The GSN SNN 4−10−30−2 is a hardware based spiking neural network that can autonomous control a remote control robot vehicle. There are 10 artificial neurons and 30 artificial synapses, and is built on 16 full-size breadboards. Four infrared proximity sensor are used on top of the vehicle to determine how far it is away from objects and walls. The sensor data is used as inputs into the first later of neurons.

A full circuit level diagram of the neural network is provided, as well as an architecture diagram. The weights on the network are set based on the resistance value. The synapses allow the weights to be set as excitatory or inhibitory.

Testing of the network went great and the robot had much smoother control than previous testing as the output now has an analog output.

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