Lifeboat Foundation

Researchers at the UPC’s Department of Electronic Engineering have developed a new type of magnetometer that can be integrated into microelectronic chips and that is fully compatible with the current integrated circuits. Of great interest for the miniaturization of electronic systems and sensors, the study has been recently published in Microsystems & Nanoengineering.

Microelectromechanical systems (MEMS) are electromechanical systems miniaturized to the maximum, so much so that they can be integrated into a chip. They are found in most of our day-to-day devices, such as computers, car braking systems and mobile phones. Integrating them into electronic systems has clear advantages in terms of size, cost, speed and energy efficiency. But developing them is expensive, and their performance is often compromised by incompatibilities with other electronic systems within a device.

MEMS can be used, among many others, to develop magnetometers—a device that measures magnetic field to provide direction during navigation, much like a compass—for integration into smartphones and wearables or for use in the automotive industry. Therefore, one of the most promising lines of work are Lorentz force MEMS magnetometers.

Researchers affiliated with the Q-NEXT quantum research center show how to create quantum-entangled networks of atomic clocks and accelerometers—and they demonstrate the setup’s superior, high-precision performance.

For the first time, scientists have entangled atoms for use as networked quantum sensors, specifically, atomic clocks and accelerometers.

The research team’s experimental setup yielded ultraprecise measurements of time and acceleration. Compared to a similar setup that does not draw on quantum entanglement, their time measurements were 3.5 times more precise, and acceleration measurements exhibited 1.2 times greater precision.

Benchmarks orient AI. They encapsulate ideals and priorities that describe how the AI community should progress. When properly developed and analyzed, they allow the larger community to understand better and influence the direction of AI technology. The AI technology that has evolved the most in recent years is foundation models, highlighted by the advent of language models. A language model is essentially a box that accepts text and generates text. Despite their simplicity, these models may be customized (e.g., prompted or fine-tuned) to a wide range of downstream scenarios when trained on vast amounts of comprehensive data. However, there still needs to be more knowledge on the enormous surface of model capabilities, limits, and threats. They must benchmark language models holistically due to their fast growth, growing importance, and limited comprehension. But what does it mean to evaluate language models from a global perspective?

Language models are general-purpose text interfaces that may be used in various circumstances. And for each scenario, they may have a long list of requirements: models should be accurate, resilient, fair, and efficient, for example. In truth, the relative relevance of various desires is frequently determined by one’s perspective and ideals and the circumstance itself (e.g., inference efficiency might be of greater importance in mobile applications). They think that holistic assessment includes three components:

Will AI kill art? Not likely, says the artist Alexander Reben, who has been working with AI for years.

UC San Diego nanoengineering professor Shyue Ping Ong described M3GNet as “an AlphaFold for materials”, referring to the breakthrough AI algorithm built by Google’s DeepMind that can predict protein structures.

“Similar to proteins, we need to know the structure of a material to predict its properties,” said Professor Ong.

“We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures.”

In this Review, Savitz and Cox consider the evidence for a model of cell-based therapy referred to as the bioreactor hypothesis, in which exogenous cells migrate to peripheral organs and reprogramme host immune cells to generate an anti-inflammatory, regenerative environment.

This CEO says generative AI is all flash, no substance — and ultimately, will fail to generate major new revenue streams for VCs’ billions.

Elon Musk’s role in Tesla’s early history has been a point of contention. Eberhard has sued Musk, alleging libel because Musk says he’s a founder.

Since then, the tech mogul and Tesla CEO has let further staff go, some through layoffs aimed at downsizing the company and others through more targeted firings.

But after scrambling to get rid of staff, Twitter is still now actually hiring, Musk said in a presentation to the company, slides from which he posted on Twitter.

“We’re recruiting,” the slide simply read.