Lifeboat Foundation

Metamaterials have recently garnered substantial research interest as they can be engineered to achieve materials properties not found in nature, thus presenting unique opportunities across various fields. In order to facilitate the rational design of metamaterials, computational methods have been widely employed, but not without numerous challenges yet to be addressed. This Focus highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.

The simplified approach makes it easier to see how neural networks produce the outputs they do.

Researchers at the University of Toronto have found that using virtual and augmented reality (VR and AR) can temporarily change the way people perceive and interact with the real world—with potential implications for the growing number of industries that use these technologies for training purposes.

The study, published recently in the journal Scientific Reports, not only found that people moved differently in VR and AR, but that these changes led to temporary errors in movement in the real world. In particular, participants who used VR tended to undershoot their targets by not reaching far enough, while those who used AR tended to overshoot their targets by reaching too far.

This effect was noticeable immediately after using VR or AR, but gradually disappeared as participants readjusted to real-world conditions.

The standard model of the universe relies on just six numbers. Using a new approach powered by artificial intelligence, researchers at the Flatiron Institute and their colleagues extracted information hidden in the distribution of galaxies to estimate the values of five of these so-called cosmological parameters with incredible precision.

Forward-looking: We’re approaching a point where traditional copper interconnections won’t be able to carry enough data to keep GPUs and other specialized chips fully utilized. The AI market is urgently demanding a next-generation solution to this interconnection bottleneck, and Broadcom appears to be working on an optics-based solution that is closer to the chip itself.

Broadcom is developing new silicon photonics technology aimed at significantly increasing the bandwidth available to GPUs and other AI accelerators. By utilizing co-packaged optics (CPOs), the fabless chip manufacturer aims to integrate optical connectivity components directly into GPUs, enabling higher data rates while simultaneously reducing power requirements.

The company has been working on CPO solutions for several years and showcased its latest advancements at the recent Hot Chips convention. Broadcom’s “optical engine” reportedly delivers a total interconnect bandwidth of 1.6 TB/sec, equivalent to 6.4 Tbit/sec or 800 GB/sec in each direction.

Reasoning, the process through which human beings mentally process information to draw specific conclusions or solve problems, can be divided into two main categories. The first type of reasoning, known as deductive reasoning, entails starting from a general rule or premise and then using this rule to draw conclusions about specific cases.

This could mean, for instance, building on the premise that “all dogs have ears” and “Chihuahuas are dogs,” to conclude that “chihuahuas have ears.”

The second widely used form of reasoning is inductive reasoning, which instead consists of generalizing (i.e., formulating general rules) based on specific observations. This could mean, for instance, assuming that all swans are white because all the swans we encountered during our lifetime were white.

Startup Deep Fission has come up with a new way to deal with the economic and safety problems of nuclear power that is, to say the least, novel. The idea is to build a reactor that’s under 30 inches (76 cm) wide and stick it down a mile-deep (1.6-km) drill shaft.

With its promise of limitless energy by breaking down matter itself, nuclear power has long held a utopian promise for humanity. However, economic and safety considerations, along with political opposition, have hindered its development – especially in the very countries that developed the technology.

The safety and economic factors are related because the high cost of building nuclear power stations has very little to do with the nuclear technology itself. Nuclear fuel, even with all the processing costs included, only comes to about US$1,663 per kilogram (2.2 lb). Because nuclear fuel has such an incredible energy density, that’s about 0.46 ¢/kWh – and the fuel costs keep dropping as the technology becomes more efficient.

Researchers at Swansea University, in collaboration with Wuhan University of Technology, Shenzhen University, have developed a pioneering technique for producing large-scale graphene current collectors.

This breakthrough promises to significantly enhance the safety and performance of lithium-ion batteries (LIBs), addressing a critical challenge in energy storage technology.

Published in Nature Chemical Engineering, the study details the first successful protocol for fabricating defect-free graphene foils on a commercial scale. These foils offer extraordinary thermal conductivity—up to 1,400.8 W m^–1 K^–1 —nearly ten times higher than traditional copper and aluminum current collectors used in LIBs.

Nvidia may have a sizable lead, but money is an excellent motivator, and tech companies old and new are striving to end its dominance of the AI chip market — or at least secure themselves a sizable slice of it.

While some of these groups, including AMD, are following Nvidia’s lead and optimizing GPUs for generative AI, others are exploring alternative chip architectures.

Continue reading “The AI chip startup that could take down Nvidia” »