Lifeboat Foundation

Researchers have used quantum computers to solve difficult physics problems. But claims of a quantum “advantage” must wait as ever-improving algorithms boost the performance of classical computers.

Quantum computers have plenty of potential as tools for carrying out complex calculations. But exactly when their abilities will surpass those of their classical counterparts is an ongoing debate. Recently, a 127-qubit quantum computer was used to calculate the dynamics of an array of tiny magnets, or spins—a problem that would take an unfathomably long time to solve exactly with a classical computer [1]. The team behind the feat showed that their quantum computation was more accurate than nonexact classical simulations using state-of-the-art approximation methods. But these methods represented only a small handful of those available to classical-computing researchers. Now Joseph Tindall and his colleagues at the Flatiron Institute in New York show that a classical computer using an algorithm based on a so-called tensor network can produce highly accurate solutions to the spin problem with relative ease [2].

Current artificial intelligence models utilize billions of trainable parameters to achieve challenging tasks. However, this large number of parameters comes with a hefty cost. Training and deploying these huge models require immense memory space and computing capability that can only be provided by hangar-sized data centers in processes that consume energy equivalent to the electricity needs of midsized cities.

The research community is presently making efforts to rethink both the related computing hardware and the machine learning algorithms to sustainably keep the development of artificial intelligence at its current pace. Optical implementation of neural network architectures is a promising avenue because of the low power implementation of the connections between the units.

New research reported in Advanced Photonics combines light propagation inside multimode fibers with a small number of digitally programmable parameters and achieves the same performance on image classification tasks with fully digital systems with more than 100 times more programmable parameters. This computational framework streamlines the memory requirement and reduces the need for energy-intensive digital processes, while achieving the same level of accuracy in a variety of machine learning tasks.

VexTrio, the shadowy entity controlling a massive network of 70,000+ domains, is finally in the spotlight. This “traffic broker” fuels countless scams & malware campaigns, including ClearFake, SocGholish, & more. Read:

The threat actors behind ClearFake, SocGholish, and dozens of other actors have established partnerships with another entity known as VexTrio as part of a massive “criminal affiliate program,” new findings from Infoblox reveal.

The latest development demonstrates the “breadth of their activities and depth of their connections within the cybercrime industry,” the company said, describing VexTrio as the “single largest malicious traffic broker described in security literature.”

Continue reading “VexTrio: The Uber of Cybercrime — Brokering Malware for 60+ Affiliates” »

Architecture practice Studio RAP has combined algorithmic design and 3D printing to create a pair of archways informed by Delft Blue porcelain at the PoortMeesters housing in the Netherlands.

Named New Delft Blue, the archways were designed to frame entrances to a courtyard garden at the centre of the housing development in Delft designed by The Hague-based VY Architects.

They were constructed using 3,000 unique tiles that were 3D-printed and arranged in a pattern determined by an algorithm created by Studio RAP.

The expansion of the universe at some stage of evolution is well described by the Friedmann model. It was derived from general relativity a hundred years ago, but it is still considered one of the most important and relevant cosmological models.

RUDN University astrophysicists have now proven the theoretical possibility of the existence of traversable wormholes in the Friedmann universe. The research is published in the journal Universe.

“A wormhole is a type of highly curved geometry. It resembles a tunnel either between distant regions of the same universe or between different universes. Such structures were first discussed in the framework of solutions to the gravitational field equations a hundred years ago. But the wormholes considered then turned out to be non-traversable even for photons—they could not move from one ‘end of the tunnel’ to the other, not to mention going back,” said Kirill Bronnikov, doctor of physical and mathematical sciences, professor of RUDN University.

The article repeats itself a bit but there’s some good parts about an exoskeleton, advanced algorithm and bipedal robots and prosthetics. It’ll basically apply to those future industries.

We typically don’t think about it whilst doing it, but walking is a complicated task. Controlled by our nervous system, our bones, joints, muscles, tendons, ligaments and other connective tissues (i.e., the musculoskeletal system) must move in coordination and respond to unexpected changes or disturbances at varying speeds in a highly efficient manner. Replicating this in robotic technologies is no small feat.

Now, a research group from Tohoku University Graduate School of Engineering has replicated human-like variable speed walking using a musculoskeletal model – one steered by a reflex control method reflective of the human nervous system. This breakthrough in biomechanics and robotics sets a new benchmark in understanding human movement and paves the way for innovative robotic technologies.

Another landmark invention in the AI industry?

A recent algorithm breakthrough is shaking things up in the machine learning discussion groups.

Mamba, a breakthrough algorithm, challenges the 21st century’s biggest algorithm, Transformer, by achieving superior language modeling, speed, and cost-effectiveness.

Biomedical engineers at Duke University have developed a new method to improve the effectiveness of machine learning models. By pairing two machine learning models, one to gather data and one to analyze it, researchers can circumvent limitations of the technology without sacrificing accuracy.

This new technique could make it easier for researchers to use machine learning algorithms to identify and characterize molecules for use in potential new therapeutics or other materials.

The research is published in the journal Artificial Intelligence in the Life Sciences.

Researchers have created a novel technology utilizing meta-optical devices for thermal imaging. This method offers more detailed information about the objects being imaged, potentially expanding thermal imaging applications in autonomous navigation, security, thermography, medical imaging, and remote sensing.

“Our method overcomes the challenges of traditional spectral thermal imagers, which are often bulky and delicate due to their reliance on large filter wheels or interferometers,” said research team leader Zubin Jacob from Purdue University. “We combined meta-optical devices and cutting-edge computational imaging algorithms to create a system that is both compact and robust while also having a large field of view.”

In Optica, Optica Publishing Group’s journal for high-impact research, the authors describe their new spectro-polarimetric decomposition system, which uses a stack of spinning metasurfaces to break down thermal light into its spectral and polarimetric components. This allows the imaging system to capture the spectral and polarization details of thermal radiation in addition to the intensity information that is acquired with traditional thermal imaging.