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Technology offers us many options, including less reliance on others, but what does that entail and how autonomous can a person get?

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Continue reading “Off The Grid: Technological Autonomy” »

Researchers at Tohoku University and the University of California, Santa Barbara, have developed new computing hardware that utilizes a Gaussian probabilistic bit made from a stochastic spintronics device. This innovation is expected to provide an energy-efficient platform for power-hungry generative AI.

As Moore’s Law slows down, domain-specific hardware architectures—such as probabilistic computing with naturally stochastic building blocks—are gaining prominence for addressing computationally hard problems. Similar to how quantum computers are suited for problems rooted in quantum mechanics, probabilistic computers are designed to handle inherently probabilistic algorithms.

These algorithms have applications in areas like combinatorial optimization and statistical machine learning. Notably, the 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for their groundbreaking work in machine learning.

Secret Blizzard has a track record of targeting various sectors to facilitate long-term covert access for intelligence collection, but their primary focus is on ministries of foreign affairs, embassies, government offices, defense departments, and defense-related companies across the world.

The latest report comes a week after the tech giant, along with Lumen Technologies Black Lotus Labs, revealed Turla’s hijacking of 33 command-and-control (C2) servers of a Pakistan-based hacking group named Storm-0156 to carry out its own operations.

The attacks targeting Ukrainian entities entail commandeering Amadey bots to deploy a backdoor known as Tavdig, which is then used to install an updated version of Kazuar, which was documented by Palo Alto Networks Unit 42 in November 2023.

Windows UI Automation exploited to bypass EDR tools, enabling data theft, phishing, and app manipulation.

Acting as an optics simulator, XLuminA explores all possible optical configurations.

Max Planck researchers developed an AI framework that autonomously designs microscopy experiments, optimizing it 10,000 times faster.

Continue reading “Max Planck’s AI-driven XLuminA boosts microscopy efficiency by 10,000x” »

Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.

Saar Yoskovitz is Co-Founder & CEO at Augury, a pioneer in AI-driven Machine Health and Process Health solutions for industrial sectors.

American manufacturers are at a crossroads, needing to decide between evolution and obsolescence. The tools that historically drove profitability and efficiency are no longer having an impact. Labor is hard to find and harder to keep. The National Association of Manufacturing projects that 2.1 million manufacturing roles will go unfilled by 2030. This hard truth is compounded by findings in Augury’s State of Production Health report, which reveals that 91% of manufacturers say that the mass exodus of industry veterans will worsen the knowledge gap.

An alarming rate of brain drain is looming over the industrial sector. As tenured employees reach retirement age and fewer professionals line up to take their place, more manufacturers are turning to artificial intelligence (AI) to bridge the gap.

‘Self-replicating Robotic Systems’ published in ‘Encyclopedia of Complexity and Systems Science’

A self-replicating machine is a type of autonomous robot that is capable of reproducing itself autonomously using raw materials found in the environment, thus exhibiting self-replication in a way analogous to that found in nature. Homer Jacobson, Edward F. Moore, Freeman Dyson, John von Neumann, Konrad Zuse and in more recent times by K. Eric Drexler in his book on nanotechnology, Engines of Creation (coining the term clanking replicator for such machines) and by Robert Freitas and Ralph Merkle in their review Kinematic Self-Replicating Machines^{moons and asteroid belts for ore and other materials, the creation of lunar factories, and even the construction of solar power satellites in space. The von Neumann probeuniversal constructor, a self-replicating machine that would be able to evolve and which he formalized in a cellular automata environment. Notably, Von Neumann’s Self-Reproducing Automata scheme posited that open-ended evolution requires inherited information to be copied and passed to offspring separately from the self-replicating machine, an insight that preceded the discovery of the structure of the DNA molecule by Watson and Crick and how it is separately translated and replicated in the cell.https://en.m.wikipedia.org/wiki/Self-replicating_machine#:~:...n_probe_is, [ 9 ] A self-replicating machine is an artificial self-replicating system that relies on conventional large-scale technology and automation. The concept, first proposed by Von Neumann no later than the 1940s, has attracted a range of different approaches involving various types of technology. Certain idiosyncratic terms are occasionally found in the literature. For example, the term clanking replicator was once used by Drexler [ 10 ] to distinguish macroscale replicating systems from the microscopic nanorobots or “assemblers” that nanotechnology may make possible, but the term is informal and is rarely used by others in popular or technical discussions. Replicators have also been called “von Neumann machines” after John von Neumann, who first rigorously studied the idea.}