Lifeboat Foundation: Safeguarding Humanity
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Category: information science

A quantum computer can solve optimization problems faster than classical supercomputers, a process known as “quantum advantage” and demonstrated by a USC researcher in a paper recently published in Physical Review Letters.

The study shows how , a specialized form of quantum computing, outperforms the best current classical algorithms when searching for near-optimal solutions to complex problems.

“The way quantum annealing works is by finding low-energy states in , which correspond to optimal or near-optimal solutions to the problems being solved,” said Daniel Lidar, corresponding author of the study and professor of electrical and computer engineering, chemistry, and physics and astronomy at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences.

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In recent years, computer scientists have created various highly performing machine learning tools to generate texts, images, videos, songs and other content. Most of these computational models are designed to create content based on text-based instructions provided by users.

Researchers at the Hong Kong University of Science and Technology recently introduced AudioX, a model that can generate high quality audio and music tracks using texts, video footage, images, music and audio recordings as inputs. Their model, introduced in a paper published on the arXiv preprint server, relies on a diffusion transformer, an advanced machine learning algorithm that leverages the so-called transformer architecture to generate content by progressively de-noising the input data it receives.

“Our research stems from a fundamental question in artificial intelligence: how can intelligent systems achieve unified cross-modal understanding and generation?” Wei Xue, the corresponding author of the paper, told Tech Xplore. “Human creation is a seamlessly integrated process, where information from different sensory channels is naturally fused by the brain. Traditional systems have often relied on specialized models, failing to capture and fuse these intrinsic connections between modalities.”

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In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives researchers a way to model complex, dynamical systems where interactions among three or more individuals—or even among groups of individuals—may play an important part.

Instead of edges that connect pairs of nodes, it is based on hyperedges that connect groups of nodes. Hypergraphs can represent higher-order interactions that represent collective behaviors like swarming in fish, birds, or bees, or processes in the brain.

Scientists usually use a hypergraph to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but don’t have access to a reliable model? Yuanzhao Zhang, an SFI Complexity Postdoctoral Fellow, has an answer.

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It would be difficult to understand the inner workings of a complex machine without ever opening it up, but this is the challenge scientists face when exploring quantum systems. Traditional methods of looking into these systems often require immense resources, making them impractical for large-scale applications.

Researchers at UC San Diego, in collaboration with colleagues from IBM Quantum, Harvard and UC Berkeley, have developed a novel approach to this problem called “robust shallow shadows.” This technique allows scientists to extract essential information from more efficiently and accurately, even in the presence of real-world noise and imperfections. The research is published in the journal Nature Communications.

Imagine casting shadows of an object from various angles and then using those shadows to reconstruct the object. By using algorithms, researchers can enhance sample efficiency and incorporate noise-mitigation techniques to produce clearer, more detailed “shadows” to characterize quantum states.

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Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the “light signatures” (optical spectra) of molecules, materials and disease biomarkers, potentially enabling faster and more precise medical diagnoses and sample analysis.

“Imagine being able to detect early signs of diseases like Alzheimer’s or COVID-19 just by shining a light on a drop of fluid or a ,” said Ziyang Wang, an electrical and computer engineering doctoral student at Rice who is a first author on a study published in ACS Nano. “Our work makes this possible by teaching computers how to better ‘read’ the signal of light scattered from tiny molecules.”

Every material or molecule interacts with light in a unique way, producing a distinct pattern, like a fingerprint. Optical spectroscopy, which entails shining a laser on a material to observe how light interacts with it, is widely used in chemistry, materials science and medicine. However, interpreting spectral data can be difficult and time-consuming, especially when differences between samples are subtle. The new algorithm, called Peak-Sensitive Elastic-net Logistic Regression (PSE-LR), is specially designed to analyze light-based data.

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Today, we’re diving into how the 2004 reboot of Battlestar Galactica didn’t just serve up emotionally broken pilots and sexy robots—it predicted our entire streaming surveillance nightmare. From Cylons with download-ready consciousness to humans drowning in misinformation, BSG basically handed us a roadmap to 2025… and we thanked it with fan theories and Funko Pops.

🔎 Surveillance culture? Check.
👤 Digital identity crises? Double check.
🤯 Manufactured realities? Oh, we’re way past that.

Turns out, the Cylons didn’t need to invade Earth. We became them—scrolling, uploading, and streaming our humanity away one click at a time.

So join me as we break it all down and honor the sci-fi series that turned out to be way more documentary than dystopia.

👉 Hit like, share with your fellow glitchy humans, and check out egotasticfuntime.com before the algorithm decides fun is obsolete!

#BattlestarGalactica.

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Love this short paper which reveals a significant insight about alien life with a simple ‘back-of-the-envelope’ calculation! — “We find that as long as the probability that a habitable zone planet develops a technological species is larger than ~10^-24, humanity is not the only time technological intelligence has evolved.” [In the observable universe]

Free preprint version: https://arxiv.org/abs/1510.

#aliens #astrobiology #life #universe

Abstract In this article, we address the cosmic frequency of technological species. Recent advances in exoplanet studies provide strong constraints on all astrophysical terms in the Drake equation. Using these and modifying the form and intent of the Drake equation, we set a firm lower bound on the probability that one or more technological species have evolved anywhere and at any time in the history of the observable Universe. We find that as long as the probability that a habitable zone planet develops a technological species is larger than ∼10−24, humanity is not the only time technological intelligence has evolved. This constraint has important scientific and philosophical consequences. Key Words: Life—Intelligence—Extraterrestrial life. Astrobiology 2016359–362.

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Strawberry fields forever will exist for the in-demand fruit, but the laborers who do the backbreaking work of harvesting them might continue to dwindle. While raised, high-bed cultivation somewhat eases the manual labor, the need for robots to help harvest strawberries, tomatoes, and other such produce is apparent.

As a first step, Osaka Metropolitan University Assistant Professor Takuya Fujinaga has developed an algorithm for robots to autonomously drive in two modes: moving to a pre-designated destination and moving alongside raised cultivation beds. The Graduate School of Engineering researcher experimented with an agricultural robot that utilizes lidar point cloud data to map the environment.

Official website for Osaka Metropolitan University. Established in 2022 through the merger of Osaka City University and Osaka Prefecture University.

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PRESS RELEASE — Quantum computers promise to speed calculations dramatically in some key areas such as computational chemistry and high-speed networking. But they’re so different from today’s computers that scientists need to figure out the best ways to feed them information to take full advantage. The data must be packed in new ways, customized for quantum treatment.

Researchers at the Department of Energy’s Pacific Northwest National Laboratory have done just that, developing an algorithm specially designed to prepare data for a quantum system. The code, published recently on GitHub after being presented at the IEEE International Symposium on Parallel and Distributed Processing, cuts a key aspect of quantum prep work by 85 percent.

While the team demonstrated the technique previously, the latest research addresses a critical bottleneck related to scaling and shows that the approach is effective even on problems 50 times larger than possible with existing tools.

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