Lifeboat Foundation: Safeguarding Humanity
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Category: information science – Page 12

How do languages balance the richness of their structures with the need for efficient communication? To investigate, researchers at the Leibniz Institute for the German Language (IDS) in Mannheim, Germany, trained computational language models on a vast dataset covering thousands of languages.

They found that languages that are computationally harder to process compensate for this increased complexity with greater efficiency: more complex languages need fewer symbols to encode the same message. The analyses also reveal that larger language communities tend to use more complex but more efficient languages.

Language models are computer algorithms that learn to process and generate language by analyzing large amounts of text. They excel at identifying patterns without relying on predefined rules, making them valuable tools for linguistic research. Importantly, not all models are the same: their internal architectures vary, shaping how they learn and process language. These differences allow researchers to compare languages in new ways and uncover insights into linguistic diversity.

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An international team of researchers, led by physicists from the University of Vienna, has achieved a breakthrough in data processing by employing an “inverse-design” approach. This method allows algorithms to configure a system based on desired functions, bypassing manual design and complex simulations. The result is a smart “universal” device that uses spin waves (“magnons”) to perform multiple data processing tasks with exceptional energy efficiency.

Published in Nature Electronics, this innovation marks a transformative advance in unconventional computing, with significant potential for next-generation telecommunications, computing, and neuromorphic systems.

Modern electronics face critical challenges, including high energy consumption and increasing design complexity. In this context, magnonics—the use of magnons, or quantized spin waves in —offers a promising alternative. Magnons enable efficient data transport and processing with minimal energy loss.

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In order to find rare processes from collider data, scientists use computer algorithms to determine the type and properties of particles based on the faint signals that they leave in the detector. One such particle is the tau lepton, which is produced, for example, in the decay of the Higgs boson.

The leaves a spray or jet of low-energy , the subtle pattern of which in the jet allows one to distinguish them from jets produced by other particles. The jet also contains about the energy of the tau lepton, which is distributed among the daughter particles, and on the way is decayed. Currently, the best algorithms use multiple steps of combinatorics and computer vision.

ChatGPT has shown much stronger performance in rejecting backgrounds than computer-vision based methods. In this paper, researchers showed that such language-based models can find the tau leptons from the jet patterns, and also determine the energy and decay properties more accurately than before.

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For the first time, a team of researchers at Lawrence Livermore National Laboratory (LLNL) quantified and rigorously studied the effect of metal strength on accurately modeling coupled metal/high explosive (HE) experiments, shedding light on an elusive variable in an important model for national security and defense applications.

The team used a Bayesian approach to quantify with tantalum and two common explosive materials and integrated it into a coupled metal/HE . Their findings could lead to more accurate models for equation-of-state-studies, which assess the state of matter a material exists in under different conditions. Their paper —featured as an editor’s pick in the Journal of Applied Physics —also suggested that metal strength uncertainty may have an insignificant effect on result.

“There has been a long-standing field lore that HE model calibrations are sensitive to the metal strength,” said Matt Nelms, the paper’s first author and a group leader in LLNL’s Computational Engineering Division (CED). “By using a rigorous Bayesian approach, we found that this is not the case, at least when using tantalum.”

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However, despite these advances, human progress is never without risks. Therefore, we must address urgent challenges, including the lack of transparency in algorithms, potential intrinsic biases and the possibility of AI usage for destructive purposes.

Philosophical And Ethical Implications

The singularity and transcendence of AI could imply a radical redefinition of the relationship between humans and technology in our society. A typical key question that may arise in this context is, “If AI surpasses human intelligence, who—or what—should make critical decisions about the planet’s future?” Looking even further, the concretization of transcendent AI could challenge the very concept of the soul, prompting theologians, philosophers and scientists to reconsider the basic foundations of beliefs established for centuries over human history.

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Recent research demonstrates that brain organoids can indeed “learn” and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also control the feedback signals, creating a biofeedback loop that allows the organoids to adapt and even demonstrate short-term memory (Bai et al. 2024).

One technique central to AI-integrated organoid computing is reservoir computing, a model traditionally used in silicon-based computing. In an open-loop setup, AI algorithms interact with organoids as they serve as the “reservoir,” for processing input signals and dynamically adjusting their responses. By interpreting these responses, researchers can classify, predict, and understand how organoids adapt to specific inputs, suggesting the potential for simple computational processing within a biological substrate (Kagan et al. 2023; Aaser et al. n.d.).

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Simulation Metaphysics extends beyond the conventional Simulation Theory, framing reality not merely as an arbitrary digital construct but as an ontological stratification. In this self-simulating, cybernetic manifold, the fundamental fabric of existence is computational, governed by algorithmic processes that generate physical laws and emergent minds. Under such a novel paradigm, the universe is conceived as an experiential matrix, an evolutionary substrate where the evolution of consciousness unfolds through nested layers of intelligence, progressively refining its self-awareness.

#SimulationMetaphysics #OmegaSingularity #CyberneticTheoryofMind #SimulationHypothesis #SimulationTheory #CosmologicalAlpha #DigitalPhysics #ontology

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The research team, led by Professor Tobin Filleter, has engineered nanomaterials that offer unprecedented strength, weight, and customizability. These materials are composed of tiny building blocks, or repeating units, measuring just a few hundred nanometers – so small that over 100 lined up would barely match the thickness of a human hair.

The researchers used a multi-objective Bayesian optimization machine learning algorithm to predict optimal geometries for enhancing stress distribution and improving the strength-to-weight ratio of nano-architected designs. The algorithm only needed 400 data points, whereas others might need 20,000 or more, allowing the researchers to work with a smaller, high-quality data set. The Canadian team collaborated with Professor Seunghwa Ryu and PhD student Jinwook Yeo at the Korean Advanced Institute of Science & Technology for this step of the process.

This experiment was the first time scientists have applied machine learning to optimize nano-architected materials. According to Peter Serles, the lead author of the project’s paper published in Advanced Materials, the team was shocked by the improvements. It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.

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In today’s AI news, OpenAI is announcing a new AI Agent designed to help people who do intensive knowledge work in areas like finance, science, policy, and engineering and need thorough, precise, and reliable research. It could also be useful for anyone making major purchases.

In what most would consider a halcyon time for AI, an anachronistic source has just added their two cents to the ethos around the AI revolution. The Vatican released a significant broadside addressing the potential and risks of AI in a new high-tech world. It’s a very interesting look at these new technologies, with a focus on human worth and human dignity.

In other advances, the one-person micro-enterprise is far from a novel concept. Cheap on-demand AI compute, remote collaboration, payment processing APIs, social media, and e-commerce marketplaces have all made it easier to “go it alone” as an entrepreneur. But what about scaling that business into something meatier — a one-person Unicorn.

And, this morning, Brussels announced plans to develop an open source AI model of its own, with $56 million in funding to do it. The investment will fund top researchers from a handful of companies and universities across EU countries as they develop a large language model that can work with the trading bloc’s 30 languages.

In videos, Lex Fridman speaks with Dylan Patel, Founder of SemiAnalysis, a semiconductor research and analysis company, and Nathan Lambert, a research scientist at Allen Institute for AI (Ai2) and author of an AI blog called Interconnects. They all discuss DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters.

Then we tune into the Big Technology podcast to hear how companies are actually deploying AI agents and what it takes to move beyond proof of concepts to real deployment. Antoine Shagoury, Chief Technology Officer of Kyndryl, an IBM spinoff, joins Alex Kantrowitz show to discuss the real-world implementation of AI in enterprise environments.

And, we take a tour of a fully automated e-commerce warehouse run by AI robots. Brightpick Autopicker is the only autonomous mobile robot (AMR) in the world that robotically picks and consolidates orders directly in the warehouse aisles, like a human with a cart.

Continue reading “OpenAI Unveils A New ChatGPT Agent For ‘Deep Research’ Analysis And Reporting” | >