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Category: information science – Page 2

What happens when AI starts improving itself without human input? Self-improving AI agents are evolving faster than anyone predicted—rewriting their own code, learning from mistakes, and inching closer to surpassing giants like OpenAI. This isn’t science fiction; it’s the AI singularity’s opening act, and the stakes couldn’t be higher.

How do self-improving agents work? Unlike static models such as GPT-4, these systems use recursive self-improvement—analyzing their flaws, generating smarter algorithms, and iterating endlessly. Projects like AutoGPT and BabyAGI already demonstrate eerie autonomy, from debugging code to launching micro-businesses. We’ll dissect their architecture and compare them to OpenAI’s human-dependent models. Spoiler: The gap is narrowing fast.

Why is OpenAI sweating? While OpenAI focuses on safety and scalability, self-improving agents prioritize raw, exponential growth. Imagine an AI that optimizes itself 24/7, mastering quantum computing over a weekend or cracking protein folding in hours. But there’s a dark side: no “off switch,” biased self-modifications, and the risk of uncontrolled superintelligence.

Who will dominate the AI race? We’ll explore leaked research, ethical debates, and the critical question: Can OpenAI’s cautious approach outpace agents that learn to outthink their creators? Like, subscribe, and hit the bell—the future of AI is rewriting itself.

Can self-improving AI surpass OpenAI? What are autonomous AI agents? How dangerous is recursive AI? Will AI become uncontrollable? Can we stop self-improving AI? This video exposes the truth. Watch now—before the machines outpace us.

#ai.

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It’s easy to take joint mobility for granted. Without thinking, it’s simple enough to turn the pages of a book or bend to stretch out a sore muscle. Designers don’t have the same luxury. When building a joint, be it for a robot or wrist brace, designers seek customizability across all degrees of freedom but are often restricted by their versatility to adapt to different use contexts.

Researchers at Carnegie Mellon University’s College of Engineering have developed an algorithm to design metastructures that are reconfigurable across six degrees of freedom and allow for stiffness tunability. The algorithm can interpret the kinematic motions that are needed for multiple configurations of a device and assist designers in creating such reconfigurability. This advancement gives designers more over the functionality of joints for various applications.

The team demonstrated the structure’s versatile capabilities via multiple wearable devices tailored for unique movement functions, body areas, and uses.

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Such questions quickly run into the limits of knowledge for both biology and computer science. To answer them, we need to figure out what exactly we mean by “information” and how that’s related to what’s happening inside cells. In attempting that, I will lead you through a frantic tour of information theory and molecular biology. We’ll meet some strange characters, including genomic compression algorithms based on deep learning, retrotransposons, and Kolmogorov complexity.

Ultimately, I’ll argue that the intuitive idea of information in a genome is best captured by a new definition of a “bit” — one that’s unknowable with our current level of scientific knowledge.

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Digital transformation is blurring the lines between the physical, digital and biological spheres. From cloud computing, to Artificial Intelligence (AI) and Big Data, technologies of the Fourth Industrial Revolution (4IR) are shaping every aspect of our lives.

In the oil and gas industry, digital transformation is revolutionizing how we supply energy to the world. By deploying a range of 4IR technologies across our business, we aim to meet the world’s energy needs while enhancing productivity, reducing CO2 emissions, and creating next-generation products and materials.

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Exactly 100 years ago, famed Austrian physicist Erwin Schrödinger (yes, the cat guy) postulated his eponymous equation that explains how particles in quantum physics behave. A key component of quantum mechanics, Schrödinger’s Equation provides a way to calculate the wave function of a system and how it changes dynamically in time.

“Quantum mechanics, along with Albert Einstein’s theory of general relativity are the two pillars of modern physics,” says Utah State University physicist Abhay Katyal. “The challenge is, for more than half a century, scientists have struggled to reconcile these two theories.”

Quantum mechanics, says Katyal, a doctoral student and Howard L. Blood Graduate Fellow in the Department of Physics, describes the behavior of matter and forces at the subatomic level, while explains gravity on a large scale.

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Ever since general relativity pointed to the existence of black holes, the scientific community has been wary of one peculiar feature: the singularity at the center—a point, hidden behind the event horizon, where the laws of physics that govern the rest of the universe appear to break down completely. For some time now, researchers have been working on alternative models that are free of singularities.

A new paper published in the Journal of Cosmology and Astroparticle Physics, the outcome of work carried out at the Institute for Fundamental Physics of the Universe (IFPU) in Trieste, reviews the state of the art in this area. It describes two alternative models, proposes observational tests, and explores how this line of research could also contribute to the development of a theory of quantum gravity.

“Hic sunt leones,” remarks Stefano Liberati, one of the authors of the paper and director of IFPU. The phrase refers to the hypothetical singularity predicted at the center of standard —those described by solutions to Einstein’s field equations. To understand what this means, a brief historical recap is helpful.

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Solving one of the oldest algebra problems isn’t a bad claim to fame, and it’s a claim Norman Wildberger can now make: The mathematician has solved what are known as higher-degree polynomial equations, which have been puzzling experts for nearly 200 years.

Wildberger, from the University of New South Wales (UNSW) in Australia, worked with computer scientist Dean Rubine on a paper that details how these incredibly complex calculations could be worked out.

“This is a dramatic revision of a basic chapter in algebra,” says Wildberger. “Our solution reopens a previously closed book in mathematics history.”

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RIKEN and Fujitsu Limited have developed a 256-qubit superconducting quantum computer that will significantly expand their joint quantum computing capabilities. The system, located at the RIKEN RQC-FUJITSU Collaboration Center, located on the RIKEN Wako campus, builds upon the advanced technology of the 64-qubit iteration, which was launched with the support of the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) in October 2023, and incorporates newly-developed high-density implementation techniques. The new system overcomes some key technical challenges, including appropriate cooling within the dilution refrigerator, which is achieved through the incorporation of high-density implementation and cutting-edge thermal design.

This announcement marks a new step toward the practical application of superconducting quantum computers and unlocking their potential to grapple with some of the world’s most complex issues, such as the analysis of larger molecules and the implementation and demonstration of sophisticated error correction algorithms.

The organizations plan to integrate the 256-qubit superconducting quantum computer into their platform for hybrid quantum computing lineup and offer it to companies and research institutions globally starting in the first quarter of fiscal 2025. Looking further into the future, Fujitsu and RIKEN will continue R&D efforts toward the launch of a 1,000-qubit computer, scheduled to be launched in 2026. For more information, see a longer press release on Fujitsu’s websiteThe webpage will open in a new tab..

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Discovering new, powerful electrolytes is one of the major bottlenecks in designing next-generation batteries for electric vehicles, phones, laptops and grid-scale energy storage.

The most stable electrolytes are not always the most conductive. The most efficient batteries are not always the most stable. And so on.

“The electrodes have to satisfy very different properties at the same time. They always conflict with each other,” said Ritesh Kumar, an Eric and Wendy Schimdt AI in Science Postdoctoral Fellow working in the Amanchukwu Lab at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME).

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A mathematician has solved a 200-year-old maths problem after figuring out a way to crack higher-degree polynomial equations without using radicals or irrational numbers.

The method developed by Norman Wildberger, PhD, an honorary professor at the School of Mathematics and Statistics at UNSW Sydney, solves one of algebra’s oldest challenges by finding a general solution to equations where the variable is raised to the fifth power or higher.

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