Microsoft announced today that it is accelerating its quantum-safe security roadmap, saying advances in quantum computing are bringing the need to replace today’s encryption standards sooner than previously expected.
Although today’s quantum computers cannot crack modern encryption, security researchers have warned about “harvest now, decrypt later” attacks. In these attacks, encrypted data that is stolen today is stored until future quantum computers become powerful enough to decrypt it, exposing sensitive information.
As a result, companies including Apple, Google, and Signal have begun integrating post-quantum cryptography (PQC) to replace existing public-key encryption algorithms with quantum-resistant versions.
To harness biological systems (plants and microbes) for next-generation energy production and advanced materials, researchers are looking to beneficial plant-microbe interactions. Because these are complex systems, it has proven difficult to reproducibly control exactly which microbes are present. And, subtle differences in materials, methods, or even the hands of the researchers themselves can lead to inconsistent results. This makes it difficult to replicate previous work, significantly slowing the leap from scientific discovery to practical application.
Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) are overcoming this bottleneck by addressing a multi-layered challenge: building reliable physical hardware, engineering accurate visual sensors, and developing predictive algorithms. Their solution, EcoBOT, stands out from typical plant phenotyping facilities by integrating these distinct components into a reliably automated workflow under strictly sterile conditions.
EcoBOT takes specialized growth chambers, called EcoFABs, and integrates them with machine-learning tools that autonomously guide the discovery cycle. This system uses advanced imaging to regularly scan the entire plant—from the tips of its leaves to the bottom of its roots. By using Gaussian Process models and AI analysis tools, it can quickly analyze and model this visual data to calculate the most informative next steps. This directs the automated hardware to determine exactly how plants adapt to environmental stressors, establishing the crucial microbe-free baseline needed to eventually study plant-microbe interactions and engineer better bioenergy crops.
Gödel’s Mind: How AI Agents Emerged from a Logical Paradox.
The Gödel Agent, a new AI research paper, represents a novel paradigm in self-referential AI agents by leveraging recursive self-improvement inspired by the Gödel machine. Traditional agentic systems have been constrained by human design, either through hand-crafted algorithms or pre-defined meta-learning routines, limiting the scope of optimization. The Gödel Agent framework bypasses these limitations by allowing agents to modify not only their decision-making policies but also their meta-learning algorithms dynamically and autonomously. The self-referential nature of Gödel Agent enables it to modify its own code during runtime, thereby continuously evolving without predefined constraints or bottlenecks imposed by human-designed modules.
Central to the Gödel Agent’s methodology is its use of large language models (LLMs) that drive recursive decision-making and self-modification. The agent operates by analyzing its performance in the environment, retrieving its current codebase from runtime memory, and employing monkey patching to alter its behavior. This process of \.
Researchers have developed light-transmitting hydrogel fibers that are just hundreds of micrometers in diameter. With further development, these soft fibers could one day make it possible to use imaging techniques to detect early breast cancer hidden inside very small breast ducts.
“While traditional, relatively rigid fiber probes may cause mechanical damage when entering narrow, curved or soft tissue spaces, our fibers are very soft with mechanical properties more similar to those of human soft tissues,” said research team leader Yu Zhang from Harbin Engineering University in China. “We made these fibers using a draw-spinning method that was inspired by spider-silk spinning.”
In research appearing in Optics Express, the researchers describe how they tested the new hydrogel fibers by incorporating them into an imaging system and using it to analyze standard pathology-stained breast tissue sections. The imaging system successfully reconstructed the microscopic features used by pathologists to evaluate tumors and, when combined with artificial intelligence algorithms, distinguished tumor subtypes with an accuracy of 93.97%.
Sulfur is one of the most abundant elements in the universe. If you peer into a diffuse interstellar cloud, you find loads of it—about the amount expected based on fusion patterns in the stars it was born in. However, if you look at a dense, cold molecular cloud—the kind where those stars actually form—it seems like 99% of the sulfur expected to be there is missing. Scientists have puzzled over this “missing sulfur problem” for decades, though a leading theory is that the element hides in icy dust grains, making it hard to detect.
A new paper published in Astronomy & Astrophysics from the Max Planck Institute for Extraterrestrial Physics and the Centro de Astrobiologia describes a new computer simulation model aimed at supporting the interpretation of laboratory results and testing our current understanding of sulfur evolution in interstellar ices.
The simulation was written in pyRate—a Python-based application that calculates how chemicals interact, especially between ice and gas phases. The paper marks the first successful model of the chemistry of a multicomponent interstellar ice analog with a rate-equation simulation. Scientists love “firsts,” but what does that actually mean in practice in this case?
Artificial intelligence has achieved remarkable breakthroughs in recent years, from generating human-like text and images to solving complex scientific and engineering problems. Yet some challenges remain extraordinarily difficult even for the most advanced AI systems. This has fueled growing interest in quantum computing, a technology that processes information in fundamentally different ways from classical computers. Researchers are now exploring whether quantum algorithms can tackle certain optimization, simulation, and computational problems that push conventional AI systems to their limits. Recent experiments and research papers have generated excitement by demonstrating situations where quantum approaches may offer unique advantages, reigniting debate about how these two revolutionary technologies could work together in the future.
Rather than viewing quantum computing and AI as competitors, many experts believe they could become powerful partners. Quantum processors may eventually help accelerate specific machine learning tasks, improve complex simulations, and solve optimization problems that are critical to industries such as logistics, finance, materials science, and drug discovery. At the same time, scientists caution that practical large-scale quantum computing remains an active area of research, and many headline-grabbing claims require careful scrutiny and independent verification. Even so, the rapid progress in both fields suggests that the future of computing may be shaped not by AI alone, but by a combination of artificial intelligence and quantum technologies working together to tackle problems once thought impossible.
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This video is intended for educational and informational purposes only. Quantum computing and artificial intelligence are rapidly evolving fields, and interpretations of research findings may change as new evidence becomes available. The content presented is based on publicly available studies, expert analysis, and current technological developments.
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A researcher at Kyushu University and his collaborators have shown that continuous parameters in quantum gravity may not be freely adjustable “dials” from outside the theory, but rather arise from operators within the theory itself, supporting the century-old claim by Albert Einstein about the fundamental laws of nature.
Einstein argued that the fundamental equations of physics contain no freely adjustable parameters. In other words, he believed that the laws of nature should not include arbitrary numbers chosen from outside a theory. Instead, such quantities should emerge naturally from physical processes.
This idea has become especially important in the search for quantum gravity, a theory that aims to combine gravity with quantum mechanics. Physicists expect that the equations governing quantum gravity should not contain freely adjustable quantities. Rather, all parameters should arise from physical fields.
For the first time, new algorithms may be able to automatically explain why some self-driving cars crash—a question crucial to answer as more autonomous vehicles take to the roads. This new approach, developed by researchers at King’s College London, reviews past events to explain why specific instances of failure happened, in the hope that this can be used to make improvements in the future.
The research was presented at the 2026 IEEE International Conference of Robotics and Automation.
Self-driving vehicles are increasingly being rolled out across the globe, in cities like London and San Francisco, but collisions and serious breaches of road safety have put pressure on manufacturers to explain why they make the mistakes they do. This is often hard to do, and current methods only provide limited explanations for these.
