Confidential Computing (CC) safeguards data during processing, not just storage or transmission. It allows sensitive data, such as cryptographic keys, AI agent reasoning stages, and proprietary algorithms, to be computed safely without external access or modification. As AI systems become more independent and interconnected, confidential computing ensures computation integrity and privacy end-to-end.
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The standard approach to satellite imagery is to snap huge batches of pictures and beam them back to Earth, where they can be sifted through by human operators and the best available algorithms.
It’s all worked well so far, but the time, transmission bandwidth, and energy required are starting to become bottlenecks. Modern satellites are simply capturing more pixels than scientists have time to look at.
However, the YAM-9 satellite has just done something different: It has identified and described features in its image scans without needing to check back with ground control.
This new approach can identify worse-case scenarios that an engineer might miss if they use a traditional method that compares an algorithm against a set of human-designed past test cases. It is also less labor-intensive than other verification tools that require engineers to rewrite an algorithm in a complex mathematical code each time they want to test it.
Instead of needing a mathematical reformulation, the new method reads the algorithm’s source code directly and automatically searches for worse-case scenarios that lead to the highest level of underperformance.
By helping engineers quickly and easily stress-test a networking algorithm before deployment, the method could catch failure modes that might otherwise only appear in a real outage. The technique could also be used to analyze the risks of deploying AI-generated code.
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?
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