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Category: information science – Page 2

Dozens of hidden star streams found in the outskirts of our Milky Way galaxy

To find them, Chen developed a computer algorithm called StarStream, which searches for streams using a physics-based model rather than relying on visual patterns alone, according to the study. The team then applied the method to Gaia data, which from 2014 to 2025 mapped the positions and motions of billions of stars in the Milky Way.

“It turns out that it’s a lot easier to find things when you have a theoretical expectation of what you’re looking for when you have a simple phenomenological picture,” Gnedin said in the statement.

The results also revealed that many streams do not match the classic expectation of thin, well-aligned trails. Instead, the study reports that some of the newfound streams are shorter, wider or even misaligned with their parent clusters’ orbits — suggesting earlier searches may have missed them by focusing only on the most obvious structures.

Google DeepMind’s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.

MICrONS Explorer: A virtual observatory of the cortex

The Machine Intelligence from Cortical Networks (MICrONS) program seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. It is an ambitious program to map the function and connectivity of cortical circuits, using high throughput imaging technologies, with the goal of providing insights into the computational principles that underlie cortical function in order to advance the next generation of machine learning algorithms.

This website serves as a data portal to release connectivity and functional imaging data collected by a consortium of laboratories led by groups at the Allen Institute for Brain Science, Princeton University, and Baylor College of Medicine, with support from a broad array of teams, coordinated and funded by the IARPA MICrONS program. These data include large scale electron microscopy based reconstructions of cortical circuitry from mouse visual cortex, with corresponding functional imaging data from those same neurons.

Have a Scientific Request? Check out the Virtual Observatory of the Cortex (VORTEX) project, a BRAIN Initiative funded program to bring the MICrONS dataset to the research community. Access proofreading resources to answer your scientific questions.

New memristor design uses built-in oxygen gradient to bring stability to reinforcement learning

In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to produce slow, stable conductance changes, enabling a reinforcement learning (RL) algorithm to learn faster and more stably than conventional approaches.

Reinforcement learning stands as one of the most promising ways to achieve continual learning in AI. The idea is to replicate how biological systems acquire and adapt knowledge slowly over time. The brain achieves this via ion gradients that regulate slow, directional signaling across cell membranes. Replicating this in hardware is a key goal of neuromorphic computing.

With their ability to mimic synaptic behavior, memristors have long been considered strong candidates for this. However, most existing devices suffer from unpredictable, abrupt conductance changes, making sustained and stable learning difficult.

World’s largest quantum circuit simulation for quantum chemistry achieved on 1,024 GPUs

A joint research team between the Center for Quantum Information and Quantum Biology (QIQB) at The University of Osaka and Fixstars Corporation has demonstrated one of the world’s largest classical simulations of iterative quantum phase estimation (IQPE) circuits for quantum chemistry on up to 1,024 GPUs, surpassing the previous 40-qubit limit. The result expands the scale of molecular systems available for the development and validation of quantum algorithms for future fault-tolerant quantum computers, supporting progress toward industrial applications in drug discovery and materials development.

The paper was presented at NVIDIA GTC 2026, held in San Jose, California, March 16–19, 2026.

Overcoming unresolved challenges in drug discovery and developing new materials to address climate change will require advanced quantum chemical calculations beyond the reach of current technology. Against this backdrop, fault-tolerant quantum computers (FTQC) are widely anticipated as a key enabling technology, making it increasingly important to develop and validate, ahead of their deployment, the quantum algorithms that will eventually run on such systems.

Social media feeds: Algorithm redesign could break echo chambers and reduce online polarization

Scroll through social media long enough and a pattern emerges. Pause on a post questioning climate change or taking a hard line on a political issue, and the platform is quick to respond—serving up more of the same viewpoints, delivered with growing confidence and certainty.

That feedback loop is the architecture of an echo chamber: a space where familiar ideas are amplified, dissenting voices fade, and beliefs can harden rather than evolve.

But new research from the University of Rochester has found that echo chambers might not be a fact of online life. Published in IEEE Transactions on Affective Computing, the study argues that they are partly a design choice—one that could be softened with a surprisingly modest change: introducing more randomness into what people see.

Hidden features in X-rays could radically change how we measure and understand them

Hidden features uncovered in X-ray signals are set to overturn a key scientific theory and fundamentally change how X-rays are interpreted across fields of physics, chemistry, biology and materials science, new research reveals. Researchers say the discovery can help scientists measure X-rays more precisely and reliably, and improve our understanding of common materials, from battery materials to biological proteins.

X-ray science focuses on the unique energy signatures of atoms. These include the specific X-rays emitted when electrons transition into inner shells—the strongest of which are known as K-alpha lines—as well as distinct energy thresholds at which atoms begin to strongly absorb X-rays.

For more than 50 years, the entire field has relied on the assumption that a core parameter in the equation used to model X-ray absorption spectra, known as the standard XAFS equation, is fixed and does not change.

The 2022 JEPA is essentially the 1992 PMAX

Yann LeCun said:

BREAKING: Schmidhuber claims to have invented JEPA in 1992!

Is anyone surprised?

At some point, when I have nothing better to do, I’ll write a piece about what it means to invent something.

Speaking of which, one day, when I was still in high school, I wrote f(x)=0.

Every theory, every algorithm, is a special case of this (with proper definitions for f and x).

Every technology is a practical application of it.

Continue reading “The 2022 JEPA is essentially the 1992 PMAX” | >

Eyal Aharoni — Breaking the Moral Turing Test

Dr. discusses one of the most provocative frontiers in technology: the automation of moral judgement — in his talk focusses on outcomes of a comparative moral Turing test (AI outperforms humans across a range of metrics), as well as AI assisted medical triage!

Link in reply🔗

Eyal Aharoni

Dr. Eyal Aharoni (Georgia State University) to the Future Day 2026 stage to discuss one of the most provocative frontiers in technology: the automation of moral judgement.

Breaking the Moral Turing Test: Studies of human attribution and deference to AI moral judgment and decision-making.

Continue reading “Eyal Aharoni — Breaking the Moral Turing Test” | >

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