Seoul National University of Science and Technology Researchers Develop a Reliable AI System for SSD Failure Prediction

Solid-state drives (SSDs), which store data in computers and servers, are critical to data center reliability. SSD failures can cause service disruptions, data loss and high maintenance costs, making ...
Opinion
Eurasia ReviewOpinion

The Most Dangerous AI Error Is A Reproducible One – Analysis

As Washington and Brussels build formal AI evaluation regimes, a repeatable but circular benchmark can convert methodological error into policy at scale ...
quantumzeitgeist

New method automatically finds errors in quantum states

A new model-based framework automatically quantifies sources of degradation in generated quantum states, crucial for ...
Goal

An attacking machine with fatal errors: a fearsome side and a worrying one for Real Madrid

Real Madrid have shown two faces at the start of the season, looking like completely different teams at the Santiago Bernabéu and on their travels. At home they are powerful and clinical in attack.
Interesting Engineering

New framework cuts errors by 80%, helps scientists predict fusion machine’s hardware changes

Inside a fusion machine, the plasma can reach temperatures hotter than the Sun’s core while powerful magnetic fields squeeze it into a tiny, unstable space. These extreme conditions are necessary for ...
Assembly Mag

Machine Learning Detects Fastening Errors

Nutrunner anomalies are rare, and historical process data are not always stored long-term. This makes it challenging to obtain sufficient data to develop datasets to train AI algorithms. April 29, ...
Phys.org

Machine learning accelerates analysis of fusion materials

Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic ...
thequantuminsider

Study Finds Exponential Quantum Advantage in Machine Learning Tasks

Small quantum computers could process massive datasets more efficiently than far larger classical systems, according to a study recently posted on arXiv that outlines a path to exponential gains in ...
IEEE

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity

Abstract: Decentralized learning, which facilitates joint model training across geographically scattered agents, has gained significant attention in the field of signal and information processing in ...
IEEE

Trend-Error Decomposition for Self-Supervised Time Series Learning in Multivariate Forecasting Task

Abstract: Self-Supervised Learning (SSL) has become a powerful paradigm in Artificial Intelligence, enabling the training of machine learning models using unlabeled data. However, in time series ...
science

Advances in machine learning–enhanced microfluidic cell sorting

Cell sorting, essential for diagnostics and early intervention, has evolved from conventional methods to sophisticated microfluidic approaches. These miniaturized systems leverage precise hydrodynamic ...