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A new algorithm uses time-series data to reveal hidden group interactions in dynamic systems like the brain.

NeuroMatch teaches AI to recognize hidden network patterns instantly, replacing slow graph searches with geometric intuition that scales across biology, chemistry, and knowledge systems.

Computer scientists use sequential pattern mining to analyze the chronological order of massive datasets, predicting consumer behavior, detecting fraud, and revealing hidden digital trends worldwide.

The new SuSiE² statistical framework integrates gene expression data directly into genetic fine mapping, allowing researchers to pinpoint true causal disease variants with unprecedented precision.

John Hopfield and Geoffrey Hinton, two pioneers of machine learning and artificial neural networks, have won the Nobel Prize in Physics.

NeuroSA, a quantum-inspired tool, mimics brain functions to find optimal solutions for logistics, drug discovery, and more.

A new small AI model with just 7 million parameters outperforms much larger ones on tough puzzles, suggesting that smart design can replace massive scale in solving complex problems.

MIT researchers craft a periodic table to connect classical machine learning algorithms, revealing new ways to improve AI models.

Correlational Elegant Normal Form integrates learned variable correlations into symbolic normalization, restoring efficient SAT search and evolutionary modeling while preserving canonical structure in highly structured domains.

Researchers say a new “multiplex” AI framework dramatically improved cultural inclusivity in chatbot responses, raising hopes for more globally representative artificial intelligence.