In a nutshell

  • A new artificial intelligence algorithm called Torque Clustering, inspired by how galaxies merge in space, can find patterns in data without human guidance. It achieved 97.7% accuracy across 1,000 diverse datasets
  • Unlike current AI that requires extensive human-labeled data for training, this breakthrough allows computers to learn independently, similar to how animals naturally observe and understand their environment
  • The algorithm could accelerate discoveries across medicine, finance, climate science and other fields by revealing hidden patterns in complex data that traditional analysis methods might miss, while making advanced AI more accessible to organizations with limited resources

SYDNEY — Artificial intelligence has made headlines for writing essays, generating art, and even passing medical exams. However, most AI systems today still require extensive human guidance to function effectively. Similar to a student who needs constant instruction, current AI relies on carefully labeled data and precise rules to learn. Now, researchers at the University of Technology Sydney have developed an innovative approach that brings AI closer to natural intelligence, allowing it to learn independently by finding patterns in data.

“In nature, animals learn by observing, exploring, and interacting with their environment, without explicit instructions. The next wave of AI, ‘unsupervised learning’ aims to mimic this approach,” says Distinguished Professor CT Lin from the University of Technology Sydney, in a statement.

Their method, called “Torque Clustering” (TC), draws inspiration from an unexpected source: the way galaxies merge in space. Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, a leading journal in artificial intelligence research, this breakthrough could transform how AI systems learn and uncover patterns across diverse fields, from detecting disease patterns in medicine to uncovering financial fraud.

“Nearly all current AI technologies rely on ‘supervised learning,’ an AI training method that requires large amounts of data to be labeled by a human using predefined categories or values, so that the AI can make predictions and see relationships,” says Lin.

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