Researchers have developed a new AI algorithm, called Torque Clustering, that is much closer to natural intelligence than current methods. It significantly improves how AI systems learn and uncover patterns in data independently, without human guidance.
Torque Clustering can efficiently and autonomously analyse vast amounts of data in fields such as biology, chemistry, astronomy, psychology, finance and medicine, revealing new insights such as detecting disease patterns, uncovering fraud, or understanding behaviour.
"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," said Distinguished Professor CT Lin from the University of Technology Sydney (UTS).
"Nearly all current AI technologies rely on 'supervised learning', an AI training method that requires large amounts of data to be labelled by a human using predefined categories or values, so that the AI can make predictions and see relationships.
"Supervised learning has a number of limitations. Labelling data is costly, time-consuming and often impractical for complex or large-scale tasks. Unsupervised learning, by contrast, works without labelled data, uncovering the inherent structures and patterns within datasets."
A paper detailing the Torque Clustering method, Autonomous clustering by fast find of mass and distance peaks, has just been published in IEEE Transactions on Pattern Analysis and Machine Intelligence, a leading journal in the field of artificial intelligence.
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