The system improves how AI systems learn and uncover patterns in data independently, without human guidance.
Torque Clustering can autonomously analyse vast amounts of data in biology, chemistry, finance, and medicine fields. It can reveal new insights, including detecting disease patterns and uncovering fraud. It is also parameter-free and can process large datasets with computational efficiency.
Advancing Unsupervised Learning
“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 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.”
Performance and Applications
Torque Clustering has been rigorously tested on 1,000 diverse datasets, achieving an average adjusted mutual information (AMI) score – a measure of clustering results – of 97.7%. In comparison, other state-of-the-art methods only achieve scores in the 80% range.
Future Impact on AI and Robotics (H2)
Torque Clustering could support the development of general artificial intelligence, particularly in robotics and autonomous systems, by helping to optimise movement, control, and decision-making with its AI algorithm.
