New AI system using light to learn associatively

An illustration of Pavlovian conditioning Credit: Z Cheng - New AI uses associative learning techniques rather than AI's traditional neural networks to challenge the conventional wisdom that artificial neurons and synapses are the sole building blocks of AI. Researchers at Oxford University's Department of Materials, working in collaboration with colleagues from Exeter and Munster have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors. The new research published in  Optica took its inspiration from Nobel Prize laureate Ivan Pavlov's discovery of classical conditioning. In his experiments, Pavlov found that by providing another stimulus during feeding, such as the sound of a bell or metronome, his dogs began to link the two experiences and would salivate at the sound alone. The repeated associations of two unrelated events paired together could produce a learned response - a conditional reflex. Co-first author Dr James Tan You Sian, who did this work as part of his DPhil in the Department of Materials , University of Oxford said: 'Pavlovian associative learning is regarded as a basic form of learning that shapes the behaviour of humans and animals - but adoption in AI systems is largely unheard of. Our research on Pavlovian learning in tandem with optical parallel processing demonstrates the exciting potential for a variety of AI tasks.' The neural networks used in most AI systems often require a substantial number of data examples during a learning process - training a model to reliably recognise a cat could use up to 10,000 cat/non-cat images - at a computational and processing cost.