Training artificial intelligences to identify faces or digitise text involves thousands or millions of iterations of a two-stage process known as back-propagation, but a new approach could save time, energy and computing power
10 March 2022
Artificial intelligence is growing ever more capable at increasingly complex tasks, but requires vast amounts of computing power to develop. A more efficient technique could save up to half the time, energy and computer power needed to train an AI model.
Deep learning models are typically composed of a huge grid of artificial neurons linked by “weights” – computer code that takes an input and passes on a changed output – that represent the synapses linking real neurons. By …