Novel Math Theory Could Upgrade Machine Vision

A team of Italian mathematicians, including one who is also a neuroscientist from the Champalimaud Centre for the Unknown (CCU), in Lisbon, Portugal, has shown that artificial vision machines can learn to recognize complex images spectacularly faster by using a mathematical theory that was developed 25 years ago by one of this new study's co-authors. Their results have been published in the journal Nature Machine Intelligence. During the last decades, machine vision performance has exploded. For example, these artificial systems can now learn to recognise virtually any human face – or to identify any individual fish moving in a tank, in the midst of a large number of other almost identical fish which are also moving. The machines we're talking about are, in fact, electronic models of networks of biological neurons, and their aim is to simulate the functioning of our brain, which is as good as it gets at performing these visual tasks – and this, without any conscious effort on our part.