Self-learning AI emulates the human brain

The research was led by Marco Zorzi at the University of Padova and funded with a starting grant from the European Research Centre (ERC). The project – GENMOD – demonstrated that it is possible to build an artificial neural network that observes the world and generates its own internal representation based on sensory data. For example, the network was able by itself to develop approximate number sense, the ability to determine basic numerical qualities, such as greater or lesser, without actually understanding the numbers themselves, just like human babies and some animals. "We have shown that generative learning in a probabilistic framework can be a crucial step forward for developing more plausible neural network models of human cognition," Zorzi says. Tests on visual numerosity show the network's capabilities, and offer insight into how the ability to judge the amount of objects in a set emerges in humans and animals without any pre-existing knowledge of numbers or arithmetic.