Why artificial intelligence is likely to take more lives

Artificial neurons for deeply intelligent machines – this is the new artificial intelligence (AI) revolution, led by Geoffrey Hinton and his team since 2012. That year, Hinton, an expert in cognitive science at the University of Toronto and a researcher at Google Brain, demonstrated the striking effectiveness of a deep neural network (DNN) in an image-categorisation task. In the wake of these remarkable results, universities – and international corporations – invested massively in the promising and fascinating field of AI. Yet despite the impressive performance of DNNs in a variety of fields (visual and vocal recognition, translation, medical imagery, etc.), questions remain regarding the limits of deep learning for other uses, such as antonymous vehicles. To understand the limits of AI in its current state, we need to understand where DNNs come from and, above all, which areas of the human brain they are modelled on – little is known about this in industrial engineering, and even in some research centres.