Séminaire GREDEG : Aldo GEUNA (University of Torino)

Abstract: "Artificial Intelligence technologies promise to revolutionize the knowledge production process. At the core of one of the most important approaches to AI is a series of Machine Learning techniques known as Deep Learning. Deep Learning has been regarded as a new method of invention and potentially a general-purpose technology in which the next industrial revolution may be based. This paper analyses the emergence of Deep Learning as a technoscientific field, that is, a domain in the middle of scientific enquiry and technical problem-solving. More specifically, it examines the role played by labelled datasets (CIFAR-10 and CIFAR-100) and the funding institute that supported the birth and growth of deep learning, the Canadian Institute for Advanced Research (CIFAR). We carried out a qualitative and quantitative analysis of the impact of CIFAR-10 and CIFAR-100 on the fields of computer vision and object recognition and how this impact relates to development of Deep Learning. We assess both scientific and technological impact. We find that CIFAR-10 was fundamental for the developments which lead to the Deep Learning revolution and still shapes the trajectory of the field. We also show that CIFAR-10 is relevant in teaching Deep Learning techniques, which highlights the profound impact of this technological artifact in the development of the field. The econometric analysis confirms that the CIFAR-10 dataset had a very significant early impact in the literature (citation count) and was and is still relevant for technological development as measured by patent-to-paper citations."

More about Aldo Geuna