Uploaded by AUTH The tutorial is an introduction to the main aspects of Artificial Social Intelligence (Social AI), the AI domain aimed at making machines socially intelligent, i.e., capable to make sense of the social landscape in the same way as people do. The focus will be on the most specific aspects of the field… Continue reading Introduction to Artificial Social Intelligence
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Quantitatively Assessing Explainable AI for Deep Neural Networks – a Crash Course
Uploaded by AUTH Deep Neural Networks (DNNs) are increasingly pervasive into society, especially in decision-making, in applications involving humans or in high-stake applications. This prompts the need for transparency, which is one of the cornerstones of the EU guidelines for Trustworthy AI. For DNNs, it is often unfeasible to attain human-understandable interpretations of the predictive… Continue reading Quantitatively Assessing Explainable AI for Deep Neural Networks – a Crash Course
On the possible outputs of human brains and intelligent machines
Uploaded by AUTH In this presentation I will argue that present-day intelligent machines, featuring the presently available modes of input and of input analysis procedures, cannot possibly produce identical outputs with those of human brains. The latter produce discriminant responses or publicly accessible behavior, more generally, on the one hand and subjective conscious experiences, on… Continue reading On the possible outputs of human brains and intelligent machines
Looking into the Future: Forecasting Quantities with Deep Learning
Uploaded by AUTH This lecture will cover recent advances in methodologies to forecast quantities using deep neural networks with applications to autonomous agents, video streaming and network traffic forecasting. We first briefly introduce sequence prediction problems introducing the main architectural choices, such as RNNs, LSTMs and Transformers. Then we will delve into forecasting of agent… Continue reading Looking into the Future: Forecasting Quantities with Deep Learning
Cyberphysical AI: Towards a (Robot) AI Scientist
Uploaded by AUTH Toward A(G)I, whatever definition one chooses for AGI, the main effort is finding the best possible self-supervised, unsupervised, and supervised objectives that scale to the foundation level. Taking a step back, some broader questions arise naturally. Are these objectives teaching our machines how the world looks or how we humans look at… Continue reading Cyberphysical AI: Towards a (Robot) AI Scientist
Trustworthy Machine Learning in Multimodal AI Applications: Case Studies and Perspectives
Uploaded by AUTH Machine learning and deep learning models are the main engines in many multimodal AI applications, which are characterized by the fusion of multiple modalities of data streams. In this lecture, we highlight the trust and robustness challenges of machine learning that arises from data fusion. To do so, we present several case… Continue reading Trustworthy Machine Learning in Multimodal AI Applications: Case Studies and Perspectives