Renan C. Moioli

Leonardo C. T. Bezerra, Alexander E. I. Brownlee, Luana Ferraz Alvarenga et al.

Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.

Caetano M. Ranieri, Renan C. Moioli, Patricia A. Vargas et al.

Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact effectively and autonomously with humans, the coupling between techniques for human activity recognition, based on sensing information, and robot behaviour selection, based on decision-making mechanisms, is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting a neurorobotics approach based on computational models that resemble neurophysiological aspects of living beings. This neurorobotics approach was compared to a non-bioinspired, heuristics-based approach. To evaluate both approaches, a robot simulation is developed, in which a mobile robot has to accomplish tasks according to the activity being performed by the inhabitant of an intelligent home. The outcomes of each approach were evaluated according to the number of correct outcomes provided by the robot. Results revealed that the neurorobotics approach is advantageous, especially considering the computational models based on more complex animals.

Caetano M. Ranieri, Jhielson M. Pimentel, Marcelo R. Romano et al.

In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.