Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Cognition as Embodied Morphological Computation

Cognitive science is considered to be the study of mind (consciousness and thought) and intelligence in humans. Under such definition variety of unsolved/unsolvable problems appear. This article argues for a broad understanding of cognition based on empirical results from i.a. natural sciences, self-organization, artificial intelligence and artificial life, network science and neuroscience, that apart from the high level mental activities in humans, includes sub-symbolic and sub-conscious processes, such as emotions, recognizes cognition in other living beings as well as extended and distributed/social cognition. The new idea of cognition as complex multiscale phenomenon evolved in living organisms based on bodily structures that process information, linking cognitivists and EEEE (embodied, embedded, enactive, extended) cognition approaches with the idea of morphological computation (info-computational self-organisation) in cognizing agents, emerging in evolution through interactions of a (living/cognizing) agent with the environment

Physical activity and sports sciences between European Research Council and academic disciplines in Italy

Also in Italy, as well as to be in all others states European Union, the academic disciplines in university system have to change to conform to the 3 areas: SH Social Sciences and Humanities, PE Physical Sciences and Engineering, LS (Life Sciences), 25 panel and 333 sub panel of European Research Council Panel Structure 2018. Nowadays, it is compulsory to have the same language within the European Union and its European Research Council Executive Agency (ERCEA). This change concerns the funding of research and the recruitment of professors; currently in Italy they follow two different procedures and the reform aims to pursue the unique way. Physical activity and sports sciences are in two different scientific areas: the human and social sciences and the life sciences. The problem is therefore to choose a single scientific area or to stay in two areas, to define the declaration of the academic discipline with the protection of the professors’ rights and the relationship with the ERC Area. The academic disciplines of Physical activity and Sport sciences field could be made by the following descriptors: Health, ageing - Social aspects of learning, curriculum studies, educational policies - Science and technology studies - Cognitive basis of human development and education, developmental disorders; comparative cognition - Personality and social cognition – Emotion - Clinical and health psychology – Neuropsychology - Attention, perception, action, consciousness - Learning, memory - Cognition in ageing - Reasoning, decision-making – Intelligence - Language learning and processing - Theoretical linguistics - computational linguistics - Comparative physiology and pathophysiology - Fundamental mechanisms underlying ageing - Sensation and perception - Neural bases of cognitive processes - Other medical technologies for diagnosis and monitoring of diseases - Epidemiology and public health - Environmental health, occupational medicine - Health services, health care research, medical ethics. In Italy its declaratory could be simplified: “Theories and methods of physical education, training, health and well-being” in Life sciences area with the exception for some professors to be structured in human and social sciences for educational profil

The Ribbon of Love: Fuzzy-Ruled Agents in Artificial Societies

The paper brings two motivations to the theoretical explorations of social analysis. The first is to enrich the agent based computational sociology by incorporating the fuzzy set theory in to the computational modeling. This is conducted by showing the importance to include the fuzziness into artificial agent’s considerations and her way acquiring and articulate information. This is continued with the second motives to bring the Darwinian sexual selection theory – as it has been developed broadly in evolutionary psychology – into the analysis of social system including cultural analysis and other broad aspects of sociological fields. The two was combined in one computational model construction showing the fuzziness of mating choice, and how to have computational tools to explain broad fields of social realms. The paper ends with some opened further computer program development

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness