Can the g Factor Play a Role in Artificial General Intelligence Research?

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach

Applications of Sparse Representations

In this dissertation I explore the properties and uses of sparse representations. Sparse representations use high dimensional binary vectors for representing information. They have many properties which make this representation useful for applications involving pattern recognition in highly noisy and complex environments. Sparse representations have a very high capacity. A typical sparse representation vector has a capacity of 10^84 distinct vectors, which is more than the number of atoms in the universe. Sparse representations are highly noise robust. They can tolerate even up to 50% noise. A very powerful and useful property of sparse representations is that they allow us to easily measure similarity between two things by directly comparing their representations. These properties allow them to have applications in a variety of fields, like Artificial Intelligence and Molecular Biology, that need to encode information that is complex and noisy in nature. In this dissertation, I show how sparse representations can be used for representing complex environments for an agent based on Learning Intelligent Decision Agent (LIDA) model. Sparse representations allowed us to achieve a two-fold goal of producing information rich representations of things in the environment while proposing a method of generating grounded representations for the LIDA model. Sparse representations also allowed us to ground the representations used by LIDA in the sensory apparatus of the agent while still allowing a perfect fidelity communication between the sensory memory of LIDA and the rest of the model. I also show how sparse representations are useful in Molecular Biology for discovering data-driven patterns in heterogeneous and noisy gene expression data. We used a sparse auto-encoder to learn sparse representations of transcriptomics experiments taken from a huge publicly available dataset. These representations were then used to identify biological patterns in the form of gene sets. The representation provided a unique signature for a set of samples originating from the same experimental condition. Applications of our method include the identification of previously undiscovered gene sets as well as supervised classification of samples from different biological classes. Overall, our results show that sparse representations are useful in a variety of fields that involve finding patterns in a complex and noisy environment

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alfonso, B.; Taverner-Aparicio, JJ.; Vivancos, E.; Botti, V. (2021). From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents. Applied Sciences. 11(22):1-29. https://doi.org/10.3390/app112210874S129112

Information literacy and peer-to-peer infrastructures: An autopoietic perspective

This article argues that an autopoietic perspective of human communities would allow to understand societies as self-organized systems and thus promote information literacy as a facilitator of social development. Peer-to-peer (P2P) social dynamics generate public information available worldwide in digital repositories, websites and bibliographic resources. However, processing such amount of data is not achievable by a single central-controlled system. We claim that distributed and heterogeneous networks of coordinated mechanisms, composed by both specialized human and artificial agents, are needed to improve information retrieval, knowledge inference and decision-making, but also to produce social value, goods and services. Handling these issues implies the collective construction of global semantic networks but also the active labor of knowledge producers and consumers. We conclude that information literacy is as much important as any technical implementation and, therefore, may lead to networks of Commons-oriented communities which would utilize P2P infrastructuresVasilis Kostakis acknowledges funding for facilities used in this research by IUT (19-13) of the Estonian Ministry of Education and Researc

La descentralización estructural y la heterogeneidad funcional en la producción colectiva de conocimiento: una justificación teórica y computacional del paradigma P2P

Esta tesis contiene artículos de investigación en anexo.Programa Oficial de Doctorado en Documentación: Archivos y Bibliotecas en el Entorno DigitalPresidente: Elías Sanz Casado.- Secretario: Rosario Arquero Avilés.- Vocal: Remedios Melero Meler

P2P Societies: The impact of Decentralization and Heterogeneity in Complex Systems

Poster presented at the 16th WOSC Congress: Our Self-organising World: from Disruption to Reparation which took place in 2014, 15-17 October, in Ibagué (COLOMBIA). The Web Site of the event: http://wosc-congress.unibague.edu.co

Cognitive modeling, ecological psychology, and musical improvisation

Understanding novelty and improvisation in music requires gathering insight from a variety of disciplines. One fruitful path for synthesizing these insights is via modeling. As such, my aim in this paper is to start building a bridge between traditional cognitive models and contemporary embodied and ecological approaches to cognitive science. To achieve this task, I offer a perspective on a model that would combine elements of ecological psychology (especially affordances) and the Learning Intelligent Decision Agent (LIDA) cognitive architecture. Jeff Pressing’s cognitive model of musical improvisation will also be a central link between these elements. While some overlap between these three areas already exists, there are several points of tension between them, notably concerning the nature of perception and the function of artificial general intelligence modeling. I thus aim to alleviate the most worrisome concerns here, introduce several future research questions, and conclude with several points on how my account is part of a general theory, rather than merely a redescription of existent work

Modeling Long-Term Intentions and Narratives in Autonomous Agents

Across various fields it is argued that the self in part consists of an autobiographical self-narrative and that the self-narrative has an impact on agential behavior. Similarly, within action theory, it is claimed that the intentional structure of coherent long-term action is divided into a hierarchy of distal, proximal, and motor intentions. However, the concrete mechanisms for how narratives and distal intentions are generated and impact action is rarely fleshed out concretely. We here demonstrate how narratives and distal intentions can be generated within cognitive agents and how they can impact agential behavior over long time scales. We integrate narratives and distal intentions into the LIDA model,and demonstrate how they can guide agential action in a manner that is consistent with the Global Workspace Theory of consciousness. This paper serves both as an addition to the LIDA cognitive architecture and an elucidation of how narratives and distal intention emerge and play their role in cognition and action