Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artifical life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways

The impact of artificial intelligence on audit profession

There is an expectation that the introduction of artificial intelligence (AI) will bring about profound changes in the current paradigm of the audit profession, ensuring better reliability and security in the analysis of financial statements. This paper reports the results of a questionnaire survey to ascertain the perceptions of certified auditors, from two Portuguese districts, regarding the impact of artificial intelligence on the audit profession. Findings reveal that the respondents believe that the profession's future depends on the implementation of AI, namely in the efficiency and effectiveness of audit procedures, sampling techniques, cost-benefit relationship and recognizing material distortions.info:eu-repo/semantics/publishedVersio

A New Constructivist AI: From Manual Methods to Self-Constructive Systems

The development of artificial intelligence (AI) systems has to date been largely one of manual labor. This constructionist approach to AI has resulted in systems with limited-domain application and severe performance brittleness. No AI architecture to date incorporates, in a single system, the many features that make natural intelligence general-purpose, including system-wide attention, analogy-making, system-wide learning, and various other complex transversal functions. Going beyond current AI systems will require significantly more complex system architecture than attempted to date. The heavy reliance on direct human specification and intervention in constructionist AI brings severe theoretical and practical limitations to any system built that way. One way to address the challenge of artificial general intelligence (AGI) is replacing a top-down architectural design approach with methods that allow the system to manage its own growth. This calls for a fundamental shift from hand-crafting to self-organizing architectures and self-generated code – what we call a constructivist AI approach, in reference to the self-constructive principles on which it must be based. Methodologies employed for constructivist AI will be very different from today’s software development methods; instead of relying on direct design of mental functions and their implementation in a cog- nitive architecture, they must address the principles – the “seeds” – from which a cognitive architecture can automatically grow. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift

The Many Faces of Edge Intelligence

Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.Peer reviewe

Repositioning the Base Level of Bibliographic Relationships: or, A Cataloguer, a Post-Modernist and a Chatbot Walk Into a Bar

Designers and maintainers of library catalogues are facing fresh challenges representing bibliographic relationships, due both to changes in cataloguing standards and to a broader information environment that has grown increasingly diverse, sophisticated and complex. This paper presents three different paradigms, drawn from three different fields of study, for representing relationships between bibliographic entities beyond the FRBR/LRM models: superworks, as developed in information studies; adaptation, as developed in literary studies; and artificial intelligence, as developed in computer science. Theories of literary adaptation remain focused on “the work,” as traditionally conceived. The concept of the superwork reminds us that there are some works which serve as ancestors for entire families of works, and that those familial relationships are still useful. Crowd-sourcing projects often make more granular connections, a trend which has escalated significantly with current and emerging artificial intelligence systems. While the artificial intelligence paradigm is proving more pervasive outside conventional library systems, it could lead to a seismic shift in knowledge organization, a shift in which the power both to arrange information and to use it are moving beyond the control of users and intermediaries alike

Towards building mobile smart-IoT service system

The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.Peer ReviewedPostprint (author's final draft