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 Role of Digital Narrative Patterns in the Metaverse Era on Human Machine Learning Interaction Systems: A Comparative Analysis of Pre and Post-Interactive Narratives

This study explores the metaverse's intriguing mysteries, including storytelling patterns and interactive narratives' effects on human-computer interactions. This study examines the influence of user-generated tales in the dynamic digital world and evaluates emotional computing models to propose a metaverse-specific framework. The study incorporates concepts from significant works in emotional computing, digital storytelling, and human-computer interaction to improve educational affective computing research. The literature study examines the emotional involvement of digital stories. The article reviews numerous authors' works on emotion-detecting and reacting AI systems. A foundation has been laid for researching metaverse emotions and narrative features. An analytical comparison approach integrates multiple methodologies. Qualitative methods allow for a complete literature review of metaverse user interactions with pre- and post-interactive narratives. Comparative analysis evaluates current emotional computing models to uncover flaws and inform new frameworks. The study's primary focus is comparing story frameworks with emotional computing models to find patterns, similarities, and contrasts. The research shows how metaverse storytelling frameworks have evolved and how user-generated stories affect human-robot relationships. Examining metaverse emotional computing models shows that there are restrictions. Addressing these issues requires a customised approach. Dynamic adaptability, context-aware computing, and a personalised user experience are proposed to improve the metaverse experience. These elements solve the issues and create a more engaging and effective atmosphere. Given the metaverse's growth, this study sheds light on the ever-changing dynamics of digital narratives and emotional computing. The research highlights the vital link between user-generated tales and machine learning systems, which might change digital storytelling. The emotive computing architecture is customised to the metaverse's dynamic and user-centric nature. Amidst the fast expansion of the digital world, it serves as a basis for discipline research and improvement

Generics, Race, and Social Perspectives

The project of this paper is to deliver a semantics for a broad subset of bare plural generics about racial kinds, a class which I will dub 'Type C generics.' Examples include 'Blacks are criminal' and 'Muslims are terrorists.' Type C generics have two interesting features. First, they link racial kinds with ​ socially perspectival predicates ​ (SPPs). SPPs lead interpreters to treat the relationship between kinds and predicates in generic constructions as nomic or non-accidental. Moreover, in computing their content, interpreters must make implicit reference to socially privileged​ ​ perspectives which are treated as authoritative about whether a given object fits into the extension of the predicate. Such deference grants these authorities influence over both the conventional meaning of these terms and over the nature of the ​ objects ​ in the social ontology that these terms purport to describe, much the way a baseball umpire is authoritative over the meaning and metaphysics of 'strike'/​ strike ​. Second, terms like 'criminal' and 'terrorist' receive default ​ racialized ​ interpretations in which these terms conventionally token racial or ethnic identities. I show that neither of these features can be explained by Sarah-Jane Leslie's influential 'weak semantics' for generics, and show how my own 'socially perspectival semantics' fares better on both counts. Finally, I give an analysis of 'Blacks are criminal' which explores the semantic mechanisms that underlie default racialized interpretations

Eliciting People’s Conceptual Models of Activities and Systems

People using computer systems are required to work with the concepts implemented by system developers. If there is a poor fit between system concepts and users’ pre-existing conceptualisation of domain and task, this places a high workload on the user as they translate between their own conceptualisation and that imposed by the system. The focus of this paper is on how to identify users’ conceptualisations of a domain – ideally, prior to system implementation. For this, it is necessary to gather verbal data from people that allows them to articulate their conceptual models in ways that are not overly constrained by existing devices but allows them to articulate taken-for-granted knowledge. Possible study types include semi-structured interviews, contextual inquiry interviews and think-aloud protocols. The authors discuss how to design a study, covering choosing between different kinds of study, detailed planning of questions and tasks, data gathering, and preliminary data analysis

Visual Focus of Attention Actively Associates Relevancy in Eye Movements

Advancements in the studies of eye movements have excelled beyond frontiers and transited into the phase of next generation splendidly. The business applications, like online shopping, advertisement, web designing, search engine optimization, of eye movement studies in real world scenarios have started to dominate as well. Tracking of eye movements can communicate the underlying mechanism of visual perception and dynamics of humans’ cognition that are of prime concerns for a number of social, economic, and scientific purposes. In this study, we conducted a series of eye tracking experiments to verify our hypothesis that during human eye movements, the visual focus of attention dynamically associated relevant constituents of artistic portrait. We collected the eye movement data of participants who regarded artistic portraits during active viewing. The trails produced from eye tracking system during portrait viewing traced connected focuses of attention in eye movements based on relevancy in visual contexts. These experimental facts validated the hypothesis that visual focus of attention actively associated relevancy in eye movements

Enhancing Automation with Label Defect Detection and Content Parsing Algorithms

The stable operation of power transmission and distribution is closely related to the overall performance and construction quality of circuit breakers. Focusing on circuit breakers as the research subject, we propose a machine vision method for automated defect detection, which can be applied in intelligent robots to improve detection efficiency, reduce costs, and address the issues related to performance and assembly quality. Based on the LeNet-5 convolutional neural network, a method for the detection of character defects on labels is proposed. This method is then combined with squeezing and excitation networks to achieve more precise classification with a feature graph mechanism. The experimental results show the accuracy of the LeNet-CB model can reach up to 99.75%, while the average time for single character detection is 17.9 milliseconds. Although the LeNet-SE model demonstrates certain limitations in handling some easily confused characters, it maintains an average accuracy of 98.95%. Through further optimization, a label content detection method based on the LSTM framework is constructed, with an average accuracy of 99.57%, and an average detection time of 84 milliseconds. Overall, the system meets the detection accuracy requirements and delivers a rapid response. making the results of this research a meaningful contribution to the practical foundation for ongoing improvements in robot intelligence and machine vision

Chapter A Fuzzy Logic Approach for Remote Healthcare Monitoring by Learning and Recognizing Human Activities of Daily Living

Systems analysis & desig

Evaluation of Quality of a Project Management & Scientific Publications Based On a New Wisdom Framework

This is a theoretical research paper. It presents a proposal for the evaluation of the quality of a project management based on a new and ‘General Cognitive Model of Wisdom’ -GCMW-. For the development of this GCMW, is proposed the conception of an ‘Information Ecosystem’ -IE-, which is composed by the following ‘cognitive units’: Data -D-; Information -I-; Knowledge(tacit, explicit) - K(tacit, explicit) = (Kt,e)- and Wisdom(tacit, explicit) -W(tacit, explicit) = (Wt,e)-, compactly written as DIKt,eWt,e. By aligning this IE with the DIKW hierarchical conception, wehave created a new, no hierarchical, integrated and generalized framework -the GCMW-. This GCMW framework aims -as an insight generator or strategic foresight- to provide a better assessment to different problems in any field of science, from information science, applied researchers or a more general audience as per example, to point out the theoretical and conceptual bases for the interaction between the project manager and this GCMW framework.It is introduced a new set of logical –general-, definitions for the DIKW to instrumentalize the GCMW framework. Finally, based on the GCMW framework, we have proposed a ‘Particular Cognitive Model of Wisdom’ -PCMW- for paper quality evaluation. Aiming at to build a comprehensive and in-depth evaluation of the quality of any scientific production, is derived from the GCMW framework a new no-hierarchical model -the PCMW framework- and a new set of logical –particular-, definitions for the DIKW are introduced to instrumentalize the PCMW towards paper quality assessment. This particular framework should provide –for any paper being written-, a better assessment and insight generator. By last, as we are admitting that any paper published has quality so; the proposal is, the quality of this paper is complete if -and only if-, the paper has also W. Both, the PCMW and the particular DIKW instruments definitions, are necessary and sufficient conditions for guaranteeing -guiding- if the paper -which is in evaluation-, has W