Futures of Responsible and Inclusive AI: How Might We Foster an Inclusive, Responsible and Foresight-Informed AI Governance Approach?

This paper seeks to investigate how we might foster an inclusive, foresight-informed responsible AI governance framework. This paper discusses the gaps and opportunities in current AI initiatives across various stakeholders and acknowledges the importance of anticipation and agility. This paper also posits that it is important for legal, policy, industry and academia to understand the specificities of each other’s domains better to build an inclusive governance framework

The scope of human creative action : created co-creators, imago Dei and artificial general intelligence

This article examines the relationship between artificial general intelligence (AGI) and the image of God. After identifying various models that Christian theologians use to classify or define the imago Dei, particular attention will be given to the 'created co-creator' model. Scholars have interpreted this model in different ways, based on the nature of human creative action. This action is seen as either subordinate to divine creation action or the human creative action is truly cooperative with divine creative action. Whether AGI would be made in the image of God in these models is then explored, highlighting the differences between humans as sub-creators versus humans as cooperative co-creators. If human creative action is cooperative, then the question arises as to whether AGI can be made in 'the image of humanity'. Some elements of this image are explored, and then the discussion turns to whether AGI would be made in 'the image of humanity', and if so, could AGI still be made in the image of God? CONTRIBUTION: The argument concludes by pointing to future work using the various models of imago Dei to help inform the relationship between humans and AGI by briefly mentioning two examples.This research is part of the research project, ‘Understanding Reality (Theology and Nature)’, directed by Prof. Dr Johan Buitendag, Department of Systematic and Historical Theology, Faculty of Theology and Religion, University of Pretoria.http://www.hts.org.zaDogmatics and Christian Ethic

The art of AI : the impact of artificial intelligence on the merger and acquisition strategy

Based on a lack of studies in this specific field and the theory that manual as well as cognitive tasks can be replaced by machines, this study explores, using a qualitative research method, the impact of artificial intelligence on the Merger&Acquisition process. An analysis of multinational interviews with experts from different industries and a framework adapted to the Due Diligence process show that there is and will be an impact of Artificial Intelligence on the Due Diligence process as the most crucial process of the Merger& Acquisitions. Although the impact of Artificial Intelligence is nowadays the greatest on Legal Due Diligence and AI-based solutions are already offered, this study, however, states that within the next 5-10 years, even 96% of all tasks of the Due Diligence will be partially or fully substituted. Furthermore, the framework reinforces the underlying theory that both manual and cognitive tasks can already be replaced by machines. Among the reasons why AI has nowadays not yet been adopted in all Due Diligence are the fact that the target companies' data is too different to train a machine, that due diligence involves a lot of communication and that humans are not yet ready for this cultural change. Based on these findings, managers of companies conducting due diligences are advised to prepare their company and employees for the implementation of Artificial Intelligence by following the steps described in Kotter's 8-step change model.Baseado na falta de estudos neste campo específico e na teoria de que tanto as tarefas manuais como as cognitivas podem ser substituídas por máquinas, este estudo explora, utilizando um método de pesquisa qualitativa, o impacto da inteligência artificial no processo de fusão e aquisição de empresas. Uma análise de entrevistas multinacionais com especialistas de diferentes indústrias e um quadro adaptado ao processo de Due Diligence mostram que existe e existirá um impacto da Inteligência Artificial no processo de Due Diligence como processo mais crucial do Merger& Acquisitions. Embora o impacto da Inteligência Artificial seja atualmente o maior em Due Diligence Legal e as soluções baseadas em IA já estejam disponíveis, este estudo, no entanto, afirma que nos próximos 5-10 anos, até 96% de todas as tarefas do Due Diligence serão parcial ou totalmente substituídas. Além disso, o framework reforça a teoria subjacente de que as tarefas manuais e cognitivas já podem ser substituídas por máquinas. Entre as razões pelas quais a IA ainda não foi adotada em todas as Due Diligences está o fato de que os dados das empresas-alvo são muito diferentes para ensinar uma máquina, que a due diligence envolve muita comunicação e que os humanos ainda não estão prontos para essa mudança cultural. Com base nessas descobertas, os gerentes de empresas que realizam as devidas diligências são orientados a preparar suas empresas e funcionários para a implementação da Inteligência Artificial, seguindo os passos descritos no modelo de mudança de 8 passos de Kotter

Visual Attention in Dynamic Environments and its Application to Playing Online Games

Abstract In this thesis we present a prototype of Cognitive Programs (CPs) - an executive controller built on top of Selective Tuning (ST) model of attention. CPs enable top-down control of visual system and interaction between the low-level vision and higher-level task demands. Abstract We implement a subset of CPs for playing online video games in real time using only visual input. Two commercial closed-source games - Canabalt and Robot Unicorn Attack - are used for evaluation. Their simple gameplay and minimal controls put the emphasis on reaction speed and attention over planning. Abstract Our implementation of Cognitive Programs plays both games at human expert level, which experimentally proves the validity of the concept. Additionally we resolved multiple theoretical and engineering issues, e.g. extending the CPs to dynamic environments, finding suitable data structures for describing the task and information flow within the network and determining the correct timing for each process

Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges

© 2021 The Authors. The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionising industries such as manufacturing, retail, and telecommunications. The subfields of AI such as machine learning, knowledge-based systems, computer vision, robotics and optimisation have successfully been applied in other industries to achieve increased profitability, efficiency, safety and security. While acknowledging the benefits of AI applications, numerous challenges which are relevant to AI still exist in the construction industry. This study aims to unravel AI applications, examine AI techniques being used and identify opportunites and challenges for AI applications in the construction industry. A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted. Furthermore, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study provides insights into key AI applications as it applies to construction-specific challenges, as well as the pathway to realise the acrueable benefits of AI in the construction industry.Engineering and Physical Sciences Research Council (EPSRC), UK (Grant Reference No. EP/S031480/

Virtual Easter Egg Hunting: A Thought-Experiment in Embodied

Abstract. The Novamente Cognition Engine (NCE) architecture for Artificial General Intelligence is briefly reviewed, with a focus on exploring how the various cognitive processes involved in the architecture are intended to cooperate in carrying out moderately complex tasks involving controlling an agent embodied in the AGI-Sim 3D simulation world. A handful of previous conference papers have reviewed the overall architecture of the NCE, and discussed some accomplishments of the current, as yet incomplete version of the system; this paper is more speculative and focuses on the intended behaviors of the NCE once the implementation of all its major cognitive processes is complete. The "iterated Easter Egg Hunt" scenario is introduced and used as a running example throughout, due to its combination of perceptual, physical-action, social and selfmodeling aspects. To aid in explaining the intended behavior of the NCE, a systematic typology of NCE cognitive processes is introduced. Cognitive processes are typologized as global, operational or focused; and, the focused processes are more specifically categorized as either forward-synthesis or backward-synthesis processes. The typical dynamics of focused cognition is then modeled as an ongoing oscillation between forward and backward synthesis processes, with critical emergent structures such as self and consciousness arising as attractors of this oscillatory dynamic. The emergence of models of self and others from this oscillatory dynamic is reviewed, along with other aspects of cognitive-process integration in the NCE, in the context of the iterated Easter Egg Hunt scenario

A model for improving knowledge generation in Design Science Research through reflective practice

Abstract : Epistemology refers to the philosophy of knowledge and aims to address central questions of how we create new knowledge. All research paradigms can be distinguished in terms of epistemological assumptions, that is, assumptions of how knowledge is produced in the respective paradigms. Design science research (DSR) is a research paradigm often used in technical disciplines for the creation of artefacts. DSR has roots in pragmatism, where beliefs and theories are evaluated based on the success of its practical application. New knowledge is produced in DSR when original artefacts are created to solve a problem. The epistemological assumption of D“‘ can then shortly be defined as 'knowledge through making'. At its core, DSR is goal-orientated and its practical approaches are focused on delivering the product according to straight- forward processes - without being affected by human factors. This process of acquiring new knowledge is efficient but not necessarily effective in terms of capturing all aspects of the experience of the practitioner. Frameworks exist for the creation of artefacts in DSR, but the process of knowledge generation is not explicit. The aim of the paper is to guide explicit knowledge generation in D“‘. The research question is "How can we make the process of obtaining knowledge in D“‘ more explicit?" DSR Frameworks are iterative in nature and focus on the creation and evaluation of artefacts. There is an implicit assumption that reflection takes place in these iterations. Schön, author of The Reflective Practitioner, writes that new knowledge is produced through reflection during and after an event has occurred. He also states that you can only have a complete understanding of a problem through the dual process of reflection-in-action and reflection-on-action. We argue that this also holds true for artefact design and development in DSR. A reflective DSR practitioner can explicitly indicate how knowledge is produced in the design science research cycle. The effective use of reflective practice changes each individual phase of a DSR framework from goal-orientated to problem-orientated. Epistemologically, knowledge is then produced through 'learning by doing', which gives D“‘ a worldview that supports reflective practice. The paper promotes the incorporation of reflective practice in DSR and provides a demonstration thereof in an example on the preparation of IT students for their chosen career

Application of Artificial Intelligence (AI)in Sustainable Building Lifecycle; ASystematic Literature Review

With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI’s potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI’s capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data‐driven analysis and prediction, while digital twins provide real‐time insights for informed decision‐making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices