Designing evolvable systems in a framework of robust, resilient and sustainable engineering analysis

This paper systematically analyzes documented cases and examines human strategic innovation against artificial intelligence systems. Drawing from peer-reviewed research and verified instances in strategic domains including complex games such as Go (Wang et al. in: Proceedings of the 40th international conference on machine learning, 2023), chess (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020), Dota 2 (Berner et al.. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and poker (Brown and Sandholm in Science 359:418–424, 2017), as well as real-world applications including cybersecurity (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019) and finance (Zhang et al., 2024), we identify patterns in human innovation when confronting AI opponents. Our analysis reveals that humans can achieve notable successes by developing novel strategies operating outside AI training distributions, exploiting specific AI limitations (Gleave et al. in International Conference on Machine Learning, 2020). Key findings demonstrate several critical mechanisms. First, pattern-breaking innovations enable humans to force AI systems into unfamiliar decision spaces where their training becomes insufficient (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019). Second, exploiting AI's bounded rationality allows strategic actors to leverage artificial systems' inherent computational and representational limitations (Simon, 1972). Third, adaptive resource distribution strategies permit dynamic capabilities reallocation based on real-time AI behavioral pattern assessment (Fatima and Wooldridge. in Proceedings of the Fifth International Conference on Autonomous Agents, 2001). In Go, adversarial policies have achieved win rates exceeding 97% against superhuman AI by forcing the system into unfamiliar game states it cannot correctly evaluate (Wang et al. in Proceedings of the 40th International Conference on Machine Learning, 2023). These attacks succeed not through superior Go play but by exploiting fundamental vulnerabilities in how AI systems process information outside their training distributions. Chess analysis indicates that human strategic choices often diverge from AI preferences, with models like Maia specifically designed to predict human moves achieving accuracies of 46–52% for targeted skill levels, highlighting fundamental differences in strategic evaluation between human and artificial intelligence (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020). While AI systems like OpenAI Five have demonstrated overwhelming dominance in Dota 2, achieving a 99.4% win rate in public games under restricted rule sets (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and Libratus significantly outperformed top poker professionals in heads-up no-limit Texas Hold'em (Brown and Sandholm in Science 359:418–424, 2017), human approaches in these contexts reveal ongoing attempts to identify and exploit AI behavioral patterns. These efforts demonstrate the persistent potential for strategic innovation even against seemingly dominant artificial systems. The implications of these findings extend beyond gaming applications to broader strategic contexts. They suggest fundamental considerations for AI system design, particularly regarding the need for enhanced strategic flexibility and adaptation capabilities when facing novel adversarial approaches (Wang et al. in Proceedings of the 40th international conference on machine learning, 2023). We propose that these insights should inform next-generation AI system development, emphasizing robust strategic frameworks that can better anticipate and respond to human innovations that operate outside conventional training paradigms. Our research contributes to the theoretical understanding of human-AI strategic interaction and provides practical frameworks for developing more resilient AI systems. The broader implications span multiple domains, including AI safety research (Russell in Human compatible: Artificial intelligence and the problem of control, Viking Press, 2019), human-AI collaboration frameworks (Vaccaro et al. in Nat Hum Behav 8:1869–1886, 2024), and strategic decision-making system design (Chen and Kumar in J Artif Intel Res 79:245–278, 2024).

Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach: This is a non-empirical review of the literature and the development of a conceptual model. Searches were conducted in key academic databases, such as Emerald Online Journals, Taylor and Francis Online Journals, JSTOR Online Journals, Elsevier Online Journals, IEEE Xplore, and Directory of Open Access Journals (DOAJ) for papers which focused on Artificial intelligence (AI), Entrepreneurial decision-making, Customer preference, Industry benchmarks, and Employee involvement. In total, 25 articles met the predefined criteria and were used. Findings: The study proposes that Artificial intelligence systems can facilitate better decision-making from the entrepreneurial perspective. In addition, the study demonstrates that employees, as stakeholders, can moderate the relationship between Artificial intelligence systems and better decision-making for entrepreneurs with their involvement. Moreover, the study demonstrates that customer preference and industry benchmark can mediate the relationship between Artificial intelligence systems and better entrepreneur decision-making. Research limitations/implications: The study assumes a perfect ICT environment for the smooth operation of Artificial intelligence systems. However, this might not always be the case. The study does not consider the personal disposition of entrepreneurs in terms of ICT usage and adoption. Practical implications: This study proposes that entrepreneurial decision-making is enriched in an environment of Artificial intelligence systems, which is complemented by customer preference, industry benchmark, and employee involvement. This finding provides entrepreneurs with a possible technological tool for better decision-making, highlighting the endless options offered by Artificial intelligence systems. Social Implications: The introduction of AI in the business decision-making process comes with many social issues in relation to the impact machines have on humans and society. This paper suggests how this new technology should be used without destroying society. Originality/value: This conceptual framework serves as a valuable organizational spectrum for entrepreneurial development. In addition, this study makes a valuable contribution to entrepreneurial development through Artificial intelligence systems.

In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

In 1998, computer scientist Ehud Shapiro returned to the Weizmann Institute in Rehovot, Israel, as a group leader after a five‐year break as a software entrepreneur. At the peak of the Internet boom, it would have been easy to find an exciting topic to pursue in computer science. Instead, Shapiro became interested in the origin of life and began to train himself in molecular biology, which eventually sparked his idea to build computers from biological molecules. His team first constructed a molecular Turing machine based on DNA, restriction nuclease and ligase to perform simple computations (Benenson et al , 2001), soon followed by a more sophisticated system that performs stochastic computations using mRNA molecules as input (Benenson et al , 2004). What seems merely to be the intellectual interest of an Israeli computer scientist—using biological compounds and systems to create logical circuits—has in fact become the hottest area in the biological sciences: synthetic biology. Other engineers are also dropping their soldering guns for micropipettes to rewire genes and genomes with the aim of reprogramming living organisms. “Synthetic biology is the other side of the coin of systems biology,” commented Victor de Lorenzo, Vice Director of the National Centre of Biotechnology in Madrid, Spain. “What you want is to create or recreate systems that have some properties of life from engineering principles.” This includes a range of techniques from recombinant cloning, to synthesizing genomes de novo , to creating completely new entities such as Shapiro's artificial systems. However, more interesting than the technology itself is the ability to create artificial metabolic and regulatory pathways and to test their viability in living systems. It allows scientists to probe the complexity of an organism's innards and thus derive further insights into how cells work. As George Church, Professor of Genetics at Harvard Medical School …

The use of artificial intelligence in Critical Infrastructure Systems has increased substantially, having evolved to become both technically possible and financially beneficial. Yet there is an emerging consensus that the consideration and management of artificial intelligence-related risks in Critical Infrastructure Systems have not been commensurate with its rapid growth. Our surveys have identified that generalised artificial intelligence principles such as those promoted by the Organisation for Economic Co-operation and Development are alone not fit for purpose in guiding use of artificial intelligence in Critical Infrastructure Systems. Evaluation is an important aspect of that, and we argue for the development of a foundational approach suited to evaluation of artificial intelligence-enhanced Critical Infrastructure Systems as a base to further research and improve practice. This study develops a novel conceptual framework for evaluation of artificial intelligence-enhanced Critical Infrastructure Systems, based on theory adaptation of Value-Focused Thinking. The framework offers simplicity and additional functionality over the default principles-based framework.

The article examines the attitude of young people to the use of artificial intelligence. Young people, due to their greater psychological flexibility and adaptive capabilities, are more familiar with digital technologies. In the period of youth, the formation of a value-semantic sphere takes place. The respondents were young people in the number of 75 people. The comparative group consisted of respondents aged 40 to 50 years in the number of 50 people. The purpose of this work is to study the attitude of young people to the use of artificial intelligence. That will allow to understand more the attitudes of young people to the use of artificial intelligence from the perspective of benefits, motives of use and emotions; will provide reflection on the relationship of a person and the sphere of artificial intelligence. As research methods, the analysis of theoretical sources was used, the author’s questionnaire was used to study the attitude to the use of artificial intelligence among young people. The data obtained were processed using the methods of mathematical statistics Student’s T-criterion. The results of a study of attitudes to the use of artificial intelligence are presented. Significant differences in attitudes towards artificial intelligence systems were revealed in two groups of respondents on the possible benefits of artificial intelligence systems, the motives for their use of artificial intelligence, and the significance of the difference was revealed in relation to the emotions experienced by respondents of different ages when faced with these systems

Une taxinomie de la connaissance technologique est developpee, en essayant de montrer l'interaction entre les scientifiques et les ingenieurs, a travers la recherche industrielle et en laboratoire

The strategic environmental assessment (SEA) process can be broadly defined as a study of the social impacts of a proposed project, plan, policy or legislative action of intelligence systems on the society, environment and sustainability. The SEA process for sustainable intelligent systems has been aimed to incorporate society, environment and sustainability factors into the project planning and decision-making process for sustainable intelligent systems. Artificial intelligence systems (AIS) should consider the titled ‘environmental impact assessment (EIA)’ process that can be defined as the systematic identification and evaluation of the potential impacts (effects) of proposed projects, plans, programmes, policies or legislative actions relative to the biological physical, physico-chemical, biological, cultural, socio-economic and anthropological components of the total environment. The SEA process protocol is important as it has been proposed for studying and checking the productivity and quality of AIS. This treaty and official government procedures of SEA were helpful in the decision-making process much earlier than the EIA process. Keywords: Artificial intelligence, business, economics, environment, industry.

Artificial intelligence is currently being used in more and more areas of production. Be it in the field of industrial robotics, automated quality inspection or cognitive support for employees in production, artificial intelligence contributes to creating smart as well as sustainable manufacturing systems. In the area of manufacturing system design, decision support models are increasingly used to facilitate the work of system designers. In this paper, we address how information models can be used to design explainable artificial intelligence decision support systems. The paper will survey and describe the information that is necessary to communicate manufacturing system design requirements to meet customer needs and use cases. The objective is to propose an information model to express system design requirements with the goal to provide a transparent representation of decisions as well as alternatives of decisions to improve the description of artificial intelligence-based decision support systems during the manufacturing system (re)design phase. The purpose of the information model is to explore the requirements and technical solutions necessary to advance manufacturing systems without losing track of alternatives, and to be able to dynamically adapt them to changing conditions in the market or the production environment.KeywordsIndustry 4.0Smart manufacturingInformation modelExplainable artificial intelligenceDecision support systems

Previous articleNext article No AccessRaffaele Pisano (Editor). A Bridge between Conceptual Frameworks: Sciences, Society, and Technology Studies. (History of Mechanism and Machine Science, 27.) lvii + 582 pp., figs., tables, bibls., index. Dordrecht: Springer, 2015. $119 (cloth).David ChannellDavid Channell Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Isis Volume 107, Number 4December 2016 Publication of the History of Science Society Article DOIhttps://doi.org/10.1086/689555 Views: 57Total views on this site Citations: 1Citations are reported from Crossref © 2016 by The History of Science Society. All rights reserved.PDF download Crossref reports the following articles citing this article: Current Bibliography of the History of Science and Its Cultural Influences, 2016, Isis 107, no.S1S1 (Jun 2017): i–240.https://doi.org/10.1086/692972

This study seeks to bridge the gap between narrative memory in human cognition and artificial agents by proposing a unified model. Narrative memory, fundamental to human consciousness, organizes experiences into coherent stories, influencing memory structuring, retention, and retrieval. By integrating insights from human cognitive frameworks and artificial memory architectures, this work aims to emulate these narrative processes in artificial systems. The proposed model adopts a multi-layered approach, combining elements of episodic and semantic memory with narrative structuring techniques. It explores how artificial agents can construct and recall narratives to enhance their understanding, decision-making, and adaptive capabilities. By simulating narrative-based memory processing, we assess the model’s effectiveness in replicating human-like retention and retrieval patterns. Applications include improved human-AI interaction, where agents understand context and nuance, and advancements in machine learning, where narrative memory enhances data interpretation and predictive analytics. By unifying the cognitive and computational perspectives, this study offers a step toward more sophisticated, human-like artificial systems, paving the way for deeper explorations into the intersection of memory, narrative, and consciousness.

Achieving adaptive behavior in artificial systems, analogous to living organisms, has been a long-standing goal in electronics and materials science. Efforts to integrate adaptive capabilities into synthetic electronics traditionally involved a typical architecture comprising of sensors, an external controller, and actuators constructed from multiple materials. However, challenges arise when attempting to unite these three components into a single entity capable of independently coping with dynamic environments. Here, we unveil an adaptive electronic unit based on a liquid crystal polymer that seamlessly incorporates sensing, signal processing, and actuating functionalities. The polymer forms a film that undergoes anisotropic deformations when exposed to a minor heat pulse generated by human touch. We integrate this property into an electric circuit to facilitate switching. We showcase the concept by creating an interactive system that features distributed information processing including feedback loops and enabling cascading signal transmission across multiple adaptive units. This system responds progressively, in a multi-layered cascade to a dynamic change in its environment. The incorporation of adaptive capabilities into a single piece of responsive material holds immense potential for expediting progress in next-generation flexible electronics, soft robotics, and swarm intelligence.

Disruptive behavior in healthcare has been identified as a threat to quality of care, nurse retention, and a culture of safety. A qualitative study elicited registered nurse experiences with disruptive clinician behavior in an acute care hospital. A conceptual framework was developed to provide a structure for organizing and describing this complex construct that includes 4 primary concepts: disruptive behaviors and its triggers, responses, and impacts.

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.