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“Can artificial intelligence produce architectural plan schemes?” discussion is the starting point of this study. The aim of this paper is to question whether this will be a new method in architectural design by producing plans with artificial intelligence interfaces working with human–computer interaction and to create a discussion environment.

The main research topic is the evaluation of architectural design decisions with the text-to-image generation AI algorithms method based on shape grammar rules. First, a sample space consisting of Palladio plans or plan diagrams was created. Plan diagram production experiments were made with different interfaces (Midjourney, Dall-e2, Stable Diffusion, Craiyon, Nightcafe), and alternative plan diagrams were recorded as outputs. The discussion of the outputs has been made over architectural design and space.

In the conceptual design phase of the architectural discipline and in the production of architectural plan scheme, AI algorithms are trending. This interaction imposes a new responsibility on architects. AI can create paradigm shifts in architectural processes with its tools with high data processing potential. On the other hand, in this study, it is emphasized that architecture is not just an act of producing visuals, but a functional act of producing visuals.

The technology is effective in producing architectural plans and directing them to artificial intelligence algorithms. With this study, multi-alternative architectural plan productions were tried with text-to-image bots with fast results. In this direction, a new method proposal has been developed for the conceptual design phase in architecture.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

The purpose of this paper is to present a novel approach based on the differential search (DS) algorithm integrated with the adaptive network-based fuzzy inference system (ANFIS) for unmanned aerial vehicle (UAV) winglet design.

The winglet design of UAV, which was produced at Faculty of Aeronautics and Astronautics in Erciyes University, was redesigned using artificial intelligence techniques. This approach proposed for winglet redesign is based on the integration of ANFIS into the DS algorithm. For this purpose, the cant angle (c), the twist angle (t) and taper ratio (λ) of winglet are selected as input parameters; the maximum value of lift/drag ratio (E_max) is selected as the output parameter for ANFIS. For the selected input and output parameters, the optimum ANFIS parameters are determined by the DS algorithm. Then the objective function based on optimum ANFIS structure is integrated with the DS algorithm. With this integration, the input parameters for the E_max value are obtained by the DS algorithm. That is, the DS algorithm is used to obtain both the optimization of the ANFIS structure and the necessary parameters for the winglet design. Thus, the UAV was reshaped and the maximum value of lift/drag ratio was calculated based on new design.

Considerable improvements on the max E are obtained through winglet redesign on morphing UAVs with artificial intelligence techniques.

It takes a long time to obtain the optimum E_max value by the computational fluid dynamics method.

Using artificial intelligence techniques saves time and reduces cost in maximizing E_max value. The simulation results showed that satisfactory E_max values were obtained, and an optimum winglet design was achieved. Thus, the presented method based on ANFIS and DS algorithm is faster and simpler.

The application of artificial intelligence methods could be used in designing more efficient aircrafts.

The study provides a new and efficient method that saves time and reduces cost in redesigning UAV winglets.

This article revisits some theories and concepts of public administration, including those related to public value, transaction costs and social equity, to analyze the advantages and disadvantages of using artificial intelligence (AI) algorithms in public service delivery. The author seeks to mobilize theory to guide AI-era public management practitioners and researchers.

The author uses an existing task classification model to mobilize and juxtapose public management theories against artificial intelligence potential impacts in public service delivery. Theories of social equity and transaction costs as well as some concepts such as red tape, efficiency and economy are used to argue that the discipline of public administration provides a foundation to ensure algorithms are used in a way that improves service delivery.

After presenting literature on the challenges and promises of using AI in public service, the study shows that while the adoption of algorithms in public service has benefits, some serious challenges still exist when looked at under the lenses of theory. Additionally, the author mobilizes the public administration concepts of agenda setting and coproduction and finds that designing AI-enabled public services should be centered on citizens who are not mere customers. As an implication for public management practice, this study shows that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.

As a fast-growing subject, artificial intelligence research in public management is yet to empirically test some of the theories that the study presented.

The paper vulgarizes some theories of public administration which practitioners can consider in the design and implementation of AI-enabled public services. Additionally, the study shows practitioners that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.

The paper informs a broad audience who might not be familiar with public administration theories and how those theories can be taken into consideration when adopting AI systems in service delivery.

This research is original, as, to the best of the author’s knowledge, no prior work has combined these concepts in analyzing AI in the public sector.

The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio.

Redesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (C_lβ) and maximum lift/drag ratio (E_max). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*_lβ and E_max are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better C_lβ and E_max, i.e. the ABC algorithm is used for two different purposes.

By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*_lβ and E_max is maximized.

It takes quite a long time for E_max data to be obtained realistically by using the computational fluid dynamics approach.

Neural network incorporation with the optimization method idea is beneficial for improving C_lβ and E_max. By using this approach, low cost, time saving and practicality in applications are achieved.

This method based on artificial intelligence methods can be useful for better aircraft design and production.

It is creating a novel method in order to redesign of morphing UAV and improving UAV performance.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

Algorithms, artificial intelligence (AI), machines, and all emerging digital technologies disrupt traditional auditing, raising many questions and debates. One of the central issues of this debate is the human-algorithms complex duality, which focuses on this investigation. This study aims to investigate the algorithms’ penetration in auditing activities, with a specific focus of a future scenario on the human-algorithms interaction in performing audits as intelligent teams.

The research uses a qualitative reflexive thematic analysis, taking into consideration the academic literature, as well as professional reports and websites of the “Big Four” audit firms and internationally recognized accounting bodies.

The results debate the complex duality between algorithms and human-based actions in the institutional settings of auditing activities by highlighting the actual stage of algorithms, machines and AI emergence in audit and providing real-life examples of their use in the audit. Furthermore, they emphasize the strengths and weaknesses of algorithms compared to human beings. Based on the results, a discussion on the human-algorithms interaction from the lens of the Human-in-the-Loop (HITL) approach concludes that the Auditor-Governing-the-Loop may be a possible scenario for the future of the auditing profession.

This study is exploratory, investigating academia and practitioners’ written debates, analyzes and reports, limiting its applicability. Nonetheless, the paper adds to the ongoing discussion on emerging technologies and auditing research. Finally, the authors address some potential biases associated with the extended use of algorithms and discuss future research implications. Future research should empirically test how the human-algorithms tandem is working and how AI and other emerging technologies will affect auditing activities and the auditing profession.

The study provides valuable insights for audit firms, auditors, professional organizations and standard-setters, and regulators revealing the implication of algorithms’ penetration in auditing activities from the human-algorithms complex duality perspective. Moreover, the academic education and research implications are highlighted, in terms of updating the educational curriculum by including the new technologies issues, as well as the need for further research investigations concerning the human-algorithms interactions issues as, for example, trust, legal restrictions, ethical concerns, security and responsibility.

The research uses HITL as a novel paradigm for responsible AI development in auditing. The study points to the strategic value of a HITL pattern for organizational reflexivity that, according to the study, ensures that the algorithm’s output meets the audit organization’s requirements and changes in the environment.

Entrepreneurs are increasingly relying on artificial intelligence (AI) to assist in creating and scaling new ventures. Research on entrepreneurs’ use of AI algorithms (machine learning, natural language processing, artificial neural networks) has focused on the intra-organizational implications of AI. The purpose of this paper is to explore how entrepreneurs’ adoption of AI influences their inter- and meta-organizational relationships.

To address the limited understanding of the consequences of AI for communities of entrepreneurs, this paper develops a theory to explain how AI algorithms influence the micro (entrepreneur) and macro (system) dynamics of entrepreneurial ecosystems.

The theory’s main insight is that substituting AI for entrepreneurial ecosystem interactions influences not only entrepreneurs’ pursuit of opportunities but also the coordination of their local entrepreneurial ecosystems.

The theory contributes by drawing attention to the inter-organizational implications of AI, explaining how the decision to substitute AI for human interactions is a micro-foundation of ecosystems, and motivating a research agenda at the intersection of AI and entrepreneurial ecosystems.