Pathway to Future Symbiotic Creativity

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed

Towards a Virtual Collaborator in Online Collaboration from an Organizations’ Perspective

In this empiric study, we present the specifications of virtual collaboration in times of the Covid-19 pandemic in an organization that worked mostly co-located beforehand, and requirements for a virtual collaborator (VC) resulting from those specifications. Related work shows that a VCs can support virtual teams in achieving their goals and promote creative work. We extend this with insights from practice by observing creative and collaborative workshops in the automotive industry and conducting interviews with facilitators and participants of these workshops. Subsequently, we identify the challenges that participants face in virtual collaboration, and derive design guidelines for a VC to address them. Main problems arise due to the virtual interaction lacking nonverbal communication and in the preparation phase that requires more planning and effort. A VC could help by influencing group cohesion and build networks between the participants, influencing the virtual working environment as well as contributing to the contents

Intelligence Augmentation: Towards Building Human-Machine Symbiotic Relationship

Artificial intelligence, which people originally modeled after human intelligence, has made significant advances in recent years. These advances have caused many to fear that machines will surpass human intelligence and dominate humans. Intelligence augmentation (IA) has the potential to turn the tension between the two intelligence types into a symbiotic one. Although IA has not gained momentum until recent years, the idea that machines can amplify human abilities has existed for many decades. Expanded from a panel discussion on Intelligence Augmentation at the 2020 International Conference of Information Systems (ICIS), we define IA in light of its history and evolution and classify IA based on its capabilities, roles, and responsibilities. Based on reviewing the IA literature in terms of research themes, enabling technology, and applications, we identify key research issues, challenges, and future opportunities

The Impact of Large Language Multi-Modal Models on the Future of Job Market

The rapid advancements in artificial intelligence, particularly in large language multi-modal models like GPT-4, have raised concerns about the potential displacement of human workers in various industries. This position paper aims to analyze the current state of job replacement by AI models and explores potential implications and strategies for a balanced coexistence between AI and human workers.Comment: 16 pages, 1 Tabl

Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda

Workplace Artificial Intelligence (AI) helps organisations increase operational efficiency, enable faster-informed decisions, and innovate products and services. While there is a plethora of information about how AI may provide value to workplaces, research on how workers and AI can coexist in workplaces is evolving. It is critical to explore emerging themes and research agendas to understand the trajectory of scholarly research in this area. This study's overarching research question is how workers will coexist with AI in workplaces. A search protocol was employed to find relevant articles in Scopus, ProQuest, and Web of Science databases based on appropriate and specific keywords and article inclusion and exclusion criteria. We identified four themes: (1) Workers' distrust in workplace AI stems from perceiving it as a job threat, (2) Workplace AI entices worker-AI interactions by offering to augment worker abilities, (3) AI and worker coexistence require workers' technical, human, and conceptual skills, and (4) Workers need ongoing reskilling and upskilling to contribute to a symbiotic relationship with workplace AI. We then developed four propositions with relevant research questions for future research. This review makes four contributions: (1) it argues that an existential argument better explains workers' distrust in AI, (2) it gathers the required skills for worker and AI coexistence and groups them into technical, human, and conceptual skills, (3) it suggests that technical skills benefit coexistence but cannot outweigh human and conceptual skills, and (4) it offers 20 evidence-informed research questions to guide future scholarly inquiries

Social Cognition for Human-Robot Symbiosis—Challenges and Building Blocks

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a “positronic” replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the “services” of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework