Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services

Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native services, where natural language prompts serve as executable "code" directly (prompt as executable code), eliminating the need for programming language as an intermediary and opening up the door to personal AI. Prompt Sapper has emerged in response, committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence, unleashing the AI innovation potential of every individual, and forging a future where everyone can be a master of AI innovation. This article will introduce the R\&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices

Sharing Leadership through Digital Collaborative Objects

This paper contributes to the understanding of the role of collaborative tools and objects in the emergence and support of shared leadership, which has been associated with positive team dy-namics and innovative outcomes. We draw on ideas from the recent discourse on viewing lead-ership as a practice that involves human actors and material or technological tools to provide the first analysis of the features of collaborative tools that can support shared leadership. We present our early findings and insights from a multiple case study of ten innovation teams in their interaction with a collaborative tool that is particularly designed for coordination. We suggest that collaborative tools can contribute to shared leadership through two facilitating fea-tures: shared problem space and shared visualisation. Through our findings, we highlight the role of collaborative tools in supporting teams in sharing leadership for the purpose of joint in-novation

Collaborative innovation ensues innovation through pastiche

One of the means to address societal challenges in the European Union is through social innovation programs and through increased call for collaboration amongst public and private entities. This paper presents a theory that innovation through pastiche is an unacknowledged trend in the field of collaborative innovation. As suggested by the researcher, Living Labsare “a human-technology interaction innovation entity utilizing a mix of methods, tools and principles drawn from known disciplines (design, science, etc.), set in a real environment and in a locale/societal scale” for the purpose of social innovation and finding business opportunities. The researcher analyzed related publications of articles within 2005 – 2011 and analysis were made as a practice and not per Living Lab.The researcher concluded that collaborative innovation as observed in the Living Lab, results to a pastiche of best methods working together towards innovation with a collaborative consensus among multiple founding actors. Key words: Collaborative innovation; Innovation through pastiche; Pastiche method; Living LabComputer Systems, Imagery and Medi

Design thinking in the scope of strategic and collaborative design

The aim of this paper is to present a reflection from a design perspective, regarding the importance of collaborative work among higher education students, in different cultures and context realities, using as a starting point a design thinking workshop. The goals of the workshop were, by introducing the principles of design thinking to a focus group of university students, in Finland, implement and develop the ability to experiment the design thinking process, and to realize how the interaction of different perspectives can lead to innovative solutions, as to the promotion of interdisciplinary work. Design thinking is a flexible methodology, which can be used in any work field, since it has valuable elements, such as iterating frequently based on continuous feedback from all the intervenient. Through rapid low-resolution prototyping, ideas are continuously tested with the potential users. “Fail early in order to succeed sooner” is the design thinking principle that helps to maximize learning and insights, crucial for human centred innovation. Collaborative work in a small groups scenario map leads to the discussion of solutions, and to the innovation that emerges from the different perspectives given by each person. Our main goal was to find business opportunities that emerge from underestimated issues from everyday life, but also to understand that exploring, understanding, and prioritizing areas can be crucial to ideating solutions.Keywords: design thinking, collaborative methodologies, collaborative work, identity

Collaborative participatory research as a learning process: the case of CIP and CARE in Peru

Participatory research (PR) has been analyzed and documented from different points of view, but particularly taking into consideration the benefits that this process generates for farmers. Studies of the benefits of PR for other actors such as field staff, researchers and organizations have been limited, with organizational learning receiving the least attention. This paper analyzes the interaction between the International Potato Center (CIP) and CARE in Peru and makes the case that PR can also contribute to creating a collaborative learning environment that generates important lessons for the individuals and organizations involved. The paper describes the evolution of the collaborative environment of these two institutions for more than a decade. Three interactive learning periods are presented, namely the “information transfer period” (1993 –1996) the “action-learning period” (1997-2002), and the “social learning period” (on-going). Several lessons from each period, as well as changes in institutional contexts and perceptions, are described. The CIP-CARE case shows that research and developmentoriented organizations can interact fruitfully using PR as a mechanism to promote learning, as well as flexibility in interaction and innovativeness, and that a process of osmosis of information occurs between groups that use PR in a specific case to other groups within the organizations, influencing behavior. However, the paper also indicates that institutional learning should be promoted more specifically in order to extract guidelines from the lessons, which can influence the way organizations plan and implement their projects in a constantly changing environment

Resolving conflicts during human-robot co-manipulation

UK Research and Innovation, UKRI: EP/S033718/2, EP/T022493/1, EP/V00784XThis work is partially funded by UKRI and CHIST-ERA (HEAP: EP/S033718/2; Horizon: EP/T022493/1; TAS Hub: EP/V00784X).This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts. © 2023 Copyright is held by the owner/author(s).2-s2.0-8515037875