Opening the Black Box of Music Royalties with the Help of Hybrid Intelligence

The ever-increasing complexity of the music industry and the intensified resentment of artists towards collecting societies call for a transformation and a change of behavior within the music ecosystem. This article introduces a hybrid intelligence system, that ameliorates the current situation by combining the intelligence of humans and machines. This study proposes design requirements for hybrid intelligence systems in the music industry. Using a design science research approach, we identify design requirements both inductively from expert interviews and deductively from theory and present a first prototypical instantiation of a respective hybrid intelligence system. Overall, this shall enrich the body of knowledge of hybrid intelligence research by transferring its concepts into a new context. Furthermore, the identified design requirements shall serve as a foundation for researchers and practitioners to further explore and design hybrid intelligence in the music industry, and beyond

Towards the Design of Hybrid Intelligence Frontline Service Technologies – A Novel Human-in-the-Loop Configuration for Human-Machine Interactions

Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets

A Machine With Human-Like Memory Systems

Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better than having just one of the two memory systems. In order to show this, we have designed and released our own challenging environment, "the Room", compatible with OpenAI Gym, where an agent has to properly learn how to encode, store, and retrieve memories to maximize its rewards. The Room environment allows for a hybrid intelligence setup where machines and humans can collaborate. We show that two agents collaborating with each other results in better performance than one agent acting alone. We have open-sourced our code and models at https://github.com/tae898/explicit-memory.Comment: Submitted to Human-Centered Design of Symbiotic Hybrid Intelligence 2022 (https://ii.tudelft.nl/humancenteredsymbioticHI/

A Research Agenda for Hybrid Intelligence:Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence

We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges

A novel hybrid intelligence approach for 2D packing through Internet crowdsourcing

Packing problems on its current state are being utilized for wide area of industrial applications. The aim of present research is to create and implement an intelligent system that tackles the problem of 2D packing of objects inside a 2D container, such that objects do not overlap and the container area is to be maximized. The packing problem becomes easier, when regular/rectangular objects and container are used. In most of the practical situations, the usage of irregular objects comes to existence. To solve the packing problem of irregular objects inside a rectangular container, a hybrid intelligence approach is introduced in our proposed work. The combination of machine intelligence and human intelligence is referred as the hybrid intelligence or semi-automated approach in the proposed methodology. The incorporation of human intelligence in the outcome of machine intelligence is possible to obtain using the internet crowdsourcing as we wish to handle the packing problem through internet crowdsourcing involving rural people. The proposed methodology is tested on different standard data sets and it is observed that it has clear advantage over both manual as well as fully automated heuristic based methods in terms of time and space efficiency

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS

Can we Help the Bots? Towards an Evaluation of their Performance and the Creation of Human Enhanced Artifact for Emotions De-escalation

We propose a hybrid intelligence socio-technical artifact that identifies a threshold where the chatbot requires human intervention in order to continue to perform at an appropriate level to achieve the pre-defined objective of the system. We leverage the Yield Shift Theory of Satisfaction, the Intervention Theory and the Nudge Theory to develop meta requirements and design principles for this system. We discuss the first iteration of implementation and evaluation of the artifact components