Conceptualization and Framework of Hybrid Intelligence Systems
Nikhil Prakash, Kory W. Mathewson
备注：
8 pages, 1 figure, HAMLETS (Human And Machine in-the-Loop Evaluation and Learning Strategies) workshop at Thirty-fourth Conference on Neural Information Processing Systems
As artificial intelligence (AI) systems are getting ubiquitous within our society, issues related to its fairness, accountability, and transparency are increasing rapidly. As a result, researchers are integrating humans with AI systems to build robust and reliable hybrid intelligence systems. However, a proper conceptualization of these systems does not underpin this rapid growth. This article provides a precise definition of hybrid intelligence systems as well as explains its relation with other similar concepts through our proposed framework and examples from contemporary literature. The framework breakdowns the relationship between a human and a machine in terms of the degree of coupling and the directive authority of each party. Finally, we argue that all AI systems are hybrid intelligence systems, so human factors need to be examined at every stage of such systems' lifecycle.
The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, d…
While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DS…
University of Kassel（卡塞尔大学）；SAP SE（SAP公司）；University of St. Gallen（圣加仑大学）
Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting soft information (information that cannot be quantified) and making predictions in unknowable risk situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the gold standard for assessing soft …
vencortex；Kassel University（卡塞尔大学）；University of St. Gallen（圣加仑大学）
Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design k…
University of Kassel（卡塞尔大学）；University of St. Gallen（圣加仑大学）
One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of earlystage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence…
Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.
Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons,…
Where's the Structure? A Systematic Literature Review of Empirical Research on Human-AI Collaboration and Hybrid Intelligence for Learning
GSIC-EMIC research group, Universidad de Valladolid (Spain)（瓦伦西亚大学GSIC-EMIC研究组）；GICAP research group, Department of Digitization, Universidad de Burgos (Spain)（布尔戈斯大学数字技术系GICAP研究组）
Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which …
Scientific Institute of System Analysis of Russian Academy of Sciences（俄罗斯科学院系统分析科学研究所）；Lesya Ukrainka Volyn National University（莱斯娅·乌克兰卡沃伦国立大学）
The problem of possible applications Polymetric Analysis for the resolution problems of artificial Intelligence is discussed. As example the hybrid super intelligence system by N. Moiseev type was selected. The bond between polymetric analysis and hybrid super intelligence system was shown. In operational sense polymetric analysis is more general system. Therefore main principles of Moiseev concept may be unify with the help of polymetric analysis. Main peculiarities of this unification are analyzed.
There are few knowledge representation (KR) techniques available for efficiently representing knowledge. However, with the increase in complexity, better methods are needed. Some researchers came up with hybrid mechanisms by combining two or more methods. In an effort to construct an intelligent computer system, a primary consideration is to represent large amounts of knowledge in a way that allows effective use and efficiently organizing information to facilitate making the recommended inferences. There are merits and demerits of combinations, and standardized method of KR is needed. In this …
Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises tha…
University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center for Cyberspace Data Analysis and Applications（北京网络空间数据分析与应用工程研究中心）；China University of Petroleum（中国石油大学）；Ulster University（阿尔斯特大学）
Along with the development of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been continuously extended. In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI. However, the conventional technologies of AI have drawbacks in dealing with more complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI) which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI i…
Center for Hybrid Intelligence（混合智能中心）；University of Gothenburg（哥德堡大学）；Centre for Collective Intelligence Design（集体智能设计中心）；Citizen Cyberlab（公民网络实验室）；Ben Gurion University（本古里安大学）；University of Oxford（牛津大学）；Northwestern University（西北大学）；Vencortex（Vencortex公司）；University College London（伦敦大学学院）；Human Computation Institute（人类计算研究所）
Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss the benefits of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Currently there is no clear framework or methodology on how to create such an effective mixture. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluab…
Leiden Institute for Advanced Computer Science (LIACS)（莱顿高级计算机科学研究所）；Leiden University（莱顿大学）；Interactive Intelligence (II)（交互智能研究所）；Delft University of Technology（代尔夫特理工大学）；Computational Linguistics & Text Mining Lab (CLTL)（计算语言学与文本挖掘实验室）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Ahsanullah University of Science and Technology（阿赫桑努拉科技大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）；University of Bristol（布里斯托大学）；Missouri University of Science and Technology（密苏里科技大学）
Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branches of science and engineering, energy system modeling being one of them. Since the idea of hybrid and sustainable energy systems is spreading rapidly in the modern world for the paradigm of the smart energy shift, researchers are exploring t…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Ceratizit Austria GmbH（奥地利森拉天时有限公司）；Otto-von-Guericke-Universität Magdeburg（奥托·冯·格里克马格德堡大学）；IBM（国际商业机器公司）
Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpo…
Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum pers…
MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture（北京建筑大学建筑与城市规划学院）
It is needless to mention the (already established) overarching importance of knowledge organization and its tried-and-tested high-quality schemes in knowledge-based Artificial Intelligence (AI) systems. But equally, it is also hard to ignore that, increasingly, standalone KOSs are becoming functionally ineffective components for such systems, given their inability to capture the continuous facetization and drift of domains. The paper proposes a radical re-conceptualization of KOSs as a first step to solve such an inability, and, accordingly, contributes in the form of the following dimensions…
In this meta-ethnography, we explore three different angles of ethical artificial intelligence (AI) design implementation including the philosophical ethical viewpoint, the technical perspective, and framing through a political lens. Our qualitative research includes a literature review that highlights the cross-referencing of these angles by discussing the value and drawbacks of contrastive top-down, bottom-up, and hybrid approaches previously published. The novel contribution to this framework is the political angle, which constitutes ethics in AI either being determined by corporations and …
Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extraction from scanned documents. However, there exists no solution to provide reliable confidence score for current state-of-the-art deep-learning-based information extractors. In this paper, we propose a complete and novel architecture to measure confidence of current deep learning models in document information extraction task. Our architecture consists of a Multi-modal Conformal Predictor and a Variational Cluster-or…
Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies
Faculty of Engineering and Computer Science（工程与计算机科学学院）；Osnabrück University of Applied Sciences（奥斯纳布鲁克应用科学大学）；Institute of Cognitive Science（认知科学研究所）；Osnabrück University（奥斯纳布鲁克大学）
Uttarakhand Technical University（北阿坎德邦技术大学）；National Institute of Technology, Hamirpur（哈米尔普尔国家理工学院）
Knowledge representation (KR) and inference mechanism are most desirable thing to make the system intelligent. System is known to an intelligent if its intelligence is equivalent to the intelligence of human being for a particular domain or general. Because of incomplete ambiguous and uncertain information the task of making intelligent system is very difficult. The objective of this paper is to present the hybrid KR technique for making the system effective & Optimistic. The requirement for (effective & optimistic) is because the system must be able to reply the answer with a confidence of so…
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）
The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings. Since traditional methods cannot dynamically generate effective scheduling strategies in face of the disturbance of environments, we formulate the DJSP as a Markov decision process (MDP) to be tackled by reinforcement learning (RL). For this purpose, we propose a flexible hybrid framework that takes disjunctive graphs as states and a set of general dispatc…
Evaluating the potential of a prospective candidate is a common task in multiple decision-making processes in different industries. We refer to a prospect as something or someone that could potentially produce positive results in a given context, e.g., an area where an oil company could find oil, a compound that, when synthesized, results in a material with required properties, and so on. In many contexts, assessing the Probability of Success (PoS) of prospects heavily depends on experts' knowledge, often leading to biased and inconsistent assessments. We have developed the framework named KAR…
Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
University of Technology Sydney, Australia（悉尼科技大学）；University of Sydney, Australia（悉尼大学）；Asia Pacific International College (APIC), Parramatta, NSW, Australia（亚太国际学院）；Lincoln Institute of Higher Education (LIHE), Sydney, Australia（林肯高等教育研究所）；The Institute of International Studies (TIIS), Sydney, Australia（国际研究学院）
