Responsible cognitive digital clones as decision-makers: A design science research study
This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their “donors” in a specific training environment based on generative adversarial networks. A trained clone can be used by a decision-maker as an additional resource for one’s own cognitive enhancement, as an autonomous representative, or even as a replacement when appropriate. The assumption regarding this approach is as follows: when someone was forced t…
An introduction to knowledge computing
This paper deals with the challenges related to self-management and evolution of massive knowledge collections. We can assume that a self-managed knowledge graph needs a kind of a hybrid of: an explicit declarative self-knowledge (as knowledge about own properties and capabilities) and an explicit procedural self-knowledge (as knowledge on how to utilize own properties and the capabilities for the self-management).We offer an extension to a traditional RDF model of describing knowledge graphs according to the Semantic Web standards so that it will also allow to a knowledge entity to autonomously perform or query from remote services different computational executions needed. We also introdu…
Taxonomy of generative adversarial networks for digital immunity of Industry 4.0 systems
Abstract Industry 4.0 systems are extensively using artificial intelligence (AI) to enable smartness, automation and flexibility within variety of processes. Due to the importance of the systems, they are potential targets for attackers trying to take control over the critical processes. Attackers use various vulnerabilities of such systems including specific vulnerabilities of AI components. It is important to make sure that inappropriate adversarial content will not break the security walls and will not harm the decision logic of critical systems. We believe that the corresponding security toolset must be organized as a trainable self-protection mechanism similar to immunity. We found cer…
TB-Structure : Collective Intelligence for Exploratory Keyword Search
In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s i…
From Deep Learning to Deep University: Cognitive Development of Intelligent Systems
Search is not only an instrument to find intended information. Ability to search is a basic cognitive skill helping people to explore the world. It is largely based on personal intuition and creativity. However, due to the emerged big data challenge, people require new forms of training to develop or improve this ability. Current developments within Cognitive Computing and Deep Learning enable artificial systems to learn and gain human-like cognitive abilities. This means that the skill how to search efficiently and creatively within huge data spaces becomes one of the most important ones for the cognitive systems aiming at autonomy. This skill cannot be pre-programmed, it requires learning…
Towards digital cognitive clones for the decision-makers: adversarial training experiments
Abstract There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveilla…
Industry 4.0 Intelligence under Attack : From Cognitive Hack to Data Poisoning
Artificial intelligence is an unavoidable asset of Industry 4.0. Artificial actors participate in real-time decision-making and problem solving in various industrial processes, including planning, production, and management. Their efficiency, as well as intelligent and autonomous behavior is highly dependent on the ability to learn from examples, which creates new vulnerabilities exploited by security threats. Today's disruptive attacks of hackers go beyond system's infrastructures targeting not only hard-coded software or hardware, but foremost data and trained decision models, in order to approach system's intelligence and compromise its work. This paper intends to reveal security threats…
Hybrid Threats against Industry 4.0 : Adversarial Training of Resilience
Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial de…
Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0
Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary…
Patented intelligence: Cloning human decision models for Industry 4.0
Industry 4.0 is a trend related to smart factories, which are cyber-physical spaces populated and controlled by the collective intelligence for the autonomous and highly flexible manufacturing purposes. Artificial Intelligence (AI) embedded into various planning, production, and management processes in Industry 4.0 must take the initiative and responsibility for making necessary real-time decisions in many cases. In this paper, we suggest the Pi-Mind technology as a compromise between completely human-expert-driven decision-making and AI-driven decision-making. Pi-Mind enables capturing, cloning and patenting essential parameters of the decision models from a particular human expert making …
Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System
Education is recognized as a fundamental enabler of human development. The adoption of information and communications technologies (ICTs) by education (especially in developing countries) contributes to educational system reforms, in addition to the traditional advantages, such as social openness and accessibility. Yet the academic community has not studied sufficiently the challenging context in which ICTs are used as instruments for the reform of inefficient, and sometimes even corrupted, educational systems rather than just as means for smarter classrooms, remote access, or content management. The object of this study is Ukrainian higher education (HE) and its quality assurance (QA) syst…
TB-Structure: Collective Intelligence for Exploratory Keyword Search
In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s i…
Industry 4.0 vs. Industry 5.0 : Co-existence, Transition, or a Hybrid
Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0 proclaims transition to digitalization and automation of processes while emerging Industry 5.0 emphasizes human centricity. This turn can be explained by unprecedented challenges being faced recently by societies, such as, global climate change, pandemics, hybrid and conventional warfare, refugee crises. Sustainable and resilient processes require humans to get back into the loop of organizational decision-making. In this paper, we argue that the most reasonable way to marry the two extremes of automation and value-based human-driven processes is to create an Industry 4.0 + Industry 5.0 hybrid, which inher…
Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means
Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such networks are known to be highly tuned to the training data (samples representing a particular problem), and they are poorly reusable to address new problems. One way to change this would be, in addition to trainable weights, to apply trainable parameters of the mathematical functions, which simulate various neural computations within such networks. In this way, we may distinguish between the narrowly focused task-specific parameters (weights) and more generic capability-spec…
Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing
Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequenti…