Deploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company

Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general

Neurosymbolic Spike Concept Learner towards Neuromorphic General Intelligence

Current research in the area of concept learning makes use of deep learning and ensembles methods to learn concepts. Concept learning allows us to combine heterogeneous entities in data which could collectively identify as individual concepts. Heterogeneity and compositionality are crucial areas to explore in machine learning as it has the potential to contribute profoundly to artificial general intelligence. We investigate the use of spiking neural networks for concept learning. Spiking neurones inclusively model the temporal properties as observed in biological neurones. A benefit of spike-based neurones allows for localised learning rules that only adapts connections between relevant neurones. In this position paper, we propose a technique allowing dynamic formation of synapse (connections) in spiking neural networks, the basis of structural plasticity. Achieving dynamic formation of synapse allows for a unique approach to concept learning with a malleable neural structure. We call this technique Neurosymbolic Spike-Concept Learner (NS-SCL). The limitations of NS-SCL can be overcome with the neuromorphic computing paradigm. Furthermore, introducing NS-SCL as a technique on neuromorphic platforms should motivate a new direction of research towards Neuromorphic General Intelligence (NGI), a term we define to some extent

Integrating Blockchains and Intelligent Agents in the Pursuit of Artificial General Intelligence

Artificial General Intelligence (AGI) is the next greatest technological milestone. AGI can be defined as a realized artificial intelligence (AI) with the ability to understand and solve problems of various scope within constantly changing environments. To take steps toward this goal, a baseline of information will be provided regarding surrounding topics and the current state of AGI, itself. Through the culmination of swarms of highly optimized narrow AI agents, a collaborative effort will be extended toward general intelligence. Blockchains have been selected to facilitate this connection. A software deliverable will accompany this thesis to illustrate how this idea might be realized. The SingularityNET platform is utilized for this end due to its advanced protocols and methods for inter-AI communication

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup

The impacts of automating manual processes in software maintenance

Abstract. The purpose of this study was to find out how automating a software maintenance task affects the software developers and end users of the information system. The study was conducted as a case study in a Finnish IT organization that provides information systems for organizations. This study focused on software maintenance tasks that are done in the production environments. The main research question was “How has the new automated process affected the stakeholders in the case?” In order to address the research question, the stakeholder groups had to be identified. Two stakeholder groups were defined and two supporting questions were formulated in order to find answers to the main question. The two supporting questions are “How do the software developers experience the changes that took place after automation?” and “How do the customers experience the changes that took place after automation?” The study utilised triangulation, combining qualitative and quantitative research methods. Qualitative data was collected with interviews and a questionnaire, and quantitative analysis was conducted to the maintenance request tickets with an MMG (Maintenance Model Graph) analysis. The findings of the quantitative MMG analysis were used to support the results found with qualitative methods. Automating a repetitive maintenance task was found to affect especially the software developers, who experienced the manual task to be time-consuming and arduous. Based on the developer interviews, four factors were found to have been affected by the automation: (1) degree of difficulty, (2) overall workload, (3) incoming maintenance requests and (4) future development. Customers were affected by the automation indirectly. The new solution was found to provide them with more accurate data and enhanced documentation, but it was also experienced to be arduous to familiarize with. Overall the results from customer questionnaire pointed out that the new solution was experienced as an upgrade. The familiarization will be handled in the case organization by providing training sessions directly to the customer organizations. The contribution of this study is the additional knowledge it provided about automating the repetitive tasks of software maintenance. As software maintenance is a very expensive part of the SW life cycle, it is beneficial to consider automating some of the most frequent tasks