Empowering IT Operations through Artificial Intelligence – A New Business Perspective

This paper aims to describe the concept of applying Artificial Intelligence to IT Operations (AIOps) and its main components, Big Data, Machine Learning and Trend Analysis. The concept was implemented by developing a multi-layered fusion of the technologies that powers the components in AIOps platforms present on the IT market. The core of an AIOps platform is represented by the Big Data organization structure and by a massive parallel data processing platform like Apache Hadoop. The ML component of the platform is able to infer the future behaviour and the regular operations that are performed from the large volume of collected data, in order to develop the ability to automate the activities. AIOps platforms find their place especially in very complex IT infrastructures, ones that require constant monitoring and quick decisions in case of failures. The case study is based on the Moogsoft AIOps platform, and its features are presented in detail, using the Cloud trial version, clearly showing the potential of such an advanced tool for infrastructure monitoring and reporting. The experiment was focused on the way Moogsoft is monitoring computing resources,    is handling events and records alerts for the defined timespan, alerts grouped by category (like web services, social media, networking). The platform is also able to display at any given moment the unresolved situations and their type of origin, and includes automated remediation tools. The study presents the features of this software category, consisting in benefits for the business environment and their integration into the Internet-of-Things model. Keywords: Big Data, Machine Learning, AIOps, business performance

Decision-Making in Real-Life Industrial Environment through Graph Theory Approach

The approach called as “graph theory and matrix approach” has been well employed in numerous research studies with a view to perform the decision-making while the situation is becoming perplexed type or where there is a very strong relative importance of one parameter over another. In such cases, the said graph theory and matrix method provides very suitable and fruitful solutions to make the decision to its final effective extent. The further improvements and the outcome enhancement can also be revealed through the use of combined practice of graph theory results along with some artificial intelligence-inspired logics and practices such as fuzzy logic, artificial neural network, etc. The significance and applicability of said method in vast fields of science, engineering, and research are also proved. Nowadays, our manufacturing sectors are getting up to date through the applications of artificial intelligence and several software-based directions. This is all to enhance the overall machine system performance with a view to improve desired performance characteristics of the process under the study. Few sections of this chapter has also elaborated the utility of the artificial intelligence-inspired fuzzy logic-based decision system which has already been a part of previous researches

Artificial Intelligence and Statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research

Case Study: Remixing Knowledge with Layered Intelligences

The case study of Degenerative Cultures explores how the layering of different forms of logic offers an opportunity for rethinking our human systems and hypothetically remixing the epistemological roots of society—through interventions into our technological systems. In Degenerative Cultures, the living organism Physarum polycephalum partners with an artificial intelligence that compiles and corrupts an archive of human texts. In the iterative art installation, which incorporates the growth cycles of microbiological organisms, protists as well as fungi cover up and effectively remix human texts. Human knowledge, contained within the philosophy books used in the project, becomes the substrate for organic growth. The living organisms grow over an actual book, and the AI, referred to as a “digital fungus,” corrupts texts on the Internet. The artists’ experiment, which links microbiological growth logic to artificial intelligence, is one step in rethinking how human knowledge may become layered and ultimately corrupted and rerouted—a forking of sorts—through integration with nonhuman logic systems, including microbiological and artificial intelligences. By orienting this work to remix theory, the article offers the hypothesis of a multispecies recombination that could, in utopian terms, reformulate the epistemological basis of modernity. In order to pursue this hypothesis, the art collective Cesar & Lois asks what role remix plays in the ongoing emergence of artificial intelligence and machine learning

Talking to Boxes, Hugging Robots

Relationships between humans and technology are at the core of my artistic research. Human-machine communication is defined by the technological level of the machines, but even more so by the way they are perceived by humans. Concepts of artificial life and artificial intelligence gradually have become part of the everyday life of growing numbers of people, and while there is an ongoing effort to design an increasingly anthropocentric technology, our minds also adapt to the new technological reality. Through immersive installations and sculptural objects my practice explores this reality. My artwork is designed to communicate with and stimulate the viewers, allowing them to examine their own perception of phenomena such as behavioral algorithms, artificial life and artificial intelligence. Not only does it provide an opportunity of self-analysis, it also facilitates a change in the way people conceptualize communication with machines

Modelling human behaviours and reactions under dangerous environment

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools (TM) Dev. Programming within Virtools (TM) Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed