Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Testing System Intelligence

We discuss the adequacy of tests for intelligent systems and practical problems raised by their implementation. We propose the replacement test as the ability of a system to replace successfully another system performing a task in a given context. We show how it can characterize salient aspects of human intelligence that cannot be taken into account by the Turing test. We argue that building intelligent systems passing the replacement test involves a series of technical problems that are outside the scope of current AI. We present a framework for implementing the proposed test and validating the properties of the intelligent systems. We discuss the inherent limitations of intelligent system validation and advocate new theoretical foundations for extending existing rigorous test methods. We suggest that the replacement test, based on the complementarity of skills between human and machine, can lead to a multitude of intelligence concepts reflecting the ability to combine data-based and symbolic knowledge to varying degrees

On Controllability of Artificial Intelligence

Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI can’t be fully controlled. Consequences of uncontrollability of AI are discussed with respect to future of humanity and research on AI, and AI safety and security. This paper can serve as a comprehensive reference for the topic of uncontrollability

Can Autonomous Machines Make Ethical Decisions?

Käesolev bakalaureusetöö küsib, kas autonoomsed masinad suudavad teha eetilisi otsuseid. Autonoomse masina all mõeldakse siin selliseid masinad, mis on võimelised tegutsema ilma pideva inimese poolse juhtimiseta ning mida kontrollib tehisintellekt. Autor arutleb eeliste ja puuduste üle, mis on erinevatel eetilistel printsiipidel, mida masin saaks kasutada alusena oma otsuste tegemisel. Siia on kaasatud nii klassikalised eetikateooriad nagu näiteks utilitarism kui ka alternatiivsed lähenemised nagu juhtumipõhine masinõpe. Samuti leitakse, et valdkonnapõhise eetika masinasse juurutamine oleks kergem, kui püüd korraga arendada üldisel eetikal põhinevat masinat. Vaadeldakse ka üldist masinapoolse otsustusprotsessi olemust ning jõutakse järeldusele, et kuna masinal puudub teadvus, vaba tahe, kavatsuslikkus ja omakasupüüdlikkus, siis ei ole masinad võimelised tegema eetilisi otsuseid sel moel nagu inimene. Sellest hoolimata on masinad võimelised tegema otsuseid, mida saab pidada eetiliseks mingis kindlas situatsioonis, juhul kui neisse on implementeeritud sobiv eetiline printsiip ning masin suudab koguda adekvaatset infot end ümbritseva kohta.https://www.ester.ee/record=b518354

Foresight Review on Design for Safety

This review explores how a culture of design for safety can enhance the safety of the world around us. Design for safety goes beyond legislation, regulations and standards. These all play an important role for established products and services but their limited scope often leads to missed opportunities to enhance safety by taking a broader perspective. Design is applied to both mature industries (which have many years of experience and a good understanding of risks and how to reduce them) and emerging industries (that use new technologies requiring new ways of controlling risk which may not yet be known or understood). An example of an emerging risk is the internet that is enabling rapid innovation of new products which generate data. This data is widely shared across the internet and the risks associated with this are as yet not fully understood by the public. A design for safety culture takes a holistic approach to understanding the influences that affect safety. Such influences are varied and take into account the broader environment within which design operates, including complex interactions, behaviour and culture. It goes beyond traditional design methods and focuses on the goal of a safer design. Implementing design for safety requires an understanding of the challenges and the methods to address them. It needs multidisciplinary teams that bring together people with the relevant skills to understand the challenges and a collaborative approach of ‘designing with’ rather than the more traditional approach of ‘designing for’. This can be achieved through an international diverse community that works together to identify and share best practices

Conversations on a probable future: interview with Beatrice Fazi

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