Dissertations / Theses: 'Embedded artificial intelligence (AI)'

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Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.

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The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.

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Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.

Full text

Abstract:

The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.

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Chollet, Nicolas. "Embedded-AI-enabled semantic IoT platform for agroecology." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG078.

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L'agriculture moderne nécessite une profonde transformation pour répondre aux défis du développement durable tout en nourrissant qualitativement et quantitativement la population mondiale croissante. Dans cette optique, les agriculteurs adoptent le "Smart Farming" ou agriculture intelligente. C'est une méthode agricole qui utilise la technologie pour améliorer l'efficacité, la productivité et la durabilité de la production agricole. Elle englobe l'usage de capteurs, l'internet des objets (IoT), l'Intelligence Artificielle (IA), l'analyse de données, la robotique et divers autres outils numériques optimisant des aspects tels que la gestion des sols, l'irrigation, la lutte antiparasitaire ou encore la gestion de l'élevage. L'objectif est d'augmenter la production tout en réduisant la consommation de ressources, minimisant les déchets et améliorant la qualité des produits. Toutefois, malgré ses avantages et son déploiement réussi dans divers projets, l'agriculture intelligente rencontre des limites notamment dans le cadre de l'IoT. Premièrement, les plateformes doivent être capables de percevoir des données dans l'environnement, de les interpréter et de prendre des décisions pour aider à la gestion des fermes. Le volume, la variété et la vélocité de ces données, conjuguées à la grande diversité d'objets ainsi qu'à l'avènement de l'IA embarquée dans les capteurs, rendent difficile les communications sur les réseaux agricoles sans fils. Deuxièmement, les recherches tendent à se focaliser sur des projets répondant aux problématiques de l'agriculture conventionnelle non durable et les projets concernant les petites exploitations axées sur l'agroécologie sont rares. Dans ce contexte, cette thèse explore la création d'une plateforme IoT composée d'un réseau de capteurs intelligents sémantiques, visant à guider les agriculteurs dans la transition et la gestion de leur ferme en agriculture durable tout en minimisant l'intervention humaine Modern agriculture requires a profound transformation to address the challenges of sustainable development while qualitatively and quantitatively feeding the growing global population. In this light, farmers are adopting "Smart Farming" also called precision agriculture. It is an agricultural method that leverages technology to enhance the efficiency, productivity, and sustainability of agricultural production. This approach encompasses the use of sensors, the Internet of Things (IoT), Artificial Intelligence (AI), data analysis, robotics, and various other digital tools optimizing aspects such as soil management, irrigation, pest control, and livestock management. The goal is to increase production while reducing resource consumption, minimizing waste, and improving product quality. However, despite its benefits and successful deployment in various projects, smart agriculture encounters limitations, especially within the context of IoT. Firstly, platforms must be capable of perceiving data in the environment, interpreting it, and making decisions to assist in farm management. The volume, variety, and velocity of those data, combined with a wide diversity of objects and the advent of AI embedded in sensors, make communication challenging on wireless agricultural networks. Secondly, research tends to focus on projects addressing the issues of non-sustainable conventional agriculture, and projects related to small-scale farms focused on agroecology are rare. In this context, this thesis explores the creation of an IoT platform comprised of a network of semantic smart sensors, aiming to guide farmers in transitioning and managing their farm sustainably while minimizing human intervention

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MAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.

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Hasanzadeh, Mujtaba, and Alexandra Hengl. "Real-Time Pupillary Analysis By An Intelligent Embedded System." Thesis, Mälardalens högskola, Inbyggda system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44352.

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With no online pupillary analysis methods today, both the medical and the research ﬁelds are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caﬀeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established oﬄine and then adapted to a real-time platform. ANN was chosen as classiﬁcation algorithm. The accuracy of the ANN from the oﬄine analysis was 94% while for the online classiﬁcation the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classiﬁcation took no longer than 2ms, faulty data detection showed consistent results. Data communication suﬀered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.

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Karlsson, Marcus. "Developing services based on Artificial Intelligence." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73090.

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This thesis explores the development process of services based on artificial intelligence (AI) technology within an industrial setting. There has been a renewed interest in the technology and leading technology companies as well as many start-ups has integrated it into their market offerings. The technology´s general application potential for enhancing products and services along with the task automation possibility for improved operational excellence makes it a valuable asset for companies. However, the implementation rate of AI services is still low for many industrial actors. The research in the area has been technically dominated with little contribution from other disciplines. Therefore, the purpose of this thesis is to identify development challenges of AI services and drawing on service development- and value-theory to propose a process framework promoting implementation. The work will have two main contributions. Firstly, to compare differences in theoretical and practical development challenges and secondly to combine AI with service development and value theory. The empirical research is done through a single case study based on a systematic combining research approach. It moves iteratively between the theory and empirical findings to direct and support the thesis throughout the work process. The data was collected through semi-structured interviews with a purposive sample. It consisted of two groups of interview participants, one AI expert group and one case internal group. This was supported by participant observation of the case environment. The data analysis was done through flexible pattern matching. The results were divided into two sections, practical challenges and development aspect of AI service development. These were combined with the selected theories and a process framework was generated. The study showed a current understudied area of business and organisational aspect regarding AI service development. Several such challenges were identified with limited theoretical research as support. For a wider industrial adoption of AI technology, more research is needed to understand the integration into the organisation. Further, sustainability and ethical aspect were found not to be a primary concern, only mention in one of the interviews. This, despite the plethora of theory and identified risks found in the literature. Lastly, the interdisciplinary research approach was found to be beneficial to the AI field to integrate the technology into an industrial setting. The developed framework could draw from existing service development models to help manage the identified challenges. Denna uppsats utforskar utvecklingsprocessen av tjänster baserade på artificiell intelligens (AI) i en industriell miljö. Tekniken har fått ett förnyat intresse vilket har lett till att allt fler ledande teknik företag och start-up:s har integrerat AI i deras marknads erbjudande. Teknikens generella applikations möjlighet för att kunna förbättra produkter och tjänster tillsammans med dess automatiserings möjlighet för ökad operationell effektivitet gör den till en värdefull tillgång för företag. Dock så är implementations graden fortfarande låg för majoriteten av industrins aktörer. Forskningen inom AI området har varit mycket teknik dominerat med lite bidrag från andra forskningsdiscipliner. Därför syftar denna uppsats att identifiera utvecklingsutmaningar med AI tjänster och genom att hämta delar från tjänsteutveckling- och värde teori generera ett processramverk som premierar implementation. Uppsatsen har två huvudsakliga forskningsbidrag. Först genom att jämföra skillnader mellan teoretiska och praktiska utvecklingsutmaningar, sedan bidra genom att kombinera AI med tjänsteutveckling- och värdeteori. Den empiriska forskningen utfördes genom en fallstudie baserad på ett systematic combining tillvägagångsätt. På så sätt rör sig forskning iterativt mellan teori och empiri för att forma och stödja uppsatsen genom arbetet. Datat var insamlad genom semi strukturerade intervjuer med två separata, medvetet valda intervjugrupper där ena utgjorde en AI expert grupp och andra en intern grupp för fallstudien. Detta stöttades av deltagande observationer inom fallstudiens miljö. Dataanalysen utfördes med metoden flexible pattern matching. Resultatet var uppdelat i två olika sektioner, den första med praktiska utmaningar och den andra med utvecklingsaspekter av AI tjänsteutveckling. Dessa kombinerades med de utvalda teorierna för att skapa ett processramverk. Uppsatsen visar ett under studerat område angående affär och organisation i relation till AI tjänsteutveckling. Ett flertal av sådana utmaningar identifierades med begränsat stöd i existerande forskningslitteratur. För en mer utbredd adoption av AI tekniken behövs mer forskning för att förstå hur AI ska integreras med organisationer. Vidare, hållbarhet och etiska aspekter var inte en primär aspekt i resultatet, endast bemött i en av intervjuerna trots samlingen av artiklar och identifierade risker i litteraturen. Till sist, det tvärvetenskapliga angreppsättet var givande för AI området för att bättre integrera tekniken till en industriell miljö. Det utvecklade processramverket kunde bygga på existerande tjänsteutvecklings modeller för att hantera de identifierade utmaningarna.

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ALAMEH, MOHAMAD. "Embedded Artificial Intelligence for Tactile Sensing." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1039756.

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Electronic tactile sensing becomes an active research field whether for prosthetic applications, robotics, virtual reality or post stroke patients rehabilitation. To achieve such sensing, an array of sensors is used to retrieve human-skin like information, which is called Electronic skin (E-skin). Humans through their skins, are able to collect different types of information e.g. pressure, temperature, texture, etc. which are then passed to the nervous system, and finally to the brain in order to extract high level information from these sensory data. In order to make E-skin capable of such task, data acquired from E-skin should be filtered, processed, and then conveyed to the user (or robot). Processing these sensory information, should occur in real-time, taking in consideration the power limitation in such applications, especially prosthetic applications. The power consumption itself is related to different factors, one factor is the complexity of the algorithm e.g. number of FLOPs, and another is the memory consumption. In this thesis, I will focus on the processing of real tactile information, by 1)exploring different algorithms and methods for tactile data classification, 2)data organization and preprocessing of such tactile data and 3)hardware implementation. More precisely the focus will be on deep learning algorithms for tactile data processing mainly CNNs and RNNs, with energy-efficient embedded implementations. The proposed solution has proved less memory, FLOPs, and latency compared to the state of art (including tensorial SVM), applied to real tactile sensors data. Keywords: E-skin, tactile data processing, deep learning, CNN, RNN, LSTM, GRU, embedded, energy-efficient algorithms, edge computing, artificial intelligence.

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Kim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.

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As there have been concerns about the excessive advancement of artificial intelligence (AI) surpassing humans, exploring reactions to AI as challenging human superiority is meaningful. By examining how the hierarchical and discriminative views on animals (speciesism) affects the views on non-living AI, this thesis has significant and novel contributions to AI adoption literature and AI product marketing.

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Fatima, Samar. "Mapping artificial intelligence affordances for the public sector." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235926/1/Samar%2BFatime%2BThesis.pdf.

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This thesis explores the affordances of artificial intelligence (AI) for the public sector. The thesis consists of three studies that answer what, why and how questions of AI affordance actualization in public sector using a combination of primary and secondary data sources. In this thesis, the affordance theory lens is used to explore AI affordance perception and actualization for the public sector through three related studies. The perception of AI affordance is investigated in the first two studies. The third study designed and evaluated artefact for public agencies to actualize AI affordance.

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Malmborn, Albin, and Linus Sjöberg. "Implementing Artificial intelligence." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20942.

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Den här uppsatsen har som syfte att undersöka huruvida det är möjligt att ta fram riktlinjer för vad privata verksamheter behöver ta i beaktande inför en planerad implementering av artificiell intelligens. Studien kommer belysa faktorer som hjälper företag att förstå vad som krävs inför en sådan omställning, men även de hinder som måste övervinnas för att lyckas. Studiens datainsamling har genomförts med två metoder, först en litteraturstudie sedan kvalitativa, semistrukturerade forskningsintervjuer. Dessa har sedan analyserats med vars en analysmetod som kompletterar varandra och därefter tolkats för att se mönster som kan besvara studiens frågeställning: Vad måste svenska organisationer inom den privata sektorn beakta för att lyckas implementera Artificiell intelligens i sin verksamhet? Resultatet har tagits fram genom att jämföra vetenskapliga texter och intervjuer, för att undersöka om den akademiska och praktiska synen skiljer sig åt. Studien resulterade i åtta faktorer som företag borde ta i beaktning inför en implementering av artificiell intelligens. Författarna hoppas att med den här studien kunna främja svensk utveckling inom artificiell intelligens och på så vis generera ett större nationellt mervärde och en starkare konkurrenskraft internationell. The purpose of this paper is to investigate the possibilities to develop guidelines for businesses to take into account before an implementation of artificial intelligence. The study will highlight different factors that will help companies to understand what is required to make this kind of digital transition, it will also highlight the obstacles companies have to overcome in order to succeed. The data collection was conducted in two parts, first a literature study and then qualitative, semi-structured interviews. These were analyzed with their own analysis which supplement each other, and interpreted to identify patterns that could answer the study's main question: What must Swedish organizations in the private sector consider in order to successfully implement Artificial Intelligence in their operations?The result of the study has been produced by comparing scientific texts and interviews, to investigate whether the academic and practical views differ. The study resulted in eight factors that companies should consider before implementing artificial intelligence. The authors hope that the study will promote Swedish development in artificial intelligence and thus generate a greater national value and international competitiveness.

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Edlund, Mattias. "Artificial Intelligence in Games : Faking Human Behavior." Thesis, Uppsala universitet, Institutionen för speldesign, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258222.

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This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions played were recorded in order for other humans to replay. Both players and spectators of the game sessions left feedbacks and reports that could later be analyzed. The data collected from these experiments was then analyzed, and reflections were made on the entire project. Tips and ideas are proposed to developers of shooter games who are interested in making human-like artificial intelligence. Conclusions are made and extra information is provided in order to further iterate on this research. Denna rapport undersöker möjligheterna att förfalska mänskligt beteende genom artificiell intelligens i datorspel, med hjälp av effektiva metoder som sparar värdefull utvecklingstid och som även skapar en rikare upplevelse för spelare. Den specifika implementationen av artificiell intelligens som utvecklas och diskuteras är ett neuralt nätverk som kontrollerar en finite-state machine. Målet var att efterlikna mänskligt beteende snarare än att simulera verklig intelligens. Ett 2D shooter-spel utvecklas och används för utförda experiment med mänskliga och artificiell intelligens-kontrollerade spelare. De sessioner som spelades under experimenten spelades in, för att sedan låta andra människor titta på inspelningarna. Både spelare och åskådare av spelsessionerna lämnade återkoppling och rapporter för senare analysering. Datan som samlats in från experimenten analyserades, och reflektioner utfördes på hela projektet. Tips och idéer presenteras till utvecklare av shooter-spel som är intresserade av en mer människolik artificiell intelligens. Slutsatser läggs fram och extra information presenteras för att kunna fortsätta iterera vidare på denna undersökning.

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Khan, Laiq. "Hybrid AI paradigms applied to power system damping controls." Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273412.

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Svedberg, Peter O. S. "Steps towards an empirically responsible AI : a methodological and theoretical framework." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-246.

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Initially we pursue a minimal model of a cognitive system. This in turn form the basis for the development of amethodological and theoretical framework. Two methodological requirements of the model are that explanation be from the perspective of the phenomena, and that we have structural determination. The minimal model is derived from the explanatory side of a biologically based cognitive science. Fransisco Varela is our principal source for this part. The model defines the relationship between a formally defined autonomous system and an environment, in such a way as to generate the world of the system, its actual environment. The minimal model is a modular explanation in that we find it on different levels in bio-cognitive systems, from the cell to small social groups. For the latter and for the role played by artefactual systems we bring in Edwin Hutchins' observational study of a cognitive system in action. This necessitates the introduction of a complementary form of explanation. A key aspect of Hutchins' findings is the social domain as environment for humans. Aspects of human cognitive abilities usually attributed to the person are more properly attributed to the social system, including artefactual systems.Developing the methodological and theoretical framework means making a transition from the bio-cognitive to the computational. The two complementary forms of explanation are important for the ability to develop a methodology that supports the construction of actual systems. This has to be able to handle the transition from external determination of a system in design to internal determination (autonomy) in operation.Once developed, the combined framework is evaluated in an application area. This is done by comparing the standard conception of the Semantic Web with how this notion looks from the perspective of the framework. This includes the development of the methodological framework as a metalevel external knowledge representation. A key difference between the two approaches is the directness by which the semantic is approached. Our perspective puts the focus on interaction and the structural regularities this engenders in the external representation. Regularities which in turn form the basis for machine processing. In this regard we see the relationship between representation and inference as analogous to the relationship between environment and system. Accordingly we have the social domain as environment for artefactual agents. For human level cognitive abilities the social domain as environment is important. We argue that a reasonable shortcut to systems we can relate to, about that very domain, is for artefactual agents to have an external representation of the social domain as environment.

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Rafiq, M. Y. "Artificial intelligence techniques for the structural design of buildings." Thesis, University of Strathclyde, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382446.

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Casten, Carlberg Carl Johan, and Elsa Jerhamre. "Artificial Intelligence in Agriculture : Opportunities and Challenges." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443576.

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Artificial Intelligence (AI) is increasingly used in different parts of society for providing decision support in various activities. The agricultural sector is anticipated to benefit from an increased usage of AI and smart devices, a concept called smart farming technologies. Since the agricultural sector faces several simultaneous challenges, such as shrinking marginals, complicated pan-European regulations, and demands to mitigate the environmental footprint, there are great expectations that smart farming will benefit both individual farmers and industry stakeholders. However, most previous research focuses only on a small set of characteristics for implementing and optimising specific smart farming technologies, without considering all possible aspects and effects. This thesis investigates both technical and non-technical opportunities and hurdles when implementing AI in Swedish agricultural businesses. Three sectors in agriculture are scrutinized: arable farming, milk production and beef production. As a foundation for the thesis, a literature review revises former research on smart farming. Thereafter, an interview study with 27 respondents both explores the susceptibility and maturity of smart farming technologies and provides examples of technical requirements of three chosen applications of AI in agriculture. Findings of the study include a diverse set of aspects that both enable and obstruct the transition. Main identified opportunities are the importance smart farming has on the strategic agendas of several industry stakeholders, the general trend towards software technology as a service through shared machinery, the vast amount of existing data, and the large interest from farmers towards new technology. Contrasting, the thesis identifies main hurdles as technical and legislative challenges to data ownership, potential cybersecurity threats, the need for a well-articulated business case, and the sometimes lacking technical knowledge within the sector. The thesis concludes that the macro trend points towards a smart farming transition but that the speed of the transformation will depend on the resolutions for the identified obstacles.

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Yang, Yikai. "Towards advanced application of artificial intelligence (AI) in epileptic seizure management." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30022.

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Epilepsy has a significant adverse impact on almost 1% of people's health and well-being globally. Clinical EEG monitoring devices that enable seizure onset detection and prediction are crucial for epilepsy patients to manage their seizure disorders. In the past three decades, many epileptic seizure detecting, and prediction methods have been developed and reported high performance. However, most of them are retrospective and lack continental and multi-dataset generalization, transparency, and reproducibility, making them hard to implement into clinical utility. Besides, the seizure prediction biomarker is yet to be fully answered, and this issue significantly limits clinician trust when using the seizure prediction algorithms. In this thesis, we propose a generalized epileptic seizure detection AI-assisted system that tested on a large scale of the clinical EEG dataset and proved to improve time efficiency while accuracy alongside the human expert. The seizure detection performance is further improved by combining EEG and ECG using a novel multimodal AI system. Secondly, we propose a Bayesian convolutional neural network to facilitate the exploration of potential seizure forecasting biomarkers. Another problem we address is the need for long recording labeled EEG data for seizure prediction. We propose a novel real-time seizure prediction AI system that learns from the on-the-fly weak label generated by the detection model. Ultimately, we focus on developing a low-power, hardware-friendly implementation method using neuromorphic-compatible Spiking Neural Networks (SNNs) for seizure detection. Overall, the work presented in this thesis has tackled several research problems related to advanced AI applications in epileptic seizure detection and prediction and drove these emerging technologies toward building reliable AI systems in real-world clinical settings.

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Othman, Amani. "Mapping Artificial Intelligence (AI) Capabilities around Human Competences : An explorative study." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44748.

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Technology in the workplace has helped increase the rate of production and speed at which business occur. As these technologies advance, humans are becoming more efficient and more productive than ever before. The impact of technology on work, has consistently changed the way human across every industry do their jobs. In fact, the latest advancements in artificial intelligence (AI) has the potential to further influence a greater shift in the way human accomplish job tasks. Because artificial intelligence is a technology based on augmenting human, we can use this advantage to further complement human intelligence. Making human more creative and smarter than they have ever been. To achieve this, we need to understand how AI’s capabilities can complement human’s competences. In a workplace context, we need to understand how AI can facilitate the support of different type of tasks. Hence, the purpose of this explorative study is to understand AI’s influence on people’s skills and on workflows. This is done by mapping AI capabilities around human competences to help individuals up- skill and to support organizations in planning effective workflows enabled by AI to achieve better performance. The insights for this qualitative study is gathered by developing an AI use- case with industrial partners, while carefully observing the influence AI poses on human skills and organization processes. Beyond the traditional technology optimizations that earlier helped human with speed and accuracy, the findings from this thesis show that AI could empower human through skills such as creativity, problem solving, and analysis. As a consequence, such advancement impacts the demand of human competences needed to thrive in AI enabled workplaces. The long-term impact of adapting AI to support in workplace tasks, shows a trend in moving toward more intellectually demanding tasks. While workflows will need to facilitate collaboration between cross-disciplinary teams and support high involvement.

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Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.

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Il grande sviluppo del mondo dell’Intelligenza Artificiale unito alla sua vastissima applicazione in molteplici ambiti degli ultimi anni, ha portato a una sempre maggior richiesta di spiegabilità dei sistemi di Machine Learning. A seguito di questa necessità il campo dell’eXplainable Artificial Intelligence ha compiuto passi importanti verso la creazione di sistemi e metodi per rendere i sistemi intelligenti sempre più trasparenti e in un futuro prossimo, per garantire sempre più equità e sicurezza nelle decisioni prese dall’AI, si prevede una sempre più rigida regolamentazione verso la sua spiegabilità. Per compiere un ulteriore salto di qualità, il recente campo di studio XAI UX si pone come obiettivo principale l’inserimento degli utenti al centro dei processi di progettazione di sistemi di AI, attraverso la combinazione di tecniche di spiegabilità offerte dall'eXplainable AI insieme allo studio di soluzioni UX. Il nuovo focus sull’utente e la necessità di creare team multidisciplinari, e quindi con maggiori barriere comunicative tra le persone, impongono ancora un largo studio sia da parte degli esperti XAI sia da parte della comunità HCI, e sono attualmente le principali difficoltà da risolvere. All’interno dell’elaborato si fornisce una visione attuale sullo stato dell'arte della XAI UX introducendo le motivazioni, il contesto e i vari ambiti di ricerca che comprende.

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Shamsolmaali, A. "Artificial intelligence and physiological models in medicine : A prototypical approach." Thesis, City University London, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382837.

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Savola, Hannimari, and Bijona Troqe. "Recruiters just wanna have...AI? : Implications of implementing AI in HR recruitment." Thesis, Linköpings universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158480.

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The development and implementation of AI is rapidly growing in the Nordic countries, yet the perception and information of AI is still limited. This paper will look deeper into the managerial aspects of implementing AI as part of the recruitment system, specifically the selection process and machine learning in text mining. The data gathering of this research has been conducted via interviews with Linköping's municipality, as well as collecting secondary data from public reports and scientific articles. Afterwards, the data was then scrutinized through theoretical analysis, using frameworks from different academic researches. A set of aspects was found, which affects the implementation of AI in an organisation in Sweden. A managerial view was taken to find a deeper significance on why an understanding of these aspects is necessary when implementing AI as a part of company's recruitment processes. However, while other elements other than the ones identified in this thesis do exist, a coherent picture of the process and the affecting variables can be thoroughly explained through these specifically chosen viewpoints. The paper concludes with drawing a bigger image of the AI in recruitment and selection processes, and the implications of it to an organisation considering to implement AI as part of these processes in near future. The thesis can be seen as a recommendation to any establishment that is making the decision of adopting the usage of AI as part of recruitment.

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O'Rell, James L. "Smart Terrain using Multiple Needs." Youngstown State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1338040302.

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Kesavan, Ajay, and not supplied. "Embedded Intelligence In Structural Health Monitoring Using Artificial Neural Networks." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080226.122746.

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The use of composite structures in engineering applications has proliferated over the past few decades due to its distinct advantages namely: high structural performance, corrosion resistance, and high strength/weight ratio. However, they also come with a set of disadvantages, i.e. they are prone to fibre breakage, matrix cracking and delaminations. These types of damage are often invisible and if undetected, could lead to catastrophic failures of structures. Although there are systems to detect such damage, the criticality assessment and prognosis of the damage is often much more difficult to achieve. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2D polymeric composite T-joint, used in maritime structures. The SHM system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all t he delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. This SHM system necessitated the development of a novel pre-processing algorithm - Damage Relativity Assessment Technique (DRAT) along with a pattern recognition tool, Artificial Neural Network (ANN), to predict and estimate the damage. Another program developed - the Global Neural network Algorithm for Sequential Processing of Internal sub Networks (GNAISPIN) uses multiple ANNs to render the SHM system independent to variations in structural loading and capable of estimating multiple delaminations in composite T-joint structures. Upto 82% improvement in detection accuracy was observed when GNAISPIN was invoked. The Finite Element Analysis (FEA) was also conducted by placing delaminations of different sizes at various locations in two structures, a composite beam and a T-joint. Glass Fibre Reinforced Polymer T-joints were then manufactured and tested, thereby verifying the accuracy of the FEA results experimentally. The resulting strain distribution from the FEA was pre-processed by the DRAT and used to trai n the ANN to predict and estimate damage in the structures. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this study.

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Srinivasan, Venkatesh. "On designing an AI based generic scheduling framework." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1061472619.

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Fyrvald, Johanna. "Mitigating algorithmic bias in Artificial Intelligence systems." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388627.

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Artificial Intelligence (AI) systems are increasingly used in society to make decisions that can have direct implications on human lives; credit risk assessments, employment decisions and criminal suspects predictions. As public attention has been drawn towards examples of discriminating and biased AI systems, concerns have been raised about the fairness of these systems. Face recognition systems, in particular, are often trained on non-diverse data sets where certain groups often are underrepresented in the data. The focus of this thesis is to provide insights regarding different aspects that are important to consider in order to mitigate algorithmic bias as well as to investigate the practical implications of bias in AI systems. To fulfil this objective, qualitative interviews with academics and practitioners with different roles in the field of AI and a quantitative online survey is conducted. A practical scenario covering face recognition and gender bias is also applied in order to understand how people reason about this issue in a practical context. The main conclusion of the study is that despite high levels of awareness and understanding about challenges and technical solutions, the academics and practitioners showed little or no awareness of legal aspects regarding bias in AI systems. The implication of this finding is that AI can be seen as a disruptive technology, where organizations tend to develop their own mitigation tools and frameworks as well as use their own moral judgement and understanding of the area instead of turning to legal authorities.

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Bahers, Quentin. "AI Planning-Based Service Modeling for the Internet of Things." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246212.

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It is estimated that by 2020, more than 50 billion devices will be interconnected, to form what is called the Internet of Things. Those devices range from consumer electronics to utility meters, including vehicles. Provided with sensory capabilities, those objects will be able to transmit valuable information about their environment, not only to humans, but even more importantly to other machines, which should ultimately be able to interpret and take decisions based on the information received. This “smartness” implies gifting those devices with a certain degree of automation. This Master’s Thesis investigates how recent advances in artificial intelligence planning can be helpful in building such systems. In particular, an artificial intelligence planner able to generate workflows for most of IoT-related use cases has been connected to an IoT platform. A performance study of a state-of-the planner, Fast Downward, on one of the most challenging IoT application, Smart Garbage Collection (which is similar to the Traveling Salesman Problem) has also been carried out. Eventually, different pre-processing and clustering techniques are suggested to tackle the latest AI planners’ inefficiency on quickly finding plans for the most difficult tasks.

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Panesar, Kulvinder. "Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective." Vernon Press, 2020. http://hdl.handle.net/10454/18140.

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Yes This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.

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FELICETTI, ANDREA. "Artificial Intelligence approaches for spatial data processing." Doctoral thesis, Università Politecnica delle Marche, 2021. http://hdl.handle.net/11566/289699.

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I ricercatori hanno esplorato i benefici e le applicazioni degli algoritmi di intelligenza artificiale (IA) in diversi scenari. Per l'elaborazione dei dati spaziali, l'IA offre enormi opportunità. Le domande fondamentali, la ricerca si inclina a capire come l'IA può essere applicata o deve essere creata specificamente per i dati spaziali. Questo cambiamento sta avendo un impatto significativo sui dati spaziali. Il Machine Learning (ML) è stato un componente importante per l'analisi dei dati spaziali e per la loro classificazione, clustering e previsione. Inoltre, il deep learning (DL) viene integrato per estrarre automaticamente informazioni utili per la classificazione, il rilevamento di oggetti, la segmentazione semantica, ecc. L'integrazione di AI, ML e DL in geomatica ha introdotto il concetto di Geospatial Artificial Intelligence (GeoAI), che è un nuovo paradigma per la scoperta della conoscenza geospaziale e oltre. Partendo da tale premessa, questa tesi affronta il tema dello sviluppo di tecniche basate sull'IA per l'analisi e l'interpretazione di dati spaziali complessi. L'analisi ha coperto diverse lacune, per esempio la definizione delle relazioni tra gli approcci basati sull'IA e i dati spaziali. Considerando la natura multidisciplinare dei dati spaziali, gli sforzi maggiori sono stati fatti per quanto riguarda i dati dei social media, le immagini termografiche a infrarossi (IRT), le ortofoto e le nuvole di punti. Inizialmente, è stata condotta una revisione della letteratura per capire le principali tecnologie di acquisizione dei dati e se e come i metodi e le tecniche di IA potrebbero aiutare in questo campo. Un'attenzione specifica è data allo stato dell'arte dell'IA, che è stata importante per affrontare quattro diversi problemi: la gestione delle destinazioni turistiche utilizzando la sentiment analysis e le informazioni di geo-localizzazione; il rilevamento automatico delle anomalie negli impianti fotovoltaici; la segmentazione dei mosaici basata sul deep learning; il rilevamento di punti del viso per la modellazione 3D della testa in ambito medico. Le applicazioni IA proposte aprono nuove e importanti opportunità per la comunità geomatica. I nuovi dataset raccolti, così come i dati complessi presi in esame, rendono la ricerca sfidante. Infatti, è fondamentale valutare le prestazioni dei metodi allo stato dell'arte per dimostrare la loro forza e debolezza e aiutare a identificare la ricerca futura per la progettazione di algoritmi IA più robusti. Per una valutazione completa delle prestazioni, è di grande importanza sviluppare una libreria di benchmark per valutare lo stato dell'arte, perché i metodi di progettazione che sono sintonizzati su un problema specifico non funzionano correttamente su altri problemi. Un'intensa attenzione è stata dedicata all'esplorazione di modelli e algoritmi specifici. I metodi di IA adottati per lo sviluppo delle applicazioni proposte, hanno dimostrato di essere in grado di estrarre caratteristiche statistiche complesse e di apprendere in modo efficiente le loro rappresentazioni, permettendo di generalizzare bene su un'ampia varietà di compiti di IA, tra cui la classificazione delle immagini, il riconoscimento del testo e così via. Le limitazioni puntano verso aree inesplorate per indagini future, servendo come utili linee guida per le future direzioni di ricerca. Researchers have explored the benefits and applications of artificial intelligence (AI) algorithms in different scenario. For the processing of spatial data, AI offers overwhelming opportunities. Fundamental questions include how AI can be specifically applied to or must be specifically created for spatial data. This change is also having a significant impact on spatial data. Machine learning (ML) has been an important component for spatial analysis for classification, clustering, and prediction. In addition, deep learning (DL) is being integrated to automatically extract useful information for classification, object detection, semantic and instance segmentation, etc. The integration of AI, ML, and DL in geomatics has lead the concept of Geospatial Artificial Intelligence (GeoAI), which is a new paradigm for geo-information knowledge discovery and beyond. Starting from such a premise, this thesis addresses the topic of developing AI-based techniques for analysing and interpreting complex spatial data. The analysis has covered several gaps, for instance defining relationships between AI-based approaches and spatial data. Considering the multidisciplinary nature of spatial data, major efforts have been undertaken in regard to social media data, infrared thermographic (IRT) images, orthophotos, and point clouds. Initially, a literature review was conducted to understand the main data acquisition technologies and if and how AI methods and techniques could help in this field. More in deep, specific attention is given to the state of the art in AI with the selected data type mentioned above, which is important to deal with four different problem: tourism destination management using sentiment analysis and geo-location information; automatic faults detection on photovoltaic farms; mosaic segmentation based on deep cascading learning; face landmarks detection for head 3D modelling for medical applications. The proposed AI applications open up a wealth of novel and important opportunities for both geomatics and computer science community. The newly collected datasets, as well as the complexity of data taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identifying future research for designing more robust AI algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmarks to gauge the state of the art, because the design methods tuned to a specific problem do not work properly on other problems. Intensive attention has been drawn to the exploration of tailored learning models and algorithms. The tailored AI methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of AI tasks, including image classification, text recognition and so on. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

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SHARMA, GAURAV KUMAR. "AI & BIG DATA IN FINANCIAL SERVICES." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18335.

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Over the past few years, technology has altered the operating psychology of companies. Artificial Intelligence (AI) is becoming increasingly important and in demand, and the financial sector is steadily turning its focus to AI. In a variety of ways, financial institutions are experimenting with and integrating technology. Artificial Intelligence is improving and becoming smarter every day. Investment management companies have embraced Artificial Intelligence at a much faster rate than other industries. This is due to the fact that the financial sector still relies heavily on human involvement in its operations. The Indian banking sector is experimenting with artificial intelligence to improve customer service. Purpose As Artificial Intelligence technology reshapes the investment management industry, the aim of this research paper is to define specific use cases so that investment practitioners and firms can take appropriate steps now to navigate the evolving environment and plan for investment success. Design/methodology/approach The basis for this research paper is exploratory research. The current analysis is focused on both primary and secondary data. Primary data is gathered from top commercial banks and investment firms' professionals, i.e. ICICI Prudential, BOI AXA, SBI, and Bajaj Finance were asked questions about using Artificial Intelligence to enhance customer service, challenges encountered, and input from customers. Fifty professionals were polled to find out how much they knew about Artificial Intelligence enabled services, how much they used them, and how satisfied they were with them. Secondary data was gathered from a variety of sources, including academic papers, blogs, and documents, in order to ensure a thorough understanding of the topic and the accuracy of the data. Findings Big data and Artificial Intelligence have the ability to bring the most dramatic improvement to the investment management industry that current practitioners will ever see. Future successful vi investment companies will begin strategically planning their incorporation of big data and Artificial Intelligence techniques into their investment processes right now. Collaborative organisational cultures, cognitive diversity, and T-shaped teams will allow successful investment professionals to recognise and leverage the opportunities brought on by these emerging technologies and applications. Research limitations This study is restricted to AI in investment management firms only.

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Arnold, Nathan S. "Reexamining Deus ex Machina: Artificial Intelligence, Theater, & a New Work." Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1569001838298508.

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Güler, Dilvan, and Mohamed Mahdi. "A Systematic Mapping Study on APIs Utilizing Artificial Intelligence Technology." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300093.

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This thesis covers the systematic mapping of established public Application Programming Interface (API)s that are employing the Artificial Intelligence (AI) technology. This due to the fact that the problem has been the lack of systematic maps of AI APIs in the present time, therefore this thesis has the purpose of increasing the insight in the area by creating the mapping study. The goal is to provide both a basis for research and to aid the general developer which uses the AI APIs. The systematic mapping of the AI APIs will be conducted by examining the information of the APIs and iterations classifying the AI APIs into categories and presented in tables. The analysis and discussion of the study was made based on the results from the study, namely the phases, the iterations, the result tables and the final systematic map. Additionally, an analysis was made on the validity threats of the study. The evaluation of each API in this study was done in cycles, by categorizing each AI API into a category that is included in the final result, which is a systematic map. The result has been proven to be useful for the target group of this study, the researchers and developers, by aiding them in finding the right API for them to use in their work. Therefore, this work will help future developers and researchers due to the fact that the thesis is providing relevant information in the development phases of employing AI in the web interfaces at the present time. Detta examensarbete omfattar en systematisk kartläggning av etablerade publika API:er som använder sig utav AI- teknik. Eftersom bristen på systematiska kartor över AI API:er varit stor, har detta examensarbete syfte att öka insikten i området genom att skapa denna kartläggningsstudie. Målet med den systematiska kartläggningen är att bygga en grund för framtida forskning inom detta område, samt förenkla processen av att hitta AI API:erna för den allmänna utvecklaren som använder dessa. Den systematiska kartläggningen av AI API:er kommer att genomföras genom att undersöka tillgänglig dokumentation och information om API:erna. Dessutom klassificerades AI API:erna i kategorier, och presenterades sedan i tabeller. Analysen och diskussionen av studien gjordes baserat på resultaten från studien, nämligen faserna och iterationerna där AI API:erna kategoriseras, samt på resultat tabellerna och den slutliga systematiska kartan på AI API:erna. Dessutom gjordes en analys av hoten mot studiens giltighet. Utvärderingen av varje API i denna studie gjordes i cykler genom att kategorisera varje AI API i en kategori som sedan inkluderades i resultatet vilket är en systematiska karta. Resultatet från detta examensarbete har visat sig vara användbar för målgruppen, forskare och utvecklare, då det hjälper målgruppen att hitta rätt API att använda i arbetet.

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Phillips, Eve M. (Eve Marie) 1977. "If it works, it's not AI : a commercial look at artificial intelligence startups." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80558.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999. Includes bibliographical references (leaves 76-80). by Eve M. Phillips. S.B.and M.Eng.

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Iqbal, Tahir, and Md Nazmul Khan. "The Impact of Artificial Intelligence (AI) on CRM and Role of Marketing Managers." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-35225.

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Background- Emergence of artificial intelligence (AI) has transformed the dynamics of the business world. AI tools are changing the way marketers used to do business and these changes are so profound that it has become difficult for marketing managers to overlook the importance of investing in and adopting AI as an integral part of the marketing function. Companies which have implemented AI in their Customer Relationship Management (CRM) and marketing as a whole,have reported significant impacts on company’s growth in terms of customer loyalty and profitability. In recent years, we also witness emergence of specialized software and IT companies which are dedicated to produce customized marketing solutions and programs targeted at customer relationship management and marketing. They produce enterprise applications for marketing automation, analytics, and application development. Consequently, the role of marketing managers has also transformed and is expected to change even more in the future. This calls for further research, as the area appears to have received little attention in relation to its weight of importance. Aim- The main purpose of our thesis is to contribute to the literature of what changes have been introduced in the function of CRM as a result of AI integration and how these ongoing changes have affected the role of marketing managers. Results- The findings of the research show a connection that exists between the 3 factors, Artificial intelligence, CRM and role of marketing managers/decision makers. The ongoing changes that are occurring in marketing as a result of AI are not only limited to transforming the marketing function of business, it is rather consequently changing the way marketing manager make decisions and the way they interpret data. It is becoming increasingly essential for marketing managers to upgrade their skillsets and acquire sound technical knowledge in addition to deep understanding of marketing concepts. Contribution- The evolution of AI is impacting all areas of business and to harvest maximum benefits, adoption and change in all aspects is equally important. This study offers guideline to marketing managers for successful application of AI and its impact on the overall performance.

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Lindstam, Tim, and Anton Svensson. "Behavior Based Artificial Intelligence in a Village Environment." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20522.

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Abstract. Autonomous agents, also known as AI agents, are staples in modern video games. They take a lot of roles, everything from being quest-givers in roleplaying games, to opposing forces in action- and shooter games. Crafting an AI that is not only easy to create, but also retains humanlike and believable behavior, has always represented a challenge to the development industry, and has in several cases ended up with open world games using AI systems that limit the AI agents to simple moving patterns. In this thesis, a form of AI systems more commonly used in simulation games such as The Sims video game series, are taken and implemented in an environment that could possibly be seen in an open world game. After the implementation, a set of tests were performed on a group of testers which resulted in the insight that a majority of the testers, when asked to compare their experience to other games, found this implementation to feel more lifelike and realistic.

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Guerra, Ana. "Millennial Consumption Values in Artificial Intelligence : An exploratory study of millennial consumer values in artificial intelligence." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39611.

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Artificial intelligence is rapidly progressing and could be the next technological revolution we see. The idea of AI is no longer farfetched and is becoming more present; individuals are showing a very diverse set of opinions regarding AI. We are currently being the first generation of people to be introduced to AI assets. As this striving new topic is developing the research existing today regarding AI is predominantly based on a technical perspective, and a gap concerning consumer values and AI, applied on millennial’s consumer values is present. The purpose of this study is to explore Millennial consumption values regarding AI with the use of The Theory of Consumption Values as a base theory. When concluded the study will add value to the field and will benefit from future research. The purpose of this study is conducted from a consumer perspective. The study is of qualitative method and the primary, empirical data is gathered through 19 semi structured interviews with millennial. An abductive approach is taken. The finding s of this study show results of the exploration of millennial consumptions values on AI. Furthermore, the study also showed the most important consumption value regarding AI and the one most talked about. Lastly, additional values were found as well as extensions to existing consumption values.

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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3" could, within the simulated environment, consistently and convincingly outperform the control policy currently in use at Ericsson’s Kista data center.

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Mehta, Manish. "Construction and adaptation of AI behaviors in computer games." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42724.

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Computer games are an increasingly popular application for Artiﬁcial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC's in computer games is that construction of high quality AI behaviors is a hard problem. There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simpliﬁed with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience. The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simpliﬁed so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach. To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation for- malism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player ex- perience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world. The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaﬀolding to help novice users modify their authored AI behaviors and achieve higher quality modiﬁed AI behaviors compared to their original unmodiﬁed behaviors.

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Jönsson, Josef. "AI acceptance and attitudes : People’s perception of healthcare and commercial AI applications." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176507.

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The relevance of AI is ever increasing. The goal of the wide implementation is usually either to boost task efficiency or for public comfort. To fuel this progression, more personal data is being used and Artificial intelligence inhabits the role of the human expert, in many different applications. This study investigated the attitudes and rates of acceptance to said AI applications and if they differed in relation to each other. Additionally, this study explored if general positive and negative attitude towards AI influence AI acceptance. Applications studied came from two different domains, E-commerce/Marketing and Healthcare. It was found that acceptance levels did in fact not significantly differ between the two domains. However, a significant positive correlation was found between positive attitude and acceptance rates, while an inverse significant correlation was found between negative attitude and acceptance rates.

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Jonson, Maja, and Prajwal Modani. "Diagnosing Doctors and AI : What the introduction of AI can tell us about the professional role of physicians." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448747.

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Artificial Intelligence (AI) is being implemented in various industries and shows promise within healthcare. AI has potential to perform tasks that a professional would traditionally do, and it thus looks to be a disruptive technology. Professional workers, like physicians, are likely to resist such implementations to protect the high status of their profession. As professional workers and healthcare hold significant roles in society, we found that research on how they are affected by AI is needed. This study takes place in the beginning of the introduction in Swedish hospitals. To gain insight into how physicians accept and relate AI to their professional work, we ask the question ‘Do physicians see AI as a threat to their professional role?’ With an abductive, qualitative case-study approach we have conducted eight semi- structured interviews with physicians with various experience and knowledge of working with AI. Theory of institutional work for maintenance allowed for more grounding in answering our question. Further, realisations from previous studies on change served to explain why our findings did not turn out as expected. We highlight a perspective of viewing technological change as a link in a chain of events, rather than studying one implementation at a time.

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Fridensköld, Jonatan. "Biased AI : The hidden problem that needs an answer." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17943.

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Franco, Giulio. "Effects of AI on driving experience." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8457.

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Realism is a very sought feature in interactive driving simulators for traffic studies, since a nonrealistic simulation could produce non-realistic human drivers behaviours. Since cars driven by artificial intelligence (AI) are one of the main components of a traffic simulation, they play an important role in making up the overall sense of realism. A good understanding of how the AI influences human drivers is thus important in avoiding biases in traffic studies with simulators, and might also come useful in simulators for traffic education, in order to induce certain behaviours in the students. The purpose of this study was to build a driving simulation with multiple AI-driven cars, and let human testers use it, in order to analyse if and how a more polite lane-change behaviour, a more realistic lateral alignment, and a slower average speed affect the perception and the behaviour of human drivers. The simulator was developed upon low-cost hardware infrastructure previously used for other traffic studies. Since the existing software is very specific and hard to modify, a new simulation software was built from scratch for this study, using the Unity3D engine and implementing design patterns developed in previous studies, in order to produce a more flexible and modifiable infrastructure than what had been done in the past studies. The test subjects gave a generally good feedback on the simulator as a whole, and cars which politely changed lanes were regarded as behaving in a slightly more realistic way. Some insights were also obtained about user perception, mainly consisting in a difficulty in perceiving absolute speeds, whereas relative speeds were estimated more accurately.

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Kardehi, Moghaddam Athena. "Automatic detection and classification of events on power wheelchairs using embedded sensors." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119653.

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Using power wheelchairs (PW) is a difficult task which needs special motor control trainings for their users. The objective of this thesis is to develop computational tools to automatically identify user driving behaviors in order to design user-specific training methods. There are many research projects on human activity recognition using wearable sensors such as accelerometers; however, PW event recognition is very rare. Moreover, for many PW applications the decision must be made with very low time complexity since accident consequences can be serious. In this thesis, we propose a machine learning framework for PW activity recognition. The framework contains three main steps: datalogging, feature extraction and event classification. In the first step, PWs are outfitted with a datalogging platform that records movement data such as acceleration. In the next step, 4 different types of features have been extracted from the preprocessed movement data and in the last step, a classifier has been trained to classify 35 different types of wheelchair activities. The classification accuracy from four different types of features has been compared: time-delay embeddings, time-domain characterization, frequency-domain features, and wavelet transforms. In a first analysis, the classification accuracy obtained when distinguishing between safe and unsafe events is compared. In a second analysis, classifcation accuracy when distinguishing between 35 different types of events is analyzed. We show that using time-delay embedding features a large proportion of activities can be detected. Specially this method, shows great performance in the detection of unsafe events. L'utilisation de fauteuils roulants motorisés (FRM) est une tâche difficile qui requiert un apprentissage moteur particulier. L'objectif de cette thèse vise à développer des outils informatiques permettant d'identifier automatiquement le profil comportemental d'un usager de manière à fournir des méthodes d'apprentissage sur-mesure. Plusieurs projets de recherche se sont penchés sur la reconnaissance d'activité humaine utilisant des capteurs portables tels que des accéléromètres; toutefois, la reconnaissance d'événements survenant sur les FRM est rare. De plus, dans la plupart des applications pour FRM les décisions de contrôle doivent être prises rapidement, sans débordement des ressource de calcul, puisque les conséquences d'un incidents peuvent être graves. Dans cette thèse, nous proposons une approche d'apprentissage machine pour la reconnaissance d'activité des FRM. Elle consiste en trois étapes principales: capture des données, extraction de caractéristiques, et classification. À la première étape, une plate-forme de capture de donnée est installée sur le FRM. Dans un second temps, quatre types de caractéristiques sont extraites à partir de données pré-traitées. Finalement, un classifieur est entraîné à distinguer 35 types d'activités pouvant survenir sur le fauteuil roulant. L'exactitude des quatre types de caractéristiques a été comparée: plongements retardés dans le temps, caractérisation dans le domaine temporel, caractérisation dans le domaine fréquentiel, et décomposition en ondelettes. Dans une première analyse, l'exactitude du classifieur à distinguer les événements sécuritaires et non-sécuritaires est comparée. Ensuite, nous nous attardons à analyser la capacité à classifier les 35 types d'événements. Nous démontrons que les plongements retardés dans le temps permettent de détecter une proportion importante des activités. De plus, cette méthode s'avère très efficace à détecter les événements non-sécuritaires.

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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.

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Sternhell, Robert. "Can we Build a Superintelligence Without Being Superintelligent?" Thesis, Department of Philosophy, 2017. http://hdl.handle.net/2123/18836.

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If we create an entity of greater intelligence to us, a superintelligence, it has the possibility to explode in intelligence, creating more and more intelligent entities. If the intelligence explosion argument holds, then the most important step to developing a powerful superintelligence is the development of the first superintelligence. This paper presents objections to the possibility of humans developing this first superintelligence. I argue that this is because we lack required knowledge about them, due to our epistemic position of not being superintelligent. I engage primarily with arguments from David Chalmers and Nick Bostrom about what superintelligences are and the nature of the intelligence explosion. I add my own detail to these areas and explore how to increase intelligence. I argue that my objections stem from flawed expectations of superintelligence such that we ought to change them. I then present my own alternative expectations for superintelligence.

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PANIGUTTI, Cecilia. "eXplainable AI for trustworthy healthcare applications." Doctoral thesis, Scuola Normale Superiore, 2022. https://hdl.handle.net/11384/125202.

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Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet three key requirements identified in the initial analysis: transparency, auditability, and human oversight. The technical transparency requirement is tackled by enabling explanatory techniques to deal with common healthcare data characteristics and tailor them to the medical field. In this regard, this thesis presents two novel XAI techniques that incrementally reach this goal by first focusing on multi-label predictive algorithms and then tackling sequential data and incorporating domainspecific knowledge in the explanation process. This thesis then analyzes the ability to leverage the developed XAI technique to audit a fictional commercial black-box clinical decision support system (DSS). Finally, the thesis studies AI explanation’s ability to effectively enable human oversight by studying the impact of explanations on the decision-making process of healthcare professionals.

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Håkansson, Marcus, and Frans Biström. "Using Genetic Programming to evolve an AI for StarCraft." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5121.

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This paper is about the possibility to use evolution to make a StarCraft AI better in some areas by using genetic programming. We aimed to use genetic programming to evolve the numbers of squad units, bunkers and turrets, which are an important part of a successful StarCraft AI. We have built a separate application for handling the evolution. This application runs in parallel with StarCraft and modifies files based on the data recieved from a played game. This is good for safety, since if StarCraft crashes the evolution is just stalled not lost. Our tests ran over the course of a few weeks. A combination of a relatively small amount of time, for something very time-consuming, and a lack of experience with genetic programming resulted in a small amount of results. The conclusion is that it is possible to improve an StarCraft AI with genetic programming, however it takes a lot of time. Denna uppsats handlar om möjligheten att använda evolution att göra en StarCraft AI bättre i vissa områden med hjälp av genetisk programmering. Vi siktade på att använda genetisk programmering att utveckla antalet trupp enheter, bunkrar och torn, som är en viktig del av en framgångsrik StarCraft AI.

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Satish, Likith Poovanna Kelapanda, and Vinay Sudha Ethiraj. "Human-like Super Mario Play using Artificial Potential Fields." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3146.

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Artifi cial potential fi elds is a technique that use attractive and repelling forces to control e.g. robots, or non player characters in games. We show how this technique may be used in a controller for Super Mario in a way create a human-like playing style. By combining fi elds of progression, opponent avoidance and rewards, we get a controller that tries to collect the rewards and avoid the opponents at the same time as it is progressing towards the goal of the level. We use human test persons to improve the controller further by letting them make pair-wise comparisons with human play recordings, and use the feed-back to calibrate the bot for human-like play. Student 1: Likith Poovanna Kelapanda Staish Mob: +46735542609 Student 2: Vinay Sudha Ethiraj Mob: +46736135683

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Uga, Brenda. "Towards Trustworthy AI : A proposed set of design guidelines for understandable, trustworthy and actionable AI." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385392.

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Artificial intelligence is used today in both everyday applications and specialised expert systems. In situations where relying on the output of the AI brings about the risk of negative consequences, it becomes important to understand why the AI system has produced its output. Previous research in human-computer trust has identified trust antecedents that contribute to formation of trust in an AI artifact, understanding of the system being one of them. In the context of Pipedrive, a sales management system, this thesis investigates how can AI predictions be designed as understandable and trustworthy, and by extension which explanatory aspects provide guidance towards actions to take, and which presentation formats support for- mation of trust. Using a research-through design approach, multiple designs for displaying AI predictions are explored for Pipedrive, leading to a proposal for a set of design guidelines that support understandability, trustworthiness and actionability of AI. Both the designs and the guidelines have been iteratively developed in collaboration with users and design practitioners. Artificiell intelligens används idag både i vardagliga applikationer och expertsystem. I situationer då förtroendet för utdata från AI innebär en risk för negativa konsekvenser blir det viktigt att förstå varför AI-systemet har producerat dess utdata. Tidigare forskning inom människa-datorförtroende har identifierat förtroendeföregångare som bidrar till att skapa förtroende för en AI-artefakt, varav förståelse för systemet är en av dem. Inom ramen för Pipedrive, ett säljhanteringssystem, utreder denna avhandling hur AI-förut-sägelser kan designas på ett förståeligt och pålitligt sätt, och i förlängningen vilka förklarande aspekter som kan ge vägledning gällande de åtgärder som ska vidtas, samt vilka presentationsformat som stödjer skapande av förtroende. Med hjälp av en metod för forskning genom design undersöks flera utföranden för att visa AI-förutsägelser för Pipedrive, vilket leder till ett förslag till en uppsättning riktlinjer för design som stödjer förståelse, pålitlighet och funktionsduglighet. Både design och riktlinjer har utvecklats iterativt i samarbete med användare och designutövare.

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Yıldız, Melih Burak. "EU’s Proposed AI Regulation in the context of Fundamental Rights : Analysing the Swedish approach through the lens of the principles of good administration." Thesis, Södertörns högskola, Juridik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-46517.

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AI has become one of the most powerful drivers of social change, transforming economies, impacting politics and wars, and reshaping how citizens live and interact. Nevertheless, the implementation of AI can have adverse effects on peoples’ lives. This dissertation first examines the relationship between artificial intelligence and public law, mainly in two domains, administrative law and criminal law. It also provides a clear insight into the potential impact of AI applications on fundamental rights in the legal context of the European Union. Four selected fundamental rights, Human Dignity, Data Protection and Right to Privacy, Equality and Non-discrimination, and Access to Justice, are examined. The dissertation further explores the European Commission's new proposed AI regulation, which was proposed in April 2021. The proposal aims to put forward a risk- based approach for a harmonized EU legislation by considering the ethical and human sides and without unnecessarily restricting the development of AI technologies. The study focuses on examples from Sweden throughout the study and lastly, examines the Swedish approach in the context of the principles of good administration.

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Podder, Tanmay. "ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4757.

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Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

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Adolfsson, Lovisa. "How will Artificial Intelligence impact the labour market, which jobs will be replaced and what will it mean for society, within the next decade?" Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412832.

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This study examines the impact of Artificial Intelligence on the Labour Market within the next decade. Methods and limitations in the technology and their correlation to work, as well as the possible developments likely to be seen in the coming decade, is presented. It also looks at whether Artificial General Intelligence (a system that meet human performance in all fields) could be invented in the next ten years. So far, methods like machine-, deep- and reinforcement learning has resulted in systems that sometimes exceed human performance but are narrow in skill and proficiency. Meaning that AGI is very unlikely to be achieved before 2030. AI is estimated to replace work in the production-, service-, care- and welfare-, transport-, and warehouse sector. The conclusion, however, is that transformation will happen in a pace such that society will be able manage it without the changes causing mass-unemployment.

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Dissertations / Theses on the topic 'Embedded artificial intelligence (AI)'

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