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Orozco, Gabriel Mario. "Artificial intelligence opportunities and an end-do-end data-driven solution for predicting hardware failures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104304.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. In conjunction with the Leaders for Global Operations Program at MIT.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 93-96).<br>Dell's target to provide quality products based on reliability, security, and manageability, has driven Dell Inc. to become one of the largest PC suppliers. The recent developments in Artificial Intelligence (AI) combined with a competitive market situation have encouraged Dell to research new opportunities. Al research and breakthroughs have risen in the last years, bringing along revolutionary technologies and companies that are disrupting all businesses. Over 30 potential concepts for Al integration at Dell Inc. were identified and evaluated to select the ones with the highest potential. The top-most concept consisted of preventing in real time the failure of hardware. This concept was investigated using a data science process. Currently, there exist a number of machine learning tools that automate the last stages of the proposed data science process to create predictive models. The utilized tools vary in functionality and evaluation standards, but also provide other services such as data and model storage and visualization options. The proposed solution utilizes the deep feature synthesis algorithm that automatically generates features from problem-specific data. These engineered features boosted predictive model accuracy by an average of 10% for the AUC and up to 250% in recall for test (out of sample) data. The proposed solution estimates an impact exceeding $407M in the first five years for Dell Inc. and all of the involved suppliers. Conservatively, the direct impact on Dell Inc. is particular to batteries under warranty and is expected to surpass $2.7M during the first five years. The conclusions show a high potential for implementation.<br>by Mario Orozco Gabriel.<br>M.B.A.<br>S.M. in Engineering Systems
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Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.
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GRIMALDI, MATTEO. "Hardware-Aware Compression Techniques for Embedded Deep Neural Networks." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2933756.
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Bedi, Abhishek. "A generic platform for the evolution of hardware." Click here to access this resource online, 2009. http://hdl.handle.net/10292/651.
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Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design. The term evolutionary computation involves similar steps as involved in the human evolution. It has been given names in accordance with the electronic technology like, Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP). In evolutionary computing, a configured bit is considered as a human chromosome for a genetic algorithm, which has to be downloaded into hardware. Early evolvable hardware experiments were conducted in simulation and the only elite chromosome was downloaded to the hardware, which was labelled as Extrinsic Hardware. With the invent of Field Programmable Gate Arrays (FPGAs) and Reconfigurable Processing Units (RPUs), it is now possible for the implementation solutions to be fast enough to evaluate a real hardware circuit within an evolutionary computation framework; this is called an Intrinsic Evolvable Hardware. This research has been taken in continuation with project 'Evolvable Hardware' done at Manukau Institute of Technology (MIT). The project was able to manually evolve two simple electronic circuits of NAND and NOR gates in simulation. In relation to the project done at MIT this research focuses on the following: To automate the simulation by using In Circuit Debugging Emulators (IDEs), and to develop a strategy of configuring hardware like an FPGA without the use of their company supplied in circuit debugging emulators, so that the evolution of an intrinsic evolvable hardware could be controlled, and is hardware independent. As mentioned, the research conducted here was able to develop an evolvable hardware friendly Generic Structure which could be used for the development of evolvable hardware. The structure developed was hardware independent and was able to run on various FPGA hardware’s for the purpose of intrinsic evolution. The structure developed used few configuration bits as compared to current evolvable hardware designs.
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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
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Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.
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Kumar, Sharad Kumar. "Analysis of Machine Learning Modeling Attacks on Ring Oscillator based Hardware Security." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1541759752027838.
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CONTI, DANIELE. "Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711511.
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Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling
of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical
behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from quantum computing to neuromorphic computing.
The present dissertation wants to be a preliminary study for understanding the opportunities enabled by neuromorphic computing based on resistive switching memories. In particular, brain inspires technology and architecture of new generation processors because of its unique properties: parallel and distributed computation, superposition of processing and memory unit, low power consumption, to cite only some of them. Such features make brain particularly efficient and robust against degraded data, further than particularly suitable to process and store in memory new nformation. Despite many research projects and some commercial products are already proposing brain-like computing processors, like spiNNaker or IBM’s Bluenorth, they only mimic the brain functioning with standard Silicon technology, that is inherently serial
and distinguish between processing and memory unit. Resistive switching technology on the other hand, would allow to overcome many of these issues, enabling a far better match between biological and artificial neuromorphic computation.
Resistive switching are, generally speaking, Metal-Insulator-Metal structures able to change their electrical conductance as a consequence of the history of applied electric signal. In such sense, they behave exactly as synapses do in a biological
neural networks. For this reason, resistive switching when modeled as memristor, i.e. memory-resistor, can act as artificial synapses and, moreover, are particularly suitable to be interfaced with artificial Silicon neurons that are designed to replicate the biological behavior when excited with electric pulses. Anyhow, from the technological standpoint, there is still no standard on the design and fabrication of resistive switching, so that multiple structure and materials are investigated.
In this dissertation, it is reported an analysis of multiple resistive switching devices, based on various materials, i.e. TiO2, ZnO and HfO, and device architectures, i.e. thin film and nanostructured devices, with the scope of both characterizing and
comprehending the physics behind resistive switching phenomena. Furthermore, numerical simulations of artificial spiking neural networks, embedding Silicon neurons and HfO-based resistive switching are designed and performed, in order to give a systematic analysis of the performances reached by this new kind of computing paradigm.
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Imbulgoda, Liyangahawatte Gihan Janith Mendis. "Hardware Implementation and Applications of Deep Belief Networks." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1476707730643462.
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Brink, Stephen Isaac. "Learning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50143.
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The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.
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Engin, Melih. "Text Classificaton In Turkish Marketing Domain And Context-sensitive Ad Distribution." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610457/index.pdf.
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Online advertising has a continuously increasing popularity. Target audience of this new advertising method is huge. Additionally, there is another rapidly growing and crowded group related to internet advertising that consists of web publishers. Contextual advertising systems make it easier for publishers to present online ads on their web sites, since these online marketing systems automatically divert ads to web sites with related contents. Web publishers join ad networks and gain revenue by enabling ads to be displayed on their sites. Therefore, the accuracy of automated ad systems in determining ad-context relevance is crucial.
In this thesis we construct a method for semantic classification of web site contexts in Turkish language and develop an ad serving system to display context related ads on web documents. The classification method uses both semantic and statistical techniques. The method is supervised, and therefore, needs processed sample data for learning classification rules. Therefore, we generate a Turkish marketing dataset and use it in our classification approaches. We form successful classification methods using different feature spaces and support vector machine configurations. Our results present a good comparison between these methods.
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Abderrahmane, Nassim. "Impact du codage impulsionnel sur l’efficacité énergétique des architectures neuromorphiques." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4082.
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Dans le contexte actuel, l’Intelligence Artificielle (IA) est largement répandue et s’applique à de nombreux domaines tels que les transports, la médecine et les véhicules autonomes. Parmi les algorithmes d'IA, on retrouve principalement les réseaux de neurones, qui peuvent être répartis en deux familles : d'une part, les Réseaux de Neurones Impulsionnels (SNNs) qui sont issus du domaine des neurosciences ; d'autre part, les Réseaux de Neurones Analogiques (ANNs) qui sont issus du domaine de l'apprentissage machine. Les ANNs connaissent un succès inédit grâce à des résultats inégalés dans de nombreux secteurs tels que la classification d'images et la reconnaissance d'objets. Cependant, leur déploiement nécessite des capacités de calcul considérables et ne conviennent pas à des systèmes très contraints. Afin de pallier ces limites, de nombreux chercheurs s'intéressent à un calcul bio-inspiré, qui serait la parfaite alternative aux calculateurs conventionnels basés sur l'architecture de Von Neumann. Ce paradigme répond aux exigences de performance de calcul, mais pas aux exigences d'efficacité énergétique. Il faut donc concevoir des circuits matériels neuromorphiques adaptés aux calculs parallèles et distribués. Dans ce contexte, nous avons établi un certain nombre de critères en termes de précision et de coût matériel pour différencier les SNNs et ANNs. Dans le cas de topologies simples, nous avons montré que les SNNs sont plus efficaces en termes de coût matériel que les ANNs, et ce, avec des précisions de prédiction quasiment similaires. Ainsi, dans ce travail, notre objectif est de concevoir une architecture neuromorphique basée sur les SNNs. Dans cette perspective, nous avons mis en place un flot de conception composé de trois niveaux, qui permet la réalisation d’une architecture neuromorphique dédiée et adaptée aux applications d’IA embarquée.Dans un contexte d'efficacité énergétique, nous avons réalisé une étude approfondie sur divers paradigmes de codage neuronal utilisés avec les SNNs. Par ailleurs, nous avons proposé de nouvelles versions dérivées du codage fréquentiel, visant à se rapprocher de l'activité produite avec le codage temporel, qui se caractérise par un nombre réduit d'impulsions (spikes) se propageant dans le SNN. En faisant cela, nous sommes en mesure de réduire le nombre de spikes, ce qui se traduit par un SNN avec moins d'événements à traiter, et ainsi, réduire la consommation énergétique sous-jacente. Pour cela, deux techniques nouvelles ont été proposées : "First Spike", qui se caractérise par l'utilisation d’un seul spike au maximum par donnée ; "Spike Select", qui permet de réguler et de minimiser l'activité globale du SNN.Dans la partie d’exploration RTL, nous avons comparé de manière quantitative un certain nombre d’architectures de SNN avec différents niveaux de parallélisme et multiplexage de calculs. En effet, le codage "Spike Select" engendre une régulation de la distribution des spikes, avec la majorité générée dans la première couche et peu d'entre eux propagés dans les couches profondes. Nous avons constaté que cette distribution bénéficie d’une architecture hybride comportant une première couche parallèle et les autres multiplexées. Par conséquent, la combinaison du "Spike Select" et de l'architecture hybride serait une solution efficace, avec un compromis efficace entre coût matériel, consommation et latence.Enfin, en se basant sur les choix architecturaux et neuronaux issus de l'exploration précédente, nous avons élaboré une architecture évènementielle dédiée aux SNNs mais suffisamment programmable pour supporter différents types et tailles de réseaux de neurones. L'architecture supporte les couches les plus utilisées : convolution, pooling et entièrement connectées. En utilisant cette architecture, nous serons bientôt en mesure de comparer les ANNs et les SNNs sur des applications réalistes et enfin conclure sur l'utilisation des SNNs pour l'IA embarquée<br>Nowadays, Artificial Intelligence (AI) is a widespread concept applied to many fields such as transportation, medicine and autonomous vehicles. The main AI algorithms are artificial neural networks, which can be divided into two families: Spiking Neural Networks (SNNs), which are bio-inspired models resulting from neuroscience, and Analog Neural Networks (ANNs), which result from machine learning. The ANNs are experiencing unprecedented success in research and industrial fields, due to their recent successes in many application contexts such as image classification and object recognition. However, they require considerable computational capacity for their deployment which is not adequate to very constrained systems such as 'embedded systems'. To overcome these limitations, many researchers are interested in brain-inspired computing, which would be the perfect alternative to conventional computers based on the Von Neumann architecture (CPU/GPU). This paradigm meets computing performance but not energy efficiency requirements. Hence, it is necessary to design neuromorphic hardware circuits adaptable to parallel and distributed computing. In this context, we have set criteria in terms of accuracy and hardware implementation cost to differentiate the two neural families (SNNs and ANNs). In the case of simple network topologies, we conducted a study that has shown that the spiking models have significant gains in terms of hardware cost when compared to the analog networks, with almost similar prediction accuracies. Therefore, the objective of this thesis is to design a generic neuromorphic architecture that is based on spiking neural networks. To this end, we have set up a three-level design flow for exploring and implementing neuromorphic architectures.In an energy efficiency context, a thorough exploration of different neural coding paradigms for neural data representation in SNNs has been carried out. Moreover, new derivative versions of rate-based coding have been proposed that aim to get closer to the activity produced by temporal coding, which is characterized by a reduced number of spikes propagating in the network. In this way, the number of spikes can be reduced so that the number of events to be processed in the SNNs gets smaller. The aim in doing this approach is to reduce the hardware architecture's energy consumption. The proposed coding approaches are: First Spike, which is characterized using at most one single spike to present an input data, and Spike Select, which allows to regulate and minimize the overall spiking activity in the SNN.In the RTL design exploration, we quantitatively compared three SNN architectural models having different levels of computing parallelism and multiplexing. Using Spike Select coding results in a distribution regulation of the spiking data, with most of them generated within the first layer and few of them propagate into the deep layers. Such distribution benefits from a so-called 'hybrid architecture' that includes a fully-parallel part for the first layer and multiplexed parts to the other layers. Therefore, combining the Spike Select and the Hybrid Architecture would be an effective solution for embedded AI applications, with an efficient hardware and latency trade-off.Finally, based on the architectural and neural choices resulting from the previous exploration, we have designed a final event-based architecture dedicated to SNNs supporting different neural network types and sizes. The architecture supports the most used layers: convolutional, pooling and fully-connected. Using this architecture, we will be able to compare analog and spiking neural networks on realistic applications and to finally conclude about the use of SNNs for Embedded Artificial Intelligence
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Janzakova, Kamila. "Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
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Les technologies neuromorphiques constituent une voie prometteuse pour le développement d'une informatique plus avancée et plus économe en énergie. Elles visent à reproduire les caractéristiques attrayantes du cerveau, telles qu'une grande efficacité de calcul et une faible consommation d'énergie au niveau des logiciels et du matériel. À l'heure actuelle, les implémentations logicielles inspirées du cerveau (telles que ANN et SNN) ont déjà démontré leur efficacité dans différents types de tâches (reconnaissance d'images et de la parole). Toutefois, pour tirer un meilleur parti des algorithmes inspirés du cerveau, il est possible de les combiner avec une implémentation materielle appropriée qui s'appuierait également sur une architecture et des processus inspirés du cerveau. L'ingénierie neuromorphique s'est principalement appuyée sur les technologies conventionnelles (CMOS circuits, memristor) pour le développement de circuits inspirés du cerveau. Néanmoins, ces implémentations sont fabriquées suivant une approche top-down. En revanche, l'informatique cérébrale repose sur des processus bottom-up tels que l'interconnectivité entre les cellules et la formation de voies de communication neuronales.À la lumière de ce qui précède, ce travail de thèse porte sur le développement de dispositifs neuromorphiques organiques programmables en 3D qui, contrairement à la plupart des technologies neuromorphiques actuelles, peuvent être créés de manière bottom-up. Cela permet de rapprocher les technologies neuromorphiques du niveau de programmation du cerveau, où les chemins neuronaux nécessaires sont établis uniquement en fonction des besoins.Tout d'abord, nous avons découvert que les interconnexions 3D à base de PEDOT:PSS peuvent être formées au moyen d'électropolymérisation bipolaire en courant alternatif, permettant d'imiter la croissance des cellules neuronales. En réglant individuellement les paramètres de la forme d'onde (tension d'amplitude de crête - VP, fréquence - f, duty cycle- dc et tension de décalage - Voff), une large gamme de structures semblables à des dendrites a été observée avec différents degrés de ramification, volumes, surfaces, asymétries et dynamiques de croissance.Ensuite, nous avons montré que les morphologies dendritiques obtenues à différentes fréquences sont conductrices. De plus, chaque structure présente une valeur de conductance qui peut être interprétée comme un poids synaptique. Plus important encore, la capacité des dendrites à fonctionner comme OECT a été révélée. Différentes morphologies de dendrites ont présenté des performances différentes en tant qu'OECT. De plus, la capacité des dendrites en PEDOT:PSS à modifier leur conductivité en réponse à la tension de grille a été utilisée pour imiter les fonctions de mémoire du cerveau (plasticité à court terme -STP et plasticité à long terme -LTP). Les réponses à la STP varient en fonction de la structure dendritique. En outre, l'émulation de la LTP a été démontrée non seulement au moyen d'un fil de grille Ag/AgCl, mais aussi au moyen d'une grille dendritique en polymère développée par électropolymérisation.Enfin, la plasticité structurelle a été démontrée par la croissance dendritique, où le poids de la connexion finale est régi par les règles d'apprentissage de type Hebbien (plasticité dépendante du moment de l'impulsion - STDP et plasticité dépendante du rythme de l'impulsion - SRDP). En utilisant les deux approches, une variété de topologies dendritiques avec des états de conductance programmables (c'est-à-dire le poids synaptique) et diverses dynamiques de croissance ont été observées. Finalement, en utilisant la même plasticité structurelle dendritique, des caractéristiques cérébrales plus complexes telles que l'apprentissage associatif et les tâches de classification ont été émulées.En outre, les perspectives futures de ces technologies basées sur des objets dendritiques polymères ont été discutées<br>Neuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
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Caldas, Júnior Carlos Roberto Dutra [UNESP]. "Implementação em hardware de um sistema inteligente para detecção de plantas daninhas em plantações de soja utilizando máquinas de vetores de suporte e redes neurais artificiais." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/98648.
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caldasjunior_crd_me_sjrp.pdf: 669834 bytes, checksum: bb1a2044c8a20046b364cf6e0b7141cb (MD5)<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>A presença de sistemas automatizados é cada vez mais comum para as pessoas. Seus exemplos vão desde máquinas de lavar, que executam praticamente todo o processo de lavagem e secagem de roupas, até linhas de produção em fábricas dos mais diversos produtos. Esses são exemplos de aplicações que exigem pouca interferência humana no processo, já que as etapas realizadas pelos sistemas são bem definidas e iterativas. Porém, outros tipos de processos podem exigir capacidade de discernimento daquele – ou daquilo – que os executam. Para automatizar esse tipo de processo uma das alternativas é o uso de técnicas de inteligência artificial. Esse trabalho visa realizar uma análise comparativa entre técnicas de inteligência artificial, quais sejam Redes Neurais Artificiais e Máquinas de Vetores de Suporte. Com essa análise espera-se estabelecer qual técnica é mais vantajosa para implementação em hardware de sistemas inteligentes, por meio do uso das principais métricas de projeto de circuitos digitais: tamanho do circuito gerado, consumo de energia e desempenho. Para tanto, foram realizados diversos testes com técnicas de pré-processamento e extração de características das imagens para determinar requisitos necessários para o funcionamento do sistema. A partir desses requisitos foram implementadas diversas arquiteturas de sistemas inteligentes para obter-se o classificador mais adequado para resolver o problema. Por fim, o classificador escolhido foi implementado em FPGA na forma de um módulo, o qual se integrará a um sistema maior, para interpretação de imagens digitais para detecção de ervas daninhas em plantações de soja<br>Automated systems have become common for people. Examples range from washing machines, which perform almost the entire cloth washing and drying process, to the production of many products. These are examples of applications that require modest human interference, since the steps taken by the systems are well defined and iterative. However, other processes may require a capacity of judgment of the natural or artificial system performing them. An alternative to automate this kind of process is the use of artificial intelligence techniques. This study aims at a comparative analysis of artificial intelligence techniques, namely Artificial Neural Networks and Support Vector Machines. With this analysis we hope to establish which technique is more advantageous for hardware implementation of an intelligent system, through the use of key metrics for digital circuit design: circuit size, power consumption and performance. Therefore, several tests were performed with image preprocessing and feature extraction techniques to determine requirements for system operation. From these requirements, various architectures for intelligent systems were implemented to obtain the most appropriate classifier to solve the problem. Finally, the chosen classifier was implemented in FPGA as a module to fit into a larger system for digital image interpretation for the detection of weeds in crops of soybeans
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Mašek, Jan. "Automatické strojové metody získávání znalostí z multimediálních dat." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-256538.
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The quality and efficient processing of increasing amount of multimedia data is nowadays becoming increasingly needed to obtain some knowledge of this data. The thesis deals with a research, implementation, optimization and the experimental verification of automatic machine learning methods for multimedia data analysis. Created approach achieves higher accuracy in comparison with common methods, when applied on selected examples. Selected results were published in journals with impact factor [1, 2]. For these reasons special parallel computing methods were created in this work. These methods use massively parallel hardware to save electric energy and computing time and for achieving better result while solving problems. Computations which usually take days can be computed in minutes using new optimized methods. The functionality of created methods was verified on selected problems: artery detection from ultrasound images with further classifying of artery disease, the buildings detection from aerial images for obtaining geographical coordinates, the detection of materials contained in meteorite from CT images, the processing of huge databases of structured data, the classification of metallurgical materials with using laser induced breakdown spectroscopy and the automatic classification of emotions from texts.
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Caldas, Júnior Carlos Roberto Dutra. "Implementação em hardware de um sistema inteligente para detecção de plantas daninhas em plantações de soja utilizando máquinas de vetores de suporte e redes neurais artificiais /." São José do Rio Preto : [s.n.], 2012. http://hdl.handle.net/11449/98648.
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Orientador: Norian Marranghello<br>Banca: Adilson Gonzaga<br>Banca: Rodrigo Capobianco Guido<br>Resumo: A presença de sistemas automatizados é cada vez mais comum para as pessoas. Seus exemplos vão desde máquinas de lavar, que executam praticamente todo o processo de lavagem e secagem de roupas, até linhas de produção em fábricas dos mais diversos produtos. Esses são exemplos de aplicações que exigem pouca interferência humana no processo, já que as etapas realizadas pelos sistemas são bem definidas e iterativas. Porém, outros tipos de processos podem exigir capacidade de discernimento daquele - ou daquilo - que os executam. Para automatizar esse tipo de processo uma das alternativas é o uso de técnicas de inteligência artificial. Esse trabalho visa realizar uma análise comparativa entre técnicas de inteligência artificial, quais sejam Redes Neurais Artificiais e Máquinas de Vetores de Suporte. Com essa análise espera-se estabelecer qual técnica é mais vantajosa para implementação em hardware de sistemas inteligentes, por meio do uso das principais métricas de projeto de circuitos digitais: tamanho do circuito gerado, consumo de energia e desempenho. Para tanto, foram realizados diversos testes com técnicas de pré-processamento e extração de características das imagens para determinar requisitos necessários para o funcionamento do sistema. A partir desses requisitos foram implementadas diversas arquiteturas de sistemas inteligentes para obter-se o classificador mais adequado para resolver o problema. Por fim, o classificador escolhido foi implementado em FPGA na forma de um módulo, o qual se integrará a um sistema maior, para interpretação de imagens digitais para detecção de ervas daninhas em plantações de soja<br>Abstract: Automated systems have become common for people. Examples range from washing machines, which perform almost the entire cloth washing and drying process, to the production of many products. These are examples of applications that require modest human interference, since the steps taken by the systems are well defined and iterative. However, other processes may require a capacity of judgment of the natural or artificial system performing them. An alternative to automate this kind of process is the use of artificial intelligence techniques. This study aims at a comparative analysis of artificial intelligence techniques, namely Artificial Neural Networks and Support Vector Machines. With this analysis we hope to establish which technique is more advantageous for hardware implementation of an intelligent system, through the use of key metrics for digital circuit design: circuit size, power consumption and performance. Therefore, several tests were performed with image preprocessing and feature extraction techniques to determine requirements for system operation. From these requirements, various architectures for intelligent systems were implemented to obtain the most appropriate classifier to solve the problem. Finally, the chosen classifier was implemented in FPGA as a module to fit into a larger system for digital image interpretation for the detection of weeds in crops of soybeans<br>Mestre
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Hoopes, Daniel Matthew. "The ContexTable: Building and Testing an Intelligent, Context-Aware Kitchen Table." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/12.
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The purpose of this thesis was to design and evaluate The ContexTable, a context-aware system built into a kitchen table. After establishing the current status of the field of context-aware systems and the hurdles and problems being faced, a functioning prototype system was designed and built. The prototype makes it possible to explore established, untested theory and novel solutions to problems faced in the field.
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Shapovalenko, A. "Artificial intelligence." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/33830.
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Technology of artificial intelligence will soon be able to help ensure the safety of your family, protecting it from the threat of international and domestic burglaries. In the USA the Department of Security also implements special software in its monitoring system, which scans phone calls and other messages. These programs can rapidly process large volumes of data and are even able to distinguish between a normal conversation from a potential threat. The old security system simply relied on sensors that reacted to a movement. Modern systems are directly related to artificial intelligence, which helps detect the presence of a stranger in the house.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/33830
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Башлак, Ірина Анатоліївна, Ирина Анатольевна Башлак, Iryna Anatoliivna Bashlak, and I. Kurinnyy. "Artificial intelligence." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13517.
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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and M. Chernyakova. "Artificial intelligence." Thesis, Вид-во СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/16890.
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Bekeniova. "ARTIFICIAL INTELLIGENCE." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33666.
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Yakushchenko, I. V., and L. M. Chuchilina. "The artificial intelligence." Thesis, Видавництво СумДУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/16003.
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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.<br>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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Chuquimia, Orlando. "Smart Vision Chip pour l’exploration du côlon." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS192.pdf.
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Le Cancer Colorectal (Cancer Colorectal (CCR)) est le deuxième le plus fréquent en terme de mortalité avec un taux de 47% (880 792 décès en 2018). 96% des CCR débutent par l’apparition d’une tumeur (un polype adénomateux) dans la paroi du côlon. La Capsule Endoscopique (CE), a été inventée par Paul Swain en 1990. Il s’agit d’une pilule intégrant une caméra et un système de radio communication, que le patient avale. Pendant cette procédure, la capsule transmet les images du tube digestif vers un boîtier externe via une communication par radiofréquence. Une fois toutes les images transmises, un médecin gastro-entérologue les télécharge pour réaliser une analyse visuelle et détecter les anomalies et les tumeurs. À l’aide de ce dispositif les médecins peuvent détecter des polypes, d’au moins 5 mm, avec une sensibilité et une spécificité respectivement de 68,8% et de 81,3%. Cette CE présente quelques limites et faiblesses liées à la résolution spatiale et temporelle des images, à son autonomie énergétique et au grand nombre d’images transmises que le gastro-entérologue doit analyser. Nous avons étudié la conception d’un système embarqué contenant une chaine de traitement capable de détecter des polypes et intégrée dans une capsule, créant un nouveau dispositif médical : une capsule endoscopique intelligente. Pour réaliser ce dispositif, nous avons pris en compte toutes les contraintes non fonctionnelles liées à l’intégration dans une capsule endoscopique. Ce dispositif pourrait ainsi être un nouvel outil de dépistage précoce des lésions pré-cancéreuses colorectales : les polypes<br>CCR is the second highest cause of death by cancer worldwide with 880,792 deaths in 2018 and a mortality rate of 47.6%. 95% of CCR cases begin with the presence of a growth on the inner lining of the colon or the rectum, called a polyp. The endoscopic capsule was invented by Paul Swain in 1990. It is a pill incorpo- rating a camera and a radio communication system that the patient swallows and transmit images from the gastrointestinal tract through the body in a workstation. Once all images are transmitted, a gastroenterologist downloads them to perform a visual analysis and detect abnormalities and tumors. Using this device doctors can detect polyps, at least 5 mm, with sensitivity and specificity respectively of 68.8% and 81.3%. This endoscopic capsule presents some limitations and weaknesses re- lated to the spatial and temporal resolution of images, its energy autonomy and the number of images transmitted to be analyzed by the gastroenterologist. We studied the design of an embedded system containing a processing chain capable of detecting polyps to be integrated into an endoscopic capsule, creating a new medical device: an intelligent endoscopic capsule. To realize this device, we took into account all the non-functional constraints related to the integration into an endoscopic capsule. This device must be a new tool for early detection of precancerous colorectal lesions : polyps
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Tennenbaum, Christopher D. "Intentionality in Artificial Intelligence." Scholarship @ Claremont, 2011. http://scholarship.claremont.edu/cmc_theses/269.
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This paper addresses the question of whether Artificial Intelligence can have intentionality. This question is part of a larger discussion of whether or not Artificial Intelligence can ever be 'conscious'. Ultimately, I come to the conclusion that while we can see how intentionality can be transferred, it has yet to be shown that intentionality can be created within Artificial Intelligence. To begin, I define intentionality. I then discuss the Turing Test (Alan Turing, "Computing Machinery and Intelligence" and the Chinese Room (John R. Searle, "Minds, Brains, and Programs"). I conclude by expressing my own opinions and where I believe Artificial Intelligence will be in the near future.
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Chaus, Oleksandr. "Artificial intelligence and robotics." Thesis, Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, 2019. https://er.knutd.edu.ua/handle/123456789/14698.
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The work deals with artificial intelligence as one of the most exciting field in robotics. AI has penetrated almost every industry, from construction, transport and manufacturing to business intelligence, education and healthcare.<br>Робота стосується штучного інтелекту як однієї з найбільш захоплюючих галузей робототехніки. Штучний інтелект проник майже в усі галузі, від будівництва, транспорту та виробництва до бізнес-розвідки, освіти та охорони здоров'я.<br>Работа посвящена искусственному интеллекту как одной из самых захватывающих областей робототехники. Искусственный интеллект проник почти во все отрасли, от строительства, транспорта и производства до бизнес-аналитики, образования и здравоохранения.
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Matorina. "ARTIFICIAL INTELLIGENCE IN MEDICINE." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33813.
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R, Maksimov K. "ARTIFICIAL INTELLIGENCE: MORAL ASPECT." Thesis, Національний авіаційний університет, 2017. http://er.nau.edu.ua/handle/NAU/28076.
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Larchenko. "ARTIFICIAL INTELLIGENCE IN ROBOTICS." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33789.
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Vaseigaran, Ajanth, and Gobi Sripathy. "Artificial Intelligence in Healthcare." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296643.
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Healthcare systems play a critical role in ensuring people's health. Establishing accurate diagnoses is a vital element of this process. As sources highlight misdiagnoses and missed diagnoses as a common issue, a solution must be sought. Diagnostic errors are common in the emergency departments, which has been recognized as a stressful work environment. Today's industries are forced to deal with rapidly changing technological advances that result in reshaped systems, products, and services. Artificial Intelligence (AI) is one of such technologies that can work as a solution to diagnosis issues but comes with technical, ethical and legal challenges. Hence, the thesis intends to investigate how AI can affect the accuracy of diagnosis as well as how its integration in healthcare relates to the technical, ethical and legal aspects. The thesis begins with a literature review, which serves as a theoretical foundation and allows for a conceptual framework to be formed. The conceptual framework is used to select interviewees, which results in 12 interviews with professors, researchers, doctors and politicians. In addition, a survey is conducted to obtain the general public’s opinion on the matter. The findings present that AI is already mature enough to make more accurate diagnoses than doctors as well as release burden from medical practitioners in the form of administrative tasks. One obstacle is the incomplete data available since laws hinder sharing of patient data. Furthermore, the AI algorithms must be fit for all social minorities and not demonstrate racial discrimination. The European AI Alliance was established in 2018 with the aim to keep the technology in check. Similar initiatives can be created on a national- and regional level to maintain some form of control over its proper use.<br>Sjukvårdssystem utgör en avgörande roll för att säkerställa människors välmående och hälsa. Att fastställa korrekta diagnoser är en viktig del av denna process. Enligt källor är feldiagnoser och uteblivna diagnoser ett vanligt problem och bör därför lösas. Diagnostiska fel är vanligt förekommande på akutmottagningar, vilka karaktäriseras som en stressig arbetsmiljö. Dagens industrier tvingas hantera snabbt föränderliga tekniska framsteg som resulterar i omformade system, produkter och tjänster. Artificiell Intelligens (AI) är en av sådana tekniker som kan fungera som en lösning på diagnosfrågor. Dock kommer den med tekniska, etiska och legala utmaningar. Examensarbetet avser därför att undersöka hur AI kan påverka diagnosens precision samt hur integrationen i vården relaterar till de tekniska, etiska och legala aspekterna. Rapporten inleds med en litteraturstudie, vilket fungerar som en teoretisk grund och bidrar till att skapa ett konceptuellt ramverk. Det konceptuella ramverket används för att välja intervjupersoner, vilket resulterar i 12 intervjuer med professorer, forskare, läkare och politiker. Dessutom genomförs en enkätundersökning för att få allmänhetens åsikt i frågan. Rapportens resultat visar att AI redan är tillräckligt utvecklad för att göra en mer precisionssäker diagnos än en läkare samt kan avlasta läkare i form av administrativa uppgifter. Ett hinder är att den data som finns tillgänglig är ofullständig på grund av lagar som hindrar delning av patientdata. AI-algoritmerna måste dessutom vara lämpliga för alla sociala minoriteter och inte leda till rasdiskriminering. European AI Alliance grundades 2018 med målet att hålla tekniken i schack i förhållande till de etiska och legala aspekterna. Liknande initiativ kan skapas på nationell och regional nivå för att bibehålla någon form av kontroll över dess korrekta användning.
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Napoli, Christian. "A-I: Artificial intelligence." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3974.
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In this thesis we proposed new neural architectures and information theory approaches. By means of wavelet analysis, neural networks, and the results of our own creations, namely the wavelet recurrent neural networks and the radial basis probabilistic neural networks,we tried to better understand, model and cope with the human behavior itself. The first idea was to model the workers of a crowdsourcing project as nodes on a cloud-computing system, we also hope to have exceeded the limits of such a definition. We hope to have opened a door on new possibilities to model the behavior of socially interconnected groups of people cooperating for the execution of a common task. We showed how it is possible to use the Wavelet Recurrent Neural Networks to model a quite complex thing such as the availability of resources on an online service or a computational cloud, then we showed that, similarly, the availability of crowd workers can be modeled, as well as the execution time of tasks performed by crowd workers. Doing that we created a tool to tamper with the timeline, hence allowing us to obtain predictions regarding the status of the crowd in terms of available workers and executed workflows. Moreover, with our inanimate reasoner based on the developed Radial Basis Probabilistic Neural Networks, firstly applied to social networks, then applied to living companies, we also understood how to model and manage cooperative networks in terms of workgroups creation and optimization. We have done that by automatically interpreting worker profiles, then automatically extrapolating and interpreting the relevant information among hundreds of features for each worker in order to create workgroups based on their skills, professional attitudes, experience, etc. Finally, also thanks to the suggestions of prof. Michael Bernstein of the Stanford University, we simply proposed to connect the developed automata. We made use of artificial intelligence to model the availability of human resources, but then we had to use a second level of artificial intelligence in order to model human workgroups and skills, finally we used a third level of artificial intelligence to model workflows executed by the said human resources once organized in groups and levels according to their experiences. In our best intentions, such a three level artificial intelligence could address the limits that, until now, have refrained the crowds from growing up as companies, with a well recognizable pyramidal structure, in order to reward experience, skill and professionalism of their workers. We cannot frankly say whether our work will really contribute or not to the so called "crowdsourcing revolution", but we hope at least to have shedded some light on the agreeable possibilities that are yet to come.
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NAPOLI, CHRISTIAN. "A-I: Artificial intelligence." Doctoral thesis, Università degli studi di Catania, 2016. http://hdl.handle.net/20.500.11769/490996.
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In this thesis we proposed new neural architectures and information theory approaches. By means of wavelet analysis, neural networks, and the results of our own creations, namely the wavelet recurrent neural networks and the radial basis probabilistic neural networks,we tried to better understand, model and cope with the human behavior itself. The first idea was to model the workers of a crowdsourcing project as nodes on a cloud-computing system, we also hope to have exceeded the limits of such a definition. We hope to have opened a door on new possibilities to model the behavior of socially interconnected groups of people cooperating for the execution of a common task. We showed how it is possible to use the Wavelet Recurrent Neural Networks to model a quite complex thing such as the availability of resources on an online service or a computational cloud, then we showed that, similarly, the availability of crowd workers can be modeled, as well as the execution time of tasks performed by crowd workers. Doing that we created a tool to tamper with the timeline, hence allowing us to obtain predictions regarding the status of the crowd in terms of available workers and executed workflows. Moreover, with our inanimate reasoner based on the developed Radial Basis Probabilistic Neural Networks, firstly applied to social networks, then applied to living companies, we also understood how to model and manage cooperative networks in terms of workgroups creation and optimization. We have done that by automatically interpreting worker profiles, then automatically extrapolating and interpreting the relevant information among hundreds of features for each worker in order to create workgroups based on their skills, professional attitudes, experience, etc. Finally, also thanks to the suggestions of prof. Michael Bernstein of the Stanford University, we simply proposed to connect the developed automata. We made use of artificial intelligence to model the availability of human resources, but then we had to use a second level of artificial intelligence in order to model human workgroups and skills, finally we used a third level of artificial intelligence to model workflows executed by the said human resources once organized in groups and levels according to their experiences. In our best intentions, such a three level artificial intelligence could address the limits that, until now, have refrained the crowds from growing up as companies, with a well recognizable pyramidal structure, in order to reward experience, skill and professionalism of their workers. We cannot frankly say whether our work will really contribute or not to the so called "crowdsourcing revolution", but we hope at least to have shedded some light on the agreeable possibilities that are yet to come.
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Lila, Serxho <1995>. "Applications of Artificial Intelligence." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18837.
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The world is changing rapidly. Developments in information technology are affecting every aspect of people’s lives, but also economies or politics all over the world.
The fourth industrial revolution started at the beginning of the twenty-first century, it is manifested by emerging technology breakthroughs in many fields, which include artificial intelligence (AI), biotechnology, 3D printing, the Internet of Things, and blockchain. In the last decade, the technology sector has driven the worldwide economy, and big tech companies such as Amazon, Apple, Microsoft, Tencent, Baidu, and Alibaba are dominating the market. Many other companies are riding the wave, while others are staying behind.
AI is impacting many industries, from transportation to agriculture, passing through healthcare, communication, media, retail, entertainment, and others. But one of the major sector involved is finance.
This qualitative research focuses on applications of AI, especially in the financial field. The theoretical part aims to introduce the concept of AI, the different types of AI, examples of AI technologies, and countries and companies that are leading the AI race. The second part of the research focuses on AI applications in different sectors, examining the influence, benefits, and challenges of this breakthrough innovation.
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Boyce, Gavin John. "Artificial intelligence : thought and content." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265914.
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Gkiokas, Alexandros. "Imitation learning in artificial intelligence." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/94683/.
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Acquiring new knowledge often requires an agent or a system to explore, search and discover. Yet us humans build upon the knowledge of our forefathers, as did they, using previous knowledge; there does exist a mechanism which allows transference of knowledge without searching, exploration or discovery. That mechanism is known as imitation and it exists everywhere in nature; in animals, insects, primates, and humans. Enabling artificial, cognitive and software agents to learn by imitation could potentially be crucial to the emergence of the field of autonomous systems, robotics, cyber-physical and software agents. Imitation in AI implies that agents can learn from their human users, other AI agents, through observation or using physical interaction in robotics, and therefore learn a lot faster and easier. Describing an imitation learning framework in AI which uses the Internet as the source of knowledge requires a rather unconventional approach: the procedure is a temporal-sequential process which uses reinforcement based on behaviouristic Psychology, deep learning and a plethora of other Algorithms. Ergo an agent using a hybrid simulating-emulating strategy is formulated, implemented and experimented with. That agent learns from RSS feeds using examples provided by the user; it adheres to previous research work and theoretical foundations and demonstrates that not only is imitation learning in AI possible, but it compares and in some cases outperforms traditional approaches.
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Polova, M. V., L. M. Mahas, М. В. Польова, and Л. М. Магас. "Major advances of artificial intelligence." Thesis, Вінницький національний аграрний університет, 2015. http://ir.lib.vntu.edu.ua/handle/123456789/5009.
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Москаленко, А. М. "Computer training and artificial intelligence." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/10734.
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Литвиненко, Галина Іванівна, Галина Ивановна Литвиненко, Halyna Ivanivna Lytvynenko, and O. Snytnikova. "Artificial intelligence: threats and promises." Thesis, Видавництво СумДУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/16063.
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There are any number of vaguely plausible reasons why Friendly AI might be humanly impossible, and it is still more likely that the problem is. But one should not so quickly write of the challenge, especially considering the stakes.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/16063
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O, Goncharenko T. "ARTIFICIAL INTELLIGENCE: PROS AND CONS." Thesis, Національний авіаційний університет, 2017. http://er.nau.edu.ua/handle/NAU/28073.
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Wong, Alison. "Artificial Intelligence for Astronomical Imaging." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30068.
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Astronomy is the ultimate observational science. Objects outside our solar system are beyond our reach, so we are limited to acquiring knowledge at a distance. This motivates the need to advance astrophysical imaging technologies, particularly for the field of high contrast imaging, where some of the most highly prized science goals require high fidelity imagery of exoplanets and of the circumstellar structures associated with stellar and planetary birth. Such technical capabilities address questions of both the birth and death of stars which in turn informs the grand recycling of matter in the chemical evolution of the galaxy and universe itself. Ground-based astronomical observation primarily relies on extreme adaptive optics systems in order to extract signals arising from faint structures within the immediate vicinity of luminous host stars. These systems are distinguished from standard adaptive optics systems in performing faster and more precise wavefront correction which leads to better imaging performance. The overall theme of this thesis therefore ties together advanced topics in artificial intelligence with techniques and technologies required for the field of high contrast imaging. This is accomplished with demonstrations of deep learning methods used to improve the performance of extreme adaptive optics systems and is deployed and benchmarked with data obtained at the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system operating at the observatory on the summit of Mauna Kea in Hawaii. Solutions encompass both hardware and software, with optimal recovery of scientific outcomes delivered by model fitting of high contrast imaging data with modern machine learning techniques. This broad-ranging study subjecting acquisition, analysis and modelling of data hopes to yield more accurate and higher fidelity observables which in turn delivers improved interpretation and scientific delivery.
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Peurifoy, John Edward. "The physics of artificial intelligence." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122844.
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Thesis: S.B., Massachusetts Institute of Technology, Department of Physics, 2018<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 83-87).<br>In this thesis, I explore both what Physics can lend to the world of artificial intelligence, and how artificial intelligence can enhance the world of physics. In the first chapter I propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. This neural network model is experimentally shown to describe the system well, and is then further used to solve the inverse design problem and propose a generalized template for how to use neural networks to enhance numerical calculations. In the second and third chapter I explore the use of Unitary matrices in neural networks to attempt to solve the exploding and vanishing gradient problem. The norm-preserving property of unitary matrices is shown through experiments to allow neural networks to retain information over many more layers. This model achieves state of the art results on a number of toy and real world tasks.<br>by John Edward Peurifoy.<br>S.B.<br>S.B. Massachusetts Institute of Technology, Department of Physics
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Ashwood, Andrew J. "Portfolio selection using artificial intelligence." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/66229/1/Andrew_Ashwood_Thesis.pdf.
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The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.
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Thompson, Adrian. "Hardware evolution : automatic design of electronic circuits in reconfigurable hardware by artificial evolution." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360588.
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Stevenson, King Douglas Beverley. "Robust hardware elements for weightless artificial neural networks." Thesis, University of Central Lancashire, 2000. http://clok.uclan.ac.uk/1884/.
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This thesis investigates novel robust hardware elements for weightless artificial neural systems with a bias towards high integrity avionics applications. The author initially reviews the building blocks of physiological neural systems and then chronologically describes the development of weightless artificial neural systems. Several new design methodologies for the implementation of robust binary sum-and-threshold neurons are presented. The new techniques do not rely on weighted binary counters or registered arithmetic units for their operation making them less susceptible to transient single event upsets. They employ Boolean, weightless binary, asynchronous elements throughout thus increasing robustness in the presence of impulsive noise. Hierarchies formed from these neural elements are studied and a weightless probabilisitic activation function proposed for non-deterministic applications. Neuroram, an auto-associative memory created using these weightless neurons is described and analysed. The signal-to-noise ratio characteristics are compared with the traditional Hamming distance metric. This led to the proposal that neuroram can form a threshold logic based digital signal filter. Two weightless autoassociative memory based neuro-filters are presented and their filtration properties studied and compared with a traditional median filter. Eachn novel architecture was emulated using weightless numericM ATLAB code prior to schematic design and functional simulation. Several neural elements were implemented and validated using FPGA technology. A preliminary robustness evaluation was performed. The large scale particle accelerator at the Theodor Svedberg Laboratory at the University of Uppsala, Sweden, was used to generate transienut psetsin an FPGA performing a weightless binary neural function. One paper,two letters and five international patents have been published during the course of this research. The author has significantly contributed to the field of weightless artificial neural systems in high integrity hardware applications.
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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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Sweat, Patricia A. "The importance of artificial intelligence for Naval intelligence training simulations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FSweat.pdf.
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Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Christian J. Darken, Perry McDowell. "September 2006." Includes bibliographical references (p. 65). Also available in print.
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Pearson, Kyle A., Leon Palafox, and Caitlin A. Griffith. "Searching for exoplanets using artificial intelligence." OXFORD UNIV PRESS, 2018. http://hdl.handle.net/10150/627143.
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In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labour intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects that, unlike current methods, uses a neural network. Neural networks, also called 'deep learning' or 'deep nets', are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
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Krebs, Peter R. History & Philosophy of Science UNSW. "Turing machines, computers and artificial intelligence." Awarded by:University of New South Wales. History & Philosophy of Science, 2002. http://handle.unsw.edu.au/1959.4/19053.
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This work investigates some of the issues and consequences for the field of artificial intelligence and cognitive science, which are related to the perceived limits of computation with current digital equipment. The Church -Turing thesis and the specific properties of Turing machines are examined and some of the philosophical 'in principle' objections, such as the application of G??del's incompleteness theorem, are discussed. It is argued that the misinterpretation of the Church-Turing thesis has led to unfounded assumptions about the limitations of computing machines in general. Modern digital computers, which are based on the von Neuman architecture, can typically be programmed so that they interact effectively with the real word. It is argued that digital computing machines are supersets of Turing machines, if they are, for example, programmed to interact with the real world. Moreover, computing is not restricted to the domain of discrete state machines. Analog computers and real or simulated neural nets exhibit properties that may not be accommodated in a definition of computing, which is based on Turing machines. Consequently, some of the philosophical 'in principle' objections to artificial intelligence may not apply in reference to engineering efforts in artificial intelligence.
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Uthus, David C. "Sports scheduling: an artificial intelligence approach." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/5839.
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This thesis looks at the Traveling Tournament Problem (TTP) from the sports scheduling literature. It presents two approaches to this problem: a metaheuristic Ant Colony Optimization (ACO) approach to nd good solutions in a reasonable time frame and a heuristic search Iterative-Deepening-A* (IDA*) approach to nd optimal solutions. The rst approach combines ACO with constraint processing techniques in order to handle the hard constraints of the TTP. The key component is creating a framework which uses forward-checking and con ict-directed backjumping to handle the constraints while using ACO for choosing the values. This is further improved by introducing new ideas of unsafe backjumping and pattern matching for constraint propagation while incorporating an old concept of ant restarts. This approach has been found to improve on past ACO approaches to the TTP and showed results which are more competitive with state-of-theart metaheuristic approaches. The second approach presents a parallel version of IDA*, combining past concepts of tree decomposition and node ordering with a new idea of subtree skipping. This new idea allows for parts of the search tree to be skipped for some iterations while still guaranteeing optimality for the nal solution that is found. Two additional ideas are presented. The rst, called forced deepening, helps to reduce node expansion when applying IDA*-like algorithms on real-world distance problems. The second, called elite paths, helps to both improve the performance of forced deepening while also allowing for the optimal solution to be found faster during the nal iteration of IDA*. The results of applying this new approach to the TTP shows that it is state-of-the-art, nding known optimal solutions in a fraction of the time of past approaches and nding new optimal solutions to some unsolved problem instances.<br>Whole document restricted until August 2011, but available by request, use the feedback form to request access.
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Van, Dyk Michael J. "In defense of strong artificial intelligence." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0004/MQ32272.pdf.
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