Tesi sul tema "Hardware for Artificial Intelligence"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Vedi i top-50 saggi (tesi di laurea o di dottorato) per l'attività di ricerca sul tema "Hardware for Artificial Intelligence".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Vedi le tesi di molte aree scientifiche e compila una bibliografia corretta.
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.
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.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-96).
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.
by Mario Orozco Gabriel.
M.B.A.
S.M. in Engineering Systems
Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network". CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.
GRIMALDI, MATTEO. "Hardware-Aware Compression Techniques for Embedded Deep Neural Networks". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2933756.
Bedi, Abhishek. "A generic platform for the evolution of hardware". Click here to access this resource online, 2009. http://hdl.handle.net/10292/651.
MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
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.
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.
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.
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.
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.
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.
Abderrahmane, Nassim. "Impact du codage impulsionnel sur l’efficacité énergétique des architectures neuromorphiques". Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4082.
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
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.
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
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.
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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
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
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.
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.
Banca: Adilson Gonzaga
Banca: Rodrigo Capobianco Guido
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
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
Mestre
Hoopes, Daniel Matthew. "The ContexTable: Building and Testing an Intelligent, Context-Aware Kitchen Table". BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/12.
Shapovalenko, A. "Artificial intelligence". Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/33830.
Башлак, Ірина Анатоліївна, Ирина Анатольевна Башлак, Iryna Anatoliivna Bashlak e I. Kurinnyy. "Artificial intelligence". Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13517.
Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko e M. Chernyakova. "Artificial intelligence". Thesis, Вид-во СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/16890.
Bekeniova. "ARTIFICIAL INTELLIGENCE". Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33666.
Yakushchenko, I. V., e L. M. Chuchilina. "The artificial intelligence". Thesis, Видавництво СумДУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/16003.
Malmborn, Albin, e 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.
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.
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.
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
Tennenbaum, Christopher D. "Intentionality in Artificial Intelligence". Scholarship @ Claremont, 2011. http://scholarship.claremont.edu/cmc_theses/269.
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.
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.
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.
Matorina. "ARTIFICIAL INTELLIGENCE IN MEDICINE". Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33813.
R, Maksimov K. "ARTIFICIAL INTELLIGENCE: MORAL ASPECT". Thesis, Національний авіаційний університет, 2017. http://er.nau.edu.ua/handle/NAU/28076.
Larchenko. "ARTIFICIAL INTELLIGENCE IN ROBOTICS". Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33789.
Vaseigaran, Ajanth, e 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.
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.
Napoli, Christian. "A-I: Artificial intelligence". Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3974.
NAPOLI, CHRISTIAN. "A-I: Artificial intelligence". Doctoral thesis, Università degli studi di Catania, 2016. http://hdl.handle.net/20.500.11769/490996.
Lila, Serxho <1995>. "Applications of Artificial Intelligence". Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18837.
Boyce, Gavin John. "Artificial intelligence : thought and content". Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265914.
Gkiokas, Alexandros. "Imitation learning in artificial intelligence". Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/94683/.
Polova, M. V., L. M. Mahas, М. В. Польова e Л. М. Магас. "Major advances of artificial intelligence". Thesis, Вінницький національний аграрний університет, 2015. http://ir.lib.vntu.edu.ua/handle/123456789/5009.
Москаленко, А. М. "Computer training and artificial intelligence". Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/10734.
Литвиненко, Галина Іванівна, Галина Ивановна Литвиненко, Halyna Ivanivna Lytvynenko e O. Snytnikova. "Artificial intelligence: threats and promises". Thesis, Видавництво СумДУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/16063.
O, Goncharenko T. "ARTIFICIAL INTELLIGENCE: PROS AND CONS". Thesis, Національний авіаційний університет, 2017. http://er.nau.edu.ua/handle/NAU/28073.
Wong, Alison. "Artificial Intelligence for Astronomical Imaging". Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30068.
Peurifoy, John Edward. "The physics of artificial intelligence". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122844.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-87).
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.
by John Edward Peurifoy.
S.B.
S.B. Massachusetts Institute of Technology, Department of Physics
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.
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.
Stevenson, King Douglas Beverley. "Robust hardware elements for weightless artificial neural networks". Thesis, University of Central Lancashire, 2000. http://clok.uclan.ac.uk/1884/.
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.
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.
Thesis Advisor(s): Christian J. Darken, Perry McDowell. "September 2006." Includes bibliographical references (p. 65). Also available in print.
Pearson, Kyle A., Leon Palafox e Caitlin A. Griffith. "Searching for exoplanets using artificial intelligence". OXFORD UNIV PRESS, 2018. http://hdl.handle.net/10150/627143.
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.
Uthus, David C. "Sports scheduling: an artificial intelligence approach". Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/5839.
Whole document restricted until August 2011, but available by request, use the feedback form to request access.
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.