Relevant bibliographies by topics / Memory (Artificial)

Academic literature on the topic 'Memory (Artificial)'

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Published: 27 July 2024
Last updated: 28 July 2024

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Journal articles on the topic "Memory (Artificial)"

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Huang, Guang-qiu. "Artificial memory optimization." Applied Soft Computing 61 (December 2017): 497–526. http://dx.doi.org/10.1016/j.asoc.2017.08.021.

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Wan, Changjin, Pingqiang Cai, Ming Wang, Yan Qian, Wei Huang, and Xiaodong Chen. "Artificial Sensory Memory." Advanced Materials 32, no. 15 (July 30, 2019): 1902434. http://dx.doi.org/10.1002/adma.201902434.

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Kim, Dongshin, and Jang-Sik Lee. "Liquid-based memory and artificial synapse." Nanoscale 11, no. 19 (2019): 9726–32. http://dx.doi.org/10.1039/c9nr02767j.

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Park, Youngjun, Min-Kyu Kim, and Jang-Sik Lee. "Emerging memory devices for artificial synapses." Journal of Materials Chemistry C 8, no. 27 (2020): 9163–83. http://dx.doi.org/10.1039/d0tc01500h.

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This paper reviews recent developments in artificial synapses that exploit various emerging memory devices. The emulation of synaptic plasticity and operation mechanism of artificial synapses using various materials and structures are presented.
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Welberg, Leonie. "Artificial activation of a memory trace." Nature Reviews Neuroscience 13, no. 5 (April 13, 2012): 287. http://dx.doi.org/10.1038/nrn3242.

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Li, Xianneng, and Guangfei Yang. "Artificial bee colony algorithm with memory." Applied Soft Computing 41 (April 2016): 362–72. http://dx.doi.org/10.1016/j.asoc.2015.12.046.

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Chen, Yujie, Chi Chen, Hafeez Ur Rehman, Xu Zheng, Hua Li, Hezhou Liu, and Mikael S. Hedenqvist. "Shape-Memory Polymeric Artificial Muscles: Mechanisms, Applications and Challenges." Molecules 25, no. 18 (September 16, 2020): 4246. http://dx.doi.org/10.3390/molecules25184246.

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Shape-memory materials are smart materials that can remember an original shape and return to their unique state from a deformed secondary shape in the presence of an appropriate stimulus. This property allows these materials to be used as shape-memory artificial muscles, which form a subclass of artificial muscles. The shape-memory artificial muscles are fabricated from shape-memory polymers (SMPs) by twist insertion, shape fixation via Tm or Tg, or by liquid crystal elastomers (LCEs). The prepared SMP artificial muscles can be used in a wide range of applications, from biomimetic and soft robotics to actuators, because they can be operated without sophisticated linkage design and can achieve complex final shapes. Recently, significant achievements have been made in fabrication, modelling, and manipulation of SMP-based artificial muscles. This paper presents a review of the recent progress in shape-memory polymer-based artificial muscles. Here we focus on the mechanisms of SMPs, applications of SMPs as artificial muscles, and the challenges they face concerning actuation. While shape-memory behavior has been demonstrated in several stimulated environments, our focus is on thermal-, photo-, and electrical-actuated SMP artificial muscles.
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Caravelli, Francesco, Gia-Wei Chern, and Cristiano Nisoli. "Artificial spin ice phase-change memory resistors." New Journal of Physics 24, no. 2 (February 1, 2022): 023020. http://dx.doi.org/10.1088/1367-2630/ac4c0a.

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Abstract We present a proposal for realization of an electrical memory reminiscent of a memristor in connected Kagome artificial spin ice. We show that current flowing through the system alters the magnetic ensemble, which in turns controls the overall resistance thus leaving memory of current passage in the system. This introduces a current-dependent effect for a dynamic resistive state. We simulate a spin-induced thermal phase-change mechanism, and an athermal domain-wall spin inversion. In both cases we observe electrical memory behavior with an I–V hysteretic pinched loop, typical of memristors. These results can be extended to the more complex geometries in which artificial spin ice can be designed to engineer the hysteresis curve.
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Izawa, Hideki, Yukio Sekiguchi, and Yasuhito Shiota. "The artificial muscle from shape memory alloy." Journal of Life Support Engineering 17, Supplement (2005): 124. http://dx.doi.org/10.5136/lifesupport.17.supplement_124.

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Querlioz, Damien. "(Invited) Memory-Centric Artificial Intelligence with Nanodevices." ECS Meeting Abstracts MA2020-01, no. 24 (May 1, 2020): 1387. http://dx.doi.org/10.1149/ma2020-01241387mtgabs.

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Dissertations / Theses on the topic "Memory (Artificial)"

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Hedberg, Charlie Forsberg, and Alexander Pedersen. "Artificial Intelligence : Memory-driven decisions in games." Thesis, Blekinge Tekniska Högskola, Institutionen för teknik och estetik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3640.

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Developing AI (Artificial Intelligence) for games can be a hard and challenging task. It is sometimes desired to create behaviors that follow some sort of logical pattern. In order to do this, information must be gathered and processed. This bachelor thesis presents an algorithm that could assist current AI technologies to collect and memorize environmental data. The thesis also covers practical implementation guidelines, established through research and testing.
Att utveckla AI (Artificiell Intelligence) i spel kan vara en hård och utmanande uppgift. Ibland är det önskvärt att skapa beteenden som följer något sorts logiskt mönster. För att kunna göra detta måste information samlas in och processas. I detta kandidatarbete presenteras en algoritm som kan assistera nuvarande AI-teknologier för att samla in och memorera omgivningsinformation. Denna uppsats täcker också riktlinjer för praktisk implementering fastställda genom undersökning och tester.
Detta är en reflekstionsdel till en digital medieproduktion.
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Bachhav, Pramod. "Explicit memory inclusion for efficient artificial bandwidth extension." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS492.

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La plupart des algorithmes ABE exploitent les informations contextuelles ou la mémoire capturée via l'utilisation de caractéristiques statiques ou dynamiques extraites de trames de parole voisines. L'utilisation de la mémoire entraîne des caractéristiques dimensionnelles plus élevées et une complexité informatique accrue. Lorsque les informations provenant de trames de prévisualisation sont également utilisées, la latence augmente également. Les travaux antérieurs montrent l'avantage pour ABE d'exploiter la mémoire sous la forme d'entités dynamiques avec un modèle de régression standard. Même dans ce cas, la littérature manque d'une analyse quantitative de l'avantage relatif de l'inclusion de mémoire explicite. La recherche présentée dans cette thèse évalue dans quelle mesure la mémoire explicite est utile et rapporte en outre un certain nombre de techniques différentes qui permettent son inclusion sans augmentation significative de la latence et de la complexité de calcul. Les avantages sont démontrés à la fois par une analyse quantitative avec une mesure basée sur la théorie de l'information et par des tests d'écoute subjectifs. Les principales contributions concernent la préservation de l'efficacité des calculs grâce à l'utilisation de la réduction de dimensionnalité sous la forme d'une analyse en composantes principales, d'auto-encodeurs superposés semi-supervisés et d'auto-encodeurs variationnels conditionnels. Les deux dernières techniques optimisent la réduction de la dimensionnalité pour offrir une performance ABE supérieure
Most ABE algorithms exploit contextual information or memory captured via the use of static or dynamic features extracted from neighbouring speech frames. The use of memory leads to higher dimensional features and increased computational complexity. When information from look-ahead frames is also utilised, then latency also increases. Past work points toward the benefit to ABE of exploiting memory in the form of dynamic features with a standard regression model. Even so, the literature is missing a quantitative analysis of the relative benefit of explicit memory inclusion. The research presented in this thesis assesses the degree to which explicit memory is of benefit and furthermore reports a number of different techniques that allow for its inclusion without significant increases to latency and computational complexity. Benefits are shown through both a quantitative analysis with an information-theoretic measure and subjective listening tests. Key contributions relate to the preservation of computational efficiency through the use of dimensionality reduction in the form of principal component analysis, semisupervised stacked autoencoders and conditional variational auto-encoders. The two latter techniques optimise dimensionality reduction to deliver superior ABE performance
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Kanar, Ege. "Photography as artificial memory: Construction of the Photographic Self." Master's thesis, Akademie múzických umění v Praze. Filmová a televizní fakulta AMU. Knihovna, 2008. http://www.nusl.cz/ntk/nusl-78095.

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Moposita, Tatiana. "Artificial Neural Network (ANN) design using Compute-in-Memory." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS682.

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De nos jours, l'ère du " More than Moore" a émergé comme une influence significative face aux limitations anticipées par la loi de Moore. Les systèmes informatiques explorent des technologies alternatives pour maintenir et améliorer les performances. Cette idée émergé pour résoudre les défis des systèmes électroniques inspirés des réseau biologiques, communément appelés Réseau Neurones Artificiels (ANN). L'utilisation des technologies emerging non-volatile memory (eNVM) est étudiée comme des alternatives prometteuses. Ces technologies offrent plusieurs avantages par rapport à la technologie CMOS traditionnelle, tels qu'une vitesse accrue, des densités plus élevées et une consommation d'énergie moindre. En conséquence, Compute-in-memory utilise les eNVM pour effectuer des calculs directement dans la mémoire, augmentant ainsi la capacité de mémoire et la vitesse de traitement. L'objectif de cette thèse se concentre sur la recherche de la conception de Réseau Neurones Artificiels en utilisant Compute-in-Memory, en employant des solutions matérielles efficaces pour les ANNs tant au niveau du circuit qu'au niveau de l'architecture. Les travaux de recherche récents dans ce contexte ont proposé des conceptions de circuits très efficaces pour optimiser les besoins de calcul énormes nécessaires au traitement des données par les ANNs. Ainsi, pour explorer les capacités d'un ANN au niveau du nœud de sortie, la conception de fonctions d'activation a été proposée. La sélection d'une fonction d'activation est significative car elle détermine la puissance et les capacités du réseau neuronal, et la précision des prédictions dépend principalement de ce choix. Pour évaluer l'efficacité d'une fonction d'activation conçue pour une implémentation analogique, les fonctions d'activation sigmoïde et softmax sont proposées. Cette thèse explore l'intégration de dispositifs mémoires émergents tels que la Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) avec la technologie CMOS. Cette approche combinée vise à tirer parti de la capacité intrinsèque de l'informatique en mémoire offerte par ces dispositifs. Perpendicular magnetic tunneling junction (MTJ) et des FinFET ont été pris en compte pour cette étude. Single-barrier (SMTJ) et double-barrier (DMTJ) sont considérés pour évaluer l'impact de la cellule STT-MRAM basée sur DMTJ par rapport à son homologue SMTJ conventionnel sur les performances d'un réseau neuronal à perceptrons multicouches (MLP) à deux couches. L'évaluation a été réalisée au moyen d'un cadre de simulation personnalisé, de niveaux de dispositif et de cellule jusqu'aux niveaux d'architecture mémoire et d'algorithme. De plus, pour améliorer l'efficacité énergétique d'une architecture Logic-in-Memory (LIM) basée sur les dispositifs STT-MTJ, une nouvelle architecture (SIMPLY+) issue de la logique Smart Material Implication (SIMPLY) et des technologies STT-MRAM basées sur MTJ perpendiculaires a été développée. Le schéma SIMPLY+ constitue une solution prometteuse pour le développement d'architectures informatiques en mémoire économes en énergie et fiables. Toutes les solutions de circuits ont été évaluées à l'aide de simulateurs de circuits commerciaux (par exemple, Cadence Virtuoso). L'activité de conception de circuits impliquant des dispositifs mémoires émergents a également nécessité l'utilisation et le calibrage de modèles compacts basés sur Verilog-A pour intégrer le comportement de ces dispositifs dans l'outil de conception de circuits. Les solutions présentées dans cette thèse impliquent des techniques qui offrent des avancées significatives pour les futures applications. Du point de vue de la conception, l'intégration de modules logiques avec la mémoire STT-MRAM est très réalisable en raison de la compatibilité transparente entre les STT-MRAM et les circuits CMOS. Cette approche est non seulement avantageuse pour la technologie CMOS standard, mais elle exploite également le potentiel des technologies émergentes
Nowadays, the era of ”More than Moore” has arisen as a significant influence in light of the limitations anticipated by Moore’s law. The computing systems are exploring alternative technologies to sustain and enhance performance improvements. The idea of alternative innovative technologies has emerged in solving challenges of electronic systems inspired by biological neural networks, commonly referred to as Artificial Neural Network (ANN). The use of emerging non-volatile memory (eNVM) technologies are being explored as promising alternatives. These technologies offer several advantages over traditional CMOS technology, such as increased speed, higher densities, and lower power consumption. As a result, Compute-in-memory employs eNVMs to perform computation within the memory itself, hence increasing memory capacity and processing speed. The objective of this thesis focuses on the research of Artificial Neural Networks design using Compute in Memory, by employing efficient hardware solutions for ANNs at both circuit- and architecture-level. Recent research work in this context has proposed very efficient circuit designs to optimize the enormous computational needs required by data processing by ANNs. Therefore, to explore the capabilities of an ANN at the output node, the design of activation functions were proposed. The selection of an activation function is significant as it determines the power and capabilities of the neural network, and the accuracy of predictions is primarily dependent on this choice. To assess the effectiveness of an activation function designed for analog implementation, the sigmoid and the softmax activation function are proposed. Besides, this thesis explores the integration of emerging memory devices like Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) with CMOS technology. This combined approach aims to leverage the intrinsic capability of in-memory computing offered by these devices. STT-MRAMs based on state-of-the-art perpendicular magnetic tunneling junction (MTJ) and FinFETs has been considered for this study. Single-barrier magnetic tunnel junction (SMTJ) and double-barrier magnetic tunnel junction (DMTJ) devices are considered to evaluate the impact of STT-MRAM cell based on DMTJ against the conventional SMTJ counterpart on the performance of a two-layer multilayer perceptron (MLP) neural network. The assessment was carried out through a customized simulation framework from device and bitcell levels to memory architecture and algorithm levels. Moreover, to improve the energy-efficiency of a Logic-in-Memory (LIM) architecture based on STT-MTJ devices, a new architecture (SIMPLY+) from the Smart Material Implication (SIMPLY) logic and perpendicular MTJ based STT-MRAM technologies was developed. The SIMPLY+ scheme is a promising solution for the development of energy-efficient and reliable in-memory computing architectures. All circuit solutions were evaluated using commercial circuit simulators (e.g. Cadence Virtuoso). Circuit design activity involving emerging memory devices also required the use and calibration of Verilog-A based compact models to integrate the behavior of such devices into the circuit design tool. The solutions presented in this thesis involve techniques that offer significant advancements for future applications. From a design perspective, the integration of logic modules with STT-MRAM memory is highly feasible due to the seamless compatibility between STT-MRAMs and CMOS circuits. This approach not only proves advantageous for standard CMOS technology but also leverages the potential of emerging technologies
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Day, Jonathan. ""Must I remember?" : artificial memory systems and early modern England." Thesis, University of Liverpool, 2014. http://livrepository.liverpool.ac.uk/2006202/.

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My thesis traces the evolution of artificial memory systems from classical Greece to early modern England to explore memorial traumas and the complex nature of a very particular way of remembering. An artificial memory system is a methodology to improve natural memory. Classical artificial memory systems employ an architectural metaphor, emphasising regularity and striking imagery. Classical memory systems also frequently describe the memory as a blank page. This thesis follows the path of transmission of these ideas and the perennial relationship between memory and forgetting and memory and fiction, as well as the constant threat of memorial collapse.
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Ludwig, Lars [Verfasser], and Thomas [Akademischer Betreuer] Lachmann. "Extended Artificial Memory. Toward an integral cognitive theory of memory and technology / Lars Ludwig. Betreuer: Thomas Lachmann." Kaiserslautern : Technische Universität Kaiserslautern, 2013. http://d-nb.info/1045194794/34.

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Ntourntoufis, Panayotis. "Aspects of the theory of weightless artificial neural networks." Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/8506.

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Turvey, Simon Paul. "Analysing and enhancing the performance of associative memory architectures." Thesis, University of Hertfordshire, 2003. http://hdl.handle.net/2299/14113.

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This thesis investigates the way in which information about the structure of a set of training data with 'natural' characteristics may be used to positively influence the design of associative memory neural network models of the Hopfield type. This is done with a view to reducing the level of connectivity in models of this type. There are three strands to this work. Firstly, an empirical evaluation of the implementation of existing theory is given. Secondly, a number of existing theories are combined to produce novel network models and training regimes. Thirdly, new strategies for constructing and training associative memories based on knowledge of the structure of the training data are proposed. The first conclusion of this work is that, under certain circumstances, performance benefits may be gained by establishing the connectivity in a non-random fashion, guided by the knowledge gained from the structure of the training data. These performance improvements exist in relation to networks in which sparse connectivity is established in a purely random manner. This dilution occurs prior to the training of the network. Secondly, it is verified that, as predicted by existing theory, targeted post-training dilution of network connectivity provides greater performance when compared with networks in which connections are removed at random. Finally, an existing tool for the analysis of the attractor performance of neural networks of this type has been modified and improved. Furthermore, a novel, comprehensive performance analysis tool is proposed.
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Tigreat, Philippe. "Sparsity, redundancy and robustness in artificial neural networks for learning and memory." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0046/document.

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L'objectif de la recherche en Intelligence Artificielle (IA) est de répliquer les capacités cognitives humaines au moyen des ordinateurs modernes. Les résultats de ces dernières années semblent annoncer une révolution technologique qui pourrait changer profondément la société. Nous focalisons notre intérêt sur deux aspects cognitifs fondamentaux, l'apprentissage et la mémoire. Les mémoires associatives offrent la possibilité de stocker des éléments d'information et de les récupérer à partir d'une partie de leur contenu, et imitent ainsi la mémoire cérébrale. L'apprentissage profond permet de passer d'une perception analogique du monde extérieur à une représentation parcimonieuse et plus compacte. Dans le chapitre 2, nous présentons une mémoire associative inspirée des réseaux de Willshaw, avec une connectivité contrainte. Cela augmente la performance de récupération des messages et l'efficacité du stockage de l'information.Dans le chapitre 3, une architecture convolutive a été appliquée sur une tâche de lecture de mots partiellement affichés dans des conditions similaires à une étude de psychologie sur des sujets humains. Cette expérimentation montre la similarité de comportement du réseau avec les sujets humains concernant différentes caractéristiques de l'affichage des mots.Le chapitre 4 introduit une méthode de représentation des catégories par des assemblées de neurones dans les réseaux profonds. Pour les problèmes à grand nombre de classes, cela permet de réduire significativement les dimensions d'un réseau.Le chapitre 5 décrit une méthode d'interfaçage des réseaux de neurones profonds non supervisés avec les mémoires associatives à cliques
The objective of research in Artificial Intelligence (AI) is to reproduce human cognitive abilities by means of modern computers. The results of the last few years seem to announce a technological revolution that could profoundly change society. We focus our interest on two fundamental cognitive aspects, learning and memory. Associative memories offer the possibility to store information elements and to retrieve them using a sub-part of their content, thus mimicking human memory. Deep Learning allows to transition from an analog perception of the outside world to a sparse and more compact representation.In Chapter 2, we present a neural associative memory model inspired by Willshaw networks, with constrained connectivity. This brings an performance improvement in message retrieval and a more efficient storage of information.In Chapter 3, a convolutional architecture was applied on a task of reading partially displayed words under similar conditions as in a former psychology study on human subjects. This experiment put inevidence the similarities in behavior of the network with the human subjects regarding various properties of the display of words.Chapter 4 introduces a new method for representing categories usingneuron assemblies in deep networks. For problems with a large number of classes, this allows to reduce significantly the dimensions of a network.Chapter 5 describes a method for interfacing deep unsupervised networks with clique-based associative memories
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Church, Dana L. "Spatial encoding of artificial flowers by bumblebees (Bombus impatiens): The contents of memory." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/29206.

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A novel methodology allowed simultaneous investigation of three elements of bumblebee foraging behaviour: spatial encoding of flower position, landmark use, and scent marking. Bumblebees were presented with a row of artificial flowers in a flight cage, one flower offering reward (S+). Testing involved empty (i.e., unrewarding) flowers. In Experiment 1, flower covers presumed to be scent marked (old covers) were switched with one of the unmarked covers, or replaced with new, scent-mark-free covers. Results confirmed previous research: presence of old covers influenced response type rather than floral choice. Choice appeared to have been made using memory. In Experiment 2, the S- were moved during testing to change the relative position of the S+. New covers were used for half of the bees. The flower in the same absolute position (wrong relative position) as the S+ was consistently chosen, suggesting that the S- did not function as landmarks. Contrary to Experiment 1, old covers influenced flower choice. Experiment 3 replicated Experiments 1 and 2. Again, bees preferred absolute position, but results suggest relative position was encoded and influenced choice under certain circumstances. The effect of old vs. new covers continued to be inconsistent: choice means were higher with new covers, and probing often occurred on new covers. Finally, when flower array independent (FAI) information and memory for a flight vector were placed in conflict in Experiment 4, bees showed a bias for using FAI cues. Taken together, these experiments show that the definition of a landmark remains to be clarified, the role of scent marking remains elusive, and bumblebees showed a consistent bias for using FAI information to locate a goal. Contributions of this thesis are placed within the context of research with vertebrate species and natural bumblebee foraging behaviour.
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Books on the topic "Memory (Artificial)"

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Kanerva, Pentti. Sparse distributed memory. Cambridge, Mass: MIT Press, 1988.

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Kolcz, A. Approximation properties of memory-based artificial neural networks. Manchester: UMIST, 1995.

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Barba, Gianfranco Dalla. Memory, Consciousness and Temporality. Boston, MA: Springer US, 2002.

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Kanerva, Pentti. Sparse distributed memory and related models. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1992.

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Mace, Mary E. Memory Storage Patterns in Parallel Processing. Boston, MA: Springer US, 1987.

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Denning, Peter J. Sparse disributed memory. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, [NASA Ames Research Center, 1989.

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Jean, Delacour, and Levy Jean-Claude, eds. Systems with learning and memory abilities. Amsterdam: North-Holland, 1988.

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K, Riesbeck Christopher, ed. Experience, memory, and reasoning. Hillsdale, N.J: L. Erlbaum Associates, 1986.

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Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Przybylski, Steven A. Cache and memory hierarchy design: A performance-directed approach. San Mateo, Calif: Morgan Kaufmann Publishers, 1990.

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Book chapters on the topic "Memory (Artificial)"

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Pratt, Ian. "Memory Organization." In Artificial Intelligence, 140–60. London: Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-13277-5_7.

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Crowder, James A., John N. Carbone, and Shelli A. Friess. "Artificial Memory Systems." In Artificial Cognition Architectures, 53–78. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8072-3_5.

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Krauss, Patrick. "Memory." In Artificial Intelligence and Brain Research, 59–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68980-6_7.

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Isaev, Peter, and Patrick Hammer. "Memory System and Memory Types for Real-Time Reasoning Systems." In Artificial General Intelligence, 147–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_15.

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Jia, Huijue. "Memory in Cells." In Neuroscience for Artificial Intelligence, 59–83. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-4.

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Özkural, Eray. "Towards Heuristic Algorithmic Memory." In Artificial General Intelligence, 382–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22887-2_47.

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Wichert, Andreas. "Quantum Associative Memory." In Quantum Artificial Intelligence with Qiskit, 183–94. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003374404-14.

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Orseau, Laurent, and Mark Ring. "Memory Issues of Intelligent Agents." In Artificial General Intelligence, 219–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35506-6_23.

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Edelkamp, Stefan. "Memory Limitations in Artificial Intelligence." In Algorithms for Memory Hierarchies, 233–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36574-5_11.

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Jia, Huijue. "Memory in Dendritic Spines." In Neuroscience for Artificial Intelligence, 85–112. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-5.

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Conference papers on the topic "Memory (Artificial)"

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Dudzik, Bernd, Hayley Hung, Mark Neerincx, and Joost Broekens. "Artificial Empathic Memory." In the 2018 Workshop. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267799.3267801.

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Ae, T., R. Aibara, and Y. Nishioka. "A memory-based artificial neural network." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170468.

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Shimizu, Y., and Y. Osana. "Chaotic Complex-Valued Multidirectional Associative Memory." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-121.

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Adda, Coline, Julien Tranchant, Pablo Stoliar, Benoit Corraze, Etienne Janod, Ralph Gay, Roger Llopis, Marie-Paule Besland, Luis E. Hueso, and Laurent Cario. "An Artificial Neuron Founded on Resistive Switching of Mott Insulators." In 2017 IEEE International Memory Workshop (IMW). IEEE, 2017. http://dx.doi.org/10.1109/imw.2017.7939071.

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Sun, Koun-Tem, Syuan-Rong Syu, and Shih-Yun Lee. "Design Semantic Memory by Artificial Neural Network." In 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 2019. http://dx.doi.org/10.1109/ickii46306.2019.9042636.

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Alexandrino, Jose Lima, Cleber Zanchettin, and Edson Costa de Barros Carvalho Filho. "Artificial Immune System with ART Memory Hibridization." In 7th International Conference on Hybrid Intelligent Systems (HIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/ichis.2007.4344028.

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Sheri, Bhagia, Pooja Kumari, Isma Farah Siddiqui, and Hira Noman. "Artificial Intelligence Based Memory Stash Alzheimer’s Aid." In 2020 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2020. http://dx.doi.org/10.1109/icisct49550.2020.9080030.

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M. Vogl, Thomas. "Artificial Intelligence and Organizational Memory in Government." In dg.o '20: The 21st Annual International Conference on Digital Government Research. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3396956.3396971.

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Alexandrino, Jose Lima, Cleber Zanchettin, and Edson Costa de Barros Carvalho Filho. "Artificial Immune System with ART Memory Hibridization." In 7th International Conference on Hybrid Intelligent Systems (HIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/his.2007.47.

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Boybat, Irem, Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian, and Evangelos Eleftheriou. "An efficient synaptic architecture for artificial neural networks." In 2017 17th Non-Volatile Memory Technology Symposium (NVMTS). IEEE, 2017. http://dx.doi.org/10.1109/nvmts.2017.8171302.

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Reports on the topic "Memory (Artificial)"

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Walden, Victoria Grace, and Kate Marrison, eds. Recommendations for using Artificial Intelligence and Machine Learning for Holocaust Memory and Education. REFRAME, January 2023. http://dx.doi.org/10.20919/elvh8804.

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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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Khan, Saif M. Maintaining the AI Chip Competitive Advantage of the United States and its Allies. Center for Security and Emerging Technology, December 2019. http://dx.doi.org/10.51593/20190013.

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The United States and its allies enjoy a competitive advantage in the production of artificial intelligence chips necessary for leading AI research and implementation. This memo identifies chokepoints for limiting China’s access to key chipmaking equipment.
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Chopra, H. D. FINAL REPORT: FG02-01ER-45906 - A novel class of artificially modulated magnetic multilayers based on magnetic shape memory alloys. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/840960.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Aroca Moya, Belén. Conceptos, fundamentos y herramientas de neurociencia y su aplicación al billete. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29749.

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El neurodiseño y el diseño emocional se aplican en el desarrollo de productos que conecten con la visión y el estilo de vida de los consumidores. Esta tendencia también afecta a los billetes: son necesarios diseños más seguros y fiables que logren representar a los ciudadanos y despertar un sentimiento de orgullo al utilizarlos. El objetivo de este documento es compilar los conceptos y fundamentos clave de la neurociencia, la percepción y el diseño, así como ofrecer una visión general de la neurociencia y de las técnicas de análisis aplicadas al ciclo de vida del billete: el diseño de billetes y de sus elementos de seguridad, la discriminación de reproducciones ilegítimas y la evaluación de defectos de fabricación para la gestión de la calidad. Las técnicas de análisis neurométrico constituyen una herramienta eficaz para cuantificar el impacto de la estimulación sensorial de la percepción del billete y evaluar diferentes procesos cognitivos, como el interés visual, la memoria, las emociones o la atención sostenida en las diferentes zonas de interés del billete, tal y como se describe en los distintos estudios de percepción analizados en este documento. Esta información, junto con el análisis de los diferentes modelos perceptivos de los usuarios (incluidas las personas con problemas de visión), permite desarrollar diseños de billetes capaces de responder a las necesidades de los usuarios y facilitar la identificación de los elementos de seguridad incluidos. La introducción de nuevas tendencias, como la tactilidad, el estudio de la voz, la realidad virtual o la inteligencia artificial, contribuye a la continua adaptación y evolución de la neurociencia y de sus herramientas.
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Memoria de Iniciación Científica 2023. Universidad Autónoma de Chile, March 2024. http://dx.doi.org/10.32457/12728/10296202484.

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«Entre lo imposible y lo posible hay una investigación», es la idea que impulsa los pasos de las y los estudiantes que forman parte del Programa de Iniciación Científica de la Universidad Autónoma de Chile, iniciativa que ha transformado vidas y marcado un hito en el mundo de la investigación. Durante nueve años, este programa ha sido el catalizador que ha fomentado la formación de habilidades críticas para el desarrollo de actividades de investigación e innovación. Una estructura sólida que ha dado lugar a investigaciones de primer nivel, con impacto en la sociedad y el crecimiento personal y profesional de los jóvenes investigadores que dan sus primeros pasos en la ciencia. Se destaca la rigurosidad científica que caracteriza a los trabajos producidos. Muchos de ellos han sido presentados en congresos o han permitido el desarrollo de publicaciones científicas, dando inicio a prometedoras carreras en el ámbito científico. La diversidad de temas abordados es impresionante: desde la producción y almacenamiento de energías limpias hasta la percepción sobre vacunas; la síntesis de fitofármacos, el fenómeno del sobreendeudamiento, la inteligencia artificial, la calidad del sueño y la seguridad alimentaria, entre otros. La investigación en pregrado es el puente que permite a los estudiantes aplicar en la práctica los conocimientos adquiridos en las aulas, profundizando en áreas específicas y obteniendo una comprensión más profunda y detallada de estos. Además, promueve el pensamiento crítico, la resolución de problemas y la capacidad de análisis, todas ellas habilidades cruciales para el desarrollo profesional.
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