Готові списки джерел за темами / Artificial Brains

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Artificial Brains"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Artificial Brains"

1

Taylor, J. G. "On artificial brains." Neurocomputing 74, no. 1-3 (2010): 50–56. http://dx.doi.org/10.1016/j.neucom.2010.06.019.

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2

Ross, Charles, and Max Jamilly. "Towards Artificial Prosthetic Brains." ITNOW 58, no. 3 (2016): 54–57. http://dx.doi.org/10.1093/itnow/bww084.

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Ross, Charles, and Max Jamilly. "Towards Artificial Prosthetic Brains." ITNOW 58, no. 4 (2016): 54–56. http://dx.doi.org/10.1093/itnow/bww114.

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Kampfner, Roberto R. "Real brains, artificial minds." Biosystems 22, no. 4 (1989): 341–42. http://dx.doi.org/10.1016/0303-2647(89)90055-5.

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5

Sun, Zhaohao, and Xuehui Wei. "Human-centered Artificial Intelligence Development." Journal of Computer Science Research 6, no. 4 (2024): 19–33. http://dx.doi.org/10.30564/jcsr.v6i4.6885.

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Анотація:
Few researchers provide a wider vision of artificial feet, hands, mouths, eyes, ears, and brains. This limits our vision of them and their significant impacts on the modern Industrial Revolution and Artificial Intelligence (AI) history. This article presents a novel perspective on human-centered social development starting from artificial feet. After briefly reviewing AI, this article explores the age of AI and artificial feet, hands, mouths, eyes, ears, and brains. It also applies AI to artificial feet and artificial brains. The research reveals that artificial feet are one of the origins of
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Squires, David. "Mechanical Brains: Autism and Artificial Intelligence." Diacritics 48, no. 1 (2020): 52–79. http://dx.doi.org/10.1353/dia.2020.0002.

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7

Buechel, Séverine D., Isobel Booksmythe, Alexander Kotrschal, Michael D. Jennions, and Niclas Kolm. "Artificial selection on male genitalia length alters female brain size." Proceedings of the Royal Society B: Biological Sciences 283, no. 1843 (2016): 20161796. http://dx.doi.org/10.1098/rspb.2016.1796.

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Male harassment is a classic example of how sexual conflict over mating leads to sex-specific behavioural adaptations. Females often suffer significant costs from males attempting forced copulations, and the sexes can be in an arms race over male coercion. Yet, despite recent recognition that divergent sex-specific interests in reproduction can affect brain evolution, sexual conflict has not been addressed in this context. Here, we investigate whether artificial selection on a correlate of male success at coercion, genital length, affects brain anatomy in males and females. We analysed the bra
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Esmaeili, Morteza, Riyas Vettukattil, Hasan Banitalebi, Nina R. Krogh, and Jonn Terje Geitung. "Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization." Journal of Personalized Medicine 11, no. 11 (2021): 1213. http://dx.doi.org/10.3390/jpm11111213.

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Анотація:
Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach
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Eiben, A. E. "Robot evolution: Artificial intelligence by artificial evolution." Open Access Government 37, no. 1 (2023): 226–27. http://dx.doi.org/10.56367/oag-037-10494.

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Robot evolution: Artificial intelligence by artificial evolution Most people think of evolution as a biological phenomenon that produced Life on Earth, plants, animals, and us, the Homo Sapiens. Within Computer Science, however, there is another view: evolution is seen as a metaphor for problem-solving, in particular, as a special approach to optimization, working with a ‘population' of solutions and improving them over time through repeated selection-reproduction cycles. This is the research of A.E. Eiben, the Professor of Computational Intelligence at Vrije Universiteit Amsterdam. He notes t
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Sokač, Mateo, Leo Mršić, Mislav Balković, and Maja Brkljačić. "Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for Electroencephalogram-Based Image Generation." Information 15, no. 7 (2024): 405. http://dx.doi.org/10.3390/info15070405.

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Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and
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Дисертації з теми "Artificial Brains"

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TEGELUND, BJÖRN, and JOHAN WIKSTRÖM. "Evolution of Artificial Brains in Simulated AnimalBehaviour." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136174.

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In this report we simulate artificial intelligence in animals using genetic algorithms. In similar models, advanced artificial neural networks have been used for decision making. We present two simpler decision-making models. Using two models based on linear and radial basis functions we find similar behaviours as those found in other studies, including food seeking, obstacle avoidance and predator-versus-prey dynamics. The results show that both decision-making models are equally efficient at gathering food and avoiding obstacles. The models differed in survival strategies when faced with dan
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2

Koperski, Jeffrey David. "Frames, brains, and chinese rooms problems in artificial intelligence /." Lynchburg, Va. : Liberty University, 1991. http://digitalcommons.liberty.edu.

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Maffei, Giovanni. "Automatic and deliberate control of action: an embodied perspective of artificial and biological brains." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/666954.

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Animals evolved to survive in dynamic environments by developing multiple behavioral strategies to adapt and to learn from their interaction with the world. Associative mechanisms and internal representations are at the core brain computation, however, to acquire a complete knowledge of their relevance for behavior it is necessary to take into consideration their embodied nature. In an interdisciplinary effort which integrates methods from computational modeling, robotics, and electrophysiology, this dissertation presents a series of studies that aim at advancing the understanding of the autom
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Foucault, Cédric. "Adaptive learning in humans, brains and neural networks : the role of uncertainty and probabilities." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS629.

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Lors de l’apprentissage, le cerveau est confronté à un problème difficile. Il apprend d’un environnement qui est à la fois stochastique et dynamique, c’est-à-dire qui se caractérise par des distributions de probabilité qui sont susceptibles de changer. La stochasticité incite le cerveau à intégrer un grand nombre d’observations passées pour arriver à une meilleure précision des connaissances acquises, tandis que la propension au changement l’incite à ne prendre en compte que les observations les plus récentes. Cette tension donne lieu à un compromis dans le taux d’apprentissage à utiliser, que
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Risi, Sebastian. "Towards Evolving More Brain-Like Artificial Neural Networks." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5460.

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An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes can be driven to produce brain-like structures, is to evolve neurocontrollers with a high density of neurons and connections that can adapt and learn from past experience. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. In this dissertation two extensions to the recently introduced Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach are presented that are a step towards more brain-lik
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Kim, Eun Young Reinhardt Joseph M. Johnson Hans J. "Multistructure segmentation of multimodal brain images using artificial neural networks." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/387.

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Kim, Eun Young. "Multistructure segmentation of multimodal brain images using artificial neural networks." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/387.

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A method for simultaneously segmenting multiple anatomical brain structures from multi-modal MR images has been developed. An artificial neural network (ANN) was trained from a set of feature vectors created by a combination of high-resolution registration methods, atlas based spatial probability distributions, and a training set of 16 expert traced data sets. A set of feature vectors were adapted to increase performance of ANN segmentation; 1) a modified spatial location for structural symmetry of human brain, 2) neighbors along the priors' descent for directional consistency, and 3) candidat
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Golesorkhi, Mehrshad. "The Brain's Intrinsic Spatiotemporal Structure and Its Potential Application in Artificial Intelligence." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42211.

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Анотація:
Neuroscience focuses largely on how the brain mediates perception and cognition. However, this leaves open the basic organization and hierarchies of the brain’s neural activity by itself prior to and independent of its role in cognition. A recent model characterizes the brain’s intrinsic features in terms of temporo- spatial dynamical (rather than cognitive) terms – the brain’s spatiotemporal hierarchies shape what is called ‘brain’s intrinsicality’. The brain’s intrinsicality may provide potential applications in designing artificial intelligence (AI). In this dissertation, I explore ‘intrins
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Chang, Spencer J. "Brain Tumor Classification Using Hit-or-Miss Capsule Layers." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2006.

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Анотація:
The job of classifying or annotating brain tumors from MRI images can be time-consuming and difficult, even for radiologists. To increase the survival chances of a patient, medical practitioners desire a means for quick and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify brain tumors using a combination of convolutional neural networks and of the concept of
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Grönqvist, Hampus, and David Zetterdahl. "METAL IN YOUR BRAIN - AI." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-36147.

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Denna rapport går igenom utvecklingen av spelet Metal in Your Brain som skickas in som ett bidrag till Swedish Game Awards (SGA), nordens största spelutvecklar-tävling och hur den artificiella intelligensen är uppbyggd och funktionerar. Den artificiella intelligensen är konstruerad på två sätt: fuzzy logic som står bakom de handlingar som en icke spelande karaktär (NPC) tar beroende på vilken situation den befinner sig i och A*-sökning som används för att en NPC ska kunna söka sig fram till ett mål och ta den kortaste vägen eller fly från spelaren för att gömma sig bakom närmsta skydd. Metal i
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Книги з теми "Artificial Brains"

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L, Casti J., and Karlqvist Anders, eds. Real brains, artificial minds. North-Holland, 1987.

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L, Reed Harold. Brains for machines, machines for brains. Nova Science Publishers, 1996.

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3

Ramacher, Ulrich, and Christoph von der Malsburg. On the Construction of Artificial Brains. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00189-5.

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Callataÿ, Armand M. de. Natural and artificial intelligence: Misconceptions about brains and neural networks. North-Holland, 1992.

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5

Slezak, Peter. Computers, Brains and Minds: Essays in Cognitive Science. Springer Netherlands, 1989.

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6

R, Morelli, ed. Minds, brains, and computers: Perspectives in cognitive science and artificial intelligence. Ablex Pub. Corp., 1992.

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7

Gerhard, Roth. The Long Evolution of Brains and Minds. Springer Netherlands, 2013.

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8

Brain IT (2nd 2005 Kitakyushu, Japan). Brain-inspired IT II: Decisions and behavioral choice organized by natural and artificial brains : proceedings of the 2nd International Conference on Brain-inspired Technology held in Hibikin, Kitakyushu, Japan, between 7 and 9 October 2005. Edited by Ishii Kazuo 1969-, Natsume Kiyohisa, and Hanazawa Akitoshi. Elsevier, 2006.

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9

Dawson, Michael Robert William. From bricks to brains: The embodied cognitive science of LEGO robots. AU Press, 2010.

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Wang, Yueming, ed. Human Brain and Artificial Intelligence. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1288-6.

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Частини книг з теми "Artificial Brains"

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de Garis, Hugo. "Artificial Brains." In Artificial General Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68677-4_5.

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Dubnov, Shlomo, and Ross Greer. "Artificial Musical Brains." In Deep and Shallow. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003240198-6.

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Garis, Hugo. "Brains, Artificial and Computational." In Encyclopedia of Sciences and Religions. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_1241.

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Prescott, Tony. "Brains and computers." In The Psychology of Artificial Intelligence. Routledge, 2024. http://dx.doi.org/10.4324/9781003088660-3.

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Schweizer, Paul. "Artificial Brains and Hybrid Minds." In Studies in Applied Philosophy, Epistemology and Rational Ethics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96448-5_10.

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Magnani, Lorenzo. "Semiotic Brains and Artificial Minds." In Cognitive Systems Monographs. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03631-6_3.

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Ogiela, Lidia, and Marek R. Ogiela. "Cognitive Systems and Artificial Brains." In Cognitive Systems Monographs. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25246-4_7.

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Solomonoff, Grace. "A Thousand Brains and a Million Theories." In Artificial General Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_26.

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Mainzer, Klaus. "Neuronal Networks Simulate Brains." In Artificial intelligence - When do machines take over? Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59717-0_7.

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Ramacher, Ulrich, and Christoph von der Malsburg. "The Difficulty of Modelling Artificial Brains." In On the Construction of Artificial Brains. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00189-5_2.

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Тези доповідей конференцій з теми "Artificial Brains"

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Li, Shiyu, Qiang Li, and Qiuli Li. "Functional Connectivity Patterns in the Brains of Deaf Adults During Tone Processing: A 1D CNN Approach." In 2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2024. https://doi.org/10.1109/icaica63239.2024.10823037.

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Borakhade, Hemant, Deepali Onkar, and Deepali Borakhade. "Comparing Neural Networks In Human Brains And AI Systems With Focus On Genetics And Experiential Differences." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842761.

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Kaushal, Shradha, Sonali, Harsh Gaur, Gazab Bhati, and Arun Kumar Rai. "Explainable Artificial Intelligence for brain Tumor Detection." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912262.

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Marteau, Benoit, Siddhanth Vashista, Matthew Chen, et al. "Brain Digital Twin Combining Artificial Intelligence and Extended Reality." In 2024 IEEE 3rd International Conference on Intelligent Reality (ICIR). IEEE, 2024. https://doi.org/10.1109/icir64558.2024.10976965.

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Luo, Zhan, Yuexin Zhang, and Xuegang Song. "Brain Age Estimation and Aging-related Brain Regions." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692827.

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Miao, Zhengqing, Anja Meunier, Michal Robert Žák, and Moritz Grosse-Wentrup. "Exploring Artificial Neural Network Models for c-VEP Decoding in a Brain-Artificial Intelligence Interface." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821771.

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Aykat, Şükrü. "Brain Tumor Detection from Brain MRI Images with Deep Learning Methods." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710648.

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Nagar, Danielle, Alexander Furman, and Geoff Nitschke. "The Cost of Big Brains in Groups." In The 2019 Conference on Artificial Life. MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00193.

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Nagar, Danielle, Alexander Furman, and Geoff Nitschke. "The Cost of Big Brains in Groups." In The 2019 Conference on Artificial Life. MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00193.xml.

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Hintze, Arend, and Jory Schossau. "Towards an FPGA Accelerator for Markov Brains." In The 2022 Conference on Artificial Life. MIT Press, 2022. http://dx.doi.org/10.1162/isal_a_00516.

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Звіти організацій з теми "Artificial Brains"

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Hannas, William, Huey-Meei Chang, Daniel Chou, and Brian Fleeger. China's Advanced AI Research: Monitoring China's Paths to "General" Artificial Intelligence. Center for Security and Emerging Technology, 2022. http://dx.doi.org/10.51593/20210064.

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China is following a national strategy to lead the world in artificial intelligence by 2030, including by pursuing “general AI” that can act autonomously in novel circumstances. Open-source research identifies 30 Chinese institutions engaged in one or more of this project‘s aspects, including machine learning, brain-inspired AI, and brain-computer interfaces. This report previews a CSET pilot program that will track China’s progress and provide timely alerts.
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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve
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Waqas, Muhammad Talha. Synaptic Symmetry: Exploring Similarities in Neural Connections between Human Brain and Artificial Neural Networks. ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.c4dckln9.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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Automated Vehicles, the Driving Brain, and Artificial Intelligence. SAE International, 2022. http://dx.doi.org/10.4271/epr2022027.

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11 Trends in Philanthropy for 2024. Dorothy A. Johnson Center for Philanthropy, 2024. https://doi.org/10.4087/vplx4378.

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When we think of “trends” in our everyday lives, we typically think of concrete things we can see, hear, or even eat. Popular songs, or brand names — the ubiquitous appetizer, the meme we can’t escape. Over eight years of producing 11 Trends in Philanthropy, the Dorothy A. Johnson Center for Philanthropy has most often taken this approach to our annual review of the sector, as well. Our team has combed the landscape of nonprofits and foundations for the most visible signs of a trend — the increased grant dollars, the emerging networks, familiar voices speaking up. This year’s trends share a fa
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