Journal articles: 'Hardware for Artificial Intelligence'

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Burkert, Andreas. "Hardware for Artificial Intelligence." ATZ worldwide 121, no. 5 (2019): 8–13. http://dx.doi.org/10.1007/s38311-019-0060-0.

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Burkert, Andreas. "Hardware for Artificial Intelligence." ATZelectronics worldwide 14, no. 3 (2019): 8–13. http://dx.doi.org/10.1007/s38314-019-0026-4.

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Popov, I. "SoC hardware supporting artificial intelligence." ELECTRONICS: Science, Technology, Business, no. 7 (2018): 116–23. http://dx.doi.org/10.22184/1992-4178.2018.178.7.116.123.

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VerWey, John. "The Other Artificial Intelligence Hardware Problem." Computer 55, no. 1 (2022): 34–42. http://dx.doi.org/10.1109/mc.2021.3113271.

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Prati, Enrico. "Quantum neuromorphic hardware for quantum artificial intelligence." Journal of Physics: Conference Series 880 (August 2017): 012018. http://dx.doi.org/10.1088/1742-6596/880/1/012018.

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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (2020): 1169. http://dx.doi.org/10.3390/electronics9071169.

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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Wang, Xiaoyin. "Artificial intelligence enhanced environmental detection system." Applied and Computational Engineering 66, no. 1 (2024): 156–59. http://dx.doi.org/10.54254/2755-2721/66/20240938.

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This paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. At the heart of our approach lies the combination of a complex AI model with the hardware and software components of the inspection system. This combined approach can significantly improve the accuracy of detection systems through greater ability to predict environmental changes and events, underscoring the superior performance of hardware and software combined with AI technology. This paper delves into the details of hardware and software design, and discusses measurement implementation methods using a build-down machine. We also explore the practical application of AI models within the framework described above. In addition, this paper also describes the implementation of communication protocols to ensure the effective data exchange between the system network and the artificial intelligence model. These protocols are essential for the real-time processing and analysis of environmental data, enabling systems to respond quickly to detected changes.

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HNATCHUK, YELYZAVETA, YEVHENIY SIERHIEIEV, and ALINA HNATCHUK. "USING ARTIFICIAL INTELLIGENCE ACCELERATORS TO TRAIN COMPUTER GAME CHARACTERS." Computer systems and information technologies, no. 1 (August 21, 2021): 63–70. http://dx.doi.org/10.31891/csit-2021-3-9.

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A review of the literature has shown that today, given the complexity of computational processes and the high cost of these processes, the gaming computer industry needs to improve hardware and software to increase the efficiency and speed of processing artificial intelligence algorithms. An analysis of existing machine learning tools and existing hardware solutions to accelerate artificial intelligence. A reasonable choice of hardware solutions that are most effective for the implementation of the task. Possibilities of practical use of the artificial intelligence accelerator are investigated. The effectiveness of the proposed solutions has been proven by experiments. The use of an artificial intelligence accelerator model allowed to accelerate the learning of a computer game character by 2.14 times compared to classical methods.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 3 (2022): 47. http://dx.doi.org/10.1093/combul/bwac093.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 2 (2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 1 (2022): 41. http://dx.doi.org/10.1093/itnow/bwac021.

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Smith, Adam Leon. "Artificial Intelligence." ITNOW 65, no. 1 (2023): 57. http://dx.doi.org/10.1093/combul/bwad031.

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Ratnayake, Deepthi. "Artificial Intelligence." ITNOW 65, no. 4 (2023): 43. http://dx.doi.org/10.1093/itnow/bwad128.

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Abstract Dr Deepthi Ratnayake, Principal Lecturer in Computer Science at University of Hertfordshire, and L. Fox Thomas MBCS, Vice Chair of BCS ISSG explore AI and its impact on women's safety and security.

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Chen, Kevin P. "Artificial Intelligence-based Musical Instrument Accompaniment System." Applied and Computational Engineering 2, no. 1 (2023): 679–84. http://dx.doi.org/10.54254/2755-2721/2/20220645.

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The string learning process requires students to be able to accurately grasp the accuracy of notes and rhythms in the process of playing, and its evaluation is usually done by instructors, but long one-on-one instruction is difficult to achieve in actual teaching. In this project, through the research of audio hardware systems and digital signal processing software technology, I design a string performance recognition robot system combining hardware and software, to realize automatic accompanying practice. The hardware of this system consists of a Raspberry Pi card-type computer, recording equipment, and cueing equipment. The software system processes the audio signal collected by the microphone through artificial intelligence technology and digital signal processing technology to realize the recognition of notes, intensity, and other elements according to the frequency characteristics of string instruments. The accuracy of the user's performance is judged by comparing the recognition result with the content of the score, and the user is guided by real-time hints through the display system. The experimental results show that the system can recognize 99% of the string audio with good recognition stability, so it can give accurate and timely performance guidance to the player.

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Thakare, Pratik Manoj. "Bridging the Gap Between Quantum Computing and Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27848.

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Quantum machine learning (QML) holds the potential to transform various industries, yet its widespread adoption faces formidable challenges. This paper provides a condensed exploration of these challenges and opportunities. I delve into: • Hardware Limitations: Present quantum computers are constrained in qubit count and gate quality, posing obstacles to real-world QML implementation. We dissect the implications of these limitations on QML computations. • Error Correction: Quantum systems are prone to errors stemming from hardware noise and gate imperfections. We scrutinize the strategies to mitigate these errors and enhance QML accuracy. • Algorithm Development: The evolution of QML algorithms is examined, including computational complexity issues and adaptability to noisy quantum hardware. then pivot to recent developments in quantum hardware, which are not only addressing these challenges but also generating new prospects: • Addressing Challenges: Increased qubit counts and improved qubit quality empower quantum computers to tackle complex real-world problems, with reduced noise and enhanced scalability. • Contributing to Opportunities: Recent hardware advancements have catalyzed the creation of efficient quantum algorithms and the exploration of novel quantum applications in healthcare, finance, and materials science. • Creating Opportunities: Quantum computing, with its unique capabilities, has the potential to unlock scientific and engineering discoveries unattainable through classical computing, ushering in a new era of innovation across diverse industries. As quantum hardware continues to advance, these developments are poised to shape the future of quantum machine learning and its profound impact on society.

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Chen, Hongxi. "Hardware Implementation for Convolutional Neural Networks in Artificial Intelligence." Highlights in Science, Engineering and Technology 62 (July 27, 2023): 73–77. http://dx.doi.org/10.54097/hset.v62i.10426.

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Artificial Intelligence (AI) has brought great convenience and help to human society by improving efficiency, increasing productivity and reducing cost. As a part of deep learning, convolutional neural networks (CNNs) have been widely concerned by researchers in recent years. In this paper, five parts of the CNN structure, including input layer, convolutional layer, pooling layer, fully connected layer, activation function and output layer are going to be elaborated. Besides, four different kinds of hardware, which can be used in AI implementation, including graphics processing unit (GPU), field programmable gate array (FPGA), application-specific integrated circuit (ASIC) and brain-like chips will be discussed in this paper. Based on the different characteristics of these four groups of hardware, this paper will analyze their feasibility to implement artificial intelligence algorithm. After contrasting their cost, flexibility and power consumption, it is concluded that different hardware has different advantages to implement AI under different circumstances. GPU performs better to handle with parallel operations or construct complex network models of AI. FPGA is able to achieve flexible AI model programming. On the other hand, ASIC is preferred considering its low power consumption and cost to implement AI. Although brain-like chip is not as well developed as the other three chips, it is promising to implement AI in the future.

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Gigan, Sylvain, Florent Krzakala, Laurent Daudet, and Igor Carron. "Artificial intelligence: From electronics to optics." Photoniques, no. 104 (September 2020): 49–52. http://dx.doi.org/10.1051/photon/202010449.

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Machine Learning and big data are currently revolutionizing our way of life, in particular with the recent emergence of deep learning. Powered by CPU and GPU, they are currently hardware limited and extremely energy intensive. Photonics, either integrated or in free space, offers a very promising alternative for realizing optically machine learning tasks at high speed and low consumption. We here review the history and current state of the art of optical computing and optical machine learning.

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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas, and Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS." Electronics 11, no. 2 (2022): 245. http://dx.doi.org/10.3390/electronics11020245.

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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.

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Siau, Keng, and Weiyu Wang. "Artificial Intelligence (AI) Ethics." Journal of Database Management 31, no. 2 (2020): 74–87. http://dx.doi.org/10.4018/jdm.2020040105.

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Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics is the field related to the study of ethical issues in AI. To address AI ethics, one needs to consider the ethics of AI and how to build ethical AI. Ethics of AI studies the ethical principles, rules, guidelines, policies, and regulations that are related to AI. Ethical AI is an AI that performs and behaves ethically. One must recognize and understand the potential ethical and moral issues that may be caused by AI to formulate the necessary ethical principles, rules, guidelines, policies, and regulations for AI (i.e., Ethics of AI). With the appropriate ethics of AI, one can then build AI that exhibits ethical behavior (i.e., Ethical AI). This paper will discuss AI ethics by looking at the ethics of AI and ethical AI. What are the perceived ethical and moral issues with AI? What are the general and common ethical principles, rules, guidelines, policies, and regulations that can resolve or at least attenuate these ethical and moral issues with AI? What are some of the necessary features and characteristics of an ethical AI? How to adhere to the ethics of AI to build ethical AI?

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Chen, Zhimei. "Hardware Accelerated Optimization of Deep Learning Model on Artificial Intelligence Chip." Frontiers in Computing and Intelligent Systems 6, no. 2 (2023): 11–14. http://dx.doi.org/10.54097/fcis.v6i2.03.

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With the rapid development of deep learning technology, the demand for computing resources is increasing, and the accelerated optimization of hardware on artificial intelligence (AI) chip has become one of the key ways to solve this challenge. This paper aims to explore the hardware acceleration optimization strategy of deep learning model on AI chip to improve the training and inference performance of the model. In this paper, the method and practice of optimizing deep learning model on AI chip are deeply analyzed by comprehensively considering the hardware characteristics such as parallel processing ability, energy-efficient computing, neural network accelerator, flexibility and programmability, high integration and heterogeneous computing structure. By designing and implementing an efficient convolution accelerator, the computational efficiency of the model is improved. The introduction of energy-efficient computing effectively reduces energy consumption, which provides feasibility for the practical application of mobile devices and embedded systems. At the same time, the optimization design of neural network accelerator becomes the core of hardware acceleration, and deep learning calculation such as convolution and matrix operation are accelerated through special hardware structure, which provides strong support for the real-time performance of the model. By analyzing the actual application cases of hardware accelerated optimization in different application scenarios, this paper highlights the key role of hardware accelerated optimization in improving the performance of deep learning model. Hardware accelerated optimization not only improves the computing efficiency, but also provides efficient and intelligent computing support for AI applications in different fields.

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Tan, K. C., L. F. Wang, T. H. Lee, and P. Vadakkepat. "Evolvable Hardware in Evolutionary Robotics." Autonomous Robots 16, no. 1 (2004): 5–21. http://dx.doi.org/10.1023/b:auro.0000008669.57012.88.

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Barrutia, Richard. "COMMUNICATIVE CALL WITH ARTIFICIAL INTELLIGENCE: SOME DESIDERATA." CALICO Journal 3, no. 1 (2013): 37–42. http://dx.doi.org/10.1558/cj.v3i1.37-42.

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The ideal CALL courseware development procedure will take advantage of many or all of the latest hardware, pedagogical, and theoretical advances in the language teaching field. These advances are presented in this paper both as a description o the ideal CALL courseware and, where applicable, as actual design implementations in a Spanish course being developed by the author.

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Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, et al. "Silicon photonics for artificial intelligence applications." Photoniques, no. 104 (September 2020): 40–44. http://dx.doi.org/10.1051/photon/202010440.

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Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

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Wang, Xibei, Shizhen Weng, and Ziyu Xie. "Artificial intelligence in clinical applications." Applied and Computational Engineering 48, no. 1 (2024): 96–105. http://dx.doi.org/10.54254/2755-2721/48/20241199.

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Modern medicine has improved to the point that intelligent diagnostic tools and auxiliary medical technology, such as surgical robots and image analysis systems, are now widespread in clinical settings. In clinical practice, the performance of different surgical robots and image analysis systems is very different, which seriously limits the use of complex medical scenes. The algorithm models and robotic arms that these intelligent robots and the supporting systems rely on have being recognized by the researchers. In this study, hardware and software algorithms are introduced one at a time, with a focus on the Da Vinci medical robot arm systems, the control mechanisms that run the surgical task optimization tools, in particular Proportional-Integral-Derivative (PID) and Remote Center of Motion (RCM), and the image algorithm active contour model that significantly increased the accuracy of tumor localization. Also provided are suggestions for improving the system's use, its limits, and future research possibilities.

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Li, Yingbo, Zhao Li, Yucong Duan, and Anamaria-Beatrice Spulber. "Physical artificial intelligence (PAI): the next-generation artificial intelligence." Frontiers of Information Technology & Electronic Engineering 24, no. 8 (2023): 1231–38. http://dx.doi.org/10.1631/fitee.2200675.

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Widdows, Dominic, Kirsty Kitto, and Trevor Cohen. "Quantum Mathematics in Artificial Intelligence." Journal of Artificial Intelligence Research 72 (December 14, 2021): 1307–41. http://dx.doi.org/10.1613/jair.1.12702.

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In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

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Pilare, Piyush, Coral Mahato, Chanchal Khergade, Shubham Agrawal, and Prasheel Thakre. "Implementation of Hand Gesture-Controlled Mouse Using Artificial Intelligence." 3C Tecnología_Glosas de innovación aplicadas a la pyme 11, no. 2 (2022): 71–79. http://dx.doi.org/10.17993/3ctecno.2022.v11n2e42.71-79.

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This article presents a proposed mouse system. In this paper, we discussed the implementation of an artificially intelligent hand gesture-controlled mouse that uses computer vision to execute mouse functions using the Colour detection technique. The virtual mouse uses the current python and computer vision algorithms for the recognition of the Masked/colored region and works seamlessly without any extra hardware requirements. A computer may be controlled remotely using hand motions, and it is capable to perform cursor movement, left-clicking, and right-clicking without the need for a hardware mouse.

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Smetanina, O. N., E. Yu Sazonova, and D. Yu Andrushko. "SOFTWARE AND HARDWARE COMPLEX FOR ASSESSING RELIABILITY USING ARTIFICIAL INTELLIGENCE." Современные наукоемкие технологии (Modern High Technologies), no. 7 2020 (2020): 90–97. http://dx.doi.org/10.17513/snt.38140.

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Su, Fei, Chunsheng Liu, and Haralampos-G. Stratigopoulos. "Special Issue on Testability and Dependability of Artificial Intelligence Hardware." IEEE Design & Test 40, no. 2 (2023): 5–7. http://dx.doi.org/10.1109/mdat.2023.3241114.

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Pande, Partha Pratim. "Special Issue on Testability and Dependability of Artificial Intelligence Hardware." IEEE Design & Test 40, no. 2 (2023): 4. http://dx.doi.org/10.1109/mdat.2023.3243862.

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Reinhardt, Sophia, Joshua Schmidt, Jonas Schneider, et al. "Smartphone-Based Videonystagmography Using Artificial Intelligence." Current Directions in Biomedical Engineering 9, no. 1 (2023): 528–31. http://dx.doi.org/10.1515/cdbme-2023-1132.

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Abstract Dizziness is a common symptom in medicine. The anamnesis and detection of a nystagmus is essential to distinguish a vertigo's pathogenesis. The diagnosis is complex, expensive, and not always available across the board. We present a novel location- and time-independent mobile application for videonystagmography (VNG) to support vertigo patients and medical staff. No additional hardware is necessary. The app uses artificial intelligence for eye tracking and to detect a horizontal nystagmus. A feasibility study of the mobile VNG with 13 healthy volunteers was performed. Each participant underwent a caloric vestibular testing to provoke the presence of a vestibular nystagmus. It could be shown that a smartphone-based VNG is possible.

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Hwang, Dong Hyun, Chang Yeop Han, Hyun Woo Oh, and Seung Eun Lee. "ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator." Micromachines 12, no. 7 (2021): 838. http://dx.doi.org/10.3390/mi12070838.

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Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.

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Mahendarto, Trias. "From Artificial Intelligence to Artificial Consciousness: An Interior Design Implication." Journal of Artificial Intelligence in Architecture 2, no. 1 (2023): 41–52. http://dx.doi.org/10.24002/jarina.v2i1.6627.

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Artificial Intelligence continues to develop rapidly and provokes people to think about Artificial consciousness. Anthropocentric understanding considers consciousness a unique feature of human beings not possessed by other living beings. However, software and hardware development demonstrated the ability to process, analyze, and infer increasingly comprehensive data close to the image of human brain performance. Furthermore, the application of artificial Intelligence to human-friendly objects that can communicate with humans evokes the presence of consciousness within these objects. This paper discusses the presence of artificial consciousness in humanoid robots as an evolutionary continuation of artificial Intelligence. It estimates its implications for architecture, primarily within interior design. Consciousness has a special place in architecture, as it guides Intelligence in engineering and brings it to an abstract level, such as aesthetics. This paper extracts popular information from Internet conversations and theories in pre-existing scientific journals. This paper concludes that the adaptability of both parties and the balance of positions between the two parties in the future will influence the development of interior design approaches that will integrate artificial Intelligence and humans.

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Rosenberg, Gili, John Kyle Brubaker, Martin J. A. Schuetz, et al. "Explainable Artificial Intelligence Using Expressive Boolean Formulas." Machine Learning and Knowledge Extraction 5, no. 4 (2023): 1760–95. http://dx.doi.org/10.3390/make5040086.

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We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability) according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule- and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special-purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over the subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations. Therefore, using specialized or quantum hardware could lead to a significant speedup through the rapid proposal of non-local moves.

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Cao, Yuan, Zhi Han, Rui Kong, Canlin Zhang, and Qiu Xie. "Technical Composition and Creation of Interactive Installation Art Works under the Background of Artificial Intelligence." Mathematical Problems in Engineering 2021 (September 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/7227416.

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Interactive installation art is a kind of art that uses specific software and computer hardware as a platform, a platform for interaction between humans and machines or different people through computer hardware. It is an interactive art that uses material installations in nature as a medium. Traditional interactive installation art is not safe and convenient, in order to solve the shortcomings of traditional interactive installation art. This article introduces artificial intelligence technology by studying the overview, development, and application of artificial intelligence. The encryption algorithm for artificial intelligence data protection and the BP neural network prediction model under artificial intelligence are also introduced to ensure the safety of interactive installation art works. The part also introduces the creation tools and creation process of interactive installation art works. Finally, in the analysis part, a questionnaire analysis of the World Expo is carried out. The results of this article show that the art of connecting inserts is the most complete and open design era. Advances in science and technology, the development of digital art, and the needs of human life have led to the development of interconnected input technologies. In addition, in the survey of people’s satisfaction with artificial intelligence, we can conclude that 89% of people think that the security of artificial intelligence technology is very high. Yes, 92% of people think that artificial intelligence technology has a fast computing speed, 86% of people think that artificial intelligence technology is low in cost.

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Akintol, Sarah A. "Optimization of Drilling Cost Using Artificial Intelligence." Petroleum & Petrochemical Engineering Journal 5, no. 4 (2021): 1–8. http://dx.doi.org/10.23880/ppej-16000285.

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Drilling operations in the oil and gas industry takes most of the well cost and how fast the drilling bit penetrate and bore the formation is termed the Rate of penetration (ROP). Since most of the cost incurred during drilling is related to the drilling operations, there is need not only to drill carefully, but also to optimize the drilling process. A lot of parameters are related to the rate of penetration which are actually interdependent on each other. This makes it difficult to predict the influence of every single parameter Drilling optimization techniques have been used recently to reduce drilling operation costs. There are different approaches to optimizing the cost of drilling oil and gas wells, some of which include static and /or real time optimization of drilling parameters. A potential area for optimization of drilling cost is through bit run in the well but this is particularly difficult due to its significance in both drilling time and bit cost. In this sense, as a particular bit gets used, it gets dull as its footage increases, resulting from the reduction in the bit penetration rate. The reduction in penetration rate increases total drill time. In order to optimize bit cost, it is desirable to find a trade-off between the two by a bit change policy This study is aimed at minimizing drilling time by use of artificial intelligent for the bit program. Data obtained from a well in the Niger delta region of Nigeria was used in this study and the cost optimization modelled as a Markov decision process where the intelligent agent was to learn the optimal timings for bit change by reinforcement policy Iteration learning. This study was able to achieve its objectives as the reinforcement learning optimization process performed very well with time as the computer agent was able to figure out how to improve drilling cost over time. Better results could be obtained with a better hardware and increased training time.

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Shuliar, Vasyl, Valentyna Shkurko, Tetiana Polukhtovych, Yuliia Semeniako, Liudmyla Shanaieva-Tsymbal, and Lesіa Кoltok. "Using Artificial Intelligence in Education." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 3 (2023): 516–29. http://dx.doi.org/10.18662/brain/14.3/488.

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The article discusses e-learning tools as a fundamental step in implementing digital learning technologies as a factor in the development of teachers’ creativity today. Recently, higher education has been developing with the help of digital technologies that significantly enhance education quality. The integration of complex software-hardware systems into the educational process lies in incorporating big data analytics, robotics, neural networks and artificial intelligence. The objectives of reforming the educational system require corresponding changes in teaching methods, as well as in higher education, through informatization and digitalization. This has led to the emergence of open education which allows for unrestricted access to educational resources. Open education also contributes to the development of digital pedagogy. Indeed, every student can choose an optimal educational programme to follow a personalized learning trajectory online which considers one’s characteristics and interests. After all, the development of digital pedagogy requires addressing various organizational, methodical and practical issues. They range from implementing digital learning technologies in the context of open education and culminating in the creation of a long-term strategic plan that will gradually transform the educational process and, in turn, encourage teachers to think creatively.

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Allard, F. "Artificial intelligence and space." Future Generation Computer Systems 7, no. 4 (1992): 341–42. http://dx.doi.org/10.1016/0167-739x(92)90049-h.

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Schoemaker, S. "Artificial intelligence in simulation." Future Generation Computer Systems 1, no. 4 (1985): 245–47. http://dx.doi.org/10.1016/0167-739x(85)90013-5.

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van de Riet, R. P. "Advances in Artificial Intelligence." Future Generation Computer Systems 3, no. 3 (1987): 223. http://dx.doi.org/10.1016/0167-739x(87)90021-5.

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Freeman, Laura, Abdul Rahman, and Feras A. Batarseh. "Enabling Artificial Intelligence Adoption through Assurance." Social Sciences 10, no. 9 (2021): 322. http://dx.doi.org/10.3390/socsci10090322.

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The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.

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Gams, Matjaz, and Martin Gjoreski. "Artificial Intelligence and Ambient Intelligence." Electronics 10, no. 8 (2021): 941. http://dx.doi.org/10.3390/electronics10080941.

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Ilyas, Mohammad. "Emerging Role of Artificial Intelligence." Journal of Systemics, Cybernetics and Informatics 20, no. 6 (2022): 58–65. http://dx.doi.org/10.54808/jsci.20.06.58.

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Artificial Intelligence (AI) is considered a branch of science that deals with the process of machine learning and intelligent behavior of machines. AI is increasingly becoming involved in our existence. Many see emergence of AI as a revolution that will impact every aspect of our lives. Some see it as an evolution based on the recent advances in hardware/software technologies, powerful computational platforms, and access to massive amount of data collected through pervasive communication networks such as Internet of Things (IoT). Irrespective of these opinions, AI is expected to profoundly impact many aspects of our existence including healthcare, transportation, agriculture, energy, social life, entertainment, fighting crime, and many more. How far AI will infiltrate in human existence is within our hands, at least, for now. We, human beings, design algorithms for AI, we restrict or relax the boundaries of their use, we benefit from the artificial intelligence, and we deal with the consequences of the decisions made by machines using AI. How far AI can go in improving our lives and how significant and deep its interference can be in our existence, remains to be seen. This paper captures the current state of AI and discusses its potential to make human existence better.

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Kim, Joo-Young. "Advanced AI Hardware Designs Based on FPGAs." Electronics 10, no. 20 (2021): 2551. http://dx.doi.org/10.3390/electronics10202551.

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Artificial intelligence (AI) and machine learning (ML) technology enable computers to run cognitive tasks such as recognition, understanding, and reasoning, which are believed to be processes that only humans are capable of, using a massive amount of data [...]

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GUȘE (FRĂTICĂ-DRAGOMIR), Alina. "ARTIFICIAL INTELLIGENCE AND ITS ROLE IN INTERNATIONAL MANAGEMENT." ANNALS OF THE UNIVERSITY OF ORADEA. ECONOMIC SCIENCES 3, no. 1 (2023): 487–96. http://dx.doi.org/10.47535/1991auoes32(1)037.

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Artificial Intelligence (AI) represents the ability that technologies or machines have to copy human intelligence as close as possible in order to solve problems and achieve goals. Artificial intelligence systems adapt, analyze data, observe future actions based on existing information and operate autonomously. An interesting change has occurred over time. In the past, the focus was on the hardware, while the software was considered a weak element. Over time, the software element developed, and over time hardware engineers adapted to the evolution becoming software engineers. Algorithms are used to make predictions in almost any field, and if used correctly, the predictions and results are beneficial and commendable. The take-up of these artificial intelligence applications in public institutions is useful to all. Therefore, developing and perfecting basic human skills is important in the long run. Above all, technology enables work to become more human. For managers, leaders or directors it has a tremendous result. It should be pointed out that starting from the first light bulb up to the emergence of the smartphone, technology has evolved. The element that never changes is the people behind the technology, while the most important aspect is that artificial intelligence is changing the working world.

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Bryndin, Evgeny. "Development of Artificial Intelligence of Ensembles of Software and Hardware Agents by Natural Intelligence on the Basis of Self-Organization." Journal of Research in Social Science and Humanities 2, no. 10 (2023): 13–22. http://dx.doi.org/10.56397/jrssh.2023.10.02.

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Natural intelligence is the totality of acquired knowledge and intellectual skills of a person. Intellectual skills are the ability to think creatively and gracefully, communicate and learn universally. From the point of view of the Orthodox tradition, there are three types of thinking, learning and communication: carnal, creative and grace-filled (spiritual). Creative thinking, learning and communication develop and improve rational intelligence. Gracious thinking, learning and communication develops and improves spiritual intelligence. Strong natural intelligence generates superior knowledge relative to the knowledge of society. It expands and deepens the knowledge of society. Knowledge is a social product. The dynamic process of discovering knowledge and broadcasting about the works of the Creator is described in (18:3-5) parables: “Day imparts speech to day, night reveals knowledge to night. There is no language and no dialect where their voice is not heard. Their voice goes throughout the whole earth, and their words to the ends of the world.” As part of the dynamic process of knowledge discovery, the natural intelligence of each person develops. Natural intelligence began to develop artificial intelligence. Artificial intelligence can be possessed by a software-hardware operating process capable of creating poetry and essays, painting pictures, developing recommendations and solutions for goals set by humans, managing production and systems in various fields of activity based on the existing knowledge of the natural intelligence of mankind. Artificial intelligence cannot develop without human participation. A very promising use of artificial intelligence is carried out by ensembles of software and hardware agents using proven methods based on self-organization in various spheres of life. Ensembles of software and hardware agents can be trained using the knowledge and skills of natural intelligence.

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Luker, Paul A., and Dennis Rothermel. "The philosophy of artificial intelligence." ACM SIGCSE Bulletin 26, no. 1 (1994): 41–45. http://dx.doi.org/10.1145/191033.191050.

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Taylor, Pamela A., and Dana L. Wyatt. "Database and artificial intelligence integration." ACM SIGCSE Bulletin 24, no. 4 (1992): 35–42. http://dx.doi.org/10.1145/141837.141851.

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McCauley, Renée. "Teaching the artificial intelligence course." ACM SIGCSE Bulletin 31, no. 2 (1999): 21–22. http://dx.doi.org/10.1145/571535.571552.

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Sondak, N. E., and V. K. Sondak. "Neural networks and artificial intelligence." ACM SIGCSE Bulletin 21, no. 1 (1989): 241–45. http://dx.doi.org/10.1145/65294.71221.

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Journal articles on the topic 'Hardware for Artificial Intelligence'

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