Review on the Status and Development Trend of AI Industry

Yang, Li; Zhu, Min

doi:10.1109/icccbda.2019.8725756

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…As technology continues to advance, artificial intelligence (AI) is also being explored as a potential tool in E-learning (Arrasmith, 2006;Newswire, 2017;Polat & Erkollar, 2021;Yang & Zhu, 2019). AI has the potential to personalize the learning experience by analyzing individual learner data and providing tailored recommendations and resources.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Literature Review on the Emerging Trends in Educational Research concerning E-learning

Loebis

2024

Int. J. Multidiscipline. Approach. Res. Sci.

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This research is a literature review study that intends to determine trends in the use of E-learning for education, The method used is a literature study method where researchers search literature from 2018 to 2023 from various articles that have been published and journals indexed by Scopus, Sinta, Wos, Ebsco, Copernicus and Google Scholar. The result of this study is that currently the use of learning media has changed which previously only used blog-based media and changed to Virtual reality and is currently most widely used as a learning media. This can make it easier for learners to visualize learning. The conclusion obtained is that the use of e-learning media can increase learning value and can facilitate the understanding of lessons. The trend that is changing today is to use Virtual reality-based learning media. Researchers hope that this article will have a significant impact on learning and teaching staff can use learning media that are currently trending. The use of Virtual reality learning media makes it easier for students to understand visual learning and in terms of cost can be more efficient.

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Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Literature Review on the Emerging Trends in Educational Research concerning E-learning

Loebis

2024

Int. J. Multidiscipline. Approach. Res. Sci.

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“…Saat ini terdapat 2 teknik yang popular untuk digunakan yakni machine learning (ML) dan deep learning (DL). Gambar 1 [6] memperlihatkan peningkatan jumlah publikasi ilmiah yang membahas kedua metode tersebut, hal ini menunjukan kredibilitas metode yang disebutkan. Perbedaan mendasar dari machine learning dan deep learning ialah cara ekstraksi fitur.…”

Section: Pendahuluanunclassified

Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam

Rizal¹

2020

ULTIMA Computing

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machine learning and edge computing currently becomes popular technology used in any discipline. Flexibility and adapt to the problem are the main advantages of its technology. In this paper, we explain step-by-step way to make a lightweight machine learning model especially intended for embedded system application. We use open source machine learning tool called as Weka to design the model. Moreover, we performed a simple stress recognition experiment to make our own dataset for evaluation. We evaluate algorithm complexity and accuracy for different well-known classifier such as support vector machine, simple logistic and hoeffding tree.

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“…A breakthrough in artificial intelligence (AI) has been associated with convolutional neural networks (CNN), which are able to surpass the human brain in image classification accuracy [1,2]. Since this discovery, the field has flourished and is enriched with new virtual data processing tasks such as machine vision and autonomous driving, intelligent object tracking and face recognition, autonomous driving, and surveillance [3][4][5][6]. However, since feature extraction is still the fundamental task of CNN, computing hardware is still a bottleneck, limited by practical performance metrics [7,8].…”

Section: Introductionmentioning

confidence: 99%

Batch processing and data streaming Fourier-based convolutional neural network accelerator

Hu¹,

Li²,

T.³

et al. 2022

Emerging Topics in Artificial Intelligence (ETAI) 2022

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Decision-making through artificial neural networks with minimal latency is critical for numerous applications such as navigation, tracking, and real-time machine action systems. This requires machine learning hardware to process multidimensional data at high throughput. Unfortunately, handling convolution operations, the primary computational tool for data classification tasks, obeys challenging runtime complexity scaling laws. However, homomorphically implementing the convolution theorem in a Fourier optics display light processor can achieve a non-iterative (1) runtime complexity for data inputs beyond 1,000 × 1,000 large matrices. Following this approach, here we demonstrate data streaming multi-kernel image batching using a Fourier Convolutional Neural Network (FCNN) accelerator. We show image batch processing of large-scale matrices as 2 million dot product multiplications performed by a digital light processing module in the Fourier domain. Furthermore, we further parallelize this optical FCNN system by exploiting multiple spatially parallel diffraction orders, achieving a 98x throughput improvement over state-of-the-art FCNN accelerators. A comprehensive discussion of the practical challenges associated with working at the edge of system capabilities highlights the problem of crosstalk and resolution scaling laws in the Fourier domain. Accelerating convolution by exploiting massive parallelism in display technology brings non-Van Neumann-based machine learning acceleration.

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Review on the Status and Development Trend of AI Industry

Cited by 7 publications

References 24 publications

A Comprehensive Literature Review on the Emerging Trends in Educational Research concerning E-learning

A Comprehensive Literature Review on the Emerging Trends in Educational Research concerning E-learning

Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam

Batch processing and data streaming Fourier-based convolutional neural network accelerator

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