AI-Driven Smart Production

Kaneko, Hiroyuki; Goto, Jun; Kawai, Yoshihiro; Mochizuki, Takashi; Sato, Shoei; Imai, Atsushi; Yamanouchi, Yuko

doi:10.5594/jmi.2019.2959173

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Nippon Hoso Kyokai (NHK, Japan's Broadcasting Corporation), has developed a new AI-driven broadcasting technology called "Smart Production". This approach extracts events and incidents from diverse sources such as social media feeds (e.g., Twitter), local government data and interviews, and integrates these into a human-friendly accessible format (Kaneko et al 2020).…”

Section: Ai For the Creative Industriesmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

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This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.

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Section: Ai For the Creative Industriesmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

View full text Add to dashboard Cite

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“…This approach extracts events and incidents from diverse sources such as social media feeds (e.g. Twitter), local government data and interviews, and integrates these into a human-friendly accessible format [231].…”

Section: Ai For the Creative Industriesmentioning

confidence: 99%

Artificial Intelligence in the Creative Industries: A Review

Anantrasirichai,

Bull

2020

Preprint

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This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the 'creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -where it is designed to augment, rather than replace, human creativity.

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Television shows ideation, and testing with smart digital twins to advance ratings

Hornik,

Rachamim

2024

Electron Commer Res

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Generating ideas for immersive television shows is fundamental to the television industry. TV channel managers are looking to stay ahead of their competitors and are turning to many advanced technologies like artificial intelligence (AI), the Internet of Things, virtual reality, cloud and fog computing. These technologies with other autonomous devices, technologies, surveys, models, and software are creating extensive, complex, and diverse television data sets. These data diversity and heterogeneity may hinder television research. Thus, there is a clear need to synthesize, synchronize, and integrate the large-scale data sets according to predefined decision rules and research objectives. Against this backdrop, this paper introduces a new platform of data integration and modeling—television digital twins. Digital twins (DTs) are virtual copies of products, services, processes, or humans encompassing all the relevant entities’ qualities. Although numerous research studies have been published on DTs, none hitherto have been conducted in media and television. This research aims to bridge two perspectives: on one side, the authors acknowledge the value of TVDT as a data fusion platform. On the other side, the authors build on previous scholarship to suggest a conceptual framework for implementing this platform in future TV studies.

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AI-Driven Smart Production

Cited by 4 publications

References 4 publications

Artificial intelligence in the creative industries: a review

Artificial intelligence in the creative industries: a review

Artificial Intelligence in the Creative Industries: A Review

Television shows ideation, and testing with smart digital twins to advance ratings

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