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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

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Front. Eng    2024, Vol. 11 Issue (1) : 159-166    https://doi.org/10.1007/s42524-023-0289-6
ChatGPT, AI-generated content, and engineering management
Zuge YU, Yeming GONG()
AIM (Artificial Intelligence and Management) Institute, Emlyon Business School, Ecully Cedex 69130, France
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Abstract

This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management. It assesses the impact of AIGC services on engineering management processes, mapping out the potential development of AIGC in various engineering functions. The study categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle. It also identifies key challenges and emerging trends associated with AIGC. The challenges highlighted are ethical considerations, reliability, and robustness in engineering management. The emerging trends are centered on AIGC-aided optimization design, AIGC-aided engineering consulting, and AIGC-aided green engineering initiatives.
Keywords engineering management      AI-generated content (AIGC)      ChatGPT      AIGC-aided engineering lifecycle     
Corresponding Author(s): Yeming GONG   
Just Accepted Date: 09 January 2024   Online First Date: 05 February 2024    Issue Date: 13 March 2024
 Cite this article:   
Zuge YU,Yeming GONG. ChatGPT, AI-generated content, and engineering management[J]. Front. Eng, 2024, 11(1): 159-166.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0289-6
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/159
Fig.1  Types of AIGC.
GAI-Text GAI-Image GAI-Audio GAI-Video GAI-3D
Techniques The first successful models were Recurrent Neural Networks (RNNs) and their variant, Long Short-Term Memory (LSTM) networks; Transformers, especially the BERT and GPT architectures, represent some of the latest advancements Generative Adversarial Networks (GANs) consist of two neural networks (a generator and a discriminator) that produce images; Variations like DCGANs, StyleGANs, and CycleGANs have expanded its capabilities WaveNet, a deep generative model, represented a breakthrough; Variants and successors have used convolutional networks and transformers GANs, especially those designed for sequential data, have been used; Video prediction models aim to predict future frames given past frames Variational Autoencoders (VAEs) and GANs adapted for 3D data, PointNet for point cloud processing
Applications Chatbots, content generation, automated journalism, language translation, and more Art creation, image super-resolution, photo-to-photo translation, face generation Voice assistants, music generation, speech synthesis, sound effects Video synthesis, deepfakes, video style transfer, video upscaling 3D object generation, scene completion, 3D model repair and enhancement
Examples OpenAI’s GPT-3, Google’s BERT NVIDIA’s StyleGAN, DALL-E by OpenAI Google DeepMind’s WaveNet, OpenAI’s MuseNet Deepfake technology, First Order Motion Model for Image Animation NVIDIA’s GANverse3D, PointNet
Tab.1  Classification of AIGC
Fig.2  The lifecycle in engineering.
Fig.3  AI-generated engineering building display drawings.
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