Authors: Daswin de Silva; Okyay Kaynak; Mona El-Ayoubi; Nishan Mills; Damminda Alahakoon; Milos Manic

Extended Abstract:

Generative artificial intelligence (Generative AI) is transforming the way we live and work. Following several decades of artificial narrow intelligence, Generative AI is signaling a paradigm shift in the intelligence of machines, an increased generalization capability with increased accessibility and equity for nontechnical users. Large language models (LLMs) are leading this charge, specifically conversational interfaces, such as ChatGPT, Gemini, Claude, and Llama (large language model meta AI). Besides language and text, robust and effective Generative AI models have emerged for all other modalities of digital data, image, video, audio, code, and combinations thereof. This article presents the opportunities and challenges of Generative AI in advancing industrial systems and technologies. The article begins with an introduction to Generative AI, which includes its rapid progression to state-of-the-art, the deep learning algorithms, large training datasets, and computing infrastructure used to build Generative AI models, as well as the technical limitations. The contribution, value, and utility of Generative AI is presented in terms of its four capabilities of accelerating academic research, augmenting the learning and teaching experience, supporting industry practice, and increasing social impact. The article concludes with an expeditious message to the academic research and industry ­practitioner communities to invest time and effort in the training, adoption, and application of Generative AI, with consideration for AI literacy for all stakeholders, human-centricity, and the responsible development and use of AI in industrial settings.

Generative AI is transforming the way we live and work, how is it impacting you?

 

This paper has been published the Industrial Electronics Magazine in https://www.ieee-ies.org/pubs/industrial-electronics-magazine

Check full paper at: https://ieeexplore.ieee.org/document/10680463