How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study

Editorial Material Radiology, Nuclear Medicine & Medical Imaging

ChatGPT and the Future of Medical Writing

Som Biswas

RADIOLOGY (2023)

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Editorial Material Multidisciplinary Sciences

ChatGPT is fun, but not an author

H. Holden Thorp

SCIENCE (2023)

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Article Business, Finance

ChatGPT for (Finance) research: The Bananarama Conjecture

Michael Dowling et al.

Summary: Based on ratings from finance journal reviewers, the AI chatbot ChatGPT demonstrates significant usefulness in finance research, with potential for application in other domains. It excels in idea generation and data identification, while facing challenges in literature synthesis and developing appropriate testing frameworks. Moreover, the quality of output is influenced by the extent of private data and researcher domain expertise input. Ethical implications and other potential impacts are also considered.

FINANCE RESEARCH LETTERS (2023)

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Editorial Material Multidisciplinary Sciences

THE PROMISE AND PERIL OF GENERATIVE AI

Chris Stokel-Walker et al.

NATURE (2023)

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Article Computer Science, Theory & Methods

Survey of Hallucination in Natural Language Generation

Ziwei Ji et al.

Summary: Natural Language Generation (NLG) has made significant progress in recent years, thanks to deep learning technologies like Transformer-based language models. This advancement has resulted in more fluent and coherent NLG, leading to improvements in tasks such as summarization, dialogue generation, and data-to-text conversion. However, deep learning-based generation is prone to producing unintended text, which affects system performance and fails to meet user expectations in real-world scenarios. To address this issue, researchers have conducted studies on measuring and mitigating hallucinated texts. This survey provides an overview of the research progress and challenges in the hallucination problem in NLG, covering metrics, mitigation methods, and task-specific research progress.

ACM COMPUTING SURVEYS (2023)

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Article

How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Aidan Gilson et al.

JMIR Medical Education (2023)

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Article Engineering, Industrial

Multilevel process monitoring: A case study to predict student success or failure

Leo C. E. Huberts et al.

Summary: This case study demonstrates the use of multilevel process monitoring in quality control, answering three research questions related to high school student progress using high school data. The results provide a clear blueprint for student progress monitoring.

JOURNAL OF QUALITY TECHNOLOGY (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Compute Trends Across Three Eras of Machine Learning

Jaime Sevilla et al.

Summary: This paper studies the trends in compute, the most readily quantifiable factor in machine learning. Three novel contributions are made, including curating a dataset with training compute of 123 milestone ML systems, framing the trends in compute in three eras, and finding a longer compute doubling time in the Deep Learning Era. The work highlights the fast-growing compute requirements for training advanced ML systems.

2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2022)

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Editorial Material Engineering, Industrial

Artificial intelligence and statistics for quality technology: an introduction to the special issue

Bianca Maria Colosimo et al.

Summary: The paper discusses the increasing attention on the use of Artificial Intelligence in quality technology in various applied and industrial settings, particularly focusing on techniques like Machine Learning, Neural Networks, and Deep Learning. It also briefly explores the limitations and advantages of AI-based solutions to stimulate creative attention towards novel solutions and future research directions.

JOURNAL OF QUALITY TECHNOLOGY (2021)

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Article Statistics & Probability

Steady-state average run length(s): Methodology, formulas, and numerics

Sven Knoth

Summary: The average run length (ARL) is the prevailing performance measure for evaluating control charts, counting observations until a change is flagged. The steady-state ARL is used for fair comparison between competing designs, postulating the appearance of change after reaching a steady state, unlike the zero-state ARL assumption.

SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS (2021)

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Article Engineering, Industrial