How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study

• URL: http://arxiv.org/abs/2302.10916v1
• Date: Fri, 17 Feb 2023 15:48:37 GMT
• Title: How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study
• Authors: Fadel M. Megahed and Ying-Ju Chen and Joshua A. Ferris and Sven Knoth and L. Allison Jones-Farmer
• Abstract summary: Generative Artificial Intelligence (AI) models have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. These tools are in the early stages of development and can be easily misused or misunderstood. We explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research.
• Score: 2.0841728192954663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Related papers

• AI and Generative AI for Research Discovery and Summarization [3.8601741392210434]
  AI and generative AI tools have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. One area that these tools can make a substantial impact is in research discovery and summarization. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future.
  arXiv  Detail & Related papers  (2024-01-08T18:42:55Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation [0.0]
  The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history. The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations.
  arXiv  Detail & Related papers  (2023-11-29T15:02:46Z)
• Best uses of ChatGPT and Generative AI for computer science research [0.0]
  This paper explores the diverse applications of ChatGPT and other generative AI technologies in computer science academic research. We highlight innovative uses such as brainstorming research ideas, aiding in the drafting and styling of academic papers and assisting in the synthesis of state-of-the-art section.
  arXiv  Detail & Related papers  (2023-11-18T21:57:54Z)
• Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier [13.613735709997911]
  This paper presents an empirical study to investigate the feasibility of automated identification of AI-generated code snippets. We propose a novel approach called GPTSniffer, which builds on top of CodeBERT to detect source code written by AI. The results show that GPTSniffer can accurately classify whether code is human-written or AI-generated, and outperforms two baselines.
  arXiv  Detail & Related papers  (2023-07-18T16:01:15Z)
• How to Do Things with Deep Learning Code [0.0]
  We draw attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems. What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models.
  arXiv  Detail & Related papers  (2023-04-19T03:46:12Z)
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face [85.25054021362232]
  Large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning. LLMs could act as a controller to manage existing AI models to solve complicated AI tasks. We present HuggingGPT, an LLM-powered agent that connects various AI models in machine learning communities.
  arXiv  Detail & Related papers  (2023-03-30T17:48:28Z)
• A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? [112.12974778019304]
  generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. This work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc.
  arXiv  Detail & Related papers  (2023-03-21T10:09:47Z)
• Probing Across Time: What Does RoBERTa Know and When? [70.20775905353794]
  We show that linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. We believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.
  arXiv  Detail & Related papers  (2021-04-16T04:26:39Z)
• Explainability in Deep Reinforcement Learning [68.8204255655161]
  We review recent works in the direction to attain Explainable Reinforcement Learning (XRL) In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box.
  arXiv  Detail & Related papers  (2020-08-15T10:11:42Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.