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ChatGPT: Applications, Opportunities, and Threats
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Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.
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“Over time, overreliance on generative AI tools for academic tasks, instead of critical thinking and mental exertion, may damage memory retention, cognitive functioning, and critical thinking abilities (Bahrini et al., 2023; Dwivedi et al., 2023).”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Bahrini et al., 2023
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Metadata
- Authors
- Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R. J., Esmaeili, M., Majdabadkohne, R. M., & Pasehvar, M.
- Affiliations
- University of Virginia (US)
- Iran University of Science and Technology (IR)
- Azarbaijan Shahid Madani University (IR)
- Alzahra University (IR)
- Universidad Americana (NI)
- Islamic Azad University, Tehran (IR)
- Venue
- 2023 Systems and Information Engineering Design Symposium (SIEDS)
- Publisher
- IEEE
- Published
- 2023-04-27
- Pages
- 274-279
- OpenAlex
- W4378805624
- Cited by
- 321
- References
- 14
- Citations by year
- 2026: 34 · 2025: 94 · 2024: 140 · 2023: 53
- FWCI
- 56.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Systems and Information Engineering Design Symposium (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- ChatGPT, large language models, transformer models, natural language generation, conversational AI, GPT-3.5, GPT-4, model performance comparison, business applications, educational software, human-AI comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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