APPLICATIONS OF «CHATGPT»: WHERE IT CAN BE USED AND WHAT CAN WE SOLVE WITH CHATGPT
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Anahtar Kelimeler:
ChatGPT, natural language processing, NLP, transformer-based language model, conversational AI, chatbots, virtual assistants, question answering systems, automated writing, language translation, responsible AI, ethical AI.Özet
ChatGPT is a transformer-based language model developed by OpenAI that has been extensively used in natural language processing (NLP) tasks. However, its most significant application lies in conversational AI. This article discusses the different applications of ChatGPT and where it can be used, including chatbots, virtual assistants, question answering systems, automated writing, and language translation.
This scientific article provides a comprehensive overview of the different applications of ChatGPT, a transformer-based language model developed by OpenAI. The article discusses the potential uses of ChatGPT in various areas of natural language processing, including chatbots, virtual assistants, question answering systems, automated writing, and language translation. The article presents evidence of how ChatGPT can improve the performance of these systems and provides insights into the potential benefits of using ChatGPT in these areas. The article also highlights the importance of using AI tools like ChatGPT responsibly and ethically. Overall, this article is a valuable resource for researchers and practitioners interested in the applications of ChatGPT and its potential impact on society.
Referanslar
REFERENCES
Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. San Francisco. 2018, «Improving Language Understanding by Generative Pre-Training». P.6–8
Tom Brown, Benjamin Mann, Nick Ryder. Califronia. 2020, «Language Models are Few-Shot Learners». P.7–8
Yizhe Zhang, Siqi Sun, Michel Galley. San Francisco. 2020, DialoGPT: «Large-Scale Generative Pre-training for Conversational Response Generation». P.5-7
Tom Brown, Benjamin Mann, Nick Ryder. San Francisco. 2020, “GPT-3: Language Models are Few-Shot Learners». P.5–8
Colin Raffel, Noam Shazeer, Adam Roberts. New York. 2019 «Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer». P.4–5
John Paul Mueller, Luca Massaron. New Jersey. 2021, Artificial Intelligence for Dummies (For Dummies (Computer/Tech)) 2nd Edition. P.120–122
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy. Boston. 2020, Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies 2nd Edition. P.30–32
Max Tegmark. New York City. 2017, Life 3.0: Being Human in the Age of Artificial Intelligence. P.60–81
Ian Goodfellow, Yoshua Bengio, Aaron Courville. Boston. 2016, Deep Learning (Adaptive Computation and Machine Learning series). P.50–53
Charu C. Aggarwal. Neural Networks and Deep Learning. New York City. 2018, P.45–46
James Stone. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning. San Francisco. 2020, P. 151–152
Stuart Russell. Human Compatible: Artificial Intelligence and the Problem of Control. New York City. 2019, P.122–123
