AI Content Generation Technology based on Open AI Language Model
PDF

Keywords

OpenAI
Language Model
GPT 3
API
Recurrent Neural Network (RNN)
Chat Bot

How to Cite

Pokhrel, Sangita, and Shiv Raj Banjade. 2023. “AI Content Generation Technology Based on Open AI Language Model”. Journal of Artificial Intelligence and Capsule Networks 5 (4): 534-48. https://doi.org/10.36548/jaicn.2023.4.006.

Abstract

The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.

PDF

References

Yu, Zhang. "A textual examination for the sake of artificial intelligence pronunciation through sound processing technology." In Journal of Physics: Conference Series, vol. 1952, no. 4, p. 042121. IOP Publishing, 2021.

George, A. Shaji, and AS Hovan George. "A review of ChatGPT AI's impact on several business sectors." Partners Universal International Innovation Journal 1, no. 1 (2023): 9-23.

K. Pearce, S. Alghowinem, and C. Breazeal, “Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture,” Dec. 2022,[Online]. Available: http://arxiv.org/abs/2212.07542

Mattas, Puranjay Savar. "ChatGPT: A Study of AI Language Processing and its Implications." Journal homepage: www. ijrpr. com ISSN 2582 (2023): 7421.

Mageira, Kleopatra, Dimitra Pittou, Andreas Papasalouros, Konstantinos Kotis, Paraskevi Zangogianni, and Athanasios Daradoumis. "Educational AI chatbots for content and language integrated learning." Applied Sciences 12, no. 7 (2022): 3239.

Lee, Mina, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin et al. "Evaluating human-language model interaction." arXiv preprint arXiv:2212.09746 (2022).

Desai, Varsha P., and Kavita S. Oza. "Fine Tuning Modeling Through Open AI." Progression in Science, Technology and Smart Computing, PRARUP (2021).

Lybarger, Kevin, Syed Abdul Hadi, Aidin Ziaee, Akash Hala Swamy, and Vaishnavi Raju Echarlu Thimmaraju. "GERALD: A Conversational AI."

Cao, Yihan, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, and Lichao Sun. "A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt." arXiv preprint arXiv:2303.04226 (2023).

Bandi, Ajay, Pydi Venkata Satya Ramesh Adapa, and Yudu Eswar Vinay Pratap Kumar Kuchi. "The power of generative ai: A review of requirements, models, input–output formats, evaluation metrics, and challenges." Future Internet 15, no. 8 (2023): 260..

Dickey, Ethan, and Andres Bejarano. "A Model for Integrating Generative AI into Course Content Development." arXiv preprint arXiv:2308.12276 (2023).

Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013).

Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee. "Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research." Machine Learning for Brain Disorders (2023): 117-138.

Zhou, Shuohua. "Research on the application of deep learning in text generation." In Journal of Physics: Conference Series, vol. 1693, no. 1, p. 012060. IOP Publishing, 2020.

Haider, Adnan, Gwanghee Lee, Turab H. Jafri, Pilsun Yoon, Jize Piao, and Kyoungson Jhang. "Enhancing Accuracy of Groundwater Level Forecasting with Minimal Computational Complexity Using Temporal Convolutional Network." Water 15, no. 23 (2023): 4041.