Is ChatGPT Leading Generative AI? What is Beyond Expectations?

Author:

AYDIN Ömer1ORCID,KARAARSLAN Enis2ORCID

Affiliation:

1. CELAL BAYAR UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

2. MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ ANABİLİM DALI

Abstract

Generative AI has the potential to change the way we do things. The chatbot is one of the most popular implementation areas. Even though companies like Google and Meta had chatbots, ChatGPT became popular as it was made publicly available. Although ChatGPT is still in the early stages of its development, it attracted the attention of people and capital groups. It has taken the public interest; people from different fields, ages, and education levels started using ChatGPT. There have been many trials with ChatGPT. It is possible to see a lot of news and shares on the Internet. The study aims to shed light on what is happening in the literature and get an insight into the user expectations of ChatGPT and Generative AI. We also give information about the competitors of ChatGPT, such as Google’s Bard AI, Claude, Meta’s Wit.ai and Tencent’s HunyuanAide. We describe technical and structural fundamentals and try to shed light on who will win the race. We also shared information about the GPT4 version of OpenAI's ChatGPT. We share the early stage due diligence and current situation analysis for all these points. We examine preprint papers and published articles. We also included striking posts on the LinkedIn platform and a compilation of various blogs and news. We also made use of ChatGPT in editing the content of these resources of this study. We can get an insight into the people's interests through their questions submitted to ChatGPT. We can also understand the capabilities of GPT3, GPT4 and also predict further enhancements.

Publisher

Academic Platform Journal of Engineering and Smart Systems

Reference87 articles.

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2. IBM (2023). Natural Language Processing (NLP). https://www.ibm.com/in-en/topics/natural-language-processing

3. Hiemstra, D. (2009). Language Models. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_923

4. Jelinek, F. (1998). Statistical methods for speech recognition. MIT press.

5. Brants, T., Popat, A. C., Xu, P., Och, F. J., & Dean, J. (2007). Large language models in machine translation.

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