Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration

According to numerous reports, ChatGPT represents a significant breakthrough in the field of artificial intelligence. ChatGPT is a pre-trained AI model designed to engage in natural language conversations, utilizing sophisticated techniques from Natural Language Processing (NLP), Supervised Learning, and Reinforcement Learning to comprehend and generate text comparable to human-generated text. This article provides an overview of the training process and fundamental functionality of ChatGPT, accompanied by a preliminary review of the relevant literature. Notably, this article presents the first comprehensive literature review of this technology at the time of publication, aiming to aggregate all the available pertinent articles to facilitate further developments in the field. Ultimately, the authors aim to offer an appraisal of the technology’s potential implications on existing knowledge and technology, along with potential challenges that must be addressed.

/pdf/chatgpt-and-open-ai-models-a-preliminary-review-104m3oz9.pdf

ChatGPT and Open-AI Models: A Preliminary Review

Generative artificial intelligence (AI) is a form of AI that can autonomously generate new content, such as text, images, audio, and video. Generative AI provides innovative approaches for content production in the metaverse, filling gaps in the development of the metaverse. Products such as ChatGPT have the potential to enhance the search experience, reshape information generation and presentation methods, and become new entry points for online traffic. This is expected to significantly impact traditional search engine products, accelerating industry innovation and upgrading. This paper presents an overview of the technologies and prospective applications of generative AI in the breakthrough of metaverse technology and offers insights for increasing the effectiveness of generative AI in creating creative content. 

Generative Artificial Intelligence in the Metaverse Era

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society. 

https://doi.org/10.1016/j.jksuci.2023.101675

Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions

ChatGPT, one of the leading Large Language Models (LLMs), has acquired linguistic capabilities such as text comprehension and logical reasoning, enabling it to engage in natural conversations with humans. As illustrated in “What Does ChatGPT Say: The DAO from Algorithmic Intelligence to Linguistic Intelligence” [1], there are three levels of intelligence: 1) Algorithmic Intelligence (AI), 2) Linguistic Intelligence (LI), 3) Imaginative Intelligence (II). ChatGPT is a powerful demonstration of Linguistic Intelligence, another milestone after AlphaGo for Algorithmic Intelligence [1]–[3]. We believe the next break-through in intelligence should be Imaginative Intelligence for artistic creation. 

/pdf/can-chatgpt-boost-artistic-creation-the-need-of-imaginative-t6b3uc83.pdf

Can ChatGPT Boost Artistic Creation: The Need of Imaginative Intelligence for Parallel Art

There are many artistic support systems available for artists, but none of them provides high-grade collaborative support for traditional artistic painting as an assistant. To collaborate with human artists, the support system should involve essential artistic procedures and be capable of imitating basic artistic techniques of human artists. In this paper, we address this issue and introduce a novel painting support system, namely Skywork-daVinci, based on a human-in-the-loop mechanism. A CPSS-based approach is proposed for collaboration between human artists and machines. Based on the approach, we develop a robotic painting system to imitate painting techniques of artists. Then, an acrylic painting case is presented to demonstrate the feasibility of our system. Furthermore, comparisons between existing robotic painting systems and ours are discussed. As experimental results show, our robotic painting system could imitate the essential painting techniques of human artists and reproduce the paintings drawn by them. This primary system acts as the basis to realize our collaborative artistic support system entirely in future work.

Skywork-daVinci: A Novel CPSS-based Painting Support System

ShadowPainter: Active Learning Enabled Robotic Painting through Visual Measurement and Reproduction of the Artistic Creation Process

Machine learning in fine art paintings is attracting increasing attention recently. Image captioning of paintings is of great importance for painting analysis, but it is rarely studied. The paintings have abstract expressions and lack annotated datasets, leading to the data-hungry problem in painting captioning. Thus, painting captioning has more significant challenges than photographic image captioning. This paper makes a novel attempt at generating content descriptions of paintings. We generate virtual paintings using the style transfer technique to deal with the data-hungry problem, then train the painting captioning model via a two-step manner. We evaluate our method on an annotated small-scale painting captioning dataset and demonstrate our improvements.

Image Captioning on Fine Art Paintings via Virtual Paintings

Could ChatGPT Imagine: Content Control for Artistic Painting Generation Via Large Language Models

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