Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods

Chinese elements : a bridge of the integration between Chinese -English translation and linguaculture transnational mobility

[Abstract] As the popularity of Chinese elements in the innovation of the translation part in Chinese CET, we realized that Chinese elements have become a bridge between linguaculture transnational mobility and Chinese-English translation.So, Chinese students translation skills should be critically improved; for example, on their understanding about Chinese culture, especially the meaning of Chinese culture. Five important secrets of skillful translation are introduced to improve students’ translation skills

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table

Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research

A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research

Manhua Modernity

From fashion sketches of Shanghai dandies in the 1920s, to phantasmagoric imagery of war in the 1930s and 1940s, to panoramic pictures of anti-American propaganda rallies in the 1950s, the cartoon-style art known as manhua helped define China’s modern experience. Manhua Modernity offers a richly illustrated and deeply contextualized analysis of these illustrations from the lively pages of popular pictorial magazines that entertained, informed, and mobilized a nation through a half century of political and cultural transformation. “An innovative reconceptualization of manhua. John Crespi’s meticulous study shows the many benefits of interpreting Chinese comics and other illustrations not simply as image genres but rather as part of a larger print culture institution. A must-read for anyone interested in modern Chinese visual culture.” CHRISTOPHER REA, author of The Age of Irreverence: A New History of Laughter in China “A rich media-centered reading of Chinese comics from the mid-1920s through the 1950s, Manhua Modernity shifts the emphasis away from ideological interpretation and demonstrates that the pictorial turn requires examinations of manhua in its heterogenous, expansive, spontaneous, and interactive ways of engaging its audience’s varied experiences of fast-changing everyday life.” YINGJIN ZHANG, author of Cinema, Space, and Polylocality in a Globalizing Chin

Rethinking Binarism in Translation Studies A Case Study of Translating the Chinese Nobel Laureates of Literature

The theorisation of translation originated in a binary opposition embodied by the debate of word-for-word vs. sense-for-sense translation methods. It is true that by now, theories in Translation Studies (TS) have become significantly more elaborate and sophisticated. However, it cannot be denied that some of its most dominant and pertinent concepts continue to get portrayed in binary concepts, such as translation vs. original, translatability vs. untranslatability and translation vs. interpreting, among many others. This study believes that TS, not different from most intellectual inquiries of the human mind, has been built upon binarism. The current research project aims to identify the traces of this epistemological tradition in the different stages of the discipline’s development, encompassing various theoretical models in the field, while reflecting upon the evolution of TS that marks its departure from such a tradition. It approaches the issue by examining three prevailing dichotomies in the field, namely source vs. target, prescriptive vs. descriptive and translation vs. non-translation. To propose an alternative to the existing binary perspective, this study borrows from the sociological models of Parsons and Giddens to portray translation as a social action. The binary concepts are then evaluated against empirical evidence obtained through a case study of two translators of Chinese Nobel Laureates, Howard Goldblatt and Mabel Lee. Both paratexts and metatexts are consulted to demonstrate that the scenario is much more complex than what is suggested by these dichotomies. It should be clarified that this study does not advocate that scholars discard these terms altogether. Instead, it acknowledges that dichotomies serve a definite purpose in certain contexts, but aims to problematise their uncritical application. Eventually, it seeks to heighten the awareness of binarism in the discipline and strives for a balance between the precision and standardisation of the metalanguage employed in discussing translation