This paper illustrates how generative AI could give opportunities for big productivity gains but also opens up questions about the impact of these new powerful technologies on the way we work and share knowledge. More specifically, we explore how ChatGPT changed a fundamental aspect of coding: problem-solving. To do so, we exploit the effect of the sudden release of ChatGPT on the 30th of November 2022 on the usage of the largest online community for coders: Stack Overflow. Using quasi-experimental methods (Difference-in-Difference), we find a significant drop in the number of questions. In addition, the questions are better documented after the release of ChatGPT. Finally, we find evidence that the remaining questions are more complex. These findings suggest not only productivity gains but also a fundamental change in the way we work where routine inquiries are solved by AI allowing humans to focus on more complex tasks.

Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Generative AI probably will not take over most people's jobs entirely but will instead automate a portion of their duties, freeing them up to do other tasks, a U.N. study said on Monday. It warned, however, that clerical work would likely be the hardest hit, potentially hitting female employment harder, given women's over-representation in this sector, especially in wealthier countries. An explosion of interest in generative AI and its chatbot applications has sparked fears over job destruction, similar to those that emerged when the moving assembly line was introduced in the early 1900s and after mainframe computers in the 1950s.

Given enough data, one can feel like it's possible to keep dead loved ones alive. With ChatGPT and other powerful large language models, it is feasible to create a more convincing chatbot of a dead person. But doing so, especially in the face of scarce resources and inevitable decay, ignores the massive amounts of labor that go into keeping the dead alive online. Someone always has to do the hard work of maintaining automated systems, as demonstrated by the overworked and underpaid annotators and content moderators behind generative AI, and this is also true where replicas of the dead are concerned. From managing a digital estate after gathering passwords and account information, to navigating a slowly-decaying inherited smart home, digital death care practices require significant upkeep.

Some of the world's biggest advertisers, from food giant Nestle to consumer goods multinational Unilever, are experimenting with using generative AI software like ChatGPT and DALL-E to cut costs and increase productivity, executives say. Generative artificial intelligence (AI), which can be used to produce content based on past data, has become a buzzword over the past year, capturing the public's imagination and sparking interest across many industries.

The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The investigation begins with an overview of the foundational technologies and current applications of AIGC. Subsequently, the study delves into the market status, policy landscape, and development trajectory of AIGC in China, utilizing keyword searches to identify relevant scholarly papers. Furthermore, the paper provides a comprehensive examination of AIGC products and their corresponding ecosystem, emphasizing the ecological construction of AIGC. Finally, this paper discusses the challenges and risks faced by the AIGC industry while presenting a forward-looking perspective on the industry's future based on competitive insights in AIGC.

Generative AIs produce creative outputs in the style of human expression. We argue that encounters with the outputs of modern generative AI models are mediated by the same kinds of aesthetic judgments that organize our interactions with artwork. The interpretation procedure we use on art we find in museums is not an innate human faculty, but one developed over history by disciplines such as art history and art criticism to fulfill certain social functions. This gives us pause when considering our reactions to generative AI, how we should approach this new medium, and why generative AI seems to incite so much fear about the future. We naturally inherit a conundrum of causal inference from the history of art: a work can be read as a symptom of the cultural conditions that influenced its creation while simultaneously being framed as a timeless, seemingly acausal distillation of an eternal human condition. In this essay, we focus on an unresolved tension when we bring this dilemma to bear in the context of generative AI: are we looking for proof that generated media reflects something about the conditions that created it or some eternal human essence? Are current modes of interpretation sufficient for this task? Historically, new forms of art have changed how art is interpreted, with such influence used as evidence that a work of art has touched some essential human truth. As generative AI influences contemporary aesthetic judgment we outline some of the pitfalls and traps in attempting to scrutinize what AI generated media means.

The selection of features for text classification is a fundamental task in text mining and information retrieval. Despite being the sixth most widely spoken language in the world, Bangla has received little attention due to the scarcity of text datasets. In this research, we collected, annotated, and prepared a comprehensive dataset of 212,184 Bangla documents in seven different categories and made it publicly accessible. We implemented three deep learning generative models: LSTM variational autoencoder (LSTM VAE), auxiliary classifier generative adversarial network (AC-GAN), and adversarial autoencoder (AAE) to extract text features, although their applications are initially found in the field of computer vision. We utilized our dataset to train these three models and used the feature space obtained in the document classification task. We evaluated the performance of the classifiers and found that the adversarial autoencoder model produced the best feature space.

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT-related research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.

This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach to enhance science, technology, engineering, and mathematics (STEM) education. We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors. To further augment the educational experience, these metaphors are subsequently converted into visual form. Our study aims to enhance the learners' understanding of STEM concepts and their learning engagement by using the visual metaphors. We examine the efficacy of our system via a randomized A/B/C test, assessing learning gains and motivation shifts among the learners. Our study demonstrates the potential of applying large language models to educational practice on STEM subjects. The results will shed light on the design of educational system in terms of harnessing AI's potential to empower educational stakeholders.