Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using text as an intermediate modality outperforms direct sketch-to-3D baselines for generating diverse and manufacturable 3D models. We find limitations in current image-to-3D techniques, while noting the value of the text modality for user-feedback and iterative design augmentation.

Detecting problematic research articles timely is a vital task. This study explores whether Twitter mentions of retracted articles can signal potential problems with the articles prior to retraction, thereby playing a role in predicting future retraction of problematic articles. A dataset comprising 3,505 retracted articles and their associated Twitter mentions is analyzed, alongside 3,505 non-retracted articles with similar characteristics obtained using the Coarsened Exact Matching method. The effectiveness of Twitter mentions in predicting article retraction is evaluated by four prediction methods, including manual labelling, keyword identification, machine learning models, and ChatGPT. Manual labelling results indicate that there are indeed retracted articles with their Twitter mentions containing recognizable evidence signaling problems before retraction, although they represent only a limited share of all retracted articles with Twitter mention data (approximately 16%). Using the manual labelling results as the baseline, ChatGPT demonstrates superior performance compared to other methods, implying its potential in assisting human judgment for predicting article retraction. This study uncovers both the potential and limitation of social media events as an early warning system for article retraction, shedding light on a potential application of generative artificial intelligence in promoting research integrity.

Recent advancements in generative AI have enabled ubiquitous access to large language models (LLMs). Empowered by their exceptional capabilities to understand and generate human-like text, these models are being increasingly integrated into our society. At the same time, there are also concerns on the potential misuse of this powerful technology, prompting defensive measures from service providers. To overcome such protection, jailbreaking prompts have recently emerged as one of the most effective mechanisms to circumvent security restrictions and elicit harmful content originally designed to be prohibited. Due to the rapid development of LLMs and their ease of access via natural languages, the frontline of jailbreak prompts is largely seen in online forums and among hobbyists. To gain a better understanding of the threat landscape of semantically meaningful jailbreak prompts, we systemized existing prompts and measured their jailbreak effectiveness empirically. Further, we conducted a user study involving 92 participants with diverse backgrounds to unveil the process of manually creating jailbreak prompts. We observed that users often succeeded in jailbreak prompts generation regardless of their expertise in LLMs. Building on the insights from the user study, we also developed a system using AI as the assistant to automate the process of jailbreak prompt generation.

Artificial intelligence has been around for a while, but suddenly it has received more attention than ever before. Thanks to innovations from companies like Google, Microsoft, Meta, and other major brands in technology. OpenAI, though, has triggered the button with its ground-breaking invention ChatGPT. ChatGPT is a Large Language Model (LLM) based on Transformer architecture that has the ability to generate human-like responses in a conversational context. It uses deep learning algorithms to generate natural language responses to input text. Its large number of parameters, contextual generation, and open-domain training make it a versatile and effective tool for a wide range of applications, from chatbots to customer service to language translation. It has the potential to revolutionize various industries and transform the way we interact with technology. However, the use of ChatGPT has also raised several concerns, including ethical, social, and employment challenges, which must be carefully considered to ensure the responsible use of this technology. The article provides an overview of ChatGPT, delving into its architecture and training process. It highlights the potential impacts of ChatGPT on the society. In this paper, we suggest some approaches involving technology, regulation, education, and ethics in an effort to maximize ChatGPT's benefits while minimizing its negative impacts. This study is expected to contribute to a greater understanding of ChatGPT and aid in predicting the potential changes it may bring about.

Since its introduction in 2022, Generative AI has significantly impacted the art world, from winning state art fairs to creating complex videos from simple prompts. Amid this renaissance, a pivotal issue emerges: should users of Generative AI be recognized as authors eligible for copyright protection? The Copyright Office, in its March 2023 Guidance, argues against this notion. By comparing the prompts to clients' instructions for commissioned art, the Office denies users authorship due to their limited role in the creative process. This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors. It argues that the current policy fails to consider the intricate and dynamic interaction between Generative AI users and the models, where users actively influence the output through a process of adjustment, refinement, selection, and arrangement. Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process that acknowledges the role of AI in creation. This approach not only aligns with the constitutional goal of promoting the progress of science and useful arts but also encourages public engagement in the creative process, which contributes to the pool of training data for AI. Moreover, it advocates for a flexible framework that evolves alongside technological advancements while ensuring safety and public interest. In conclusion, by examining text-to-image generators and addressing misconceptions about Generative AI and user interaction, this Article calls for a regulatory framework that adapts to technological developments and safeguards public interests

We conducted a survey of 135 software engineering (SE) practitioners to understand how they use Generative AI-based chatbots like ChatGPT for SE tasks. We find that they want to use ChatGPT for SE tasks like software library selection but often worry about the truthfulness of ChatGPT responses. We developed a suite of techniques and a tool called CID (ChatGPT Incorrectness Detector) to automatically test and detect the incorrectness in ChatGPT responses. CID is based on the iterative prompting to ChatGPT by asking it contextually similar but textually divergent questions (using an approach that utilizes metamorphic relationships in texts). The underlying principle in CID is that for a given question, a response that is different from other responses (across multiple incarnations of the question) is likely an incorrect response. In a benchmark study of library selection, we show that CID can detect incorrect responses from ChatGPT with an F1-score of 0.74 - 0.75.

Generative AI (GenAI) can synthesize extremely realistic-looking images, posing growing challenges to information authenticity on the Internet. Watermarking [1-7] was suggested as a key technology to distinguish AI-generated and non-AI-generated content in the Executive Order on AI security issued by the White House in October 2023. In watermarkbased detection, a watermark is embedded into an AI-generated image before releasing it; and an image is detected as AI-generated if the same watermark can be decoded from it. Watermarking AI-generated images has been widely deployed in industry. For instance, Google's SynthID watermarks images generated by Imagen [8]; OpenAI embeds a watermark into images generated by DALL-E [9]; and Stable Diffusion enables users to embed a watermark into the generated images [10]. An attacker can use evasion attacks [11] to remove the watermark in a watermarked image to evade detection. Specifically, an evasion attack strategically adds a perturbation into a watermarked image such that the target watermark-based detector falsely detects the perturbed image as non-AI-generated. The literature has well understood the robustness of watermark-based detector against evasion attacks in the white-box setting (i.e., the attacker has access to the target watermarking model) and black-box setting (i.e., the attacker has access to the detection API) [11]. Specifically, in the white-box setting, an attacker can find a small perturbation for a given watermarked image such that the perturbed image evades detection while maintaining the image's visual quality; and in the

The rapid adoption of Generative Artificial Intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices, and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. This paper presents the findings of a pilot study conducted at British University Vietnam (BUV) exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from 'No AI' to 'Full AI', enabling educators to design assessments that focus on areas requiring human input and critical thinking. Following the implementation of the AIAS, the pilot study results indicate a significant reduction in academic misconduct cases related to GenAI, a 5.9% increase in student attainment across the university, and a 33.3% increase in module passing rates. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in HE, promoting academic integrity while leveraging the technology's potential to enhance learning experiences.

If you're in the US, you might see a new shaded section at the top of your Google Search results with a summary answering your inquiry, along with links for more information. That section, generated by Google's generative AI technology, used to appear only if you've opted into the Search Generative Experience (SGE) in the Search Labs platform. Now, according to Search Engine Land, Google has started adding the experience on a "subset of queries, on a small percentage of search traffic in the US." And that is why you could be getting Google's experimental AI-generated section even if you haven't switched it on. The company introduced SGE at its I/O developer conference in May last year, shortly after it opened up access to its ChatGPT rival Bard, now called Gemini.

This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work (Stiennon et al., 2020). We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant gains in response quality that scale with model size with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint.