On Thursday morning, Meta released its latest artificial intelligence model, Llama 3, touting it as the most powerful to be made open source so that anyone can use it. The same afternoon, Yann LeCun, Meta's chief AI scientist, said an even more powerful successor to Llama is in the works. He suggested it could potentially outshine the world's best closed AI models, including OpenAI's GPT-4 and Google's Gemini. Meta released two versions of Llama 3 today, one with 8 billion parameters--an industry term that roughly conveys a model's power--and another with 70 billion parameters. LeCun said that bigger models are in the works and that the most powerful, with more than 400 billion parameters, is currently in training.

This paper examines the impact of Generative Artificial Intelligence (GenAI) tools like ChatGPT on the creation and consumption of terminological definitions. From the terminologist's point of view, the strategic use of GenAI tools can streamline the process of crafting definitions, reducing both time and effort, while potentially enhancing quality. GenAI tools enable AI-assisted terminography, notably post-editing terminography, where the machine produces a definition that the terminologist then corrects or refines. However, the potential of GenAI tools to fulfill all the terminological needs of a user, including term definitions, challenges the very existence of terminological definitions and resources as we know them. Unlike terminological definitions, GenAI tools can describe the knowledge activated by a term in a specific context. However, a main drawback of these tools is that their output can contain errors. For this reason, users requiring reliability will likely still resort to terminological resources for definitions. Nevertheless, with the inevitable integration of AI into terminology work, the distinction between human-created and AI-created content will become increasingly blurred.

Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content, raising concerns about digital authenticity and potential misuse in creating deepfakes. This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier. We propose a novel loss that can improve the detector's robustness and handle imbalanced datasets. Additionally, we flatten the loss landscape during the model training to improve the detector's generalization capabilities. The effectiveness of our method, which outperforms traditional detection techniques, is demonstrated through extensive experiments, underscoring its potential to set a new state-of-the-art approach in DM-generated image detection. The code is available at https://github.com/Purdue-M2/Robust_DM_Generated_Image_Detection.

This study explores a new method in food development by utilizing AI including generative AI, aiming to craft products that delight the senses and resonate with consumers' emotions. The food ingredient recommendation approach used in this study can be considered as a form of multimodal generation in a broad sense, as it takes text as input and outputs food ingredient candidates. This Study focused on producing "Romance Bread," a collection of breads infused with flavors that reflect the nuances of a romantic Japanese television program. We analyzed conversations from TV programs and lyrics from songs featuring fruits and sweets to recommend ingredients that express romantic feelings. Based on these recommendations, the bread developers then considered the flavoring of the bread and developed new bread varieties. The research included a tasting evaluation involving 31 participants and interviews with the product developers. Findings indicate a notable correlation between tastes generated by AI and human preferences. This study validates the concept of using AI in food innovation and highlights the broad potential for developing unique consumer experiences that focus on emotional engagement through AI and human collaboration.

Meta Platforms on Thursday released early versions of its latest large language model, Llama 3, and an image generator that updates pictures in real time while users type prompts, as it races to catch up to generative AI market leader OpenAI. The models will be integrated into virtual assistant Meta AI, which the company is pitching as the most sophisticated of its free-to-use peers. The assistant will be given more prominent billing within Meta's Facebook, Instagram, WhatsApp and Messenger apps as well as a new standalone website that positions it to compete more directly with Microsoft-backed OpenAI's breakout hit ChatGPT. The announcement comes as Meta has been scrambling to push generative AI products out to its billions of users to challenge OpenAI's leading position on the technology, involving an overhaul of computing infrastructure and the consolidation of previously distinct research and product teams. The social media giant equipped Llama 3 with new computer coding capabilities and fed it images as well as text this time, though for now the model will output only text, Chris Cox, Meta's chief product officer, said in an interview.

A year and a half into the AI boom, there's growing evidence that the hype machine is slowing down. Drastic warnings about AI posing an existential threat to humanity or taking everyone's jobs have mostly disappeared, replaced by technical conversations about how to cajole chatbots into helping summarize insurance policies or handle customer service calls. Some once-promising start-ups have already cratered and the suite of flashy products launched by the biggest players in the AI race -- OpenAI, Microsoft and Google -- have yet to upend the way people work and communicate with each other. While money keeps pouring into AI, very few companies are turning a profit on the tech, which remains hugely expensive to build and run.

Fortunes are being made on that interest, in a way hauntingly reminiscent of the crypto boom and subsequent bust. And absolutely everyone wants to get in on the former and avoid the latter. "Everyone" in this case includes pretty much every possible technology company. While OpenAI is at the center of this particular gold rush, and Nvidia is selling the shovels to digital forty-niners, more conventional players aren't resting on their haunches. As happens with buzzwords, it's quickly becoming diluted -- every new PC is an "AI PC," which means very little for actual users.

This research paper will bring forth the innovative pedagogical approach in computer science education, which uses a combination of methodologies borrowed from Artificial Intelligence (AI) and DevOps to enhance the learning experience in Content Management Systems (CMS) Development. It has been done over three academic years, comparing the traditional way of teaching with the lately introduced AI-supported techniques. This had three structured sprints, each one of them covering the major parts of the sprint: object-oriented PHP, theme development, and plugin development. In each sprint, the student deals with part of the theoretical content and part of the practical task, using ChatGPT as an auxiliary tool. In that sprint, the model will provide solutions in code debugging and extensions of complex problems. The course includes practical examples like code replication with PHP, functionality expansion of the CMS, even development of custom plugins, and themes. The course practice includes versions' control with Git repositories. Efficiency will touch the theme and plugin output rates during development and mobile/web application development. Comparative analysis indicates that there is a marked increase in efficiency and shows effectiveness with the proposed AI- and DevOps-supported methodology. The study is very informative since education in computer science and its landscape change embodies an emerging technology that could have transformation impacts on amplifying the potential for scalable and adaptive learning approaches.

The advent of generative artificial intelligence (GenAI) technologies has revolutionized research, with significant implications for Digital Humanities (DH), a field inherently intertwined with technological progress. This article investigates how digital humanities scholars adopt, practice, as well as critically evaluate, GenAI technologies such as ChatGPT in the research process. Drawing on 76 responses collected from an international survey study, we explored digital humanities scholars' rationale for GenAI adoption in research, identified specific use cases and practices of using GenAI to support various DH research tasks, and analyzed scholars' collective perceptions of GenAI's benefits, risks, and impact on DH research. The survey results suggest that DH research communities hold divisive sentiments towards the value of GenAI in DH scholarship, whereas the actual usage diversifies among individuals and across research tasks. Our survey-based analysis has the potential to serve as a basis for further empirical research on the impact of GenAI on the evolution of DH scholarship.

This paper addresses the contentious issue of copyright infringement in images generated by text-to-image models, sparking debates among AI developers, content creators, and legal entities. State-of-the-art models create high-quality content without crediting original creators, causing concern in the artistic community. To mitigate this, we propose the \copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination. Addition involves training a \copyright plug-in for specific copyright, facilitating proper credit attribution. Extraction allows creators to reclaim copyright from infringing models, and combination enables users to merge different \copyright plug-ins. These operations act as permits, incentivizing fair use and providing flexibility in authorization. We present innovative approaches,"Reverse LoRA" for extraction and "EasyMerge" for seamless combination. Experiments in artist-style replication and cartoon IP recreation demonstrate \copyright plug-ins' effectiveness, offering a valuable solution for human copyright protection in the age of generative AIs.