Deep learning [6, 29] is a technology that has revolutionized many areas of modern life. The term describes the gradient-based training of deep neural networks. Since its breakthrough in image classification in 2012 [28], deep learning is essentially the only viable technology for this application. Moreover, it is the basis of multiple recent breakthroughs in science [25] and even mathematical research [14]. Recently, deep learning has received wide public attention through the advent of generative AI in the form of large language models such as ChatGPT [39]. It is well-documented that deep learning in modern applications can have extreme requirements on computational resources and the hardware requirements scale in an unsustainable way [52]. In constrained settings, this can become a serious bottleneck preventing the employment of deep learning methods. In addition, these comprehensive computations come with an immense environmental cost.

Meta, owner of Facebook and Instagram, announced major changes to its policies on digitally created and altered media on Friday, before elections poised to test its ability to police deceptive content generated by artificial intelligence technologies. The social media giant will start applying "Made with AI" labels in May to AI-generated videos, images and audio posted on Facebook and Instagram, expanding a policy that previously addressed only a narrow slice of doctored videos, the vice-president of content policy, Monika Bickert, said in a blogpost. Bickert said Meta would also apply separate and more prominent labels to digitally altered media that poses a "particularly high risk of materially deceiving the public on a matter of importance", regardless of whether the content was created using AI or other tools. Meta will begin applying the more prominent "high-risk" labels immediately, a spokesperson said. The approach will shift the company's treatment of manipulated content, moving from a focus on removing a limited set of posts toward keeping the content up while providing viewers with information about how it was made.

Elon Musk says Tesla is raising compensation for its AI engineers, saying OpenAI is "aggressively recruiting" them. 'CRAZIEST TALENT WAR': Tesla CEO Elon Musk said the electric vehicle giant is giving its artificial intelligence engineers a raise as the automaker tries to fend off poaching efforts by ChatGPT creator OpenAI. COSTLY GAME: More and more sports bettors appear to be turning to artificial intelligence to help counter the notoriously unpredictable tournament, which is often referred to as March Madness. LEISURE TIME: Billionaire investor and New York Mets owner Steve Cohen said in a Wednesday appearance on CNBC's "Squawk Box," that he believes that the majority of workers will eventually have a four-day work week and three-day weekend, which will expand opportunities for individuals to engage in leisurely pursuits. FIGHT AGAINST AI: Comedian George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence.

AI models using individual's work without permission (or compensation) is nothing new, with entities like The New York Times and Getty Images initiating lawsuits against AI creators alongside artists and writers. In March, OpenAI CTO Mira Murati contributed to the ongoing uncertainty, telling The Wall Street Journal she wasn't sure if Sora, the company's new text-to-video AI tool, takes data from YouTube, Instagram or Facebook posts. Now, YouTube's CEO Neal Mohan has responded with a clear warning to OpenAI that using its videos to teach Sora would be a "clear violation" of the platform's terms of use. In an interview with Bloomberg Originals host Emily Chang, Mohan stated, "From a creator's perspective, when a creator uploads their hard work to our platform, they have certain expectations. One of those expectations is that the terms of service is going to be abided by. It does not allow for things like transcripts or video bits to be downloaded, and that is a clear violation of our terms of service. Those are the rules of the road in terms of content on our platform."

Even before OpenAI released ChatGPT in late 2022 and kicked off the AI boom, camera makers Nikon and Leica began developing ways to imprint special "metadata" that lists when and by whom a photo was taken directly when the image is made by the camera. Canon and Sony have begun similar programs, and Qualcomm, which makes computer chips for smartphones, says it has a similar project to add metadata to images taken on phone cameras.

This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties.

Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services. In this paper, we share our experiences in developing and operating generative AI models within a national-scale search engine, with a specific focus on the sensitiveness of user queries. We propose a taxonomy for sensitive search queries, outline our approaches, and present a comprehensive analysis report on sensitive queries from actual users.

Scaling law principles indicate a power-law correlation between loss and variables such as model size, dataset size, and computational resources utilized during training. These principles play a vital role in optimizing various aspects of model pre-training, ultimately contributing to the success of large language models such as GPT-4, Llama and Gemini. However, the original scaling law paper by OpenAI did not disclose the complete details necessary to derive the precise scaling law formulas, and their conclusions are only based on models containing up to 1.5 billion parameters. Though some subsequent works attempt to unveil these details and scale to larger models, they often neglect the training dependency of important factors such as the learning rate, context length and batch size, leading to their failure to establish a reliable formula for predicting the test loss trajectory. In this technical report, we confirm that the scaling law formulations proposed in the original OpenAI paper remain valid when scaling the model size up to 33 billion, but the constant coefficients in these formulas vary significantly with the experiment setup. We meticulously identify influential factors and provide transparent, step-by-step instructions to estimate all constant terms in scaling-law formulas by training on models with only 1M~60M parameters. Using these estimated formulas, we showcase the capability to accurately predict various attributes for models with up to 33B parameters before their training, including (1) the minimum possible test loss; (2) the minimum required training steps and processed tokens to achieve a specific loss; (3) the critical batch size with an optimal time/computation trade-off at any loss value; and (4) the complete test loss trajectory with arbitrary batch size.

Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age. Over the last decade, there has been a notable surge in the recognition and application of data storytelling, both in academia and industry. Recently, the rapid development of generative AI has brought new opportunities and challenges to this field, sparking numerous new questions. These questions may not necessarily be quickly transformed into papers, but we believe it is necessary to promptly discuss them to help the community better clarify important issues and research agendas for the future. We thus invite you to join our workshop (Gen4DS) to discuss questions such as: How can generative AI facilitate the creation of data stories? How might generative AI alter the workflow of data storytellers? What are the pitfalls and risks of incorporating AI in storytelling? We have designed both paper presentations and interactive activities (including hands-on creation, group discussion pods, and debates on controversial issues) for the workshop. We hope that participants will learn about the latest advances and pioneering work in data storytelling, engage in critical conversations with each other, and have an enjoyable, unforgettable, and meaningful experience at the event.

Text-to-image generative models, specifically those based on diffusion models like Imagen and Stable Diffusion, have made substantial advancements. Recently, there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However, the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps, which requires significant manual intervention. To address this, we introduce the \textbf{P}rompt \textbf{A}uto-\textbf{E}diting (PAE) method. Besides refining the original prompts for image generation, we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider aesthetic score, semantic consistency, and user preferences. Experimental results demonstrate that our proposed method effectively improves the original prompts, generating visually more appealing images while maintaining semantic alignment. Code is available at https://github.com/Mowenyii/PAE.