The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment.

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the Grammy awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.

Is there a right way for Google's generative AI to create fake images of Nazis? Gemini, Google's answer to ChatGPT, was shown last week to generate an absurd range of racially and gender-diverse German soldiers styled in Wehrmacht garb. It was, understandably, ridiculed for not generating any images of Nazis who were actually white. Prodded further, it seemed to actively resist generating images of white people altogether. The company ultimately apologized for "inaccuracies in some historical image generation depictions" and paused Gemini's ability to generate images featuring people.

Microsoft announced an artificial intelligence partnership Monday with the French startup Mistral AI that could lessen the software giant's reliance on ChatGPT-maker OpenAI for supplying the next wave of chatbots and other generative AI products. Mistral AI emerged less than a year ago but is already what Microsoft described Monday as an "innovator and trailblazer" at the vanguard of building more efficient and cost-effective AI systems. Microsoft and Mistral didn't disclose the financial terms of the deal, though Microsoft said it involves a small investment in the Paris-based startup. That suggests it is far smaller than Microsoft's investment of billions of dollars into OpenAI, a years-long relationship that has attracted the scrutiny of antitrust regulators in the U.S. and Europe. Mistral on Monday released a public test version of its own chatbot, called Le Chat, that apparently was flooded with so much interest that a company executive said it was temporarily unavailable for part of the day.

The brave new world of Generative AI has become the latest battleground for U.S. culture wars. Google issued an apology after anti-woke X-users, including Elon Musk, shared examples of Google's chatbot Gemini refusing to generate images of white people--including historical figures--even when specifically prompted to do so. Gemini's insistence on prioritizing diversity and inclusion over accuracy is likely a well intentioned attempt to stamp out bias in early GenAI datasets that tended to create stereotypical images of Africans and other minority groups as well women, causing outrage among progressives. But there is much more at stake than the selective outrage of U.S. conservatives and progressives. How the "guardrails" of GenAI are defined and deployed is likely to have a significant and increasing impact on shaping the ecosystem of information and ideas that most humans engage with.

Everything reminds me of Her. While ChatGPT is not as powerful as the artificial intelligence from Spike Jonze's sci-fi romance movie, OpenAI's experimental memory tool for its chatbot seems to suggest a future where bots are highly personalized and capable of more fluid, lifelike conversations. OpenAI just soft-launched a new feature for ChatGPT called Memory, where the AI chatbot stores personal details that you share in conversations and refers to this information during future chats. Right now, ChatGPT's Memory feature is available only to a small group of users to test--it's unclear when a wider rollout for more chatbot users will happen. The feature is expected to be available for all chatbot users, not just subscribers to ChatGPT Plus.

It is undeniable that technological advances and cultural shifts have transformed our online universe over the years--especially with the recent surge in AI-generated content--but Deckelmann still isn't afraid of people on the internet. She believes they are its future. In the summer of 2022, when she stepped into the newly created role of CPTO, Deckelmann didn't know that a few months later, the race to build generative AI would accelerate to a breakneck pace. With the release of OpenAI's ChatGPT and other large language models, and the multibillion-dollar funding cycle that followed, 2023 became the year of the chatbot. And because these models require heaps of cheap (or, preferably, even free) content to function, Wikipedia's tens of millions of articles have become a rich source of fuel. To anyone who's spent time on the internet, it makes sense that bots and bot builders would look to Wikipedia to strengthen their own knowledge collections.

Booking a badminton court at one of Singapore's 100-odd community centers can be a workout in itself, with residents forced to type in times and venues repeatedly on a website until they find a free slot. Thanks to artificial intelligence (AI), it could soon be easier. The People's Association, which runs the community centers, worked with a government tech agency to build a chatbot powered by generative artificial intelligence to help residents find free courts in the city-state's four official languages. The booking chatbot, which could be rolled out shortly, is among more than 100 generative AI-based solutions spurred by the AI Trailblazers project, launched last year to find AI-based solutions to everyday problems.

Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on their underlying techniques, applications across different domains, and their challenges. We delve into the theoretical foundations of diffusion models, including concepts such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling. Furthermore, we explore the diverse applications of these models in text-to-image, image inpainting, and image super-resolution, along with others, showcasing their potential in creative tasks and data augmentation. By synthesizing existing research and highlighting critical advancements in this field, this survey aims to provide researchers and practitioners with a comprehensive understanding of generative AI diffusion and legacy models and inspire future innovations in this exciting area of artificial intelligence.

Generative AI platforms and features are permeating many aspects of work. Entrepreneurs from lean economies in particular are well positioned to outsource tasks to generative AI given limited resources. In this paper, we work to address a growing disparity in use of these technologies by building on a four-year partnership with a local entrepreneurial hub dedicated to equity in tech and entrepreneurship. Together, we co-designed an interactive workshops series aimed to onboard local entrepreneurs to generative AI platforms. Alongside four community-driven and iterative workshops with entrepreneurs across five months, we conducted interviews with 15 local entrepreneurs and community providers. We detail the importance of communal and supportive exposure to generative AI tools for local entrepreneurs, scaffolding actionable use (and supporting non-use), demystifying generative AI technologies by Figure 1: We designed an introductory generative AI workshop emphasizing entrepreneurial power, while simultaneously deconstructing series with entrepreneurs and tech providers which centered the veneer of simplicity to address the many operational communal experience, supportive exposure, tangible skills needed for successful application.