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.

Generative AI, which can create text and chat with users, presents a unique challenge because it can make people feel like they're interacting with a human. Anthropomorphism is the ascription of human attributes or personality to nonhumans. People often anthropomorphize artificial intelligence (especially Generative AI) because it can create human-like outputs. Among them, information transmission activities based on artificial intelligence technology have received more and more attention. With the help of artificial intelligence technology to obtain information and transmit information, it can be more convenient and accelerate the realization of information interaction, industry marketing, user interaction, brand publicity, and advertising, and create more creative content. Artificial intelligence technology has brought great changes and more availability to everyone's daily life and receiving information channels. However, the collection of personal data is more and more extensive, which also makes the problem of personal data privacy and security more serious. Therefore, combined with the double-sided nature of artificial intelligence, this paper analyzes the advantages and disadvantages of intelligent data processing in personal data privacy, applies the machine learning differential privacy algorithm combined with intelligent data processing to the research, and realizes the risk prediction and protection of personal data. This serves as a reminder for everyone on how to use artificial intelligence to protect their information security more effectively."

Generative AI image models may inadvertently generate problematic representations of people. Past research has noted that millions of users engage daily across the world with these models and that the models, including through problematic representations of people, have the potential to compound and accelerate real-world discrimination and other harms (Bianchi et al, 2023). In this paper, we focus on addressing the generation of problematic associations between demographic groups and semantic concepts that may reflect and reinforce negative narratives embedded in social data. Building on sociological literature (Blumer, 1958) and mapping representations to model behaviors, we have developed a taxonomy to study problematic associations in image generation models. We explore the effectiveness of fine tuning at the model level as a method to address these associations, identifying a potential reduction in visual quality as a limitation of traditional fine tuning. We also propose a new methodology with twice-human-in-the-loop (T-HITL) that promises improvements in both reducing problematic associations and also maintaining visual quality. We demonstrate the effectiveness of T-HITL by providing evidence of three problematic associations addressed by T-HITL at the model level. Our contributions to scholarship are two-fold. By defining problematic associations in the context of machine learning models and generative AI, we introduce a conceptual and technical taxonomy for addressing some of these associations. Finally, we provide a method, T-HITL, that addresses these associations and simultaneously maintains visual quality of image model generations. This mitigation need not be a tradeoff, but rather an enhancement.

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits.