Advanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of AI for citizens' ability to make informed choices about political representatives and issues (epistemic impacts). We ask how AI might be used to destabilise or support democratic mechanisms like elections (material impacts). Finally, we discuss whether AI will strengthen or weaken democratic principles (foundational impacts). It is widely acknowledged that new AI systems could pose significant challenges for democracy. However, it has also been argued that generative AI offers new opportunities to educate and learn from citizens, strengthen public discourse, help people find common ground, and to reimagine how democracies might work better.

Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' potential intentions. Consequently, machines need to interact with users multiple rounds to better understand users' intents. The unpredictable costs of using or learning image generation models through multiple feedback interactions hinder their widespread adoption and full performance potential, especially for non-expert users. In this research, we aim to enhance the user-friendliness of our image generation system. To achieve this, we propose a reflective human-machine co-adaptation strategy, named RHM-CAS. Externally, the Agent engages in meaningful language interactions with users to reflect on and refine the generated images. Internally, the Agent tries to optimize the policy based on user preferences, ensuring that the final outcomes closely align with user preferences. Various experiments on different tasks demonstrate the effectiveness of the proposed method.

With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

Police departments are often some of the tech industry's earliest adopters of new products like drones, facial recognition, predictive software, and now–artificial intelligence. After already embracing AI audio transcription programs, some departments are now testing a new, more comprehensive tool--software that leverages technology similar to ChatGPT to auto-generate police reports. According to an August 26 report from Associated Press, many officers are already "enthused" by the generative AI tool that claims to shave 30-45 minutes from routine officework. Initially announced in April by Axon, Draft One is billed as the "latest giant leap toward [the] moonshot goal to reduce gun-related deaths between police and the public." The company--best known for Tasers and law enforcement's most popular lines of body cams--claims its initial trials cut an hour of paperwork per day for users.

Nvidia's rise to a 2-trillion valuation at the beginning of 2024 underscored the extraordinary computing demands of artificial intelligence systems that power ChatGPT and a host of other cloud services that create videos, music, and computer programs on demand. The power of computing and memory scaling has provided much of the impetus behind the surge in interest in generative AI based on large language models (LLMs). As models get bigger they seem to harness emergent behavior, making them more useful. But, as the growth in parameter counts has easily outstripped Moore's Law, such scaling comes at a high cost. Much of the concern around resource usage has been focused on the enormous arrays of graphics processing units (GPUs) and accelerators in training grids used to train models for weeks at a time.

There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings. However, with generative AI, these personalized offerings are elevated by incorporating tailored communication that considers the customer's persona, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual handler might overlook, enhancing efficiency and maximizing recovery potential.

Every time someone uses ChatGPT to write an essay, create an image or advise them on planning their day, the environment pays a price. A query on the chatbot that uses artificial intelligence is estimated to require at least 10 times more electricity than a standard search on Google. If all Google searches similarly used generative AI, they might consume as much electricity as a country the size of Ireland, calculates Alex de Vries, the founder of Digiconomist, a website that aims to expose the unintended consequences of digital trends. Yet someone using ChatGPT or another artificial intelligence application has no way of knowing how much power their questions will consume as they are processed in the tech companies' enormous data centers. De Vries said the skyrocketing energy demand of AI technologies will no doubt require the world to burn more climate-warming oil, gas and coal.

Earlier this month, executives from Alphabet's Google DeepMind, Microsoft and Meta Platforms joined tech founders in Bangalore to watch one of India's top artificial intelligence startups unveil a new product that might change how the world's most populous country uses the technology. Sarvam AI, often described as India's OpenAI, introduced software for businesses that can interact with customers using spoken voice rather than just text. The technology was developed with data from 10 native Indian languages and is priced at a rupee per minute to capture the market.

This paper explores the potential of generative AI within interactive art, employing a practice-based research approach. It presents the interactive artwork "Visions of Destruction" as a detailed case study, highlighting its innovative use of generative AI to create a dynamic, audience-responsive experience. This artwork applies gaze-based interaction to dynamically alter digital landscapes, symbolizing the impact of human activities on the environment by generating contemporary collages created with AI, trained on data about human damage to nature, and guided by audience interaction. The transformation of pristine natural scenes into human-made and industrialized landscapes through viewer interaction serves as a stark reminder of environmental degradation. The paper thoroughly explores the technical challenges and artistic innovations involved in creating such an interactive art installation, emphasizing the potential of generative AI to revolutionize artistic expression, audience engagement, and especially the opportunities for the interactive art field. It offers insights into the conceptual framework behind the artwork, aiming to evoke a deeper understanding and reflection on the Anthropocene era and human-induced climate change. This study contributes significantly to the field of creative AI and interactive art, blending technology and environmental consciousness in a compelling, thought-provoking manner.

Ongoing advancements in Generative AI (GenAI) have boosted the potential of applying long-standing "learning-by-teaching" practices in the form of a teachable agent (TA). Despite the recognized roles and opportunities of TAs, less is known about how GenAI could create synergy or introduce challenges in TAs and how students perceived the application of GenAI in TAs. This study explored middle school students' perceived roles, benefits, and challenges of GenAI-powered TAs in an authentic mathematics classroom. Through classroom observation, focus-group interviews, and open-ended surveys of 108 sixth-grade students, we found that students expected the GenAI-powered TA to serve as a learning companion, facilitator, and collaborative problem-solver. Students also expressed the benefits and challenges of GenAI-powered TAs. This study provides implications for the design of educational AI and AI-assisted instruction.