This literature review interrogates the intersections between artificial intelligence, poetry, and art, offering a comprehensive exploration of both historical evolution and current debates in digital creative practices. It traces the development of computer-generated poetry from early template-based systems to generative models, critically assessing evaluative frameworks such as adaptations of the Turing Test, the FACE model, and ProFTAP. It also examines how these frameworks endeavour to measure creativity, semantic coherence, and cultural relevance in AI-generated texts, whilst highlighting the persistent challenges in replicating the nuance of human poetic expression. The review contributes a Marketing Theory discussion that deconstructs the figurative marketing narratives employed by AI companies, which utilise sanitised language and anthropomorphic metaphors to humanise their technologies. This discussion reveals the reductive nature of such narratives and underscores the tension between algorithmic precision and the realities of human creativity.The review also incorporates an auto-ethnographic account that offers a self-reflexive commentary on its own composition. By acknowledging the use of AI in crafting this review, the auto-ethnographic account destabilises conventional notions of authorship and objectivity, resonating with deconstruction and challenging logocentric assumptions in academic discourse. Ultimately, the review calls for a re-evaluation of creative processes that recognises the interdependence of technological innovation and human subjectivity. It advocates for interdisciplinary dialogue addressing ethical, cultural, and philosophical concerns, while reimagining the boundaries of artistic production.

As the video games industry continues to face massive layoffs, narrative jobs are taking the biggest hit. The industry's job cuts over the past couple of years--more than 30,000 roles were eliminated in 2023 and 2024--disproportionately affected narrative designers, the creative professionals who craft the story elements of the game and give a title its emotional punch. Even the director of the game Avowed, Carrie Patel--a successful author and narrative developer with over a decade of experience at the game studio Obsidian Entertainment--feels lucky she was able to start her career years ago. She can't imagine trying to break into the industry under today's conditions. "It just seems to be harder and harder to find a path in," Patel says. "I've heard colleagues hired within the last three or five years say essentially the same thing."

Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception performance. However, compared with single-vehicle perception, the openness of a CP system makes it more vulnerable to malicious attacks that can inject malicious information to mislead the perception of an ego vehicle, resulting in severe risks for safe driving. To mitigate such vulnerability, we first propose a new paradigm for malicious agent detection that effectively identifies malicious agents at the feature level without requiring verification of final perception results, significantly reducing computational overhead. Building on this paradigm, we introduce CP-GuardBench, the first comprehensive dataset provided to train and evaluate various malicious agent detection methods for CP systems. Furthermore, we develop a robust defense method called CP-Guard+, which enhances the margin between the representations of benign and malicious features through a carefully designed Dual-Centered Contrastive Loss (DCCLoss). Finally, we conduct extensive experiments on both CP-GuardBench and V2X-Sim, and demonstrate the superiority of CP-Guard+.

Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks. The study spanned the entire creative journey from ideation to releasing these songs on Spotify. Participants leveraged AI for music and lyric production, cover art, and distribution. Our findings highlight how AI transforms creative workflows: accelerating ideation but compressing the traditional preparation stage, and requiring novices to navigate a challenging idea selection and validation phase. We also identified a new "collaging and refinement" stage, where participants creatively combined diverse AI-generated outputs into cohesive works. Furthermore, AI influenced group social dynamics and role division among human creators. Based on these insights, we propose the Human-AI Co-Creation Stage Model and the Human-AI Agency Model, offering new perspectives on collaborative co-creation with AI.

Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system precision@k of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.

Creative and disruptive insights (CDIs), such as the development of the theory of relativity, have punctuated human history, marking pivotal shifts in our intellectual trajectory. Recent advancements in artificial intelligence (AI) have sparked debates over whether state of the art models possess the capacity to generate CDIs. We argue that the ability to create CDIs should be regarded as a significant feature of machine superintelligence (SI).To this end, we propose a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind. We propose the Einstein test: given the data available prior to the emergence of a known CDI, can an AI independently reproduce that insight (or one that is formally equivalent)? By achieving such a milestone, a machine can be considered to at least match humanity's past top intellectual achievements, and therefore to have the potential to surpass them.

So much of this year felt like a fever dream: The attempted assassination of Donald Trump. Which is why, this year, I'm leaning into my nerdish tendencies and rounding up some good, interesting, or inspiring news stories from the science world--promising discoveries, exciting new data, historic events, and unsung heroes. In the hope of providing relief from the hell that has been 2024, here's a non-comprehensive list of the year's coolest science stories, both big and small: Wildlife filmmaker Carlos Gauna and University of California, Riverside, PhD student Phillip Sternes spotted what appears to be a baby great white shark off the coast of California last year. In January, the team published the photos in the journal Environmental Biology of Fishes. "Where white sharks give birth is one of the holy grails of shark science. No one has ever been able to pinpoint where they are born, nor has anyone seen a newborn baby shark alive," Gauna said in a UC Riverside press release.

The birth of ChatGPT brought a collection of anxieties regarding how large language models allow users to quickly subvert processes that once required human time, effort, passion, and understanding. And further, the tech sector's often stormy relationship with regulation and ethical oversight have left many fearful for a future where artificial intelligence replaces humans at work and stymies human creativity. While much of this alarm is well founded, we should also consider the possibility that human creativity can blossom in the age of AI. In 2025, we will start to see this manifest in our collective cultural response to technology. To examine how culture and creativity might adapt to the age of AI, we'll use hip-hop as an example.

Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology.

Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creativity. This paper explores the neurobiological machinery that underlies these internal processes and describes the experiential component of creativity. It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different. The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the integration of knowledge, and the diversity of ideas.