Generative large language models (LLMs) have become central to everyday life, producing human-like text across diverse domains. A growing body of research investigates whether these models also exhibit personality- and demographic-like characteristics in their language. In this work, we introduce a novel, data-driven methodology for assessing LLM personality without relying on self-report questionnaires, applying instead automatic personality and gender classifiers to model replies on open-ended questions collected from Reddit. Comparing six widely used models to human-authored responses, we find that LLMs systematically express higher Agreeableness and lower Neuroticism, reflecting cooperative and stable conversational tendencies. Gendered language patterns in model text broadly resemble those of human writers, though with reduced variation, echoing prior findings on automated agents. We contribute a new dataset of human and model responses, along with large-scale comparative analyses, shedding new light on the topic of personality and demographic patterns of generative AI.

This paper explores the rising concern of utilizing Large Language Models (LLMs) in spear phishing message generation, and their performance compared to human-authored counterparts. Our pilot study compares the effectiveness of smishing (SMS phishing) messages created by GPT-4 and human authors, which have been personalized to willing targets. The targets assessed the messages in a modified ranked-order experiment using a novel methodology we call TRAPD (Threshold Ranking Approach for Personalized Deception). Specifically, targets provide personal information (job title and location, hobby, item purchased online), spear smishing messages are created using this information by humans and GPT-4, targets are invited back to rank-order 12 messages from most to least convincing (and identify which they would click on), and then asked questions about why they ranked messages the way they did. They also guess which messages are created by an LLM and their reasoning. Results from 25 targets show that LLM-generated messages are most often perceived as more convincing than those authored by humans, with messages related to jobs being the most convincing. We characterize different criteria used when assessing the authenticity of messages including word choice, style, and personal relevance. Results also show that targets were unable to identify whether the messages was AI-generated or human-authored and struggled to identify criteria to use in order to make this distinction. This study aims to highlight the urgent need for further research and improved countermeasures against personalized AI-enabled social engineering attacks.

A novelist responds to Jeff Hewitt's "The Big Four v. ORWELL." For the first time in history, a machine is capable of crafting flash fiction stories, poems, parody Bible verses, and spoof My Little Pony episode summaries, to everyone's delight (or horror). Narrative art, once thought the sole province of humans, has been invaded by large language models. Hollywood writers have told me they're terrified that studios will fire them all and fill writers' rooms with robots in a few years. Before we've even had a chance to absorb the fact that the Turing test (used to determine if an artificial intelligence can pass as human) has been demolished, it seems we writers are being handed pink slips.

There's a chance that ChatGPT knows personal details about you--and if it doesn't, it might just make something up. As OpenAI's generative text chatbot has boomed in popularity over the past six months, the risks of the system being trained on data vacuumed up from the web have become clearer. Data regulators around the world are investigating issues with how OpenAI gathered the data it uses to train its large language models, the accuracy of answers it provides about people, and other legal concerns about the use of its generative text systems. Europe's data regulators have joined forces to look at OpenAI after Italy temporarily banned ChatGPT from the country. And Canada is also investigating the technology's potential privacy risks.

This is how ChatGPT works. You go to your device, you sign up, it prompts you to ask any question in the world, and you do so. Because what spits back, instantly, is the answer to almost anything, in clear, basic language that sounds like someone is talking to you. Which is kind of the idea. ChatGPT is the latest darling from the world of AI, which, depending on your level of fear, stands for artificial intelligence, allegedly innocent, or alien invasion.

What do our creations think of us? Generative Pre-trained Transformer 3 is a language model released by OpenAI in 2020 that uses deep learning to produce text that seems like it could have been written by a human. Taken individually, the AI's lines don't smack much of poetry or strictly cohere, but in aggregate, they gesture at something more. What would it produce if asked to meditate on the human soul and to produce spiritual poetry like ours? What does it think of our religious beliefs?

Natural language generation (NLG) has been featured in at most a handful of shipped games and interactive stories. This is certainly due to it being a very specialized practice, but another contributing factor is that the state of the art today, in terms of content quality, is simply inadequate. The major benefits of NLG are its alleviation of authorial burden and the capability it gives to a system of generating state-bespoke content, but we believe we can have these benefits without actually employing a full NLG pipeline. In this paper, we present the preliminary design of Expressionist, an in-development mixed-initiative authoring tool that instantiates an authoring scheme residing somewhere between conventional NLG and conventional human content authoring. In this scheme, a human author plays the part of an NLG module in that she starts from a set of deep representations constructed for the game or story domain and proceeds to specify dialogic content that may express those representations. Rather than authoring static dialogue, the author defines a probabilistic context-free grammar that yields templated dialogue. This allows a human author to still harness a computer's generativity, but in a capacity in which it can be trusted: operating over probabilities and treelike control structures. Additional features of Expressionist's design include arbitrary markup and realtime feedback showing currently valid derivations.

In October last year, for example, AI-generated art hit the headlines when auction house Christie's New York sold an AI-created artwork for $432,000. AI is also being used in music production, with a new industry being built around the use of AI in music. The musician Taryn Southern has used an artificial intelligence platform called Amper to create an entire album, called I AM AI. The album was the first LP to be entirely composed and produced using AI. A patented AI system called "DABUS", created by Dr Stephen Thaler, can devise and develop new ideas.

AI is capable of making music, but does that make AI an artist? As AI begins to reshape how music is made, our legal systems are going to be confronted with some messy questions regarding authorship. Do AI algorithms create their own work, or is it the humans behind them? What happens if AI software trained solely on Beyoncé creates a track that sounds just like her? "I won't mince words," says Jonathan Bailey, CTO of iZotope. "This is a total legal clusterfuck."

The goal of an interactive narrative system is to immerse users in a virtual world such that they believe that they are an integral part of an unfolding story and that their actions can significantly alter the direction or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -- the ability to organize experience into narrative form -- is central to the cognitive processes employed across a range of experiences, from entertainment to active learning.