After ChatGPT broke cover in late 2022 and the tech industry embarked on its contemporary rendering of tulip mania, people started to wonder why the biggest tech giant of all – Apple – was keeping its distance from the madness. Eventually, the tech commentariat decided that there could be only two possible interpretations of this corporate standoffishness: either Apple was way behind the game being played by OpenAI et al; or it had cunning plans to unleash upon the world its own world-beating take on the technology. Finally, at its annual World Wide Developers' Conference (WWDC) on 10 June last year Apple came clean. For Apple, "AI" would not mean what those vulgar louts at OpenAI, Google, Microsoft and Meta raved about, but something altogether more refined and sophisticated – something called "Apple Intelligence". It was not, as the veteran Apple-watcher John Gruber put it, a single thing or product but "a marketing term for a collection of features, apps, and services". Putting it all under a single, memorable label made it easier for users to understand that Apple was launching something really novel.

Higher use of chatbots like ChatGPT may correspond with increased loneliness and less time spent socializing with other people, according to new research from OpenAI in partnership with the Massachusetts Institute of Technology. Those who spent more time typing or speaking with ChatGPT each day tended to report higher levels of emotional dependence on, and problematic use of, the chatbot, as well as heightened levels of loneliness, according to research released Friday. The findings were part of a pair of studies conducted by researchers at the two organizations and have not been peer reviewed. The launch of ChatGPT in late 2022 helped kick off a frenzy for generative artificial intelligence. Since then, people have used chatbots for everything from coding to ersatz therapy sessions.

This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.

We present generative artificial intelligence (AI) to empirically validate fundamental laws of physics, focusing on the Stefan-Boltzmann law linking stellar temperature and luminosity. Our approach simulates counterfactual luminosities under hypothetical temperature regimes for each individual star and iteratively refines the temperature-luminosity relationship in a deep learning architecture. We use Gaia DR3 data and find that, on average, temperature's effect on luminosity increases with stellar radius and decreases with absolute magnitude, consistent with theoretical predictions. By framing physics laws as causal problems, our method offers a novel, data-driven approach to refine theoretical understanding and inform evidence-based policy and practice.

As robots increasingly operate in dynamic human-centric environments, improving their ability to detect, explain, and recover from action-related issues becomes crucial. Traditional model-based and data-driven techniques lack adaptability, while more flexible generative AI methods struggle with grounding extracted information to real-world constraints. We introduce RAIDER, a novel agent that integrates Large Language Models (LLMs) with grounded tools for adaptable and efficient issue detection and explanation. Using a unique "Ground, Ask& Answer, Issue" procedure, RAIDER dynamically generates context-aware precondition questions and selects appropriate tools for resolution, achieving targeted information gathering. Our results within a simulated household environment surpass methods relying on predefined models, full scene descriptions, or standalone trained models. Additionally, RAIDER's explanations enhance recovery success, including cases requiring human interaction. Its modular architecture, featuring self-correction mechanisms, enables straightforward adaptation to diverse scenarios, as demonstrated in a real-world human-assistive task. This showcases RAIDER's potential as a versatile agentic AI solution for robotic issue detection and explanation, while addressing the problem of grounding generative AI for its effective application in embodied agents. Project website: https://raider-llmagent.github.io/

New studies from OpenAI and MIT Media Lab found that, generally, the more time users spend talking to ChatGPT, the lonelier they feel. The connection was made as part of two, yet-to-be-peer-reviewed studies, one done at OpenAI analyzing "over 40 million ChatGPT interactions" and targeted user surveys, and another at MIT Media Lab following participants' ChatGPT use for four weeks. MIT's study identified several ways talking to ChatGPT -- whether through text or voice -- can affect a person's emotional experience, beyond the general finding that higher use led to "heightened loneliness and reduced socialization." For example, participants who already trusted the chatbot and tended to get emotionally attached in human relationships felt lonelier and more emotionally dependent on ChatGPT during the study. Those effects were less severe with ChatGPT's voice mode, though, particularly if ChatGPT spoke in a neutral tone.

The researchers found some intriguing differences between how men and women respond to using ChatGPT. After using the chatbot for four weeks, female study participants were slightly less likely to socialize with people than their male counterparts who did the same. Meanwhile, participants who interacted with ChatGPT's voice mode in a gender that was not their own for their interactions reported significantly higher levels of loneliness and more emotional dependency on the chatbot at the end of the experiment. OpenAI plans to submit both studies to peer-reviewed journals. Chatbots powered by large language models are still a nascent technology, and it's difficult to study how they affect us emotionally.

That was how long Google was giving Sissie Hsiao. A hundred days to build a ChatGPT rival. By the time Hsiao took on the project in December 2022, she had spent more than 16 years at the company. She led thousands of employees. Hsiao had seen her share of corporate crises--but nothing like the code red that had been brewing in the days since OpenAI, a small research lab, released its public experiment in artificial intelligence.

This study provides the first comprehensive assessment of consistency and reproducibility in Large Language Model (LLM) outputs in finance and accounting research. We evaluate how consistently LLMs produce outputs given identical inputs through extensive experimentation with 50 independent runs across five common tasks: classification, sentiment analysis, summarization, text generation, and prediction. Using three OpenAI models (GPT-3.5-turbo, GPT-4o-mini, and GPT-4o), we generate over 3.4 million outputs from diverse financial source texts and data, covering MD&As, FOMC statements, finance news articles, earnings call transcripts, and financial statements. Our findings reveal substantial but task-dependent consistency, with binary classification and sentiment analysis achieving near-perfect reproducibility, while complex tasks show greater variability. More advanced models do not consistently demonstrate better consistency and reproducibility, with task-specific patterns emerging. LLMs significantly outperform expert human annotators in consistency and maintain high agreement even where human experts significantly disagree. We further find that simple aggregation strategies across 3-5 runs dramatically improve consistency. Simulation analysis reveals that despite measurable inconsistency in LLM outputs, downstream statistical inferences remain remarkably robust. These findings address concerns about what we term "G-hacking," the selective reporting of favorable outcomes from multiple Generative AI runs, by demonstrating that such risks are relatively low for finance and accounting tasks.

This study employs the Paul-Elder Critical Thinking Model and Tan's argumentative writing framework to create a structured methodology. This methodology, ChatGPT Guideline for Critical Argumentative Writing (CGCAW) framework, integrates the models with ChatGPT's capabilities to guide L2 learners in utilizing ChatGPT to enhance their critical thinking skills. A quantitative experiment was conducted with 10 participants from a state university, divided into experimental and control groups. The experimental group utilized the CGCAW framework, while the control group used ChatGPT without specific guidelines. Participants wrote an argumentative essay within a 40-minute timeframe, and essays were evaluated by three assessors: ChatGPT, Grammarly, and a course instructor. Results indicated that the experimental group showed improvements in clarity, logical coherence, and use of evidence, demonstrating ChatGPT's potential to enhance specific aspects of argumentative writing. However, the control group performed better in overall language mechanics and articulation of main arguments, indicating areas where the CGCAW framework could be further refined. This study highlights the need for further research to optimize the use of AI tools like ChatGPT in L2 learning environments to enhance critical thinking and writing skills.