As generative AI diffuses through academia, policy-practice divergence becomes consequential, creating demand for auditable indicators of alignment. This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use. The framework extracts empirical and epistemic signals from academics, yielding three filtered indicators of such voids: (1) AI-integrated assessment capacity (proxy) - within a three-signal screen (AI skill, perceived teaching benefit, detection confidence), the share who would fully allow AI in exams; (2) sector-level necessity (proxy) - among high output control users who still credit AI with high contribution, the proportion who judge AI capable of challenging established disciplines; and (3) ontological stance - among respondents who judge AI different in kind from prior tools, report practice change, and pass a metacognition gate, the split between material and immaterial views as an ontological map aligning procurement claims with evidence classes.

Tools for analyzing character portrayal in fiction are valuable for writers and literary scholars in developing and interpreting compelling stories. Existing tools, such as visualization tools for analyzing fictional characters, primarily rely on explicit textual indicators of character attributes. However, portrayal is often implicit, revealed through actions and behaviors rather than explicit statements. We address this gap by leveraging large language models (LLMs) to uncover implicit character portrayals. We start by generating a dataset for this task with greater cross-topic similarity, lexical diversity, and narrative lengths than existing narrative text corpora such as TinyStories and WritingPrompts. We then introduce LIIPA (LLMs for Inferring Implicit Portrayal for Character Analysis), a framework for prompting LLMs to uncover character portrayals. LIIPA can be configured to use various types of intermediate computation (character attribute word lists, chain-of-thought) to infer how fictional characters are portrayed in the source text. We find that LIIPA outperforms existing approaches, and is more robust to increasing character counts (number of unique persons depicted) due to its ability to utilize full narrative context. Lastly, we investigate the sensitivity of portrayal estimates to character demographics, identifying a fairness-accuracy tradeoff among methods in our LIIPA framework -- a phenomenon familiar within the algorithmic fairness literature. Despite this tradeoff, all LIIPA variants consistently outperform non-LLM baselines in both fairness and accuracy. Our work demonstrates the potential benefits of using LLMs to analyze complex characters and to better understand how implicit portrayal biases may manifest in narrative texts.

The improved generative capabilities of large language models have made them a powerful tool for creative writing and storytelling. It is therefore important to quantitatively understand the nature of generated stories, and how they differ from human storytelling. We augment the Reddit WritingPrompts dataset with short stories generated by GPT-3.5, given the same prompts. We quantify and compare the emotional and descriptive features of storytelling from both generative processes, human and machine, along a set of six dimensions. We find that generated stories differ significantly from human stories along all six dimensions, and that human and machine generations display similar biases when grouped according to the narrative point-of-view and gender of the main protagonist. We release our dataset and code at https://github.com/KristinHuangg/gpt-writing-prompts.

They flag offensive content on social networks and delete spam from our inboxes. At the hospital, they help convert patient--doctor conversations into insurance billing codes. Sometimes, they alert law enforcement to potential terrorist plots and predict (poorly) the threat of violence on social media. Legal professionals use them to hide or discover evidence of corporate fraud. Students are writing their next school paper with the aid of a smart word processor, capable not just of completing sentences, but generating entire essays on any topic.

Behavioural health tech provider Holmusk is banking on that, partnering authorities in Singapore to develop a suite of digital tools for hospitals and clinics. One solution the firm is looking to introduce is a "smart pill" to track when patients forget or skip their medication. How that works, is through a small, grain-sized biosensor embedded within the pill, and a sticky patch on the patient's body that can detect when the pill is ingested. The technology is approved in the United States. "Let's say schizophrenia, depression patients with some psychosis – not taking the pill for a few days can be bad enough to drive them off the cliff. And if you knew that they have stopped taking the pill two days in a row, you can intervene. You can catch them early", chief analytics officer of Holmusk, Joydeep Sarkar, told CNA.

This article was made in collaboration with Sean Eugene Chua, an undergraduate student at the University of Toronto who has shared experiences across fields that include data science, programming, and machine learning. By definition, it is when a machine can imitate human behavior and emulate how they think and act. A published paper written by logician Walter Pitts and neuroscientist Warren S. McCulloch entitled "A logical calculus of the ideas immanent in nervous activity" was regarded as a breakthrough in laying the first foundations of machine learning. It indicates the usage of mathematical principles to detail the science and psychology behind human decision-making. However, in 1950, Alan Turing introduced what computer scientists now know of as the "Turing Test" to determine whether a machine can be considered intelligent or unintelligent.

New Delhi, June 4 (IANSlife) You've probably heard for years that the workforce would be supplanted by robots. AI has changed several roles, such as using self-checkouts, ATMs, and customer support chatbots. The goal is not to scare people, but to highlight the fact that AI is constantly altering lives and executing activities to replace the human workforce. At the same time, technological advancements are producing new career prospects. AI is predicted to increase the demand for professionals, particularly in robotics and software engineering. As a result, AI has the potential to eliminate millions of current occupations while also creating millions of new ones.

You've probably heard for years that the workforce would be supplanted by robots. AI has changed several roles, such as using self-checkouts, ATMs, and customer support chatbots. The goal is not to scare people, but to highlight the fact that AI is constantly altering lives and executing activities to replace the human workforce. At the same time, technological advancements are producing new career prospects. AI is predicted to increase the demand for professionals, particularly in robotics and software engineering.

In the last 10 years, the field of robot awareness has made significant progress, and the advances are expected to proceed toward intellect and independent decision-making. Ongoing research is trying to better understand what the new AI programs will be able to do. In today's AI, connections emulate synapses, the relationships between neurons. The problem-solving ability is expected to improve with the size and complexity of links. Today's AI can translate text, make personal recommendations for books and movies, recognize people in images and videos, and more specialized applications in industry and personal life constantly surprise us.

Today, we look at the basics of artificial intelligence, which permeates almost every aspect of our lives. This article will explore the main concepts revolving around artificial intelligence and the answers to frequently asked questions without getting into technical complexities as much as possible. Artificial intelligence (AI) is a field of computer science that focuses on developing smart machines capable of accomplishing tasks that require human intellect. Most people immediately think of Artificial General Intelligence (AGI) when they hear about AI. It can perform anything that a human being can, but it does so far superior. However, the fact is that we are nowhere near to creating one.