Science Biology Evolution Biologists discover gene that may determine'good' and'bad' dads Social cues and surprising genetics may affect mammal fathers. Breakthroughs, discoveries, and DIY tips sent six days a week. Most mammals grow up in single parent homes. It's estimated that over 95 percent of the planet's nearly 6,000 known mammalian species rely almost exclusively on mothers to nurture and raise their offspring. But even when dads stick around, it's not always smooth sailing.

User profiling, as a core technique for user understanding, aims to infer structural attributes from user information. Large Language Models (LLMs) provide a promising avenue for user profiling, yet the progress is hindered by the lack of comprehensive benchmarks. To bridge this gap, we propose ProfileBench, an industrial benchmark derived from a real-world video platform, encompassing heterogeneous user data and a well-structured profiling taxonomy. However, the profiling task remains challenging due to the difficulty of collecting large-scale ground-truth labels, and the heterogeneous and noisy user information can compromise the reliability of LLMs. To approach label-free and reliable user profiling, we propose a Confidence-driven Profile reasoning framework Conf-Profile, featuring a two-stage paradigm. We first synthesize high-quality labels by leveraging advanced LLMs with confidence hints, followed by confidence-weighted voting for accuracy improvement and confidence calibration for a balanced distribution. The multiple profile results, rationales, and confidence scores are aggregated and distilled into a lightweight LLM. We further enhance the reasoning ability via confidence-guided unsupervised reinforcement learning, which exploits confidence for difficulty filtering, quasi-ground truth voting, and reward weighting. Experimental results demonstrate that Conf-Profile delivers substantial performance through the two-stage training, improving F1 by 13.97 on Qwen3-8B.

Breakthroughs, discoveries, and DIY tips sent every weekday. Just as birth rates decline in many wealthy and developed nations, dog parenting is remaining steady and even gaining in popularity. Up to half of households in Europe and 66 percent of homes in the United States have at least one dog and these pets are often regarded as a family member or "fur baby." To dig into what this shift says about our society, researchers from Eötvös Loránd University in Budapest, Hungary conducted a literature review to analyze the data. They propose that while dogs do not replace children, they can offer a chance to fulfill an innate nurturing drive similar to parenting, but with fewer demands than raising biological children.

Social psychologist and NYU professor Jonathan Haidt joins'Fox & Friends Weekend' to discuss banning social media for children in the United States. Babies born in the year 2025 will begin the newest generation – Generation Beta. Following Generation Alpha (2010 to 2024), Gen Beta will comprise a new group of kids born between 2025 and 2039. The Australian research firm McCrindle predicted that Gen Beta will make up 16% of the world's population by 2035, and many will live to see the 22nd century. The research and analysis group, led by demographer and futurist Mark McCrindle, wrote in an article that Gen Beta "represents a pivotal chapter in our evolving world."

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge. However, existing methods lack effective control mechanisms for integrating internal and external knowledge. Inspired by human cognitive processes, we propose Parenting, a novel framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. Specifically, Parenting utilizes a key parameter mining method that combines forward and backward propagation signals to localize subspaces representing different capabilities. Then, Parenting employs a type-tailored tuning strategy, applying specific and appropriate optimizations to different subspaces, aiming to achieve a balanced enhancement of both adherence and robustness. Extensive experiments on various datasets and models validate the effectiveness and generalizability of our method.

Origins With Cush Jumbo Widely available, episodes weekly Cush Jumbo is always good fun when doing press interviews for her work (The Good Wife, Criminal Record, Hamlet) – and she's just as great now the tables are turned in her first podcast. She speaks to stars about their origin stories, including Kate Nash, Harlan Coben, David Schwimmer and, in episode one, Anna Wintour, who says she hates people who waffle and recalls getting fired from Harper's Bazaar because she couldn't pin a dress. Rebel Spirit Widely available, episodes weekly Comedian Akilah Hughes gives her serious mission a light touch as she returns to her Kentucky home town to try to change her high school's racist mascot from a Confederate general to a biscuit. Can she drag the school into the modern age – and what will the change mean to her and other pupils? Sara & Cariad's Weirdos Book Club Widely available, episodes weekly Sara Pascoe and Cariad Lloyd go beyond the usual selections with season two of their book club for people who don't want to discuss reading over cheese and wine.

Recently, Meta has shifted towards AI-mediated ad targeting mechanisms that do not require advertisers to provide detailed targeting criteria, likely driven by excitement over AI capabilities as well as new data privacy policies and targeting changes agreed upon in civil rights settlements. At the same time, Meta has touted their ad preference controls as an effective mechanism for users to control the ads they see. Furthermore, Meta markets their targeting explanations as a transparency tool that allows users to understand why they saw certain ads and inform actions to control future ads. Our study evaluates the effectiveness of Meta's "See less" ad control and the actionability of ad targeting explanations following the shift to AI-mediated targeting. We conduct a large-scale study, randomly assigning participants to mark "See less" to Body Weight Control or Parenting topics, and collecting the ads and targeting explanations Meta shows to participants before and after the intervention. We find that utilizing the "See less" ad control for the topics we study does not significantly reduce the number of ads shown by Meta on these topics, and that the control is less effective for some users whose demographics are correlated with the topic. Furthermore, we find that the majority of ad targeting explanations for local ads made no reference to location-specific targeting criteria, and did not inform users why ads related to the topics they marked to "See less" of continued to be delivered. We hypothesize that the poor effectiveness of controls and lack of actionability in explanations are the result of the shift to AI-mediated targeting, for which explainability and transparency tools have not yet been developed. Our work thus provides evidence for the need of new methods for transparency and user control, suitable and reflective of increasingly complex AI-mediated ad delivery systems.

"Hum," Helen Phillips's third novel, begins with a needle being drawn, steadily and irreversibly, across a woman named May's face. She is participating in a paid experiment in "adversarial tech," undergoing a procedure that will ever so slightly alter her features, making her harder for surveillance cameras to identify. As the book opens, May is mid-op, the needle advancing its "slender and relentless line of penetration" across her temple, toward the skin of her eyelid. What lies on the other side of the surgery? "Some sort of transformation, undeniable but undetectable," Phillips writes.

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. New large language models offer the possibility to automate annotation, saving time and cost. But validating these approaches requires both high quantitative performance as well as an understanding of how automated methods fit in existing workflows, and what they really offer. In this paper we present a case study of adding an automated system to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants. Our team of experienced death-penalty lawyers and NLP technologists pursued a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing human annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials was a complicated task that involves with many individual judgment calls. In contrast to standard arguments for automation that are based on efficiency and scalability, legal experts found the computational models most useful in challenging their personal bias in annotation and providing opportunities to refine and build consensus on rules for annotation. This suggests that seeking to replace experts with computational models is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies.

CyberGuy explains how to leave FaceTime messages on iOS 17. Whether you're a new parent or a seasoned one, we know how stressful and challenging it can be to raise children in this fast-paced and ever-changing world. Many parents today are looking for ways to leverage technology to enhance their parenting skills and support their children's development. And the best part is, you don't need to be a tech expert to use them. All you need is a device, an internet connection, and a chatbot named ChatGPT.