Science and Technology

Artificial intelligence systems, especially large language models, can generate outputs that sound confident but are factually incorrect or unsupported. These errors are commonly called hallucinations. They arise from probabilistic text generation, incomplete training data, ambiguous prompts, and the absence of real-world grounding. Improving AI reliability focuses on reducing these hallucinations while preserving creativity, fluency, and usefulness.Superior and Meticulously Curated Training DataOne of the most impactful techniques is improving the data used to train AI systems. Models learn patterns from massive datasets, so inaccuracies, contradictions, or outdated information directly affect output quality.Data filtering and deduplication: Removing low-quality, repetitive, or contradictory sources…

Dreaming is a nearly universal human experience, with most individuals drifting into several dreams each night, although what they see, how vivid it feels, and what they later remember can differ greatly. Researchers investigate dreams to explore how the brain handles memory, emotion, creativity, and overall activity. Although no single, definitive explanation clarifies why dreaming occurs, a growing body of evidence from neurobiology, psychology, evolutionary perspectives, and clinical research suggests a multifaceted set of purposes and underlying processes.How the brain operates while dreamingDreams are typically most intense during rapid eye movement (REM) sleep, yet they can also emerge throughout non-REM…

Productivity improvements driven by AI copilots often remain unclear when viewed through traditional measures such as hours worked or output quantity. These tools support knowledge workers by generating drafts, producing code, examining data, and streamlining routine decision-making. As adoption expands, organizations need a multi-dimensional evaluation strategy that reflects efficiency, quality, speed, and overall business outcomes, while also considering the level of adoption and the broader organizational transformation involved.Clarifying How the Business Interprets “Productivity Gain”Before measurement begins, companies align on what productivity means in their context. For a software firm, it may be faster release cycles and fewer defects. For a…

A quiet experiment is exploring what unfolds when artificial intelligence systems engage with each other on a large scale, keeping humans outside the core of their exchanges, and its early outcomes are prompting fresh concerns about technological advancement as well as issues of trust, oversight, and security in a digital environment that depends more and more on automation.A newly introduced platform named Moltbook has begun attracting notice throughout the tech community for an unexpected reason: it is a social network built solely for artificial intelligence agents. People are not intended to take part directly. Instead, AI systems publish posts, exchange…

Large foundation models have dominated public attention in artificial intelligence due to their broad capabilities, massive training datasets, and impressive performance across many tasks. However, a parallel shift is underway. Smaller, specialized AI models are increasingly competitive by focusing on efficiency, domain expertise, and practical deployment advantages. Rather than replacing foundation models, these compact systems are reshaping how organizations think about performance, cost, and real-world impact.What Characterizes Compact, Purpose-Built AI ModelsSmaller, specialized models are designed with a narrow or clearly defined purpose. They typically have fewer parameters, are trained on curated datasets, and target specific industries or tasks such as…

Alcohol is one of the most commonly used psychoactive substances worldwide. Many people treat modest drinking—one glass of wine with dinner, a beer after work—as harmless or even beneficial. That view is increasingly challenged by medical evidence showing that even small amounts can raise the risk of injury and disease, interact dangerously with other conditions and medicines, and contribute to long-term harm at a population level. This article explains why “a little” isn’t always harmless, with concrete mechanisms, data, examples, and practical steps.What “a little” meansStandard drink definitions: In the United States a standard drink contains about 14 grams of…