Explains: Let our writers untangle the complex, messy world of technology to help you understand what's coming next.

Baldness does not only come from your mother's side--and other myths debunked. There are plenty of myths about hair loss. While not always true, some reveal surprising truths. Breakthroughs, discoveries, and DIY tips sent every weekday. Bald men are more fertile.

Recent advances in large language models (LLMs) highlight the importance of training data structure and quality in shaping reasoning behavior. However, most existing approaches focus on transforming data formats while neglecting the internal reasoning complexity of training samples, leaving the reasoning potential of data under-explored and underutilized. In this work, we posit that LLM logical reasoning performance is jointly constrained by the potential of the training data and the cognitive capacity of the model. To make this relationship measurable, we introduce Data Reasoning Intensity (DRI), a novel metric that quantifies the latent logical reasoning complexity of samples by decomposing and aggregating their logical structures. This allows us to analyze how well current LLMs utilize logical reasoning signals and identify performance gaps relative to data potential. Based on this insight, we introduce a re-cognizing optimization strategy that systematically enhances the logical reasoning intensity of training data. Rather than increasing data volume, our method re-optimizes existing samples to better align with the LLM's logical reasoning boundary. Extensive experiments show that our approach significantly improves performance and generalization over data-centric strategies. We further validate our method under a reinforcement learning framework. Our results indicate that prioritizing reasoning complexity in data rather than sheer scale or superficial form is essential to realizing LLMs' full cognitive potential.

Breakthroughs, discoveries, and DIY tips sent every weekday. When the brain releases oxytocin during sex, childbirth, breastfeeding, and social interactions, the hormone supports strong feelings such as attachment, trust, and closeness. That's why oxytocin is frequently nicknamed the love, cuddle, or happy hormone--even though it's also linked with aggression. To continue investigating the biological role of oxytocin, a team of researchers studied it with scientist's poster species for love and friendship, the prairie vole (Microtus ochrogaster). The small rodents found throughout central North America have bonds that are "similar to human friendships in the sense that they are selective and long-lasting. Voles form strong, stable bonds with specific peers," Markita Landry, a chemist from the University of California (UC), Berkeley, tells Popular Science.

The human brain is one of the most complex objects in the universe – and that complexity may be due to a surge of hormones released by the placenta during pregnancy. While numerous ideas have been proposed to explain human brain evolution, it remains one of our greatest scientific mysteries. One explanation, known as the social brain hypothesis, suggests that our large brains evolved to manage complex social relationships. It posits that navigating large group dynamics requires a certain degree of cognitive ability, pushing social species to develop bigger brains. For instance, other highly sociable animals, such as dolphins and elephants, have relatively large brains too.

CES 2025 is here, and that means a whole fleet of new gadgets has been introduced to the world. As we do annually, the Engadget team has battled jet lag, sleep deprivation and the static shocks of those horrible casino carpets to bring you all of the most pressing news coming out of the show this year. You can follow along with our CES liveblog here, but if you don't want to keep a browser tab locked on the site, here's a handy recap for you. Below may not be everything we covered, but it's a rundown of the biggest, most important and generally interesting news coming out of the first day of the show. There was a strong showing from the biggest names in the PC space, with Intel showing off its latest crop of Arrow Lake chips.

CES 2025 has begun, which means a whole fleet of new gadgets has been unleashed onto the world. As usual, team Engadget has battled jet lag, sleep deprivation and the static shocks of those horrible casino carpets to bring you all the news that's fit to print. But if you're too busy to keep your browser locked on the site (or our handy dandy liveblog) then here's a recap. This may not be everything we covered, but it's a rundown of the biggest, most important and generally interesting news for your delectation. There was a strong showing from the biggest names in the PC space, with Intel showing off its latest crop of Arrow Lake chips.

That was the average length of time an adult could focus on a screen for in 2021, according to research by Gloria Mark, a professor of informatics at the University of California. Twenty years ago, in 2004, that number stood at two-and-a-half minutes. Our attention spans – how long we're able to concentrate without being distracted – are shrinking. Our focus – how intensely we can think about things – is suffering too. The causes: technology that's designed to demand our attention; endless tools for procrastination at our fingertips; rising stress and anxiety disorders; and poor sleep quality.

It's well known that spending too much time gaming could lead to weight gain and trouble sleeping. However, it could spell trouble in the bedroom, a study suggests. Researchers in China studied more than 200,000 men while they performed'leisure' activities like watching TV, going for a drive, and playing games on the computer. The team measured participants' sex hormones, as well as feelings of depression and anxiety. They found that every 1.2 hours spent playing video games or doing other leisure activities per day at the computer led to a three times greater risk of erectile dysfunction (ED).

The field of women's endocrinology has trailed behind data-driven medical solutions, largely due to concerns over the privacy of patient data. Valuable datapoints about hormone levels or menstrual cycling could expose patients who suffer from comorbidities or terminate a pregnancy, violating their privacy. We explore the application of Federated Learning (FL) to predict the optimal drug for patients with polycystic ovary syndrome (PCOS). PCOS is a serious hormonal disorder impacting millions of women worldwide, yet it's poorly understood and its research is stunted by a lack of patient data. We demonstrate that a variety of FL approaches succeed on a synthetic PCOS patient dataset. Our proposed FL models are a tool to access massive quantities of diverse data and identify the most effective treatment option while providing PCOS patients with privacy guarantees. Our code is open-sourced at https://github.com/toriqiu/fl-pcos.