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If you are frequently getting the ick from potential romantic partners, it might not be them. The problem might be you. A new study has found that if you possess certain personality traits, you might be more susceptible to the dreaded ick than others. Researchers Brian Collisson, Eliana Saunders, and Chloe Yin from Azusa Pacific University in Southern California found that those who are prone to disgust, hold others to high standards, or score higher in narcissism are most at risk. Even if youre unsure what were talking about, youve likely experienced it. A now ubiquitous term in dating, the ick is used to describe the feeling of disgust that arises toward a love interest. They stumble on the side of the curb? Ick. There are remnants of red sauce around their mouth? Instant ick. Although the concept itself is not new (the ick was first coined in the 1990s TV show Ally McBeal), the term has more recently found a new lease on life online, with more than 120 million related posts on TikTok. Personally, I became interested in learning more about the ick when I heard that a friend of mine kept a running list on her phone notes app of every ick shed ever experienced from a guy (it was several pages long), Saunders, a graduate student at Azusa Pacific and the studys lead author, told Psypost. For the study, researchers asked 74 men and 51 women, ranging in age from 24 to 72, if they knew what getting the ick meant and whether they had ever experienced it. The study then measured the likelihood of participants experiencing the ick in response to specific behaviors. Participants also completed personality tests and answered questions about their dating lives. The findings are clear: Certain personality traits make participants more vulnerable to the ick. These include higher disgust sensitivity, which increases the intensity of reactions to triggers rather than the frequency of the ick occurring. Narcissism is also linked to the likelihood, though not the frequency, of experiencing the ick. Those who tend to place high expectations on others are triggered by a wider range of behaviors. Women are more likely than men to recognize the ick, though both men and women experience a similar average number of ick moments. For women, misogynistic behavior or annoying speech are immediate turnoffs. For men, its vanity or overly trendy behavior. While the ick often acts as a bucket of ice-cold water on a blossoming romance (about a quarter of participants reported ending a relationship immediately upon experiencing the ick), Saunders said people should look inward before making any hasty decisions. Before dumping a partner because their feet dangle when they sit in a chair, we should think critically about why were feeling icked out, Saunders told Psypost. Ask yourself: Is this something I truly cant deal with, or am I being overly critical? Is this ick their fault, or is it mine?
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Earlier this year, a robot completed a half-marathon in Beijing in just under 2 hours and 40 minutes. Thats slower than the human winner, who clocked in at just over an hourbut its still a remarkable feat. Many recreational runners would be proud of that time. The robot kept its pace for more than 13 miles (21 kilometers). But it didnt do so on a single charge. Along the way, the robot had to stop and have its batteries swapped three times. That detail, while easy to overlook, speaks volumes about a deeper challenge in robotics: energy. Modern robots can move with incredible agility, mimicking animal locomotion and executing complex tasks with mechanical precision. In many ways, they rival biology in coordination and efficiency. But when it comes to endurance, robots still fall short. They dont tire from exertionthey simply run out of power. As a robotics researcher focused on energy systems, I study this challenge closely. How can researchers give robots the staying power of living creaturesand why are we still so far from that goal? Though most robotics research into the energy problem has focused on better batteries, there is another possibility: Build robots that eat. Robots move well but run out of steam Modern robots are remarkably good at moving. Thanks to decades of research in biomechanics, motor control, and actuation, machines such as Boston Dynamics Spot and Atlas can walk, run, and climb with an agility that once seemed out of reach. In some cases, their motors are even more efficient than animal muscles. But endurance is another matter. Spot, for example, can operate for just 90 minutes on a full charge. After that, it needs nearly an hour to recharge. These runtimes are a far cry from the eight- to 12-hour shifts expected of human workersor the multiday endurance of sled dogs. The issue isnt how robots moveits how they store energy. Most mobile robots today use lithium-ion batteries, the same type found in smartphones and electric cars. These batteries are reliable and widely available, but their performance improves at a slow pace: Each year new lithium-ion batteries are about 7% better than the previous generation. At that rate, it would take a full decade to merely double a robots runtime. Animals store energy in fat, which is extraordinarily energy dense: nearly 9 kilowatt-hours per kilogram. Thats about 68 kWh total in a sled dog, similar to the energy in a fully charged Tesla Model 3. Lithium-ion batteries, by contrast, store just a fraction of that, about 0.25 kilowatt-hours per kilogram. Even with highly efficient motors, a robot like Spot would need a battery dozens of times more powerful than todays to match the endurance of a sled dog. And recharging isnt always an option. In disaster zones or remote fields, or on long-duration missions, a wall outlet or a spare battery might be nowhere in sight. In some cases, robot designers can add more batteries. But more batteries mean more weight, which increases the energy required to move. In highly mobile robots, theres a careful balance between payload, performance, and endurance. For Spot, for example, the battery already makes up 16% of its weight. Some robots have used solar panels, and in theory these could extend runtime, especially for low-power tasks or in bright, sunny environments. But in practice, solar power delivers very little power relative to what mobile robots need to walk, run, or fly at practical speeds. Thats why energy harvesting like solar panels remains a niche solution today, better suited for stationary or ultra-low-power robots. Why it matters These arent just technical limitations. They define what robots can do. A rescue robot with a 45-minute battery might not last long enough to complete a search. A farm robot that pauses to recharge every hour cant harvest crops in time. Even in warehouses or hospitals, short runtimes add complexity and cost. If robots are to play meaningful roles in society assisting the elderly, exploring hazardous environments, and working alongside humans, they need the endurance to stay active for hours, not minutes. New battery chemistries such as lithium-sulfur and metal-air offer a more promising path forward. These systems have much higher theoretical energy densities than todays lithium-ion cells. Some approach levels seen in animal fat. When paired with actuators that efficiently convert electrical energy from the battery to mechanical work, they could enable robots to match or even exceed the endurance of animals with low body fat. But even these next-generation batteries have limitations. Many are difficult to recharge, degrade over time, or face engineering hurdles in real-world systems. Fast charging can help reduce downtime. Some emerging batteries can recharge in minutes rather than hours. But there are trade-offs. Fast charging strains battery life, increases heat, and often requires heavy, high-power charging infrastructure. Even with improvements, a fast-charging robot still needs to stop frequently. In environments without access to grid power, this doesnt solve the core problem of limited onboard energy. Thats why researchers are exploring alternatives such as refueling robots with metal or chemical fuelsmuch like animals eatto bypass the limits of electrical charging altogether. An alternative: Robotic metabolism In nature, animals dont recharge; they eat. Food is converted into energy through digestion, circulation, and respiration. Fat stores that energy, blood moves it, and muscles use it. Future robots could follow a similar blueprint with synthetic metabolisms. Some researchers are building systems that let robots digest metal or chemical fuels and breathe oxygen. For example, synthetic stomach-like chemical reactors could convert high-energy materials such as aluminum into electricity. This builds on the many advances in robot autonomy, where robots can sense objects in a room and navigate to pick them up, but here they would be picking up energy sources. Other researchers are developing fluid-based energy systems that circulate like blood. One early example, a robotic fish, tripled its energy density by using a multifunctional fluid instead of a standard lithium-ion battery. That single design shift delivered the equivalent of 16 years of battery improvements, not through newchemistry but through a more bioinspired approach. These systems could allow robots to operate for much longer stretches of time, drawing energy from materials that store far more energy than todays batteries. In animals, the energy system does more than just provide energy. Blood helps regulate temperature, deliver hormones, fight infections, and repair wounds. Synthetic metabolisms could do the same. Future robots might manage heat using circulating fluids or might heal themselves using stored or digested materials. Instead of a central battery pack, energy could be stored throughout the body in limbs, joints and soft, tissue-like components. This approach could lead to machines that arent just longer-lasting but are more adaptable, resilient, and lifelike. The bottom line Todays robots can leap and sprint like animals, but they cant go the distance. Their bodies are fast and their minds are improving, but their energy systems havent caught up. If robots are going to work alongside humans in meaningful ways, well need to give them more than intelligence and agility. Well need to give them endurance. James Pikul is an associate professor of mechanical engineering at the University of Wisconsin-Madison. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Artificial intelligence began as a quest to simulate the human brain. Is it now in the process of transforming the human brains role in daily life? The Industrial Revolution diminished the need for manual labor. As someone who researches the application of AI in international business, I cant help but wonder whether it is spurring a cognitive revolution, obviating the need for certain cognitive processes as it reshapes how students, workers, and artists write, design, and decide. Graphic designers use AI to quickly create a slate of potential logos for their clients. Marketers test how AI-generated customer profiles will respond to ad campaigns. Software engineers deploy AI coding assistants. Students wield AI to draft essays in record timeand teachers use similar tools to provide feedback. The economic and cultural implications are profound. What happens to the writer who no longer struggles with the perfect phrase, or the designer who no longer sketches dozens of variations before finding the right one? Will they become increasingly dependent on these cognitive prosthetics, similar to how using GPS diminishes navigation skills? And how can human creativity and critical thinking be preserved in an age of algorithmic abundance? Echoes of the Industrial Revolution Weve been here before. The Industrial Revolution replaced artisanal craftsmanship with mechanized production, enabling goods to be replicated and manufactured on a mass scale. Shoes, cars, and crops could be produced efficiently and uniformly. But products also became more bland, predictable, and stripped of individuality. Craftsmanship retreated to the margins, as a luxury or a form of resistance. Today, theres a similar risk with the automation of thought. Generative AI tempts users to conflate speed with quality, productivity with originality. The danger is not that AI will fail us, but that people will accept the mediocrity of its outputs as the norm. When everything is fast, frictionless, and good enough, theres the risk of losing the depth, nuance, and intellectual richness that define exceptional human work. The rise of algorithmic mediocrity Despite the name, AI doesnt actually think. Tools such as ChatGPT, Claude, and Gemini process massive volumes of human-created content, often scraped from the internet without context or permission. Their outputs are statistical predictions of what word or pixel is likely to follow based on patterns in data theyve processed. They are, in essence, mirrors that reflect collective human creative output back to usersrearranged and recombined, but fundamentally derivative. And this, in many ways, is precisely why they work so well. Consider the countless emails people write, the slide decks that strategy consultants prepare, and the advertisements that suffuse social media feeds. Much of this content follows predictable patterns and established formulas. It has been there before, in one form or the other. Generative AI excels at producing competent-sounding contentlists, summaries, press releases, advertisementsthat bears the signs of human creation without that spark of ingenuity. It thrives in contexts where the demand for originality is low and when good enough is, well, good enough. When AI sparksand stiflescreativity Yet, even in a world of formulaic content, AI can be surprisingly helpful. In one set of experiments, researchers tasked people with completing various creative challenges. They found that those who used generative AI produced ideas that were, on average, more creative, outperforming participants who used web searches or no aids at all. In other words, AI can, in fact, elevate baseline creative performance. However, further analysis revealed a critical trade-off: Reliance on AI systems for brainstorming significantly reduced the diversity of ideas produced, which is a crucial element for creative breakthroughs. The systems tend to converge toward a predictable middle rather than exploring unconventional possibilities at the edges. I wasnt surprised by these findings. My students and I have found that the outputs of generative AI systems are most closely aligned with the values and worldviews of wealthy, English-speaking nations. This inherent bias quite naturally constrains the diversity of ideas these systems can generate. More troubling still, brief interactions with AI systems can subtly reshape how people approach problems and imagine solutions. One set of experiments tasked participants with making medical diagnoses with the help of AI. However, the researchers designed the experiment so that AI would give some participants flawed suggestions. Even after those participants stopped using the AI tool, they tended to unconsciously adopt those biases and make errors in their own decisions. What begins as a convenient shortcut risks becoming a self-reinforcing loop of diminishing originalitynot because these tools produce objectively poor content, but because they quietly narrow the bandwidth of human creativity itself. Navigating the cognitive revolution True creativity, innovation, and research are not just probabilistic recombinations of past data. They require conceptual leaps, cross-disciplinary thinking, and real-world experience. These are qualities AI cannot replicate. It cannot invent the future. It can only remix the past. What AI generates may satisfy a short-term need: a quick summary, a plausible design, a passable script. But it rarely transforms, and genuine originality risks being drowned in a sea of algorithmic sameness. The challenge, then, isnt just technological. Its cultural. How can the rreplaceable value of human creativity be preserved amid this flood of synthetic content? The historical parallel with industrialization offers both caution and hope. Mechanization displaced many workers but also gave rise to new forms of labor, education, and prosperity. Similarly, while AI systems may automate some cognitive tasks, they may also open up new intellectual frontiers by simulating intellectual abilities. In doing so, they may take on creative responsibilities, such as inventing novel processes or developing criteria to evaluate their own outputs. This transformation is only at its early stages. Each new generation of AI models will produce outputs that once seemed like the purview of science fiction. The responsibility lies with professionals, educators, and policymakers to shape this cognitive revolution with intention. Will it lead to intellectual flourishing or dependency? To a renaissance of human creativity or its gradual obsolescence? The answer, for now, is up in the air. Wolfgang Messner is a clinical professor of international business at the University of South Carolina. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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