American Airlines and Google said Thursday that they significantly reduced the climate impact of some of the airline’s flights using an AI-based forecasting tool to help prevent contrails.American Airlines and Google said Thursday that they significantly reduced the climate impact of some of the airline’s flights using an AI-based forecasting tool to help prevent contrails.Energy & Green Tech[#item_full_content]
Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.Robotics[#item_full_content]
Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.[#item_full_content]
Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.Robotics[#item_full_content]
Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.[#item_full_content]
NUS researchers have developed a platform that lets lab-grown muscle tissues train themselves to record-breaking strength, with no external stimulation required. By mechanically coupling two muscle tissues so they continuously pull against each other, their own natural contractions become a round-the-clock workout. The resulting muscles powered OstraBot, an ostraciiform (a type of fish locomotion) swimming robot that reached 467 millimeters per minute—the fastest speed reported for any skeletal muscle-driven biohybrid robot.NUS researchers have developed a platform that lets lab-grown muscle tissues train themselves to record-breaking strength, with no external stimulation required. By mechanically coupling two muscle tissues so they continuously pull against each other, their own natural contractions become a round-the-clock workout. The resulting muscles powered OstraBot, an ostraciiform (a type of fish locomotion) swimming robot that reached 467 millimeters per minute—the fastest speed reported for any skeletal muscle-driven biohybrid robot.[#item_full_content]
MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.Robotics[#item_full_content]
MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.[#item_full_content]
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.[#item_full_content]
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.Computer Sciences[#item_full_content]
