Study finds that very unattractive people make more money

Study finds that very unattractive people make mora moneyStudy finds that very unattractive people make more moneyIn Amy Schumers new film I Feel Pretty she plays a woman, Renee, who struggles with low self-confidence and views herself as unattractive.It doesnt help that she works for a cosmetics brand that seems to only employ models. About 20 minutes into the film after wishing to be better looking at a fountain (do those things still work in the wish-delivering department?) she hits her head during spin class the next day and awakes to believe she looks completely different and is absolutely and imperatively attractive (she looks exactly the same.) Well jokes on you Renee because it turns out people who are deemed very unattractive end up making more money at a younger age, according to a recent study.A study from theJournal of Business and Psychologyof 20,000 young Americans, interviewed the subjects at home at age 16 and then three more times before they turned 29-years-old. The researchers looked atthe correlation between attractiveness and income of the participants based on a five-point scale of physical attractiveness from very unattractive to very attractive.The ugliness premiumNow attractive people are still winning at life as overall there was a positive association between attractiveness and earnings. This is also known as the beauty premium and ugliness penalty and has beendocumentedwidely by economists. However, those rated as very unattractivealso saw a positive correlation when it came to money. The VUs (who only made up 1 to 2% of the survey sample) were found to always earn more than the just regular unattractive people and sometimes even more than just average-looking people and even the attractive people. This is turning previous research on its head as this demonstrates an ugliness premium.Ladders is now on SmartNewsDownload the SmartNews app and add the Ladders channel to read the latest career news and advice wherever you go.The research ers,Satoshi Kanazawa and Mary Still, believe that the reason attractive people earn more is not just because of their looks but what their looks signify. Physically more attractive workers may earn more, not necessarily because they are more beautiful, but because they are healthier, more intelligent, and have better personality traits conducive to higher earnings, such as being more Conscientious, more Extraverted, and less Neurotic, Kanazawa said in a press release. The very unattractive people also tested higherin those three categories which contributes to their earnings bump.However, the very unattractive group was quite small which also can throw off results, Daniel Hamermesh, a professor of economics at the Royal Holloway University of London, told CBS News.It looks like Snow White will still get the higher salary over the ugly witch in the woods, but she better watch her back. That witch has got personality.and poisoned apples.

Study confirms Cat people are more enthusiastic than dog people

Study confirms Cat people are more enthusiastic than dog peopleStudy confirms Cat people are more enthusiastic than dog peopleI welches only a cat person for about 17 years. After Hairy Pawter passed away, I decided to fully commit to becoming a productive member of society. Forty-seven percent of U.S households have cats, and 60% have dogs. Last year, these pet owners dished out a collective $72 billion on their animals, which is an 8.1% increase from 2016. Americans love animals. Thirty-five percent of the US pet owners surveyed in Rover.coms new report said that their animals needs informed what kind of furniture they purchased, 29% said that it affected the kind of motor vehicle they bought, and 29% said that having animals impacted the kind of apartment or home they rented.Follow Ladders on FlipboardFollow Ladders magazines on Flipboard covering Happiness, Productivity, Job Satisfaction, Neuroscience, and moreCat people vs. dog peopleClearly, the majority of the pet owners surve yed did not take the role lightly. Seventy percent of both dog people and cat people give theirpets nicknames, 61% of both let theiranimals take over the bed and couch, the average amount of cuddle time a day was one to two hours for both. EIghty-four percent of cat lovers and dog lovers said that pictures of their respective animals take up half of the photo space on their phones, and the vast majority said that when they enter the house from work they say hello to their pets before family members. But of the two kinds surveyed, which were the more enthusiastic? Cat people. By a lot.It is true that, on balance. dog people talk to their animals a lot more than cat people do, (on average cat people talked to their felines one to five times a day, the average chat-time reported by dog owners? Too many time to count) but the reason cat people, dont talk to their cats as often as dog people is because they sing to them instead. Seventy percent more cat owners than dog owners reported do ing so Making up new songs all the time or singing to their pet at least sometimes.Cat people are also 16% more likely to get ticked off if they see their pet cuddling up with someone other than themselves. Ninety-one percent of cat owners claimed to be able to interpret their cats individual meows. Fifty-two percent of cat fanatics preferto spend time with their animals rather than humans, which is 9% percent more than the dog owner respondents that agreed.You might also enjoyNew neuroscience reveals 4 rituals that will make you happyStrangers know your social class in the first seven words you say, study finds10 lessons from Benjamin Franklins daily schedule that will ersatzdarsteller your productivityThe worst mistakes you can make in an interview, according to 12 CEOs10 habits of mentally strong people

How a Jenga-playing Robot Will Affect Manufacturing

How a Jenga-playing Robot Will Affect Manufacturing How a Jenga-playing Robot Will Affect Manufacturing How a Jenga-playing Robot Will Affect Manufacturing Humans are born with intuitive abilities to manipulate physical objects, honing and perfecting their skills from an early age through play and practice. But its not as easy for robots.Computers may have beaten the best human players of abstract, cognitive-based games such as chess and Go, but the physical abilities and intuitive perceptions acquired over millions of years of evolution still give humans a definite edge in tactile perception and the volksverdummung of real-world objects.That human-machine skill gap makes it difficult to develop machine learning, or ML, algorithms for physical tasks that require not just visual information, but tactile data. A team of researchers in the Department of Mechanical Engineering of the Massachusetts Institute of Technology has taken a fresh approach to that aufgabe by teaching a robot to p lay Jenga. Their work was published in Science Robotics.Read mora on Engineers Teaching Machines Game Theory Helps Robot DesignJenga involves building a layered tower by carefully moving rectangular blocks from lower layers to the top, without collapsing the tower. That requires careful consideration of which blocks to move and which to leave in place, a choice thats not always apparent. The robots interpretation of the game. Image MITIn the game of Jenga, a lot of the information that is necessary to drive the motion of the robot isnt apparent to the eyes, in visual information, noted the studys co-author Alberto Rodriguez. You cannot point a camera at the tower and tell which blocks are free to move or which blocks are blocked. The robot has to go and touch them.The researchers overcame that physical element of the game by providing a means for the robot to integrate both visual and tactile cues.The experimental setup consisted of an off-the-shelf ABB IRB 120 roboti c arm fitted with an ATI Gamma force and torque sensor at the wrist and an Intel RealSense D415 camera, along with a custom-built gripper.Our system feels the pieces as it pushes against them, said Nima Fazeli, an MIT graduate student and the papers lead author. It integrates this information with its visual sensing to produce hypotheses as to the type of block interaction, block configurations, and then decides what actions to take.Listen to the latest episode of ASME TechCast Breakthrough Could Bring New Cancer TreatmentMost modern ML algorithms begin by defining a problem as the world is like this, what should be my next move? Rodriguez said. A typical strategy would be to simulate every possible outcome of how the robot might interact with every Jenga block and the tower, a bottom-up approach. But this would be both time-consuming and data intensive, requiring enormous computational power.Playing Jenga is a real-world physical task in which every possible move can have various o utcomes. Its very difficult to come up with a simulator version that can be used to train a machine-learning algorithm, Rodriguez said. Training the algorithm is even more complicated by the fact that every time the robot makes a wrong move and the tower collapses a human being has to rebuild the structure before the robot can try again.Instead, MIT engineers decided on a top-down learning methodology that would more closely simulate the human learning process. The intent is to build systems that are able to make useful abstractions (top-down) that they can use to learn manipulation skills quickly, Fazeli said. In this approach, the robot learns about the physics and mechanics of the interaction between the robot and the tower.Read about another Robotic Invention The Rise from BattleBot to Corporate RobotRather than going through thousands of possibilities, Fazeli and his colleagues trained the robot on about 300, then grouped similar outcomes and measurements into clusters that the ML algorithm could then use to model and predict future moves.The AI builds useful abstractions without being told what those abstractions are, i.e., it learns that different types of blocks that are stuck or free exist, Fazeli said. It uses this information to plan and control its interactions. This approach is powerful because we can change the goal of the robot and it can keep using the same model. For example, we can ask that it identify all blocks that dont move, and it can just do that without needing to retrain. It is also data-efficient thanks to its representation that allows for abstractions.By using the visual and tactile information collected through its camera and force sensor, the robot is thus able to learn from experience and plan future actions.While the system wont be challenging human Jenga champs anytime soon, the top-down ML approach demonstrated in this work may have a more significant impact.Were looking toward industrial automation where we hope to have flex ible robotic systems that can quickly acquire novel manipulation skills and behave reactively to their mistakes, Fazeli said. Modern day assembly lines change rapidly to align with consumer interests so we need systems that can keep up.Mark Wolverton is an independent writer.Read Latest Exclusive Stories from ASME.orgYoung Engineer Takes Great Strides with Prosthetic FootVR and Drone Technology in a Paper AirplaneFive Job Interview Questions Young Engineers Can ExpectIts almost certainly cheaper to capture carbon emissions from their sourceor never emit them in the first place.Matt Lucas, Carbon180