Whether it's taking on more responsibilities or staying late in the office, many employees will go above and beyond to try to get a pay rise. But now a study suggests that if you're not good looking, your efforts may be futile. Researchers from the Institute for Operations Research and the Management Sciences in Baltimore have uncovered a'striking' link between physical attractiveness and career success. In their study, the team analysed the careers of more than 40,000 graduates who had completed MBAs. They found attractive respondents earned up to 11 per cent more than their colleagues who were seen as less good looking.

In the world of generated imagery, you’re either drop-dead gorgeous or a wrinkled, bug-eyed freak.

'Gutfeld!' panelists discuss whether every generation has become less attractive. We'll get to the indictments and the Bidens in the next block. GUTFELD MUSIC VIDEO: So many stories too upsetting to look into. A bunch of news that just makes you wanna cry. Then suddenly everything gets less depressing.

The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body mod- els. To cope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. We formulate the pose estimation problem as one of probabilistic inference over a graphi- cal model where the random variables correspond to the individual limb parameters (position and orientation). Because the limbs are described by 6-dimensional vectors encoding pose in 3-space, discretization is im- practical and the random variables in our model must be continuous- valued. To approximate belief propagation in such a graph we exploit a recently introduced generalization of the particle filter.

One reason for the widespread interest in AI is that it has the potential to reduce the degree of bias underpinning human decisions. For example, meta-analytic studies have long highlighted the pervasive nature of bias in hiring and recruitment. Even in the rich and liberal world, there are many biases at play in the workplace, which account for the unmeritocratic or unfair advantage that some groups have over others, irrespective of their actual talent or potential: sexism, racism, and ageism, to name just a few. But one of the most prominent biases is hardly ever discussed or acknowledged, namely the beauty bias -- also known as "lookism." Indeed, the existence of a beauty premium in the labor market is well-documented.

A new study has suggested that a great personality trumps good looks when finding a match. Researchers found that people's perceptions of potential dates' attractiveness goes up after they have a positive face-to-face interaction - but only for those who were rated mid to low attractiveness based on their photo. Because those who were deemed good looking could not increase in attractiveness, it was those in the middle who received higher ratings for being friendly and having a good sense of humor. Researchers found that people's perceptions of potential dates' attractiveness goes up after they have a positive face-to-face interaction - but only for those who were rated mid to low attractiveness based on their photo The recent study, conducted by researchers at the University of Kansas, investigated how a person's perception changes of person they'meet' on a dating app when they come face-to-face in real life. By rating someone's attractiveness before meeting them diminishes the rater's evaluation of that person afterward, probably because the rater is comparing their conversation partner to all the other potential partners they saw online.

I'd seen everything that Grindr had to offer and was growing weary of unsolicited dick pics and random old balls. So I turned to Tinder and had a couple failed dates. The first was a disgruntled state worker who wore Tom's, winced when I told him I had two pitbulls and spent the better part of two hours mansplaining ethics to me. The next was a waifish first-year English teacher and self-professed INFJ who, curiously, didn't do a lot of reading due to his workload. I powered through, reminded of months of success, but couldn't help thinking of my failure.

The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body models. To cope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. We formulate the pose estimation problem as one of probabilistic inference over a graphical model where the random variables correspond to the individual limb parameters (position and orientation). Because the limbs are described by 6-dimensional vectors encoding pose in 3-space, discretization is impractical and the random variables in our model must be continuousvalued. To approximate belief propagation in such a graph we exploit a recently introduced generalization of the particle filter. This framework facilitates the automatic initialization of the body-model from low level cues and is robust to occlusion of body parts and scene clutter.

The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body models. Tocope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. We formulate the pose estimation problem as one of probabilistic inference over a graphical modelwhere the random variables correspond to the individual limb parameters (position and orientation). Because the limbs are described by 6-dimensional vectors encoding pose in 3-space, discretization is impractical andthe random variables in our model must be continuousvalued. To approximate belief propagation in such a graph we exploit a recently introduced generalization of the particle filter. This framework facilitates the automatic initialization of the body-model from low level cues and is robust to occlusion of body parts and scene clutter.