“Our personality and behaviour defines who we are and how people perceive us. In many ways, the same thing goes for products, as we humans have a tendency to perceive inanimate objects as having personality, even if they don’t really have one by design. This project was an exploration in how to apply the basic elements of human personality to product design. (…)

In order to make the three traits easier to discuss in relation to product design and behaviours, I decided to build three small everyday objects representing each trait through an added layer of behaviour. I chose three objects that most people know and interact with every day. To put emphasis on the object’s added personality trait, I chose objects that are normally quite passive. ”

Read more at http://portfolio.tobiastoft.dk/#271073/Synthesising-Personality

“We tend to think of colors as ideas which all humans agree on – grass is green, flames are orange, the sky is light blue – even if different languages have different names for these colors.
As English speakers, we also tend to think of color names in terms of the “basic” ones and the more specific, secondary ones (e.g. turquoise, ochre). Think of the words that are taught to young children for color. A quick look at baby books shows that English generally has 11 basic color words:

Many people are surprised to learn, therefore, that different languages do not consider the basic colors to be the same. Some New Guinea Highland languages, for example, still have terms only for black and white (perhaps better translated as “dark” and “light”). Hanuno’o language, spoken in the Philippines, has only four basic color words: black, white, red and green. Looking at the chart below: Berlin & Kay’s landmark study (1969) of 98 languages showed that if a language has a name for a color in a higher-numbered column it always has a name for the ones to the left (i.e. if a language has only 2 color words they will always be white and black; if it has 5 they will always be white, black, red, green and yellow, etc.).”

GestureWorks ships with a library of over 200 built-in gestures. This library is built upon an open source gesture framework, allowing developers to customize and extend the “gesture object” to create support for new gestures. Try GestureWorks multitouch software.

Laser guide stars are a form of artificial star created for use in astronomical adaptive optics imaging.

Adaptive optics (AO) systems require a wavefront reference source in order to correct atmospheric distortion of light (called “astronomical seeing“). Sufficiently bright stars are not available in all parts of the sky, which greatly limits the usefulness of natural guide star adaptive optics. Instead, one can create an artificial guide star by shining a laser into the atmosphere. This star can be positioned anywhere the telescope desires to point, opening up much greater amounts of the sky to adaptive optics. Because the laser beam is deflected by astronomical seeing on the way up, the laser light moves around in the sky in a random fashion. In order to keep astronomical images steady, a natural star nearby in the sky must be monitored in order that the motion of the laser guide star can be subtracted using a tip-tilt mirror. However, this star can be much fainter than is required for natural guide star adaptive optics, which means many more stars are suitable and a correspondingly larger fraction of the sky is accessible.

There are two main types of laser guide star system, known as sodium and Rayleigh beacon guide stars. Sodium beacons are created by using a laser specially tuned to 589.2 nanometers to energize a layer of sodium atoms which is naturally present in the mesosphere at an altitude of around 90 kilometers. The sodium atoms then re-emit the laser light, producing a glowing artificial star. The same atomic transition of sodium is used to create bright yellow street lights in many cities. Rayleigh beacons rely on the scattering of light by the molecules which make up the lower atmosphere. In contrast to sodium beacons, Rayleigh beacons are a much simpler and less costly technology, but do not provide as good a wavefront reference as the artificial beacon is generated much lower in the atmosphere. The lasers are often pulsed, with measurement of the atmosphere being time-gated (taking place a few microseconds after the pulse has been launched so that scattered light at ground level is ignored and only light which has traveled for several microseconds high up into the atmosphere and back is actually detected).

Hacking might be characterized as ‘an appropriate application of ingenuity’. Whether the result is a quick-and-dirty patchwork job or a carefully crafted work of art, you have to admire the cleverness that went into it.

An important secondary meaning of hack is ‘a creative practical joke’. This kind of hack is easier to explain to non-hackers than the programming kind. Of course, some hacks have both natures (…)

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When BMW introduced one of the most sophisticated navigation and telematics systems into its 5 Series car in Germany a decade ago, it represented the pinnacle of German engineering excellence, with great advances in accuracy and functionality. Yet BMW was forced to recall the product—because the system had a female voice. The service desk had received numerous calls from agitated German men who had the same basic complaint. They couldn’t trust a woman to give them directions.

While this might seem like a story of men’s weird attachment to cars or gender stereotyping run amok, a growing body of research suggests that there is something much deeper at work: People respond to computers and other technologies using the same social rules and expectations that they use when interacting with other people. These responses are not spur-of-the-moment reactions. They run broadly and deeply.

If you were asked how much you liked, say, a plate of lasagna, you would undoubtedly say nicer things to the chef than you would to a person who had no connection to the chef. This would be the polite thing to do. Would you also be overly nice to a computer that tutored you for 30 minutes and then asked how well it taught you?

To find out, I ran an experiment at Stanford University. After being tutored by a computer, half of the participants were asked about the computer’s performance by the computer itself and the other half were asked by an identical computer across the room. Remarkably, the participants gave significantly more positive responses to the computer that asked about itself than they did to the computer across the room. These weren’t overly sensitive people: They were graduate students in computer science and electrical engineering, all of whom insisted that they would never be polite to a computer.

Another social rule is illustrated by the ubiquitous color wars at summer camps, in which half of the camp is arbitrarily assigned to the Red Team and the other half is assigned to the Blue Team. Even though the assignments are random, the Red Team campers suddenly notice that the members of their team are faster, bigger, more skilled, more attractive and have more of every other positive trait compared with the members of the Blue Team; seemingly violating the laws of physics, the Blue Team discovers the same positive attributes about their teammates.

In a set of experiments, my lab determined whether computers could leverage this bonding. For half of the participants, we gave people a blue wristband, put a blue border around the computer, and told the participant that they and the computer were “the blue team.” The other half of participants were also given a blue wristband, but they worked with a green-bordered monitor and were told that they were the “blue person working with the green computer.” Although every other aspect of the 40-minute interaction was the same, the “team” participants thought that the computer was smarter and more helpful and they worked harder because of the special “bonds” between the two teammates.

More than 100 experiments have shown that one can take virtually any finding from the social sciences and apply it to people’s interactions with computers. This isn’t just a scientific oddity; it has been used to improve the design of a number of products. One of the most reviled software designs of all time was Clippy, the animated paper clip in Microsoft Office. The mere mention of his name to computer users brought on levels of hatred usually reserved for jilted lovers and mortal enemies. There were “I hate Clippy” websites, videos and T-shirts in numerous languages. One of the first viral videos on the Internet—well before YouTube made posting videos common—depicted a person mangling a live version of Clippy, screaming, “I hate you, you lousy paper clip!”

Clippy’s problem was that he was utterly oblivious to the appropriate ways to treat people. Every time a user typed “Dear…” Clippy would dutifully propose, “I see you are writing a letter. Would you like some help?”—no matter how many times the user had rejected this offer in the past. Clippy never learned anyone’s names or preferences. If you think of Clippy as a person, of course he would evoke hatred and scorn.

To have Clippy learn about his users would have required advanced artificial intelligence technology, along with a great deal of design and development time. An alternate approach is to use a social strategy. The simplest and most effective way for dislikable people to become more accepted is for them to find a scapegoat.

In an experiment, we revised Clippy so that when he made a suggestion or answered a question, he would ask, “Was that helpful?” and then present buttons for “yes” and “no.” If the user clicked “no,” Clippy would say, “That gets me really angry! Let’s tell Microsoft how bad their help system is.” He would then pop up an email to be sent to “Manager, Microsoft Support,” with the subject, “Your help system needs work!” After giving the user a couple of minutes to type a complaint, Clippy would say, “C’mon! You can be tougher than that. Let ‘em have it!”

The system was showed to 25 computer users, and the results were unanimous: People fell in love with the new Clippy. A long-term business user of Microsoft Office exclaimed, “Clippy is awesome!” An avowed “Clippy hater” said, “He’s so supportive!”

Without any fundamental change in the software, the right social strategy rescued Clippy from the list of Most Hated Software of all time; creating a scapegoat bonded Clippy and the user against a common enemy. Of course, that enemy was Microsoft, which didn’t pursue this strategy. When Microsoft retired Clippy in 2007, it invited people to shoot staples at him before his final burial.

Other research shows that the Wizard of Oz was right when he made the Scarecrow intelligent simply by labeling him a “Doctor of Thinkology.” To test the power of labels, we set out to determine if even a television could benefit from being a “specialist.” Participants were brought into the laboratory and watched segments from news shows and situation comedies. Half of the participants were told that they would watch an ordinary TV that showed both news and entertainment programs. The other half were told that they would watch programs on two identical ordinary televisions: one that happened to only show news, and the other that happened to only show entertainment.

After watching the shows, we asked participants to evaluate what they had seen. Participants who watched the segments on the “specialist” TV thought the news segments were significantly higher in quality, more informative, interesting and serious than did participants in the “generalist” condition. Similarly, the entertainment segments were significantly funnier and more relaxing when watched on the “specialist” television. Thus, even meaningless assignments of “expertise” can result in powerful effects.

The ineffective or effective use of social rules can also affect safety. In 2002, a Japanese car company developed a system that would monitor drivers’ performance and warn them when improvements were appropriate. During one demonstration, the participant exceeded the speed limit and made a turn a little too sharply. “You are not driving very well,” the car said. “Please be more careful.”

The driver was not delighted to hear this valuable information from an impartial source; instead, he became somewhat annoyed. He started to over-steer, making rapid, small adjustments to the wheel; the system reported an increase in driving speed and a decrease in driving distance from the next car. “You are driving quite poorly now,” the car announced. “It is important that you drive better.”

Was the driver now appropriately chastened? No. His face contorted in anger as he started driving even faster, darting from lane to lane without signaling. He swerved back and forth from one side of the lane to the other at a frightening pace, tailgating the cars in front of him. This spiral of negative evaluation, anger, worse driving and more negative evaluation escalated until, in a rage, he smashed into another car in the simulation.

Trying to cheer up unhappy drivers by giving a car an enthusiastic voice, on the theory that “misery loves company,” doesn’t work either. It turns out that the correct saying is “misery loves miserable company.” When you’re angry, there are few things worse than having someone bounce in and say, “Let’s turn that frown upside down!” A much better strategy is to sound negative and subdued, thereby being sympathetic while reducing the driver’s arousal.

This idea was tested in a car simulator study, in which happy or upset drivers used a car with a voice that was either clearly upbeat or morose and downbeat. Not surprisingly, happy drivers had fewer accidents and paid more attention to the road with the happy voice. However, the happy voice made upset participants’ driving much worse: Sad drivers hearing the happy voice had approximately twice as many accidents on average as with the sad voice. Upset drivers also enjoyed driving more, liked the voice more, and thought that the car was of a higher quality when the virtual passenger was sad. Upset drivers even spoke much more with the sad passenger than they did with the happy one. Of course, happy drivers enjoyed driving more, liked the voice more, thought the car was better and spoke more with the happy voice than the sad voice.

While computers are usually thought of as the antithesis of sociality and caring, understanding the value of conformity to social rules by technology has made computers and other machines more likeable, effective and persuasive. Indeed, we may be reaching the point at which our technologies are actually more socially effective than our colleagues.

We now see software that is superior to all but the most suave people with respect to effective praising and criticizing (if you need to give a mix of positive and negative feedback, it turns out it’s better to criticize first, then praise); maintaining consistency when managing people with different personality types; reinforcing team bonding (by emphasizing similarity and shared goals); handling frustration and other negative emotions (don’t allow people merely to vent—empathize); and giving more compelling recommendations by leveraging expertise. It would be ironic if in the future, people will be turning to computers to learn how to win friends and influence people rather than the other way around.