In this week’s reading of The Circle, an app called LuvLuv is introduced. The app makes all of the data surrounding an individual available for the lover in question to use in order to make wise decisions on dates and ultimately, win over the individual.
I immediately compared this fictitious app to present dating sites including Eharmony and Match.com. I personally do not have much experience with the sites, but they do expedite the process of matching people with similar interests. The main difference between these sites and LuvLuv is that people can edit their profiles on today’s dating sites. This ability often leads to people altering some (Read: most) of their details in order to seem more attractive; in this sense forming an artificial data double. The classic example leads to profile pictures that are taken at a way earlier date or of a completely different person. Additionally, it is questionable whether a perfect match of interests leads to a happy couple (See Video). The saying, “Opposites attract obviously does not apply to dating sites.
LuvLuv ultimately cuts out this flaw entirely, by using truthful data out of people’s everyday lives, leading to a pretty accurate depiction of an individual. Although useful, I think there is another weakness in this system. By making this information known to the general public, it makes dating into a somewhat rigged game rather than real life. If I knew all of a person’s interests before meeting them, I could artificially make myself exactly like them. I could take her to her favorite restaurant, put on her favorite music, and ultimately connect with her using the power of information. The problem the person on that date would not be “me.” I would be just as fake as the 80 year old using an Eharmony profile picture from the 1970s.
Although both methods attempt to precipitate relationships, I think love is something that cannot be created with wires.
The Disney MagicBand is an interesting appliance. Rakim’s blog post explains greater research in the band and comes to the conclusion that the “more customized experience” might be worth the personal data breach.
I find it very interesting that on the DisneyWorld site (link below), there is plenty of talk about the MagicBand’s perks, including ease of room entrance, food purchases, and FastPass+ access to a multitude of experiences. But there is absolutely no notice of any of the “behind the scenes” features of the bands, especially Disney’s ability to track your every waking move. With this data, Disney can increase their efficiency, making more profits, while making more people “happy.”
I personally think it is an ingenious strategy and will ultimately do wonders for the amusement park. But I do believe that this trickery is not natural. Personally, the best part about Disney World for me was that everything was unexpected. I never knew what ride, structure, or cartoon characters I was going to see at every turn. It was exciting and incredibly appealing to me in my adolescence. I feel like the band takes away from this experience. It makes actions predictable and ultimately less fun for a couple extra bucks. The data will definitely make the park more efficient, but playing on people’s affinity towards “immediate payoffs,” (Dr. Sample’s Comment) could have consequences that affect the unpredictability of the park and human nature as a whole in the future.
This week, I observed how students’ posture and positioning affected their overall class participation. I looked at the correlation between characteristics, such as slouching forward/back and straight sitting to the amount of questions answered in class. I also collected data on whether the student was facing Dr. Sample and if they were cross legged. The data is as follows.
Total Amount of Questions Answered: 28
Slouched Forward: 13 (46.4%)
Slouched Back: 9 (32.1%)
Straight Sitting: 6 (21.4%)
Facing Professor: 12 (42.8%)
Cross Legged: 8 (28.5%)
Based on the data, it seems like students who were slouched forward as well as facing the professor were more likely to answer questions. In addition, I found it interesting that students who were slouched back were more likely to answer questions than those sitting up straight. I would guess that this is most likely because people usually do not practice perfect posture anywhere.
A common method that coaches tell their players in basketball is to visualize their shot going into the hoop before the action takes place. This is a trick that can help a shooter’s confidence. But are their other ways that sports data can be “visualized” for human use?
This week’s reading by Lev Manovich analyzed the usefulness of data visualization. Manovich provides a rough definition of infovis “as a mapping between discrete data and a visual representation.” The question that arises out of this definition is how accurately does a visual embody the raw data? The answer varies. Manovich mentions data reduction as well as the use of special variables as two main characteristics of infovis. These methods effectively smooth the data in order to represent key aspects to the viewers.
Although useful, I think that these two characteristics of visual analysis can have a tendency to digress from a dataset’s overall meaning. They do offer the viewer an opportunity to make sense of the datapoints, but by omitting aspects, the individualized data could lead to different results. For example, a graph can show a player’s field goal percentage in several games, but the analysis will leave out variables such as whether the shot was contested, minutes played, and the location of the shot.
A new system of NBA tracking has been implemented that tracks the movement of players 25 times per second. This new form of data allows for far more advanced statistics concerning touches, rebound opportunities, drives, and catch and shoots. The system also allows viewers to see video as well as movement animations to form a more complete level of analysis. Manovich would characterize this type of data as “direct visualization.” In this fashion, the NBA tracking system uses actual images and video to make a new depiction of the data. Will this new way of visualization change the way players and coaches think about stats? No way to tell now, but I encourage you to check out the site.
In “Developing an Opinion on Edward Snowden’s Leaks”, the author describes some of the negative consequences of privacy violations. I think he hits three important points, but I want to highlight some of my thoughts on the posted Ted Talk by Glenn Greenwald.
- Throughout the presentation, Greenwald attempts to legitimize the importance of privacy in our lives. He says that humans will inherently act differently when they know they are being watched and that people everywhere will refuse to give up private information even when they have nothing to hide. I completely agree with this statement, but I think a problem arises when humans are under undetected surveillance. Does their behavior actually change without them knowing? And does surveillance make human nature inherently bad or unnatural and which is worse?
- Greenwald also makes reference to the social characteristics of humans. People interact with others everyday and peoples’ actions are often influenced by others around them. Does the fact that our behavior is molded by our environment in some way similar to the change in behavior brought about by a privacy breach?
- Lastly, people do have the option to fight back against privacy breaches by limiting their internet use or using private search engines (see link below), but many people do not. Does this mean that they are not worried about their privacy or just too lazy to do something about it?
Overall, I think we are in an ever-changing battle with technology. In order to gain the benefits of today’s connected world, people must display their “private” information for others to see. Snowden’s file breach has made people realize the consequences of surveillance, but whether people will actually change their actions is a different question entirely.
Time Magazine Article “11 Simple Ways to Protect Your Privacy”
10 pairs of blue jeans
2 coffee cups
2 fraternity shirts
2 econ pens
1 Ticonderoga pencil
13 computers being used
-6 apple computers
7 green markings on board
4 blue markings on board
18 questions asked by Dr. Sample
7 Nike shoes
25 questions asked my Dr. Sample
4 other phones
5 drinks on tables
26 computers being used
4 Davidson shirts
1 fraternity shirt