Responders – Page 2 – Data Culture

Disney: Behind Closed Doors

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.

https://disneyworld.disney.go.com/faq/bands-cards/understanding-magic-band/

Augmented Memory of Today

From the wearable technology movement discussed in Peterson’s article, to the utopian idea of “total recall” presented in Penderson’s book chapter, the storage of human experience is presented as a new movement initiated by new technologies. While we have discussed augmented memory as a possibly worrisome experience that lacks creativity and human emotion, how do we feel about photographs today?

Isn’t documenting our daily lives and experiences through IPhone pictures, Facebook or Instagram forms of augmented memory? I would say it is a way to store our experience at that moment of time. Although a photograph only captures a snapshot of that experience, most of the time it is the experience behind a picture that makes it meaningful. When looking at your favorite photo, does it bring back positive memories as well as emotion? In this case, a form of augmented memory has possibly succeeded in capturing and storing a human experience.

However, augmented memory is less successful when the experience behind the picture is less meaningful than the picture itself. If your phone was out of memory and you needed to clear some space, which pictures would you consider deleting first? You would probably delete the pictures that mean the least to you. Even though a picture is a record of your past, it’s not worth keeping if theres no emotion to back it up.

Are we remembering our experiences or just the pictures we take?

A movement towards complete augmented memory should not be feared because it is a possibility, but feared if used improperly. Augmented memory can still capture emotion, as long as emotion was present in the experience in the first place. What we must avoid is reliance on these forms of memory as the only experience of the world around us. How often do we see tourists more focused on their camera lens than the scenery around them? A photograph, or any other form of augmented memory cannot be used to replace memories and experiences if we hope for them to have any meaning to us in the future.

The Human Condition without the Humanity

In Augmented Memory, Digital Life, and Computers that Promise to Remember Everything, Isabel Peterson examines the implications of self-tracking to the point where you are able to recall everything that you see, hear, read write or feel. Her analysis suggests that this new form of the quantified self negates the fundamental purpose of human memory.

If we expect that human memory exists to store rather than generate reality, then humans are for information and not the other way round, a rhetoric that ultimately betrays posthuman conditions in these subtle assumptions.

Humanists would suggest that information is for humans to learn, use and wrestle with. What happens to our humanity when we exist and experience simply to recall? Is an emotion still an emotion when we only remember having it, not the way it actually felt? As this idea progresses, the functionality of the human brain diminishes and we approach a robotic society where we do rather than feel and analyze.

This begs the question that we asked at the end of class: do we need a computer to tell us how we feel? As one student asked, do I need my tinder app to tell me if I am having a good time on a date? I would argue that if we do not already know these things, then ultimately, how we feel does not matter. The readings highlight the argument that there is intrinsic value in the existence of the record for posterity. However, at this point the human condition is no longer worth reflecting on. What is the human condition without the humanity that is evident in our emotions, conflicts and questions?

Hashtags and Graphs

This post will focus on the post by Peter Saunders about the hashtags. I absolutely agree with the point that hashtags sorted various information into certain categories and “handle the dirty work of aggregating” for the users. It is really interesting that Peter points out the inefficiency caused by the misspelling of hashtags. If you really think about how hashtags work, you will keywords, or tags, used in some blog websites including WordPress work the exactly same way. If you put the network into graphs, you might be able to see tags as endpoints that link to the endpoints on the other side, which are the posts. It resonates with the other reading, “Graphs” from Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg.

For instance, on instagram, you post a photo with #dataculture. The hashtag serves as a hyperlink between the category and the photo and thus the photo is the endpoint. The other way around, if you search the hashtag #dataculture, the hashtag becomes an endpoint with leads to the category. Just like the graph shown below:

If you have multiple of photos with multiple hashtags, the graph will go more complicated. And then with a large user group, the graph goes even crazier. This is where Cloud technology came in.

To draw a conclusion for this response, the hashtags are certainly inefficient sometimes because of the misspelling, but, instead of creating different databases, the hashtags create multiple hyperlinks to sort information into categories. Comparatively, hashtags are very efficient.

Graphs credit to:

http://en.wikipedia.org/wiki/Planar_graph

http://en.wikiversity.org/wiki/Web_Science/Part2:_Emerging_Web_Properties/Emerging_Structure_of_the_Web

http://en.wikipedia.org/wiki/Linked_data

Hashtags and Tagging

This response will pay particular attention to the blog post “Harnessing the Power of #Hashtag”. This post was a very interesting and straightforward response to Mejia’s chapter on “Computers as Socializing Tools”. This post focusses on the implications of Hashtags in modern social media, paying particular attention to Twitter. This post describes various benefits that hashtags offer social media users.

I really enjoyed how the post divulged into how hashtags served as a “beautifully simple way to sort through and classify information on a particular topic”. I strongly agree with this, as hashtags truly make information on a certain topic easily accessible to everyone, as they organize the information into particular categories. Also, I agree that the small percentage of mistypes in hashtags are a small price to pay for all of the benefits that tagging can create. In my opinion, when most people use hashtags, they are careful to use the exact wording as the original hashtag.

The argument that hashtags help to bridge the gap between celebrity and “regular folks”is another intriguing point. Although this is true that hashtags enable everyone to have their tweets or posts categorized in the same place, it is very hard for a hashtag about a non-celebrity function to get much traction. So in my eyes, hashtags do not strongly help the celebrity/regular folk divide.

Also, I believe that photo tagging is a very important topic in the field of social media information exchange. The ability to make oneself known and tagged in someone else’s photo is a very helpful resource enabling people to widen their net of people who can view their photos.

The world of tagging and hashtags truly do serve as simple and efficient resources that enable information to be effectively categorized and spread.

http://media02.hongkiat.com/quicktips/fb-tagging.jpg

Inaccuracy of Maps

In class the other day we were told to draw maps around campus to other places that we may or may not have known the location. After thinking about the drawings that were made and talking about how different maps could be inaccurate. I wanted to see what different ways in which a map could be inaccurate. So as I was looking on the internet I came across a study done by Colorado University at Boulder looking at the different types of inaccuracies that can be found in maps. The article, “Error, Accuracy, and Precision” talks about “the problems caused by error, inaccuracy, and imprecision in spatial datasets.” Throughout the article it talks about the different inaccuracies. The different types that they bring up are format of the data, age of the data, relevance, density of observations, map scales which all deal with the characteristics of the map itself. It also goes over inaccuracies made through general error such as numerical error and analyzing the data gathered. It was interesting to see the different types of inaccuracies, and it goes with the notion that was brought up in class stating that all maps have errors and are inaccurate.

Questions are constantly brought up about how to make maps more accurate. The article raises some of these questions. How accurate are positional and attribute features? What projection, coordinate system, and datum were used in maps? These are just two of the many questions that can be asked on how to make maps more accurate. What can cartographers do to make sure their maps are as accurate as possible? What are the most important aspects of a map that need to be the most accurate in order for amble data to be taken from it?

A Mercator Mapping Mystery

After our discussion Tuesday, I began to do some research on my own. While searching for some interactive inaccurate maps, I found a blog that boldly declared to me that every map I had ever seen was radically wrong. The blogger began to explain that one of his co-workers mentioned a thing to him called the Mercator project. Being unfamiliar with the term, as was I, the blogger browsed the term “Mercator.”

As Cam Bard previously noted during his discussion, it is impossible to represent a three dimensional world on a two dimensional scale. Apparently, a cartographer from the 1500s named Gerard Mercator created the ovular map that we recognize today. In an attempt to preserve the shapes of the world’s countries, Mercator drew the countries as we commonly see them on the map. However, he did not account for the scaling with relation to the Equator. On most maps, countries and landforms appear larger and larger as they are placed farther from Equator. The blogger’s primary example was Greenland. The point was made that Greenland is roughly 60% smaller than we believe it to be according to modern maps.

A Mercator Puzzle was created to show how warped depictions are on most maps. It was quite intriguing to find that, according to the Mercator Puzzle, Greenland was roughly the size of the Democratic Republic of Congo (an average-sized country in the middle of Africa). The thing that stuck me after investigating the Mercator mystery was, how can the people who designed the Mercator Puzzle be so sure that their map predictions are accurate??

The Role of the Cartographer and the daily map user

In response to cabard’s post, I believe that he has a very good logical standpoint on who gets to decide what goes on maps, but I feel like he downplays the role of the user. He states that it is up to cartographer to decide what goes on maps, and how truthful that information is. I believe that while the cartographer does have this power, those who utilize the services have power as well. If a particular person wishes to have something on a map, then they should be able to suggest this to the cartographer. Since this information is for the users, then the users should be the people kept in mind when creating maps. These people can band together to get a say in what they believe to be most important for representation purposes. Regarding the point made about getting the bigger picture but losing the details, I believe that this does not always have to be true. While the little bits of information can be lost, this can be prevented if we decided what the little bits information are. If a map wishes to have information regarding the local area, then the little information will not be relevant as the overall purpose is to graph a small area. Once again, the user should decide what the purpose is, so if they wish to have a big area mapped then they must be conscious that certain details will be left out. If they want small, details should not be left out. Finally, I agree with the point that we can have either distances or shapes, but i do not believe that this will be a problem for much longer. With the increasing amount of technology in our surroundings, soon we will be able to bridge the gap that has been established. It would be crazy to think that this issue regarding maps will be a problem in the future.

Scaling: How Map Makers Make Mistakes

In the discussion following Tuesday’s map making exercise, which required us to create three maps leading from Studio D to Nummit, Davidson Pizza Company, and Chipotle, a number of people mentioned how they had difficulty managing the scale of their maps. I encountered this scaling issue when I ran out of space while attempting to draw the path from Studio D to Nummit. Despite the distance between the two points amounting to only a few hundred yards and the fact that I make this walk on a daily basis, the other two maps that I drew were far more accurate. While I am clearly a terrible cartographer, I believe that my familiarity with the path from Studio D to Nummit actually hindered my ability to successfully depict the route. Because I know the route so well, I found myself trying to add every little detail to the map that I fit, and as a result, I had to drastically alter the scale on the map to fit Nummit on the page. Conversely, when drawing the other two maps, I had far better spacing and was able to fairly accurately show how to get from one place to the other. I believe this was the case as I was not as concerned with drawing every minute detail, but instead I focused more showing the route from point A to point B.

While others may not have had similar experiences drawing their maps, mine left me asking if too much information can be a detriment when constructing a map. Ultimately, this question relates to Monmonier’s article and highlights the tradeoff between accuracy and usefulness that all map makers face. I found through this exercise that the more information one tries to incorporate into a map, more difficult it becomes to use the map. On the other hand, too little information appearing on a map is potentially dangerous as the omission of an important detail could render a map completely ineffective.

Data is the Oxygen to Machine Learning’s Fire

cat detection — YouTube’s Conception of “Cat”

This past week’s discussion of Big Data seems incomplete without mentioning machine learning. For the uninitiated, machine learning is a method of computing that “trains” algorithms to achieve a desired result using large amounts of data. While machine learning has been around since 1959, it has only recently come back into fashion as businesses are waking up to its potential in the age of Big Data.

The key ingredient in effective machine learning applications, from FaceBook to Baidu, is data. Lots of data. Because of their scale, these companies are able to gather trillions of data points for everything, including individuals’ emotions, shopping habits, facial features and much more. To a human, or even an army of humans, one trillion pictures of peoples’ faces would yield little utility. But, to an elite team of computer scientists, such pictures allow them to construct systems that can recognize identity and emotion more accurately, and at exponentially greater scale, than human beings.

The paradigmatic shift from silos of individual networks to aggregated data and computing resources known as cloud computing brings with it greater efficiency and more data. Joseph Sirosh, Microsoft’s VP of Machine Learning, was recently interviewed by the cloud and data experts at GigaOM. Sirosh explains that Microsoft is rapidly transitioning from an operating system provider to a cloud provider with expertise in big data and machine learning. He goes so far as to state that computing itself is less important than the data that it provides:

“I think you should even first ask, ‘How big is the world of data to computing itself?’” he said. “I would say that in the future, a huge part of the value being generated in the field of computing . . . is going to come from data, as opposed to storage and operating systems and basic infrastructure. It’s the data that is most valuable.”

Microsoft has made its billions by providing software and services. Pivoting the business model of a $360B company is a herculean task; it’s safe to say that Microsoft and every other major player in technology wouldn’t be chasing desperately after data science if it wasn’t a huge deal. Data is often described as the new oil of the 21st century – machine learning is the new refinery.

Continue reading Data is the Oxygen to Machine Learning’s Fire