Category: [Old] Your Work

The main goal of this text is to create awareness of the importance of digitalization in these times. Digitalize everything uses as evidence the examples of digital applications that have affected our lives such as Waze, an app that can tell you the current best route possible to your destination, and google translate, an app that translates text among different languages.  Also, the text shows examples of the current uses of digital applications, just as how it is possible to predict the success of a movie in the box office by analyzing the tweets made about that movie.

The main take away of the reading is that we are currently living in the second machine age and digitalization lies in its heart.

One compelling idea, amongst many, stood out to me in today’s readings. The idea that digitization restructures the capitalist nature of artistic production. It’s written that “The old business saying is that ‘time is money, ‘ but what’s amazing about the modern Internet is how many people are willing to devote their time to producing online content without seeking any money in return”.

In an ever growing society, and subsequently, an expanding database of knowledge, one must consider the worth of their work. Often, as creative people, we are placed at the lower end of capital, because of monetary desire. Physical money or work to be exchanged for money become the centre of the conversation of production. What makes me happy about the process of digitization, is that people need a more sustainable resolve for producing work outside of monetary value. With the volatility of the art market, and the whims of large financial powers, the monetary value of what we produce is unstable, but the impact of work on people, their thinking, and being, transcend this instability.

 

Here is my data visualization for this week.

It takes a list of three values and uses them to create the x-position, y-position, and diameter of a series of circles.

 

//DataVisualization_Nisala

int data[];
String things[];
int radius = 200;

void setup() {

  size(600, 800);

  things = loadStrings("data.csv");
  // This array has one element because the file only has one line. 
  println(things.length);
  // Convert String into an array of integers using ',' as a delimiter
  //string array is returned, which we cast to an int array


  //println(data.length);
  noStroke();

  //noLoop();
}

void draw() {

  background(20);
  noStroke();
  strokeWeight(1);
  
  stroke(255,80);
  noFill();
  ellipse(width/2, height/2, 400, 400);


  for (int i =0; i<things.length; i++) {

    data = int(split(things[i], ',' ));

    float distance= dist(width/2, height/2, data[0], data[1]);
    if (distance<radius) {

      float c = map (data[0], 0, 600, 0, 255);
      noStroke();
      fill (50, c, c, data[2]);
      ellipse(data[0], data[1], data[2], data[2]);
    }
  }
}

 

 

Although Waze is not the focus of the paper, I actually find that part very interesting. As someone who drives in NYC, I’m not sure I agree entirely with how efficient Waze is. Technically speaking, traffic moves and there’s a chance that by the time you get to the point with traffic, it has already subsided. If you are rerouted to a route that is further away, the time you spent to get there might be equivalent to the time you spend in the traffic. Similarly, if you are spending a lot of time in local traffic, with all the lights, in the long run, you might actually be spending longer on the road than if you spend a short time in traffic.

Besides that point, I think the question “But what would happen to the digital world if information were no longer costly to produce?” the author poses is interesting. It’s great that users are creating data that can be used by other technologies, but if technologies like Watson are using such data, those data are not necessarily accurate. How do we know when to trust these data since it’s not actually proofread? Regardless, I agree with  Andrew McAfee and Erik Brynjolfsson about how great digitalization is and how it’s provided a lot of data that are very useful for other technologies. Despite some inaccurate information, overall, those data probably do not skew conclusions that are formed from user data.

Right from the start, this reading made some great points. For example, the idea that word of mouth is one of the best advertising strategies. You can advertise as much as you want and spend as much money as you want, but people trust their friends and family. If someone they trust recommends a service or experience, they’re more likely to buy into it. That’s something to keep in mind for IM, but also in whatever I do in the future, with my career, with SIG events, with everything, really.

It was funny reading about Waze, because I use Waze when I’m home and my mom absolutely hates it. I find it really useful. It keeps me really focused on the road, especially when I hear “object in the road ahead” or “heavy traffic in 2 miles”. But my mom finds it so distracting. She doesn’t trust what it’s saying so she tends to overthink what she should do. Instead of slowing down and paying more attention to what could be on the road, she panics and stops thinking. It’s interesting how the same app can cause such different reactions in people.

But in its function, one of the coolest things for me is that, yes, it does follow set traffic patterns, speed limits, and general data inputs, but then it also grows in efficiency as more people use it and add information in the moment. This is one of the most fascinting subjects to me, this user-generated content, because it really stimulates this need for interaction between humans that the reading points out. While the reading focuses on Wikipedia, I find its application in Waze to be even cooler. Frustrated drivers and passengers sitting in traffic, logging in that they’re stuck in bumper-to-bumper traffic. And then that sets off a little signal to all of the Waze users that there’s heavy traffic up a few miles, or the route redirects. It’s an incredible feeling of “well I’m suffering in this traffic, but I’m helping the people behind me”.

On another note, I found a connection with the idea that information used to be very costly to produce, but easy and cheap to reproduce. I feel like we’re starting to experience this with our processing sketches. We have to take the time to create the classes and functions, but once we do, we can easily reproduce them with almost no time.

This chapter was a very insightful read in terms of showing me a different way to look at IT advancements in regards to economy and profit.

The interaction between computers was of particular interest to me. We tend to frame the discussion about interaction just around people and objects – not between two objects. That’s why the experiment regarding the searches for housing and consequent increase in the prices introduced a new way to look at it for me. It sounded a way too familiar to the experience I – and pretty much everyone – keeps encountering when buying flight tickets.

Under no circumstances can I let the algorithms know that I need the tickets for a specific day, to a specific destination – otherwise the prices always skyrocket the next time I search for the same thing. Therefore I always feel like an ultimate criminal, using a different browser in an incognito window to look for and compare flights. I never looked at it from the perspective of interaction – that pretty much with no human interference whatsoever, the portals are able to communicate and exchange the information of when and where I want to go and make a profit out of it. This a force that was not even thought of a couple of years ago.

It’s fascinating how such advancements in the field of IT can be transferred into the advantage of economic profit. This is also reflected in the approach the authors take at the beginning of the chapter when describing how innovations in software can be costly initially, but replicated and reproduced with almost no cost. The analogy of atoms in products and products made of bits made it very easy to understand.

What is more, the redefinition of “time is money” really resonated with me. As discussed in the chapter, since people can publish anything pretty much anywhere online, a lot of them do it without seeking profit and produce information freely. However, the world of the internet created new conditions that laws have troubles adjusting to – especially in terms of copyrights. Since anyone can create, copy and share, when it comes to actually claiming what is supposed to be yours, it becomes increasingly more difficult.

The main theme of this chapter of the book The Digitization of Just About Everything by Erik Brynjolfsson and Andrew McAfee (which I would like to note is a digital book, with PDF copies on sale for 8.95) is that the easy replication and distribution of digital information is the backbone of modern information systems such as the internet. Building off of this, they comment on how this creates a system in which it is difficult to produce information and digital products, but easy to reproduce them (there is nothing quite as useful in life as the copy paste function). I think their argument applies heavily to the work we do in class in multiple ways. For one, it applies literally. It takes time and energy to write code, but using functions such as classes, we are able to duplicate information that we have designed and spent time processing.

In a more metaphorical sense, however, This theory applies to students as well. There is a lot of effort needed to learn how to code. Once we have learned how to code however, we are able to more easily replicate, and even alter based upon what we have learned. Each of these systems evolves and grows over time as new information is added.

Another thing that the chapter details is how the immense amount of data on the internet (already measured in terms of zettabytes even in 2015 when it was written) serves to continuously allow the second machine age to progress, as more information equals the ability to further develop technology. As databases from academic search engines to wikipedia and even services like yelp expand, they become more accurate and expansive. Information is available at the press of a button, and it keeps getting better.

I’ve noticed it myself over the years. I was born in 1999, and was just barely able to understand the significance of the smartphone when the first Iphone came out in 2007. Since then, the effects of  Moores law have continuously unveiled  before my eyes. Sometimes it feels like I am growing at the same time as the internet. Sometimes it is difficult to distinguish the internet from a living, thinking thing. This is partly because of the similarity between how humans build information and experiences over the course of their lives, and how this web of interconnected information gathering processes does the same over the course of its life.

I think that this article is a fantastic introduction into data and the information age, where statistics and data can allow us to live more “efficient” lives through its processing by computers. When I read this article, I thought “this is fantastic that Waze is able to analyze traffic routes and Siri can dig through databases of information and users’ sound recordings,” but when data is used to create things more convenient for us, make us live more efficient lives, we are also giving this network data about ourselves. I think that this chapter, and I do recognize that this is a chapter of a book and probably does not discuss everything the whole book does, is not able to show that this is a double-edged sword, that there is a huge risk involved in providing this data that makes the auto-entry Gmail emails of today possible. I greatly appreciated reading this chapters because it provided statistics and examples that seemed objective in its analysis of how technology and data are utilized today. Oftentimes, most of what I read about the implications of data and technology are either completely against technology or completely confident that we can be living in a utopia because of all of this information and the proper technology used to process it. But this chapters focus on statistics of how data is being used and how much data is present, which was a welcome change from the often extreme viewpoints I see in the media.

So this week’s reading was very interesting in the sense that I was able to relate the content and examples of the article with lessons from another class. In “Cyberwarfare”, the other class I am taking, we have discussed how users are unexpectedly always providing data to businesses such as Facebook or Netflix when we are using their products. I feel like the same is happening with Waze. In order to have a better experience, as it says in the article, more people have to use it, since the app has to record date and time and therefore can reuse the log once another person makes the same trip. I know this data gathering from people is for a positive use as shown in the Waze example, but I would be careful since privacy invasion is also a significant matter these days and giving your information to navigator apps like Waze also could expose yourself to anyone who wants to track you down.

In the second part, to see digitization in an economic perspective, I get the author’s point, and it is certainly true that digitization has made our lives much more convenient in numerous methods. However, I do think that the author is too positive about this change, and I have a few reasons on why I politely disagree with this notion.

To begin with, digitization also means that any sort of data can be pirated. This is extremely harmful in the cases of starting musicians or artists who want to make cash by selling their work online. It is cool if I share my pdf book file to Hank, but to do that for free? If I pay and he doesn’t, but I still share it to him because he is my friend, the author gets half the price of the book in the real world. Currently, torrenting has become a huge problem in our modern society, especially in terms of copyright claims and what not. I think, in that sense, there are still perks as to why keeping some objects in the physical world might be more safe.

Similarly, data breaches are also very dangerous. This ties to the first argument, but I want to talk more about subjects such as finance or personal information. Let’s look at finance, for example. If I have my money in a bank or a safe, it will be arduous for a robber to steal my money. Even if they do, chances are I will use CCTV cameras or fingerprint scanning, even collecting hair in the suspected areas to go on to find who the suspect is. However, cyber-security is not as developed yet, and in the cyber world it is way harder for police to digitally track down someone. This of course, leads to individuals getting away and even sometimes groups of people working for nations, as we have seen in the Russian hacking of elections a few years ago.

Overall, I like the concept of the article and it has taught me new things that I previously did not know, but I wish the author put in the opposite perspective’s views, especially in terms of how digitization can be disruptive and negative so that we should always be cautious but it can still be beneficial if used correctly. Unfortunately, there does not seem to be too much negative connotation that can provide alert and warning to the readers, which I personally believe is very significant.

The author illustrates digitization through Waze, a mobile GPS application that provides users the fastest way to a destination depending on the time, traffic, and other variables. We are situated in the moment where nearly all of the “offline” data are becoming digitalized – and these raw data are floating around the medium we know of as “the internet”. Some are being efficiently utilized, such cases shown through Waze where it uses the sensor data of its users’ mobile devices on top of general map information and GPS data. However, I would say that most of other raw data are still waiting for their turn to be polished into a proper use – which leads to new terminologies like Big Data.

As the author describes as “data explosion”, I agree with his claim on how statisticians will be one of the sexy jobs. To put it simply, we have tons of increasing number of ingredients but we do not have enough people to refine those ingredients to make something new – and this is where the hype of data science comes in. A lot of the innovations are hidden under these sets of data and once we can analyze these septillions of data, maybe we can answer many questions about behavioral sciences as well as automation of complex human tasks.

I liked how simple and straightforward this reading was, and I was especially drawn to the concept of the network effect that the author referred to in relation to the navigation app Waze. Essentially, the network effect is a “situation where the value of a resource for each of its users increases with each additional user.” So as the amount of users increases, the overall quality of the app increases, and this is because user input is crucial to the accuracy of the app’s results. User input, in this instance, does not only mean manual information provided by users (typing in traffic updates, warnings of accidents, etc.), it also means gathering information from each mobile device’s sensors, processors, and transmitters.

Another interesting, relevant point raised in the chapter was the idea that “new ideas yield new recipes”. The author uses the example of Waze to contextualize the idea of using layers and generations of digitization within the application. Here, the oldest layer would be digital maps, then GPS location information, then social data. Social data refers to the point raised above about the network of everything and how users directly contribute information about various factors affecting one’s transportation from A to B, almost like crowdsourcing data. After social data comes sensor data, which utilizes every car using the app to collect data. This is where sensors, transmitters, and processors come to play. This data is calculated and converted into the app to help people find the quickest, most reliable routes, amongst other services.

So, Waze used an already existing technology or system: GPS navigation. However, they increased its potential to a very great degree by adding two more layers to the system: social data, and sensor data. I think this is a very important aspect of modern-day innovation. Technology has come a far way, and sometimes we become too pre-occupied with the idea of creating entirely new technology, or finding brand new breakthroughs, that we don’t think of the ways we can massively improve already existing technology with our newly-found knowledge and capabilities.