He Talks the Talk but Does He Walk the Walk?

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Tuesday during the beginning of class Prof. Sample specifically mentioned his desire to move around the class more in order to keep our attention. The above is his movement for class on March 31st (Tuesday) during lecture time. As we can see Prof. Sample went on 2 long trips around the room, but most of his time was spent going between his main desk, his podium, and the 2 front whiteboards.

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Thursday we had a lot less lecture time, so as expected their is less movement from Prof. Sample during his lecture. Again we see 2 larger trips around the room, but this time without his podium he spent more time going between the two front whiteboards, and spent a little more time near the rear one.

Old Information Systems and New

saint chapelle gold


Built in the mid 13th century, Saint-Chapelle, the windows of the of the church were probably the first “clock” that the Parisians had ever seen. Though lacking in precise accuracy, the displays were able to change the color of the chapelbetween night and day, and the shifting seasons. This is a great example to see just how far our information systems have gone. As we saw them boast in class, Apples’ new Apple Watch claims to be accurate to within 50 milliseconds of the actual time. Not only is it accurate, but it can sync with your phone to receive texts, calls, app notifications and much more. The advancement from old information systems too new is highlighted by speed, precision, and size. The old information systems were public, accessible to anyone anytime. As information systems have grown more complex, they have held more personal data. Data people may not be comfortable being public. It is in this struggle that people debate the privacy rights of information system users. What rights does Apple have to the information that is loaded up on their devices? Are they allowed to store that information? Can they sell that information?

ENIAC, completed in the early 1940’s by the U.S. army is credited for being the first computer in the world. This was just a general-purpose computational machine. It wasn’t until 1969 that information could actually be sent and received by different computers. The first nodes were UCLA and Stanford, only a few blocks apart from each other at the time. Now today, we have the ability to instantaneously send more information than ENIAC could process in a day any where across the globe. Health monitors implanted in peoples chests, camera’s that monitor eye movement for advertisers, and cookies downloaded onto my computer by websites track every moment of my life and are analyzed almost immediately. It is for these reasons that I agree with John Foreman that the mysterious humanity is destroyed. With enough many spent anybody can learn more about a person than the person probably knows about themselves.


Background of ARPANET: http://www.webopedia.com/TERM/A/ARPANET.html


Going through the readers responses to the works, one thing that I was surprised that didn’t come up was the Bacon number. Although one of the readers did mention nodes and connections, nobody talked about the factor that Kevin Bacon is within eight friend layers of every single actor in every single film that the study could find. This is ridiculous when you think of the human race as a network because it shows how close we are truly interconnected.

Another reader wrote about how artificial intelligence is becoming more increasingly life life, there are still short comings. It seems as if my generation is obsessed with imagining a future without those short comings.  We see newly released movies like the new Robocop or Chappie. We have corporations like IBM creating some of the most complex AI ever in Watson, the all time leader in Jeopardy. As of today we are only scratching the surface of AI’s, but eventually they will grow into the backbone of our industries, our households, and our lives. One can only speculate about what the future holds for us as a the Human network collides with artificial networks.

Entering and Leaving Class

This week I wanted to examine how the class filled up by table, how many jackets were on seat-backs, and how the class exited.

On Tuesday, between the snow and some of the sports teams missing we had 20 students, and the picture below shows how many people sat at each table (the number below the square) and the order that in which the tables filled up (the number in the square).

Tuesday:Screenshot (3)

On Thursday we had a few more people with 22 students. Image follows the same rules as above.


Thursday:Screenshot (4)

I was also interested to see how many jackets would be placed on seat-backs for the “snow day” vs. non-snow day.

Tuesday: 8 students had jackets on chair-backs.

8/20= 40% of people

Thursday: 12 students had jackets on chair-backs.

12/22= 54.5% of people

The perception of a snow day could have put people in a mind set that told them they were cold and so more people kept their jacket on.


When exiting the room the class has two options: they can leave what I call office side (side facing the front of the library) or writing center side (facing the back of the library).

Tuesday: 16 students left library side.

16/20= 80% of people

Thursday: 13 students left library side.

13/22= 59.1% of people

These numbers could very as people may not have had a class to rush off too on Tuesday, but did on Thursday.

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In an era of fiber optic cables, enabling data to travel up to speeds of 2/3rds the speed of light, and the continuous flow of information across the web puts mass amount of data right at the fingertips of the NSA. As we have already talked about, anonymity is practically impossible when attempting to live a convenient life. However, it wasn’t until 2013 with the release of the Snowden files did we realize that not only were we not anonymous, but in fact we were actually quite well known.

Using handy counter on The Guardian website and a stop watch, I calculated that the NSA collects, according to The Guardian, around 20 terrabytes of information per minute. Most of this data would be turned around and stored in the NSA’s new Utah Data Center which, according to Fox News, cost the taxpayers $1.7 billion to build. Forbes.com estimated that this site could hold up to 12 exabytes of information. To put that in perspective lets do a calculation:

1 exabyte= 1,000,000 terabytes

12 exabytes= 12,000,000 terabytes

@ 20 terabytes per minute (12,000,000/20)

The NSA could hold 600,000 minutes of data

600,000 @ 60 minutes per hour (600,000/60)

The NSA could hold 10,000 hours of data

10,000 hours @ 24 hours per day (10,000/24)

The NSA could hold over 416 days worth of data

The NSA could hold all the data they collect, which using the talked about dragnet and the “three hops” would be most of the data available, for over 416 days. After they filter through the data to determine threat levels of information and remove the information deemed non-threatening, they would have enough space to hold enough data on all terrorist SUSPECTS AND THEIR FIRST TWO HOPS OF FRIENDS for far greater than any of them will actually live. At a time where cyber attacks are a real threat to not only nation security, but also things like economic attacks, identity theft, and anti-western hacking groups, can we trust the government with out data? What right does the government have to bully corporations without warrants like Lavabit to give over users data? These are both questions that need to be answered and are finally being talked about thanks to Edward Snowden.


More information can be found here:



Your Package Has Arrived

amazon box

Does dataveillance let companies know more about ourselves then we know about ourselves? Everyone knows that Google tracks their users searches and emails to create targeted advertisement, but Amazon is taking to the next level. Amazon applied for a patent in August of 2012 that was approved in December of 2013 for an idea called “Anticipatory Shipping”. The theory behind this is that through tracking of peoples Amazon.com searches, previous buys, and wishlists, Amazon will expect you to buy a specific item as a certain time. Their computers would run their algorithm, based off their calculated probability of buying the product, and would then ship your product. If the computers found a very high certainty of a future buy, Amazon.com would ship the product to your doorstep automatically. For those will a little lower certainty, Amazon would ship the product to your area’s local distribution center, so the wait time on the product in minimal.

Now, obviously, this will only work for people they have collected enough data on, so the buyer would have to be a frequent shopper at the site. It is also important to note that any pre-shipping has not yet commenced, and they are still working on the program but wanted to make sure their idea was protected first. If only I could patent my privacy. This would be dataveillance you could avoid by just avoid Amazon; but if it works for Amazon, and considering american patents only last twenty years, how soon before other companies use programs like this? Do you think that other corporations would be willing to buy rights to the patent from Amazon in return for the ability to do this? Twenty years is a long time for business owners to let competitors get ahead.

You can find out more about Amazon’s anticipatory shipping here: