Blog 6

 

Computer science has evolved from a combination of different fields. It is related to psychology, in that computer programs are built with the ability to predict human behavior. For example, if you are searching for something on Google, what you are searching for will already be listed for you to simply press rather than having to type it out. Computer science is related to biology, in that a computer is similar to a human brain (Pinker, n.d). A computer operates due to a number of components operating and functioning together, similar to how interconnected neurons in the human brain fire action potentials that relay information to the rest of the body. Furthermore, computer science is strongly related to the field of mathematics.

Applied mathematics is the term used for mathematics being incorporated into another field. Applied mathematics could be applied for predicting weather patterns, calculating potential profits and losses of a business, and even in psychology. For example, applied mathematics is used in psychology to determine whether an experiment has found statistically significant results. Furthermore, applied mathematics is closely incorporated with computer science.

Mathematics is a ginormous part of computer science. From programming, algorithms, logarithms, search results, to data storage; in essence, the majority of computer science consists of simple algebraic binary mathematics. From search engines finding what individuals are looking for by using a ‘matching algorithm’ that indexes (similar to a book) and ranks search queries, to recognizing digital signatures, the book ‘Nine Algorithms that Changed the Future’ (2012), by John MacCormick provides readers with numerous examples of how mathematics and computer science are related. A part of computer science that involves mathematics that is very interesting is public key cryptography. According to the book, Whitfield Diffie and Martin Hellman founded public key cryptography. Public key cryptography is the reason why when you enter your credit card number into a website, only that website can read the number (even though the internet is entirely public). Key cryptography is often how individuals on the dark market communicate amongst each other. Programs like PGP encryption encrypt messages between two individuals so people whom may want to read the information (i.e., the police) are unable to.

‘Nine Algorithms that Changed the Future (2012),’ break down how computers are able to communicate in secret with public key cryptography. The book first uses the example of mixing paint, then puts the analogy into numbers and applies math. According to the textbook, each computer attempts to communicate in private between each other (without having anybody else eavesdropping) by first picking a private number. The computers then multiply their private number with a public number, and that number is put into the public (the middle of the circle).   That number is then taken, and then multiplied by the receiving computer’s own private number, which then results in a shared secret number that can be decoded. This approach implements a one-way action; which means other computers can’t decode the information due to not being able to divide (or figure out the code by inverting the equation). Because it is a one-way action in that other people can’t decipher the code, the private number must be a prime number. Public key cryptography also uses clock arithmetic. Although somewhat confusing, clock arithmetic means that numbers ‘wrap around,’ or start at zero once a certain value has been reached.

In general, most algorithms that are related to computer science can be also related to other fields. For example, the bubble sort algorithm can be used to sort anything from laundry to a list of numbers. However, in computer science bubble sort is often used to quickly create and replicate graphics in games. Statistical algorithms that can be used to predict what people search for on search engines are similar to algorithms that look for analysis of variance in psychological testing. According to Wikipedia, there are a few algorithms that are strictly used for computer science.  The Cantor–Zassenhaus algorithm is an algorithm used in public key cryptology. Although it is very complex, the Cantor–Zassenhaus works by factoring all of the possible inputs from the received private-public key and generate an output that is equivalent the receivers own.

Without mathematics, algorithms that make computers run would not exist. As ‘Nine Algorithms that Changed the Future (2012),’ exhibited, there were algorithms (i..e, indexing) before the field of computer science arose. However, now that it has risen, computer science and applied mathematics fall hand in hand more so compared to almost any other field.

http://www.emcp.com/intro_pc/reading11.htm

https://en.wikipedia.org/wiki/Applied_mathematics#Computer_science

https://en.wikipedia.org/wiki/Modular_arithmetic

https://en.wikipedia.org/wiki/Cantor%E2%80%93Zassenhaus_algorithm

MacCormick, J. (2012). 9 algorithms that changed the future: The ingenious ideas that drive today’s computers. Princeton, New Jersey: Princeton University Press

Blog 5: Making Meaning (Tubes)

Tubes: A Journey to the Center of the Internet (Blum, 2012) takes readers on an expedition that explores the physical infrastructure of the internet.  The book is supposedly inspired by a squirrel whom had successfully chewed through a fiber optic cable outside of the authors house, thus disconnecting him from the interconnected matrix we are encompassed by (known as the internet).  Traveling to various locations (i.e., Amsterdam, London), Blum initially takes readers on a technical history of the internet (chapters one through three) by discussing its origins and founders.  The remaining material in the book describes what the internet has come to be today and its prospected future directions.

Written in first person, the book seemed to have a sense of identity confusion as to its genre.  The underlying basis of the book was to teach readers about how the internet works.  In doing this, Blum incorporated a significant amount of bias personal experience and philosophy.  Although this was employed to help readers who have no knowledge of the internet be able to grasp the concept, he frequently wondered so far off topic that it would take an entire page to describe something that could be written in one sentence.  The vast number of quotes from individuals not related to technology was overwhelming.  Furthermore, he made so many comparisons that I frequently forgot what he was initially attempting to describe. Perhaps Blum should stick to what his degree entails (humanities) and less about being technologically and instructionally informative.

Prior to reading the book, I had always wished our school textbooks had more similes and comparisons that I could relate to my personal life.  I wished they be more interesting and less direct- for I thought that would help me in gaining a better understanding of the overall concept.  However, after reading Blum’s (2012) novel, I now understand why textbooks don’t stray far from what needs to be taught; because of the potential to go off topic.  This book was a clear cut example of that.

To me, reading the book was an endless cycle of boredom and frustration.  I was interested in what the author was attempting to convey, but I just wished he would get to the point!!  The authors style of writing would have been better if he wasn’t trying to explain such a technical topic.   Because of this, I would hesitate to recommend this book to a friend who wanted to learn about the internet.  Having said all that, the most significant thing about the book was that it viewed technical concepts from multiple perspectives that related to humanity and sociology.  For example, in chapter four when visiting Amsterdam’s Internet Exchange (AMS-IX), he talked about how company was shaped around the country’s culture and belief system.  Furthermore, he questioned the ethics of Google and why they were so secretive when it came to sharing information (although there was much bias in the section).  Google seemed like the only company out of all of them that acted like the author was more of a nuisance than anything else (which is kind of scary, beings how colossal they really are).  I also thought it was interesting when talking about why the internet started on the west coast as opposed to other geographical locations (because the west coast was more open minded).

In an effort to be more interesting and appeal to the general audience, Blum made some statements that are not true.  Blum makes the statement, “every IP address is by definition public knowledge; to be on the Internet is to want to be found.” Although IP addresses are public knowledge, you do not have to have a want to be found to be on the internet. Perhaps the author does not want to go into detail about TOR, Orbot, and other IP-hiding devises that are sketchy but have hit the mainstream.

Although I admittedly did a lot of skimming during the last 75 or so pages due to being bored and frustrated with reading mundane and pointless information (i.e., how international airport hotels look and feel), I thought the introduction about the startup of the internet (chapter two) was the best, easiest, and most interesting part of the book to understand.   Perhaps it could have been because we already discussed it in class, but by the end of chapter two I had a good understanding of what, and the original intentions, of the ARPANET and IMP-1 were. Although I just discussed how the book had too much involvement in humanities, I thought it was interesting how UCLA students had no interest in the first IMP even though it was just in the other classroom. This displays that today’s generations have little interest in the past, even though it created what is going on today. I think this way of thinking may be partially due to how the internet has caused people to not see the bigger picture of things, and simply focus on what is interesting at the present time. https://en.wikipedia.org/wiki/Interface_Message_Processor does a good job at helping gain a further understand of the installation of west coast IMPs, and how and when they communicated amongst each other. Furthermore, the website describes the 24-bit error detection method, and a number of other ‘state of the art’ features the mechanism had. Who would have known in 1969 that Kleinrock log that stated “talked to SRS host to host” would come to be what it is today?

In conclusion, I did not enjoy reading and learned little from Tubes: A Journey to the Center of the Internet.  I think the authors global ventures to see the physical structures of the internet first hand may be more interesting than the book itself.  However, I did learn about how the internet is an actual thing that circumferences the globe, and how it all interconnects to bring us together.  One of the most interesting things in the book was that the intentions of the original innovators was to connect person to machine.  However, although this has been accomplished, the internet’s most prominent feature is that it connects person to person.

References

Blum, A. (2012). Tubes: A journey to the center of the internet. New York, NY: HarperCollins Publishers.

Interface Message Processor (n.d.) In Wikipedia. Retrieved April 3, 2017, from https://en.wikipedia.org/wiki/Interface_Message_Processor

Blog 4

The Internet is designed to confine users to a bias matrix that prevents them from experiencing the realities of the world.   This unsettling phenomenon, known as the filter bubble, was exposed in a Ted Talk presented by Eli Pariser titled ‘Beware of Online Filter Bubbles, (2014).’ According to the Ted Talk, the Internet utilizes an immense number of signals (i.e., type of web browser, previously clicked on links, geographical location) to create a ‘safe space’ for users, only displaying to them what they want to see and what they feel comfortable with.  Upon the surface, the filter bubble seems to promote efficiency: why present users with a clutter of information they have no interest in, when you can quickly display exactly what they want to find? However, hiding from users what they don’t want to see creates a single-minded view of how the world operates.  It does not paint the whole picture; it does not show the other side of the coin.  This fabricated version of reality not only modulates personal viewpoints, but also can have an immense impact on decisions that affect the world (e.g., presidential elections, the use of fossil fuels).

To test the filter bubble phenomenon, I compared my girlfriend’s social media, Netflix, and news sources to my own.  First stop, Facebook:

Facebook

Facebook has admitted, and has been sued for, filtering ads based on race and ethnicity.  Furthermore, Facebook filters individual newsfeeds based geographic locations, relationship status, what links people have clicked on first, etc.  Additionally and most likely having the most impact, newsfeeds are strongly filtered by the type of friends people have and what they post.

Surprisingly, my girlfriend and I had similar Facebook newsfeeds.  Although I had more information and posts regarding politics, both newsfeeds had a pro-conservative, anti-democrat taste.  This can be explained by both of us growing up in the South, thus having friends that are eager to get that wall built ASAP.  In reality, I am more Liberal on a lot of issues; however the links I press on first are usually from my family or close friends-in which despise everything about Obama.

Other then the few political posts, the majority of my girlfriends newsfeed consisted of comical memes usually involving cats or shiba inu’s, relationship quotes, DIY projects, and sponsored ads by fast food chains (e.g., Wendy’s) and retail (e.g., Spencers).  On the flip side, my newsfeed consisted of a lot of workout routines (my friends are workoutaholics), wrestling and waterskiing videos, and inspirational quotes.  Although some of the memes were the same, overall her newsfeed more aligned with Reddit while mine aligned more with a news website (I think hers was more interesting).

Netflix

In relation to every other difference between my girlfriend and I’s filter bubbles, Netflix was the most dramatic.  My recommended movies consisted of one-star kung-fu action films (I purposefully watch a lot of low rated movies, looking for that hidden treasure), and acclaimed classics such as Pulp Fiction and Blow.  Parks and Rec, Scrubs, and other comedies were the first to appear on the list of TV shoes.   Conversely, her ‘recommended to watch’ consisted of a handful of documentaries, dramas (i.e., Gray’s anatomy), and animas.  Because we often watch Netflix together, we did have some similarities in our recommended to watch, as American Horror Story and Shameless were on the top (if you have never seen Shameless, please watch it).  Not to stereotype, but I felt like the differences in movie and TV-show recommendations were based on that I enjoy watching things most men would prefer, while she enjoys watching what most women would prefer.

YouTube

Poker, Kid Cudi, poker, Red Hot Chili Peppers, Ted Talk, Poker, NCAA wrestling finals, how to run statistical software, different workouts, poker.. That’s primarily what my YouTube filter Bubble consisted of.  My overabundance of poker recommendations is most likely due to me utilizing YouTube as a coping mechanism when I come home with my pockets empty after a ‘bad beat’ at a house-game; watching other people lose a lot more money then me gives me a sense of comfort (even though they have a lot more money to lose).  Kind of comical, after doing a YouTube Search about how to run a statistical analysis of variance on a program we use on my research team, all of my recommended videos below were still about poker.  I guess this is proof that I lose more money then I win most of the time J.

Exotic animals, Keith Urban, DYI projects, and ‘WhatsTrending..’ that’s primarily what my girlfriends YouTube filter Bubble consists of.   She is into country music while I like alternative and rap.  She would rather watch grass grow then play poker, and she is much more interested in wildlife as compared to my interest statistical psychology. A lot of her YouTube recommendations consisted of horse back riding, how to do your makeup, and how to complete basic algebraic equations (she is in algebra).

 

News Sources

There were notable differences when searching news sources such as CCN.com, The Huffington post, and Foxnews.  Although we both had news about upcoming weather in our area, the ‘top stories’ differed significantly.  The majority of my top stories dealt with how the repeal of Obama Care care would cause millions of citizens to go broke (which is interesting considering most of my Facebook News Feed is conservative), and other articles related to Trumps political Agendas.  My girlfriend’s top stories via news sources were mainly about relationship struggles among celebrities (EXTREMELY IMPORTANT STUFF).  The search also displayed how she was more pro LGBT while I didn’t have much care on the topic; as one of her top stories was “Aarow Star Colton Haynes Is Engage to Jeff Leatham,” while mine was “Todd Starnes: Hey, Sean, I was Attacked by a Raging Feminist, too.”

In conclusion, I was surprised by what my girlfriend saw on the Internet as compared to what I saw on the Internet.  This is a reflection of our difference in interests, difference in age, where we were raised and whom we are associated with. Because she is still new to my life, if we decide to stay together I would hypothesize that our filter bubbles will become more similar in the future (as our values and interests will become more similar).

Both of our filter bubbles primarily consisted of what was relevant near us. There was much about Trump, recent storm destruction, and police arrests; and so little about what was happening globally.  We may not know that a school in Africa was just got shot up and thirty children died, if the people who committed the attacks were allied with the American government.  We won’t know when a white man kills ten individuals in Australia, but we will sure know when a Muslim shoots someone in America.

At last, I wanted to do an incognito search to see what the Internet would show me if I was anonymous.  It should be noted that I am not using an IP changer; thus the Internet can still present information relevant to my geographical location.

Going Incognito

Youtube

Recommendations on Incognito YouTube are primarily based on what others have been recently viewing.  From a ‘Thai Noodle Challenge,” to “What Happens When You put 20,000 Volts into a Watermelon,” the variety is limitless.  Unfortunately, there are a lot of anti-Muslim videos, exemplifying how the majority of the United States scapegoats the religion.  Thus, although I feel incognito is less bias, it is still bias in regards what the common internet user in America is thinking/interested in.

New Sources

Although the new stories were practically the same, they were arranged in a different order.  While my top stories dealt with politics (mentioned above), top stories going incognito were about the dangers of marijuana and fake weed.  Furthermore, there were a lot of posts related to childcare and rearing; things I have zero interest in.

Conclusion/In Text References

According to Wikipedia and other internet sources, the filter bubble is an algorithmic approach; which is a procedure that guarantees to achieve a goal (Hillis, 1998).  However, I am confused about why the filter bubble is considered an algorithm and not a heuristic, as it is not right 100% of the time.  Although it combines a number of factors to determine what should and what should not be present on a selected browser, it is not 100% accurate; rather it gives the ‘almost right answer (Hillis, 1998). For instance, I have more interest in what natural disasters are currently occurring in the world as opposed to the tornado warning in Manhattan last week; thus, if the filter browser was 100% accurate shouldn’t it have presented world news first?

I found difficulty in referencing Andrew Blum’s novel ‘Tubes,’ as the first four chapters mainly covered the development of the internet.  However, Blum’s (2013) novel discusses how infinite networks and servers peer together so the internet becomes less congested.  If filter bubbles did not exist, and websites would supply everyone with every single bit of information, the exchange of traffic would be immense and (I think) the internet would become slower.  Thus, filter bubbles are in place to monitor and limit the amount of traffic that is being sent to a server, and to ensure the traffic is relevant to the viewer.  Furthermore, the novel said the most important people that contribute to the internet today are not hardware engineers anymore, but analysts that can predict what people want to view and see.  These are the people that assess all of the factors that go into a filter bubble.

References

Blum, A. (2013). Tube: A journey to the center of the internet. New York, NY: HarperCollins.

Hillis, D. W. (2015). The pattern on the stone: The simple ideas that make computers work. New York, NY: Basic Books.

Pariser, E. (2014). Beware of the filter bubble . Retrieved from http://blog.ted.com/how-do-i-cite-a-tedtalk/

 

 

 

 

 

 

Blog 3

Blog Three: Making Meaning: POTs

Hillis’s (1998) ‘The Pattern on the Stone,’ introduces readers to the world of computer science. From examining the basics of programming (chapter three), defining and discussing basic algorithms and heuristics (chapter five), to touching on more advance subjects such as parallel computers (chapter seven), each chapter covers an entirely different area of computing. Chapters begin with the basics of each topic (i.e., defines what it is and how it relates to computing), and gets more complex (i.e., talking about its different aspects) as the chapter continues forth.

Because the book covered such a broad range of subjects I was unfamiliar with in fewer than two hundred pages, I felt most chapters were difficult and hard to follow. I would understand the beginning of each chapter; however as the chapter would go by I would find myself having to reread paragraphs (more so than textbooks in my 500 level psychology classes) to get a grasp at what the author was attempting to convey.  For example, during chapter six (Memory: Information and Secret Codes) I understood when Hillis (1998) was describing memory and its unit of measure (bits); however I hard difficulties in understanding when he was describing the various techniques (using bits) that can be used to measure memory.  I feel that if I had more time to reread the book again while taking notes, I would gain a better comprehension of the material and the fine details described within.

Overall, I was more bored and frustrated than anything when reading this textbook. Coming into this class, my knowledge regarding computer science was extremely minute; however I thought I would enjoy the class and that my interest in computer science would increase as time progressed.  Unfortunately, thus far my aspirations have not been met as I find most of the content confusing and uninteresting.  In regards to the book, I did enjoy how each chapter up until about halfway aligned with our in-class activities.  Although I didn’t understand everything the author was describing in each chapter, gaining the basic knowledge of the topic helped me complete each in class assignment (i.e., Turing Machine, algorithms).

There were two notable chapters that I had a high level of interest in. I thought the concepts and material within chapter seven (Speed: Parallel Computers) was interesting when describing how computer technology was/is progressively changing from sequential processing to each processor working together to solve a problem (similar to the human brain).  This was the rebuttal needed to disprove Amdahl’s law.  To further understand parallel computing, Blaise (2016) writes an in-depth article regarding the subject.  The article introduces parallel computing, implementations of its use today (e.g., finance and economic modeling, virtual reality, atmospheric pressure, weather readings), limits of parallel processing (e.g., mentions Amdahl’s law), what makes a good parallel processer (e.g., incorporating both fine and coarse grained detail), and the predicted future (e.g., ‘supercomputers’).  Blaise (2016) reiterated a lot of the information found within Hillis’s (1998) book, but includes imagery and a more current outlook on the subject.  On a final note regarding chapter seven, I thought it was interesting how close Hillis (1998) was at successfully predicting the future.  For example, Hillis made the prediction that the internet would eventually become connected to a number of household appliances (i.e., refrigerator; a prediction most computer scientists would have laughed at), which is something that is currently occurring in most middle and upper class households (I can log onto Face Book from my friend’s refrigerator).

Another chapter I found interesting was chapter nine (Beyond Engineering). Although the brain is much more complex and less prone for failure (i.e., neuroplasticity) than computers, the two are strongly related.  Technology has been on a path to become more like the human brain in that each part of the computer works together in the most efficient matter possible.  This is similar to the millions of neural networks that connect the parasympathetic and central nervous system; firing neurotransmitters between neurons (when sodium and potassium channels create an action potential) that cause our entire body to function as one.  Another aspect interesting in the chapter is how the advancement of computer technology is like Darwin’s survival of the fittest.  Because technology is greatly influenced by cultural and artificial selection, it is increasing at monumental speeds; however both technology and Darwin’s natural selection are influenced by the Baldwin Effect- the theory that social learning shapes evolution as much as evolution shapes social learning.

Overall this book definitely helped in dipping my feet into the water of computer science. Although I found it personally confusing, and there were many aspects I still don’t understand, I learned a significant amount throughout the novel.  As noted previously, learning was increased when we applied the book to in-class assignments; and I was able to understand the topics better when it was compared it to the human brain.  I would recommend this book to anyone who (like me) has a very limited knowledge regarding computer science.  I’d imagine there were many people in this class (for those that actually read it) who did not learn anything; and for those individuals I would recommend a more advanced textbook.

I don’t really have any questions that have been left unanswered. I read the book, but I feel that if I took a test on it I would not perform well as (previously noted) there was too much to comprehend in only two hundred words; thus I would need to reread and takes extensive notes on it.  Having said that, the most significant thing about the book was it’s ability to cover such a broad range of topics.  Although I may not know the fine details about any specific topic, I now know what computer science entails and the facets the lie within it.

References

Hillis, D. W. (1998). The pattern on the stone: The simple ideas that make computers work. New York, NY: Basic Books.

Blaise, B. (2016). Introduction to parallel computing. Livermore Computing Center. Retrieved from https://computing.llnl.gov/tutorials/parallel_comp/

Blog 2-Algorithms

Bubble Sorting Algorithm
Spencer Ingermanson

            The bubble sorting method is one of the most basic types of algorithms implemented to accurately place items in either ascending or descending order.   According to Astrachan (2003), the algorithm was first introduced in 1959 by the name ‘exchange sort.’ For unknown reasons, a few years later (1962) a computer scientist published a journal article in which he changed the name from ‘exchange sort,’ to ‘bubble sort.’ Throughout the next few decades, many synonyms to the algorithm have been proposed (i.e., ‘sorting by repeated comparisons and exchanging,’ ‘shuttle sort’), however most computer scientists today still use the term bubble sort (Astrachan, 2003).

Bubble sorting is one of the most basic types of sorting methods. The method consists of looking at the first two items (generally starting at the left side) that are next to each other, and switching them if they are in the wrong order relative to an ascending or descending pattern. After the two items have been switched if in the wrong order, or left untouched if in the correct order, the procedure shifts one number to the right and repeats until the entire list has been sorted. The following is an example of the bubble sorting method sorting the numbers in ascending order; given the numbers, 5, 9, 2, 0, 1:

Step 1. 5, 9, 2, 0, 1

The numbers 5 and 9 are compared. Because the number 5 is less than the number 9, the numbers do not switch.

Step 2. 5, 2, 9, 0, 1

The numbers 9 and 2 are compared. Because the number 9 is greater than the number 2, the numbers switch.

Step 3. 5, 2, 0, 9, 1

The numbers 9 and 0 are compared. Because the number 0 is less than the number 9, the numbers switch.

Step 4. 5, 2, 0, 1, 9

The numbers 1 and 9 are compared. Because the number 1 is less than 9, the numbers switch.

At this point we now have 5, 2, 0, 1, 9. Because the highest number is now in the correct position, it can be ignored and the other numbers can be swapped using the same technique until all of the numbers fall in ascending order.

The bubble sorting method can be written as О(n2), in which n represents the total number of items being sorted, whereas O represents how running time grows as n grows.   Because n is squared, which significantly increases running time, bubble sorting is not efficient when dealing with a larger number of items (Hillis, 2015). Furthermore, the bubble sorting method is inefficient if the numbers are in the worst-case scenario (i.e., all the largest numbers are on the left side when sorting in ascending order from left to right), as the method would require numerous steps before all of the items are placed in the correct order. To combat these issues, a number of other algorithms are more efficient and produce the same results. One algorithm, known as the merge sorting, involves a recursion method (Hillis, 2015). The merging method involves dividing the total number of items in half and sorting those two halves in ascending order. Next, combine the two halves back together by successively taking the lowest numbered item on top of the stack and placing it in order. The merge sorting method can be written as n log n. Using the example we used with the bubble sorting method (i.e., using the number 5, 9, 2, 0, 1), here is how the merge sorting method would work:

Step 1. Divide 5, 9, 2 into one stack, and 0, 1 into another stack

Step 2. Assort 5, 9, 2, into ascending order (i.e., 2, 5, 9)

Step 3. Compare the top card of the two stacks. 0 would be compared to 2, so zero would be placed first as it is a lower number. Additionally, 1 is also smaller than 2, so 1 would be placed before 2, which would then be followed by 2, 5, and 9.

As displayed, the bubble method can be easily done manually. Due to the amount of time it takes to put a large number of items in order, and due to the possibility of the worst-case scenario, this algorithm is rarely used in programming. However, the bubble method is occasionally implemented when fixing minute computer graphics that can be adjusted with using only linear complexity (Bubble Sort, 2017).
What makes bubble sorting special and unique is its simplicity. Although it may not be the best and most efficient algorithm to use, it is an ideal technique to teach when introducing algorithms to students who have no background in computer science (e.g., the author of this paper). Many computer scientists have ridiculed teaching this method due to its potential misuse (i.e., using it when n is a large value; Astrachan, 2003); however if taught the drawbacks to the method, it can be the initial stepping-stone to delving into the world of algorithms and heuristics.

Switching to a first person perspective, on a personal note, I think the bubble sorting method is useful in teaching the basics to algorithms and heuristics to individuals who have no experience in computer science. Before reading the chapter, I didn’t know what an algorithm even was. However, because Hillis started as simple as possible (and used the sock analogy), I quickly learned their use. As Hillis described, and as we practiced in class, the bubble method was my first step in learning about algorithms.

Prior to writing about the bubble sorting method (perhaps the most simple algorithm out there), I was going to write about an algorithm that found variance. Because I run many ANOVAs on my cognitive psychology research team, I thought it would be very interesting to see how the programs I use (i.e., SPSS, JMP, MATLAB) can analyze variance and regression analyses. After failing to interpret any of the equations, I decided to give up on the topic and go for something more basic. However, what I did learn was that before these computer programs were developed, the job of a psychologists and a mathematician was very similar. Hopefully, once I complete more statistics, I will be able to understand and comprehend algorithms that are more complex than the bubble sorting method.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

 

Astrachan, O. (2003). Bubble sort: An archaeological algorithmic analysis (Doctoral Dissertation). Retrieved from https://users.cs.duke.edu/~ola/papers/bubble.pdf

Bubble Sorting Method. (n.d.). In Wikipedia. Retrieved February 7, 2017, from https://en.wikipedia.org/wiki/Bubble_sort

Hillis, D. W. (2015). The pattern on the stone: The simple ideas that make computers work. New York, NY: Basic Books.

 

 

 

Blog 1

(Narcissism is high in most millennials living in industrialized countries. Students are probably enjoying writing about themselves. I can see why this is blog #1)

 

Born and raised in the Midwest, hunting bucks and riding trucks is all I’ve ever known.   I can’t go a second without a dip under my lip and a spitter in my hand. If we ain’t going to Dirty Dogs, we ain’t going to the Ville, yall understand?

 

Just kidding.
Even though I have never lived an hour away from Salina (a more conservative town in Kansas), I significantly differ from how individuals living in other states stereotype us ‘folks.’ Never have I rode a horse, never milked a cow, and if a girl asks me to square dance with her I’m probably going home alone that night. Even though I am not a modern cowboy (especially not the ones who go to the bar wearing decorative jeans), I still enjoy the vast number of opportunity and beauty the Midwest has to offer. During the summer you can find me with a poll in the water and some aluminum in my hand. We could feed a Dorm hall with the amount of deer meat we have stored in the freezer. Probably like everyone else who has to endure it, when the cold wind during Kansas’s dry winter is hitting me in the face I get the urge to move to Florida. However, in my opinion, nothing beats a Kansas summer (I’m talking about when your seatbelt is too hot to put on). Thus, having said that, the reason I chose to go to K-State was a combination between being able to continue enjoying the benefits Kansas has to offer while still being close to home. Looking back at it now, due to the connections and friendships I have made with colleagues and professors in my field of study, I wouldn’t choose to receive my Bachelors from any other college (even if I had a time portal at my disposal).

Not only was I born into white, male, heterosexual, and non-handicapped privilege, I had the best mom and dad in the world. Being the baby of the family (i.e., brother is 35 and sister is 34) nothing surprised my parents. They let me figure out my mistakes on my own. Not only were my parents always by my side, my brother and sister were positive role models to look up to. According to Bandura’s social learning theory in human development my family hit the nail on the head. My family, coupled with a hundred and some mistakes I’ve learned from, have helped me get to where I am today- a senior with a 3.9 whose resume shines bright.

I’m a psychology major who graduates in May. Because our blogs can be informal (correct me if I am wrong) you are currently not reading my writing at it’s maximum potential. Whether it be an APA based scientific paper with a specific maximum word limit or a poetic script I jot down when I am bored, writing is my forte’ (thus I plan to hopefully write our entire group paper assuming my teammates have in someway contributed). Although I can quickly use a number of search engines to find the most relevant, accurate, and modern psychological journals; my ‘go to’ search engine when it comes to computer science is practically non-existent. I don’t read scholarly articles regarding computing, nor do I ever read non-scholarly sites or blogs. As I previously mentioned I enjoy fishing, perhaps the most technologically savvy articles I read are in Cabela’s magazines about the latest fishing boats and trolling motors. Its fascinating that technology has progressed to a point where you can undock your boat, have the trolling motor automatically direct it to the closest and safest body of water, then automatically pick you up at the dock once you have parked your truck. Authors note: I am not sure how to further this rubric criteria without being dishonest, thus I am really hoping to not miss any points due to this.

            Along with being a full-time student, I have worked in the food industry for nine years and am past ready to throw in the towel. Every time a guest tells me at Olive Garden to “put the whole block of cheese on their salad, haha,” all I can think about is walking across that stage in May. Post graduation I will take my GRE and apply for I/O psychology grad schools. After grad school I plan to work as an organizational psychologist in a major company (Google ‘I/O psychologists’ if you don’t have any idea of what they are).

Currently I despise sitting in front of a computer. I would rather be at the Rec lifting or wrestling, running with my two dogs at the Dog Park or Linear Trail, or be losing money in a poker game at someone’s house (the only time I ever use math). Years ago I was very technology savvy but I have lost a lot of these skills. Most of my computer skills were illegal or at least strongly looked down upon in the computing community. If you have ever played World of Warcraft, and you got a phony email saying your account was compromised and you needed to enter your username and password into a website that looked similar to Blizzards, there was a chance it was my website and you got phished. Sorry pal, your WoW character is now naked and just transferred all their gold to a Chinese company (if you have gotten hacked before and you are reading this, please don’t give me a zero). Similarly, although I didn’t make well over a thousand dollars as I did in WoW, my soft-modded Xbox really frustrated some Halo 2 MLG gamers trying to hit level 50 (I was honestly a huge jerk, ‘lawling’ them over the headset as they rage quit).

I decided to take a computer science class to step out of my pseudo-science moral matrix the building of Bluemont has confined me to.   This is my last semester and I only have one ‘required’ class, so why not? I picked up this class, a couple business classes, a fitness class, and my 12th and final psychology class. Talk about a schedule full of variance. I think computer science is interesting for a variety of reasons. For one, because technology is expanding at such an exponential rate, I don’t want to be that technological laggard who everyone wonders how he or she made it in industrialized America. I want to be the early adaptor who jumps on technological innovations before they hit mainstream. This will not only provide me with the newest and most effective gadgets, but will help shed light about which products are worth investing in. However, due to the limited amount of knowledge this class teaches (due to it being an introductory level course) I probably won’t learn as much about computer science as I had hoped.

If I were to choose two textbook chapters that seem the most interesting, ‘Algorithms and Heuristics,’ and ‘Speed: Parallel Computers,’ would be my selections.   The former (i.e., algorithms and heuristics) raises interest due to wanting to learn the basics of coding. Being on a psychological research team I am proficient in programs such as SPSS and MATLAB. However, although I know how to find correlation coefficients, statistical significance, and other such measures, I am unknowledgeable as to how the program executes such functions. On my research team we have convinced a computer programming graduate student to write us various codes, and I think it would be interesting to get a taste as to what he is doing.

The latter chapter I had chosen, ‘Speed: Parallel Computers,’ seems interesting as it is seemingly similar to the human brain. I question if parallel computers is similar to humans ability to perceive various aspects of an object when encoding. I wonder if the millions of neurons that are rapidly firing at synapses during every cognition that comes to mind is similar to how computers process information at almost an instantaneous rate.

All in all I am excited to be apart of this class and look forward to gaining knowledge that lies outside of my realm.   I hope your as excited for the semester as I am!

 

 

References

Hillis, D. W. (2015). The pattern on the stone: The simple ideas that make computers work. New York, NY: Basic Books.

Ross, F. (n.d.) Trolling Motor Buyer’s Guide. Retrieved from http://www.cabelas.com/product/Trolling-Motor-Buyers-Guide/532011.uts