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Where do I go from Here?

Well, I’m finally done with my first semester of Computer Science. The first semester had its ups and downs but overall I’d say that I enjoyed it. While I’m looking forward to taking the higher level, fundamental Computer Science and programming courses, I’m also really looking forward to taking some more specialized Computer Science classes in the future, and while I won’t be taking any next semester, I still think that Computer Engineering will be interesting. In the coming years I would like to take some classes that delve more into subjects such as robotics, artificial intelligence and neural networks, computer graphics, and video game design and programming. In the meantime, I’m sure I’ll keep busy Computer Science wise with the Game Design Club and other personal projects, as well as the regular Computer Science courses.

I really enjoyed CIS 115, much more than CIS 200 actually because CIS 200 ended up being mostly review of things that I learned in my high school Computer Sciences classes which were also taught using Java. I did learn a few new things from CIS 115, especially once we got into C# which I had used very rarely before, but for the most part, it was old information. CIS 115 on the other hand dealt with a lot of topics that I had had little or no exposure to in the past and I found it very interesting. In particular, I really liked talking about neural networks, how the internet works, algorithms and heuristics, and Turing machines. Turing machines in particular were very interesting to me because it was basically breaking down exactly what a computer was into only the absolutely essential components. As Hillis put it in The Pattern on the Stone, “In terms of what they are able to do, all computers … are fundamentally identical”. I also really liked hearing about the limits of computability, both in class and through the textbooks. MacCormick raised a good point in 9 Algorithms that Changed the Future, when he raised the question of our brains having the same problem with incomputable problems that mechanical computers do.

Speaking of which, the textbooks were also very interesting (well two out of three of them, but that’s a better ratio than any of my other classes). My favorite chapters across all three textbooks would have to be “Public Key Cryptography” and “What is Computable?”, from 9 Algorithms that Changed the Future by John MacCormick and “How Universal are Turing Machines?”, from The Pattern on the Stone by W Daniel Hillis. I don’t have a favorite chapter from Tubes by Andrew Blum because, as I said in blog post review of the book, I felt that it focused more on the narrative of the author in his journey to find out how the internet works than on explaining how the internet actually works.

In terms of what kind of job I would want after I’m done with college, I think that I would honestly be happy with any job in Computer Science but would definitely like some fields more than others. I think that robotics is an interesting field and that I wouldn’t mind working in it, especially if I got to work with artificial intelligence as a result, but my number one dream job would really be to work in the video game industry doing both programming and design. I’ve been pretty active in the Game Development Club this semester and even attended the Chillenium Game Jam at Texas A & M University which lasted 72 hours and was extremely fun. If it was anything like working in the actual industry, I think that going into game development would definitely be my best option in terms of jobs that I would enjoy.

9 Algorithms - Review

After reading 9 Algorithms that Changed the Future, by John MacCormick, I would have to say that I thought it was actually pretty good. My favorite chapters were chapter 4 and chapter 10. Chapter 4, “Public Key Cryptography: Sending Secrets on a Postcard” talked about shared secrets which I found very interesting. The initial conditions of the problem seem at first to make the problem unsolvable, how can you share a piece of secret information with someone without using any sort of private channel? It was kind of like reading a locked room mystery (not quite that interesting, but it had the same feeling). Chapter 10, “What Is Computable?” was about proving logically that not all logic problems have answers, and while the idea was not entirely new to me it was presented in a very interesting way and using examples that I hadn’t heard of before.

On an entertainment level, I liked this book much more than Tubes but not quite as much as The Pattern on the Stone. While the information was much more interesting and relevant than the information in Tubes, the book was incredibly long winded in most of its explanations. For example, in chapter 4, when the book is explaining the concept of a shared secret, it says that if you and a friend used to live in a house together, then you can say, “our old house number plus the secret number is 329”. Pretty self-explanatory right? Well, apparently the author didn’t think so. He spent three more paragraphs, almost an entire page, explaining how your friend can use the information you told him to get the secret number and why someone who overheard the conversation wouldn’t be able to. This is really more of a minor inconvenience than a real problem, but it can still be pretty annoying sometimes.

In terms of information gained from the book, I would say I probably learned more useful information from this book than I did from either Tubes or The Pattern on the Stone. The Pattern on the Stone talked a little bit about algorithms, but it was in more of a theoretical sense of what an algorithm is and why they are useful and not so much in a practical sense. 9 Algorithms on the other hand explained things in such a way that you could actually use the described algorithms. For example, chapter 7,”Data Compression: Something for Nothing”, talks, unsurprisingly, about data compression, but it is explained in depth enough that I could definitely replicate the procedure in a program if I needed to. There were lots of new concepts in The Pattern on the Stone, but purely in terms of practicality I think that I would still have to go with this one. The Pattern on the Stone had a lot of interesting concepts, they just don’t seem that useful to me at my current programming level.

If a friend was learning about programming and asked me whether they should read this book, I would tell them not to buy it, just to get it from the library or something. The information in it is important for computer scientists, but I feel like if you had already read it once and wanted to review something, a google search would probably suffice and would probably yield some much more succinct results as well. I do not feel the same way about The Pattern on the Stone. That book has ideas which are much more complicated and it is good to have a complete explanation of them that you can go back to whenever you need to.

All in all, it was a pretty good book.

Computer Science & Mathematics

Mathematics is at the very core of what makes computers work. If you dig all the way down through the user interfaces, through the programming languages, all the way down to the absolute basics of digital computer decision making, it’s just pure math.

I’ve always been interested in logic for as long as I can remember. My favorite board game has always been chess, I’ve always liked solving puzzles, and just using logic to solve problems has always been fun to me. Logic is the basis that all math is built upon and math is the basis that all programming is built upon so it follows logically that logic is the basis of how computers work and is one of the main reasons that I’ve always been interested in computers. I became more interested in math and how it relates to computers more recently. A few years ago, when I was first learning how to program in Java, I stumbled upon the Fast Inverse Square Root method. I’ve talked about it a couple times in these blog posts. It is a heuristic that was used in one of the Quake games to calculate light angles for shading. The reason that I found it so interesting was that it wasn’t an exact calculation. The programmers needed a way to make the calculation much, much faster, so they broke down the logic of the problem, took out all the parts that weren’t necessary, and put it back together. It doesn’t calculate the inverse square root exactly, but it does calculate it close enough for what they needed it for and it does it very, very quickly. I think that the reason I found it so fascinating was that it showed how even something as exact as math can still be broken down further into pure logic. Ever since then, I’ve been interested in heuristics, particularly with how they relate to computer science. Even though heuristics don’t find exactly what you were looking for, the computer still interprets it as pure, hard math. The computer still finds an exact answer using the instructions you gave it, you’re just rigging the instructions. According to W. Daniel Hillis in his book The Pattern on the Stone, “In many cases, it is more practical to use a procedure that only almost always gets the right answer.”

Another thing that I find interesting in the math of computer science is data compression. As described in 9 Algorithms that Changed the Future by John MacCormick, “…you might be thinking that this kind of compression is cheating”. Compression is another trick in computer science for getting around the math by using a little logic. Basically, with lossless compression at least, any time that you have repetition in a data set, you can replace the repeated chunks with a single character or short marker that indicates that the large chunk should go there. It’s like saying “a half million” rather than saying “five hundred thousand”. “Five hundred thousand” is a more exact term, but just telling someone the instructions for getting the answer is alright too, especially if just letting them follow the instructions is faster than actually spelling it out for them.

In conclusion, I guess I’m really just more interested in finding clever was to not have to do the math than in the math itself and computer scientists do that kind of thing very often. They still have to use logic, a kind of math, to find ways around it, but it can still be better than doing the actual math. I hope that someday I’ll be smart enough that I won’t have to do any math at all.

Tubes - Review

Tubes is a book written by Andrew Blum about where the internet comes from and how it gets to your house. To be completely honest, I was not a huge fan of this book. I think that the main reason for my dislike is that the book is written as more of a narrative about the author’s journey to figure out how the internet works, as opposed to being a book explaining how the internet actually works. There is so very much time spent explaining things that don’t really matter if you’re just reading the book, trying to learn about the internet. My primary example of this would be the first seven pages of chapter one, The Map, which the author uses to describe the printing of a large map of the internet’s various connections across the world. In these first few pages the author describes, in detail, the city where the map is printed, his drive to the factory where it’s printed, the machine it’s printed on, the man who is repairing the machine that prints the map, and the man who drew the map. In none of these seven pages does the author talk about how the internet actually works which, in my opinion, is somewhat important in a book that’s supposed to be about how the internet works.

I’m not saying that narrative has no place in a book whose primary purpose should be to inform, but I am saying that that place should be to help the reader understand the concepts being explained and not to tell the reader how they should feel about the concept being explained. Take W. Daniel Hillis’s The Pattern on the Stone for example. On page seventy-seven, at the very beginning of the chapter Algorithms and Heuristics, he tells a story about his college roommate who came up with a better algorithm for sorting his socks into pairs. This story only takes up a little bit less than one page, is a useful example of how an algorithm works that makes the concept easy for the reader to understand, and comes as a nice break from his usual, more factual, explanation of the various computing concepts that he covers in the book, especially right after the previous chapter which ended with him discussing the philosophy behind the differences between a computer and a human brain. Much more helpful and much more entertaining in my opinion.

Another gripe that I had with this book was how hard it was to find anything in it. While working on my last blog post about the filter bubble, I had a very difficult time trying to make a connection between the book and the blog post topic. I couldn’t think of any connections off the top of my head, so I tried looking in the index to see if maybe there was something I had forgotten or if there was even something later in the book that might help me. The index has no listings for the words filter, bubble, cookies, tracking, or history. The closest thing that I could find to the topic was the term “privacy issues” which was talked about one time for half of a paragraph. It might just be me, but I feel like these are some pretty important topics for the book to be skipping over.

The one part of the book that I did find somewhat interesting was his trip to the Google data center and how tough it was for him to get into compared to the other data centers.

All in all, I wouldn’t recommend this book to a friend who was trying to learn about how the internet works. I might recommend it to a friend looking for a story about the internet.

The Filter Bubble

For this assignment I decided to use my neighbor’s computer. Neither of us use Amazon or Facebook so I looked mainly at the differences between recommended videos on Youtube and Netflix, as well as Google searches. Right off the bat, I noticed that Google tried to suggest things that weren’t suggested on my computer. While the first recommendation for the letter “s” on my computer was “scantron”, hers recommended “Star Trek”. The letter “j” on my computer recommended several things related to java, while her computer recommended Jessica Jones. On Youtube, the results were very different. While most of my recommended videos are gaming, animation, or comedy related, hers were mostly musicals and music videos. I was surprised to see that this also carried over to the related videos that were shown on the right while watching a video. The first few were usually videos on the same channel as the current video, but scrolling down a bit would reveal videos that were more like the ones on each of our respective recommended videos pages. Netflix yielded similar results. My Netflix account was mostly series, but when we pulled up my friend’s Netflix account on her TV, the majority of the recommendations were movies. Both of us share our accounts with our families, so some of the suggestions were obviously not generated from our own use of the account, such as the westerns that popped up on mine because of my dad or the much greater number of indie movies on my friend’s account due to her sister being interested in them. I also found it interesting that her suggestions seemed much more focused, while mine were pretty all over the place, which I assume is because I have double the number of family members using my account than the number of people using hers and all of her family members seem to watch relatively similar genres while my family is very varied in their Netflix consumption. My brothers watch more horror movies, documentaries, and serious films, I watch more current television series and anime, my sister watches older television series and cartoons, my dad watches classics, westerns, and comedies, and my mom watches romantic comedies and 80s movies with my sister every once in a while. The “Recommended for you” tab on my account was full of things that I couldn’t see anyone in my family watching, while my friend’s account had quite a few things that actually looked interesting to her in it. This makes sense when looking at Netflix’s suggestion algorithm. As soon as you open Netflix, you see a screen that lets you select which user you are, my friend’s family and mine all just pick the default user though. Speaking of Netflix’s suggestion algorithm, according to an interview with Netflix’s vice president of product innovation, Chris Jaffe, on businessinsider.com, the algorithm is designed in such a way that, rather than the team thinking about what they think would improve the product and then implementing it, they simply look at data coming in from users and base adjustments to the algorithm entirely on what the data says would make users watch more Netflix. I think that this is a very good business model because it’s based entirely on empirical evidence.

All of this begs the question, what are companies doing with our data? With sites like Netflix or Youtube, it doesn’t much matter, as long as your credit card information doesn’t get out. I wouldn’t much mind if the world knew what types of videos I watch in my spare time, but with a site like Google or Facebook, I feel that it’s extremely important. They could give your data to interested parties and suddenly you can’t find a job because companies have figured out that people that make certain types of Google searches tend to be less effective employees. Or you can’t buy a house because data from your Facebook page is effecting your credit score. In the book Tubes which we are reading for class, Andrew Blum doesn’t address this issue directly, but, on pages 245-248, he does talk about how hard it is to get into Google’s data center, even for senators and politicians, so at least we know that it would be incredibly hard for our data to be stolen.

Link to article about Netflix’s algorithm development process: http://www.businessinsider.com/how-the-netflix-recommendation-algorithm-works-2016-2

Pattern on the Stones Review

Our class has just finished reading The Pattern on the Stone by W. Daniel Hillis and, I can’t really make up my mind as to whether I liked it or not. There were several parts that I really liked and several parts that I really disliked. I think I would have enjoyed the book more had I not been exposed to a lot of the concepts in the book before I read it, which made the book feel like it was mostly review, going a bit further in depth in the topic than what I was used to towards the end of each chapter. For example, in the first chapter, “Nuts and Bolts,” he talks about beings able to make computers using things besides silicon and electricity, such as building a computer out of sticks and string, or building a computer using water pressure. I have heard about and thought about the concept that it would be possible to build computers using other properties of physics besides electricity, but Pattern on the Stone went further in depth than simply mentioning it. Hillis explained exactly how you would go about building one of these computers by constructing different logic gates using either sticks or pipes. He even showed a picture of a computer he had built himself out of Tinker Toy parts which could play Tic-Tac-Toe. The problem that the book had with telling me things I already knew did drop off in some of the later chapters, and while the concepts were interesting, most of them still weren’t entirely new to me.

The chapters that I enjoyed most were “How Universal are Turing Machines?”, “Memory Information and Secret Codes”, “Speed: Parallel Computers”, and “Computers that Learn and Adapt”. Turing machines and parallel computing were both concepts that I hadn’t heard about previously so they were both interesting to read about. Encryption is something that I’ve always been kind of interesting in but had never come across a resource that went as in depth about encryption as this book, although I think that it still could have gone deeper. Artificial intelligence, genetic programming, and artificial neural networks are all concepts that interest me and are all things that I had learned a bit about before reading the book, but I’m interested enough in them that I still found that particular chapter and interesting read.

If someone else was interested in going into computer science, I would probably recommend this book. It does a very good job of explaining computing concepts in a completely objective way. For example, almost the entire first chapter is spent defining exactly what is and what isn’t a computer, this way the reader isn’t thinking only about electronic computers while reading the book, and it makes a lot of the concepts much easier to understand and easier to make analogies for, since so many of the concepts in the book were being used before the invention of the electric computer, such as when he used Morse Code as an example of data compression.

All in all, it was alright.

Algorithms

The algorithm I’ve chosen to write about is the Fast Inverse Square Root algorithm which is used, unsurprisingly, to estimate the inverse of the square root of a floating point number using a very small amount of computational power. It was probably written by Silicon Graphics Inc., a company that dealt with various aspects of 3D rendering, although the exact date of the algorithm’s creation is unknown, and in fact, its creation may have even been before Silicon Graphics Inc. (code apparently wasn’t very well documented back in the day). The algorithm wasn’t widely known about until after the release of Quake III Arena, which used it to calculate lighting as the game was running. This would mean that the algorithm was in use at least before 1999 which is when Quake III Arena came out.

The reason it was created, or at least the reason it was most widely used, was because, in 3D rendering, the inverse square root needed to be found for tons and tons of numbers, in order to calculate the way light would bounce off of surfaces. This was a big problem for 3D programs that wanted to have dynamic lighting, because finding square roots and dividing are two computations that are incredibly taxing on the processor as far as single operations go. Before the method was invented, dynamic lighting was pretty much out of the question for any program that needed to generate 3D images quickly, like say, a video game.

The Fast Inverse Square Root method is able to make this calculation much faster than the conventional way, although, it doesn’t have 100% accuracy. According to The Pattern on the Stone, by W. Daniel Hillis, a method must get an exact answer in order to be an algorithm and not a heuristic, which would seem to disqualify the Fast Inverse Square Root method. However, the method’s answer can be made more and more accurate by repeating Newton’s method on the result, this would defeat the purpose of the FAST Inverse Square Root method though. So, although the method doesn’t return a 100% accurate answer, it could easily be extended to find an answer with more precision than the number of digits that the average computer variable can hold. If anything it could be classified as an algorithm that is cut short in order to save time.

The Fast Inverse Square Root algorithm finds its answer in very few lines of code, although they are very complicated. Basically, it takes the original number, estimates log₂ of the number by aliasing it to an integer, shifts the new number’s binary digits one digit to the left, takes the number 1,597,463,007 and subtracts the shifted number from it, changes the number back to a float, and finally, uses Newton’s method to make the approximation more precise. The entire algorithm only consists of six lines worth of computations.

If you think it sounds like dark magic from space, that’s because it probably is. After its creation, it was commented by one of the other programmers on whatever team developed it. On the line where log₂ is estimated by aliasing the float to an integer, he commented “evil floating point bit level hacking”. On the very next line where the binary digits are shifted to the right and subtracted from 1,597,463,007, he wrote the question on everyone’s mind after reading such a line, ”what the f&!#?”.

Nobody is sure exactly where the number 1,597,463,007 came from. It isn’t actually the constant that gets the most accurate result. Charles McEniry, in The Math Behind the Fast Inverse Square Root method determined that it may have been found using brute force rather than actual math.

Biography

Hello. I’m a student currently attending Kansas State University for Computer Science. I grew up in a medium sized town just outside of Kansas City, Kansas. My high school was a good mix of both rural and urban people. I was on my high school’s cross country team and am still a runner to this day. I spent a year on my school’s mass media team, helping shoot the morning announcements and sometimes being an anchor on them.

My family consists of two older brothers, a younger sister, my parents, and a lot of other relatives who live in Montana. Because of this, we end up visiting Montana most summers and last summer on our way back, we stopped in Colorado to spend a day hiking in the mountains which was amazing.

I first got into computer science in middle school, mainly because I wanted to make video games. Ever since I learned to code I’ve been programming games. I started out using Scratch when I was twelve years old and have now been using Java for about three years. I know a little bit about coding in Basic and tried to use C++ once but decided to just stick with Java for the time being. I haven’t released any games yet but I just joined the KSU game development club and am hoping to eventually get into the industry professionally. The best case scenario would be that I start my own game development company and it takes the world by storm. I think that I would probably be fine with working for another game development company, but it would have to be the right one because I’ve heard lots of stories about horrible working conditions, long hours, and low pay in that particular part of the computer science industry. In case you’re wondering, my top three games would probably have to be, in no particular order: Fez, Street Fighter, and Megaman X.

I’m currently living with my brother and another unrelated housemate while I attend college. As luck would have it, the unrelated housemate and I share the same first name which is a little confusing at times, I won’t be revealing what that name is here, however.

I get most of my technology related news from either https://m.reddit.com/ or http://www.cnet.com/. Specifically, I’m subscribed to r/programming, r/gadgets, and r/technology on reddit. I really like reddit because the articles come from differing news outlets and are ranked and given their order of appearance based entirely on user ratings, rather than their priority being decided solely by an algorithm.

The book we are reading for class is W Daniel Hillis’s The Pattern on the Stone and what I’ve read of it so far has been pretty interesting. Looking at the table of contents, I would have to say that the two chapters I am looking forward to the most would have to be Chapter 5: Algorithms and Heuristics, and Chapter 8: Computers that Learn and Adapt. Chapter 5 seems interesting to me because new algorithms can revolutionize computer science. Algorithms are at the core of the kind of problem solving that makes programming fun for me. Chapter 8 sounds interesting to me because it sounds like it’s about artificial intelligence and artificial intelligence has such a vast number of applications once it’s advanced enough. Imagine being able to delegate any task to a machine that a human could do, except that the machine gets it done much faster, much more efficiently, and with much less complaining, plus there’s the added bonus of the machine being able to exist in many more places than a human ranging from the vacuum of outer space to a completely virtual environment.

I guess that about wraps it up for this post. I’m really looking forward to computer science this year.

#biography #weekly entry

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