On Dumpster Diving

I honestly thought what Eighner had to say in "On Dumpster Diving" was fascinating. The lifestyle that was drawn by Eighner was one that I had never thought of or experienced. I also really enjoyed his explanation of the evolution of the dumpster diver. At first, the fresh diver will take everything, regardless of worth or use. However, as time goes by, there is a realization that taking useless items is just that: useless. So, the diver eventually only hunts for the essentials: food, clothing, etc. His writing seems almost useful in the unfortunate event that I become homeless.

I do have a couple points that stuck with me. The first is that he stated that he tried not to judge the people that threw things into the dumpster. However, the writing seemed somewhat judgmental or condescending at times. The way he described the supposed thought process of why a certain item was disposed annoyed me. He assumed the lifestyle or choices of his "patrons" according to what else he found in the dumpster. Then, he would go on to tell the tale of the disposed item in anyway he would like. Am I being too cynical towards a man who scavenges dumpsters to get by? Perhaps I am. Another point that annoyed me is the language that he used. I may have read too much into his style, but it almost seemed like a humble brag. Some sentences used unnecessary words that screamed: "I may be homeless, but I do know my English!" There is, however, a huge chance that this means I'm just a lackluster reader/writer.

Overall, I enjoyed reading through "On Dumpster Diving". Eighner's description of the little tricks and tips to successful diving really kept me interested and on my feet. If anything, I can probably get by for a little while if I ever find myself in a dire situation.

Writing Friday

1. How did you learn to write?
I did not. All jokes aside, I can only say that I learned to write by just trying to please my teachers. I wrote what they wanted to hear in the form that they wanted to see. I've never really wrote or figured out my "own style". I barely wrote for fun or to express myself, which probably put a huge detriment on my actual writing skills. My learning was watching how others do, and copy what seemed to work.


2. What kind of writer are you?
I think that I'm simply just an average writer. I am most definitely not a proficient writer off the top of my head. I usually need multiple looks at my own writing before anything can start to make an ounce of sense. Though this post was done off the top of my head. I'm also the type to take forever to find an entrance to anything I write. A lot of people advise me to start with the meat, but I cannot do it. It's very hard for me to come up with any sort of meat unless I have some sort of introduction. It's !kind boggling that some can write out of order, yet combine it to make chronological and logical sense.

Narrow your topic

1. 1. What is the general topic area you are considering?
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3. Can machine learning be used as a tool of medical diagnosis? 
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1. 2.Why? Are you truly fascinated/curious/passionate about the topic? How did you become interested in this topic?
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3. I am truly interested in seeing if there is a viable way to implement medical knowledge in poor places. It could be more efficient or cheaper to implement a program that can diagnose patients without needing to deploy doctors overseas. These programs would most likely be able to operate with minimal help . insert more rambling here.
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1. 3.What surprising facts have you gathered so far about your topic? What further questions do you have that you need answered with data? And what sources are you thinking of using?
2. One of the surprising facts that I've seen is the difference in vision between engineers and doctors. There seems to be some sort of disapproval or mistrust from doctors towards engineers that is putting a block in the road for further research. Some further questions include the reliability of the program, the cost of implementation, and what areas would benefit the most from implementation. 
3. Some sources I was thinking of using is:
4. Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109.
5. Leung, M. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015). Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. Proceedings Of The IEEE, doi:10.1109/JPROC.2015.2494198
6. Liu, N. T., & Salinas, J. (2015). Review: Machine learning in burn care and research: A systematic review of the literature. Burns, 411636-1641. doi:10.1016/j.burns.2015.07.001
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8. 4. Do reasonable people disagree about the topic? If so, what aspects of the topic to they disagree about? Who disagrees with whom? Name names. Articulate at least three positions you have found.
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10. Some argue on the reliability of a computer program diagnosing patients. There seems to be a stigma around the fact that patients will be diagnosed without any human interaction. How reliable can this program be? Is it better or on the same level as human doctors in terms of accuracy?
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12. 5.Is the topic researchable in the time you have?
13. Yes it is.
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15. 6.What are some subtopics that have emerged in your research?
16. Some subtopics in the area of machine learning is deep learning, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Bayesian statistics, data analysis, and data aggregation. Some subtopics included in the medical side would be the data necessary to reasonably diagnose a patient, areas with poor medical knowledge, and cost of doctors vs program.
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18. 7.What questions might you pursue in further research, based on what you’ve discovered during preliminary research?
19. Is implementation of a machine learning program actually more cost efficient than actually sending doctors? Does the long term benefit of implementation trump building a hospital and donating funds? How can we assume a reasonable percentage of reliability in the program?
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21. 8.What are some key terms that keep coming up in relation to this topic?
22. Some key terms that come up is deep learning, statistics, data aggregation, data analysis, trend prediction, and medical reliability.
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Machine Learning and Medicine

First of all, what is machine learning? Simpy put, machine learning is a field of computer science that uses pattern recognition and computational learning theory. A more in depth explanation can be found with this video by Android Authority. For example, a program can be set to learn chess through watching/playing thousands of iterations of the game, and learning the ins and outs at a hefty speed. With machine learning, a computer can make predictions on what will happen based on past data. There are three main classifications of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Each have their own way of organizing input toward different desired or unknown outputs.

So how can machine learning be used in conjunction with medicine? The first idea that popped in my head was diagnosing patients. In areas where there may be a lack of medical knowledge or doctors, a program can help immensely. Computers are starting to be able to recognize and label pictures as seen here by Fei-Fei Li of Stanford's Artificial Intelligence Lab. With the help of few volunteers, these programs may be able to help diagnose patients using pictures, x-rays, etc. With these information, the computer can speed up diagnosis by analyzing the patient. Often times, data on proper or correct diagnoses are available in records. This can be used as a reference point for the computer to determine an algorithm. More information on the history and implications of machine learning in medicine can be found here.

Currently there are some limits to machine learning being used in conjunction with medicine. Most medical professionals feel that the program is not as reliable or trustworthy as a real human. It is speculated that the disconnect between engineers and doctors must be relieved for a machine learning algorithm to be considered into use. I'm not yet sure on how such a program can be implemented in areas with a lack of medical knowledge and doctors. Hopefully with my research, I can find a way to aid doctors in lacking areas.

The Funds, Friends, and Faith of Happy People

In the article, Myers discusses many things that can correlate to someone’s happiness such as wealth and relationships.  It is interesting yet obvious that there is no real correlation between wealth and happiness. We’ve all heard the saying “money doesn’t buy happiness”. However, many people in the article agreed that having “a little more money [could make them] a little happier.”  So what’s the difference? I think the difference is in the lifestyle. I don’t think that most people can handle living a high maintenance, wealthy lifestyle, and would prefer to have an “average” lifestyle. Thus, having slightly more money would not put people over the top, yet would be enough to give an extra boost in confidence and morale. In other words, I believe that most would only prefer to be slightly above the average.

In the article, there are many discussions on what makes a person happy. The first statistic that surprised me was the fact that, in Detroit, 9 in 10 people selected a happy expression to portray their lives. Although this survey does not have a large sample size, I think that it greatly reflects on how most view their life. Maybe I’m a cynic, but I can’t really believe that there is this many people that think so highly of their lives. I am not saying that it is impossible to lead a happy life; however, I think that there are more than 10 percent of people that are in unhappy situations. Are people inflating their views on their lives? Or do people actually believe that they are living a happy life? What constitutes a happy life?

Overall, there doesn’t seem to be a conclusive reason to why someone is happy. That is completely expected and understandable. Happiness is subjective. I’d be afraid if happiness can be quantified or boosted artificially. We’d be living in a world similar to that of Brave New World. We all have different views, and constantly challenge each other’s views and ideas; it’s how we grow as people. People view their lives differently from one another, and appreciate their lives in their own ways. In the end, I think that it is up to yourselves to find some sort of happiness in your own lives in any shape or form.