Summary of reading: May – July 2017

It’s has been quite a long time since last time I wrote a book review.  This year my goal on goodreads is to finish 30 books and I’m way behind my schedule. In addition, I have been thinking about whether to port my “book review” section in this blog to my technical blog. The reasoning behind it is that I have spending most of time reading technical books and wordpress is not very friendly when drafting a technical-heavy blog (why? see this post). However, when I actually start drafting this post, I realize that book review is not that technical: it involves the mixture of the text from writer, and thoughts and feelings from reader. So, I’ll keep it here. The really downside for keeping the reviews here is that my “bookReview” tag doesn’t really give a list of titles for quick browsing. By the way, I want to thank my work that offers me four hours daily commute that allows me to concentrate on reading.

“The Road Less Traveled: A New Psychology of Love, Traditional Values and Spiritual Growth” by M.Scott Peck

I finish this book maybe May or maybe June. I cannot remember the exact date. That has always been a problem for me because I usually do highlight when I read a book and once I finish it, I want to take note of good points for future reference. This delays the progress of my next book and also messes up my memory on what exactly date I finish the book. This problem usually happens to some book that are worth lots of re-read in the future. “The Road Less Traveled” by M. Scott Peck is exactly one of those books.

I read through the Chinese translation of the book. It’s not a thick book: only around 250 pages for translation edition. To me, this is somewhat a sequel to “There is nothing wrong with you” book even they are from different authors. The problem discuss in this book is about life and love. The theme is truly cliche on some level. When I recommend this book to my friends, some of them even laugh when I do some briefing of the content to them. However, to me, the author does mange to provide some new perspectives to the issue.

The book starts out by emphasizing one important mindset: life is hard. People may often complain about life: “Why this thing happens to me?” “Why can’t I have an easy life like John has?” There is a subtle assumption that people may not be aware of when they say those stuff: they assume that life should be easy. That’s the problem that can cause lots of people’s mental issue. I was shocked when I read this part because it was just so obvious and I had never thought about this. Once you have a really high expectation to life, you cannot easily go through the obstacles you will encounter. However, if you tune down your expectation by accepting the fact that life is hard, then you’ll be in a positive mindset when trouble happens to you because you know this is how the life really is. And more importantly, when you actually achieve something or getting some help from others or something good happens to you, you can appreciate those things to another level. You’ll feel lucky and satisfied. This will make you stronger when face the next obstacles.

Another good point the author makes is about love. Love is a term that is really hard to define. People have been trying thousands of years from many perspectives trying to define it precisely. I think there will never be a universally-accepted definition to this term but we can learn about love based upon each person’s definition. I guess that’s the beauty of love. The definition offered by the author is that one is truly willing to help someone grow. This definition is really new to me but if you take a pause and think about it, it makes sense. Parents show their love to their children by helping them from different perspectives with the hope that their children can grow healthily and have a good life. Lovers care about each other so much that sometimes they may directly point out each other’s weakness. Those scenarios shows the love. However, as pointed out by the author, love is not easy as people may think. I myself face issues with love. Chinese parents are overly critic when their children face the problem and they think criticizing is better than compliment.  That’s not the love as they may think and that causes problems like self-hate as thoroughly explained in the “there’s nothing wrong with you” book. In order to find the cure, the author emphasizes the role of psychiatrist. To the author, one can hardly get cured without the help from psychiatrist. That feels too depressing to me. The author tries to deliver this message by giving out lots of cases that show how he helps his patients to achieve better mental state. I have doubts if this is the only way to the cure especially when the problem for a person isn’t as serious as the patients described in the book.

One thing I realized lately is the role of toastmasters club. Even though the organization is described as a way to help people improve their communication and leadership skill, it can also be thought as a way for people gaining love. In the book, the author explains that listening as a way to show your focus to someone is a crucial way to show your love. That’s why the children requires parents constantly attention.  People in the club pay attention to the speaker and provide positive feedbacks to the speaker as a way to help the speaker acquire self-confidence. This is exactly how people give love to others. With the practice of speech, speakers are no longer afraid of stage is a signal for gaining love. I think the mental issue or whatever problem caused by lacking of love can be cured as long as we can acquire love from outside. Psychiatrist may be an important way but I hardly believe that it’s the only way.

The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century” by David Salsburg

I know this book since the junior year of college when my PhD roommate carries this book around as a way to take a break from intense research. The title looks interesting to me and one day, I ask him what good about this book. He answers with big smile: “This book is all about gossip in statistics”.  I know back that time he is also taking a statistics course in sociology.

Four years after that conversation, this book never appears in my life again. However, for no reason, his comment about this book, especially the word “gossip”, is engraved on my mind. So, I give it a try once I’m done with “The Road Less Traveled”. This book looks a good fit to me because based upon his comment, it’s not a really technical book that may require a silent study room, pencil and paper. Also, it’s about statistics on some level is exactly what I need because I’m always attracted to the machine learning, which has deep root in statistics.

To be honest, this book is probably not best described by the word “gossip” because there is not really a lot of gossip involved. Although, I do learn that R.A Fisher really dislikes Jerzy Neyman and his “overly-abused” hypothesis testing; Egon Pearson overthrows his father, Karl Pearson’s work. To me, this book is more like a survey of the field with the aim to tell general audience about the development of mathematical statistics. I myself is also part of general audience in the sense that I’m not really into a mathematical-formula-dense book on a noisy subway. From this perspective, I think the author does a very good job by delivering idea of mathematic term without invoking mathematical formulas.  This is particular useful for me because I can get an overview of each component in the field and their connections without studying a great deal of math. Lots of intuitions are actually from seeing “big picture” instead of worrying about some tedious calculation. Another good point of this book is that you can feel the excitement of the development of the field. Scientific development is motivated by the problems. This is especially true with statistics because statistics is essentially about using mathematical tools to discover the true nature of the data. Here, “the true nature” invokes lots of controversy in the field. The author does a good job to naturally develop the story and motivation behind the argument. Each chapter isn’t too long and it is self-contained as a complete story.  One takeaway from reading this book is the idea about mathematical tools. Formulas are just formulas and unlike computer program, I never think about those mathematical formulas as “tools” for statistician to query the data. This has psychological impact on me because thinking about formulas as tools are just not as intimidating as thinking of formulas as formulas. Another good point about this book is that it lets me realize the assumption behind some mostly-commonly used technique, like hypothesis-testing. Every tool has its assumption, and it’s danger to abuse it without knowing what’s the motivation behind the invention of the tool.

This book is definitely the book I’ll revisit a lot and I think it can be used to its full potential while you taking a class in statistics when you read the book.

 

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“Research” Interest

This week Friday, I meet with my future roommate in Beijing. During the lunch, we had a conversation about each one’s research interest. My roommate, likes me, is also a CS graduate student at Austin. However, unlike me, he has a clear vision about what direction he is going to pursue in graduate school. He just finished his undergraduate degree in Automation department at Tsinghua University. Automation department, as he explained, is similar to a mixture of mechanical engineering and electrical engineering. He has interest in mathematics since high school and naturally, he wants to work on machine learning theory in graduate school with emphasis on computer vision (CV).

Now comes to my turn. That’s a hard question I have been thinking about for a while. I don’t have clear vision on what I’m going to pursue next. I think maybe I’m too greedy and want to keep everything. However, I also realize that I may not be as greedy as I thought initially. I know I don’t want to work on computer architecture, computation theory, algorithm, compiler, network. Now, my options really just choosing among operating system, database, and machine learning. For the machine learning, I even know I probably won’t choose computer vision eventually (still want to try a course though) and I more lean towards the natural language processing (NLP). However, picking one out of those areas is just too hard for me now, even after I did some analysis in my last post trying to buy myself into picking machine learning only. There is always a question running in my head: why I have to pick one? Sometimes I just envy the person like my future roommate who doesn’t have this torture in his mind (maybe he does? I don’t know).

This feeling, to be honest, doesn’t new to me. When I was undergraduate facing the pressure of getting a job, a naive approach is just locking oneself in the room and keeping thinking what profession might suit me the best. After two years of working, I grow up enough to know that this methodology on making choice is stupid and I also grow up enough to know that “give up is a practice of art”. Why I’m in this rush to pick the direction I want to pursue even before I’m taking any graduate course yet? Why can’t I sit down and try out several courses first? Because I want to get a PhD in good school so bad. Let’s face the fact that people get smarter and smarter in generations. Here “smarter and smarter” doesn’t necessarily mean that people won’t repeat the mistake that happened before. It means that people will have better capability to improve themselves. Machine learning is not hot in 2014 from my experience in college. Back that time, Leetcode only has around 100 problems. I have no particular emotional attachment to machine learning material when I’m taking the AI class. Maybe because wisconsin has tradition in system area? I don’t know. However, in 2017, everyone, even my mother who is a retired accountant, can say some words about AI, machine learning. Isn’t that crazy?

On my homepage,  I write the following words:

I like to spend time on both system and machine learning: system programming is deeply rooted in my heart that cannot easily get rid of; machine learning is like the magic trick that the audience always want to know how it works. I come back to the academia in the hope of finding the spark between these two fascinating fields.

Trust me, I really mean it. Maybe because I graduate from wisconsin, I have naturally passion for system-level programming, no matter it from operating system or database. Professor Remzi’s system class is just a blast for anyone who wants to know what’s going on really under the software application layer. Professor Naughton’s db course is fully of insights that I can keep referring to even I begin to work a DBMS in real world. Wisconsin is just too good in system field and this is something that I can hardly say no even I have work so hard lie to my face saying that “system is not worth your time”. What about machine learning? To be honest, great AI dream may never accomplish. Undergraduate AI course surveys almost every corner of AI development but only machine learning becomes the hottest nowadays. Almost every AI-related development nowadays (i.e. NLP,  Robotics, CV) relies on machine learning technique support. Why I’m attracted to machine learning? Because it’s so cool. I’m like a kid who is eager to know what is going on behind magic trick. Machine learning is a technique to solve un-programmable task. We cannot come up with a procedure to teach machine read text, identify image object, and so on. We can solve these tasks only because the advancement of machine learning. Isn’t this great? Why both? I think machine learning and system becomes more and more inseparable. Without good knowledge about system, one can hardly build a good machine learning system. Implementing batch gradient descent using map-reduce is a good example in this case.

I just realized that I haven’t answered the question about rushing towards the making decision. In order to get a good graduate school to pursue PhD, you need to demonstrate that you can do research. This is done by publishing papers. Most of undergraduates nowadays have papers under their belt. That’s huge pressure to me. Master program only has two years. I cannot afford the time to look around. I need to get started with research immediately in order to have a good standing when I apply to PhD in 2018.

So, as you can tell, I have problem. So, as a future researcher, I need to solve the problem. Here is what I’m planning to do:

  • Take courses in machine learning in first semester and begin to work on research project as soon as I can. I’ll give NLP problem a chance.
  • Meanwhile, sitting in OS class and begin to read papers produced by the Berkeley Database group. People their seem to have interest in the intersection between machine learning and system. This paper looks like promising one.
  • Talk to more people in the area and seek some advice from others.
  • Start reading “How to stop worrying and start living

Will this solve the problem eventually? I don’t know. Only time can tell.

What are some useful, but little-known, features of the tools used in professional mathematics?

What's new

A few days ago, I was talking with Ed Dunne, who is currently the Executive Editor of Mathematical Reviews (and in particular with its online incarnation at MathSciNet).  At the time, I was mentioning how laborious it was for me to create a BibTeX file for dozens of references by using MathSciNet to locate each reference separately, and to export each one to BibTeX format.  He then informed me that underneath to every MathSciNet reference there was a little link to add the reference to a Clipboard, and then one could export the entire Clipboard at once to whatever format one wished.  In retrospect, this was a functionality of the site that had always been visible, but I had never bothered to explore it, and now I can populate a BibTeX file much more quickly.

This made me realise that perhaps there are many other useful features of…

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