Just recently, I became aware of this campaign: http://philosophy.fas.harvard.edu/files/phildept/files/harvard_teaching_campaign_statement.pdf?m=1393650319
A few weeks ago, I met a guy who was a friend of a friend of mine who has finished his undergrad in Mechanical Engineering, one of the most sought after field of studies in Iran. He then continued his studies in Canada in the same field. After finishing his masters, he has decided to go back to Iran to start following his dream of studying Physics academically, the very field of my interest. Of course, everybody in his family was trying to convince him that he is making (at least) three BIG mistakes. First, returning back to Iran, second, starting from zero again, and third (and probably the worst of all), studying physics! I should say that physics is one of those fields that everybody seems to like but not many people want to do!
Anyways, long story short, he told us that he always wanted to do physics but everybody convinced him not to do that, and after these many years he could no longer suppress his inner interest in physics, and have finally decided to do what he should have done a long time ago. After he learned that I am a physicist, he got all excited and started to share his views and his reasons why he wanted to be a physicist. Oh boy, it was such an amusing night. He kept saying that they [his friends and family] can not understand. Well, sure enough, I knew what he was talking about. It was really great to see someone who has this courage to sit back and rethink his interests and do what he thinks is right, despite all the cost! I felt so honored to meet a few of those people in my life.
He showed me his little book on the history of quantum mechanics by F. Hund. This reminded me of a quote by Steven Weinberg: “After you learn quantum mechanics you’re never really the same again”. Actually, looking at him, I started to remember my old days way before my first encounter with quantum mechanics. When I was 3-4 years old, I used to take my little bag and go to school with my elder brothers, as my mother describes. of course, I was not allowed to enter but anyway I liked to go to school doorstep. My mother and brothers taught me numbers, alphabets, and writing before I go to school. Even now, sometimes I remember some scenes that I was forcing my parents to teach something before I go to bed. I don’t know why, but I was so passionate about learning and this passion was with me throughout my whole studies. Frankly, the subject did not matter to me that much, I was just happy to learn. Of course, there were courses I liked more, but I liked them all, from Persian literature to mathematics and physics.
The passion of mathematical science and learning, in general, was with me in high school, undergrad and grad schools. However, unfortunately, during the course of my life, I gradually became more disappointed to see how following one’s passion is dead in academia, and almost anywhere! One should have expected this, as it is an unfortunate plague of modern time that something that is not measurable is considered as a nonexistent. Unfortunately, passion is no exception.
I remember, despite all my interest in doing experimentation, I decided to become a theorist, since I could not imagine seeing myself doing a single experiment over and over in a lab which possibly located down the stairs somewhere. I should say that I appreciate all people who are involved in experiments, as they are responsible for most of things around us. However, I could not consider myself to be a sole experimentalist. I wanted to learn and practice different subjects. I always admired people like John von Neumann who was a quantum physicist, game theorist, abstract mathematician, economist, computer scientist, and almost anything interesting. Not only, he mastered those fields, he actually made a great contribution and basically defined some of those fields. Sure, one can say that not everyone is like him intellectually, but the point is more devastating, as nobody seems to want people like him around. I always looked at knowledge as the greatest intellectual heritage of the human being, during the course of history, and I could not isolate a single subject out of many to focus on.
The bottom line is that I became a physicist for the pleasure of finding things out and I advise everyone to become one. However, there is a big danger here. After a few years, you can no longer satisfy your hunger for the joy of this pleasure. Your brain will not be satisfied by money, or a mundane and repetitive jobs and life. Basically, you could loose the capability of enjoying anything less intellectually challenging. The addiction to this pleasure of understanding and new findings is no less than the strongest drugs. There are other dangers as well, such as starting to understand the illogical behavior of people around you, which turns you down, from time to time. So be a physicist, but be careful what you are wishing for!
I really like this quote by Gandhi. However, it is hard to really grasp what it means, practically. Well, to be honest, it is hard to find people who are the examples for this statement. I think that Stephen Wolfram (SW) could be one good example. I have met him in person only once, but during these years, I have come across his name many times. In particular, because he is a particle physicist, also as I was exposed to Mathematica software, for scientific computations since my undergrad.
What is really interesting, regarding SW, is the fact that, he loudly speaks up his beliefs, he writes about them, and even further he invites other people to test his theories. He has spent almost 12 years to write his idea about Cellular Automata in his book entitled New Kind of Science (NKS). Despite many positive criticisms from the general audience, this book received lots of negative criticism by physicists such as Steven Weinberg, Freeman Dyson, etc. However, this did not stop him from organizing summer schools every year, and inviting people to learn more about NKS and do some projects over there.
I have heard rumors about his arrogance, however, what is important for me here is that he really stands by his ideas and he tries to bring them to reality. This, not only has made him a fortune, but also provided a framework for himself to interact with many brilliant people around the world. I believe that we need more intellects like him. Someone who generates ideas, revises his ideas and advertises them. I should emphasize, there are many ideas out there without any references or any notable points for humanity. We need ideas which are attached to some basic principles, and let others falsify it.
The reason that I started this post was to aspire to reach our deep interests. However, we should keep in mind, choosing a path toward achieving our goal is as important as the goal itself. We should ensure that we enjoy the path, otherwise, we may be bored, tired, or disappointed before achieving to our goals. To enjoy the path, I believe, we should stay tangent to public interest. Of course, there is always a gap between our dreams and public interest (otherwise change would be unnecessary). In short, our dreams should give us the sense of direction, and manifold of public interest determines the actual path. For example, In the above mentioned example, to make fundamental changes in mathematics (or physics), one can detour by starting off a company in computation which is the focus of public interest during last three decades.
Before getting into details of what is Mathematica, and why I am writing this post, I should remind about the very interesting lecture by Richard Feynman one of my older posts (here). One of the important message of that lecture was the fact that, naming the so called computer is misleading, as actually no computation is done. Today computers are basically a Fast Filing System. Every software is composed of few definitions for data (string, integer, double, etc) and a set of rules on how to do certain operation. However, nothing internally is making a meaning of those data or the rules. The task of computer is to break down every other programs to the set of those internal rules and data, and finally outputs the result.
Internally all the signs (such as summation, multiplication, devision, etc) has no meaning, and they are just a symbol which imply a rule for data which might come before and after this symbol. This fact is greatly appreciated in one of the earliest programming language, LISP, basically the second oldest language after FORTRAN.
At this point, I should also mention a few words about functional vs imperative programming. Basically, many current programming are imperative programming in a sense that the state of variables changes during the evaluation. However in a pure functional program, nothing is changed and everything is calculated as set of function acting on each other. It is long discussion on what is what, and how is done, and you can see more details here. The bottom line is a more natural (i.e., closest to the real mathematical world) way of programming is the functional way, again such as LISP. Of course, there is always a discussion on being *natural* matters or being *effective*, and so on!
It is so unfortunate that most of current programming languages such as (Java, C/C++, Python, etc) are imperative rather than functional, and most of us are not used to the idea of writing nested functions rather than defining ever-changing intermediate states.
Putting everything together, the best approach which utilize the main capability of “computers” is the language where everything is just a symbol (there is no difference between data and code) and every program is set of nested functions acting on these symbols and finally DISPLAYing the output in a one way or the other. To make this last point more clear, consider an image, for example, image of a flower. In practice it is wide spectrum of colors DISPLAYED at the pixel of our monitors. These colors are basically numbers which determines the combinations of different primitive colors. So, an image of a flower is no different than expanding a function in terms of some basis.
So the ultimate software which is inline with the internal design of computers is a symbolic functional programming. Mathematica if it is not the only, is the best programming language which is based on those first principles. I should make myself clear, I never claim that Mathematica is implemented the best way possible. What I am trying to achieve here is the fact that Symbolic Functional programming is the best possible way, and Mathematica is in that direction.
One last thing is interesting about Mathematica is the fact that is kind of “Evolutionary programming”. Basically, everyday new functions are added to the main body of the program, and the best one, in terms of speed and quality survives, where the rest of them just fade away. Mathematica is not only a mathematical software, rather and old (and the best) programming paradigm which is rare to find these days.
After 9 months of silence, I am back again to my weblog. It is hard to explain why I was not around in one post, since past few months were full of ups and downs at least for me. To be short, after many years being in academia, either as a student or researcher, I started a position as a R&D in one of my favorite topics on the intersection of mathematics and computation while working with real world examples, i.e., as a machine learning data scientist. That by itself is a good reason to be away for a few months. However, in my case this was not the only thing.
The other thing that I have realized is the fact that, after being away for a few months, it is hard to come back, as there are many things to say and you basically don’t know where to start. To choose one particular out of many, I just quickly write about why I partially left academia, at least for the moment.
First of all, it is almost clear to everyone who is already engaged in academic activities that the whole subject of “academic interest” does no longer exist. By academic interest, I do not mean a useless scenario that people came up these days to publish a few more papers. No, I really mean to spend time and energy on something for the sake of curiosity. I can’t say that there is no one doing that, but I can certainly say that the new generation of scientists, are either businessmen or engineers. (Nothing wrong is with being businessman/engineer, just they should practically be different from scientists, and now they merged somehow. )
Second, again those who have been involved in academic setting are realizing that, for many reasons, creativity is kind of discouraged. Most of the times finding a more creative solution is not desired. During these many years, I came across many important subjects worth studying, which were dismissed because they considered HARD to do in a few months or seemed irrelevant.
Thirdly, computer science in north america has cast a shadow on many professions, and academia is not excluded. Hence, everybody tries to codify some mathematical problems and in the best case scenario bring it close to experiments. I should say that, I do not discourage this activity, the point is theoretical studies should be different from computational studies, but theory almost cease to exist these days. Finally, not to mention the fact that, academic fields are tightly narrowed these days that even in one particular fields, it is very hard to move around. Part of it is due to the fast growth of science, but it is mostly due to the crowdedness of the environment! Indeed, this list goes on, but I guess these are the main reasons.
Putting altogether, I realized for people like me who are interested in wide breadth of research on different aspects of life, data science seems a way to go. I reserve the right for me to change the above statement as time goes on!
In case you have not come across the Coursera website, I strongly suggest you to take a look at this wonderful initiative. You can simply explore the courses menu to locate possible courses you might be interested in. I have personally followed some of the courses, such as the Machine Learning course by Andrew Ng, and it was amazing. At first, it might seem similar to other online lectures! However, there are some subtle but essential differences. The main difference is that, each course is divided into several small lectures, where each lecture is very well designed with some follow-up questions (or quiz). It is very accessible and practical, and more importantly, it is not designed as A PART OF curriculum, but each course is a stand alone subject.
Since the beginning of blogging, I tend to believe the future hiring would change from the current passive process to something more interactive. For example, I thought employers can initiate a blog to post essential materials for applicants who want to undertake a particular position. Then, interact with those members who continuously contributes to the blog and in the end, hire one or some of them, instead of just trusting applicants resumes. Not to mention, not everybody is good in demonstrating their abilities in hypothetical situations introduced in the interviews.
Now that I know more about Coursera, I believe it is even better than blogging. Basically, an employer can post a course (or series of courses) to freely teach applicants about the subject and evaluate the ones have taken the course, at the end, the best possible candidates can be called up for an interview. This way, employers can make sure that the applicants are at least familiar with the subject and have been evaluated once. Additionally, applicants have this opportunity to learn something new throughout the hiring process. So, each job application can be a new experience and new lesson instead of a disappointing process.
I also believe that the future of graduate studies would change similarly. For example, one can design their own curricula through Coursera. After taking enough courses, the corresponding certificate can be added to their resume. For getting the certificate, applicants are asked to do a project. Then for pursuing higher degrees, they can apply for those professors who either posted a project or a course or both on Coursera. This way, both sides are certain about the common interest and background. At the same time, professors are encouraged to broadcast their knowledge, freely. University expenses for graduate courses would also drop down.
After Wikipedia this is the next internet product which I am really excited about. The cool thing about Coursera is that, with a little money one can earn certificates of the courses they have taken. Or they can choose to watch the course and learn something (without getting a certificate) for free.
در نوشته گذشته (اینجا)، یک مرور کوتاهی بر واقعیّتهای اجتماعی زندگی در کانادا داشتم. در این نوشته میخوام به واقعیّت تفاوتهای اجتماعی در زمینه شغلی بپردازم. چند روز پیش وقتی داشتم با یکی از دوستان صحبت میکردم، یک خاطره رو گفت که شنیدنش خالی از لطف نیست. دوستم میگفت که درسفری با یک مسول مهاجرت کانادا هم مسیر بوده، و اون فرد از ایشون پرسیده که نمیدونم چرا وقتی ایرانیا به کانادا میان، از همه بیشتر تو دانشگاه برای گرفتن دکترا مشغول تحصیل میشن، بجای اینکه در بازار کار مشغول بشن.
در این نوشته دوست دارم جوانب این نکته رو باز کنم. در ابتدا اجازه بدید که به طور خیلی خلاصه به منشأٔ داخلی این تفکر بپردازم. در ایران، یه تفکر کاذبی شایع شده که تحصیلات بیشتر به پول بیشتر منجر میشه. البته دلیل این امر به دولت زدگی و مدرک گرایی در ایران مربوطه. در ایران، خیلی از پدران و مادران ما در مشاغل دولتی مشغول هستند و در این مشاغل، هرچه مدرک بالاتر باشه، در مدارج بالاتری مشغول میشن و به طبع از امکانات مالی و حقوقی بیشتری برخوردار خواهند بودند. در واقع تفکر در ایران اینجوریه که “یک سال بخور نون و تره، یک عمر بخور نون و کره”. یعنی اگه یه چند سالی به خودت سختی بدی، یک عمر میتونی پات رو بندازی رو پات، و ریاست کنی و پول خوب در بیاری. در ضمن، جایگاه اجتماعی خوبی هم داره. بررسی جایگاه دانش و دانشگاه در ایران موضوعی است که شاید در پستی دیگر بر اون پرداختم. البته جا داره، که همینجا به تفاوت علم و دانش هم، به عقیده من البته، که در یکی از نوشتههای گذشته به اون پرداختم اشاره کنم. لازم به ذکره که قوانین سختگیرانه ویزا برای ایرانیا، به این نکته بیشتر دامن میزنه که موجب این میشه که فقط تحصیل کرده هامون بتونن به طور قانونی از کشور خارج بشن.
فهم این نکته که هر کشوری یک سری امکانات محدودی داره که باید طبق یک سیاست گذاری این امکانات رو در زمینه تولید دانش و زمینه ی شغلی سرمایه گذاری کنه. فهم دقیق این سیاستها به تعیین مسیر شغلی افراد خیلی کمک میکنه. اگه به لیست ۱۹ شغلی که برای اونها دولت کانادا تمایل به جذب مهاجر داره، رجوع کنید شاید این تفاوت سیاست گذاری براتون واضحتر بشه. در کشور کانادا، سیاستگذاری طوریه که حد وسط جامعه بتونن دخل و خرجشون رو بدن. یا اینکه اگه یک زوج به تحصیلات متوسط مثلا لیسانس، یا دبیرستان، در یک شغل ساده که تخصصی نمیخواد مشغول باشن بتونن حدوداً ۵۰ هزار دلار در سال در بیارن که با این پول میشه یک ماشین و بعد از حدود ۵-۶ سال کار یک خونه به طور ۳۰ سال قسط خریداری کرد. البته شرطش اینه که این زوج به طور پیوسته به مدت ۸ ساعت در روز، در طول این مدت کار کنن. البته اگه بجای رفتن به دانشگاه، به مؤسسات فنی و حرفه ی رجوع کنن، حقوقشون بالاتر میره. مثلا یک مکانیک حدود ۲۲ دلار به ازای هر ساعت میگیره، که با احتساب ۸ ساعت در روز کار، حقوقی معدل ۴۵ هزار دلار سر سال میشه. در حالیکه که یه متخصص ژئوفیزیک حدود ۶۸ هزار دلار میگیره.
بطور کلی قانون نانوشته اینجوریه. هرچه مدرک بالاتر بره، با یک شیب بسیار ملایمی حقوق هم بالاتر میره. مثلا یک مکانیک با تحصیلات فنی حرفهای که شاید ۲ سال هم وقت نذاشته باشه، حدود ۴۵-۶۰ هزار دلار در سال میگیره، و یک دکترای ژئوفیزیک که حدود ۸-۱۰ سال تو دانشگاه سپری کرده (و کلی هزینه ی تحصیل داده)، حدود ۶۰-۸۰ هزار دلار در سال میگیره. البته باید به این نکته ی مهم و ظریف هم توجه داشته باشید، که تعداد مشاغل با بالا رفتن مدرک کمتر و کمتر میشن. مثلا اگه تو هر شهری واسه یک مکانیک یا یک منشی شغل هست، واسه یک دکترای عمران شغل نیست. این نکتهای که خیلی از مهاجرین ایرانی از اون غافل هستند، واسه همین در دور و بر خودم کلی ایرانی با مدارک بالا میبینم که جویای کار هستند، در حالیکه اگه همونا به ایران برگردن به راحتی شغل میگیرند. باید این نکته رو هم یاد آور بشم که چندین رشته از این قانون مستثنی هستند. رشته ی کامپیوتر، حسابداری، امور مالی، و مشاغل مرتبط به سلامت در کانادا و آمریکا بازار خوبی دارد. البته واسه شغل گرفتن تو شرکتهای بزرگ نیازی به دکترا نیست، و با گرفتن یک فوق لیسانس، یا حتا یک دوره کوتاه حرفهای کفایت میکنه.
در نهایت اینطور جمع بندی میکنم که بطور کلی با بالا رفتن مدرک حقوق با یک شیب ملایم بالا میره، ولی موقعیت شغلی با یک شیب تند پائین میاد. در ضمن کلی از عمرتون رو باید در راه دریافت مدرک سپری کنید. پس اگه به صورت مهاجرت و یا حتا به صورت دانشجویی (حتا اگه با پذیرش و دریافت کمک هزینه تحصیل) به کانادا آمدید ، بجای ادامه تحصیل، یک دوره کوتاه در یکی از مشاغل مورد علاقتون بگذرونید و بلافاصله وارد بازار کار بشید. اینجوری مهاجرت دلپذیرتری در کانادا تجربه خواهید کرد. در نهایت هم توصیه میکنم که به این لیست بهترین مشاغل در کانادا خوب نگاه کنید که راحت تر بتونید واسه آیندتون انتخاب کنید
Some days ago, I read an article in the national geographic website, with the same title (here). This article first starts by stating that people, even scientists, ignore science since in accepting scientific facts we “cling” to our “naive beliefs”. There are several examples, in the article which the author argues they are just scientific facts, but people have the hard time believing in them, simply because human nature is naive!
Recently, I came across several articles with the same type of logic, and discussions about the “naive beliefs”. I find the reasoning used in this article and similar ones ridiculous. In another post, I explained what is wrong with “scientific facts”. However, please let me explain in some more details why people doubt science. First of all, it happened to all of us when we started our day by looking at the weather forecast. However, once we looked outside, we saw something completely different! So you could obviously see there are many unexpected things that can happen which was not included in our scientific models. Everybody sees this shortage and understands that, particularly, when we are dealing with large multivariable systems.
Somewhere in the article says that “We’re asked to accept, for example, that it’s safe to eat GMO (genetically modified organisms)…because, the experts point out, there’s no evidence that it isn’t and no reason to believe that altering genes precisely in a lab is more dangerous than altering them wholesale through traditional breeding.” This statement is wrong at so many levels. First of all, when we are talking about our health we tend to stay close to our tradition simply because we know that it has been working fine for many many years. Second, I have seen no clear data to rule out the correlation between these many new diseases and GMOs. For example, we don’t know at what time scale we should expect to see the correlation. It may take more than 10 or 50 years for something to affect us. Or even worse, it is not clear for what type of disease we should look for. Finally, we don’t know what could be the effect of GMO on environment, the time scale for the environment could be much longer than 50 or 100 years. Just as an example, look at the history of DDT.
People doubt science due to the failures of science throughout the years, not because they cling to their naive beliefs. They doubt science because of many things that can go wrong in one scientific research. As I emphasized in previous posts (e.g., here), science is not for believing, but it is a process to falsify our understandings. I should say, although I agree that scientific process is the only trustworthy method to investigate each and every question, we have all the reasons to doubt “scientific facts”, particularly those sciences which rely heavily on statistical inferences.
It doesn’t matter which religion, in all of them you see storytelling, and extensive usage of metaphors and expressions in communicating a message. When I was learning writing in English, I was told that I should write concise short sentence with a clear message, and try to avoid any complication in delivering my message. Compared to what I learned, the religious texts are way off! The question is why? Why in communicating spiritual messages we urge to use stories, instead of directly cutting into the chase. As a Persian, I can name several well known books by religious authors which are filled with delicate religious stories. Ancient Persian poetry is also famous for that. There are thousands of pages written with high caliber of ingenuity just to deliver simple spiritual messages!
I have been puzzled by this question for a while. Now, I believe that I understood why that’s the case, and I try to express my understanding with minimum complications. To answer this question, let me start by answering a similar mathematical question. How can one explain a three-dimensional object with the two-dimensional mathematics? The best way is to slice it up to many two-dimensional surfaces and then introduce each and every surface, one by one. For example, introducing a simple tree to a 2D universe, needs many (infinite to be exact) layers of 2D images. So, as you see whenever we aim to explain even a simple object, but from a higher dimension, we would face a challenge.
In another example, how one can explain the taste of an apple (say McIntosh) to a society who have never had an apple in their life! The first step is to find out what type of taste that particular people are being exposed to. Then the idea is to mix different tastes to get closer and closer to the taste of apple. So, this is exactly the process of delivering something which does not belong to our ordinary common senses.
In the spiritual world, I believe that is the same. Once a person observe something which does not well fit in our current realm of understanding, one needs to slice it up to many different little stories where the role of each of this stories is to lighten up one side of the observation. For the sake of this conversation, I don’t mind if the origin of this observation is due to the ecstasy of meditation, or due to some chemical compound. What I do care, in this post, is that sometimes a simple observation requires a lot of explanations, simply because it doesn’t belong to our everyday life experiences, and yet you can not make the point exactly.
I remember talking to a friend of mine mentioning the fact that I am really skeptic about the results of the studies in the multi-variable fields with no underlying theories, such as life sciences, social sciences, and earth sciences. My argument is as following, there are many parameters to change, and since we have no theory to compare the experimental predictions with, we could easily get misled by the results. In fact, in most of these fields we can’t even answer the basic questions, such as how many parameters are there to estimate the right sample size!
Recently, I came across this article in newscientists which basically proves that many scientific results are probably wrong! What I like about physics, in general, is that we have theoretical guidelines which help us to better understand the experiments. Of course, our theories are based on many approximations and assumptions, however, it is still unreasonably accurate. In fact, the main difference between theoretical physics and applied mathematics is how to lay down different approximations. This is one of the main reasons that, I believe, we have a steady progress in physics, and sorts of a random walk in other fields. By the random walk, I do not mean that we are not making progress, of course, we are. However, there is no sense of direction in what we are doing, at least by an outsider like me!