Categories
Physics Thinking

The Two Things about Physics

Yesterday I’ve read an article that  about every topic, one has to know only Two Things.

For every subject, there are really only two things you really need to know. Everything else is the application of those two things, or just not important.

Of course it is a fascinating idea, and I started to think about my profession, physics. If I simplify my experience and knowledge down to this minimal level, what would be the two things I get to? I do think it is not a straightforward stuff, and one can only get to the bottom of this, can find the hidden truth below, if one spends a lot of time with the subject, gets to know it inside and out. I feel that I’m still just scratching the surface of the wast knowledge of the universe (even after being a physicist for about 12 years now). Still this doesn’t stop me from trying.

Drawing in my Wreck This Journal
At Geek Dinner Taipei someone contributed this drawing of the Einstein Field Equations to my Wreck This Journal. Of course, this is ignoring a possible non-zero cosmological constant.

I’m an experimental physicist, worked in Solid State Physics first, now mostly in Atomic & Laser Physics – all kind of fun stuff. I had very good professors, great inspiration and I have learnt a lot from them. If I think back all the things I’ve learned there are still things that come to me as my first thoughts, and usually those are the right guesses, for whatever intuition I have.

My Two Things about Physics:

  1. Pure math can take you very far along the way, though in the end still need experiment to see whether the results describe something real.
  2. Everything is an approximation, but that’s fine. Just pick your approximations carefully.

Maybe it is worth expanding a little bit on that these:

Pure math can be used to describe things extremely well. It’s maybe even too good at that, which got other, much cleverer people to think as well, like a fellow Hungarian physicist, Wigner Jenő (or Eugen Wigner), writing about The Unreasonable Effectiveness of Mathematics in the Natural Sciences. I frequently got myself really excited when after doing some complicated calculation to predict the behaviour of a physical system, the experiment matches up to all of its nuances. Let it be atomic spectroscopy, polarization of light reflected from metallic mirrors, magnetic field of a Zeeman-slower, I had a lot of fun figuring out the physical theories of different phenomena then matching it up with what happens in the lab. Of course, there were loads of times when they didn’t match, but it turns out my math was off. Really, if I want to understand the world, math is one of the most useful and versatile tool to have.

Having that tool is of course not enough. When people come up with new and interesting math trick, it often turns out (to many people’s amusement) that those tricks have some physical meaning, they often also give some insight into the world around us. It’s often, but not always. Mathematics can really easy take one to very strange places and give a result, which is completely aphysical. To complicate things even further, those aphysical results sometimes turn out to be actually correct and predicting real but insofar unobserved things. An example of this is the Dirac equation which gives two solution, one for electron, and one particle with negative mass that first people dismissed, but later it was understood as the representation of positron. How to distinguish between really wrong solution and “wrong as current understanding”, that’s a whole different level of problems.

The second point was really a revelation for me. Whatever equations we have, they all just describe things to a certain level. If we can look closer, we often find differences from the theory, that are harder and harder to explain as we get closer. On the other hand, using intuition and physical understanding, people often choose to ignore certain parts of the situation, or certain features of the problem since it cannot affect the results to a level that would be observed in the given experiment. This makes everything solvable, and once solved, one can advance on top of the new understanding even deeper into the problem. Finding the good approximations is almost as valuable as finding the right theory, that’s why often these approximations are named after the people who came up with them, or given other shorthand names so everyone can quickly recall and use them.

There’s a whole methodology built to help come up with approximations and handle them, called perturbation theory: if the given problem is very similar to a simpler, already solved problem, then treat it as the simple one plus some small effect that changes relatively little on the behaviour of the system. Not everything can be handled like this, but surprisingly many problems fit very well.

Others’ Two Things about Physics

On the original site there were other people’s Two Things as well:

1. Energy is conserved.
2. Photons (and everything else) behave like both waves and particles.
-Tim Lee

1.  Draw a diagram.
2.  Get the dimensions straight.
-Eric Schafer

I personally don’t like these that much. The first one is just stating two theories that can be superseded in the future, and right now they kind of limit instead of enable. The second one is good advice, but can’t say that’s the only thing there about physics. Having said that, I have more adventures with incorrect dimensions and units than I’d prefer to have.

What other Two Things choices one can make, in physics or in other sciences, other topics?

Categories
Programming

Facebook Hacker Cup 2012 Qualifier 1

This is that time of the year once again, when coders gather to take part in some good programming fun, the Facebook Hacker Cup. It’s only the first qualifier round, and while I hoped it will go better than last year, well, it didn’t. Not that I’m really surprised.

My Facebook Hacker Cup 2012 Qualifier score
I needed one right to qualify, but I wish I haven't messed up the easiest problem.

After that 72 hours results are in, as is the explanation and example source code for the solutions. It’s good that the example solutions are in Python, I might even learn a trick or too.

Alphabet Soup

The problem setting is easy enough. Funny thought that no matter how many times I counted the letters in the world “HACKERCUP”, I didn’t notice that there are two Cs. I mean, duh! As usual, the example input set was designed such that it wouldn’t trigger the bug of miscounted Cs. This carelessness is one thing that comes up quite often in my programming, probably should take better care of it.

Billboards

This problem actually worked, which means I’m in Round 2, but I guess it can be improved quite a bit, make it more efficient or come up with some heuristics. Or maybe it doesn’t matter much.

Auction

This problem was on a whole different level. While the first two had apparently more than 5000 and 3000 correct solutions respectively, this had only 28… I was thinking about it for quite a while, drawing diagrams, trying to use my intuition and imagination to see where the trick is since the naive O(N^2) algorithm is definitely unusable on the N~10^18 level. On the other hand, I might have tricked myself. Reading the solution the trick was completely different than I expected – I thought there’s some weakness in the random number generator that can be used to express everything analytically, while it is actually just about keeping good track of things. There’s no fancy algorithm to break this problem, just pure logical thinking. Now that’ll teach me as well…

Looking out

This of course means that I have a lot more to learn, and most likely I’m not cut out to be a Facebook caliber hacker. That’s no problem, but good to know. Whenever I think about it, the picture that comes to me is the hacking competition scene from The Social Network, where they hire their first employee. I’d love to be in the middle of such brainfest, such intense creation, such inspired learning from one another while having an an amazing time. Well, fortunately there are other places where I can have that experience, like the Startup Bus. And maybe, I can also set out to create that environment over here in Taiwan.

But first, let’s get ready for round 2, should make that one better.

 

Categories
Learning

Adventures into online learning

Last year I tried quite a few new things, and many of those things were quite a bit fun so I will continue experimenting with them. One of such fun thing was a different type of online learning, when I don’t just watch videos and class material already shared at e.g. MIT OpenCourseware and Stanford’s Youtube channel, but I’m actually part of a class, doing real homework, working with real deadlines, taking real exams in the end. And hopefully getting real knowledge too (though that part depends on me more than on the class).

It was possible because Stanford announced 3 online classes for their 2011 Fall Semester: Databases, Machine Learning and Artificial Intelligence. All three looked very interesting so I signed up for each of them. Since the 2012 Spring Semester will see even more classes, I just take some notes here, how this first truly large scale experiment went.

Stanford classes 2011 fall

Databases

This class was the one I was most hesitating if it’s interesting enough to sign up, but I’m really glad I did.

Database class screenshot
Database class – Relational design theory, the most complicated part, the rest of it was much more straightforward :)

Professor Jennifer Widom turned out to be an excellent teacher, and the material was also very interesting and fun to work with. Their team came up with pretty good infrastructure for the videos, exercises and exams as well. It’s really impressive what they have built in such a short time, under the pressure of tens of thousands of students relying on them (as much as I remember, about 90.000 signed up, though maybe ~30.000 had enough work done by the end to have any score and certificate of accomplishment).

Things were thoroughly explained, the exercises were usually challenging enough to make me think and it was all the excitement of solving puzzles. The exams covered a lot of material, and I didn’t score as high as I expected, though I think almost all the losses can be explained by me not paying enough attention to the questions, or misunderstanding/second-guessing myself.

In the end I had a score of 308 out of 323 (which, looking at the statistics, in the 60-70 percentile). On the other hand, the best part is that I could almost immediately use many of the ideas I learned there about SQL, and the different ways of thinking about databases.

Machine Learning

This one I hesitated about, because I thought there must be too much overlap with the AI class, but actually they had pretty different aim.

Machine learning class screenshot of neural networks section
Machine learning class, there was a lot of math, but with plenty of examples and it made complete sense in the end

Taught by Professor Andrew Ng, this class really went into practical implementations, so could even be called “Machine Learning for the working professional”. Lots of ideas and good explanation how to implement regression, classification, neural networks, and all of these applied to a number of different topics.

From what I’ve seen, some people complained that the programming exercises were too easy, and indeed usually it could be solved in a few lines – for those who mostly already know how to solve it. From experience, my friends who asked me to study a bit together were having much harder time. I’d think more about those exercises as blueprints and guidelines if someone really want to implement some of the algorithms in a real setting.

This class used pretty similar architecture than Databases: 10-18 minutes of videos, 2-4 of those for one class. It was a nice touch that I could speed up the sound, so I was listening to all of these and the other classes at 1.5x speed – somehow that was just the right pace.

In the end I had 79.25/80 for the review questions (forgot to go back to the last one to correct it) and 800/800 for the programming exercises. The best result, though, is that now I’m thinking a lot what data do I have that can be hacked on with the tools I acquired.

Artificial Intelligence

This one was the first class that was advertised, and the most popular, probably because both of the topic and the lectures.

Artificial Intelligence class screenshot
Artificial Intelligence class with its myriad of videos, low tech presentation and interesting topics

If the other two courses were pretty similar in setting and tech, this one was very different from them in almost all respect. There were two lecturers, Sebastian Thrun and Peter Norvig; they were using short, <1 minute to 4 minute videos hosted on Youtube, and up to about 30 of them for each class. Instead of using slides and tablets to “write” on those, they used actual paper and pen, and occasional printouts.

The low-tech presentation was okay most of the time, and it was easy enough to follow what’s happening, but the first couple of classes (before they got the hang of it) was pretty hard to see sometimes.

The teaching skills were not really equal: Professor Thrun explained things really well, I enjoyed his classes a lot and was a quite easy to follow, and his enthusiasm is very contagious. This probably explains what he did after the classes finished – but let’s come back to this later.

Professor Norvig on the other hand, was pretty difficult to follow, jumping between topics and explanations, often felt like he was (probably unintentionally) making things much harder than they really are. Some times the quiz questions asked about things that was said in a rather misleading way or it wasn’t explained yet. On the forums plenty of people complained about it, and I was a bit upset too (how can they make those score count into the final result if they doing it so badly?), but in the end it felt I was often just excusing myself from thinking deep enough about the given problems. It’s Stanford after all, don’t look for shortcuts just fight through.

It was interesting to see how they covered most of the Machine Learning class’ material in 2 lectures, and many of the topics were a bit rushed as well, since there was just so much to say. On the other hand, I had enough initiation to loads of topics and ideas to have a feeling where to follow up if I wanted to.

With score of 95.6% I was apparently in the top 25%, which means there was a really tough crowd taking this class. I have plenty to think about as well, and more hacking ideas.

Future

Of course learning does not stop here, I think it is just an amazing beginning to explore the real potential of online learning, now that someone did this experiment.

More Stanford classes

Apparently the success surprised a lot of people, both at Stanford and outside. Now they have announced about a dozen new classes for the 2012 Spring Semester at Stanford. This time not even just computer science but a lot of other interesting things. They are slightly delayed, supposed to have started this week (I have my note paper prepared) but now will do gradually in February-March. I still haven’t decided which ones to take, 3 of them last time took up a considerable amount of time, but there are just way too many cool ones:

  • Technology Entrepreneurship, this I definitely going to, that’s where my future leads so let’s see what can I learn at this stage
  • Making Green Buildings, architecture is awesome, and I like high tech designs, curious what they can teach
  • Cryptography, this is an all time favorite topic
  • Probabilistic Graphic Models, looking at the schedule there are too many good topics covered, lots of interactive computing and physical-computational world connection
  • Design and Analysis of Algorithms I, algorithms are like puzzles, and there’s never enough of them
  • Game Theory, something to understand the world a bit better and make better choices
  • Natural Language Processing, covered a little in the AI and ML classes, just enough to get me all excited about its power
  • Information Theory, being a physicist, I got a little of this, but just enough to see how powerful it can be and get me hungry
  • Model Thinking, I used to say that complex planning is my favorite past time, this apparently can make me better at it
  • Human-Computer Interaction, this can be very useful, these days technology enables so many new things in the topic and has a lot of hacking potential
  • Anatomy, having a lot of medical doctor friends, I’d definitely would love to know more about this most amazing machine of ours, the human body.

Of course I can only take a couple of them, I just hope the videos will be available for the rest afterwards, so I can catch up with the interesting courses later on.

Udacity

Another development is about Udacity, an online university which was reported a few days ago: Professor Thrun apparently gave up his tenure at Stanford to start this initiative. He must be indeed very convinced about the future of the project to do this. And I think if anyone then he can follow through. Their first courses are Building a Search Engine and Driving a Robotic Car, both of which he has plenty of experience (he was on the team building Stanley, the self-driving car that won the 2005 Darpa Grand Challenge). I’m very curious of what will this become.

Khan Academy

Of course there’s one more big name in the online learning scene, Khan Academy, which is probably aimed at different audience, but also a much wider audience. I had a lot of fun with these other projects, so I want to see more what they are capable of. Salman Khan is also a very passionate speaker and that enthusiasm did rub off me somewhat.

Afterword

If someone wanted to learn by themselves they could always do that. Now, however, it feels it is easier than ever, and one can learn much higher level things than before. I can only wish that the society can become more educated and this more clever and resilient this way. Let’s see what I can do about that too.

(Edit: Just a little bit more about about the changing role and importance (or rather lack of importance) of universities, by Matt Welsh. This link is not agreement or disagreement but food for thought.)

Categories
Life

Retro-spectacular: 2011

Last day of the year is customarily used for reflection. Very useful artificial boundary that makes us think in a time span (a year) that is still quite manageable for humans. One year is quite long to create change, but still short so most of one’s projects are only on the way and didn’t reach their full potential. Taking time to think about those projects and the change make the good parts more permanent and the bad parts more temporary.

This year was really a game changer for me. It’s different in quantities and qualities as well. I remember thinking at the end of 2010 that I missed a lot of opportunities and time just passed me by for one year. Actually, for quite a few years I think there wasn’t too many things to speak of – though all of them have sown the seeds of the awesomeness that was 2012.

Calendar with all days checked off except today, December 31.
The year is almost out

Let’s take some (probably incomplete) stock.

Writing

This blog itself is almost one year old, I have started it in January. Haven’t written as much this year as I wanted (like how come there’s nothing since October?). This is not good, and it could be conveniently be blamed on being too busy to with all the projects I’ve been up to, but cannot completely. It benefits me greatly to write as much as possible, sometime in an organized way (like here), sometimes completely just in a flow. It is not really an excuse either that “I couldn’t find enough topic”, anyone who talks to me off-line often cannot get me to shut up about a thousand things. So better get to it.

Doing NaNoWriMo was limited to a few days of novel writing for me, got only 10% done, which is almost nothing. I said almost, because in those few days there were times indeed when storytelling was working, never before felt so good about writing, and changed the way I now read other people’s writing. So it’s not a total loss.

Doing 750 words was probably the best influence: write at least 750 words every day, about anything. I signed up for every monthly challenge  to write each and every day. I made the first one in October I think, since then I failed every time and often in a stupid way just forgetting about it. Next year I got to get myself off the Wall of Shame again. One thing I learned from it that once something is not perfect (i.e. I missed a day), I tend to let it go and miss more days: failures aggregate if one lets them. A habit worth getting rid of.

Brain stuff

I really enjoy programming, and the Language of the Month series was great, to learn some about Scala, Lua, Prolog, Javascript, and a bit of Go that I haven’t written up yet. It wasn’t every month in the end, so maybe should rethink the project, but I want to continue: there are just too many programming languages and they are awesome way to exercise one’s brain and learn completely new ways of thinking.

I could create a couple of small tools and sites as well, like WatchDoc that I use myself all the time. Nothing too big and still looking for a project that I can build something substantial for. But being able to make your own tools in the online when you need them is just as rewarding as working with your hands offline. The computers/internet is the next generation of Lego if you know how to talk to it.

Took part in the first online Stanford Classes: Machine Learning, Artificial Intelligence and Databases. All three totally worth it, it’s an experience I’m yet to write up, but I’m already using so many things I have learned. Also, my friend’s friends come to me saying “I heard you were doing the Stanford online courses this year, do you want to do it together next year?” There are many more really promising courses announced for the spring semester next year, now I have the problem of having to choose between them. 3 was kinda manageable, so right now I still have to cut down from the 7 noted down.

Somehow I had time to read more this year as well, though I have failed my Yearly Reading Challenge, totaling out at 30 books. I planned one for each week, but it’s just the motivation, the ones I had read this year all really worth it. I have hundreds of books on my to-read list, many of them from friends’ recommendations, so looking forward what will  next year’s 52 books be.

Community

The biggest change with regards of community and my drive as a force of change was taking riding the StartupBus. I cannot overstate how much that changed me: the people, the things we did, the travel and sights that were connected. I’m really glad to still be in touch with many of the people there – and hope to get back in touch with quite a few more. It is a community I’m proud to be part of and can’t wait to see what else comes out of it.

The bus set a few different things in motion, one of them ended up being Ignite Taipei. It is probably the single most important thing that I did this year. Or actually did 3 of them, together with the most inspiring people I can ever wish for. It is something I want to continue on for the foreseeable future, and want to grow it, as well as I’m sure I’ll be growing with it.

A small side project that was Geek Dinner, which is getting together, eat, and being able to talk in a way that I don’t have to hold back on anything. Programming, art, social networks, photography, microcontrollers, laptops, phones, all fair game and when you people don’t tune out  within 10 seconds. It seems there’s really a need for something like this in Taipei, people were really happy and eager. I hope I can carry it on.

A lot has changed about how do I use communities, how do I interact with people on Facebook/Google+, because I realized a few different things how I prefer to be interacted with. This lead for example to the No-Like Manifesto where I try whenever it’s possible to give a (meaningful) comment and not use likes/+1s, just as a last resort and when that’s meaningful in itself. This lead me to so much more discussion and been able to connect to people better. I can indeed say I know my friends a bit better now than I did before, and in big part because of the 24/7 interaction availability of whatever technology or network there is available for it.

Looking forward

This is just a short summary, I must have missed countless things. Now, however, it is really time to look forward. I’m already feeling so excited about a lot of projects that I’m planning for next year, and if I can carry over the excitement of 2011, it will be extraordinary too. Will include films, art, electronics, definitely include a lot, a LOT of people, friends, a lot of kicking ass.

And hopefully a lot of happiness. ^^
Cheers to You!

Categories
Life

Blog mixtape #1

Today ’bout 5am in the morning I just thought I just make a mixtape to match my mood.

I was playing around with Grooveshark more and more recently, and today I spent half the night sorting through songs. I guess I really prefer this to YouTube in terms of finding the songs I know and want to listen to again. Not as good for music discovery, but most of the time (say 4 out of 5) it has what I’m looking for. It likely would need some proper cleanup, most if not all artist’s songs are a mess, loads of duplicates and all. One can upload songs too to be able to listen everywhere, but not sure what goes into the main collection. None of the things I uploaded did.

Tape clip art

So here’s the mix, give it a try. Mostly things I have already known before, probably the majority of the people don’t.  Not very well researched, just something that comes naturally at this wee hour. The sky starts to brighten by now, fortunately it’s a public holiday today in Taiwan, so it won’t make much difference.

Let me know what you think.