Archis's Blog

First look at some of the screenshots from the link below:
http://content.zdnet.com/2346-11470_22-188303-5.html

Let’s first make allowances for the following:
1. I work for Microsoft and will by definition criticise anything-Google.
2. ….. well, there is no point number two, but I know most of you will be just repeating point one constantly instead of really giving me reasons to like the Android UI.

Good heavens! After all that hype and after all the criticism I dished out to WiMo, I was expecting something more! I admit this may just be a prototype, but after the iPhone, I was expecting something a lot cooler.

The UI is just a been-there-done-that kinda thing. No fancy scrolling, or multitouch, or stuff like that. Some stuff I find on my Pocket PC at the bottom is at the top. Some stuff I find at the top on my PPC is sometimes at the top. But apart from that, I saw all the same options I’m used to seeing.

Sarcastic as I may be, an idiot I am not. One thing conspicously missing from the screenshots is even the slightest hint of the browser/web interface – the real killer everyone is expecting from the device. So perhaps, I may be a bit premature in my criticism, and perhaps the best of Android is yet to come……

A common comment in any debate involving computer science or software or technology is, “Let’s leave the politics out of it.” More than often, it is the losing side that makes this comment.

Over time, I have learnt that there is no such thing as a “non-political” decision. Every decision is opinionated (opinionated by the decision-makers). Every decision has a reason behind it – people don’t just take them randomly. Now while you may say that a decision was objectively taken to fit certain criteria, who guaranteers that the criteria to begin with are themselves non-opinionated?

I’m not trying to lecture in some kind of patronising holistic manner here. I’m speaking out of experience. Till about two years ago, I lived in my own virtual shell where I believed there is no politics. I believed there can be places where people work purely towards altruistic goals. Over time I realised that my views were not necessarily mutually-exclusive. Altruism requires politics. Mohandas Gandhi (for those who may disagree with his Mahatma status) may have been altruistic, but you don’t get independence without laws, and a government. You don’t rally people towards a common cause without being political.

“Open Source”, “Freedom”, “Copyrights”, “Usability”, etc. is all politics. That doesn’t mean either that being politics makes it bad, or that it’s not important or necessary. But you need to factually accept the fact that we live in a world with passions, whims, opinions, and egos.

And you know what? That’s what makes the world so great! Richard Stallman firmly believes in his altruistic vision where software shall be free. To come and tell me that he’s not a political guy would make me lose all respect for him. Of course he’s political. Of course he’s passionate about what he feels. On the other hand, I also know people who have been ruined by opening up their ideas and others making money out of them. Therefore there are people who passionately believe in intellectual property rights. Afterall, it takes hard work to publish a paper and if you’ve ever studied in the University of Pune, you’d know just how valuable “intellectual property” is. The value is as complex to measure as the intellectual property itself. Many a student has been harassed, tortured and abused before they could do anything productive. That’s the “price” of intellectual property, and it certainly doesn’t come cheap (let alone free).

It’s important to separate the election-oriented party-politics from politics in general. If we didn’t have passions, if we didn’t have opinions and if we didn’t have beliefs, humanity would have been lovable, cute, adorable and ultimately inconsequential pets of some alien species.

So think again before you try “taking the politics out of computer science”.

During discussions on various algorithms communities, and with friends and colleagues, I found many people having skewed notions of the words “probability”, “randomness” and non-determinism. They use the words out-of-context or with inaccurate semantics.

This blog was motivated in part to dismiss some inaccurate notions regarding these phrases, and also to present some interesting examples which may help you appreciate the subtleness of expression science requires. I am writing this on a very tight schedule, it may end abruptly (I shall follow up with entries in the near future).

This topic is highly “academic”, and you may appreciate that academicians, not throwing fancy words all the time, when they do use a word, they mean it! And I hope to cultivate some regard amongst CS students for the highly precise nature of the mathematical science they chose to study.

Randomness:

Let’s begin with a fun example my dad used to tell me (he’s a statistician). Imagine there are two people reciting numbers progressively. And there are two observers writing down the numbers spoken by the former candidates. Some information regarding the numbers they’ve provided:

1. Candidate one has said: 1, 5, 2, 9, 7, 3, 2, 1

2. Candidate two has said: 2, 2, 2, 2, 2, 2, 2

Checkpoint1: Now I ask you, can you predict the next number that either of the candidates will utter? Also, please do write down the reason(s) for your answer.

Keep that answer to yourself for the moment. In the meantime, some more info about our contestants:

1. Candidate one is a professor of statistics who specialises in pseudo-random number generation.

2. Candidate two is a mentally retarded person (so far as our contemporary understanding of his condition goes).

Checkpoint2: Now I ask – can you predict the next number either of them will utter? Keep your answer to yourself once more.

In the meantime, some more gossip about these candidates: both people were initially told to produce random numbers.

Checkpoint3: Again, note down your answer and the reason for thinking so.

Let us now try and debate which of the next number to be obtained from either candidate would qualify as “random”. Let us assume that randomness is the property of being unable to predict the next number that will be produced.

1. Candidate one: He knew he was supposed to generate “random numbers”. He is a professor aware of the properties of randomness of a sequence of numbers. And the sequence he has generated does exhibit some of those properties. I shall venture to make a comment here and you can make up your own mind – given enough numbers that he has generated, and if we assumed he is going to follow the “statistical properties of randomness”, it would fairly narrow down the possibilities of the next number he would generate (since it would try to maximise the adherence to the properties of randomness).

2. Candidate two: He probably has no idea what he is supposed to do beyond generating numbers. While we do know that he has generated a sequence of ’2′s consequtively, we cannot assume any properties or any process by which he is generating the sequence. Maybe he has OCD and is generating a specific number of ’2′s before he switches to another number? Maybe the only number he knows is ’2′? Maybe he’ll stop at the next number just for the heck of it?

Based on the definition of randomness that I stated above (conceding that it is my subjective definition), I am compelled to find that the series of ’2′s is a random series, whereas the first series is not, in fact, random.

This is a critical property of randomness that very few people seem to grasp. Randomness of an outcome is a property of the process used to generate the outcome, and not the outcome that was actually generated.

To provide a simpler analogy, will you assume that just because your home had tap water for the previous one hour, you are going to get tap water indefinately? Or conversely that because tap water at your house keeps getting disconnected abruptly, it will never come back? As you can see, the way you’d predict the outcome of the supply of tapwater is based on the process used to provide it. It is the same with random series, and making assumptions based on the numbers themselves is synonymous with predicting tap water availability based on how much water you collected in the bucket.

Sidebar: Frequently, people use the word “random” to indicate “high variance”, which is technically inaccurate.

Law of Averages:

Another fun statistician joke: A guy goes to his doctor for heart surgery. The doc tells him his chances of survival are 100%. The happy, yet skeptical patient asks his doc, “How come? I was told that the survival rate in this surgery is only 10%.” The doc promply replies, “That is precisely correct. And it is because all my previous nine patients have died during this surgery, that I am supremely confident of your survival!”

This example in part shows the danger of predicting future outcomes of a process, based on purely the past outcomes, without looking into the process. Look at it this way, would you yourself go to a doc, who’s had a 100% fatality rate for all his prior patients? However, if it were the best heart surgeon in the world who’s had extreme-stage cases for the past nine times, would you rather trust him or another doc who’s has a zero fatality rate but has left patients with a permanent disabilities where any other doc would have been able to provide a full recovery?

Essentially, I want to reinforce two points:
1. Randomness is a property of the process, not the outcome of the process. Never ever make assumptions of randomness based on purely the outcomes of the process (cryptographers would tell you the extreme dangers of that).

2. Randomness is about unpredictability! It is not about a high variance in outcomes. It is about not being able to predict the outcome. Basically, even if you get a million ’2′s in a row, if you are unable to predict the million-and-first number, it is a random sequence. On the other hand, if you find a sequence of four statistically random numbers, and can predict the 5th number, it is NOT a random sequence.

Non-determinism of outcome vs. Non-determinism of process:

Everyone, I hope is familiar with Schr��dinger’s cat. I keep facing many queries in algorithms forums for “non-deterministic” algorithms, when people really cannot distinguish between determinism and accuracy.

Schr��dinger’s cat is a perfect example of non-determinism or unpredictability or randomness. There is really no parameter in the experiment that allows us to explain the state of the cat while the box is closed. The outcome of this experiment is non-deterministic in the sense that a decision based on this outcome will change drastically since the outcome is discrete and binary.

Another example of non-determinism is that of probabilistic primality testing. Given a number, you may have false positives and false negatives.

Let me put it this way:
In non-deterministic outcomes, the outcomes themselves are well-defined and you know all possible outcomes, only that you don’t know whether the outcome you obtained was correct or incorrect. A non-deterministic Turing machine is a good example of this. Like Schr��dinger’s cat, an NTM can be in multiple states at the same time. However, for each state, the decision to be taken next is well-defined and deterministic. Similarly in a probabilistic primality testing algorithm, each step is well-defined and accurately executed. The algorithm itself has no non-determinism built into it. Given the same input, the algorithm will behave exactly the same every time (although the answer you get may be different). To put in another way, an algorithm to test for the survival of the cat calls for the opening up of the box. This alg
orithm is invariant. You always deterministically open the box and look inside to obtain the outcome. The outcome itself is something you don’t know until the algorithm has completed.

Now don’t get me wrong. I’m not saying a non-deterministic-outcome algorithm has to be deterministic in it’s process. These two are orthogonal properties. Let’s look at non-deterministic-process algorithms:

Random algorithms, on the other hand, use some kind of entropy within the algorithm itself. The processes of such algorithms are themselves non-deterministic. Ideally, until you reach a decision point in the algorithm, you’d have no way of forecasting what decision would be taken at the point. Monte-Carlo methods are an example of such algorithms (but pseudo-random numbers do in fact allow us to predict their decision points). Let me put that another way – if you run the same Monte Carlo method twice, you’d follow different steps. So a Schr��dinger’s cat experiment with Monte Carlo methods would vary based on the parameter you’re making non-deterministic – for example, it may open the the box after different time intervals each time you ran it and you’d have no way (ideally) of predicting when the box would be opened.

An example of a non-deterministic process leading to a deterministic outcome is the use of Monte Carlo methods used to approximate PI (the most common example of Monte Carlo methods), or to get more deterministic, roots of polynomials. The algorithms used generally won’t ever follow the same state transitions twice, but they always lead to the results which are always deterministic and identical (in any case, even if the algorithm doesn’t reach the deterministic answer, there does exist a deterministic answer and the algorithm gets _closer_ to it progressively).

Which brings us finally to:

Approximations:

Approximations or near-optimal solutions are the third orthogonal property we need to consider. The notion of “close to” the answer comes into play here. And this is difficult to differentiate from the property of high-probability of an answer.

An easy way to understand this contrast is to think of prime number generation vs. primality testing.

1. Assume a function F(x) which returns the x-th prime number. Due to computational limitations, there will be loss in numerical accuracy and the number we obtain won’t exactly be a prime itself (and in all likeliness not an integer at all). In this case, the better the numerical accuracy we provide during our computations, we arrive “closer” to the prime number. If our numerical accuracy is infinite, we obtain the perfect integer. But in every case, we are aware that there exists in fact a prime number without any doubt in a neighbourhood of the number we obtain. You can bet your cat’s life expectancy to be propertional to closeness of the outcome, and you’d get a pretty healthy cat.

2. On the other hand, consider an F(x) that returns 1 if x is prime, and 0 if x is non-prime, and any x in (0, 1) by attributing a confidence level to the primality of x. Essentially if you tested a 100 Xi’s, (define Yi = F(Xi)) and if Yi > Yj, then more Xi’s are primes than Xj’s on average over time. However, by increasing Yi, it doesn’t make the number Xi any primer. There is no such thing as “more prime” or “less prime”. Even with a Yi of 0.99, Xi may turn out to be composite. Hence, this is not an “approximation”. This is non-deterministic, probabilistic and perhaps random. But it is not approximate. To say that “X is approximately a prime number” would be a gross misrepresentation (and I’ve heard this statement made more than once). If you bet your cat’s life on this, you’d be playing russion roulette with your cat.

Essentially, in a surgery with chance of 20% tissue damage, all 100% patients would come out with (upto a maximum of) 20% damage to their muscles. In a surgery with 20% mortality rate, 80% people would come out alive.

Such distinctions become very important when you’re in any kind of business that supports decision-making (programming being one of them). Imagine if someone sold you an investment-advice program that had a 0.01% failure in predictions, and another guy sells you a program with 10% loss of accuracy in predictions. It’s important to know these distinctions before evaluating any decision choices. Don’t misunderstand me – I’m not biasing on either side. I personally would buy the first one (since I’d like to make lots of money fast and risk the total loss of all my money). But that doesn’t mean one shouldn’t understand and appreciate the distinction between the two.

This concludes the post for now. I need to go watch He-Man for a while and get motivated and punch in a lot of code.

This question keeps getting asked again and again, on mailing lists, and forums, and programming contests.

What’s different for the BCS crowd, is that this question gets asked by “Alumni” to show of their mediocre skills. What’s more wierd is that most “Alumni” that I know of have always demonstrated suboptimal solutions to this problem (refer to definition of “Alumni” below).

For the benefit of the small band of rebels that I’m trying to encourage to purge the empire, here are the hidden plans that destroy the empire. The FFT method would out-run absolutely any method on earth when you’re talking about integer multiplications to the scale of 1000! or above.

All feedback/comments will be appreciated.

The code and a descripton will be found here:
http://www.geocities.com/archisgore/code_samples/fft_mult.html

I hope this mitigates those “I wanna do a project, do you have a project?” kind of questions and explains all my views in excruciating detail. For any questions, you’re always welcome to mail me and I’ll revert back with another entry. Me not being either an “industry expert” (I’ve exhausted all industry-expert humor for now – but I promise to come up with new insults soon), and neither am I an “alumni” (if you’re new to Pune culture, this isn’t an adjective which means past-student, but a very special honour – it’s a bit hard to explain here unless you’ve faced some “alumni” yourselves), so there’s little scope of me ever being able to speak on these topics directly to students.

Let’s get down to business. I’ll post suggesstions here and justifications later, so that any of you who want to get a quick overview won’t waste time:

1. Projects should always be focussed on a “problem”. They must begin with a problem, and only end in solving the problem. If you’re beginning with, “I want to do something in Java/.Net/”, you’re in trouble. Now, this could be a valid assertion, assuming you’re talking about extending the language/technology in question. If you want to increase the “Java platform” itself, this makes sense. But if you’re talking about using Java to do (where is as of yet undefined), you’d better rethink your choice of becoming a computer scientist. Not sure if you even know how a “problem” is defined.

2. A problem must be something real and substantial. It must be something people can relate to. Something that, if “solved”, must no longer exist. Let’s say you pick up “Base64 encoding a string” as a problem, then after your solution, base64 encoding of a string should no longer bother your target audience.

A problem must always have a target audience. If nobody needs a base64 encoding of a string, don’t do it. If someone needs a base64 encoding of a string, and won’t use your program for it, don’t do it. Do something that someone real uses. This doesn’t have to be a “live” project. Do something at home. I don’t care. But do something that works! Even if you’re mom or dad or grandpa uses it, it’s okay.

In my opinion, a great project is one that achieves something – something that at least someone cares about – even if it’s one person on earth (including you). Let’s take an example later.

3. Write “REAL” code: Go to your own college. I’m sure everyone’s complaining about some missing tool, or “I wish I had a tool to do this”. Now think to yourself. In your own college, one that is admittedly “academic” and “non-industry-oriented”, there isn’t a single tool written by many of you today, that solves any of the problems they crib about. Now if your so-called “academic” college won’t use it (which is why you bring in “industry experts” to deliver lectures), why should a multi-billion-dollar industry trust you?

Your own HoD won’t trust the code that you write, but overnight, you expect a multibillion-dollar company to just trust you? And let me make this very clear – the industry, once they hire you, implicitely trusts you. I had a disagreement about this issue with a senior many years ago, and he had said I would “learn that trust isn’t everything” when I joined the industry. As I have yet to apaprently join the “industry”, I maintain my original opinion, and actively promote it. When I write code, I am questioned by nobody. My manager doesn’t ask me why I did something a certain way. If I say something can’t be done, he takes it at face value and defends my decision higher up. He’ll never ask for a second opinion. My mails are not monitored and I could burn all my team’s code on a CD and walk out with it without anyone checking. It’s that simple. You can now imagine just how much trust Microsoft has in all it’s 70,000+ employees worldwide (vendors and interns over and above this number). That’s how the industry works. And this trust has to be earned.

“How do I write real code?” It’s simple. Write code. Distribute it. If one person uses your code for their problem, it’s “real” code. Now it is very important that they really do use it. Some friend shouldn’t just fire up your program, run it, and then fire up something else later. You’re program needs to be the only program they use for the specific problem your program attempts to solve.

4. “Impressive projects” (IPs for short), are mostly for cowards. Let me explain. In my day, I’ve seen (and continue to see) extremely fancy-sounding names like “kernel-module-janky-panky-thingy”, or “hyper-threaded-multi-headed-monster” to name the remotely sane ones. Now while those of you doing “small” projects like GUI’s for a database might get intimidated by these names, let me attempt to give you some confidence.

If the comment, “My project is 90% done”, can be made about any project, that project is cowardly. How many of those kernel modules being made is the college using in their labs? How many of those jhanky-panky things are being used by your own teachers? So far as I’ve noticed, ZERO! The cool thing about an IP (impressive project) is that the minute you begin it, you’ve already defined an exit-route. Regardless of what you end up doing, you just say, “Hey it’s 90% done”, and you got 100% marks for doing 90% of a very complex project.

The really brave are those who take up those silly-sounding database GUI projects. Due to their silly-sounding nature, everyone is already critical of them before the evaluation begins. Being a silly-sounding project, they really need to “deliver”. If it doesn’t work, they’re screwed. Forget 99%, they need it to be done 100% or even more sometimes. Think about this for a moment.

One of the reasons I have never been, nor will never be asked to get involved in industry-student activities (where industry jerks go to the college and tell students how stupid they are), is that I keep asking the wrong questions. I distinctly remember at least 3 IPs where I asked the teacher praising it, “So ma’am, does your machine use this file-system driver?”, and she didn’t reply, and I didn’t need to ask anything further. That’s why “defining the problem” is the most important step. Define it and solve it goddamit!

If you’re still not convinced, let me put this in another way. Most of you know I love making my own toothpaste just for fun. Now I recently made a toothpaste that’s 90% done. It can kill off absolutely any bacteria, virus, fungus and any other pathogens known to man! Wow! It’s super-awesome! Just one minor issue – we can’t use it on our teeth yet. But wait! It’s 90% done! We’re almost there! We solved the major issue that no other toothpaste in the world solves! We killed off all germs! Take that you idiotic dentists studying for 1000 years! I, a mere computer programmer, invented a better toothpaste than you! And now, I want you all to invest 100 million dollars in my new invention so I can make it safe for teeth, and we’ll all be rich!

Just tell me how many if you will invest in my super-awesome toothpaste? The moral of the story is, just go do a project you enjoy and have fun with, and don’t be intimidated by these IP’s. If you’re doing a small GUI project, you’re braver than many of them, and you’ve got a lot more to be afraid of – because you will be scrutinized, you’ll be interrogated, you’ll be broken down into bits before you get a single point for your project, which needs to do everything you said it would do. Your project needs to be safe for teeth, _and_ kill as many germs as possible. So take heart and trust me – the industry will value you. Have you ever heard of the standard comment at college that goes something like…. “sometimes low-scorers are hired by the industry and high-scorers remain jobless”? Believe me – the industry isn’t stupid. They never hired low-scorers. They hired those who were scrutinized the most and didn’t have the escape routes that the others had.

As an ending note: if you do manage an IP and only deliver the solution
- well, you’re beyond all praise. I honestly mean it. This blog entry was not meant for you.

5. Commitment: I know most of you have never met people who’re committal, but hey, they do exist you know! Now, I don’t mean this in the context of management-style blazer-wearing people. I mean it in the plain human definition of it. If you want to be brave, learn to write down the problem you’ll commit to solve, and only go to the examiner with, “It’s solved”, or “It’s not solved” (unless you’d rather back me a million dollars for my toothpaste).

Define the problem in use-case terms ONLY! That’s the only way to really commit. If you’re making a Linux installer, and if I were an examiner, I’d ask it to install Linux on my PC without asking you a single question otherwise. If it didn’t install Linux, but instead did some hyper-threaded-multi-headed thingy, I wouldn’t give a damn! Use-case scenarios are frightening to commit to, and that’s why you need to learn this while you’re students. Now is the time to make those mistakes.

And believe me, there’s a difference when I say this. I work for a company that produces products that _you_ personally use (well, I hope so anyway; and if you don’t use our products, I’ve proven my point even better). I don’t come to you and say, “I’m some bigshot industry fellow who knows everything and you’re stupid.” The code I write, physically reaches you. You’re being, while you’re reading this blog, my direct judges. You’re my evaluators. You’re directly responsible for my bread-and-butter. How many of you use Live Search? What if I told you Live Search uses some jhanky-panky-super-cool technology that Google doesn’t have, will you use it? If I said, Live uses C#, will you use it? Think of orkut. What technology does it use? What Os does it run on? What AJAX engine does it employ? Have you even once considered these questions? All you care about is the quality of search results, and the ability to _communicate_ using orkut. Orkut’s problem is a use-case scenario. “To enable Person A to talk to Person B”. Even if it’s 90% done, it’s worthless. Even if it’s 99% done, it’s worthless. Only if it enables Person A to talk to Person B, do you – the judges, the evaluators, the jury – use orkut! Then why is it, that having expressed a desire to work for this industry, you hate these “academic questions” asked by allegedly stupid university examiners? In my opinion, the university examiners ask the perfect questions – the very questions that you would ask me when I come and ask you to use my product. In a way, calling them stupid, is calling yourself stupid.

I admit, they don’t care what technology you’ve used, or how hard you’ve worked, or that cool kernel-thingy you did. But then again, neither do you care for all these things. Learn to live with it – better now than later. That’s what life in the industry-without-double-quotes is. That’s how we live daily. If Person A cannot talk to Person B, I’m worthless to the world regardless of what transport protocol I may have used and how many layers of encryption I may have put on it.

The one final suggesstion I would make to you – learn to make commitments and learn to live with them. Never have excuses based on technology, hard-work, or impressive-sounding names – that’s for cowards.

6. Ensure you’re thinking of deployment: How many projects in our labs are run directly from IDEs, and how many are binary executables? Even more so, how many of those executables are packaged? On Linux, there’s deb and rpm, on Windows there’s MSI. How many projects can you “install->double-click->run”? Again, would you use my products if I gave you a large source tree? This is a tough call, but it’s an important one. Being able to write something that’ll run on even one machine that you don’t have control over provides immense pleasure.

7. Use all the tools at your disposal: Use a source versioning system. Store your code in a CVS/SVN/VSS repository. Keep incremental changes as diffs.

Compare those diffs. Look at how you wrote the code. If something goes wrong, you can revert back to a working build instantly. It’s important to be able to use these tools regardless of where you work. If you’re handling any content, a versioning system is critical – even if you’re only working with office documents and not source code. Your company/clients/stakeholders are going to want your sharepoint to hold all incremental changes to documents that are made. Use source-analysis tools to find memory holes. Even knowing that a certain tool exists for a certain job can be valuable.

8. “If I do all this, when will I do my project?” The answer is simple – do a simple project, but one in which you can focus on all aspects of development. I had attempted to make this suggesstion in the syllabus, but the university is still living in the 1980′s so we’ve got to give them some time before they can catch up on almost 22 years. In the meantime, I personally recommend build only notepad if you have to. Build sudoku. Build minesweeper. But build something that you yourself should use for hours on your own. Not for testing, but for actual “using”. Build something that allows you to explore various challenges – how to version source code being edited by 20 people at a time, how to write comments, how to build binaries, how to build packages, how to ensure and test packages will install and run and take care of dependencies for unknown configurations. These are all fun and interesting aspects, but most importantly, they’re just plain old bread-and-butter aspects of a programmer.

When I say real code, go out to a public CVS. Contribute just 100 lines to Apache or MySQL, or Postgres, or Linux. Instead of a 10000-line kernel-thingy, submit 100 lines to _the_ kernel. 100 lines that will be used by hundreds of thousands of users all over the world! Now that’s “real” code! It’s not the quantity but the quality that counts.

Naturally, I don’t discourage building a kernel driver, but then have the guts to commit to it. It shouldn’t be done for “40 marks”. The only acceptibility criteria is that it be merged into the kernel tree. There should be no other criteria. If you’re solving the problem of “non-existence of a driver”, after your solution, this problem must go away (as I mentioned above). After your solution, there should “exist a driver” – not on your PC or in your lab, but in the kernel production tree. Being able to make this commitment is what the thrill is all about. Make a commitment on notepad, but then ship it! It should go into production. If you can’t commit on even notepad, then you’re seriously in the wrong place and reading the wrong blog. Its upto you – whether you want to be a coward and be non-committal, or be courageous and commit to a small deliverable!

Being a fan of C S Lewis’s Narnia books, I thought I’d write a blog entry based on a very excellent paper I found on the net describing the two magics (can’t find the link right now, will post it as soon as I get it).

I wanted to comment on another aspect of the two magics.

This aspect of activeness and passiveness. JKR Magic is very active in nature. Whereas CSL magic is extremely passive. In JKR magic, the characters positively use magic or influence it to induce certain results. They perform spells, make potions and stuff of that sort. CSL magic, on the other hand is invoked passively, and in most cases unknowingly (remember, “Thou shalt not tempt God!”). CSL was most obviously promoting Christianity through his books (which I loved).

In CSL, all active magic is also “evil” magic (has a lot of Jedi philosophical similarities). The White Witch, the Lady of the Green Kirtle, etc. were all evil because they “invoked” magic. This has obvious similarities to using power for personal gain. Invoked magic always has the motivation of personal gain (or is implied to do so in the books). All “good” magic is that which is invoked by principle, by definition or by moral superiority. Essentially, if you’re a good person, good things will happen to you. It is actually a brilliant and ingenious way of explaining religion to young children – I certainly got the point much clearly than having read the Bible. The movie even has the quote, “There is a deep magic, more powerful than any of us, that rules over Narnia. It defines right from wrong. It governs all our destinies, your’s and mine.”

Hence, when the children are called to help out Narnians, rarely do we see the good characters actively engaging in magical acts. In fact it is unspokenly tabood to do so.

In the JKR universe, magic is very active. Both good and bad sides use it. Magic is more like a knife – it’s use depends on it’s user. A knife may save lives in a surgery and also may take lives in murders. JKR magic is invoked rather heavily – except when Harry was protected by a deep ancient magic – which sound similar to the Stone Table’s magic from CSL.

The Chronicles of Narnia! Of course! In all the book-to-movie translations that I’ve seen so far, this one comes really close. It’s actually perfect. There wasn’t ONE SINGLE suggesstion I could have made to change the movie to reflect the book any better.

You can read my really long review about this, but the fact is that they really did not make it more Hollywood-ish like the Harry Potter books were made when turned into movies. Narnia is just what it was supposed to be. No changing of spell names, no changing of characters and preserving the basic theme.

Most of you will criticize me that the Harry Potter books were way bigger than the Narnia books – and this is true – the Narnia books are possibly one-fourth the size of the smallest harry potter book. But it’s about the basic theme. There’s that internal gut feeling that you get. The Narnia movie completely portrays _what_ it’s all about – the whole concept of the Narnian world, the concept of the books, the principals behind the story. The Harry Potter books are great – they have this element.

The movies, however, are pure entertainment. I mean, for people like me, that was the major downer in LOTR. I’m a guy who wants a strong story line, a strong underlying principle, and if you can cram them in, loads of special effects. But a compromise in the story’s weight to increase battle scenes or to have Harry fighting dragons and all just doesn’t impress me. I like dialogs. I like philosophy. I like to see characters in challenging situations, as humans where they must use their brains, their courage, their wit, their personna to escape. That’s what books are all about. The Narnia movies did a great job of it. I feel Prizoner of Azkaban is the best HP movie so far – a really great one where the story is kept strong.

I am probably the first Indian and only single undergrad student to participate in the Brain Computer Interface Competition III. I participated only in the first dataset due to lack of time, college pressures and the fact that I was developing Fergusson Linux alongside. Anyways, I managed to score a 79% accuracy with the highest being 91%. I single-handedly ranked 13th and my results are better than some very huge universities. View their results page for more authentic information.

It feels great to be the first Indian (both by origin and location) to have participated in the BCI competition and perhaps also the only undergraduate student in the world to have done it. What’s more is that achieving the 13^th position did a lot to give me a moral boost and the confidene that my abilities to conduct research were only limited due to lack of resources. From this event onwards, I’ve decided to do research because I like it – I dont give a damn about degrees or paperwork. And I dont care what people think about me either. I’m much happier this way as compared to the submissive person I used to be trying to please everyone to earn a good degree in hopes of pursuing research.

Let me tell you a little secret. If you think that sucking up to someone and getting good grades is going to help you, you’re unimaginably mistaken. I tried that for a few years and realised that people will always be demeaning and will try to pull you down. The lack of good grades is just an excuse they use to throw you out. When you do have good grades, they’ll say that the education system is so mundane that anyone who manages to get good grades is not creative enough. It’s better to be happy doing what you like.

Although BCIs are my primary love and only possible career choice for me, I’m still having trouble finding funding. For all those science enthusiasts who’re being roped in to do someone else’s work under false promises of research grants for crazy ideas, let me hit you with the reality – its all fake. Nobody really cares about pure science anymore. They just use it to portray themselves as higher or more holistic human beings compared to others. These people completely miss out on the whole point of science – which is to do stuff for its own sake!