Perl poetry

 Perl is said to be close to natural language in many ways. But, can you write a poem in it that expresses your heart? Let's see:

my $sweetheart;
my $heart = $yours for $ever;
do { accept my $love, my $dear }, or kill $me;

bless \$me, my $Lord; join($us, $together) until eternity; 
heaven: our $home, wait for $us; 

my $soul, my $darling, say "I love you";
let's, $honey, splice(@together), not split $ever,  
                                   and goto heaven, $together;

Perl indeed does a good job, isn't it?
So, why am I into this crazy stuff suddenly? Well, there was this post in my friend Sharmi's blog that said:

Love is the language between two hearts; for everything else there is C#

which is apparently a praise of C#. Well, I thought, why for everything else only? :)

The real deal here is that the above is valid Perl code, and when executed, prints "I love you" repeatedly on the screen.

Now take that, C#. :)

*Execution instructions: You need Perl 5.10 or above, and probably need to enable the 'say' feature. If you saved the above program as perlpoetry.pl, the invocation might be: perl -Mfeature=say perlpoetry.pl
This would fill your screen with text, so be ready to press Ctrl-C immediately. :)
 
PS: I do like C#, and in fact think MS did a great service to humanity by relieving many developers from Java. It's just that I love Perl even more. ;)

The Internet vs a cycler

I’ve been reading Programming Pearlsfor some time now. It’s a great book, the kind of book that makes you realize what programming really is about.

Among the exercises behind Column 6 in it, there was this strange problem: “At what distances can a courier on a bicycle with a reel of magnetic tape be a more rapid carrier of information than a telephone line that transmits 56,000 bits per second? Than a 1200-bps line?”

The thought that a bicycle can be faster than an electronic communication line seems ridiculous indeed, but it turns out it’s not difficult. For the sake of modernity, let’s replace the ‘magnetic tape’ with a hard disk. 1TB disks are common enough these days that I’ll use that for the calculations.

Now, let’s calculate the time taken for transmitting ‘g’ Gigabytes of data using both the bicycle method and an Internet line, between two places which are ‘d’ kilometres apart. In the bicycle method, we have two kinds of ‘time’s to consider: the time taken to transfer things to and from the hard disk, and the time taken in the bicycle itself. These days, data transfer rates in hard disks lies around 25MB/s from my experience, so I’ll use that as the transfer rate here. Then, 1 gigabyte will take 1GB/25MB ~= 40 seconds. For g GB, it is 40g seconds. This is for one transfer. Since we do this twice (transferring to the hard disk, and later back from it), it’d be 80g seconds in total. In addition, we have the time taken in the bicycle itself. Assuming a minimal speed of 10kmph by the bicycler, the time taken would be d/10 hours, which in seconds would be 360d. So, the total time is 360d + 80g.

For a 56kbps Internet line, the time taken for transferring the same g gigabytes would be  the upload time + download time. From discussions in the Internet, it appears that upload speeds were “from 1/2 to less of the the download speed”. Of course, this is not ‘authoritative’, but is good enough for our purposes. The download speed here is 7KB/s (since 56kbps = 7KB/s). Let’s take the upper limit and take the upload speed to be half of download i.e. 3.5KB/s. Then, time taken = 1000,000g/3.5 + 1000,000g/7 seconds (since 1GB ~= 1000,000KB) = 3000,000g/7. So we’ve got to solve for d in:

360d + 80g < 3000,000g/7

360d < (3000,000 – 560)g/7

d < 2999440g/2520

d < 1200g (approx.)

If g = 1GB, the bicycle is faster for upto a distance of 1200 km.

If we are to use the 1TB hard disk to its full capacity, g ~= 1000GB. Substituting that,

d < 1,200,000 km

which means that upto an astounding distance of about 1200,000 kilometres (that’s about 750,000 miles), a bicycle with a 1TB hard disk is faster than a 56kbps line for transferring 1TB of data!

Now, 56kbps is one of those old dialup lines – the more common internet connection these days is a broadband connection. For purposes of modernity, let’s now assume a 1Mbps connection, which transfers at a rate of 128KB/s. Again, the upload speed is less. And in this case, it appears that upload speeds of 1/4th of download are most common. Hence, let’s assume an uplink of 128/4 = 32KB/s. Then, the inequality is:

360d + 80g < 1000,000g/32 + 1000,000g/128

360d < 4999920g/128

d < 108g

Again, substituting g = 1,

d < 108 km

For 1024 GB (i.e., 1TB data),

d < 110,000 km

Given that the earth’s circumference itself is just 40,000 km, this means that if you need to transfer 1TB of data to anywhere on earth, you’re better off sending a bicycle courier than sending it through today’s internet connections (of course, there’s the issue of crossing the oceans, which we’ll ignore for simplicity ;) )

Now, the same is not true if you need to transfer only 1GB – as we saw above, a bicycle is faster only upto 108 km. So, for what amount of data is a bicycle faster to anywhere on earth? Let’s do one final calculation.

The maximum distance you’d need to travel on earth is half its circumference (can you see why?). This is 20,000 km. So, let’s make d=20,000 km in the inequality and see what g we get.

360*20,000 < 4999920g/128

g > 184 GB

So, the moral of the story is, even if you have a 1Mbps broadband line, if you need to transfer more than 184 GB of data, you’d achieve better speeds to anywhere on earth by carrying a hard disk on a bicycle than by transferring it through the 1Mbps connection!

Programming for the Finance industry

The last two days, I had a training in finance. While the class was entirely about finance, the trainer mentioned quite a few times how programmers (he called us "IT professionals") had made everything very easy and transparent.

So, I thought about the various things he had taught, and realised that finance is probably the best application of textbook programming. Ok, maybe there are other fields like scientific programming, etc. to compete to that title, but finance is indeed an extremely suitable field.

If you think about it, almost all of finance is about numbers and logic. The two things computers do very well. The only two things computers do 'naturally'.

The trainer told us about one common task that was a chore to do manually until computers took over - checking a stock price every now and then, and performing an action when the price reached a certain value. You can imagine how boring and difficult a job this would have been when done manually.

I thought about it from a programmer's point of view, and couldn't help smile when I realized how easy this would be to program. Just keep fetching the value, comparing and waiting in a loop until you get to the required value. As I said, a textbook application of programming.

There were many other things, obviously - finance is full of calculations: calculating the interests, computing dividends, finding the tax taking into account all the various exemptions, etc. Every one of them is just an application of the most basic operations of current computers - number crunching and logic evaluation.

But, this is looking at only one face of finance - the part which involved tedious manual calculations which are algorithmically specifiable. However, there is another face to finance which is what makes the experts who they are. The part that involves judgement.

If you are any familiar with Artificial Intelligence, you'll know how difficult it is to teach computers to do this. Even most humans find it difficult to give a reasonably correct prediction of how a stock price will change or how an investment will turn out beforehand. How can mere computers which are just number crunching machines do it?

However, we humans are not an easily-giving-up lot. Using these mere number crunching machines, we're trying to build things that 'think'. We have things like neural networks which try to learn from previous data and project them to the future. We have Genetic Programming methods to evolve good models of the stock price variations. Of course, it's not foolproof and will probably never be. But, it's a wonder that we've even managed to even take a number-crunching-machine to the level of a (sometimes faulty) thinker. Also, as we give the system more and more data about the economics involved, it will get more and more accurate. Currently, we're giving them only numbers - previous stock prices, possibly the company's profits and losses, and such relevant data. However, we're currently mostly unable to give some important data that a human expert has access to - the news surrounding the company, the quality of the current management, etc. When we one day find a way to feed these things to the computer (or better, have the computer find those things itself), I believe we can expect much better predictions.

Finance industry has benefited a lot from the 'primitive' types of computation we are now easily able to do - extremely fast mathematical calculations and reliable logic evaluation. It appears it also stands to benefit from the 'newer generations' of computing which are coming up - where computers will cease to be mere machines and will 'understand' and 'think' about things!