The Future of Coding

Posted by admin on Jun 3rd, 2010 | 0 comments home title

The Future of Coding

The number system is actually a system of codes to represent information that is not intrinsic to the code itself. So is the alphabet.

Since encryption also involves coding, some people confuse between codes and encryption. While there are elements common to both, the purpose of coding is to represent information efficiently, enabling arithmetical and logical manipulation, while the purpose of encryption is to hide information from unauthorized people. In that sense, code is information that is potentially available to all who who know the rules of coding and code-manipulation, while encryption is the process of restricting the meaning of the code to a select few. A good example of code would be DNA where the information is coded but not hidden. An example of encryption would be the way user-passwords are stored by most website databases.

Our concern in this article is with coding, and the use of technology for coding, and not with encryption. Since all encryption rides upon coding, knowing about the past and future of coding would automatically open up the way to understand the past and future of encryption, which can then be explained more efficiently in forthcoming write-ups devoted exclusively to encryption.

The arrival of computers made manipulation of codes easy, even when there are several levels of codes riding one on top of the other. So much so that what a man can do with paper and pencil in a thousand years can be done by a super computer in one second. However, contrary to what was expected in the last few decades, it has become clear that this raw increase in computational power and corresponding code-handling capacity has not resulted in a proportional capacity to mimic the human thought process.

If computers have to effectively take over all what men have envisaged them doing, then they need to mimic the human thought process. Raw computation power of a million supercomputers might be able to do that, we do not know, but then nobody in his right mind ever envisages a situation where a million supercomputers are connected together to guide a robot to sweep the house or go shopping. It has become increasingly clear that for the so-called Artificial Intelligence to arrive one needs more efficient ways of handling code and that is the bottleneck where things more or less the stand today.

Algorithms: The Demand For Future

Machines with better efficiency will be needed to handle code-manipulation in future. Better efficiency here means both the raw computing power as well as the ways in which code is handled to arrive at results.

Man handles code through addition, subtraction multiplication etc. But he also handles multiplication blazingly fast through tables, memorizing which at least up to the table of 30 was compulsory in Indian schools. In a previous generation stone masons also used to memorize tables of fractions, and were able to mentally compute area and volumes which for most of us requires books of references and calculators. Thus tables gave them unusual efficiency for mental computation in that bygone generation. What was done in these generation through tables is a good example of ”algorithms”.

An algorithm is a technique, method, or approach for solving a computational problem in an unusually efficient way. It is also a technique, method or approach for solving problems that demand complex logic. Mathematical tables are a good example. Logarithm that reduce very large multiplication and division to very simple addition and subtraction is another example. There are numerous other algorithms, some of which might look very complex to the reader, but which are easy for computers to handle. These algorithms give tremendous speed and accuracy to computers. However, even the best algorithm has not yet come to a point where it can mimic human thought even remotely. This is where the future