C Language – The code of God

One could easily say “In the beginning there was C language. All else were variants” and not be far from the truth. As I headed to work today I was ruminating on the impact C language has had to the computing landscape for the past 4 decades. No other language has had such a significant impact.

C Language was created by Dennis Ritchie & Brian Kernighan in Bell Labs, around 1972. C was the trigger for many seismic shifts in the computing industry. The language is terse and compact. C language strikes a rich balance between brevity and readability.

C language, in my opinion, is the code of God.

We can easily divide the epoch of programming languages as before C and after C. Before C, there was a babel of languages from FORTRAN, COBOL, Pascal, Basic, Prolog, Ada, Lisp and numerous others. When C language, entered the scene, many other languages simply faded away. C set the tone for programming and spawned an entire industry.

Many of the popular constructs like the if-then-else, for, while loops had a crisp simplicity in C. C included in its repertoire the ability to manipulate the bits of registers all the way to creating complex and rich data structures with the help of structures and pointers. In fact C was probably one of key enablers for the development of the legendary Operating System (OS), UNIX from Bell Labs.

Building the innards of an OS is an undertaking of gigantic proportions and requires the need to be able to manipulate the registers of the numerous input/output devices, the processors and the memory. C was eminently suited for this the job. Also the complex algorithms of OS for e.g. process scheduling, memory management, IO management, disk management could now be programed simultaneously in a bottom-up fashion by working at the ‘bit’ level and in a top-down fashion allowing for complex data structures and algorithms required for scheduling, memory and IO management. . With a powerful language C, the birth of UNIX was a given. When AT&T distributed UNIX to the universities in the late 1970 it created serious shock waves in the industry. Since then UNIX has resulted in numerous variants – Solaris, HP-UX, AIX, iOS, Linux and then Android and so on. Well, that’s another story!

C came at an opportune time when the internet was at its infancy. C proceeded to be useful also for protocol of the internet namely TCP/IP. C spawned an army of programmers all keen to take on this new language twiddling bits, bytes and complex data structures of the OS and protocols.

C, UNIX and TCP/IP almost entirely power the internet and the WorldWideWeb.

The beauty and brevity of the language enabled programmers to easily express complex problems as units of C functions. Pointers, and bit manipulation gave it a power that was unparalleled at that time. Soon C became the de facto programming standard. C, in fact, became a way of thinking for problems!

So it was not surprising that languages that came after C used the same or similar constructs. C++ maintained identical constructs of C to maintain backward compatibility as well as to allow the already existing millions of C programmers easily assimilate the OO paradigm. Java, from Sun Micro Systems, followed suit. Java, a very powerful and popular language, also retained the flavor of C.

Many interpreted and dynamic languages like Perl, Python, and Ruby all have C look-alike constructs,

Even in the languages of the Word Wide Web, C familiarity is extremely useful. JavaScript, PHP look familiar to one who is proficient in C.

The only other language which is entirely different from C from the bottom up, in my opinion is Lisp. Lisp is older than C and requires an entirely different way of thinking. There are possibly others too.

C balances economy of syntax, style and structure in programming exquisitely. It does have a few shortcomings, as its detractors would like to say. For e.g. C in the hands of a novice can spell disaster. It has also been accused of allowing programmers to create impregnable code. But in the hands of an experienced programmer it is possible to create really, robust code. UNIX and its variants are considered to be more resilient than OS’es to hackers.

C is really the soul of programming!

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The promise of predictive analytics

Published in Telecom Asia – Feb 20, 2012 – The promise of predictive analytics

Published in Telecoms Europe – Feb 20, 2012 – Predictive analytics gold rush due

We are headed towards a more connected, more instrumented and more data driven world. This fact is underscored once again in Cisco’s latest Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016.The statistics from this report is truly mind boggling

By 2016 130 exabytes (130 * 2 ^ 60) will rip through the internet. The number of mobile devices will exceed the human population this year, 2012. By 2016 the number of connected devices will touch almost 10 billion.

The devices that are connected to the net range from mobiles, laptops, tablets, sensors and the millions of devices based on the “internet of things”. All these devices will constantly spew data on the internet and business and strategic decisions will be made by determining patterns, trends and outliers among mountains of data.

Predictive analytics will be a key discipline in our future and experts will be much sought after. Predictive analytics uses statistical methods to mine information and patterns in structured, unstructured and streams of data. The data can be anything from click streams, browsing patterns, tweets, sensor data etc. The data can be static or it could be dynamic. Predictive analytics will have to identify trends from data streams from mobile call records, retail store purchasing patterns etc.

Predictive analytics will be applied across many domains from banking, insurance, retail, telecom, energy. In fact predictive analytics will be the new language of the future akin to what C was a couple of decades ago. C language was used in all sorts of applications spanning the whole gamut from finance to telecom.

In this context it is worthwhile to mention The R Language. R language is used for statistical programming and graphics. The Wikipedia defines R Language as “R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others”.

Predictive analytics is already being used in traffic management in identifying and preventing traffic gridlocks. Applications have also been identified for energy grids, for water management, besides determining user sentiment by mining data from social networks etc.

One very ambitious undertaking is “the Data-Scope Project” that believes that the universe is made of information and there is a need for a “new eye” to look at this data. The Data-Scope project is described as “a new scientific instrument, capable of ‘observing’ immense volumes of data from various scientific domains such as astronomy, fluid mechanics, and bioinformatics. The system will have over 6PB of storage, about 500GBytes per sec aggregate sequential IO, about 20M IOPS, and about 130TFlops. The Data-Scope is not a traditional multi-user computing cluster, but a new kind of instrument, that enables people to do science with datasets ranging between 100TB and 1000TB. The Data-scope project is based on the premise that new discoveries will come from analysis of large amounts of data. Analytics is all about analyzing large datasets and predictive analytics takes it one step further in being able to make intelligent predictions based on available data.

Predictive analytics does open up a whole new universe of possibilities and the applications are endless. Predictive analytics will be the key tool that will be used in our data intensive future.

Afterthought

I started to wonder whether predictive analytics could be used for some of the problems confronting the world today. Here are a few problems where analytics could be employed

– Can predictive analytics be used to analyze outbreaks of malaria, cholera or AID and help in preventing their outbreaks in other places?

– Can analytics analyze economic trends and predict a upward/downward trend ahead of time.

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The Future of Programming Languages

How will the computing landscape evolve as we move towards the next millennium? Clearly the computer architecture will evolve towards a more parallel architecture with multiple CPUs each handling a part of the problem in parallel. However, programming parallel architectures is no simple task and will challenge the greatest minds.

In the future where the problems and the architectures will be extremely complex, the programming language will itself evolve towards simplicity. The programming language will be based on natural language that we use to define problems. Behinds the scenes of the natural language interface will be complex algorithms of Artificial Intelligence which will perform the difficult task of specifying the definition of problem into a high level programming language like C++, Java etc.

The Artificial Intelligence interface will handle the task of creating variables, forming loops and actually defining classes and handling errors. The code generated by the machine will have far less syntactical errors than those created by human beings. However while a large set of problems can be solved by the AI interface there will be a certain class of problems which will still require human intervention and the need to work in the languages of today.

One of the drivers for this natural language of programming of the future, besides the complexity of the computer architecture is the need to bring a larger section of domain experts to solve the problems in their fields without the need to learn all the complex syntax, and semantics of the current programming languages.

This will allow everybody from astrophysicists, geneticists, historians and statisticians to be able to utilize the computer to solve the difficult problems in their domain.

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