From the initial days of number-crunching by languages of FORTRAN, to the procedural methodology of Pascal or C, and later, the object-oriented paradigm of C++ and Java, we have come a long way. In this age of information overload, technologies that can just solve problems through steps and procedures are no longer adequate.
We need technology to detect complex patterns, trends, understand nuances in human language and to automatically solve problems. In this new era, the following three technologies are furthering the frontiers of computing technology:
By 2016, 130 exabytes will rip through the Internet. The number of mobile devices will exceed the human population this year, and by 2016, the number of connected devices will touch 10 billion.
Devices connected to the Net will range from cellphones, laptops, tablets, sensors and the millions of devices based on the ‘Internet of things’. A hot and happening trend in computing is the ability to make business and strategic decisions by determining patterns, trends and outliers among mountains of data. Predictive analytics will be a key discipline in our future and its experts will be much sought after.
Predictive analytics uses statistical methods to mine intelligence, information and patterns in structured, unstructured and streams of data. Predictive analytics will be applied across many domains, including banking, insurance, retail, telecom and energy.
There are also applications for energy grids and water management, besides those that determine user sentiment by mining data from social networks.
The most famous technological product in the domain of cognitive computing is IBM’s supercomputer, Watson, an artificial intelligence computer system capable of answering questions posed in natural language.
Watson is best known for successfully trouncing a national champion in the popular U.S. TV quiz competition, Jeopardy. What made this victory more astonishing was that the supercomputer was able to decipher the nuances of natural language and pick the correct answer.
Following the success at Jeopardy, Watson has now been employed by a leading medical insurance firm in the U.S. to diagnose medical illnesses and recommend treatment options for patients. Watson will be able to analyse 1 million books, or roughly 200 million pages of information. Another well-known example of cognitive computing is Siri, the voice recognition app on the iPhone. The earlier avatar of cognitive computing was expert systems based on artificial intelligence. These expert systems were inference engines that were based on knowledge rules.
The most famous among the expert systems were ‘Dendral’ and ‘Mycin’.
This is another computing trend that is set to become prevalent in the networks of tomorrow. Autonomic computing refers to the self-managing characteristics of a network. Typically, it signifies the ability of a network to self-heal in the event of failures or faults.
Autonomic network can quickly localise and isolate faults in the network while keeping other parts of the network unaffected. Besides, these networks can quickly correct and ‘heal’ the faulty hardware without human intervention. Autonomic networks are typical in smart grids where a fault can be quickly isolated and the network healed without resulting in a major outage in the electrical grid.
These are truly exciting times in computing as we move towards true intelligence.
(The author is infrastructure architect at IBM, India)