It’s pretty certain that you must have come across multiple articles/blogs on artificial intelligence. If not that, then surely the various fiction novels/scripts. All describing the meaning of artificial intelligence, its jargons, its pros, and cons. It’s vastness of possibilities. It’s plausibility of being able to ever evolve. But let’s just take a moment here, and rather going forward take a step backward and understand how and why exactly did artificial intelligence come into existence.
The idea of machines being able to think, analyze, predict and understand like humans (even better and faster) has been a fantasy for centuries. So it would rather be tough to say where and how exactly did the concept of artificial intelligence came into being, but one of the first algorithms for machine learning (a sub-set of Artificial Intelligence) was designed in 1970, but its true potential was unleashed in a famous paper in 1986 by David Rumelhart, Geoffrey Hinton and Ronald Williams (and is still vastly used as a basic model for machine learning) and the first ever breakthrough idea to be sprouted was in 1950. But what then was only a sci-fi story is now gradually coming into existence. The peripheral of the same ‘not-so-human’ idea has been scratched.
So, why something that was designed in 1980 is being used and developed now? The times lacked the resources needed for training a machine i.e the availability of humongous amount of data and fast computational power. Yes, training. Like a child is trained the basics of languages and numbers. That is how a machine is made to learn, how it is made smarter. And what, is this intelligent heap of 0’s and 1’s capable of? The accuracy of search engines and the all-rounder Siri in your phones are just the trailer of a ‘yet-to-be-climaxed’ movie.
AI today stands at a point where a computer can dream like a human, where a machine can depict the explanation of a picture like a human, wherein the computers can not only read and understand but also write, create content, poems/stories, and quotations. From where it began, it has phased out to many branches (each of them being milestones in their own) like machine learning, deep learning, natural language processing, natural language generation.
Thus said, there is yet too much to learn and study in this field. The growth, research, and advancements in AI are being made at an exponential rate. As fascinating as it sounds on the outside, it is even more enthralling on the inside. An entire world, of exorbitant potential, untapped!