Although it was coined way back in 1955, the perspective and definition of Artificial Intelligence (AI) has evolved significantly over the years. In the initial days, this was a concept with a vague understanding amongst the masses, which related to a system’s ability to process tasks and operations which otherwise needed human intelligence to be pulled off.
Over the years, there has been a paradigm shift in this sector as well, where the former understanding has grown deeper and is now perceived as a system’s ability to learn and evolve, to achieve a specific goal, through a series of rational actions. This has now given birth to multiple sub-domains that decide the maturity, decision-making capabilities, accuracy, and other aspects of the AI Engine.
Growth
The growth of AI has been witnessed through the countless breakthroughs it has had in recent years. In the last decade itself we have seen the development of AI applications that were smart enough to beat humans at games; Go and Jeopardy, to name a few. You might have also noticed the term autonomous vehicles (read Tesla) gaining popularity recently. According to reports, the AI sector projects growth of over $190 billion by 2025. For all the right reasons, the number of start-ups operating in this sector has also increased 14x since 2000. With more VCs pumping in money on this sector, and the ever-rising demand for speed and efficiency at both the B2C and B2B markets, AI seems to be the only road ahead. There has been a reported growth of 4.5x since 2013 in the number of jobs that require skills in AI.
Components
Going through the statistics mentioned above, one might fathom how are lines of code turned into programs, which are at times, much more reasonable, efficient, and fast than their human counterparts. This brings us to the three main concepts in Artificial Intelligence:
- Machine learning: Every individual will have a specific usage pattern while interacting with the software. A feature that is useful to one might not be as useful to another. Machine learning helps the software to learn and adapt to a user’s behavior pattern without writing any extra lines of code. The auto-email filtering feature of G-mail or virtual personal assistants such as Siri and Google Assistant.
- Deep learning: This deals with the development of the algorithm which enables the software to learn and improve itself in more than one specific task. Whereas machine learning comes from mainly analyzing huge sets of data, deep learning is more concerned with structuring the algorithms in layers which enables the software to make intelligent decisions on its own.
- Neural networks: As the name might suggest, this is related to the functioning of the neurons in the brain. There are thousands and in some cases millions of connected neurons (nodes), which try to mimic the functioning of the brain, which allows the program to think and learn like a human.
Conclusion
To sum it up, AI is here to stay and there is a fair share of evidence that projects its steep rise in the days to come. However, as every rose has its thorn, there are controversies about the development of AI as well. There have been instances where companies have adopted AI-based automation software and countless employees have been laid off as a result. No doubt it’s a boon for the corporates, but AI does pose a threat to a good chunk of employees out there.
