Past, Present and Future of Machine Learning

"Machine Learning" as a term has gained such traction in the technology world today that it is highly improbable that you would meet a person who hasn't heard this term, either in a positive or a negative connotation.

We have industry experts, CS professionals, hiring experts and practically every other person directly or indirectly related to the Computer Science domain, harping about how ML/AI is going to be the future of computing.

I am here to just explore how this came out to be, and also why I think although assuming that ML is going to be THE future of computing might be over exaggeration, it is going to play an important role in the future nonetheless.

Where did ML start?

It might be surprising to know for a majority of you that one of the corner stones of ML, the humble Naive Bayes classifier has been around for more than 2 centuries. The Bayes theory was first published in the second half of the 18th century.

One of the cutting edge of ML research and applications, the Neural Networks has been around since the 1950s! Some of you might even be surprised to learn that this was even before the von Neumann architecture was introduced! For those of you who might not know what von Neumann architecture is, it is the basic architecture on which ALL the computers till date were ever built.

How did ML/AI become such a rage in the latter half of this decade?

So why did it peak in the last 5 years that didn't happen in the past half a century? The answer lies in DATA!

The success of any ML model solely depends on the type, amount and quality of data fed to the model for training. The reason why it was abandoned was because of the data. There was never enough data to train even a simple naive Bayes model. With the advent of the World Wide Web in the 2000s, although there was a lot of data being generated, the quality was still too bad to be of any use for any modelling. With the advent of iPhone and personal computers and the penetration of these personal computing devices made sure that there was no dearth in the production of data. Hence, applications of models came to the fore.

With data being produced at an unprecedented rate which might even surpass the consumption rate, these ML and AI based applications become ever more relevant to our day to day lives.

How is ML affecting our society and employment?

Nowadays we are observing a deep uncertainty over the implications of a machine capable of making decisions based on incomplete data available and its potential to disrupt the human society as a whole, with some even suggesting that the Terminator-esque scenario where the human race is hunted by the machines will come to fruition in the near future. I personally believe that such predictions are completely unfounded for various reasons. The popular media has the ability to attach negative connotation to any subject that might be hard to comprehend. If you look at the world today, we can already see a vast majority of our lives being penetrated by ML or AI, without us noticing at all. That Google search you just did to find out how the weather would be today, or your Facebook or Instagram feed, were all powered by ML.

A couple of decades ago, blue collar factory workers were looked up to as they were considered skilled employees. Youth were drawn towards the glamourous appeal of urban and factory life compared to the agricultural livelihood, as can be seen from the mass migration of rural youth to urban centres which were primarily built around factories. But in the last decade, most of these blue collar jobs were replaced by automated machines, which were both reliable and efficient. Although there were large scale protests against the loss of jobs, the society did not complain when the prices of their day to day products went down and the quality increased. The job market corrected itself after the people who lost their jobs were able to find other means of livelihood made possible by the booming economy brought about by the reduction in production costs.

Similarly, the society is unsettled with the advent of self driving vehicles, personal assistants etc and its potential to cut human jobs and increase the chances of machines taking over the world. But if we look around us, ML has already taken over our lives and the market has always self corrected itself after a short term decrease in employment.

In the long term, the ability of a machine to take over mind numbing and routine tasks opens up other avenues for human kind. We can now explore the more creative and intellectually stimulating pursuits that we couldn't afford before. By assigning all the manual and dirty work to the machines, we are actually left free to advance mankind in a more creative venture. This is analogous to getting a maid at home. Whether we will utilize the new found freedom to advance mankind or to other self destructing avenues remain to be seen. But one thing is for certain, if the society does enter an apocalyptic phase, it is only us to be blamed and not the ML/AI.

Where is ML headed?

In the near future, software development in services is headed towards the same path as the blue collar factory jobs of yesteryears. Since most of the basic architectures and systems are already in place, majority of the software servicing roles have become repetitious. As history as shown us, these repetitious and monotonous jobs are going to be automated with new jobs being created for more intellectually challenging and creative ventures. This is not to say that the entire software development industry is going to be shut down. Some of the software development roles require a lot of human intellect and creativity, which can never be replaced by a machine.

How will ML/AI change in the future?

Right now, we are limited by the processing capabilities of the current computing machines. In the status quo, the processing machines are deterministic i.e given an input, the result can be determined. We are trying to use this machine in order to model a system that is highly probabilistic in nature. I think, in another decade or so, we would come up with an entirely new architecture of computing, that would be probabilistic, instead of the deterministic nature of current processors. This would signal a huge leap in the ML/AI space as a probabilistic machine can better represent a human who has never been a deterministic being.