Human Remains The Backbone of Machine Learning
We are living in an exciting and innovative time with futuristic technology literally at our fingertips. It’s no secret that recently there has been momentous hype surrounding machine learning (ML) as it has evolved from a sci-fi pipe dream to reality. But, no matter how much a business integrates emerging technology into their systems, the human element will remain integral.
First, let us understand what is machine learning. Machine learning is about understanding data and statistic. It’s a technical process where computer algorithms find patterns in data, then predict problem outcomes and solutions. Given the unique attribute of machine learning, it works best with big data due the volume and complexity of the data themselves. For better understanding, this simply means that, the bigger or the more data that a company or organization has, the more pattern that ML can determine which leads to higher accuracy of results generated later on. ML aspires to mimic the human brain - learning by observing. For example, the ability to determine an incoming email whether it is spam email or not by running some verifications such as words in the subject line, links included in the message, or pattern identified in a list of recipients.
Where Do Humans Play A Part?
Despite the benefits that ML has to offer, it will not reach its potential without the human element. Why? Consider a system that recommends certain drug treatments based on medical records. In order for the system to produce a certain diagnosis, it uses the training set data where the data are preset based on similar medical cases by physicians. Hence, if the system encounters a patient who has symptoms that do not match the data in the system, a wrong diagnosis might be produced. Therefore, this is where the human elements come in where a physician will attend the patient.
Likewise, think about the prevailing autonomous vehicles programmed to sense its surroundings and navigate without human input. What if it encounters a situation or object, say a huge explosion or a plunging hazard, to which it can not classify with certainty? The driver might then have to resume control of the wheel. Inevitably, decisions produced by machine would still have to rely on lots of data input, and these inputs come from our real life happenings.
It is an undeniable fact that the existence of ML has aided mankind in many ways and most importantly, it has increased the efficiency of producing desired results in various fields. But, we should not fully depend on ML alone because despite how advanced and convenient ML seems to be in today’s world, it still has its loophole. The concept of machine learning is not to replace human, but rather capitalize on the combination of algorithms, machines learning, and data science to better inform decision-making abilities.