Machine learning has been trending from the past few years and companies love the insights that machine learning offers to them. While it has become popular, a lot of people still are unaware of how it works and also the role of humans in the success or failure of machine learning. As one understands that machine learning is not about a machine developing capability to learn like humans but being programmed to use existing data with algorithms to figure out patterns that are useful.
This article aims to provide answers to questions that a common man with a basic knowledge of machine learning would have. It will bust the myths around machine learning or artificial intelligence as some like to call it. The article will also discuss the aspects that can make your machine learning initiative a hit or a flop.
Did You Know Machine Learning Relies on Human Input?
Machines are created by man and human involvement at one stage or another is important in their functioning. Machine learning is no different. It operates on data sets that are selected by humans. The data might be acquired through electronic means but the source and quality of data as an input affects the output. So, to say that machine learning can be independent would be wrong because the input is required for it to function effectively.
One important aspect to touch here is how our perception about machine learning is derived from popular entertainment mediums that almost make us believe that machines can outsmart humans. This can be a root cause of misconceptions that people have about machine learning. For correct knowledge on how machine learning can help and what are its limitation, read books and informative journals rather than watching fiction.
Did You Know That Machine Learning Is Not Infallible?
There is a common misconception that machine learning is not capable of mistakes because it is functions with algorithms and calculations. This is true in a way but with a condition. Machine learning systems use data to provide answers to whatever we are seeking. What if the data entered is not correct? In such a case, the output is not completely correct, which proves the vulnerability of the systems.
A simple way of understanding this is to think of a situation where you are using a calculator to find answer to a complex mathematical question. The calculator would in all cases (except if it is a fault one) would show correct answers as per the numbers that you have put. The only reason for incorrect answer would be that you have either input incorrect number or your calculation method is incorrect. ML operates on similar principle.
Did You That the Success of Your ML Initiative Depends on the Quality and Quantity of Your Data?
We have already discussed the importance of data accuracy in order to get the right benefits. Very often, organizations tend to use training data which has been generated by individuals who might have biases or prejudices. This data when fed into the system can cause your machine learning system to recognize patterns that are biased and thus result in irregular insights which can be a nightmare.
Additionally, machine learning is useful only when you have a considerable amount of data. After all, if it is just a few sets of paper, you would not want to invest in a sophisticated system that will cost you a fortune. This implies that the quantity of data is important in deciding whether you should opt for machine learning or not and the quality decides the kind of output you get.
While the machine is doing all the so called hard work of recognizing the patterns and helping us make informed decisions, the work that we are required to do is not less. The data source and the information in the data has to be verified and that in itself is a tedious task. Data cleansing might require a lot of time but it is in no way secondary task because the success depends on the quality of data that is available in bulk.
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