5 Common Myths about Machine Learning

Machine learning has been the main media coverage lately, and several articles and emotional stories have been published every second. Machine learning is proving to be the most useful, and there is no doubt that we have begun to penetrate commercial work models to make many notable advances such as language translation, speech recognition, recommendation systems, etc. . Indeed, artificial intelligence and machine learning beat our experts on certain complex problems. Ultimately, this advance is, in one way or another, the main motivator for being excited and occupied by reading and researching machine learning.

As we study machine learning and its progress, we are often tempted to believe that there are countless ways to discover machine learning to solve all of our problems and apply it to any situation. But the sad truth is that any organization has not yet fully exploited the BC due to misunderstandings that have arisen around it and that resolve from the first step. Break through the prevailing myths and misunderstandings about machine learning to create more amazing things.

Myth#1 Machine learning will soon pave the way for superhuman intelligence

Well, from the daily headlines about advances in artificial intelligence, we often get the impression that computers will soon take control. Many popular AI films talk about how machines develop their ability to speak, see, and argue, and ultimately leave people in the dust. It is true that we have come a long way in digital advances, and the main reason for recent success is the rise of AI, machine learning, and deep learning, but we still have a long way to go. The machines are super-fast and can do tedious tasks at lightning speed, but they lack one of the most important things, common sense, and no one knows how to teach them.

Myth#2 Both Machine Learning and Data Mining are same

Thousands of articles are published every day about the difference between data mining and machine learning, but it is often confused that it is the same. Data mining is similar to the work of a miner who wins and wins coal, but doesn't know how to make a beautiful diamond ring out of it. Data mining involves digging data to identify unknown properties or patterns. Machine learning is later used to use paving data with specific properties or models to feed machines and obtain useful information. Although data mining and machine learning operate on similar principles, there is a thin line between the two that illustrates the differences.

Read More: Top 12 ways in which machine learning can help your business.

Myth#3 All Machines will start Learning like Human Beings

We see that these lively trends still speak of AI algorithms learning like humans, but the fact is they don't come close to chimpanzee learning. Compare the machine learning process to that of a child. A child shows curiosity and intuitively creates their learning strategy by watching other people walk around and setting their goals, while a machine needs advice and support at every learning stage. In addition, the machine does not have a sense organ to carry out an effective learning process. Therefore, at every step, it must be instructed on how to synthesize and integrate the inputs of several channels such as sound. View and text to understand things. Can you now see how difficult this job is?

Myth#4 Unbiased Result Produced by Machine Learning

As much as we wish, this is not the case. In order to achieve unbiased results, the data fed in internally must be undamaged or not one-sided. If you supply the system with one-sided source data, the results obtained are distorted. We cannot blame the machines for this defect, but it is a limitation for all machine learning experts working on the solution. You shouldn't blindly rely on the analysis, but also make sure that the results obtained are unbiased.

Myth#5 Machine learning Works Just Great Everywhere

Are you ready to spend hundreds and thousands of dollars on personalization if you have financial difficulties managing your business? If cheap human labor is available to do the same job for less than half the money, the machine learning solution won't win the situation here. While it is possible to apply machine learning to small businesses with fewer records, given the cost, only those who use big data services will make headway. It is therefore obvious that machine learning has its limits and we cannot blindly say that it can be used anywhere. However, some initiatives are being taken to overcome this dependency on large amounts of data and enormous costs. We can probably expect more startups to join machine learning in the future.

Conclusion:

I hope you have understood these myths about the machine learning.

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