Before machine learning was acceptable globally, multinational tech companies like Google, Facebook and Amazon used machine learning secretly. Google with the help of machine learning took care of ad placement whereas Facebook with the help of machine learning showed post feeds according to their convenience. Amazon showed recommendations in the e-commerce website which impacted the clicks of the user. For example, if the user purchased a shoe recently then Amazon used this information to show recommendations for other shoes of the same shape and structure to entice the user for further exploration.
Though machine learning has undergone modifications from very recently, but is now also the centre of an important topic for debate - Is machine learning the end of privacy for the human race? What else can machine learning do other than driving cars automatically and communication? Is machine learning harmful to mankind? These are some questions that researchers and IT professionals debate when the topic of machine learning training comes up. Since machine learning training happens to be the future of evolution there are certain misconceptions that surround it. This article will provide a quick view of several misconceptions that have taken a hold over the time.
Machine learning only means generating compact data
Table of Contents
- 1 Machine learning only means generating compact data
- 2 Algorithms in machine learning are there just to discover the relationship between events
- 3 Machine learning is useless when it comes to relationships
- 4 Machine learning is not helpful when it comes to unseen events
- 5 More data in machine learning leads to hallucinating patterns
- 6 There is no space for pre-existing knowledge in machine learning
- 7 Complex models are not accurate
- 8 Face value determines the patterns
- 9 Machine learning will be as same as human intelligence
- 10 Models are incompressible
Most of the people have a common misconception that machine learning only means summarising data. What they fail to realize is that the main function of machine learning is to predict the future. For example, if a user has watched some movies in the past then with the help of machine learning the system would able to tell the user what are the types of movies that the user wants to watch next.
Algorithms in machine learning are there just to discover the relationship between events
The media has presented just one side of AI to the minds of the people. What people watch is what they believe in. The media has played an important role in making people believe machine learning algorithms is only used for discovering relationship but in reality, they are used to discover knowledge.
Machine learning is useless when it comes to relationships
Most of the people believe that machine learning is only helpful in identifying correlations but when it comes to a causal relationship, it serves as a complete failure. This view is completely faulty as the machine can look at the entire data and derive relationship from the past data to the current one.
Machine learning is not helpful when it comes to unseen events
Most of the people think that machine learning is unable to predict unseen events which are commonly known as “Black Swan” events. In reality, machine learning is designed to predict events with high accuracy.
More data in machine learning leads to hallucinating patterns
For example, consider an example where NASA checks their records and accidentally matches an innocent to their terrorist identification rule. These “hallucinating patterns” are made because more mining is done with people sharing same entities and attributes. In reality, machine learning keeps hallucinating at a very low rate.
There is no space for pre-existing knowledge in machine learning
Many IT professionals feel that machine learning has a blank state meaning that it only derives knowledge from the algorithms. According to these professionals, real reasoning power can only be derived from past experimentation and reasoning. In reality, all algorithms in machine learning do not have blank state and some user data is required to modify the systems knowledge.
Complex models are not accurate
This means that many people think that simple infrastructure of machine learning tends to be more accurate. This view is completely untrue because a simple infrastructure can come with a complex data and the complex model can come with the simplest of data which is very easy to understand.
Face value determines the patterns
Consider a user making the search for a mole in their skin, then there can be results saying that a tumor which can lead to cancer. This view is completely wrong as the slight change in data could modify the patterns, for example, if the user now searches for mole with a red patch which would only tell that it is just a simple mole and there is no reason to worry about.
Machine learning will be as same as human intelligence
Yes, machine learning can modify from day to day basis but it can never arise in level with human intelligence because humans are the ones who have designed machine language. Scientist and the IT professionals who deal with them on regular basis know their Importance.
Models are incompressible
This means that some of the models which have machine learning implemented in them may not have a trustworthy recommendation and some may have a trustworthy recommendation. This misconception can be better be understood by people who have studied machine learning in India like Balaram Ravindran and Bidyut Baran Chaudhuri. Machine learning in India is not a popular concept but one of the major cities that famous for it is Bengaluru.