Machine Learning techniques are relatively recent issues around the world. Although the theoretical basis has existed for decades, it was the dissemination of the use of technology and the evolution and reduction of the costs of electronic components that facilitated the growth and popularization of these tools.

Still, the computational processing capacity of the tasks helped immensely in the application of the theory. Today, with computers and devices becoming more and more powerful, we have the resources to solve complex problems on a daily basis.

However, as with any new development, there are also several points to clarify. Based on that, we’ve compiled a list of the top 12 Machine Learning myths you need to know today. Good reading!

1. Machine learning and Artificial Intelligence are the same thing

This is the first of the Machine Learning myths that needs to be eliminated. Despite being related areas, Machine Learning is not synonymous with Artificial Intelligence — and vice versa.

In practice, Machine Learning is a subset of Artificial Intelligence. It acts as an intelligence development stage that will later be applied to simulate aspects of human reasoning — which includes the ability to learn from new information and even make decisions.

Source: Inteligência Artificial vs Machine Learning vs Deep Learning

2. Machine Learning can work with any set of data

Machine Learning can even handle any set of data, but its effectiveness will be greatly compromised if this is done unprepared. In order to function, ML needs data that is adequate for its purpose.

Thus, after learning patterns are established and put into practice, it will be possible to achieve more reliable results that, in some way, are useful for your purpose.

3. Machine Learning only works with a huge set of data

Another myth about Machine Learning that needs to be cleared up is the premise that the tool only works with a lot of data. It is clear that the volume of data is very important for the proper functioning of several ML techniques, but it is far from being mandatory.

ML techniques can be used with a limited data set, as long as this information is prepared to be recognized and worked by machines. So, the main point must be the quality of the data and the adequate choice of techniques, as this is what will allow for more satisfactory results.

4. Machine Learning Will End Jobs

Another of the machine learning myths that often appear in news headlines around the planet concerns unemployment caused by the tool. Obviously, like any technological evolution, ML will eliminate some jobs, but it is far from leaving all human beings without a job.

Then, as expected, there will be a reorganization of some jobs.

The main change can be seen in mechanical routines, where the computer — due to its ability to read and process data — has the advantage of performing repetitive tasks over anyone. However, the interpretation and decision of the knowledge obtained will always be up to the human professional — and this will hardly change.

5. Machine Learning is totally impartial

This is also a big fallacy on the subject.

It is important to remember that Machine Learning is performed by humans and is subject to guidelines that people determine. As complex and highly efficient as they are, ML can work based on bias and thus produce unexpected and partial results.

A famous and relatively recent example was an experiment carried out by Microsoft: an artificial intelligence that learned racist concepts over the internet. Thus, we see that technology can be constructed or manipulated to act with partiality.

6. Machine Learning is smarter than people

The technology has incredible capabilities, but the concept of intelligence — when compared to the human brain — is far from being achieved. It is a fact that the brain is an extremely complex organ and, therefore, it is very difficult to be reproduced.

Therefore, this is one of the myths about Machine Learning that needs to be abolished, as the tool is capable of many things, but it will hardly reach the level of intelligence and autonomy of the human brain.

7. Machine Learning is a handy tool to use

Although quite widespread, Machine Learning is not a tool accessible to everyone. First, the number of people who can use it is low. Second, the computational resources that ML uses are often unavailable to most businesses.

So, in summary, Machine Learning is still a concept far removed from most businesses — even though it receives a lot of attention in the market and is already present in many companies.

8. Machine Learning Can Solve Any Business Problem

Definitely not. As we said before, Machine Learning’s processing power is great, but that doesn’t allow any business problem to be solved.

Most business questions, for example, require the intelligence and creativity of human brain analysis. It is the ability to connect dots and think outside the box that makes it impossible to solve problems with the exclusive use of ML algorithms.

9. Machine Learning can learn anything on its own

One more point that needs to be clarified, since Machine Learning is not a magical and/or miraculous concept. It is true that there are techniques that make the tool more autonomous, but in general, the ML needs to learn from inputs provided by humans.

That way, once that’s met, it can deliver the best results — within its limits, of course. Without this input, Machine Learning cannot perform its tasks with assertiveness.

10. Machine Learning can interpret data better than humans

As we can see, the list of myths about Machine Learning is extensive. In this sense, data interpretation is also not better for computers using the tool.

The reading capacity is infinitely greater, but the resulting quality of the analyzes is not always superior to the human brain. The computer only has the speed of analysis in its favor, being able to easily reveal relationships underlying the data, but the whole process before or after that must be stipulated by us humans.

11. Machine Learning is never wrong

Myth. Machine Learning, in many cases, produces erroneous results for a number of factors.

Whether due to poor quality data or mistaken programming, Machine Learning techniques will always respond to input from programmers and software engineers. So, obviously, they can produce results in the wrong way.

12. Machine Learning is a risk to humans

Lastly, the biggest myth about Machine Learning and Artificial Intelligence needs to be cleared up. Although we see robots dancing and doing improbable things, these tasks are actions programmed by humans.

That is, robots cannot put human lives at risk on their own. They need to be programmed to attack and somehow try to “dominate” the human race.

Therefore, as we could see throughout the content, Machine Learning is not a perfect and independent technology. It needs human intelligence to, at the very least, set the scene and analyze the results.

Despite this, it remains one of the most incredible and powerful concepts in the modern world, and it will certainly be very important for the evolution of humanity.

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