What is true and what is not? Myths and facts about AI

Artificial intelligence is a hot technology right now. Everyone is talking about it and there is a lot of speculation and discussion about it. But what is true and what is not? In this blog post, we look at some of the myths and facts about AI that are widely discussed in the media.

What is AI?  

Let's start by looking at what artificial intelligence really is. In a nutshell, it is an automated system that performs tasks that previously required human intelligence.

AI systems work on the basis of algorithms (sequences of instructions) written in software. They recognise patterns and then make their own decisions. And just like humans, they get better with practice.

Some forms of the technology are, among others:

  • Software AI: virtual assistants, image analysis software, search engines, and systems for speech and face recognition.  
  • Physical AI: robots, self-driving vehicles, drones, and devices with built-in WiFi, such as a fridge or vacuum cleaner.  

Myths and facts about artificial intelligence

As we said, there is a lot of speculation about AI at the moment. Much of it is true, but there are also urban myths going around. Therefore, we have put together some myths, along with the corresponding facts.

Data quality

If you want to start using AI, it is advisable to start with a limited and clearly defined test case. Such a test case provides insight into the potential of AI for your organisation. This test case should have an impact on operations, but be limited in scope. Next, it is important to analyse what data is needed to feed the technology, how it is obtained, and how clean it is. This involves cleaning only the data that is needed, so that the value of AI can be demonstrated and exploited without the need for huge data cleansing projects. 

The fact is that to get started with artificial intelligence, you need not just one test case, but several, so that the software can learn from them properly.  

Bakkerij Amstelveld, for example, uses artificial intelligence for bread quality control. To train the system, ten thousand photos of correct and ten thousand photos of incorrect bread rolls are needed. The AI software cannot decide whether a bread roll is incorrect on the basis of one picture of an incorrect bread roll. That is too little data for the software to draw conclusions from, which leads to errors.

An important fact about data quality in the context of AI is that the accuracy and effectiveness of AI models is highly dependent on the quality of the training data. If the training data is of poor quality, this can lead to biases and errors in the predictions and decisions made by an AI model. 

It is therefore crucial to ensure that training data is representative of the real world and free from misinformation and bias. Data quality issues such as missing data, duplicates, inconsistencies, and inaccuracies can have a significant impact on the performance of AI systems. Therefore, much attention is being paid to data cleaning, verification, and validation to ensure that AI input data is of high quality and produces reliable results.  

Data security  


Everyone knows how useful and fast artificial intelligence is, but it is not always safe to just hand over all your information to the software. With ChatGPT, for example, the information you give to the platform is used to help the program learn. This means that all the information you provide is available to the whole world.

The fact is that this is not always the case, because when you use ChatGPT's closed or secure environment, the information you provide remains there. The platform does not use this information for additional learning.


An important fact about data security in the context of this technology is that the increasing use of AI systems increases the need for robust cybersecurity measures. AI systems process large amounts of data and may contain sensitive information, making them vulnerable to various forms of cyber attacks, such as data theft through USB drive hacking and phishing attempts. It is essential to implement security protocols to protect AI systems and ensure that sensitive data remains secure. This can include encryption, authentication, and continuous monitoring to ensure data security.  

The technology is still too unfamiliar for a lot of people

There are many myths about AI technology, but there is also much that is true. For many people, the technology is still too new to know what is true and what is not. AI is growing fast and will make great strides in the coming years. It is therefore important to learn about the technology before you start using it, for example by discussing it with an AI expert. First, learn how to use it properly and make sure all your information is stored securely so you don't fall victim to data theft.

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