Machine Learning, Artificial Intelligence, Big Data, Data Annotation – what is the difference?

In the past couple of decades, artificial intelligence, machine learning, and big data have become the center of the technological world. From simple computer programs to live robots, applications of these phenomena are widespread. However, another thing about these terms that is widespread is the misinformation regarding their real meaning. People, tech-savvy or not, have been using these terms along with data annotation interchangeably. This creates an idea that these terms all mean the same thing when in reality, they are all as unique as can be.

Let’s take a look at how all these concepts differ from one another.

What is Artificial Intelligence? 

Artificial intelligence refers to a machine’s ability to use logic, analyze its surroundings, interpret audio and visual inputs and finally make a decision based on these attributes. According to IBM, artificial intelligence (AI) “mimics the problem-solving and decision-making capabilities of the human mind.”

AI is widely used in numerous applications around the world. The closest example is the Google Assistant or Apple’s Siri that are available on our phones.

What is Machine Learning? 

Machine learning is often confused with AI but they are both very different. Machine learning is a part of artificial intelligence and it deals with a machine’s ability to “learn” things without being programmed to do them.

Machine learning is a method used to help computers identify different patterns in big data and apply them to make several decisions. One of the most common examples of machine learning is search engines that use your search history and other data to show you relevant ads across your web browser.

What is Big Data? 

Big Data is exactly what it sounds like. It is a large amount of data that is used in machine learning to help computers and machines identify patterns and make suggestions based on them. Big Data is only useful when it has the three Vs: Volume, Velocity, and Variety.

The data has to have a large volume to enable computers to identify patterns easily. However, only the volume isn’t enough. The velocity of data is also very important. It simply means the rate at which data is being received. Greater velocity allows machines to learn faster. Without variety, even the largest volumes of data will be useless. Variety helps machines decipher a wide range of patterns.

What is Data Annotation? 

Data annotation is simply the labeling of data to make it easier for machines to categorize and use them in different AI techniques. With accurate data annotation, AI performance can be improved greatly. AI data annotation is very important for supervised machine learning as it allows machines to interpret inputs faster.

While all four terms are related to one another, each of them represents a completely different idea. We can say that artificial intelligence is a superset that contains machine learning. Similarly, machine learning uses big data with the help of data annotation to improve AI techniques. Using these terms interchangeably creates confusion amongst people who might be interested in the technology.

Jenna Jose
Jenna Jose
Jenna Jose is an experienced gaming editor with a journalism degree and a passion for RPGs and strategy games. She's your go-to source for the latest gaming news and comprehensive game lists. Off the clock, she's all about retro games and board game nights.


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