In today’s article, we will focus on various Artificial intelligence topics and terms that are widely used in the field of AI.
As a modern marketer, it is useful to get an insight into different artificial intelligence topics and concepts of AI and understand more in-depth each of them. Therefore, we have gathered into this article relevant artificial intelligence topics that are good to know as a marketer.

These following terms are explained in detail:

1. Big Data

2. Machine Learning

3. Algorithms

4. Chatbots

5. Cognitive Computing

6. Computer Vision

7. Deep Learning

Big Data

Big data is a topic that has been discussed for a while and is gaining increasing importance. With the development of social media and the Internet of Things, large amounts of data are being collected. Whoever can access this data can use it as a market advantage. This is made easier with artificial intelligence.

Imagine how much data self-driving cars have to gather in order to master the Herculean task of driving autonomously. They have to register and process data regarding every element of their surroundings.

Herculean task of driving autonomously

The data volume explodes

In the near future, the majority of household tools will be connected to the internet, just like most electric devices are in the office. That is in addition to the already vast automated connections between computer systems. Every mobile device delivers vast quantities of data, allowing insight into the behaviour of consumers in a way that was unimaginable in the past.

household electronics

Therefore, large amounts of data are available. The question is how this can be applied correctly and processed systematically. The speed of data streams is growing exponentially, and the orientation of businesses towards customers is adapting quickly.

Machine Learning

Another worth to mention artificial intelligence topic is Machine learning. It is an artificial system that learns patterns according to regularity and from examples and that can generalize these patterns after a learning phase.

Machine learning is based on the generation of knowledge through experience. Traditional programs use data to process these. The output of traditional programs is what we as humans can understand and process further. The output is always the same; the rules of the program don’t change on a fundamental level. In contrast to this, machine learning uses data to constantly adapt to the program, and the output is always changing.

Big data and algorithms

Big quantities of data are not beneficial on their own. Only with algorithms can value be generated out of the data. Dynamic algorithms are at the center of the futuristic business model. They adapt to the requirements independently.

Training data as the foundation for AI

The synergy effect between artificial intelligence and big data is based on the idea that in order to model and train artificial intelligence, large quantities of data are necessary. When training neural networks with large datasets, the results improve substantially.
The more data, the better the results. With 10 million labelled images, for example, an algorithm for image recognition can soon perform as well as a human or achieve even better results.

Understanding algorithms

It is predicted that the amount of data available doubles about every two years. From the beginning of the internet era on, data has been the real gold of digitalization. Now it is about the challenge to analyse big data correctly and extract new values from it. Those who know how to use data wisely generate new opportunities and receive market advantages.

data volume doubles every year

Vision: The logarithmic business

A company that runs automatically with the usage of intelligent algorithms because they are able to automatically deduct actions correctly is often called a logarithmic business. For these businesses, value creation is increased comparable to how one can imagine with an autonomous car.

Personalized addressing with algorithms

Algorithmic addressing is often used in the media as an example of artificial intelligence topics. Examples are the newsfeed on Facebook and personalized Google searches which have been in use since 2009. Newer examples are Netflix and Spotify which use algorithms to personalize recommendations for customers. Naturally, algorithmic addressing is also used to make purchase recommendations (Amazon) and even recommendations for potential partners.


A chatbot is a virtual agent which can make phone calls or chat with humans based on artificial intelligence. Chatbots use machine learning to replicate the human language. They are used to deliver specific content or for automated services such as support or sales consulting.

Chatbot is your virtual agent

Cognitive computing

This term encompasses technologies that combine artificial intelligence and language detection. Applications of cognitive computing use machine-learning algorithms to simulate natural speech and try to function like a human.

Computer vision

This part of artificial intelligence works on the question of how a computer can build a visual system in order to see digital images and interpret them. An example of its usage is adding a short description to a picture which can then be used in a different system.

Deep learning

Deep learning is a specialized branch of machine learning which is used for many different applications. The main difference is in the way an AI model is set up in deep learning.
Deep learning models concentrate on the training of neural networks in different layers. Each layer of a network can recognize different patterns and deliver different results. Just like most other machine learning networks, deep networks are able to classify and sort large datasets and detect anomalies in data patterns. They are said to be one of the most complex models in AI.