Artificial intelligence in a nutshell: what is it and how do companies use it?

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In today's digital age, artificial intelligence (AI) is no longer a futuristic concept, but a reality that affects our lives in many ways. At, we are on the front lines of this technological evolution, integrating AI into digital products to take businesses to the next level. In this article we delve deeper into the fascinating world of artificial intelligence, its different forms and the ways in which AI learns and evolves.

What is Artificial Intelligence?

Artificial intelligence is a broad field within computer science that focuses on creating machines capable of performing tasks that traditionally require human intelligence. From simulating human reasoning to understanding natural language, AI has revolutionized how we experience and use technology. Various forms of artificial intelligence

Weak AI (Narrow AI)

The most common form of AI, these machines are designed and trained for a specific task. Companies use this form of AI for chatbots, recommendation systems, and other task-specific solutions. Siri and Alexa are good examples, answering our questions and completing tasks, but without a broader understanding of the world.

General AI (AGI)

The holy grail of AI research, AGI would produce machines with the ability to perform any intellectual work a human can do. The road to AGI is still long and full of unknowns, but the promises are limitless.

Super intelligent AI

A step beyond AGI, super intelligent AI would theoretically surpass human intelligence in virtually every conceivable domain, a concept that is both exciting and terrifying.

How does artificial intelligence learn?

AI's ability to learn and grow is central to its potential. The four broad ways an AI model can learn are as follows:

.Supervised Learning

With a labeled dataset as a basis, the AI ​​model learns to make relationships between input and output, similar to a student learning from a teacher. This form of learning is ideal for companies with well-structured data. This allows models to make accurate predictions and then make decisions independently.

. Unsupervised Learning

Without pre-labeled data, the AI ​​model looks for hidden patterns and structures in the raw data, like an explorer in an unknown world. Suitable for unstructured or semi-structured data, it helps companies discover hidden patterns and trends.

. Semi-supervised Learning

In a mix of supervised and unsupervised learning, semi-supervised learning uses some labeled data to guide the learning of much more unlabeled data, giving the AI ​​model a little more guidance about what needs to be learned. This middle ground helps companies with less labeled data gain valuable insights about the entire data set.

. Reinforcement Learning

Through a system of rewards and punishments, AI that uses reinforcement learning navigates complex, unpredictable environments, just like a child learning through trial and error. Particularly useful in complex, dynamic environments where AI must learn from trial-and-error, such as in automated trading or robotics.

Explanation of common concepts in the field of artificial intelligence

In addition to the above levels of AI and the different forms of learning, there are a number of common concepts in the field of artificial intelligence that you want to know in order to be able to discuss them.


At its core, an algorithm is a set of instructions that a machine follows to perform a certain task. In the context of AI, algorithms help machines learn from data, recognize patterns and make decisions. They are the backbone of any AI application and the efficiency of an algorithm can contribute significantly to the performance of an AI system.

Machine Learning (ML)

At the heart of many AI systems, ML allows machines to learn from data, making them better as they gain experience. ML automation accelerates decision-making processes and provides a competitive advantage through the ability to quickly generate insights from large data sets.

Deep Learning (DL)

An advanced form of ML, deep learning uses neural networks to gain deeper insights from large amounts of data. By identifying complex patterns in data, deep learning can help solve challenging problems, from speech recognition to image recognition.

Neural Networks

Neural networks are computational models designed to learn from data and recognize patterns, just as our brain learns from experience. They consist of layers of nodes, similar to neurons, that work together to identify and learn complex relationships in data. These structures imitate the functioning of the human brain, and form the basis for many deep learning systems.


Deepfake technology uses AI to create realistic fake videos or audio recordings. Using deep learning algorithms, deepfakes can accurately mimic the way people talk and move, leading to impressive, but sometimes disturbing, results. In business, deepfakes can be used for realistic simulations or training, but they also pose ethical and safety challenges.

Generative AI

Generative AI is about creating new content. Whether it concerns images, text or music, generative AI can create new, unique creations. This has enormous potential for the design, content creation, and entertainment industries, but also brings challenges in terms of copyright and authenticity.

Generative Pre-trained Transformer (GPT)

GPT is a type of machine learning model for natural language processing. With its latest iteration, GPT-4, it has reached a new level of proficiency in understanding and generating human language. GPT-4 can be used to answer questions, write text, and even perform simple tasks within certain software programs, opening the door to powerful, language-driven interfaces and services.

Large Language Model (LLM)

Language models such as GPT and others are core components of NLP (Natural Language Processing). They are trained to understand and generate human language, allowing machines to better communicate with humans. LLMs are critical for creating intelligent chatbots, translation services, and other language-focused AI applications.

Natural Language Processing (NLP)

NLP is the field of AI that focuses on the interaction between computers and human language. It enables machines to read, understand and generate text in a way that is valuable to humans. Through NLP, companies can improve customer service, gain insights from social media, and increase efficiency in document management and other task-oriented processes.

Conclusion on artificial intelligence

The world of artificial intelligence is wide and deep, with a wealth of concepts and technologies waiting to be explored. By understanding the fundamental forms and learning processes of AI, companies can be better prepared to take advantage of the many opportunities this technology presents. At, we are passionate about helping businesses unlock the power of AI, and look forward to working together to build an intelligent, efficient and engaging future.

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