AI Components: What They Are, Examples, and Applications

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Rafael Timbó
Rafael Timbó
Chief Technology Officer

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Learn about the basic components of artificial intelligence, how they work, and about real-world applications.
Published on
December 6, 2023
Updated on
April 11, 2024

The term artificial intelligence (AI) has been thrown around quite a bit recently. But beyond a surface-level understanding, many people find themselves lost as to what AI is, how it works, its main components, and how these components work together to enable groundbreaking deep learning applications and technological developments.

This article will help you understand the various basic components of artificial intelligence, how they work, and in which products and services you will most likely find them. We’ll also help you find the best industry professionals to introduce AI to your business operations.

What Is AI?

At its core, AI is a branch of computer science that studies the development of complex computer programs capable of performing tasks that typically require human intelligence and input. Such tasks can range from understanding spoken language and recognizing patterns to creative problem-solving and learning from past exposure to information. Simply put, it’s a simulation of human intelligence processes, allowing machines to reason, self-correct, optimize, and acquire a level of perception of their environment.

Artificial intelligence has evolved into a broad subset of computer science, encompassing various applications. Modern applications of artificial intelligence have evolved past the creation of intelligent systems and reached a point of understanding intelligence and how to replicate it. 

How Does AI Work?

AI technologies combine large amounts of data with intelligent algorithms, allowing the software to identify patterns and learn from them. Depending on the algorithm in use, the AI model can adapt to new inputs, allowing it to learn and grow independently of its creator and based on the provided input.

Creating and training an AI begins with collecting relevant amounts of data, which could be in the form of text, images, video, or audio files. The AI software can then process and analyze the data to identify patterns and relationships between various points. After sufficient exposure and training, the AI system uses what it’s learned to make predictions and decisions and perform tasks independent of human intervention. Additionally, AI systems are able to learn and improve the more they interact with their environment and receive accurate feedback.

Beneath the surface, the AI algorithms employ several components crucial to its operation, such as Machine Learning. Other components include Natural Language Processing (NLP), computer vision, and expert systems, which also play a crucial role in allowing AI infrastructure to understand and interpret input without human assistance.

Machine Learning (ML)

Machine learning (ML) is considered one of AI's most crucial key components, as it allows it to learn from provided data without needing explicit programming. Instead, it utilizes statistical techniques to improve with time and experience. ML algorithms use complex computational methods to “learn” information and gain experience.

Types of Machine Learning

Machine Learning algorithms are categorized into three main types:

  • Supervised learning: The ML algorithm is trained using labeled data along with corresponding correct outputs.
  • Unsupervised learning: The data used in training the ML algorithm isn’t labeled, leaving it to freely identify patterns and relationships in the provided data.
  • Reinforcement learning: The ML algorithm is provided with an end goal, and then it’s allowed to behave and experiment freely until it finds the optimal way to achieve the desired result.

Applications of Machine Learning

Machine Learning has a wide range of applications like predictive analytics, image recognition, and even speech recognition. That’s especially true when combined with other AI components, such as computer vision and NLP.

Examples of Machine Learning

Machine Learning is used in various industries and sectors. Some examples include:

  • Google's search algorithms: Google uses ML algorithms to improve search result accuracy and relevance to users.
  • Recommendation engine: Sites like YouTube and Netflix use ML to analyze viewer behavior and suggest content they might like.
  • Self-driving cars: Machine learning is used to predict and respond to different scenarios on the road.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is the part of AI that focuses on understanding the interactions between humans and machines. The goal of NLP is to read, decipher, and understand human language and be able to produce meaningful responses.

Types of NLP

NLP can be classified into two main types:

  • Syntax: The arrangement and categorization of words and sentences using the grammatical rules of the language to produce and comprehend meaningful sentences.
  • Semantics: This involves the meaning that is communicated by a text. It includes processes like named entity recognition, word sense disambiguation, and natural language generation.

Applications of NLP

NLP has various applications, including text and audio translation from one language to another, sentiment analysis of the emotion and meaning behind sentences, and interactive chatbots capable of understanding and participating in a human conversation.

Examples of NLP

Some examples of NLP being used in various sectors include:

  • Siri: Apple's virtual assistant uses NLP to understand and respond to voice and text user commands.
  • Google Translate: NLP automatically and instantaneously translates text from one language to another.
  • Gmail Smart Reply: This feature uses NLP to suggest quick email responses.

Computer Vision

As the name suggests, computer vision is dedicated to analyzing and comprehending visual media, whether images or videos. It’s the component that enables AI algorithms to accurately and reliably identify objects that the machine “sees” and react accordingly.

Types of Computer Vision

Computer Vision can be classified into several types:

  • Image recognition: This is the process of accurately recognizing an object or feature in a digital image or video.
  • Object detection: This involves identifying certain objects' presence location, and type in a digital image or video.
  • Image segmentation: This process involves dividing an image into multiple parts to simplify image analysis.

Applications of Computer Vision

Computer Vision has various applications, including facial recognition, autonomous and semi-autonomous driving, and medical imaging. The AI applications using this model can be trained to accurately detect a specific type of object, whether it’s a human face, the movement of a vehicle, or the presence of an illness in a medical scan.

Examples of Computer Vision

Computer Vision is used in different sectors. Examples include the following:

  • Facebook's automatic photo tagging: Computer Vision is used to identify friends in your photos.
  • Tesla's Autopilot system: It is also used to identify and respond to objects on the road.
  • Google Lens: Computer Vision identifies objects and provides relevant information.


Rather than a component, robotics is typically considered a branch of AI that focuses on robotic machinery's design, construction, and operation. Robots are typically used to automate not only highly difficult and dangerous tasks but also mundane and high-accuracy tasks.

Types of Robotics

Robotics can be classified into several types:

  • Industrial robots: These are primarily made and used in manufacturing and production.
  • Service robots: Often used in the service industry, they’re much safer for humans to be around.
  • Medical robots: These are used in healthcare settings to perform surgeries or assist in patient care.

Applications of Robotics

Robotics has countless applications, from manufacturing and healthcare to exploring remote regions and even search-and-rescue operations using nearly autonomous vehicles. 

Examples of Robotics

Robotics is used in various sectors. Examples of robotics include:

  • Amazon's warehouse robots: These robots are used to move items around Amazon's warehouses to fulfill deliveries.
  • Da Vinci Surgical System: This robot performs minimally invasive surgeries that are impossible with humans alone.
  • NASA's Mars rovers: These robots are used to explore the surface of Mars.

Expert Systems

Expert Systems are also a sub-category of AI in which software has access to a wealth of information that it uses for decision-making on a human level. They’re typically designed to solve complex problems by reasoning through their vast body of knowledge.

Types of Expert Systems

Expert Systems can be classified into three types:

  • Rule-based systems: These are expert systems that follow a specific set of rules in their decision-making.
  • Fuzzy systems: These systems use logic to handle vague and incomplete information.
  • Neural networks: These systems use algorithms inspired by the human brain to rationalize decisions.

Applications of Expert Systems

Expert Systems have multiple applications, particularly in the healthcare and financial industries, where decisions must be made accurately and promptly based on newly acquired information. In medical diagnosis, expert systems can identify diseases based on symptoms. Meanwhile, they can predict stock prices and make split-second trading decisions in finance.

Examples of Expert Systems

Some examples of Expert Systems in different sectors include:

  • MYCIN: This is one of the first expert systems developed by Stanford University to diagnose bacterial infections.
  • Credit card fraud detection systems: These expert systems can identify suspicious financial transactions with a combination of neuro-fuzzy systems.

Leverage AI In Your Business

Whether you want to improve customer service or streamline operations with intelligent chatbots, implementing artificial intelligence in your business may be your most efficient option. However, acquiring a team of AI experts can be daunting, costly, and time-consuming. Luckily, Revelo can help.

Revelo connects US-based companies with AI experts in Latin America. Our tech professionals are vetted for technical skills, soft skills, and English proficiency. They all work in the same time zone as the U.S. while still charging significantly less than their U.S. counterpart due to the lower cost of living in South America.

Hire talent with Revelo today and let our team handle everything from negotiations and benefits administration to payroll, local compliance, and onboarding. Let us help you leverage the power of AI in your business.

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