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How Does AI Work? – Discover Basics From A to Z

Software Development   -  

April 18, 2024

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We’ve heard of the term Artificial Intelligence, or AI, multiple times these days. But what is AI? And how does AI work? These questions spark curiosity in people of all ages. 

So in this comprehensive guide, Designveloper will uncover all the mysteries behind this advanced tech. Particularly, we’ll discover its definition, how it works, and the best resources for beginners to study AI from scratch. 

Whether you’re looking for ways to enter this field or simply curious about AI, this guide is the right place for you. Ready to discover? Keep reading! 

Understanding Artificial Intelligence (AI)

In the first section, DSV will guide you through the fundamentals of this advanced tech.

What is Artificial Intelligence?

What is AI?

Artificial Intelligence (AI) is a subsegment of computer science. 

Vasant Honavar, a Huck Chair in Biological Data Sciences and AI at Penn State University gave an interesting definition of this tech. He said AI was a collection of advanced techs to do tasks that could require intelligence if they’re performed by humans.

As such, AI aims to empower machines, especially computer systems, to observe their surroundings and perform cognitive functions like a human (e.g., play chess, recognize speech, and even make informed decisions). 

Without your recognition, AI is everywhere around us. It can appear as a virtual smartphone assistant (e.g., Siri or Galaxy AI), a data analytics tool (e.g., Tableau or Qlik), finance management software (e.g., AlphaSense or SongNhi), and more. 

How Does AI Work?

Hoes does AI work? - Image

To understand how AI works, we first need to grasp the structure of AI models. 

According to Honavar, AI models are composed of two core parts: engineering and the science of intelligence. The former refers to developing computer systems and applications that can use intelligence in many aspects of our lives and work. Meanwhile, the latter mentions how AI and machine learning techniques empower machines to produce similar outcomes to what human brains can do. 

To achieve that goal, AI models must use intelligent algorithms – in other words, a series of instructions – to process and analyze vast amounts of data. Based on that data, they can detect complex patterns used to help the models perform human-like functions. This could range from simple tasks like sorting data into categories based on specific requests, to more advanced ones like understanding human language. 

These AI systems can even test and measure their performance. They then develop continuous learning capabilities to achieve new knowledge and make progress.

AI systems like chatbots, robots, or drones rarely take a break, except for updates or maintenance. So they can perform millions of tasks very fast. This is why AI is widely used to support humans in doing repetitive tasks like handling simple requests of customers 24/7 or analyzing data.  

6 Core Disciplines Behind AI

Our human brains have different parts to perform different functions. The visual cortex, for example, handles visual information. Similarly, AI also covers numerous subfields, commonly known as “core disciplines” that make up AI. 

To further understand the question “How does AI work?”, we have to dig into these six key models. They include:

Machine Learning (ML)

Machine learning

As a key part of AI, ML gives machines the ability to self-learn from data to make decisions or predictions. 

For example, you want AI models to automatically filter spam emails. First, these models can be trained on the data which could be a large quantity of emails labeled as either “spam” or “not spam”. After extracting the patterns from the data (like email addresses or frequent phrases in the emails), AI algorithms will self-learn from these patterns and then filter new emails. 

With this capability, ML has become more popular across fields, especially in advertising & media with the largest revenue share of 20% in 2022. Thanks to ML, businesses can deliver highly custom ads, optimize their advertising campaigns across multiple channels, and identify ad fraud. 

For this reason, the global ML market is projected to soar at an impressive CAGR of 34.8% during the forecast period 2023-2030.

Neural Networks

Neural networks - How does this AI work?

Neural networks are a type of machine learning. Like human brains, they contain “neurons”, or nodes connected by “synapses”. They use machine perception to understand and analyze sensory data, then label or cluster this data to identify patterns. 

One of the most practical applications of neural networks is image recognition. They aim to recognize people, objects, actions, and more in images.

So, how does this AI work? Let’s take this example. 

Imagine you want a neural network to detect different kinds of animals in your images. So first, the network would be trained on big databases of animal images, each labeled with the kind of animal it represents (e.g., panda, eagle, or dog). Accordingly, the network learns to identify patterns in the pixel data of the images. 

When you show the trained network a new image of any animal, it can recognize the animal exactly by comparing the patterns in the image to what it’s learned. 

With this capability, neural networks have a wide application in photo tagging on social media and even medical imaging to discover diseases.

Deep Learning (DL)

Deep learning

One popular type of ML is deep learning. This model uses multi-layered neural networks to mimic the behaviors of human brains in handling data for use in predictions and decision-making. 

All deep learning models are neural networks. But not all neural networks are considered “deep”. While neural networks can produce approximate predictions, deep learning models optimize the accuracy of these predictions. These models hence ensure a more reliable decision.

For this reason, deep learning is widely applied in various aspects. One typical example is voice-activated assistants like Apple’s Siri and Amazon’s Alexa. Besides, automotive giants like Tesla use this model to enable self-driving cars to identify objects around them, choose the best route for travel, and more. 

Especially during the COVID-19 pandemic, DL-based models could detect infected patients. This has boosted the growth of its market, with a CAGR of 40.3% from 2023-2030.

Natural Language Processing (NLP)

Natural language processing

NLP can interpret and analyze text or voice commands in human language. This AI model works by using statistical, machine learning, and deep learning models with computational linguistics (rule-based modeling of human language). 

With this capability, NLP allows you to communicate with machines intuitively to process a bunch of tasks like humans can do.

Therefore, it has a wide application across industries. One common example is search engines like Google and Bing using NLP to understand and provide responses to your search queries. Further, you can find NLP in translation services (like Google Translate), speech recognition software (like Microsoft’s Cortana), sentiment analysis tools (like Amazon Comprehend,) and more. 

Computer Vision

Computer vision

Computer vision enables computer systems to interpret and analyze visuals (e.g., images and videos) by using advanced techniques and algorithms. 

These techniques include deep learning, pattern recognition, and even neural networks, especially Convolutional Neural Networks (CNNs) which excel at handling grid-like data like images. Therefore, computer vision’s outcomes are symbolic or numerical information for decision-making. 

You may find this AI model works in captchas, security systems for facial identification, “Amazon Go” stores for automatic checkout, and more. Even in healthcare, this AI model proves helpful in medical imaging to identify diseases (e.g., cancer) via X-ray images and MRI scans.

Cognitive Computing

Cognitive computing - How does this AI work

The final one is cognitive computing. Here’s how: It involves tech platforms mimicking human thought processes to handle complex situations. By using artificial neural networks, machine learning, NLP, and other advanced techniques, cognitive computing systems can learn from data, constantly improve their performance, and interact with humans. 

One simplest example of how this AI model works is car driving. On the road, drivers must use their cognitive abilities to observe objects, track how other vehicles move, choose a safe route, or decide when to turn. Cognitive computing systems also operate in the same way.

For this reason, cognitive computing has become a powerful assistant in many industries. For example, it helps doctors diagnose diseases, manage patient care, and build new treatments. 

Why Should You Learn AI?

Why should you learn AI?

Understanding “How does AI work?”, you might realize AI is a thriving industry and provides massive benefits. But does it offer promising jobs for those wanting to pursue this field? The answer is yes. Below are two main reasons why you shouldn’t miss a chance to master this advanced tech:

Growing Demand

According to the 2023 report of the World Economic Forum (WEF), an AI and machine learning specialist is ranked the fastest-growing job in the future. Its demand is predicted to increase by 40%, equivalent to a million jobs, during the forecast period 2023-2027.

This impressive growth is fueled by the wide adoption of these cutting-edge techs. The WEF stated that over 75% of businesses are looking to apply AI features in their operational routines. 

However, the effective management and utilization of AI models require a skilled workforce. With a growing demand for AI expertise, learning AI now opens more avenues to keep abreast of current trends and secure a promising job in this field.

High Salary Potential

No wonder higher demands for AI skills can lead to lucrative pay. 

According to Glassdoor, AI Engineers require an estimated annual compensation of USD 151,438 in the US. Meanwhile, the average salary of machine learning specialists stands at USD 167,738. 

These salary ranges highlight the high value and importance of these skill sets in the current job market. Also, they turn into a big motivation for many people to start their AI learning path. 

5 Best Resources to Study AI

5 best sources to study AI

You’ve already answered two questions: “How does AI work?” and “Why should you learn AI now?” 

However, to smoothly start in this burgeoning field, you first must master the prerequisite skills such as basic mathematics and programming. Once you have a solid foundation, you can dig into aspects of AI and machine learning, including algorithms, techniques, and practical applications. 

Finally, you can choose the perfect specialization based on your interests and career direction. This could be anything from MLOps, deep learning, natural language processing, and more. 

But to embark on this education path smoothly, you can’t abandon the importance of having the right resources to study AI. They offer the knowledge and practical skills needed for you to excel in this industry. Here, DSV will showcase the 5 best resources to study AI:

Online Courses

Online courses provide structured learning paths with video lectures, quizzes, and hands-on projects from top-tier experts and professors. 

They cover a wide range of topics at any level. This could be the basics of AI, GenAI, and ML for beginners or more advanced topics on deep learning, natural language processing, and more. Besides, you can find online AI courses for specific industries, for example, AI in Healthcare by Standford University

Today, there are different online courses from top universities and corporations. These courses are often offered in prestigious learning platforms like edX, Coursera, and Udacity.

Here are some of the best AI courses you might consider:

  • AI/ML Courses by Google: This tech giant offers a course series to help even beginners start with AI. Accordingly, you’ll learn how to build scalable AI systems from A to Z with Google’s experts and through hands-on examples. Some sources offer certifications to enhance your portfolio.
  • Data Science and Machine Learning by MIT: This 12-week program helps you master advanced AI skills like Python, machine learning, or predictive analytics through live virtual sessions, case studies, hands-on projects, and even mentorship from industry experts. At the end of the course, you’ll receive a certification of completion by MIT IDSS.


Although we’re living in the tech era, books still play an important role in providing us with in-depth knowledge about AI models. These books also cover a variety of topics written in an understandable way. So they become a great resource for even beginners. 

Below are the best AI books:

  • Artificial Intelligence for Dummies by John Paul Mueller and Luca Massaron: This book mentions different topics like machine learning, robotics, and more in clear and understandable language. Therefore, it’s a good start for those without prior knowledge.
  • Make Your Own Neural Network by Tariq Rashid: If you want to explore neural networks without programming knowledge, this book is a great start. It shows you how to use Python to build and train a neural network. 


Don’t ignore bootcamps if you’re looking for hands-on training programs or projects to hone practical skills.

Further, these programs connect you with a large network of recruiters, securing you a job after completing the bootcamps.

Here are the best AI bootcamps you can join:

  • AI Bootcamp by Columbia Engineering: This program lasts 24 weeks. It provides live online classes led by leading instructors, 24/7 learning assistance, a career engagement network, and more.
  • Machine Learning & AI Bootcamp by Springboard: Partnering with UC San Diego Extended Studies, Springboard offers a 9-month program to prepare you on the way to becoming an ML engineer. It helps you master industry-valued skills through projects that simulate what you’ll do in reality and secure a job through career coaching.

Blog Posts

Besides the solid knowledge and practical skills you acquire from the mentioned resources, you must also stay updated with the latest trends and advancements in AI. And blog posts are good sources. 

Here are some of the best blog posts for reference:

  • MarkTechPost: This news platform offers updates on AI-related news, research, tools, and more. Its articles – albeit technical – are still easy to understand for those without prior experience with AI.
  • Designveloper Blog: Our blog is a treasure trove of knowledge, providing insightful articles, guides, and tutorials for both beginners and experts in AI/ML. Whether you’re looking for AI fundamentals, complex algorithms, or the latest developments, we have you covered.

Online Communities

You can also learn from industry experts by engaging in online communities. These are where AI enthusiasts and experts around the world share their meaningful insights and experiences about AI techs. There, you can ask questions, get feedback, or share knowledge. 

Some of the best AI communities include:

  • AI Stack Exchange: This is a hub for those interested in different aspects of AI to ask questions, learn from others, and share experiences.
  • Stack Overflow: This is the largest online community for developers to share knowledge, learn from experience, and even build careers. With OverflowAI, this community allows for advanced search, question-asking, and more.


Through this article, we expect you to understand “How does AI work?” and the six core disciplines (or techniques) that form AI today. Also, you might understand why learning AI now is crucial and which sources you can use to master this tech. If you want more updates and developments about AI models, subscribe and follow our blog. 

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