AI Concepts Explained: AI vs GenAI vs ML vs DL vs NLP

Have you ever been confused by AI concepts like AI vs GenAI vs ML vs DL vs NLP? Yes, precisely the same here. All of these buzzwords sounded cool when I first started learning about Artificial Intelligence, but trying to get the core AI concepts explained clearly was a challenge. I continued to watch videos and read articles, but the more I tried to figure it out, the more confused I became.

Back then, I needed someone to explain these AI concepts in plain language, without the jargon or complexity. I hope to accomplish that for you here.

So let’s simplify this AI concepts: Artificial intelligence is the big idea that makes machines act smart. Machine Learning is how machines learn from data. Deep Learning is a potent type of ML with neural networks. Natural Language Processing enables machines to understand human language, and Generative AI generates new content such as text, images, or code.

Although these technologies are frequently combined, each has a unique function and set of capabilities. As time passed, I saw that the true game-changer is realising how they relate and differ. 

Whether you’re exploring this space out of curiosity or looking to build something with AI, having the core AI concepts explained clearly can make a world of difference.

Let’s break it all down together, one concept at a time.

What is Artificial Intelligence?

You probably heard about AI when ChatGPT started making headlines, right? That’s how it caught my eye, too. Suddenly, everyone—from students to CEOs— discussed how AI could write emails, create apps, make jokes, and even pass tests. But AI didn’t just pop up out of nowhere; it has been quietly advancing the technology we use daily for years.

When I first learned about artificial intelligence (thanks, Hollywood!), I had a vision of robots with glowing eyes conquering the world. But AI is more than science fiction or robots; it’s creating machines that can think, learn, and make choices like humans—or at least try.

The science of making machines learn to perform tasks that would otherwise require human intelligence is the core of Artificial Intelligence. These tasks may be as trivial as identifying spam mail or as sophisticated as detecting diseases or navigating a vehicle.

AWS Example: Amazon Alexa is a good example of AI at work. It uses several services in AWS that allow it to listen to your voice, interpret intent, and respond like another person, whether requesting the weather or controlling smart devices. 
General Example: A self-driving car uses AI to decide when to stop or accelerate based on traffic signals and surroundings.

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What is Machine Learning (ML)?

After learning about AI, I began to hear about Machine Learning(ML), which, to be honest, sounded like a fancy version of AI. However, I soon discovered that Machine Learning is the fundamental mechanism behind the majority of what we currently refer to as Artificial Intelligence.

Simply put, ML is the process of teaching machines to learn from data similarly to how humans learn from experience. We provide the machine with several examples and let it identify the patterns on its own, rather than writing comprehensive instructions for each task.

That blew my mind at first. I always assumed that coding entailed issuing detailed instructions. With machine learning, however, it’s more like “Here’s a tonne of data—now figure it out yourself.“

AWS Example: Amazon Personalize is an AWS service that automatically creates real-time product recommendations with machine learning. It enables capabilities such as “Customers who bought this also bought….” by analyzing users’ behavior and preferences on Amazon’s retail platform.

General Example: Netflix uses machine learning to recommend shows based on your viewing history and preferences.

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What is Deep Learning (DL)?

I remember hitting a roadblock when I first heard the term Deep Learning. I had just started wrapping my head around Machine Learning, and now there was something even deeper? It honestly felt like opening a door to another layer of complexity.

The reality is that deep learning (DL) is not as frightening as it may seem.

Using neural networks—systems modelled after the way our brains process information—to teach machines is merely a subset of machine learning.

The name “deep” refers to these neural networks having many layers. Deep learning selects the important information from the data on its own, whereas traditional ML models frequently require human intervention.

AWS Example: Amazon Rekognition uses deep learning models to provide advanced capabilities for image and video analysis. For instance, a deep learning model can automatically detect faces, objects, or even hazardous elements in uploaded photos and videos from surveillance devices. 

General Example: Facial recognition on smartphones uses deep learning to identify and unlock the device accurately.

What is Natural Language Processing (NLP)?

Having tried out ChatGPT and Claude, I had a single question: How in the world can a machine understand language, in which even human beings sometimes falter?

Then, I learnt about Natural Language Processing, or NLP in short.

The underlying link between machines and human language is NLP. The aim of this field of AI is to enable computers to comprehend, understand, and even generate human language, whether spoken or written. It commonly overlaps with deep learning and machine learning.

You employ language that is rich in emotion, vagueness, grammatical abnormalities, and slang when you talk or write. For NLP to be able to figure out what you meant, it first decodes that into a machine-readable format, like numbers or patterns.

AWS Example: Amazon Comprehend is an NLP service that can review text and extract insights from the text. It can also automatically review customer reviews and identify if they are positive or negative sentiments, extract key phrases from text, or identify person’s names, dates and locations from longer documents.

General Example: Siri or Alexa understanding and responding to voice commands in natural language.

What is Generative AI (GenAI)?

Let me guess: ChatGPT’s viral success on social media most likely marked the beginning of your introduction to AI, right?. So did mine. AI wasn’t just a catchphrase all of a sudden. It could create logos, write poetry, write code, and even offer relationship advice. At that point, my curiosity really began to grow: What kind of AI is capable of producing things?

That’s when I learnt about Generative AI, or GenAI.

Located deep within the AI family tree, generative AI is a subset of deep learning that overlaps with NLP. GenAI’s unique feature is that rather than merely evaluating or categorising data, it produces new content that appears to have been created by a human, including text, images, music, voice, and even videos. Imagine GenAI as a highly intelligent creative assistant that has been trained on enormous amounts of data. It creates new content by “learning” patterns in the data that already exists.

AWS Example: With Amazon Bedrock, developers can build GenAI applications using foundation models developed by leading AI startups such as Anthropic (Claude), Stability AI and more, all with a common API. This can generate text summaries, chatbots or even generate images with minimal effort. 

General Example: ChatGPT generates human-like responses to text prompts, enabling conversational AI.

Till this point, you’ve seen how each of these concepts, AI, ML, DL, NLP and GenAIbuild upon one another. But if you’re like me, visualizing it makes the connections much clearer.

Visual Recap: How They All Relate

To better understand how Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, and Natural Language Processing (💬) all fit together, let’s break them down with a visual diagram. 

This representation helps simplify their relationships and aids in having the core AI concepts explained clearly:

At some point, I had all these phrases floating around my brain: AI, ML, DL, GenAI, NLP, and I just could not understand how they connected to each other. So I did what I generally do when I am stuck: I sketched a diagram.

Picture large rectangles nested within rectangles, with a few of them overlapping along the edges.

• Artificial Intelligence:(🧠):
  At the most superficial level, there’s Artificial Intelligence, the parent discipline that encompasses everything to do with machines emulating human intelligence.
  
• Machine Learning:(📈):
  Within AI resides Machine Learning, where machines begin to learn from experience instead of being instructed precisely what to perform. This was my initial “aha!” moment.
  
• Deep Learning:(🧠🔗):
  Going deeper, there’s Deep Learning ,a rich subset of ML that relies on neural networks to learn automatically from raw input, such as images or voice.
  

• NLP:(💬): Now here’s where it gets interesting. Natural Language Processing,the technology that enables machines to comprehend and respond in human language,overlaps both ML and DL. That made perfect sense when I understood that earlier NLP models were constructed using classical ML, and the newer ones (such as ChatGPT) depend largely on DL.

• Generative AI:(🎨):
  And finally, right in the mix is Generative AI,nestled within DL and overlapping with NLP, because it creates new content such as text and images, typically in natural language. This is where ChatGPT and Claude hang out.

That graphic helped me tie everything together, and I hope it makes things click for you as well.

Summary of AI Concepts Explained: AI vs GenAI vs ML vs DL vs NLP

Definitions

Term	In Short
AI	AI is when machines are designed to think, learn, and solve problems like humans.
ML	ML is a way for machines to learn from data and improve their performance without being specifically programmed for every task.
DL	DL uses layered neural networks to help machines automatically learn complex patterns from large amounts of data.
NLP	NLP helps machines understand and respond to human language, whether spoken or written.
GenAI	GenAI creates new content—like text, images, or music—by learning patterns from existing data.

Comparison of AI vs GenAI vs ML vs DL vs NLP

Parameter	AI	ML	DL	NLP	GenAI
Definition	Machines mimicking human intelligence	Machines learning from data	Neural-network-based learning from large datasets	Machines understanding and processing human language	Creating new content (text, images, etc.) using learned data patterns
Core Idea	Imitate human intelligence	Learn patterns from data	Use multiple neural network layers to process complex data	Translate human language into machine-understandable format	Generate original outputs from training data
Dependency	Parent domain of all	Subset of AI	Subset of ML	Overlaps with ML & DL	Subset of DL; overlaps with NLP
Human-like Ability	Reasoning, problem-solving, decision-making	Learning from examples	Perception (seeing, hearing, etc.)	Language comprehension	Creativity, content generation
Example	Amazon Alexa, Siri, self-driving cars	Netflix recommendations, fraud detection	Face recognition, voice assistants	Chatbots, sentiment analysis, auto-summarization	ChatGPT, DALL·E, Claude, Stable Diffusion

The similarities and differences between AI, ML, DL, GenAI, and NLP should now be clear to you, especially with these AI concepts explained in a simplified manner. Knowing these basics opens the door to more exploration, whether you want to become a researcher, developer, or just an informed user. The AI space is rapidly evolving, and this is just the beginning.

FAQs about AI vs GenAI vs ML vs DL vs NLP

Luis Chavez | Co-author