. Machine Learning and Deep Learning are mentioned just as often.
And now, Generative AI seems to dominate nearly every technology conversation.
For a lot of professionals outside the AI field, this vocabulary will be confusing. These terms are sometimes used interchangeably, sometimes mixed together, and sometimes presented as competing technologies.
If you will have ever asked yourself:
- What exactly is AI?
- How are Machine Learning and Deep Learning connected?
- What makes Generative AI different?
This text is for you 😉
The target here is clarity — not simplification through approximation, but accurate explanation in plain language. No technical background is required for the remaining of the article.
The important thing idea: the Matryoshka doll
A useful option to understand the connection between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI is to assume Matryoshka dolls.
- Each concept accommodates the following one inside it:
- Nothing replaces what got here before,
- Each layer builds upon the previous one.
Let’s open them one after the other.
Artificial Intelligence: the outer shell
Artificial Intelligence (AI) is the broadest definition.
At its core, AI refers to systems designed to perform tasks that typically require human intelligence. In practice, AI includes systems that may:
1. Make decisions. Example: A navigation system selecting the fastest route based on real-time traffic conditions.
2. Draw conclusions. Example: A system deciding whether to approve or reject a loan application based on multiple aspects.
3. Recognize patterns. Example: Detecting fraudulent bank card transactions by identifying unusual spending behavior.
4. Predict outcomes. Example: Estimating future energy consumption or product demand.
Rule-based AI: intelligence written by humans
Within the early many years of AI, particularly within the Nineteen Seventies and Nineteen Eighties, systems were primarily rule-based. What I mean is that humans explicitly wrote the logic. The pc didn’t learn — it executed predefined instructions.
- -> A rule looked like this in human natural language:
“If a house has no less than three bedrooms and is positioned in a superb neighborhood, then its price ought to be around €500,000.” - -> In programming terms, the logic is comparable but written in code with something that may looks like this :
This was considered Artificial Intelligence because human reasoning was encoded and executed entirely by a machine.
Why rule-based AI was limited
Rule-based systems work well only in controlled environments.
Real-world conditions will not be controlled. If we’re still with our real estate example.
- markets evolve,
- contexts change,
- exceptions multiply.
The system cannot adapt unless a human rewrites the foundations.
This limitation led to the following layer.
Machine Learning: letting data speak
Machine Learning (ML) is a subset of Artificial Intelligence.
The important thing shift is easy but profound:
As an alternative of telling the pc what the foundations are, we let the system learn them directly from examples.
-> Let’s return to the home price example. As an alternative of writing rules, we collect data:
- surface area,
- variety of rooms,
- location,
- historical sale prices.
1000’s, sometimes thousands and thousands, of past examples.
This data is provided as training data to a machine learning model.
But what does “training a model” using data actually mean?
Training isn’t a black box. We start by selecting a mathematical model — essentially an equation — that would describe the connection between inputs (surface, location, etc.) and output (price).
We don’t test one equation. We test many (We call them models).price = 2 × surface + 3 × location
The model adjusts its parameters by comparing prices with real prices across many examples.
No human could manually analyze a whole bunch of hundreds of homes without delay. A machine can.
How can we know a model works?
Before adopting a model — that’s, the equation that best represents the phenomenon we’re studying — we evaluate it.
A part of the information is intentionally hidden. That is generally known as test data.
The model:
- Never sees this data during training,
- Must make predictions on it afterward.
- Predictions are then in comparison with reality.
If performance is sweet on unseen data, the model is beneficial.
If not, it’s discarded and one other model is tried.
This evaluation step is crucial.
Machine learning excels at tasks humans struggle with:
- Analyzing large volumes of knowledge,
- Detecting subtle patterns,
- Generalizing from past examples.
- Examples of applications:
- Healthcare
-> disease risk prediction,
-> evaluation of medical images. - Industry
-> predicting equipment failures,
-> optimizing production processes. - Consumer products
-> suggestion systems,
-> fraud detection.
The bounds of traditional machine learning
Nevertheless, traditional Machine Learning has essential limitations. It really works thoroughly with structured data:
- tables,
- numerical values,
- clearly defined variables.
Nonetheless, it struggles with varieties of data that humans handle naturally, resembling:
The rationale for this limitation is key ->
Computers don’t understand images, sounds, or words the best way humans do.
They only understand numbers.
When working with images, text, or audio, these data must first be transformed into numerical representations.
For instance, a picture is converted right into a matrix of numbers, where each value corresponds to pixel information resembling color intensity. Only after this conversion can a machine learning model process the information.
This transformation step is mandatory.
Feature extraction: the standard approach
Before the rise of deep learning, this transformation relied heavily on manual feature engineering.
Engineers had to choose upfront which characteristics is likely to be useful:
- edges or shapes for images,
- keywords or word frequencies for text,
- spectral components for audio.
This process, generally known as feature extraction, was:
- time-consuming,
- fragile,
- strongly depending on human intuition.
Small changes in the information often required redesigning the features from scratch.
Why deep learning was needed
The restrictions of manual feature extraction in complex settings were a key motivation for the event of Deep Learning. (I’m not covering the more technical motivations in this text. My goal is to present you a transparent understanding of the massive picture).
Deep Learning doesn’t eliminate the necessity for numerical data.
As an alternative, it changes how features are obtained.
Quite than counting on hand-crafted features designed by humans, deep learning models learn useful representations directly from raw data.
This marks a structural shift.
Deep Learning: the structural shift
Deep Learning still works as Machine Learning. The training process is identical:
-> data,
-> training,
-> evaluation.
What changes is what we call the architecture of the model.
Deep learning relies on neural networks with many layers.
Layers as progressive representations
Each layer in a deep learning model applies a mathematical transformation to its input and passes the result to the following layer.
These layers will be understood as progressive representations of the information.
Within the case of image recognition:
- Early layers detect easy patterns resembling edges and contrasts,
- intermediate layers mix these patterns into shapes and textures,
- later layers capture higher-level concepts resembling faces, objects, or animals.
The model doesn’t “see” images the best way humans do.
It learns a hierarchy of numerical representations that make accurate predictions possible.
As an alternative of being told explicitly which features to make use of, the model learns them directly from the information.
This ability to robotically learn representations is what makes deep learning effective for complex, unstructured data (see the representation above).
And once this level of understanding is reached, a crucial shift becomes possible.
Up thus far, deep learning models have mainly been used to analyze existing data.
They’re trained to:
- recognize what’s present in a picture,
- understand the structure of a text,
- classify or predict outcomes based on learned patterns.
Briefly, they assist answer the query:
But learning wealthy representations of knowledge naturally raises a brand new query:
If a model has learned how data is structured, could it also produce latest data that follows the identical structure?
This query is the inspiration of Generative AI.
Generative AI: from evaluation to creation
Generative AI doesn’t replace deep learning. It builds directly on top of it.
The identical deep neural networks that learned to acknowledge patterns can now be trained with a special objective: generation.
As an alternative of focusing only on classification or prediction, generative models learn the way data is , step-by-step.
Consequently, they can create latest content that’s coherent and realistic.
A concrete example
Consider the prompt:
“Describe a luxury apartment in Paris.”
The model doesn’t retrieve an existing description.
As an alternative:
- It starts from the prompt,
- predicts the most certainly next word,
- then the following one,
- and continues this process sequentially.
Each prediction depends upon:
- What has already been generated,
- The unique prompt,
- And the patterns learned from large amounts of knowledge.
The ultimate text is latest — it has never existed before — yet it feels natural since it follows the identical structure as similar texts seen during training.
The identical principle across data types
This mechanism isn’t limited to text. The identical generative principle applies to:
- images, by generating pixel values,
- audio, by generating sound signals over time,
- video, by generating sequences of images,
- code, by generating syntactically and logically consistent programs.
That is why these models are sometimes called foundation models: a single trained model will be adapted to many various tasks.
Why Generative AI feels different today
Artificial Intelligence, Machine Learning, and Deep Learning have existed for a few years.
What makes Generative AI feel like a turning point isn’t only improved performance, but how humans interact with AI.
Up to now, working with advanced AI required:
- technical interfaces,
- programming knowledge,
- infrastructure and model management.
Today, interaction happens primarily through:
- natural language,
- easy instructions,
- conversation.
Users now not must specify to do something.
They’ll simply describe .
This shift dramatically reduces the barrier to entry and allows AI to integrate directly into on a regular basis workflows across a big selection of professions.
Putting every thing together
These concepts will not be competing technologies. They form a coherent progression:
- Artificial Intelligence defines the goal: intelligent systems.
- Machine Learning enables systems to learn from data.
- Deep Learning allows learning from complex, unstructured information.
- Generative AI uses this understanding to create latest content.
Seen this fashion, Generative AI isn’t a sudden break from the past.
It’s the natural continuation of every thing that got here before.
Once this structure is evident, AI terminology stops being confusing and becomes a coherent story.
But, Have we finished? Almost.
At this point, we’ve covered the core AI ecosystem: artificial intelligence, machine learning, deep learning, and generative AI — and the way they naturally construct on each other.
If you happen to are reading this text, there’s a superb likelihood you already use tools like ChatGPT in your each day life. I won’t go much deeper here — this deserves an article of its own.
Nonetheless, there’s one essential final idea value remembering.
Earlier, we said that Generative AI is a continuation of Deep Learning, specialized in learning patterns well enough to generate latest data that follows those self same patterns.
That’s true — but with regards to language, the patterns involved are way more complex.
Human language isn’t only a sequence of words. It’s structured by grammar, syntax, semantics, context, and long-range dependencies. Capturing these relationships required a serious evolution in deep learning architectures.
From Deep Learning to Large Language Models
To handle language at this level of complexity, latest deep learning architectures emerged. These models are generally known as Large Language Models (LLMs).
As an alternative of trying to know the total meaning of a sentence suddenly, LLMs learn language in a really particular way:
They learn to predict the following word (or token) given every thing that comes before it.
This might sound easy, but when trained on massive amounts of text, this objective forces the model to internalize:
- grammar rules,
- sentence structure,
- writing style,
- facts,
- and even elements of reasoning.
By repeating this process billions of times, the model learns an implicit representation of how language works.
From these Large Language Models, conversational systems resembling ChatGPT are built — combining language generation with instruction-following, dialogue, and alignment techniques.
The illustration above shows this concept visually: generation happens one word at a time, each step conditioned on what was generated before.
The ultimate big picture
Nothing you see today got here out of nowhere.
ChatGPT isn’t a separate technology. It’s the visible results of a protracted progression:
- Artificial Intelligence set the goal.
- Machine Learning made learning from data possible.
- Deep Learning enabled learning from complex, unstructured data.
- Generative AI made creation possible.
- Large Language Models brought language into this framework.
I hope this text was helpful. And now, you’re now not lost in tech conversations — even at your end-of-year family gatherings 🙂
If you happen to enjoyed this text, be happy to follow me on LinkedIn for more honest insights about AI, Data Science, and careers.
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