When I started my journey in AI, I was very confused with all these terms of Machine learning, Data Science, Deep learning and later Generative AI and now there is Agentic AI. Even AGI (Artificial General Intelligence) is coming up. What is the exact distinction between all these terms? Are they the same with slight differences? Can they be used interchangeably?
It took some time to understand the difference and I'm still discovering more similarities and distinctions.
1. Artificial Intelligence: The Big Picture
AI is the superset that encompasses all the other domains. It's the broad field of computer science dedicated to creating systems that can perform tasks normally requiring human intelligence.
The aim is to make machines think and act just like, or better than, human beings.
It includes problem-solving, reasoning, planning, perception, and learning.
2. Machine Learning (ML): The Core Learning Engine
ML is a subset of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
It involves making predictions or decisions based on training data by finding relationships (patterns) between inputs and outputs.
How it Works: Algorithms find a function f such that for a given input X, the output is approximately Y, (Y=f(X) + Error). Based on the pattern, the output is predicted.
3. Deep Learning (DL): The Neural Network Approach
DL is the subset of Machine Learning that uses Artificial Neural Networks (ANNs) with multiple layers (hence "deep") to learn complex patterns.
It involves building an architecture similar to the human brain, with neurons/units in a series of layers interconnected to each other.
Input data is processed layer-by-layer. The output of one layer's activation function becomes the input for the next. This structure allows the system to learn more complex patterns with vastly more parameters compared to traditional ML.
4. Generative AI (GenAI): Creating New Content
GenAI is a subset of Deep Learning focused on models that can generate new, novel data (text, images, audio, code, etc.) that is similar to the data they were trained on.
It heavily relies on advanced DL architectures like Transformers (used in models like GPT) and Generative Adversarial Networks (GANs).
Instead of just classifying or predicting an existing value, GenAI creates entirely new content.
5. Data Science: The Interdisciplinary Field
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Relationship to AI/ML: ML is a tool used by Data Scientists to make predictions or classifications. A Data Scientist's job is broader—it involves everything from cleaning and preparing data to visualizing results and advising on business strategy.
The entire data lifecycle: collection, cleaning, analysis, modeling (often using ML), interpretation, and visualization.
6. Agentic AI: The Action Takers
Agentic AI (or AI Agents) refers to a system built upon a foundation model (like a GenAI model) that can reason, plan, and execute multi-step tasks autonomously to achieve a specific goal.
It involves the model using a defined loop of planning --> action --> observation --> reflection --> replanning until the goal is achieved. It can decide which external tools (e.g., Google Search, code interpreter, external APIs) to use and when.
While GenAI is about generating content, Agentic AI is about taking action and solving multi-step problems.
7. The Future: Artificial General Intelligence (AGI)
AGI is a theoretical form of AI that would possess the ability to understand, learn, and apply its intelligence to solve any problem that a human being can.
Status: AGI does not exist yet. All current AI, ML, DL, and GenAI systems are forms of Artificial Narrow Intelligence (ANI), which is designed to perform a specific, limited task.
What this journey through AI terminology reveals is a field that is constantly evolving and deepening its capabilities. This move from prediction and generation to autonomous agency is the single most important difference separating the current wave of AI from everything that came before it.
As these disciplines continue to converge and grow, the lines may occasionally blur, but the ultimate goal remains the same: to build increasingly intelligent, useful, and transformative machines. Whether you're a Data Scientist cleaning a dataset or an AI engineer deploying a new Agent, you are contributing to a monumental future.
The journey is just beginning. Given the rise of Agentic AI, what do you think is the biggest ethical challenge we face in deploying these autonomous systems? Share your thoughts below!
Top comments (2)
🙂
Thank you