AI vs. ML vs. Deep Learning: Decoding the Hierarchy

Understanding the Relationship Between AI, ML, and Deep Learning.

The AI Hierarchy: Where ML and DL Fit

AI, Machine Learning, Deep Learning

Machine learning (ML) has moved from being a niche academic concept to the engine powering our modern digital world from recommended videos on YouTube to complex agentic AI systems.

But how exactly does ML fit into the broader landscape of Artificial Intelligence (AI), and where does Deep Learning (DL) come in?

Understanding this hierarchy and the fundamental learning paradigms is key to grasping today’s technological innovations.

The AI Hierarchy:

Where ML and DL Fit

It is often asked if machine learning is synonymous with AI, or if deep learning and machine learning are the same. The answer is no; they exist in a clear hierarchy.

1. Artificial Intelligence (AI):The overarching field.
2. Machine Learning (ML): A subset of AI. ML focuses on algorithms that can learn patterns from training data and then make accurate inferences about new, unseen data. ML allows models to make predictions or decisions without explicit hard-coded instructions, relying instead on pattern recognition.
3. Deep Learning (DL): A subset of Machine Learning. DL utilizes neural networks with many layers to learn complex, hierarchical representations of data.

In short: DL sits inside ML, and ML sits inside AI.

Understand AI

The Central Premise of Machine Learning

The core idea of ML is straightforward: if a machine’s performance is optimized on a dataset of tasks that accurately resemble the real world a process called model training that trained model can then make accurate predictions on new data.

A trained model essentially applies the patterns it learned from the training data to infer the correct output for a real-world task. The deployment of this fully trained model, where new data is fed into it to generate predictions, is called AI inference.

The Three Pillars of Machine Learning Paradigms

Most machine learning is categorized into three major learning paradigms:

1. Supervised Learning

Supervised learning trains a model to predict the correct output for an input by utilizing labeled examples, often referred to as ground truth. It is called "supervised" because it generally requires human involvement to provide this ground truth data.

Key Model Types in Supervised Learning:

Regression Models: These models predict continuous numerical values.
Examples: Price prediction or temperature forecasting.
Types: Linear regression (finding the best fit line through data points) and polynomial regression (capturing nonlinear relationships).
Classification Models: These models predict discrete classes.
Examples: Binary classification (e.g., fraud or legit), Multi-class classification (one of many categories), or Multi-label classification (multiple tags simultaneously).
Ensemble Methods: Modern supervised learning often uses a combination of models, known as ensemble methods, to achieve better accuracy.
Semi-Supervised Learning: This method sits between supervised and unsupervised learning. It improves a supervised model by training with a small, costly labeled dataset combined with a large pool of unlabeled data, allowing generalization over the unlabeled examples.

2. Unsupervised Learning

Unsupervised learning uses unlabeled data to autonomously discover structure within the data.

Key Model Types in Unsupervised Learning:

Clustering: This involves grouping similar items so that things that behave alike end up together. This is useful for tasks like customer segmentation.
K-means Clustering: This well-known method chooses $k$ groups, repeatedly assigns each item to the nearest group average, and recomputes the averages until they stabilize. For instance, customers might be split into segments like "loyal repeaters" or "bargain hunters".
Hierarchical Clustering: This method starts with every item separate, then continuously merges the most similar groups to construct a tree structure. Cutting the tree later allows for obtaining a desired number of clusters (e.g., themes for IT support tickets like "password reset" or "laptop won’t boot").
Dimensionality Reduction: These algorithms reduce the complexity of data points by representing them with a smaller number of features (fewer dimensions) while still retaining all meaningful characteristics.
Dimensionality reduction is often used for preprocessing data, data compression, or data visualization.
Examples: Principal Component Analysis (PCA) and Encoders.

3. Reinforcement Learning (RL)

Reinforcement learning optimizes a policy through continuous trial and error using rewards and penalties.

In RL, an agent interacts with an environment. It observes the current state and chooses an action. The environment responds by either rewarding the correct action or punishing the incorrect action with a penalty. Over time, these interactions teach the agent a policy that maximizes long-term rewards.

Balancing Act: The agent must balance exploration (trying new actions) with exploitation (repeating actions that worked).
Real-World Example: In a self-driving car, the state comes from sensors (GPS, cameras, LiDAR). Actions include steering, braking, and accelerating.

The model rewards safe, smooth progress (staying in the lane, obeying signals) and penalizes negative outcomes (hard braking, collisions).

Classic ML in the Age of Generative AI

The concepts of regression, classification, clustering, and reinforcement learning are considered classic machine learning techniques. These methods are still foundational and widely used in business today for everything from predicting prices to segmenting customers.

However, these established ideas are being applied in new, scaled-up ways. The most prominent example is Large Language Models (LLMs), which are built upon transformer architectures a newer neural network design.

Crucially, even these advanced systems still rely on the same fundamental ML principles: pattern recognition, data, model training, and inference.

Furthermore, reinforcement learning has seen a significant comeback through RLHF (Reinforcement Learning with Human Feedback). Instead of training an agent to play a game, RLHF trains LLMs to align better with human preferences. Human annotators provide rewards and penalties on the model’s outputs, effectively fine-tuning the system’s behavior.

While today’s technology buzz revolves around generative AI and agentic AI, their powerful foundations are undeniably rooted in classic ML concepts, demonstrating that human learning continues to find new ways to apply machine learning.

AI

Artificial Intelligence

Machine Learning

Deep Learning