Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interconnected fields that differ in their scope, complexity, and application:

*Artificial Intelligence (AI)*

1. Scope: Developing intelligent systems that mimic human behavior.
2. Goal: Automate tasks, reason, and solve problems.
3. Techniques: Rule-based systems, decision trees, optimization algorithms.
4. Applications: Expert systems, natural language processing, robotics.

*Machine Learning (ML)*

1. Scope: Subset of AI, focusing on learning from data.
2. Goal: Enable systems to improve performance on tasks without explicit programming.
3. Techniques: Supervised, unsupervised, and reinforcement learning.
4. Applications: Image classification, speech recognition, recommendation systems.

*Deep Learning (DL)*

1. Scope: Subset of ML, focusing on neural networks with multiple layers.
2. Goal: Automatically learn complex patterns in data.
3. Techniques: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
4. Applications: Image recognition, natural language processing, autonomous vehicles.

*Key differences:*

1. Complexity: AI > ML > DL (in terms of scope and complexity).
2. Data dependency: ML and DL rely heavily on data, whereas AI can operate with or without data.
3. Learning style: ML learns from data, while DL learns hierarchical representations.
4. Accuracy: DL typically outperforms ML and AI in tasks requiring complex pattern recognition.

*Relationships:*

1. AI encompasses ML and DL.
2. ML builds upon AI foundations.
3. DL is a specialized form of ML.

*Real-world examples:*

1. AI: Chatbots, expert systems.
2. ML: Image classification, sentiment analysis.
3. DL: Self-driving cars, language translation.