AI vs Machine Learning vs Deep Learning vs Generative AI

1 - Artificial Intelligence (AI)
It is the overarching field focused on creating machines or systems that can perform tasks typically requiring human intelligence, such as reasoning, learning, problem-solving, and language understanding. AI consists of various subfields, including ML, NLP, robotics, and computer vision.

2 - Machine Learning (ML)
It is a subset of AI that focuses on developing algorithms that enable computers to learn from and make decisions based on data.
Instead of being explicitly programmed for every task, ML systems improve their performance as they are exposed to more data. Common applications include spam detection, recommendation systems, and predictive analytics.

3 - Deep Learning
It is a specialized subset of ML that utilizes artificial neural networks with multiple layers to model complex patterns in data.
Neural networks are computational models inspired by the human brain's network of neurons. Deep neural networks can automatically discover representations needed for feature detection or classification. Use cases include image and speech recognition, NLP, and autonomous vehicles.

4 - Generative AI
It refers to AI systems capable of generating new content, such as text, images, music, or code, that resembles the data they were trained on. While many modern generative AI models use transformer architecture, others employ different approaches like GANs or diffusion models.