In his comprehensive guide, digital marketing consultant George Abraham categorizes Artificial Intelligence, Machine Learning, and Deep Learning, explaining their fundamental principles, types, and applications. He examines narrow, general, and super AI, outlines supervised, unsupervised, and reinforcement learning, and details CNNs, RNNs, and transformer models to inform aspiring technologists.

Key points

• Classification of AI into narrow, general, and super categories illustrating task-specific to hypothetical self-aware systems.
• Explanation of machine learning paradigms—supervised, unsupervised, and reinforcement learning—and their applications in spam filtering and autonomous navigation.
• Overview of deep learning networks including CNNs for image tasks, RNNs for sequential data, and transformer architectures powering advanced NLP.

Why it matters: Clarifying distinctions among AI, ML, and DL guides curriculum development, informs strategic technology investments, and accelerates adoption of intelligent systems.