AI Hardware Acceleration in ASIC Design Lecture 1: Introduction & History of Artificial Intelligence

This lecture provides a comprehensive introduction to Artificial Intelligence (AI), beginning with its historical roots stretching back to the 19th century and progressing through key milestones like the development of early computers and modern deep learning systems. It defines AI as computer systems performing tasks traditionally done by humans—reasoning, decision-making, and complex problem-solving—and highlights the shift from rule-based programming to systems that learn from vast amounts of data through training and inference modes. The lecture emphasizes that current AI, while inspired by biological intelligence, operates on a vastly different scale and efficiency.
The discussion then delves into the foundational elements of AI systems, specifically neural networks modeled after biological neurons, and explains how these networks learn through weighted inputs and signal triggering. It clarifies the distinction between training—where the network adapts based on data—and inference—where it applies learned knowledge to new inputs. Ultimately, the lecture establishes a broad understanding of AI’s evolution, core principles, and current capabilities, setting the stage for exploring hardware acceleration techniques in subsequent lectures.