Part 2/9:
In the early days, the goal of AI was to automate thinking. Early predictions suggested that it would soon be feasible to create “giant electronic brains” capable of complex reasoning similar to human thought processes. Throughout the decades, two primary approaches emerged: symbolic reasoning and statistical methods, particularly through the use of neural networks. While symbolic approaches attempted to formalize rules and logic, the statistical side focused on pattern recognition and data-driven learning, leading to the modern deep learning systems that are prevalent today.