Basics of AI
👨💻 Computer Scientist | 🌌 Physicist Enthusiast 💡 Always exploring new technologies and seeking to bridge the gap between science and technology.
The main goal of Artificial Intelligence(AI) is to design systems capable of performing complex tasks that require human intelligence. Many approaches have been proposed, over the years, to achieve this goal. Today, the discussions about AI are dominated by Deep Learning, Machine Learning or Large Language Model(LLM). But these technologies only represent a fraction of the vast field of AI.
Alongside these approaches lies another branch of AI: symbolic AI. Rather than learn pattern from large amount of data, Symbolic AI focus on reasoning, problem solving, problem representation and search algorithms operating on structured representations of the problems. In this paradigm, real-world problems are modeled using states, symbols, rules, and graphs.
In this series of articles, we will explore the foundations of symbolic AI, how it works, and how complex real-world problems can be represented as structured models that machines can reason about. We will also study some of the field's most fundamental search algorithms, including Breadth-First Search (BFS), Depth-First Search (DFS), and A* Search, before introducing more advanced concepts such as probabilistic models and Markov processes.
