Introduction-to-Artificial-Intelligence.pdf
- 1. Introduction to Artificial Intelligence Artificial intelligence (AI) is the simulation of human intelligence processes by computers. AI systems are designed to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. by Roshana Udayakumar RU
- 2. What is AI? AI encompasses a wide range of technologies and techniques that enable computers to learn from data, reason logically, and interact with the world in intelligent ways. Machine Learning Computers learn from data without explicit programming, improving performance over time. Deep Learning AI models with multiple layers of artificial neural networks, capable of complex pattern recognition. Natural Language Processing Computers understand and process human language, allowing for communication and interaction. Computer Vision Computers "see" and interpret images and videos, enabling applications like facial recognition and object detection.
- 3. History and Evolution of AI The field of AI has evolved significantly over decades, with advancements in computing power and algorithms driving innovation. 1 Early AI (1950s-1970s) Focus on symbolic reasoning, expert systems, and game-playing. 2 AI Winter (1970s-1980s) Limited computing power and unrealistic expectations led to a period of decline. 3 Machine Learning Era (1980s- present) Emergence of machine learning algorithms, including neural networks and support vector machines. 4 Deep Learning Revolution (2010s- present) Breakthroughs in deep learning, enabling AI to achieve remarkable results in various domains.
- 4. Fundamental Techniques in AI AI techniques involve a wide range of approaches to enable machines to exhibit intelligent behavior. 1 Supervised Learning Training models on labeled data to predict outputs based on inputs. 2 Unsupervised Learning Discovering patterns and structures in unlabeled data without explicit guidance. 3 Reinforcement Learning Learning through trial and error, receiving rewards for desired actions and penalties for undesired actions. 4 Knowledge Representation Encoding knowledge in a structured format for computers to understand and reason about.