AI thinks using various algorithms and techniques that enable machines to process, analyze, and learn from data. Here's a simplified overview:
Machine Learning (ML) Paradigms👇
1. Supervised Learning: AI learns from labeled data.
2. Unsupervised Learning: AI discovers patterns in unlabeled data.
3. Reinforcement Learning: AI learns through trial and error.
AI Thinking Processes👇
1. Data Processing: AI receives and processes input data.
2. Pattern Recognition: AI identifies patterns and relationships.
3. Decision-Making: AI makes predictions or takes actions.
4. Learning: AI adapts and improves through experience.
Key AI Techniques
1. Neural Networks: Inspired by human brain structure.
2. Deep Learning: Multiple layers of neural networks.
3. Natural Language Processing (NLP): AI understands human language.
4. Genetic Algorithms: AI uses evolutionary principles.
AI Decision-Making👇
1. Rule-Based Systems: AI follows predefined rules.
2. Decision Trees: AI uses hierarchical decision-making.
3. Probabilistic Reasoning: AI uses uncertainty and probability.
AI Learning Strategies👇
1. Heuristics: AI uses experience-based shortcuts.
2. Gradient Descent: AI optimizes parameters.
3. Backpropagation: AI adjusts neural network weights.
Cognitive Architectures👇
1. SOAR: Simulates human cognition.
2. ACT-R: Models human memory and reasoning.
3. LIDA: Integrates attention, perception, and reasoning.
Neural Network Types👇
1. Feedforward Networks: Simple, layered networks.
2. Recurrent Neural Networks (RNNs): Feedback loops.
3. Convolutional Neural Networks (CNNs): Image processing.
AI Inspiration from Nature👇
1. Swarm Intelligence: Inspired by flocking behavior.
2. Evolutionary Algorithms: Mimic natural selection.
3. Artificial Immune Systems: Inspired by biological immunity.
Photo Credit: Martin Onyisi
Note: The guy on the photograph is my classmate😏, and a staunch C/Java programmer😊, and that was him reading 2 days before our exams 😪
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