How does AI think?

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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