Super important to know #ai #artificialintelligence
The seven processes of Artificial Intelligence (AI) generally refer to key functional areas or stages that define how AI systems operate. These aren’t universally codified like a strict framework, but a common and widely taught model includes the following:
1. Learning
AI systems acquire knowledge through data. This can be:
• Supervised learning (learning from labeled data),
• Unsupervised learning (finding patterns in unlabeled data),
• Reinforcement learning (learning from feedback/rewards).
2. Reasoning
Drawing inferences or conclusions from facts or data. AI uses logical rules or probabilistic models to reason about uncertain information.
3. Problem-Solving
Planning and determining a sequence of actions to achieve a goal. This includes searching through possible solutions and selecting the best one (e.g., pathfinding in navigation).
4. Perception
Interpreting sensory data such as vision, sound, or touch. For example, computer vision (image analysis) or natural language processing (understanding spoken or written language).
5. Language Understanding
Processing and understanding human language. This includes natural language understanding (NLU) and natural language generation (NLG).
6. Interaction
Engaging with users or environments, often in natural ways. This could include chatbots, voice assistants, robotics, or human-computer interfaces.
7. Self-Correction / Adaptation
Continuously improving based on feedback or new data. This is often seen in machine learning models that retrain or adjust over time for better performance.
The seven processes of Artificial Intelligence (AI) generally refer to key functional areas or stages that define how AI systems operate. These aren’t universally codified like a strict framework, but a common and widely taught model includes the following:
1. Learning
AI systems acquire knowledge through data. This can be:
• Supervised learning (learning from labeled data),
• Unsupervised learning (finding patterns in unlabeled data),
• Reinforcement learning (learning from feedback/rewards).
2. Reasoning
Drawing inferences or conclusions from facts or data. AI uses logical rules or probabilistic models to reason about uncertain information.
3. Problem-Solving
Planning and determining a sequence of actions to achieve a goal. This includes searching through possible solutions and selecting the best one (e.g., pathfinding in navigation).
4. Perception
Interpreting sensory data such as vision, sound, or touch. For example, computer vision (image analysis) or natural language processing (understanding spoken or written language).
5. Language Understanding
Processing and understanding human language. This includes natural language understanding (NLU) and natural language generation (NLG).
6. Interaction
Engaging with users or environments, often in natural ways. This could include chatbots, voice assistants, robotics, or human-computer interfaces.
7. Self-Correction / Adaptation
Continuously improving based on feedback or new data. This is often seen in machine learning models that retrain or adjust over time for better performance.
1 month ago
