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AI Fundamentals and Responsible AI Principles
Basic AI concepts, common AI workloads and the 6 responsible AI principles
⏱️ Estimated reading time: 20 minutes
Common AI Workloads
Artificial intelligence encompasses various technologies that enable machines to perform tasks that traditionally require human intelligence. Common AI workloads include:
Machine Learning (ML): Algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
Computer Vision: Analysis and interpretation of images and videos to extract visual information.
Natural Language Processing (NLP): Understanding and generation of human language by machines.
Knowledge Mining: Extraction of valuable information and insights from large volumes of unstructured data.
Generative AI: Creation of new content such as text, images, or code using deep learning models.
Machine Learning (ML): Algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
Computer Vision: Analysis and interpretation of images and videos to extract visual information.
Natural Language Processing (NLP): Understanding and generation of human language by machines.
Knowledge Mining: Extraction of valuable information and insights from large volumes of unstructured data.
Generative AI: Creation of new content such as text, images, or code using deep learning models.
🎯 Key Points
- ✓ Machine Learning focuses on predictions based on historical data
- ✓ Computer Vision processes visual content like images and videos
- ✓ NLP enables natural interaction between humans and machines
- ✓ Knowledge Mining extracts insights from unstructured data
- ✓ Generative AI creates original new content
The 6 Responsible AI Principles
Microsoft has established six fundamental principles to ensure that AI systems are developed and used ethically and responsibly:
Fairness: AI systems must treat all people fairly, avoiding bias and discrimination.
Reliability & Safety: Systems must behave consistently and safely, even under unexpected conditions.
Privacy & Security: Protection of users' personal and confidential data.
Inclusiveness: AI systems must be accessible and beneficial to all people, regardless of their abilities.
Transparency: Users should be able to understand how AI systems work and how decisions are made.
Accountability: People and organizations are responsible for the consequences of AI systems.
Fairness: AI systems must treat all people fairly, avoiding bias and discrimination.
Reliability & Safety: Systems must behave consistently and safely, even under unexpected conditions.
Privacy & Security: Protection of users' personal and confidential data.
Inclusiveness: AI systems must be accessible and beneficial to all people, regardless of their abilities.
Transparency: Users should be able to understand how AI systems work and how decisions are made.
Accountability: People and organizations are responsible for the consequences of AI systems.
🎯 Key Points
- ✓ Fairness: Avoid bias in data and algorithms
- ✓ Reliability: Consistent behavior and error handling
- ✓ Privacy: Protection of user's sensitive data
- ✓ Inclusiveness: Accessibility for people with disabilities
- ✓ Transparency: Explainability of AI decisions
- ✓ Accountability: Humans are responsible for AI systems