Describe Artificial Intelligence workloads and considerations
Identify common AI workloads and understand responsible AI principles for ethical AI development.
Covers identification of common AI workloads including computer vision, natural language processing, document processing, and generative AI. Also includes understanding guiding principles for responsible AI such as fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability in AI solutions.
5 minutes
5 Questions
Artificial Intelligence (AI) workloads in Azure encompass various categories of tasks that machines can perform to simulate human intelligence. These workloads are fundamental to understanding how AI solutions are implemented in real-world scenarios.
**Machine Learning Workloads** involve training models on data to make predictions or decisions. This includes classification (categorizing data), regression (predicting numerical values), and clustering (grouping similar items together).
**Computer Vision Workloads** enable machines to interpret and analyze visual information from images and videos. Applications include image classification, object detection, facial recognition, and optical character recognition (OCR) for reading text from images.
**Natural Language Processing (NLP) Workloads** allow machines to understand, interpret, and generate human language. This covers text analytics, sentiment analysis, language translation, and conversational AI through chatbots and virtual assistants.
**Document Intelligence Workloads** focus on extracting information from documents, forms, and receipts, automating data entry processes that traditionally required manual effort.
**Generative AI Workloads** involve creating new content such as text, images, or code based on learned patterns from existing data.
**Key Considerations for AI Workloads:**
1. **Fairness**: AI systems should treat all groups of people equitably and avoid bias in decision-making.
2. **Reliability and Safety**: Solutions must perform consistently and safely under various conditions.
3. **Privacy and Security**: Personal data must be protected, and systems should be secure against threats.
4. **Inclusiveness**: AI should be accessible and beneficial to people of all abilities.
5. **Transparency**: Users should understand how AI systems make decisions.
6. **Accountability**: Organizations must take responsibility for their AI systems and their outcomes.
These principles guide responsible AI development and deployment, ensuring that AI solutions benefit society while minimizing potential harms.Artificial Intelligence (AI) workloads in Azure encompass various categories of tasks that machines can perform to simulate human intelligence. These workloads are fundamental to understanding how AI solutions are implemented in real-world scenarios.
**Machine Learning Workloads** involve training…