What Is Artificial Intelligence?

Quick Answer: Artificial intelligence (AI) is software that imitates human capabilities such as visual perception, speech recognition, decision-making, and language understanding. AI encompasses machine learning, deep learning, computer vision, NLP, and generative AI. On Azure, these capabilities are delivered through services like Azure AI Vision, Azure AI Language, Azure AI Speech, and Azure OpenAI Service.

Defining AI

At its core, artificial intelligence refers to computer systems that can perform tasks that typically require human intelligence. This includes:

• Visual perception — recognizing objects, faces, and text in images
• Speech recognition — converting spoken words into text
• Decision-making — making predictions or recommendations based on data
• Language understanding — comprehending and generating human language
• Content generation — creating new text, images, code, or other content

AI is not a single technology. It is an umbrella term that covers many approaches, techniques, and specializations:

The AI Hierarchy

Understanding how AI concepts nest within each other is critical for the AI-900:

Artificial Intelligence (broadest)
├── Machine Learning (learning from data)
│   ├── Supervised Learning (labeled data)
│   │   ├── Regression (predict numbers)
│   │   └── Classification (predict categories)
│   ├── Unsupervised Learning (no labels)
│   │   └── Clustering (find groups)
│   └── Deep Learning (neural networks)
│       ├── Computer Vision
│       ├── Natural Language Processing
│       └── Generative AI
└── Rule-Based Systems (explicit rules, no learning)

On the Exam: Questions often present a scenario and ask you to identify the type of AI workload. The key is recognizing the INPUT and OUTPUT: images = computer vision, text understanding = NLP, new content creation = generative AI, numerical predictions = regression, category predictions = classification.

Common AI Workloads

Microsoft organizes AI capabilities into several workload types that you must recognize on the exam:

1. Prediction and Forecasting

Using historical data to predict future outcomes. Examples include sales forecasting, stock price prediction, and weather modeling. This typically involves regression machine learning models.

2. Classification

Assigning items to categories based on their features. Examples include spam detection (spam or not spam), medical diagnosis (disease or no disease), and credit risk assessment (approve or deny). This uses classification machine learning models.

3. Anomaly Detection

Identifying unusual patterns that do not conform to expected behavior. Examples include fraud detection, manufacturing quality control, and network intrusion detection. Azure provides the Anomaly Detector service for this workload.

4. Computer Vision

Extracting meaningful information from images and videos. This includes image classification, object detection, facial recognition, and optical character recognition (OCR). Azure provides Azure AI Vision, Custom Vision, and Face API services.

5. Natural Language Processing (NLP)

Understanding, interpreting, and generating human language. This includes sentiment analysis, entity extraction, translation, and speech recognition. Azure provides Azure AI Language, Azure AI Speech, and Azure AI Translator services.

6. Conversational AI

Building AI-powered chatbots and virtual assistants that can hold natural conversations. Azure provides Azure AI Bot Service and Azure OpenAI Service for this workload.

7. Generative AI

Creating new content such as text, images, code, and audio. This uses large language models (LLMs) like GPT-4o and image generation models. Azure provides Azure OpenAI Service for this workload.

8. Knowledge Mining

Extracting insights from large volumes of unstructured data (documents, images, audio). Azure provides Azure AI Search with cognitive skills enrichment for this workload.

AI on Azure — Service Landscape

On the Exam: You do not need to know implementation details of these services for the AI-900. You need to know WHAT each service does and WHEN to use it. The AI-102 tests implementation; the AI-900 tests awareness and selection.