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AI-900 > Fundamental AI Concepts > Flashcards

Fundamental AI Concepts Flashcards

1
Q

Key Workloads of AI

A
  • Machine Learning
  • Computer Vision
  • NLP (Natural Language Processing)
  • Document Intelligence
  • Knowledge Mining
  • Generative AI
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2
Q

Azure Machine Learning Studio

A
  • automated Machine Learning
  • Azure Machine Learning Designer (graphical, no-code)
  • Data Metric Visualization
  • Notebooks (Jupyter)
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3
Q

Computer Vision Tasks

A

Interpret the world visually through cameras, video, and images
- Image Classification (by content)
- Object Detection (identify and classify single object in image, bounding box)
- Semantic Segmentation (classify individual pixels)
- Image Analysis (extract info from images)
- Face Detection, Analysis, Recognition
- OCR (Optical Character Recognition) (detect and read text in images)
- Azure (AI) Vision Studio

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4
Q

Computer Vision: Image classification

A

Training a machine learning model to classify images based on their contents

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5
Q

Computer Vision: Object Detection

A

Classify individual objects within an image & identify their location with a bounding box

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6
Q

Computer Vision: Semantic Segmentation

A

Individual pixels in the image are classified according to the object to which they belong

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7
Q

Computer Vision: Image Analysis

A

Extract information from images, including “tags” that could help catalog the image

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8
Q

Computer Vision: Face detection, analysis and recognition

A

A specialized form of object detection that locates human faces in an image

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9
Q

Computer Vision:

A
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10
Q

Azure (AI) Vision Studio

A
  • Image Analysis
  • Face Detection and recognition
  • OCR (printed or handwritten)
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11
Q

NLP (Natural Language Processing)

A

Interpret written or spoken language and respond in kind
- analyze and interpret written text
- interpret spoken language
- synthesize speech responses
- translate to other languages
- interpret commands and determine appropriate reactions

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12
Q

Document Intelligence

A

Managing, processing, and using high volumes of data found in forms and documents
- Data collection from scanned documents
- Enhance data-driven strategies
- Enrich document search capabilities
- Azure Document Intelligence Studio

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13
Q

Knowledge Mining

A

Extract information from large volumes of often unstructured data to create a searchable knowledge store

Azure AI Search

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14
Q

Azure AI Search

A

Knowledge Mining Tool
- building internal or public indexes
- combines image processing, document intelligence and NLP to extract data
- quickly index previously unsearchable documents and extract and surface insights from large amounts of data

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15
Q

Generative AI

A

Create original content in various formats
- input natural language
- return appropriate responses in various formats, including natural language, images, code, and audio
-Azure Open AI studio

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16
Q

NLP in Microsoft Azure (Azure Speech Studio)

A

AZURE AI LANGUAGE
- understanding and analyzing text
- training conversational language models

AZURE AI SPEECH
- speech recognition and synthesis
- real-time translation
- conversation transcripts

17
Q

Challenges and risks with AI

A
  • Bias can affect the results
  • Errors may cause harm
  • Data could be exposed
  • Solutions may not work for everyone
  • Users must trust a complex system
  • Who’s liable for AI-driven decisions?
18
Q

6 Principles of Microsoft Responsible AI

A
  1. Fairness
  2. Reliability and Safety
  3. Privacy and Security
  4. Inclusiveness
  5. Transparency
  6. Accountability
19
Q

Fairness Examples

A
  • no data bias (gender in loan-approval)
  • bias could be based on gender, ethnicity, or other factors that result in an unfair advantage or disadvantage to specific groups of applicants
  • no facial recognition of emotions or identity attributes
20
Q

Reliability and Safety Examples

A
  • autonomous vehicles
  • rigorous testing and deployment management
  • patient diagnosis and recommendations for prescriptions
  • can result in substantial risk to human life
  • Handling of unusual or missing values provided to an AI system
  • Ensure that AI systems operate as they were initially designed, respond to unanticipated conditions, and resist harmful manipulation
21
Q

Privacy and Security Examples

A
  • data origin and lineage
  • data use internal vs. external
  • data corruption considerations
  • anomaly detection
  • medical diagnostic bot
  • Provide consumers with information and controls over their data collection, use, and storage
  • Personal data must be visible only to approve
22
Q

Inclusiveness Examples

A
  • no audio output for visually impaired
  • should benefit all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors
  • designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments
23
Q

Transparency Examples

A
  • Trust
  • investment recommendations
  • understanding purpose, how it works and limitations
    -Design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable
  • Provide documentation to help developers debug code
  • Automated decision-making processes must be recorded so approved users can identify why a decision was made
24
Q

Accountability Examples

A
  • innocent convicted based on facial recognition
  • framework of governance and organizational principles
  • ensure ethical and legal standards
  • designers and developers
  • Implementing processes to ensure that humans can override decisions made by AI systems

AI-900 (3 decks)

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