AI - Cognitive - basic

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AI capabilities:
  • Visual perception
    Use computer vision capabilities to accept, interpret, and process input from images, video streams, and live cameras.
  • Text analysis
    Use natural language processing (NLP) to read & extract semantic meaning from text-based data.
  • Speech
    Recognize speech as input and synthesize spoken output. Speech capabilities together with analysis of text enables a form of human-compute interaction that's become known as conversational AI.
  • Decision making
    Use past experience and learned correlations to assess situations and take appropriate actions.
  • Data science
    Discipline that focuses on the processing and analysis of data; applying statistical techniques to uncover and visualize relationships and patterns in the data, and defining experimental models that help explore those patterns.
  • Machine learning
    Subset of data science that deals with the training and validation of predictive models. Used by Data Scientist to predict values for unknown labels.
  • Artificial intelligence
    Most common is built on machine learning, can be a software that emulates one or more characteristics of human intelligence.
  • Azure Cognitive Services
    Cloud-based services that encapsulate AI capabilities.
Azure Cognitive Services capabilities:
  • Language
    • Text analysis
    • Question answering
    • Language understanding
    • Translation
  • Speech
    • Speech to Text
    • Text to Speech
    • Speech Translation
    • Speaker Recognition
  • Vision
    • Image analysis
    • Video analysis
    • Image classification
    • Object detection
    • Facial analysis
    • Optical character recognition
  • Decision
    • Anomaly detection
    • Content moderation
    • Content personalization
You can use:
  • Multi-service resource
    Cognitive services - single resource that enable Language, Computer Vision, Speech, etc.
  • Single service resource
    Each service must be provision separately. See list below.
Azure Cognitive services - Single service resource:
  • Language
    • Language
    • Translator
  • Speech
    • Speech
  • Vision
    • Computer Vision
    • Custom Vision
    • Face
  • Decision
    • Anomaly detection
    • Content moderation
    • Personalizer

Azure out-of-box solutions.
  • Azure Form Recognizer
    OCR solution
  • Azure Metrics Advisor
    Real-time monitoring and response to critical metrics (Iot).
  • Azure Video Analyzer for Media
    Video analysis solution.
  • Azure Immersive Reader
    Supports for people of all ages and abilities.
  • Azure Bot Service
    Deliver conversational AI solutions.
  • Azure Cognitive Search
    Extract insights from data and documents.

AI based services relay on trained models (found relations between features and labels and can predict unknown labels).
Any prediction is in fact a probability and has associated a confidence score.

When predictions affect people (most of them do so) ethical considerations mut be enforced.
Responsible AI:
  • Fairness
    Treat all people fairly (banks, insurance)
  • Reliability and safety
    Ex: all industry automations.
  • Privacy and security
  • Inclusiveness
    When training models make sure you include subject from all social, demographic, etc. categories.
  • Transparency
    Easy to understand what system is doing.
  • Accountability
    If there are problems, not the AI but the one who train & test the model are responsible.
  • Data Scientist
    • Ingest and prepare data.
    • Run experiments to explore data and train predictive models.
    • Deploy and manage trained models as web services.
  • AI Engineers
    Integrates AI capabilities into applications and services consumed by end users.
    • Use Azure ML designer to train machine learning models and deploy them as REST services that can be integrated into AI-enabled applications.
    • Collaborat with data scientists to deploy models based on common frameworks.
    • Use Azure ML SDKs or CLI scripts to orchestrate DevOps processes that manage versioning, deployment, and testing of machine learning models as part of an overall application delivery solution.

Hands-on REST Interface, Login to view

Hands-on Python SDK, Login to view

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