Introduction to AI
Table of contents
- Understand machine learning
- Understand anomaly detection
- Understand computer vision
- Natural language processing
- Knowledge Mining
- Challenges and risks with AI
- Responsible AI
What is AI?
- AI is the creation of software that imitates human behaviours and capabilities.
Key workloads of AI
Machine learning - "Teaching" a computer model to make predictions and draw conclusions from data.
Anomaly detection - Automatically detecting errors or unusual activity in a system.
Computer vision - Interpreting the world visually through cameras, video, and images.
Natural language processing - Interpreting written or spoken language, and responding in kind.
Knowledge mining - Extracting information from large volumes of often unstructured data to create a searchable knowledge store.
Understand machine learning
How does machine learning work?
Machines learn from data.
Huge volumes of data are generated in our everyday lives.
Data scientists use this data to train machine-learning models.
Models make predictions and inferences based on the relationships found in the data.
A real-world example of machine learning in sustainable farming
The Yield, an agricultural technology company in Australia, uses sensors, data, and machine learning to help farmers make informed decisions.
Machine learning is used to analyze weather, soil, and plant conditions.
Machine learning in Microsoft Azure
Azure Machine Learning service is a cloud-based platform for creating, managing, and publishing machine learning models.
Provides features and capabilities for machine learning.
|Automated machine learning
|This feature enables non-experts to quickly create an effective machine-learning model from data.
|Azure Machine Learning designer
|A graphical interface enabling no-code development of machine learning solutions.
|Data and compute management
|Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale.
|Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks.
Understand anomaly detection
Credit card transaction monitoring to detect fraud
Automated production line activity tracking to identify failures
Racing car telemetry system to proactively warn about mechanical failures
Anomaly detection is a machine learning-based technique
It analyzes data over time and identifies unusual changes
Anomaly Detection in Microsoft Azure
Microsoft Azure Anomaly Detector service offers an API for developers
Developers can use this API to create anomaly detection solutions.
Sensors in the car collect telemetry, such as engine revolutions, brake temperature, and so on.
An anomaly detection model is trained to understand expected fluctuations in the telemetry measurements over time.
If a measurement occurs outside of the normal expected range, the model reports an anomaly that can be used to alert the race engineer to call the driver in for a pit stop to fix the issue before it forces retirement from the race.
Understand computer vision
Computer Vision is an area of AI that deals with visual processing.
Let's explore some of the possibilities that computer vision brings.
The Seeing AI app is a great example of the power of computer vision. Designed for the blind and low-vision community, the Seeing AI app harnesses the power of AI to open up the visual world and describe nearby people, text and objects.
Computer Vision models and capabilities
Most computer vision solutions are based on machine learning models that can be applied to visual input from cameras, videos, or images.
The following table describes common computer vision tasks.
|Image classification involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.
Object detection machine learning models are trained to classify individual objects within an image, and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicle.
Semantic segmentation is an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with "mask" layers to highlight different vehicles using specific colors.
You can create solutions that combine machine learning models with advanced image analysis techniques to extract information from images, including "tags" that could help catalog the image or even descriptive captions that summarize the scene shown in the image.
Computer vision services in Microsoft Azure
|Azure AI Vision
|You can use this service to analyze images and video, and extract descriptions, tags, objects, and text.
|Azure AI Custom Vision
|Use this service to train custom image classification and object detection models using your own images.
|Azure AI Face
|The Azure AI Face service enables you to build face detection and facial recognition solutions.
|Azure AI Document Intelligence
|Use this service to extract information from scanned forms and documents.
Natural language processing
Definition of NLP
- Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language
Capabilities of NLP
Analyze and interpret text in documents, email messages, and other sources
Interpret spoken language, and synthesize speech responses
Automatically translate spoken or written phrases between languages
Interpret commands and determine appropriate actions
Example of NLP in a VR game
Starship Commander is a virtual reality (VR) game that uses NLP
Players can control the narrative and interact with in-game characters and starship systems using NLP
Natural language processing in Microsoft Azure
|Azure AI Language
|Use this service to access features for understanding and analyzing text, training language models that can understand spoken or text-based commands, and building intelligent applications.
|Azure AI Translator
|Use this service to translate text between more than 60 languages.
|Azure AI Speech
|Use this service to recognize and synthesize speech, and to translate spoken languages.
|Azure AI Bot Service
|This service provides a platform for conversational AI, the capability of a software "agent" to participate in a conversation. Developers can use the Bot Framework to create a bot and manage it with Azure Bot Service - integrating back-end services like Language, and connecting to channels for web chat, email, Microsoft Teams, and others.
Knowledge mining involves extracting information from large volumes of unstructured data
Creates a searchable knowledge store
Azure Cognitive Search
A knowledge mining solution in Microsoft Azure
Private, enterprise search solution with tools for building indexes
Indexes can be used for internal or public-facing content
Azure Cognitive Search and AI
Utilizes AI capabilities of Azure AI services
Includes image processing, content extraction, and natural language processing
Can index previously unsearchable documents
Extracts and surfaces insights from large amounts of data quickly.
Challenges and risks with AI
Artificial Intelligence is a powerful tool that can be used to greatly benefit the world. However, like any tool, it must be used responsibly.
|Challenge or Risk
|Bias can affect results
|A loan-approval model discriminates by gender due to bias in the data with which it was trained
|Errors may cause harm
|An autonomous vehicle experiences a system failure and causes a collision
|Data could be exposed
|A medical diagnostic bot is trained using sensitive patient data, which is stored insecurely
|Solutions may not work for everyone
|A home automation assistant provides no audio output for visually impaired users
|Users must trust a complex system
|An AI-based financial tool makes investment recommendations - what are they based on?
Treating all people fairly
AI systems should treat all people fairly
Avoid bias based on gender, ethnicity, or other factors that result in an unfair advantage or disadvantage
Azure Machine Learning and fairness
Azure Machine Learning includes the capability to interpret models and quantify the extent of each feature's influence on predictions
This helps identify and mitigate bias in the model
Responsible AI and facial recognition
Microsoft's implementation of Responsible AI includes retiring facial recognition capabilities
These capabilities can be misused to infer emotional states and identity attributes
Subjecting people to stereotyping, discrimination, or unfair denial of services
Reliability and Safety
Importance of reliability and safety in AI systems
AI systems in critical applications like autonomous vehicles or medical diagnoses must perform reliably and safely
Unreliable AI systems can pose a significant risk to human life
Testing and deployment management for AI software
AI-based software applications must undergo rigorous testing and deployment management processes
This ensures that the systems work as expected before their release
Security and privacy considerations in AI systems
AI systems should be secure and respect privacy
Machine learning models rely on large volumes of data, which may contain personal details
Privacy and security considerations need to be maintained even after training the models and deploying the system
As new data is used to make predictions or take action, both the data and decisions made may raise privacy or security concerns
AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.
AI systems should be understandable. Users should be made fully aware of the purpose of the system, how it works, and what limitations may be expected.
People should be accountable for AI systems. Designers and developers of AI-based solutions should work within a framework of governance and organizational principles that ensure the solution meets ethical and legal standards that are clearly defined.