Learning Objectives

concepts followed: 

Lesson 1 AI

• Common AI Workloads
• AI in Azure

Lesson 2 Responsible AI 

• Chanllenges and Risks with AI
• Principles of Responsible AI

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Lesson 1 AI

What is AI?

Software that imitates human capabilities

Software that exhibits human-like capabilities

Making decisions based on data and past experience

Recognizing abnormal events

Interpreting visual input

Understanding written and spoken language

Engaging in dialogs and conversations

The relationships of Data science, Machine learning and AI

AI: Intelligent software apps and agents.

ML: Use of data and agorithms to train predictive models.

Data Science: Application of mathematical and statistical techniques to analyze data. 

Common AI Workloads

Common AI Workloads

Machine Learning	Predictive models based on data and statistics - the foundation for AI
Anomaly Detection	Systems that  detect unusual patterns or events, enabling pre-emptive action.
Computer Vision	Applications that interpret visual input from cameras, images, or videos.
Natural Language Processing(NLP)	Applications that can interpret written or spoken language.
Conversatonal AI	AI agents, or bots, that can engage in dialogs with human users.

AI in Azure

Scalable, reliable cloud platform for AI  

 Data storage, compute, services.

AI in Azure

	Azure Machine Learning	A platform for trainning, deploying, and managing machine learning models.
	Cognitive Services	A suite of services developers can use to build AI solutions.
	Azure Bot Service	A cloud-based platform for developing and managing bots.

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Lesson 2 Responsible AI

Chanllenges and Risks with AI

Challenges and Risks with AI

Challenge or rRisk	Example
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 predictive app provides no audio output for visually impaired users.
Users must trust a coplex system	An AI-based financial tool makes investment recommendations - what are they based on?
Who's liable for AI-driven decisions?	An innocent person is convicted of a crime based on evidence from facial recognition - who's responsible?

Principles of Responsible AI

6 principles: 

Fairness | Reliability and safety | Privacy and Security

Inclusiveness | Transparency | Accountability