Syllabus

What is AI?

• Machine learning - This is often the foundation for an AI system, and is the way we "teach" a computer model to make predictions and draw conclusions from data.
• Computer vision - Capabilities within AI to interpret the world visually through cameras, video, and images.
• Natural language processing - Capabilities within AI for a computer to interpret written or spoken language, and respond in kind.
• Document intelligence - Capabilities within AI that deal with managing, processing, and using high volumes of data found in forms and documents.
• Knowledge mining - Capabilities within AI to extract information from large volumes of often unstructured data to create a searchable knowledge store.
• Generative AI - Capabilities within AI that create original content in a variety of formats including natural language, image, code, and more.

Challenges and risks with AI

Six principles of Responsible of AI

1. Fairness:- AI systems should be designed and deployed to avoid bias and discrimination. ****
2. Reliability and safety:- AI systems should be reliable and perform as intended. They should be designed to minimize the risk of errors and should include safeguards to prevent harm to users and society.
3. Privacy and security:- AI systems should be designed with strong privacy protections to ensure that personal data is handled responsibly and securely.
4. Inclusiveness:- AI systems should empower everyone and engage people.
5. Transparency:- AI systems should be understandable.
6. Accountability

What is Machine Learning ?

• A Machine Learning model is a software application that encapsulates a function to calculate an output value based on one or more input values.