Getting started with AI

What is Artificial Intelligence?

AI is the ability of a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Examples include— making decisions based on past happenings, seeing, understanding, relating things, and engaging in conversations.

Areas of Artificial Intelligence

1. Machine Learning — is the foundation of an AI system. It's how we teach computer models to learn and make decisions based on past data.
2. Anomaly Detection — is the capability to automatically detect errors or unusual activity in a system.
3. Computer Vision — is the capability of a system to interpret the world through cameras, images, and videos.
4. Natural Language Processing (NLP) — is the capability of a computer to interpret written or spoken words, interpret and respond.
5. Conversational AI — the capability of a software agent (bot) to participate in a conversation.

AI services available in Microsoft Azure

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

Principles of Responsible AI

AI is powerful and as a superhero saying goes, “With great power, comes great responsibility” hence it's important we make AI responsible. We do this by setting some principles.

1. Fairness — AI systems should treat all people fairly without any bias.
2. Reliability & Safety — AI systems should perform reliably and safely.
3. Privacy & Security — AI systems should be secure and must respect privacy.
4. Inclusiveness — AI systems should empower everyone and bring benefits to all.
5. Transparency — AI systems must be understandable. Users should be made fully aware of the purpose of the system and what its limitations are.
6. Accountability — People should be accountable for AI systems. Designers and developers should work within a framework and organizational principles that ensure the solution meets ethical and legal standards.

Some more terms

1. Regression — a form of ML that is used to predict a numeric value based on an item's features.
2. Classification — is used to predict which category an item belongs to. This is a supervised ML technique.
3. Clustering — is a form of ML used to group similar items based on their features. This is an example of an unsupervised ML technique.