Key Concepts (A)

   In spite of having a limited knowledge of AI, the most important concepts I learned in module 1 are considered for me a huge achievement in my life because it gave me the opportunity to learn about the astonishing world of AI. For instance, some of the terms I learned are,

Artificial Intelligence: AI is a branch of computer science that deals with the creation of intelligent agents and systems that can reason, learn, act, etc. 

Generative AI: is a type of artificial intelligence technology that can produce various types of content- including text, imagery, audio, and synthetic data.

Machine learning: machine learning which is a subfield of AI gives the computer the ability to learn without explicit programming.

Generative AI: is a type of Artificial Intelligence that creates new content based on what it has learned from existing content.

Large Language Models: (LLM) are one type of generative AI since they generate novel combinations of text in the form of natural-sounding language.

Prompt design: is the process of creating a prompt that will generate the desired output from a large language model.

Prompt engineering: is the process of creating a prompt that is designed to improve performance. This may involve using domain-specific knowledge, providing examples of the desired output, or using keywords that are known to be effective for the specific system.

The seventh AI principles:

1. Socially beneficial

2. Avoid creating or reinforcing unfair bias

3. Built and tested for safety

4. Accountable to people

5. Incorporate privacy design principles

6. Uphold high standards of scientific excellence

7. Made available for uses that accord with these principles

Responsible AI: is an iterative practice - the impact you want to make with your products, will go a long way in building AI responsibly.

Hallucinations: The AI model generates content that is unrealistic, fictional, or completely fabricated.

AI bias: also called machine learning bias or algorithm bias, refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithm—leading to distorted outputs and potentially harmful outcomes.

Design thinking: is a systemic, intuitive, customer-focused problem-solving approach that organizations can use to respond to rapidly changing environments and to create maximum impact. Design thinking is a methodology that we use to solve complex problems, and it’s a way of using systemic reasoning and intuition to explore ideal future states.

The five stages of design thinking are:

1. Empathize: research your users' needs.

2. Define: state your users' needs and problems.

3. Ideate: challenge assumptions and create ideas.

4. Prototype: start to create solutions.

5. Test: try your solutions out.