Your Top AI Questions Answered: AI Literacy for Everyone

Course details

Explore the dynamic landscape of artificial intelligence and gain a strong understanding of both traditional and generative AI. Join instructor and generative AI expert Dr. Marily Nika as she offers an overview of the history of AI—from its conceptual inception to its current state. Discover the differences between traditional AI and generative AI, before diving into constructing effective prompts to enhance the usefulness of AI responses in various contexts. Learn about key technologies including transformer architecture, large language models (LLMs), and real-world applications like chatbots, translators, and agents. Along the way, Marily covers the importance of data quality, expert human contribution, and the back-end technology, such as GPUs, all of which are essential for training AI systems. This course is an ideal fit for tech enthusiasts, data scientists, professionals in tech-driven fields, and anyone looking to leverage AI for personal or professional growth.

Learning objectives
Compare traditional AI systems with generative AI models and describe their unique capabilities and applications.
Explain what a GPU is and how it leads to overall cost for running AI models.
Outline the key components of an effective generative AI prompt.
Identify the ethical considerations in AI development and explain strategies for practicing responsible AI.
Summarize the impact of data quality on AI outcomes.

Concepts

0. Introduction

01 - Answers to your most common AI questions

1. Understanding AI and GenAI's Unique Capabilities

02 - A quick history of AI
03 - What's the difference between traditional AI and generative AI
04 - Why does AI cost so much to create

2. Generative AI Technical Concepts

05 - What's an LLM
06 - What does it mean to train an AI model
07 - What is a GPU
08 - What is an AI agent and how can it help you

3. Working with Generative AI Tools

09 - What makes an effective prompt
10 - What is Chain of Thought prompting

4. Data and Quality Considerations

11 - Why does quality matter in AI
12 - How can we ensure data integrity in AI
13 - How does data quality impact AI outcomes

5. Ethics and Compliance

14 - What are the ethical considerations in AI
15 - How can we practice responsible AI
16 - What are the regulations and compliance requirements for AI
17 - Could we have coverage of the environmental impact of AI when we shouldn't use it to avoid overuse wasting computing power