Artificial "Intelligence" Basics

In this 3-day course you’ll learn the fundamentals required for A.I. up-skilling. When the term “Artificial Intelligence” was coined by John McCarthy, it was challenged by his contemporaries. Some thought “Complex Information Processing” was a better moniker. It’s unfortunate that this descriptive term didn’t stick, as it might have prevented much of the confusion around the capabilities of A.I. If you’re seeking a non-technical, practical, comprehensive overview of fundamental A.I. concepts, this course is for you! It will help you distinguish A.I. fact from A.I. fiction. Designed around our proprietary B.A.S.I.C.S. moniker, your team will learn the fundamentals required for basic A.I. up-skilling, including:

B - Basic Terms and Concepts
A - Applications and Use Cases
S - Separating Truth from Fiction
I - Interactive Practice Exercises
C - Comparing Popular LLMs
S - Social Concerns and Solutions

The course covers basic AI terminology, the role of algorithms and data, the difference between Generative AI and Predictive AI, AI use cases in various business functions, popular misconceptions about AI, the limitations of AI, how algorithms work, and ethical considerations in AI systems.

You'll Learn To:

• Explain basic AI terminology
• Explain the role of algorithms
• Explain the role of data
• Distinguish between Generative AI and Predictive AI
• Identify AI use cases across 7 business functions, including Software Development, Marketing and Sales, Customer Support, HR, Cybersecurity, Finance and Legal
• Distinguish between AI facts and AI fiction
• Identify 6 popular misconceptions about A.I.
• Explain which types of human intelligence AI lags in and excels in
• Explain why humans will always be needed
• Explain the limitations of AI
• Gain a practical understanding of how algorithms work by training a simple learning machine model
• Practice writing a simple algorithm and testing its effectiveness
• Identify 6 popular LLMs
• Observe prompt responses from 6 popular LLMs
• Describe common ethical considerations when using AI systems
• Propose solutions to ethical case studies
• Describe the tenets of AI Explainability