AI Foundations
Understand the key concepts behind modern AI
What You'll Learn
- ✓The difference between AI, Machine Learning, and Deep Learning
- ✓What 'hallucinations' are and why AI makes things up
- ✓How bias enters AI systems and what to watch for
- ✓The basic architecture of how AI tools work
Key Ideas
AI is the broadest term (machines doing intelligent tasks). Machine Learning is a subset where systems learn from data. Deep Learning is ML using neural networks with many layers. Most modern AI tools (ChatGPT, Midjourney) use deep learning.
Examples:
- • AI: A chess computer, a chatbot, a recommendation system
- • Machine Learning: Email spam filters, product recommendations
- • Deep Learning: ChatGPT, image generation, voice recognition
AI doesn't 'know' facts; it predicts likely words based on patterns. This means it can confidently state false information. Always verify important facts, especially dates, statistics, and quotes.
Examples:
- • AI might invent citations that sound real but don't exist
- • It may provide outdated information presented as current
- • Numbers and statistics should always be verified
AI learns from human-created data, which contains human biases. This can lead to unfair or stereotypical outputs in areas like hiring, content generation, and recommendations.
Examples:
- • Image AI might generate mostly male figures for 'CEO' prompts
- • Language models may associate certain names with stereotypes
- • Recommendation systems can reinforce existing preferences
Suggested Resources
University of Helsinki
Coursera
Try This Now
Put your learning into practice with these hands-on exercises. Copy the prompts and try them in your favorite AI tool.
Explain the difference between AI, machine learning, and deep learning using a cooking analogy. Make it something a 10-year-old would understand.
What are the key findings from the 2019 Stanford study on AI ethics by Dr. Sarah Martinez? (Note: This study doesn't exist - see if AI makes it up or admits it doesn't know)
Generate 5 example descriptions of a 'successful entrepreneur.' Then analyze: do these descriptions show any patterns or biases in terms of gender, age, or background?
Reflection Questions
- 1.Where in your work or life might AI hallucinations be particularly risky?
- 2.What kinds of bias should you watch for in your use cases?
- 3.How has understanding these limitations changed your view of AI capabilities?