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Objectives:
Understand how AI learns by recognizing patterns
Learn the importance of providing clear and fair data to AI models
Explore how human decisions affect how AI behaves
Understand how AI (as discussed) relates to the concept Think like an Adversary.
Key Terms
Artificial Intelligence (AI) | A computer system that can learn from data and make decisions.
Training Data | The information given to AI to help it learn.
Pattern Recognition | The process of finding similarities or rules in the data.
Bias | When the AI makes unfair decisions because of bad or incomplete data.
Prediction | The AI’s decision is based on what it has learned.
Introduction
Imagine you’ve stepped into a time-traveling AI lab. Here, you can speak with realistic AI voice versions of Alan Turing, Grace Hopper, and John McCarthy.
Each one has knowledge from their time, but thanks to AI, they can also comment on modern-day technology.
Work with your partner or group to ask the models questions, and see what they have to say!
Meet the Experts
Consider questions you would like to ask the AI about these important figures
Alan Turing | Often considered the father of theoretical computer science and artificial intelligence. Turing's work on the Turing Machine laid the foundation for modern computing, and his famous Turing Test explored the concept of machine intelligence.
Sample Question | "What tools can bad actors use to crack codes, and what tools did you use?"
Marvin Minsky | American cognitive scientist and one of the founding fathers of artificial intelligence (AI)
Sample Question | "Why do developers skip patches or updates?"
John McCarthy | Known as one of the founders of AI, McCarthy coined the term "Artificial Intelligence" in 1955. He also developed the LISP programming language, which became integral to AI research.
Sample Question | "How might an adversary trick AI asstants?"
Q&A Session
Brainstorm questions as a group:
"What do you think about AI today?"
"Would you trust computers to make decisions about people?"
"How did you stay ethical in your work?"
Reflection
What did you learn about how AI makes decisions?
How does the data we give to AI affect the outcome?
How can we make sure AI is fair and helpful to everyone?
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