AI Foundations

AI Foundations

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• Learning Objectives:

• Define Artificial Intelligence (AI) and distinguish types.

• Identify six key categories of AI: Machine Learning, Computer Vision, Ethics, Robotics/Automation, NLP, and Data/Algorithms.

• Apply AI concepts to real-world examples, including autonomous vehicles and datasets.

AI in Daily Life

How are you using AI?

Definitions

Artificial Intelligence (AI)

Narrow AI (ANI)

General AI (AGI)

Concepts

Here, computers are trained to ‘see’ and interpret the visual world. Think of facial recognition, self-driving cars, or even apps that identify plants from photos.

This is about teaching computers to learn from data, so they can make predictions or decisions without being explicitly programmed for every scenario. For example, recommending a song based on your listening habits.

AI isn’t just technical—it has real-world consequences. We need to consider fairness, bias, privacy, and how AI affects jobs, society, and decision-making.

Computer Vision

Ethics & Societal Impact

Machine Learning

Artificial Intelligence

At the heart of all AI is data. Algorithms process that data to recognize patterns and make predictions. Understanding how data works and how algorithms operate is essential to building and evaluating AI systems.

This area focuses on how computers understand, interpret, and generate human language. Tools like chatbots, translation apps, or virtual assistants all rely on NLP.

This branch applies AI to physical systems, helping machines perform tasks autonomously, like warehouse robots or surgical assistants.

Natural Language Processing (NLP)

Data & Algorithms

Robotics & Automation

Activities

Finding Patterns in Data

AI in Autonomous Vehicles

1. Examine the dataset table.
2. Highlight trends or patterns.
3. Predict what actions AI might take.
4. Identify missing or biased data.
5. Share your findings with a partner.

1. Look at the car outline.
2. Label where each AI category is used (Machine Learning, Computer Vision, Ethics, Robotics/Automation, NLP, Data/Algorithms).
3. Write or draw examples in the boxes.

2. Discuss: How do these AI systems work together?

Lesson References

• Bird, K. A., Castleman, B. L., & Song, M. (2023). Are Algorithms Biased in Education? Exploring Racial Bias in Predicting Community College Student Success. Virginia Community College System. https://www.vccs.edu

• Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

• National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://www.nist.gov/itl/ai-risk-management-framework

• Future of Life Institute. (2023). AI Principles & Ethics. https://futureoflife.org/ai-principles

• Ayeshasal89. (2024). AI Assistant Usage in Student Life (Synthetic Dataset). Kaggle. https://www.kaggle.com/datasets/ayeshasal89/ai-assistant-usage-in-student-life-synthetic

• OpenAI. (2024). AI and Society: Foundations for Responsible Development. Retrieved from https://openai.com/research

Definition: AI that is designed to perform a specific task or a narrow set of tasks. Key Info: Examples include voice assistants (Siri, Alexa), recommendation engines (Netflix, Amazon), and spam filters. ANI does not possess general intelligence.

Definition:Hypothetical AI that can perform any intellectual task a human can do. Key Info: AGI remains theoretical; current AI technologies do not have human-level reasoning or consciousness.

Definition: The simulation of human intelligence processes by machines, including learning, reasoning, problem-solving, perception, and language understanding. Key Info: AI can range from simple automation to complex decision-making systems.