Introduction to Artificial Intelligence

Introduction

• Why you need to know about artificial intelligence

1. What Is Artificial Intelligence?

• Define general intelligence
• The general problem-solver
• Strong vs. weak AI

2. Popular Uses for Artificial Intelligence

• Predictive AI
• Generative AI

3. The Rise of Machine Learning

• Machine learning
• Artificial neural networks

4. Common AI Systems

• Searching for patterns in data
• Robotics
• Natural language processing
• The internet of things
• Generative systems

5. Learn from Data

• Labeled and unlabeled data
• Massive datasets
• Data models

6. Identify Patterns

• Classify data
• Cluster data
• Reinforcement learning

7. Machine Learning Algorithms

• Common algorithms
• K-nearest neighbor
• K-means clustering
• Regression
• Naive Bayes

8. Fit the Algorithm

• Select the best algorithm
• Follow the data
• Overfitting and underfitting

9. Artificial Neural Networks

• Build a neural network
• Weighing the connections
• The activation bias

10. Improve Accuracy

• Learning from mistakes
• Step through the network

11. The Rise of Generative AI

• Self-supervised learning
• Foundation models
• Large language models (LLM)
• Image diffusion models

12. Generative AI Architecture

• Generative adversarial networks (GAN)
• Variational autoencoder (VAE)
• Transformers

13. Ethical and Legal Challenges

• The alignment problem
• Decision traceability
• Copyright challenges
• Privacy concerns

14. Where to Go from Here

• Using AI systems
• Applying AI to solve problems