Introduction to Artificial Intelligence

Contents

• Introduction Introduction

  • Why you need to know about artificial intelligence

    49s
• 1. What Is Artificial Intelligence? 1. What Is Artificial Intelligence?

  • Define general intelligence

    3m 13s
  • The general problem-solver

    3m 7s
  • Strong vs. weak AI

    3m 1s
• 2. Popular Uses for Artificial Intelligence 2. Popular Uses for Artificial Intelligence

  • Predictive AI

    3m 18s
  • Generative AI

    3m 3s
• 3. The Rise of Machine Learning 3. The Rise of Machine Learning

  • Machine learning

    3m 20s
  • Artificial neural networks

    3m 5s
• 4. Common AI Systems 4. Common AI Systems

  • Searching for patterns in data

    3m 35s
  • Robotics

    3m 19s
  • Natural language processing

    3m 26s
  • The internet of things

    3m 18s
  • Generative systems

    3m 27s
• 5. Learn from Data 5. Learn from Data

  • Labeled and unlabeled data

    3m 7s
  • Massive datasets

    3m 40s
  • Data models

    3m 7s
• 6. Identify Patterns 6. Identify Patterns

  • Classify data

    3m 22s
  • Cluster data

    3m 24s
  • Reinforcement learning

    3m 9s
• 7. Machine Learning Algorithms 7. Machine Learning Algorithms

  • Common algorithms

    3m 19s
  • K-nearest neighbor

    3m 19s
  • K-means clustering

    3m 31s
  • Regression

    3m 25s
  • Naive Bayes

    3m 29s
• 8. Fit the Algorithm 8. Fit the Algorithm

  • Select the best algorithm

    3m 21s
  • Follow the data

    3m 3s
  • Overfitting and underfitting

    3m 19s
• 9. Artificial Neural Networks 9. Artificial Neural Networks

  • Build a neural network

    3m 22s
  • Weighing the connections

    3m 3s
  • The activation bias

    3m 2s
• 10. Improve Accuracy 10. Improve Accuracy

  • Learning from mistakes

    3m 23s
  • Step through the network

    4m 3s
• 11. The Rise of Generative AI 11. The Rise of Generative AI

  • Self-supervised learning

    3m 39s
  • Foundation models

    3m 38s
  • Large language models (LLM)

    3m 40s
  • Image diffusion models

    3m 35s
• 12. Generative AI Architecture 12. Generative AI Architecture

  • Generative adversarial networks (GAN)

    3m 28s
  • Variational autoencoder (VAE)

    3m 28s
  • Transformers

    3m 30s
• 13. Ethical and Legal Challenges 13. Ethical and Legal Challenges

  • The alignment problem

    3m 42s
  • Decision traceability

    3m 23s
  • Copyright challenges

    3m 26s
  • Privacy concerns

    3m 14s
• 14. Where to Go from Here 14. Where to Go from Here

  • Using AI systems

    3m 14s
  • Applying AI to solve problems

    1m 22s