Overview
This lecture provides a concise introduction to artificial intelligence (AI), explaining its main fields, how AI relates to human abilities, and the core machine learning techniques.
What is Artificial Intelligence?
- AI is a branch of computer science aimed at creating systems that function intelligently and independently.
- AI seeks to replicate human abilities such as speaking, seeing, moving, and recognizing patterns.
Key Fields within AI
- Speech Recognition: Enables machines to understand spoken language through statistical learning.
- Natural Language Processing (NLP): Allows machines to read and interpret written text.
- Computer Vision: Enables machines to process and understand images and visual information.
- Image Processing: Prepares images for computer vision, though not directly AI.
- Robotics: Focuses on machines understanding their environment and moving autonomously.
- Pattern Recognition: Identifies patterns in data; machines often outperform humans due to their ability to process more data.
- Machine Learning: Machines learn patterns from data to make decisions or predictions.
Neural Networks and Deep Learning
- The human brain is a network of neurons, inspiring artificial neural networks in AI.
- Complex, multilayered ("deep") networks are used for learning complex tasks (deep learning).
- Convolutional Neural Networks (CNNs): Specialized for recognizing objects in images, crucial for computer vision.
- Recurrent Neural Networks (RNNs): Designed to remember sequences or past events, mimicking human memory.
Machine Learning Approaches
- Symbolic-Based AI: Processes information using rules and symbols.
- Data-Based AI (Machine Learning): Learns from large datasets to find patterns and make predictions.
- Machines can analyze high-dimensional data and recognize patterns beyond human capability.
Types of Machine Learning Tasks
- Classification: Assigns data to categories, e.g., grouping customers.
- Prediction: Uses patterns in data to forecast outcomes, e.g., customer behavior.
Types of Learning Algorithms
- Supervised Learning: Trains with labeled data where correct answers are provided (e.g., recognizing friends by name).
- Unsupervised Learning: Finds patterns without labeled answers (e.g., categorizing celestial objects).
- Reinforcement Learning: Learns through trial-and-error to achieve a goal (e.g., a robot learning to climb a wall).
Key Terms & Definitions
- AI (Artificial Intelligence) — Computer systems that perform intelligent tasks independently.
- Speech Recognition — Technology that translates spoken language into text.
- Natural Language Processing (NLP) — Technology that enables machines to understand written language.
- Computer Vision — Enables computers to interpret and process visual information.
- Pattern Recognition — Identifies patterns and regularities in data.
- Machine Learning — Algorithms that allow computers to learn from data.
- Neural Network — Computer model inspired by the human brain’s network of neurons.
- Deep Learning — Use of complex neural networks to learn from large data sets.
- CNN (Convolutional Neural Network) — Neural network type suited for image and object recognition.
- RNN (Recurrent Neural Network) — Neural network that processes sequential data.
Action Items / Next Steps
- Review definitions of key AI fields and learning types.
- Practice identifying examples of supervised, unsupervised, and reinforcement learning in real life.