Overview
This lecture introduces the basics of artificial intelligence (AI), compares AI functions to human abilities, and explains key subfields and learning methods.
What is Artificial Intelligence?
- AI is a branch of computer science aimed at creating systems that function intelligently and independently.
- AI is often compared to human intelligence to explain its concepts and goals.
Core AI Subfields and Human Analogies
- Speech Recognition allows computers to understand and process spoken language, similar to humans conversing.
- Natural Language Processing (NLP) enables machines to read and write text in human languages.
- Computer Vision lets machines interpret visual data, analogous to human sight.
- Image Processing helps computers handle images, supporting computer vision.
- Robotics enables machines to perceive environments and move, like humans navigating surroundings.
- Pattern Recognition allows identification of patterns within data; machines excel here due to handling high-dimensional data.
- Machine Learning involves computers learning from data to identify patterns, classify, or predict outcomes.
Connection to Human Brain and Neural Networks
- The human brain is a network of neurons; mimicking this creates neural networks in AI.
- Neural Networks are algorithms modeled after brain structure for learning tasks.
- Deep Learning uses complex neural networks to solve advanced tasks.
- Convolutional Neural Networks (CNNs) scan images for object recognition in computer vision.
- Recurrent Neural Networks (RNNs) help machines remember sequences, similar to human memory.
AI Learning Methods
- Symbolic-based AI uses programmed rules to process information.
- Data-based AI (Machine Learning) requires large datasets for learning.
- Supervised Learning trains algorithms with labeled data (with correct answers).
- Unsupervised Learning lets machines find patterns in unlabeled data.
- Reinforcement Learning involves machines learning by trial and error to reach a goal.
AI Tasks: Classification and Prediction
- Classification assigns data points to categories (e.g., grouping customers as "young adults").
- Prediction uses past data to forecast future outcomes (e.g., predicting customer defection).
Key Terms & Definitions
- Artificial Intelligence (AI) — Computer science field for making intelligent, independent systems.
- Speech Recognition — Technology for understanding spoken language.
- Natural Language Processing (NLP) — Processing and understanding text data.
- Computer Vision — Enabling computers to interpret visual information.
- Machine Learning — Algorithms learning from data to identify patterns.
- Neural Network — Computer models mimicking the brain’s learning structure.
- Deep Learning — Advanced neural networks for complex tasks.
- Convolutional Neural Network (CNN) — Neural network for image analysis.
- Recurrent Neural Network (RNN) — Neural network for sequential memory tasks.
- Supervised Learning — Training with input and known output.
- Unsupervised Learning — Training with input only, no answers given.
- Reinforcement Learning — Training using rewards and trial-and-error.
Action Items / Next Steps
- Review these AI subfields and learning types.
- Prepare examples of classification and prediction tasks for discussion.
- Read an introduction to neural networks and machine learning basics.