AI 101 - Course & Competition - Grades 7-12 - Sun@11:30am EST-Recording Workshop 7

Video Transcription

Video Summary

The video transcript covers a detailed classroom session on data analysis and natural language processing (NLP). Initially, the class reviews homework about calculating mean and standard deviation for datasets involving student grades, number of classes taken, and extracurricular activities, emphasizing interpreting formulas and frequency-weighted standard deviations for feature splits. The instructor clarifies concepts and answers questions about statistical calculations and dataset partitioning.<br /><br />The lesson then transitions to NLP fundamentals, explaining applications including author identification, sentiment analysis, machine translation, chatbots, text summarization, and named entity recognition. The "bag of words" model is introduced to represent text as word frequency vectors without preserving word order, followed by large language models (LLMs) such as GPT with billions of parameters that learn word sequence probabilities to generate text. The importance of converting words to numerical embeddings is explained, including methods like Word2Vec, cosine similarity for vector comparison, and the superiority of context-specific embeddings in LLMs over simpler models.<br /><br />Training stages of ChatGPT are outlined: supervised learning with prompt-answer pairs, reward modeling with human feedback ranking outputs, and reinforcement learning for optimizing responses. Prompt engineering and retrieval-augmented generation (RAG) are discussed as methods to improve chatbot accuracy by fetching relevant documents through embeddings and cosine similarity.<br /><br />Finally, homework and a competition format involving Google Forms with a mix of multiple-choice and free-response questions are covered, along with reminders on rules and upcoming lessons on reinforcement learning.

Keywords

data analysis

natural language processing

statistical calculations

bag of words

large language models

word embeddings

ChatGPT training

prompt engineering

retrieval-augmented generation