ChatGPT Explained From Scratch An Intuitive Technical Understanding of AI Chatbots
AI Summary
Summary of the Video: Understanding ChatGPT
Introduction
- ChatGPT is one of the fastest-growing applications, reaching around 100 million active users in just 2 months.
- The video aims to provide a foundational understanding of how ChatGPT works.
Key Concepts
Large Language Models (LLMs)
- LLMs are machine learning models with a large number of parameters, often implemented through Deep Neural Networks.
- GPT (Generative Pre-Trained Transformer) is a specific type of LLM.
Reinforcement Learning with Human Feedback (RLHF)
- RLHF involves using human feedback to fine-tune the model for better performance.
Historical Context
- Early attempts to model language using grammatical rules were unsuccessful.
- Statistical NLP in the late ’90s and early 2000s aimed to predict the next word based on preceding words.
Language Model Development
- Models needed to consider a larger context for accurate predictions. Simple models like unigram or bigram fail as they lack long-term dependency recognition.
- Higher-order models (like n-grams) increase computational complexity.
Neural Networks
- The introduction of neural networks allowed more complex representations of language.
- Features of data can be automatically extracted to make predictions.
Word Representation Techniques
- One-Hot Encoding: High-dimensional and inefficient for large vocabularies.
- Word2Vec Embeddings: Low-dimensional representations (around 300 dimensions).
Transformers and Attention Mechanism
- Introduced in 2017 by Google, attention allows the model to consider the context of words for better predictions.
- Positional Encoding: Added to word embeddings to retain word order sensitivity.
The Transformer Block
- Consists of self-attention, layer normalization, and feedforward layers.
- GPT-3 has 96 layers and 175 billion parameters, processing up to 2048 tokens at a time.
From GPT to ChatGPT
- Fine-Tuning: Supervised learning on a human-created dataset to generate expected outputs.
- Human Feedback: Outputs are rated by humans to train a rewards model that predicts output quality.
- Reinforcement Learning: The fine-tuned GPT model’s parameters are updated based on rewards from the rewards model.
Conclusion
- Understanding ChatGPT involves grasping the underlying technology of LLMs and reinforcement learning. The landscape of AI is rapidly evolving, and this information may soon be outdated.