ChatGPT for Beginners

Introduction

• ChatGPT has significantly changed daily coding work for many, especially with its AI code generation capabilities.
• Feelings encountered: excitement, relief, fear of it getting better than human coding.
• Aim: Understand how ChatGPT works at a high level and create a beginner-friendly guide.

What is ChatGPT?

• A conversational AI capable of intelligent dialogue with humans.
• Comparable to Iron Man's Jarvis for building and diagnostics tasks through conversation.
• Represents a leap in AI, capable of better-than-human conversations in some areas.

Challenges in Conversational AI

• AI research: around 80 years old, with conversational AI being particularly challenging to crack.
• Natural languages are nuanced and context-dependent, making precise programming difficult.
• Example: Same word can mean different things based on context (like in English or Korean).
• Humans learn languages gradually; hence, creating an AI that mimics this process is tough.

How Scientists Cracked It

• Inspiration from human brain's neural structure and functions.
• Simulation of brain neurons in a computer to process conversations and generate responses.
• AI is reactionary: waits for user input, processes, and then generates a response.

Brain and Neurons

• Neurons are cells in the brain connected by thin connections for electrical signals.
• Different brain regions have neurons arranged uniquely based on their functions (memory, logic, etc).

Simplified AI Neuron Model

• Neurons receive inputs (0-9) and activate other neurons with different signals (5, 1, 9 is an example).
• AI simulates many neurons connected in a program to mimic brain functions.
• Training involves adjusting connections to improve response accuracy over iterations.

Real-World AI Applications

• Image Recognition: Neural networks recognize images (e.g., dog vs bird vs cat) with training data.
• Starts with random neural connections and adjusts based on feedback (reinforcement learning).
• Different structures of neural networks (e.g., recurrent networks) for various tasks.

ChatGPT Development

• Built upon advanced neural network research, notably patterns from human brain structure and Google’s research papers.
• Two neural networks in ChatGPT:
  1. Understanding input context: trained unsupervised with massive text data from the internet.
  2. Generating responses: fine-tuned by human judges for supervised learning.

Training Process

• Unsupervised learning for understanding context from extensive data scraping (takes about 1 year).
• Supervised learning for response generation (takes about 6 months) involving human feedback on correctness and ethics.

Comparisons: ChatGPT vs Humans

• Humans: 25 years to fully develop, fluid and adaptable, autonomous, energy-efficient (biofuel like food).
• ChatGPT: 1.5 years to train, fixed post-training, massive energy consumption, relies on continuous updates for improvement.

Future Prospects

• Current AI limitations and rigidness may improve, becoming more adaptable and efficient.
• Potential for young scientists to delve deeper and innovate further, making AI even more beneficial.