Understanding GPT and Large Language Models

Introduction to GPT

• GPT: Generative Pre-trained Transformer
• A type of large language model (LLM)
• Can generate human-like text

Overview of the Video

1. What is an LLM?
2. How do LLMs work?
3. Business applications of LLMs?

1. What is an LLM?

• LLM: A type of foundation model
  • Pre-trained on large amounts of unlabeled and self-supervised data
  • Learns patterns in the data to produce adaptable output
• Applied specifically to text and text-like data (e.g., code)
• Trained on large datasets of text (books, articles, conversations)
  • Example: text dataset in petabytes (1 petabyte = 1 million gigabytes)
• Parameter count:
  • Parameters: Values a model can change independently while learning
  • GPT-3 example: 45 terabytes of data, 175 billion parameters

2. How do LLMs work?

Components of LLMs

• Data: Large amounts of text data
• Architecture: Neural network, specifically transformers for GPT
  • Handles sequences of data (e.g., sentences, code)
  • Understands context of each word by relating it to every other word
• Training: Predicting the next word in a sentence
  • Iteratively adjusts internal parameters
  • Improves accuracy over time (e.g., "the sky is... bug" to "the sky is... blue")
  • Can be fine-tuned on specific datasets for specialized tasks

3. Business Applications of LLMs

Customer Service

• Intelligent chatbots for handling customer queries
• Frees up human agents for complex issues

Content Creation

• Generates articles, emails, social media posts, video scripts

Software Development

• Generates and reviews code

Future Applications

• Continual evolution leading to more innovative uses

Conclusion

• Enamored with the potential of LLMs
• Encouragement to ask questions and engage with the content