5. Genetic AI and Large Language Models Study Notes

5. Genetic AI and Large Language Models Study Notes Module Overview on Genetic AI and Large Language Models

• Introduction to the study of genetic AI and large language model fundamentals.

• Overview of topics covered:

  • Definition and explanation of genetic AI.

  • Understanding large language models and their operational mechanisms.

  • Deep dive into transformer architecture.

  • Interaction with large language models: Bronze vs Fine-tuning.

  • Customization of models for personal data.

Introduction to Generative AI

• Definition of AI: The capability of machines to replicate human-like intelligence.

  • Machine Learning: Subset of AI using algorithms to learn from data and predict outcomes or identify trends.

  • Deep Learning: Further subset of machine learning employing neural networks for learning from complex data.

What is Generative AI?

• Generative AI Definition: A type of AI capable of creating new content from training data.

• Types of Outputs: Can create text, images, music, videos, and other data types.

• Zen AI: A subset of deep learning involving models that autonomously generate outputs, enabling innovative ideas and automation.

How Generative AI Works

• Learning Patterns: Models learn underlying patterns in datasets to produce new data resembling these patterns.

  • Example: To teach a model to draw a dog, it learns from various dog images identifying common features (pointy ears, tails, etc.).

  • Output Generation: After training, the model generates a new dog image based on learned patterns without copying any existing picture.

Comparison with Traditional Machine Learning

• Machine Learning: Identifies patterns and requires labeled training data.

  • Example: Given pictures of cats and dogs along with their labels, the model learns to classify new images.

• Generative AI Models: Learn patterns from unstructured content without labels during pretraining.

Types of Generative AI Models

1. Text-based Models: Generate text, code, dialogues, and articles by learning from extensive collections of textual data.

2. Multimodal Models: Process and generate various types of data (text, images, audio, video) simultaneously.

Applications of Generative AI

• Utilized across various industries.

  • Creative Content Generation: Used for writing, creating images, and videos.

  • Medical Imaging and Drug Discovery: Accelerating scientific advancements in medicine, diagnostics, and drug development.

Understanding Large Language Models (LLMs)

• Definition of Language Model: Probabilistic model predicting the likelihood of word sequences in a sentence based on prior words.

• Example: The sentence "I wrote to the zoo to send me a pet, they sent me ___"; a model predicts the next word based on probabilities of potential choices (e.g., dog, lion).

• Large Language Models: Defined by the number of learnable parameters, with no universally agreed threshold for categorization.

Operational Mechanism of LLMs

• Word Emission: The word with the highest probability is selected and appended to the input for further predictions.

• Example of Predictive Process: The model selects 'dog,' appends it, and produces a high probability for the end-of-sequence token.

Capabilities of LLMs

• Answering questions.

• Composing essays.

• Translating text between languages.

• Based on the deep learning transformer architecture enabling contextual awareness in word predictions.

Transformer Architecture

• Introduction to Transformers: Designed for understanding language with improved retention of contextual relationships across sentences.

• Recurrent Neural Networks (RNNs) Limitation: Struggles with long sequences and dependencies due to their sequential processing methodology (vanishing gradient issue).

Self Attention Mechanism

• Definition: Mechanism allowing transformers to weigh the importance of words in a sequence for understanding context.

• Key Features: Enables the model to grasp long-range dependencies and contextual relationships across the sentences.

Components of Transformer Architecture

• Encoder: Processes input, encoding it into a numerical representation.

• Decoder: Takes these representations to generate text outputs.

• Both encoder and decoder utilize multiple layers connected by self-attention mechanisms.

Tokenization and Embeddings

• Tokens: Elements (words, parts of words, punctuation) recognized by models; essential for understanding language.

  • Example: "friendship" might be two tokens, "friend" and "ship."

• Embeddings: Numerical representations of text elements that help models understand semantic relationships.

Retrieval-Augmented Generation (RAG)

• Concept Overview: Allows models to query external knowledge bases for grounded responses.

• Utilization in customer service applications (e.g., retrieving return policies from databases).

Fine-Tuning LLMs for Customization

• Fine-Tuning: Tailoring a pretrained model on domain-specific data for improving performance.

• Advantages: Enhances model contextual understanding and response relevance.

Comparison of Customization Techniques

1. Prompt Engineering: Quick method to instruct models without training costs.

2. Retrieval-Augmented Generation: Use when data changes often; requires quality data sources but grounds answers.

3. Fine-Tuning: Necessary for new tasks where model performance requires improvement; needs labeled datasets for effective training.

Conclusion

• Generative AI and LLMs provide groundbreaking opportunities across various tasks and industries, particularly with advancements in transformers and various customization techniques like prompt engineering and fine-tuning.

• Recognition of challenges such as hallucination in generated text and the need for ongoing research for improvement in these areas.

Future Learning Directions

• Continue exploring transformer architecture and prompt engineering strategies for successful applications of LLMs in diverse contexts.