WHY AI Works

Step-by-Step Tutorial: Understanding the Functionality of LLMs and Neural Networks

1. Introduction to LLMs and Neural Networks:

   • LLMs (Large Language Models) and neural networks are designed to understand and process information using a combination of knowledge and reasoning.

2. Input Processing in Neural Networks:

   • Neural networks take digital input and convert it into a vector of numbers, which are then normalized typically between 0 and 1 for processing.

3. Math Function in Neural Networks:

   • The output in neural networks is computed by multiplying each input by a weight, adding a bias, and applying a normalization activation function to ensure the output stays within desired bounds.

4. Training Neural Networks:

   • Training neural networks involves providing input-output pairs, computing errors, and adjusting parameters using methods like gradient descent to minimize errors and optimize performance.

5. Gradient Descent in High-Dimensional Spaces:

   • In high-dimensional spaces, gradient descent helps to find the global extremum by navigating through points efficiently, avoiding local minima, and utilizing the curse of dimensionality to reach optimal solutions.

6. Autoencoders for Data Compression:

   • Autoencoders use two neural networks to compress and then reconstruct data, effectively creating a fuzzy representation that can be decoded back to the original data.

7. Encoding Text with Neural Networks:

   • Encoding text involves representing characters as vectors in high-dimensional spaces, ensuring meaningful distances between characters and words to capture semantics effectively.

8. Word Vectors and Semantic Embeddings:

   • Word vectors represent words as coordinates in semantic spaces, preserving relationships and enabling operations like analogy properties between words.

9. Applications of Embeddings:

   • Embeddings are versatile tools used for encoding various concepts, including images, audio signatures, and profiles, by representing them in compact, high-dimensional spaces.

10. LLMs and Attention Mechanism:

    • LLMs utilize attention blocks to focus on important signals in the input, helping the neural network process information more efficiently and accurately.

11. Compression of Knowledge in LLMs:

    • LLMs leverage the blessing of dimensionality to compress vast amounts of knowledge and patterns found on the internet into more manageable sizes, optimizing performance and reasoning abilities.

12. Reasoning Abilities of LLMs:

    • LLMs excel at mimicking human reasoning by encapsulating human knowledge, patterns, and grammar rules, enabling them to process information and generate outputs effectively.

By following these steps, you can gain a deeper understanding of how LLMs and neural networks function, process information, and reason effectively, showcasing the power of AI in various applications.