wtf:How does all this new AI actually work?

The technology of a "GPT" (Generative Pre-trained Transformer) is really just a storyteller with an infinite imagination. Just as a human writer crafts stories, Any GPT (a GPT is a technology, not a product by OpenAI "ChatGPT" is their product) can generate coherent and engaging text on virtually any topic because their library contains the whole internet, and the whole internet is the best complete database of human knowledge we have.

But GPT is more than just a storyteller; it's a programming makes it spesifically a language specialist with the ability to understand and generate mostly all human language in ways that were once thought impossible. Using its "Transformer" (The T in GPT!) architecture, GPT is like a librarian who doesn't just process books one by one and understand where it is, it but connects relevant words across entire sentences, capturing long-range dependencies and contextual relationships, a GPT has the best "organization" system we know of. It has an unimaginably brilliant version of the Dewey Decimal Classification System.

The Transformer Architecture

Think of reading a complex historical document. To understand a particular word or phrase, you might need to look at other parts of the text, regardless of how far away they are, maybe even in another book alltogether. The Transformer's self-attention mechanism works similarly. It allows the model to consider the relationship between all words in a sentence simultaneously, rather than processing them in a strict order. For example, imagine you're analyzing a document from multiple perspectives - political, economic, social, etc. Each perspective might highlight different connections and insights. Multi-head attention in the Transformer works like this. It allows the model to attend to different aspects of the text in parallel, enriching its understanding.

The Encoder-Decoder Structure

Building upon the Transformer architecture, GPT employs an encoder-decoder structure. In this structure, the encoder acts like a skilled researcher analyzing a document, identifying key points and connections. The decoder then uses these insights to generate new text, much like a writer crafting a story from a researcher's notes. This dynamic duo works together to create coherent and contextually appropriate text.

Pre-training and Fine-tuning

Before GPT can work at all, it undergoes "pre-training", learning from a vast library of texts, in the case of OpenAI and Anthropic, the whole internet. It's akin to an apprentice absorbing as much knowledge as possible. Then comes fine-tuning, where GPT specializes in a specific task, adapting its broad knowledge to specific requirements. This allows GPT to be applied to various tasks, such as language translation, text summarization, or content generation. Much like ADHD, if the GPT was not fine tuned it would go off and end up doing something totally unrelated to the other task, so you can think about fine tuning like introducing Adderall to the mix, the GPT stays focused on it's task and doesn't get lost in the massive library it has access to.

Generative Capabilities and Scalability

Generation is achieved through very sophisticated algorithms, like seriously shitloads of math, that calculate the likelihood of each word in a sequence, ensuring the text is not only fluent but also contextually appropriate.

On the scalability front, GPT’s architecture resembles a modular skyscraper. Its design allows for straightforward enhancements and scaling, akin to adding floors or extensions to a building. This flexibility is enabled by the addition of more layers, heads, and parameters, each increasing the model's depth and breadth of understanding, much like expanding the capabilities of a complex, multi-functional structure. This scalability ensures that GPT can be adapted to a wide range of applications and can evolve alongside advancing technology and growing datasets.

How the Transformer Constructs Outputs

Imagine the transformer model as an intelligent architect moving through a skyscraper, meticulously designing each floor according to the blueprint of prior levels. Each floor in this analogy represents a layer in the transformer architecture. The architect, or the transformer, starts at the ground floor with the input data, like the foundation of a building. As it ascends from one floor to the next, it refines and expands upon the information, integrating new elements based on the underlying structure.

The transformer uses attention mechanisms—tools that determine which parts of the text to focus on and how different words relate to each other. This is akin to the architect deciding where to place walls, windows, or doors to best suit the floor’s design, all while considering the overall integrity and purpose of the building. By the time the architect reaches the top floor, the output is fully constructed: a coherent and contextually enriched piece of text, polished and ready for presentation.

This dynamic process of moving through the skyscraper not only allows for the construction of sophisticated outputs but also illustrates the transformer's ability to handle and adapt to a variety of linguistic tasks, making it a versatile tool in the field of natural language processing.

Deep Learning

Under the hood, GPT is powered by deep learning, a subset of artificial intelligence that mimics how the human brain learns. Just like a magician pulls a rabbit out of a hat, GPT can generate coherent text out of seemingly thin air. But instead of magic, it's the result of complex mathematical algorithms and vast amounts of data. GPT's ability to learn and adapt is what makes it so remarkable and revolutionary.

Unlocking the Power of Self-Attention: A Step-by-Step Guide

Self-Attention is a insane mechanism, it's just a bunch of math, but it's what is enabling the model to weigh the importance of different words in a sentence, thus capturing contextual relationships and long-range dependencies. Let's explore this concept step-by-step:

1. Input Embeddings: Think of a sentence as a recipe with ingredients (words). Each ingredient has a unique flavor (meaning), represented in a way the model can understand.
2. Query, Key, and Value Matrices: These matrices are like roles in a team. The Query acts as the Team Leader, understanding the context; the Key is the Expert, knowing the details; the Value is the Contributor, providing important information.
3. Computing Attention Scores: Picture a networking event where attendees exchange business cards. The relevance of the connection dictates the attention they pay to each other.
4. Scaling and Normalization: This process is akin to balancing a seesaw, ensuring that no single word dominates the conversation unfairly.
5. Computing Contextualized Embeddings: A master chef combining ingredients with the right amount of seasoning to create a harmonious dish represents how the model blends words to form a contextually rich sentence.
6. Output: The final output is like a rich tapestry, weaving together contextualized embeddings to represent the sentence in a nuanced and connected way.

Human-Machine Interaction

GPT is not just another AI model; it represents a new paradigme of human-machine interaction. Imagine having a virtual assistant that can not only understand your commands but also engage in meaningful conversations. GPT's ability to understand context and generate human-like responses opens up endless possibilities for how we interact with machines, from customer service to education and beyond.

GPT's language skills are not just impressive, but they are net new technologies.

Transformer architecture, encoder-decoder structure, and self-attention mechanism enable it to understand and generate human language with unprecedented accuracy and fluency.