Название: Generative AI Applications: Planning, Design and ImplementationАвтор: David Spuler, Michael Sharpe
Издательство: Aussie AI Labs Pty Ltd.
Год: November 10, 2024
Страниц: 347
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
Launch your Generative AI application from idea to implementation. Understand the various options and trade-offs in using LLMs for applications.
An AI application is really two components and it’s not very complicated:
• Engine — Transformer
• Model — LLM
Transformers are a type of neural network engine that calculates the answers in Generative AI. The Large Language Model (LLM) contains all of the data about the relationships between words and their relative positioning.
In terms of technology, the distinction between engines and models is also very simple:
• Engine — code
• Model — data
The runtime code is the “engine” and the grunt work is often done in C++ under a Python wrapper. The data is the “model” which is literally all numbers, and no code. So far, not so exciting. Where it gets more interesting is in the complex meshing between engines and models. Not all engines work with all models, and vice-versa. Even Transformers are tightly interwoven with their LLM data. There are many variants of Transformer architectures, and the data won’t work with an architecture that’s different. Engines and models are symbiotic and you need both to get anything done. An engine without a model means you ran out of compute budget, whereas a model without an engine cannot really occur because engines create models via training.
What’s an engine? The engine is code that you have to write. All of the fast low-level code is usually written in C++, but the higher-level control code is often written in Python. Somebody has probably used Java to do AI engines, but I’m not a fan of having ten directory levels.
What’s an LLM? There’s nothing really special about Large Language Models (LLMs) used by ChatGPT, Gemini, or Llama, compared to other types of AI model files, except that they’re:
(a) large,
(b) language-focused (not images), and
(c) a model.
More specifically, LLMs tend to be model files that are processed by Transformers, rather than other types of AI engines.
The Transformer was a breakthrough in the evolution of neural networks. One of its main advantages was its capacity to perform calculations in parallel, allowing it to increase intelligence through sheer brute-force algorithms. This led to a massive increase in the size of models into multi-billion parameter scale, which we now call Large Language Models (LLMs). Before the Transformer, there were many different neural network architectures. Several of these designs are still being used today in areas where they are stronger than Transformers.
Recurrent Neural Networks (RNNs). An early type of neural network that worked iteratively through a sequence. An RNN processes its inputs one token at a time, creating its output response, and then re-enters its own output as an input to its next phase. Hence, it is “recursive” in processing its own output, which is also known as “auto-regressive” mode when this same idea occurs in Transformers. Transformers have largely displaced RNNs for applications in text processing and generative AI. However, there are still research papers attempting to revive RNNs with advancements, or to create hybrid Transformer-RNN architectures.
Generative Adversarial Networks (GANs). These are an advanced image-generating neural network. The idea is to combine two models, one that generate candidate images (the “generator”), and the other model that evaluates them (the “discriminator”). By a weird kind of “fighting” between the two models, the generator model gradually creates better images that please the discriminator. The results are surprisingly effective, and this technology is still in use today.
Convolutional Neural Networks (CNNs). Whereas RNNs and Transformers are focused on input sequences, CNNs are better at input data that has a structure, especially the spatial structure inherent in images. Modern image processing and computer vision technology still uses CNNs, although enhanced Transformer architectures, such as multimodal or vision transformers, can also be used. CNNs are good at splitting an image into separate input “channels” and then applying a “filter” to each channel. Hence, CNNs have been holding their own against Transformers in areas related to image processing.
Main Points
- Deciding on your AI project
- Planning for success and safety
- Designs and LLM architectures
- Expediting development
- Implementation and Deployment
Who This Book is For:
This book is for anyone whose boss has just thrown them into the deep end of an ocean rip by assigning them the task of doing “something with LLMs” or any other similarly clear project description related to Generative AI. Alternatively, this book may help you if you’re the CEO or other C-suite executive of a company, and you want to assign someone such a project.
Contents:
Скачать Generative AI Applications: Planning, Design and Implementation