Generative AI Architecture

Generative AI technology goes beyond mere industry jargon – that is, it is an advanced instrument used for reshaping business operations by automating essential tasks such as content generation, image creation, and knowledge assistance. Generative AI represents a thrilling leap forward in the tech world, igniting significant enthusiasm among those passionate about technological innovation. Referred to as GenAI, an abbreviation of Generative artificial intelligence, this form of technology stands out for its remarkable ability to independently produce new content, such as text, images, music, videos, coding, and so on, with capabilities that closely mimic human-like creativity.

The use of generative AI is increasing in different business areas. It can greatly reduce the time, resources, and costs needed to operate a business when used well. For instance, ChatGPT can assist in creating marketing campaigns for products or serve as a travel planner...

What is generative AI?

Generative AI is artificial intelligence with the remarkable ability to develop new content and ideas. This includes things like having conversations, creating stories, producing images and videos, and even making music.

In December 2022, the design team at the Laboratory for Artificial Intelligence in Design (AiDLab) located in Hong Kong orchestrated a groundbreaking fashion exhibition titled Fashion X AI (https://www.fashionxai.com/event-highlights-fashionshow). This showcase was unique because every design featured in the event was created by AI, drawing inspiration from mood boards, color palettes, and concepts provided by human designers.

Like other types of AI, generative AI relies on machine learning (ML) models. These models are quite large and are pre-trained using vast amounts of data. We often call these models foundation models (FMs).

The FMs we have today (like OpenAI GPT-4 or Google Gemini for large language tasks, Stable Diffusion...

Generative AI use cases

Let’s look at various use cases across different categories such as customer experience, employee production, and business operations efficiency, and learn how generative AI is enhancing existing AI capabilities and bringing forth entirely new possibilities:

Customer experience transformation

Generative AI is changing the game in how customers interact with businesses. Imagine you’re shopping online for shoes. A generative AI-based virtual assistant on the website greets you and helps you find the perfect pair based on your style and size preferences. It can even show you images of the shoes and answer any questions you have. Let’s look at some more such use cases where generative AI can help to improve customer experience and engagement:

• Chatbots and virtual assistants: Imagine you visit a website and a chatbot pops up to help you out. Generative AI powers these chatbots. They can talk to you like humans, understand...

The basic architecture of generative AI systems

At the heart of generative AI systems is a massive FM. FMS are large-scale, pre-trained models that have been trained on vast datasets and can be fine-tuned or adapted for a wide range of tasks and applications. To understand the architecture of generative AI systems, let’s break it down into simple components:

• Generator: The core element that generates new data, whether it’s images, text, music, or other forms of content. The generator learns patterns and relationships from existing data and uses this knowledge to produce new, similar content. For example, the generator takes random noise in image generation and produces images that resemble the training data.
• Latent space: A conceptual space where the model represents data in a compressed form. It’s like a compact representation of the data that the generator uses to create new content. This is a lower-dimensional vector space from which the generator...

Popular generative AI FMs

The field of generative AI is rapidly evolving, with various organizations pushing the boundaries and launching powerful foundation models to drive innovation. The launch of models like ChatGPT has undoubtedly contributed to the acceleration of this trend. Both established tech giants and emerging start-ups are actively participating in the generative AI boom, aiming to develop more sophisticated and capable FMs. Here’s a list of some of the most popular FMs in generative AI:

• Amazon: Amazon Web Services (AWS) is one of the top cloud providers and has a large set of offerings in ML and generative AI. AWS has launched a generative AI service called AWS Bedrock with accessibility to popular FM models using APIs in a serverless manner. Amazon SageMaker JumpStart is another offering that provides access to a wide range of FM models and the ability to tune them as needed. Amazon Titan is AWS’s flagship generative AI model. Amazon’...

How to start with generative AI

Starting with generative AI involves selecting the right tools and platforms that suit your needs. Whether you’re an end user looking to engage in AI-generated conversations or a developer/ML scientist aiming to create sophisticated applications, numerous resources are available from different providers to help you embark on your generative AI journey. Getting started with generative AI can be exciting! The following subsections provide a breakdown of how different types of users can begin their exploration into generative AI.

For end users

For individuals seeking to harness the capabilities of generative AI in their day-to-day activities such as content creation, marketing materials, email composition, and efficient learning, several accessible tools can be employed:

• ChatGPT offers a user-friendly chatbot experience driven by GPT-3.5, an advanced language model. This tool responds with natural language based on the input...

Generative AI reference architecture for building a mortgage assistant app

The homebuying process can often appear daunting to prospective buyers, primarily due to the overwhelming amount of paperwork involved. Frequently, buyers find themselves needing more time and an in-depth understanding of the intricate details within these documents. Consequently, they experience feelings of being overwhelmed, confused, and occasionally frustrated as they grapple with grasping the significance of what they are signing, particularly in the context of mortgage-related paperwork.

Addressing these challenges becomes paramount in enhancing the overall customer experience and building trust between buyers and lenders throughout the loan application and closing process. To alleviate this burden and empower homebuyers, a generative AI solution can assist them in comprehending their loan terms and conditions better without relying on mortgage experts or attorneys.

This section delves into the...

Challenges in implementing generative AI

Implementing generative AI, while highly promising, comes with its set of challenges and considerations. In the following subsections, we delve into some of the primary challenges associated with generative AI.

Training stability issues

One of the significant challenges encountered in generative AI is training stability issues. These issues can manifest as convergence problems, slow training, or even divergence, making it difficult to obtain high-quality generative models.

One prevalent application of generative AI involves using a GAN to create high-definition images. Training stability issues may arise during the training of a GAN for image generation. For instance, the generator may produce nonsensical or highly distorted images. These issues can hinder the GAN from converging to a satisfactory solution, leading to poor image generation quality.

Addressing and preventing training stability issues in generative AI involves...

Summary

In this chapter, we delved into the fascinating world of generative AI, starting with a comprehensive exploration of what it is. We explored the diverse use cases that generative AI enables, from transforming customer experiences to enhancing employee productivity and optimizing various aspects of business operations.

To understand the basic architecture of generative AI systems, we broke down the different types of generative models, including GANs, VAEs, and transformer-based models. We highlighted the importance of hyperparameter tuning and regularization in constructing effective generative AI architectures.

In the context of FMs, we provided insights into some of the popular generative AI FMs offered by key players in the field, such as Amazon, OpenAI, Google, Nvidia, and several others. These models serve as the backbone of generative AI applications.

For those eager to start their journey with generative AI, we offered guidance tailored to different user...