Generative AI Models – A Comprehensive Guide

Despite the daily emergence of new AI tools and companies, the generative AI models powering these tools are fewer and play a crucial role in advancing the field. These models are the behind-the-scenes drivers of generative AI’s progress.

Over the past couple of months, large language models, or LLMs, such as chatGPT, have taken the world by storm. Whether it’s writing poetry or helping plan your upcoming vacation, we are seeing a step change in the performance of AI and its potential to drive enterprise value. Continue reading to explore more about generative AI models, their functioning and how they differ from other AI types, as well as some leading generative AI models available today.

What are Generative AI Models?

Generative artificial intelligence (AI) models are platforms designed to create diverse outputs using large training datasets, neural networks, deep learning frameworks, and user prompts.

Depending on their specific type, these models can produce images, convert text into images, synthesise speech and audio, create original video content, and generate synthetic data.

The creation of generative models involves multiple intricate steps, usually performed by teams of researchers and engineers. Models like GPT (generative pre-trained transformer) from OpenAI and similar architectures are designed to generate new content that reflects the patterns found in their training data.

Here’s a step-by-step overview of the process:

• Data Collection

Initially, data scientists and engineers define the project’s objectives and requirements to guide the collection of a suitable dataset. They often rely on public datasets, which provide extensive amounts of text or images. For example, training ChatGPT (GPT-3.5) required processing 570 GB of data, equivalent to 300 billion words from public internet sources, including nearly all of Wikipedia’s content.

• Model Selection

Selecting the appropriate model architecture is crucial. This choice depends on the task, the type of data, the desired output quality, and computational constraints. Architectures such as VAEs, GANs, and transformer-based and diffusion models will be discussed later. Typically, new models build on existing architecture frameworks, using proven structures as a foundation to allow for specific refinements and innovations.

• Model Training

The selected model is trained using the collected dataset. This stage often requires significant computing power, utilising specialised hardware like GPUs and TPUs. Although training methods vary based on the model, all models undergo hyperparameter tuning, where data scientists adjust specific settings to optimise performance.

• Evaluation and Fine-Tuning

Finally, the model’s performance is assessed, often through real-world testing. Evaluating generative models differs from traditional machine learning models because generative models create new outputs, and the quality is subjective. Evaluation methods depend on the output type and often involve human raters. Generative models might even evaluate each other. Insights from this stage inform further fine-tuning or retraining. Once validated, the model is ready for deployment.

Types of Generative AI Models

Generative AI models are a category of AI that holds the ability to create new and engaging data. There are several different types of generative AI models, each with its unique uses and features. Some of these include:

● Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a type of model consisting of two neural networks, a generator and a discriminator. These networks are trained together through adversarial learning, which helps the generator create progressively more realistic data.

GANs operate without human intervention and have applications across various fields, including art creation, video enhancement, and data generation for training. They are also extensively used in tasks such as image-to-image translation.

● Transformer-based Models

Transformers are neural networks designed to understand context by identifying and tracking relationships in sequential data, such as the words in a sentence. They are widely used for natural language processing (NLP) tasks and form the basis of many foundational models. Transformer models operate through a series of layers that process sequential information, which can include text, code, or other types of data.

● Variational Autoencoders (VAEs)

VAEs learn to generate new content by analysing patterns in a dataset. They do this by compressing data into a lower-dimensional space and then learning how to generate new data by sampling from this compressed space.

● Autoregressive Models

Auto-regressive models produce new samples by estimating the conditional probability of each data point based on the preceding context. These models generate data sequentially, enabling the creation of complex sequences. Auto-regressive models are trained to predict the next data point based on the preceding context. During inference, they generate new samples by drawing from the learned conditional distributions.

● Deep Convolutional Generative Adversarial Networks (DCGANs)

Deep Convolutional Generative Adversarial Networks (DCGANs) are a type of deep learning model used to generate synthetic images. They leverage the architecture of convolutional neural networks (CNNs).

DCGANs have shown remarkable proficiency in creating realistic images, driving progress in image synthesis and reconstruction tasks.

Applications of AI-Generative Models

Generative AI is utilised across various industries, significantly impacting many companies. Our Generative AI report reveals that text applications are the primary driver for adopting generative AI tools, accounting for 40.8% of their use. Let’s talk about all the applications, including:

• Audio applications
• Text applications
• Conversational applications
• Data augmentation
• Video/visual applications

Audio Applications

Generative AI audio models utilise machine learning, artificial intelligence, and algorithms to generate new sounds from existing data, such as musical scores, environmental sounds, audio recordings, or speech-to-sound effects. Once trained, these models can produce original and unique audio. The models employ various types of prompts to generate audio content, including:

• Environmental data
• MIDI data
• Real-time user input
• Text prompts
• Existing audio recordings

Text Applications

Artificial intelligence text generators leverage AI to produce written content, making them useful for applications such as website content creation, report and article generation, social media post creation, and more. By utilising existing data, these AI text generators ensure that the content aligns with specific interests. Additionally, they assist in recommending products or information that someone is most likely to find appealing.

There are several applications of generative AI text models:

• Language translation
• Content creation
• Summarisation
• Chatbot and virtual assistants
• SEO-optimised content

Conversational Applications

Conversational AI aims to enhance natural language conversations between humans and AI systems. Utilising technologies like Natural Language Generation (NLG) and Natural Language Understanding (NLU) enables smooth and seamless interactions. There are several applications of generative AI conversational models:

• Natural Language Understanding (NLU)
• Speech recognition
• Natural language generation (NLG)
• Dialogue management

Data Augmentation

By employing advanced algorithms, particularly generative models, it’s possible to generate fresh synthetic data points for integration into an existing dataset. This technique is commonly applied in machine learning and deep learning scenarios to boost model performance by expanding both the scale and variety of the training data.

Data augmentation serves to address issues of dataset imbalance or scarcity. By generating additional data points resembling the original dataset, data scientists can reinforce models, improving their ability to generalise with unseen data. Here are some applications of data augmentation:

• Medical imaging
• Natural language processing (NLP)
• Computer vision
• Time series analysis
• Autonomous systems
• Robotics

Visual/Video Applications

The significance of AI in video applications is on the rise, given its capacity to create, alter, and scrutinise video content in manners previously unfeasible. Nevertheless, the increasing utilisation of generative AI in video applications gives rise to ethical considerations. Instances like Deep Fakes have been exploited maliciously, prompting a demand for tools to identify and mitigate such occurrences. Challenges such as authenticity verification, obtaining informed consent for using individuals’ likenesses, and potential implications on employment within the video production sector remain unresolved issues that need careful navigation.

• Content creation
• Video enhancement
• Personalised content
• Virtual reality and gaming
• Video compression
• Video synthesis from other inputs

Benefits of Generative AI Models for Business

The 2023 Global Trends in AI Report from S&P Global highlights that 69% of respondents have implemented at least one AI deployment into production. The undeniable value derived from AI is evident, with 70% of organisations identifying revenue generation as their primary motivation. Additionally, 67.2% of enterprises intend to adopt LLMs and Generative AI by the end of the year. According to McKinsey & Company’s economic potential report, these technologies could contribute an astounding $2.6 trillion to $4.4 trillion annually to the global economy.

Here are some of the major advantages of Generative AI Models for Business:

• Automated Content Production
• Personalisation
• Time Savings
• Cost Reduction
• Routine Task Automation
• Customisation
• Improved Customer Experience

Evaluation of AI-Generative Models

To assess the performance of a generative AI model, you need to look into its effectiveness, resilience, and ethical implications.

• Determining its effectiveness covers scrutinising the precision and pertinence of the model’s generated content. However, as models become more complex, their behaviour may become erratic, yielding results that aren’t always reliable.

• Resilience, on the other hand, refers to the model’s capacity to handle diverse inputs adeptly.

• The existence of biases in AI models raises significant concerns. Due to the biased nature of human-provided data, biases can inadvertently infiltrate models. Addressing these biases and ethical considerations poses a formidable challenge for the AI community.

Limitations of Generative AI Models

Although generative AI showcases remarkable capabilities in generating creative content, it’s crucial to acknowledge its boundaries and realise that it cannot supplant human ingenuity. Generative AI frequently falls short in conveying the emotional depth, intuition, and cultural acumen that human creators infuse into their creations.

• Constrained creativity and innovation
• Inadequate comprehension of intricate contexts
• Restricted adaptability and personalisation

Challenges of Generative AI Models

Despite the surge in popularity of generative AI since November 2022, the scarcity of startups venturing into AI model development can be attributed to the considerable financial investment and extensive resources required, coupled with the intricate nature of the field. Below, we outline some prominent challenges associated with generative AI models.

• Mode Collapse in GANs
• Training Complexity
• Adversarial Attacks
• Fine-tuning and transfer learning

To Cut It Short

In general, generative AI holds immense potential to revolutionise numerous industries and applications, marking a pivotal domain in AI research and advancement. Companies like NVIDIA, Cohere, and Microsoft are actively committed to fostering the ongoing expansion and enhancement of generative AI models through the provision of services and tools aimed at addressing prevailing challenges. These offerings and platforms streamline the process of setting up and deploying models at scale by abstracting away complexities. To provide yourself with a comprehensive and accessible resource for you to keep updated with AI development, join Integrated Program in Data Science, Artificial Intelligence & Machine Learning.