5 Different Types of Generative AI Models

The rapid advancement of Generative Artificial Intelligence (Generative AI or GenAI) has accelerated the need for businesses to adap
t quickly. Research from McKinsey reported that one-third of organizations already incorporate Generative AI into at least one business function. Also, Gartner forecasts that over 80% of companies will either deploy Generative AI applications or utilize Generative AI APIs by 2026.

Adding to the urgency, Generative AI could contribute between $2.6 trillion and $4.4 trillion annually to the global economy, highlighting its potential for significant competitive advantage.

However, it is by no means an easy feat to realize these benefits unless businesses are equipped with the relevant knowledge and skills required. Especially given the numerous Generative AI models available, selecting the right one stands as a crucial yet complex step that hinders a lot of companies embarking on the Generative AI implementation journey.

In this article, we’ll give you a comprehensive overview of Generative AI models, including their definition, operational mechanisms, major types, notable examples currently in use, and much more.

What Is A Generative AI Model?

Generative AI models are sophisticated AI systems that create a wide range of outputs by leveraging extensive training data, neural networks, deep learning techniques, and user input. Depending on the specific Generative AI model, you can create images, convert text into visuals, produce speech and audio, craft unique content, and even generate synthetic data.

These advanced capabilities start from training Generative AI on large datasets. As a result, Generative AI applications have been adopted across various sectors, including healthcare, marketing, retail, and eCommerce.

Types of Generative AI Models 

With a growing number of Generative AI models available and even more in the pipeline, it can be challenging to navigate your options without some background knowledge.

Following are the 5 main Generative AI models and their capabilities.

1. Generative Adversarial Networks (GANs)  

First introduced by Ian Goodfellow and his team in 2014, generative adversarial networks have significantly advanced generative modeling. This model features a duel between two neural networks: the generator and the discriminator. The generator creates new data, like images, while the discriminator evaluates whether the data is real or synthetic. This competitive process helps GANs produce highly realistic and convincing synthetic outputs.

GANs are primarily known for generating images and videos, but their capabilities extend to creating high-quality content in various areas. They also excel in tasks such as style transfer—e.g., turning a photo into a pencil sketch—and data augmentation—i.e., creating synthetic data to enhance training datasets.

2. Variational Autoencoders (VAEs)  

VAEs are intricate models composed of two parts: an encoder and a decoder. The encoder compresses input data into a smaller, denser representation, focusing on the essential features needed for reconstruction. The decoder then rebuilds the original data from this compressed form. This approach makes VAEs efficient in generating new data points from learned patterns.

While VAEs can produce new content, they are often more effective for compressing and decompressing data than generating high-quality content. Besides, they are particularly useful for reducing noise in images and detecting anomalies in data. With this flexibility, they can be tailored to accommodate organizations’ unique needs, from generating images in marketing to detecting fraud in finance.

3. Transformer-Based Model

Transformers have become the driving force behind many of today’s most talked-about Generative AI tools, such as ChatGPT, GPT-4, Copilot, Gemini, and Midjourney. These models rely on a technique called attention, which helps them focus on the most relevant parts of the data within a sequence. This allows them to:

• Analyze entire sequences of data all at once.
• Understand the context within the sequence.
• Encode training data into embeddings (or hyperparameters) representing the data and its context.

This approach not only speeds up the training process but also enhances performance in natural language processing (NLP) and natural language understanding (NLU). Thus, they can generate detailed and coherent content, from social media posts to essays and poems, with high accuracy.

Additionally, transformers can be fine-tuned to use various tools, such as spreadsheet applications and drawing programs, to produce content in different formats.

4. Diffusion Model

Diffusion models do a good job of generating high-quality images and videos by studying how data spreads or diffuses through a system. They are known for their ability to create stunning visuals but tend to require longer training times due to their complex architecture. As a result, this trade-off between quality and training time is worth considering when businesses choose a model.

5. Autoregressive Model 

Autoregressive models take a straightforward approach to generation. They predict the next part of a sequence based on previous inputs—essentially forecasting the future based on the past. These models are particularly useful for:

• Text generation and language modeling.
• Sequential data tasks, such as predicting text, audio, and time series data.
• Generating new images and video frames based on previous frames.
• Forecasting stock prices and the weather.

Examples of Popular Generative AI Models 

Many companies build their Generative AI tools on well-established foundation models. These models serve as robust starting points, which can then be customized for particular applications. Some of the most notable foundation models are the GPT series, BERT, and XLNet.

Let’s have a closer look at the 4 most prominent Generative AI models currently available below.

1. GPT Series (OpenAI) 

Generative pre-trained transformer (GPT) series are advanced large language models known for their extensive general knowledge and strong reasoning abilities. This series includes models like GPT-4o, GPT-4o-mini, GPT-4-turbo, GPT-4, and GPT-3.5-turbo, each offering improvements over its predecessors.

Among these, GPT-4o (where “o” stands for “omni”) is the most advanced. GPT-4o is multimodal, meaning it can handle both text and image inputs and generate text. It is highly efficient, offering text generation that is twice as fast and 50% more cost-effective than GPT-4 Turbo. Additionally, GPT-4o is great at vision tasks and supports numerous non-English languages.

2. Llama (Meta)

Meta introduced Llama in 2023. Llama comes in different sizes and versions, with the latest Meta Llama 3.1 405B. Meta claims this model is the largest and most capable open-source foundation model in existence. As an open-source model, Llama can be fine-tuned, distilled, and deployed anywhere, providing users with flexibility.

The Llama 3.1 405B is notable for its advanced capabilities in general knowledge, adaptability, mathematical problem-solving, tool usage, and multilingual translation. With this release, Meta aims to drive innovation and open up new possibilities, such as generating synthetic data for training smaller models and advancing model distillation, a groundbreaking achievement at this scale in open-source AI.

3. Gemini (Google) 

Previously known as Bard, Google’s Gemini is among the top cutting-edge multimodal large language models (LLMs) that integrate seamlessly across different platforms, from data centers to mobile devices.

Built specifically for multimodal use, Gemini processes and understands text, code, images, audio, and video. The first iteration, Gemini 1.0, comes in three sizes, offering versatility for various tasks. The latest versions, Gemini 1.5 Pro and Gemini 1.5 Flash have a context window of up to one million tokens, the longest of any large-scale model. This capability allows it to handle extensive documents, large codebases, and lengthy audio or video files. For 1.5 Pro, a two-million-token context window is available to developers and enterprise customers.

4. BERT (Google)

Google introduced BERT (Bidirectional Encoder Representations from Transformers) in 2018. This model has 340 million parameters and is built on the transformer framework. BERT uses bidirectional self-attention to learn from large amounts of text data, making it highly effective for numerous natural language tasks, from text classification to sentiment analysis and named entity recognition.

All these four high-tech models are at the forefront of natural language processing, facilitating a range of applications, from conversational AI to content creation, and pushing the limits of understanding and generating human-like text.

How Do Generative AI Models Work?

Generative AI models are designed to mimic human-like content by using advanced neural networks that simulate the connections between neurons in the human brain. These models improve over time by learning from extensive datasets, sophisticated machine learning algorithms, and continuous updates.

Here is a breakdown of 2 key technologies behind Generative AI models:

• Neural networks are computational systems inspired by the brain’s structure. They consist of interconnected nodes, or neurons, organized in layers. These networks learn by adjusting the connections between nodes through a process known as backpropagation. A prominent example of a neural network is Google’s search algorithm, one of the largest and most complex in use today.
• Deep learning is a branch of machine learning that employs neural networks with several hidden layers, called deep neural networks. These layers allow the model to learn intricate patterns and relationships within data, mimicking human decision-making processes.

Generative AI models use different training methods, including unsupervised and semi-supervised learning:

• Unsupervised learning allows machine learning models to learn from data without any human-provided labels. They identify patterns and relationships within the data on their own. This approach comes in handy when labeled data is limited.
• Semi-supervised learning combines supervised and unsupervised learning. It uses a small amount of labeled data and a larger pool of unlabeled data. The labeled data helps the model learn initial patterns, which it then applies to the unlabeled data.

Best Strategies for Training Generative AI Models

Different types of Generative AI models require varied approaches to training. Nevertheless, several effective strategies can improve the training process for all these models.

1. Select the Right Model Architecture 

Choosing the appropriate model is a critical first step in generating high-quality Generative AI tools. Some popular models are VAEs, GANs, transformer-based models, diffusion models, and autoregressive models. Each model has its strengths and limitations. For example:

• VAEs are excellent for learning latent representations and generating smooth data but may sometimes produce blurry results or suffer from mode collapse.
• GANs are known for creating sharp, realistic data but can be more challenging to train effectively.
• Autoregressive models generate high-quality data but may be slower and more memory-intensive.

To choose the best model, you should carefully evaluate its performance, scalability, and efficiency to match your project’s specific needs and constraints.

2. Leverage Transfer Learning and Pre-Trained Models 

A time-efficient approach to training generative models is using transfer learning and pre-trained models. Transfer learning allows you to apply knowledge from one task or domain to another, while pre-trained models have already been trained on massive datasets like ImageNet, Wikipedia, or YouTube.

By fine-tuning pre-trained models such as GPT-3, BERT for text, or VAE and GAN for images, you can significantly reduce the time and resources required to train models. Adapting these models to your own data can result in more accurate and efficient outputs.

3. Apply Data Augmentation and Regularization 

Improving the performance of generative models can be achieved through data augmentation and regularization techniques.

• Data augmentation involves creating variations of the training data through transformations like cropping, rotating, or adding noise. This increases the diversity of the dataset and makes the model more robust.
• Regularization helps prevent overfitting by adding constraints, such as dropout, weight decay, and spectral normalization, ensuring the model generalizes well to new data.

Together, these techniques can improve the quality of generated content by making the model more versatile and resilient.

4. Use Distributed and Parallel Computing 

Training generative models, especially on large datasets, can be time-consuming. One way to accelerate this process is by using distributed and parallel computing, which splits the workload across multiple devices like GPUs, CPUs, and TPUs.

Data parallelism, model parallelism, and pipeline parallelism are some common techniques that facilitate faster training and enable models to handle more complex datasets. This approach also helps manage memory usage and bandwidth, making it easier to scale up your model.

5. Implement Efficient and Adaptive Algorithms 

Optimizing the training process requires efficient algorithms to adjust parameters like the learning rate and batch size. Such algorithms as SGD, Adam, and AdaGrad can help fine-tune these settings, improving the model’s accuracy and performance over time.

In addition, several methods that are useful for hyperparameter tuning include Bayesian optimization, grid search, and random search. They help you find the optimal settings for your generative model without excessive trial and error.

By applying these techniques, you can guarantee optimal outcomes in training Generative AI models for many tasks.

Leverage Generative AI Models Successfully with Neurond Today

Generative AI encompasses diverse models such as GANs, VAEs, transformers, diffusion models, and autoregressive models. As research progresses, we can anticipate advancements in the quality and quantity of Generative AI models.

Businesses are increasingly tapping into the potential of Generative AI to automate tasks, enhance workflows, and perform complex data analyses. With a variety of models available, each with its unique advantages and challenges, companies should carefully choose the models that best fit their specific needs.

At Neurond, we specialize in adopting cutting-edge Generative AI models to create and analyze human-like text, images, audio, and more. Our expertise includes but not limited to:

• Multimodal models: GPT-4, GPT-4o, Claude 3, Claude 3.5, Gemini Pro
• Text generation: Mistral, Mixtral, Llama
• Image generation: Diffusion, DCGAN, CLIP
• Code generation: GitHub Copilot, AlphaCode, CodeT5

Contact us now to see how Neurond’s Generative AI consulting services can help you harness the full potential of Generative AI models for your unique business needs.