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Generative artificial intelligence (AI) models are AI platforms that generate a variety of outputs based on massive training datasets, neural networks, deep learning architecture, and prompts from users.
Depending on the type of generative AI model you’re working with, you can generate images, translate text into image outputs, synthesize speech and audio, create original video content, and generate synthetic data.
While many AI companies and tools are popping up daily, the generative AI models that work in the background to run these tools are fewer and more important to the growth of generative AI’s capabilities. Todays’ generative AI models are the “unsung” heroes of AI.
KEY TAKEAWAYS
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- •Generative AI models utilize massive datasets and neural networks to create outputs such as images, text, and audio. They operate using unsupervised or semi-supervised learning to recognize patterns in data and generate human-like content. (Jump to Section)
- •Various generative AI models, including transformer-based models, GANs, and diffusion models, are trained through different processes involving large-scale data, neural networks, and methods like forward and reverse diffusion to refine output quality. (Jump to Section)
- •Generative AI models enhance data augmentation, natural language processing, and creative applications. However, they face challenges like training complexity, resource intensity, and susceptibility to adversarial attacks, necessitating robust ethical and security practices. (Jump to Section)
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Read on to learn more about generative AI models, how they work and compare to other types of AI, and some of the top generative AI models available today.
TABLE OF CONTENTS
How Do Generative AI Models Work?
Generative AI models are the massive, big-data-driven models that power the emerging artificial intelligence technology that can create content.
Using unsupervised or semi-supervised learning methods, generative AI models are trained to recognize small-scale and overarching patterns and relationships in training datasets that come from all kinds of sources — the internet, wikis, books, image libraries, and more.
This training enables a generative AI model to mimic those patterns when generating new content, making it believable that the content could have been created by or belonged to a human rather than a machine.
Generative AI models are able to so closely replicate actual human content because they are designed with layers of neural networks that emulate the synapses between neurons in a human brain. When the neural network design is combined with large training datasets, complex deep learning and training algorithms, and frequent re-training and updates, these models are able to improve and “learn” over time and at scale.
Among the many types of generative AI models, there are text-to-text generators, text-to-image generators, image-to-image generators, and even image-to-text generators. In this example, I used a text-to-image generator, Img2Go. I provided an AI prompt – a text description – and the model generated a new image that matched my prompt.
The prompt I used was: “A laughing robot in the sunset.”
For more information about how generative AI is used in business, see our guide: Generative AI Examples
How Are Generative AI Models Trained?
Generative AI models are all trained a little differently, depending on the model type you’re training. Let’s look at how transformer-based models, GANs, and diffusion models are trained:
Transformer-Based Model Training
Transformer-based models are designed with massive neural networks and transformer infrastructure that make it possible for the model to recognize and remember relationships and patterns in sequential data.
To start, these models are trained to look through, store, and “remember” large datasets from a variety of sources and, sometimes, in a variety of formats. Training data sources could be websites and online texts, news articles, wikis, books, image and video collections, and other large bodies of data that provide valuable information.
From there, transformer models can contextualize all of this data and effectively focus on the most important parts of the training dataset through that learned context. The sequences this type of model recognizes from its training will inform how it responds to user prompts and questions.
Essentially, transformer-based models pick the next most logical piece of data to generate in a sequence of data. Encoders and/or decoders are built into the platform to decode the tokens or blocks of content that have been segmented based on user inputs.
GAN Model Training
GAN (generative adversarial network) models are trained with two different sub-model neural networks: a generator and a discriminator. The generator generates content based on user inputs and training data, while the discriminator model evaluates generated content against “real” examples to determine which output is real or accurate.
First, the generator creates new “fake” data based on a randomized noise signal. Then, the discriminator blindly compares that fake data to real data from the model’s training data to determine which data is “real” or the original data.
The two sub-models cycle through this process repeatedly until the discriminator is no longer able to find flaws or differences in the newly generated data compared to the training data.
Diffusion Model Training
Diffusion models require both forward training and reverse training, or forward diffusion and reverse diffusion.
The forward diffusion process involves adding randomized noise to training data. The model is trained to generate outputs using the noisy data (not as refined or specific) as input. The noise introduces variations and perturbations in the data, making the model robust and helping it to learn different possible outputs for a given input.
When the reverse diffusion process begins, noise is slowly removed or reversed from the dataset to generate content that matches the original’s qualities. This process encourages the model to focus on the underlying structure and patterns in the data, rather than relying on the noise to produce the desired outputs. By gradually removing the noise, the model learns to produce outputs that closely match the desired qualities of the original input data.
Noise, in this case, is best defined as signals that cause behaviors you don’t want to keep in your final dataset but that help you to gradually distinguish between correct and incorrect data inputs and outputs.
To better understand generative AI models, read our AI chatbot comparison: ChatGPT vs. GitHub Copilot
Types of Generative AI Models
Many types of generative AI models are in operation today, and the number continues to grow as AI experts experiment with existing models.
With the classification of AI models below, keep in mind that it’s possible for a model to fit into multiple categories. For example, the latest updates to ChatGPT and GPT-4 make it a transformer-based model, a large language model, and a multimodal model.
- Generative adversarial networks (GANs): Best for image duplication and synthetic data generation.
- Transformer-based models: Best for text generation and content/code completion. Common subsets of transformer-based models include generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT) models.
- Diffusion models: Best for image generation and video/image synthesis.
- Variational autoencoders (VAEs): Best for image, audio, and video content creation, especially when synthetic data needs to be photorealistic; designed with an encoder-decoder infrastructure.
- Unimodal models: Models that are set up to accept only one data input format; most generative AI models today are unimodal models.
- Multimodal models: Designed to accept multiple types of inputs and prompts when generating outputs; for example, GPT-4 can accept both text and images as inputs.
- Large language models: The most popular and well-known type of generative AI model right now, large language models (LLMs) are designed to generate and complete written content at scale.
- Neural radiance fields (NeRFs): Emerging neural network technology that can be used to generate 3D imagery based on 2D image inputs.
Generative AI vs. Discriminative AI Models
The primary difference between generative and discriminative AI models is that generative AI models can create new content and outputs based on their training.
Discriminative modeling, on the other hand, is primarily used to classify existing data through supervised learning. As an example, a protein classification tool would operate on a discriminative model, while a protein generator would run on a generative AI model.
Generative AI vs. Predictive AI Models
Generative models are designed to create something new while predictive AI models are set up to make predictions based on data that already exists.
Continuing with our example above, a tool that predicts the next segment of amino acids in a protein molecule would work through a predictive AI model while a protein generator requires a generative AI model approach.
To gain a fuller understanding of the differences between generative and predictive models, read our guide: Generative AI vs. Predictive AI.
What are the Challenges of Generative AI Models?
Even though generative AI has been trending since November 2022, one of the major reasons you see only a limited number of startups developing AI models is that it requires deep pockets and vast resources and is very complex. We highlighted some key challenges of generative AI models below.
Mode Collapse in GANs
GANs may suffer from mode collapse, which is when the generator learns to fool the discriminator by producing a limited set of outputs, ignoring the diversity present in the training data. This can result in repetitive or less varied generated content.
Training Complexity
As stated above, generative models often require large amounts of data and computational resources for training. The resource-intensive nature of training limits accessibility for smaller research labs and individual researchers. It also requires domain-specific knowledge, as a lack of domain-specific expertise can result in the AI model giving suboptimal outputs, or even outputting AI hallucinations.
Adversarial Attacks
Generative models, especially GANs, are susceptible to adversarial attacks where small perturbations to input data can lead to unexpected or malicious outputs. Learning to effectively combat adversarial attacks is an active area of research.
Fine-tuning and transfer learning
Adapting pre-trained generative models to specific tasks or domains may be challenging. The ability to fine-tune without causing catastrophic forgetting or degradation in performance is an another ongoing research concern, and requires more work and investment.
To learn about the companies actively developing generative AI models, see our guide: Generative AI Companies: Top 12 Leaders
What are the Benefits of Generative AI Models?
The benefits of generative AI models are numerous and very important to AI’s future, particularly in the area of data augmentation and natural language processing.
Data Augmentation
Generative models can be used to augment datasets by generating synthetic data. This is helpful in scenarios where sufficient real-world labeled data is not available, making it useful for training other machine learning models.
Natural Language Understanding and Generation
Generative AI models can be used to create AI chatbots and virtual AI assistants capable of understanding and generating human-like responses in natural language. They can generate human-like text for content creation, including articles, stories, and more.
Creative Applications
Generative AI models can be used to create art, poetry, music and other artistic works. For example, OpenAI’s Jukebox, a generative model, can compose music in different genres. Generative AI models can also be leveraged for content synthesis, as it is capable of producing diverse and creative content, and assisting in brainstorming and ideation processes.
Versatility
AI models can be fine-tuned for various tasks, such as translation, summarization, and question answering. They can also be adapted to different domains and industries with proper training and fine-tuning.
For example, depending on the tuning, the output can be very serious and proper, or casual and recreational. The mode and mood of the output can be tuned to a remarkably specific degree.
Examples of Generative AI Models
Below you’ll find some of the most popular generative AI models available today. Keep in mind that many generative AI vendors build their popular tools with one of these models as the foundation or base model. For example, many of Microsoft’s new Copilot tools run on GPT-4 from OpenAI.
- GPT-3/3.5/4, etc.: GPT-3, GPT-3.5, and GPT-4 are different generations of the GPT foundation model managed, owned, and created by OpenAI. The latest version, GPT4, uses a multimodal LLM that is the basis for ChatGPT.
- OpenAI Codex: Another model from OpenAI, Codex is able to generate code and autocomplete code in response to natural language prompts. It is the foundation model for tools like GitHub Copilot.
- Stable Diffusion: One of the most popular diffusion models, Stability AI’s Stable Diffusion is primarily used for text-to-image generation.
- LaMDA: A transformer-based model from Google, LaMDA is designed to support conversational use cases.
- PaLM: Another transformer-based LLM from Google, PaLM is designed to support multilingual content generation and coding. PaLM 2 is the latest version of the model and is the foundation for Google Bard.
- AlphaCode: A developer and coding support tool from DeepMind, AlphaCode is a large language model that generates code based on natural language inputs and questions.
- BLOOM: Hugging Face’s BLOOM is an autoregressive, multilingual LLM that mostly focuses on completing statements with missing text or strings of code with missing code.
- LLaMA: LLaMA is a smaller large language model option from Meta, with a goal of making generative AI models more accessible to users with fewer infrastructural resources.
- Midjourney: Midjourney is a generative AI model that operates similarly to Stable Diffusion, generating imagery from natural language prompts that users submit.
Learn how AI’s development is driving the growth of AI models: Generative AI Landscape: Current and Future Trends
What Can Generative AI Models Do?
Generative AI models support various use cases, allowing you to complete a variety of business and personal tasks when trained appropriately and given relevant prompts. You can use generative AI models to handle the following tasks and many more:
| Language | Visual | Auditory |
|---|---|---|
| Generate and complete text | Image generation | Music generation |
| Code documentation | Video generation | Voice synthesis |
| Answer questions and support research | 3D models | Voice cloning |
| Design proteins and drug descriptions | Optimize imagery for healthcare diagnostics | |
| Supplement customer support experiences | Create immersive storytelling and video game experiences | |
| Generate synthetic data | Synthetic media | |
| Code generation | Procedural generation |
Bottom Line: Generative AI Models
Generative AI models are highly scalable, accessible artificial intelligence solutions that are rightfully getting publicity as they supplement and transform various business operations.
However, there are many concerns about how these tools work, their lack of transparency and built-in security safeguards, and generative AI ethics in general. Whether your organization is working to develop a generative AI model, build off of a foundational model, or simply use ChatGPT for daily tasks, keep in mind that the best way to use generative AI models is with comprehensive employee and customer training and clear ethical use policies in place.
For a deeper understanding of how generative AI models are used in business, see our guide: Best AI Chatbots 2024