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Generative AI models are the massive, big-data-driven artificial intelligence models that are powering the emerging generative AI technology. Generative AI models use large language models, complex algorithms and neural networks to produce original text, audio, synthetic data, images, and more.
While many generative AI companies and tools are popping up daily, the models that work in the background to run these tools are fewer and more important to the growth of generative AI’s capabilities.
Read on to learn more about what a generative AI model is, how they work and compare to other types of AI, and some of the top generative AI models that are available today.
Also see: Top Generative AI Apps and Tools
Table of Contents: A Closer Look at Generative AI Models
- Generative AI Model Definition
- Types of Generative AI Models
- How Do Generative AI Models Work?
- How Are Generative AI Models Trained?
- Examples of Generative AI Models
- What Can Generative Models Do?
- Bottom Line: The Potential and Limitations of Generative AI Models
Generative AI Model Definition
Generative AI models are artificial intelligence platforms that generate a variety of outputs based on massive training datasets, neural networks and deep learning architecture, and prompts from users.
Depending on the type of generative AI model you’re working with, you can possibly generate images, translate text into image outputs and vice-versa, synthesize speech and audio, create original video content, and generate synthetic data. Although there are many different subsets and new formats of generative AI models emerging, the two primary designs are:
Generative adversarial networks
With generative adversarial networks (GANs), the components of the AI model include two different neural networks: the generator and the 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.
Transformer-based models
With transformer-based models, 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.
Also see: Generative AI Companies: Top 12 Leaders
Generative 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 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.
Also see: Generative AI Startups
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 classifications 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.
Also see: 100+ Top AI Companies 2023
How Do Generative AI Models Work?
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, etc.
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.
The reason generative AI models are able to so closely replicate actual human content is that 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.
Also see: 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. Here, we’ll discuss 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 corpora 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.
GAN model training
GAN models are trained with two different sub-model neural networks: a generator and a discriminator.
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. When the reverse diffusion process begins, noise is slowly removed or reversed from the dataset to generate content that matches the original’s qualities.
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.
Also see: ChatGPT vs. GitHub Copilot
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, GPT-4, 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 that exists with the purpose 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.
Keep learning: Generative AI Landscape: Current and Future Trends
What Can Generative Models Do?
Generative models can 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:
- Generate and complete text.
- Generate and complete code and code documentation.
- Generate imagery, videos, and audio.
- Generate synthetic data.
- Design proteins and drugs.
- Answer questions and support research.
- Optimize imagery for healthcare diagnostics.
- Create immersive storytelling and video game experiences.
- Supplement customer support experiences.
- Automate and create more visibility for cybersecurity and risk management.
More on this topic: Generative AI: Enterprise Use Cases
Bottom Line: The Potential and Limitations of 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 — and even the resourceful 10th grader’s English paper.
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 foundation 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.
