What Are Foundation Models in Generative AI?

March 9, 2024 • April Miller


The world has seen some exciting changes in AI (artificial intelligence with the release of generative AI and foundation models. These advancements — including technologies like ChatGPT — have significantly improved different industries. Foundation models can learn from the data developers feed it and help companies complete numerous tasks. 

Since the release of generative AI like ChatGPT, humans have become faster and more efficient at work. They’re capable of generating text, images, video and audio. Together, foundation models in generative AI change how people work and create, making AI more useful than ever.

What Is Generative AI?

Generative AI is a type of artificial intelligence that focuses on creating new content that resembles the work humans produce. This technology learns from existing data, understanding patterns, styles and structures within the data. The market size for generative AI is $13 billion, with a compound annual growth rate (CAGR) predicted to grow 36.5% from 2024 to 2030.

Experts also predict it will contribute $4.4 trillion to the world’s economy each year. Professionals expect it will grow significantly due to the various applications it allows. When in use, generative AI can create realistic images from textual descriptions. It can also compose music, write stories and generate code. 

Its uses are vast, offering potential benefits in fields such as entertainment, education, design and software development. By automating the creative process, generative AI opens groundbreaking possibilities for innovation.

What Are Foundation Models in Generative AI?

Foundation models in generative AI are large, pre-trained models that generate various types of content. These models possess a deep understanding of language, images and patterns, enabling them to perform different tasks. Foundation models like a generative pre-trained transformer (GPT) are pivotal in generative AI because they provide the knowledge and capabilities to build specialized applications. 

By fine-tuning these models with additional data specific to a particular task or domain, they can produce contextually relevant outputs. Foundation models are crucial for the advancement of generative AI because of how they create diverse content. With their flexibility and efficiency, they’re driving practical applications forward in the field.

What Are the Types of Foundation Models in Generative AI?

In generative AI, there are two main types of foundation models — generative adversarial networks (GANs) and variational autoencoders (VAEs).

Generative Adversarial Networks

GANs are a powerful class of neural network architectures in generative AI. The main idea behind GANs involves two neural networks contesting with each other in a game-like scenario — the Generator and the Discriminator. 

The Generator’s role is to create fake data indistinguishable from real data, starting from random noise. It learns to produce outputs that mimic genuine articles. 

On the other hand, the discriminator evaluates the presented data, attempting to distinguish between real data and fake data the Generator produces. This process is iterative, with both networks improving over time through competition. 

This training method allows GANs to generate highly realistic and detailed content, making them especially useful for various tasks. 

Variational Autoencoders

VAEs are another key foundation model commonly used in generative AI. They’re distinct from GANs in their approach and applications. Introduced in machine learning for efficient data encoding and generation, VAEs are built on probabilistic graphical models and deep learning techniques. 

VAEs learn the underlying probability distribution of a data set. They achieve this through two main components — an encoder and a decoder. The encoder takes the data and condenses it into a lower-dimensional form. The decoder then takes this representation and reconstructs its original input data.

What makes VAEs interesting is their ability to generate new data similar to the training data. By sampling values from the latent space, the decoder can create new instances that closely resemble the original training inputs.

How Do Foundation Models Work?

Foundation models leverage data to learn patterns, relationships and structures inherent in the data. This process allows them to develop a broad understanding of the world, which they can apply across different tasks and domains. How foundation models function involves:

  • Training on large datasets: Foundation models require extensive collections of data for pre-training. This enables the models to grasp diverse concepts, contexts and nuances.
  • Learning representations: During training, these models learn to represent information in a way that captures the explicit and implicit features of the data. For example, this could include understanding grammar and context in written text.
  • Transfer learning: A key characteristic of foundation models is they can transfer knowledge learned from one task to another. This is possible because the models develop a general understanding not too specific to the initial tasks they were trained on.
  • Fine-tuning: While foundation models acquire broad capabilities through initial training, they can be fine-tuned with smaller datasets. This process adjusts the model’s parameters to specialize in a particular domain, enhancing its performance on the task.

The Characteristics of Foundation Models in Generative AI

Foundation models in generative AI exhibit several defining characteristics that set them apart from traditional AI models. Some of their key features include:

  • Scalability: Foundation models improve as they process more data. This scalability allows them to learn from vast datasets, capturing data patterns, nuances and complexities.
  • Generality: Unlike models designed for specific tasks, foundation models possess a broad understanding across multiple domains. This enables them to apply knowledge from one domain to solve problems in another.
  • Adaptability: Foundation models are adaptable to specific tasks with new information, making them capable of excelling in various applications. 
  • Efficiency in learning: Due to their large-scale pre-training, foundation models require less data to learn new tasks than a training model from scratch. This is particularly beneficial where data is scarce or expensive to obtain. 

Examples of Foundation Model Applications

Foundation models like GPT-3 and BERT have enabled groundbreaking applications. For instance, GPT-3 can streamline patient care by developing AI assistants. These AI assistants can understand and process natural language queries, enhancing medical information retrieval and patient interaction.

Another striking example is AlphaFold by Google DeepMind. It’s revolutionizing the biology field by predicting protein structures with incredible accuracy. Thus, it’s accelerating drug discovery and research into diseases. 

Tools like DALL-E have opened new potentials for artists and designers by generating high-quality images from descriptive prompts. The legal profession has also used AI-based assistants like BERT. It can understand legal documents, aiding in research and case preparation more efficiently. 

Shaping the Future With Foundation Models in Generative AI

Foundation models in generative AI are at the center of a technological revolution, changing industries with unimaginable capabilities. From improving health care to sparking creativity, these technologies are redefining what’s possible. As generative AI continues to innovate, the potential impact is immense, causing people to reimagine the future. Responsibly leveraging these advancements will ensure their benefits are great for society.