Distinguishing Generative AI, Large Language Models, and Foundation Models: A Comparative Quick Study

Distinguishing Generative AI, Large Language Models, and Foundation Models:

A Comparative Quick Study

Artificial Intelligence (AI) has seen a rapid evolution over the past decade, with advancements in various subfields such as image classification, speech recognition, and reinforcement learning. In recent years, three terms have gained prominence in AI discussions: Generative AI, Large Language Models (LLMs), and Foundation Models. These terms often appear interchangeably, causing confusion about their precise meanings and differences. This essay aims to provide a comprehensive understanding of these terms, their overlaps, and distinctions.

Generative AI:

Generative AI refers to AI systems primarily designed to create content. This term emphasizes the content-creating function of these systems, distinguishing them from other AI systems that perform tasks such as classifying data, grouping data, or choosing actions. Examples of generative AI systems include image generators like Midjourney or Stable Diffusion, large language models like GPT-4 or PaLM, code generation tools like Copilot, and audio generation tools like VALL-E or resemble.ai.

Large Language Models (LLMs):

LLMs are a subset of AI systems that work with language. These models aim to create a simplified yet useful digital representation of language. The term "large" refers to the trend of training language models with more parameters, which has been found to consistently improve performance. Modern language models may have thousands or even millions of times as many parameters as those trained a decade ago. Examples of LLMs include OpenAI’s GPT-4, Google’s PaLM, and Meta’s LLaMA. However, the term LLM is somewhat vague, as there is no consensus on what should count as a language model or what size of model should be considered "large".

Foundation Models:

The term "Foundation Model" was popularized by an institute at Stanford University. It refers to AI systems with broad capabilities that can be adapted to a range of different, more specific purposes. The original model provides a base (or "foundation") on which other things can be built. This contrasts with many other AI systems, which are specifically trained and then used for a particular purpose. Examples of foundation models include many of the same systems listed as LLMs. For instance, an LLM called GPT-3.5 served as the foundation model for the original ChatGPT.

Comparisons and Differences:

At present, "foundation model" is often used synonymously with "large language model" because language models are currently the clearest example of systems with broad capabilities that can be adapted for specific purposes. The key distinction is that "large language models" specifically refers to language-focused systems, while "foundation model" is a broader function-based concept, which could stretch to accommodate new types of systems in the future.

In Conclusion, while Generative AI, LLMs, and Foundation Models are all part of the AI landscape, they each point to different clusters of systems of interest. There are no clear boundaries separating these terms, and their definitions are likely to evolve as the field of AI continues to advance. Therefore, it is crucial to understand the context in which these terms are used and the specific systems they are intended to cover.

Hope this helps in our understanding of AI.

@Jude Joseph (July 20, 2023)