The Center for Research on Foundation Models of the Stanford Institute for Human-Centered Artificial Intelligence coined the term “foundation models” in August 2021 to refer to artificial intelligence models “trained on broad data that can be adapted to a wide range of downstream tasks.” The introduction of these models is now accelerating the developments in the different fields of artificial intelligence and expanding their respective applications.
Explaining and Understanding the Purpose and Importance of Foundation Models in Advancing Artificial Intelligence and Promoting its Wider Applications
Purpose: Definition and Development
Prior models in artificial intelligence are often developed with a single purpose in mind. A particular model could perform a collection of tasks or solve a specified problem. Foundation models are now central to recent advances in machine learning and deep learning modeling, artificial neural networks, natural language processing, and computer vision. These models are economical and practical because of their multiple purposes.
Stanford Institute defines a foundation model as an AI model trained on a broad quantity of data at scale using self-supervised or semi-supervised learning that can be adapted or modified to perform different ranges of downstream tasks through trough fine-tuning or remodeling. Foundational models are fundamentally flexible and reusable artificial intelligence models that can be applied to different AI domains and use case scenarios.
The institute explained further that existing terms in the field of artificial intelligence such as large language models and self-supervised learning are overlapping but not adequate to capture the essence of models that can be adapted to different purposes. The term “foundation model” puts emphasis on the intended function behind their development. This function centers on serving as a base model for the development of other models.
Nevertheless, based on the aforementioned, the purpose of foundation models is to provide a starting point for developing more complex models and advanced applications or end-use models with specific uses without needing to create an entire model from scratch. These models provide a common framework for researchers and developers to build upon while also providing a benchmark for evaluating newer and more advanced models.
The development and introduction of foundation models mark a paradigm shift in artificial intelligence. Their further promotion is helping bring about changes in how AI is developed and deployed while facilitating the wider adoption and utilization of artificial intelligence systems. It is still important to underscore the fact that these models are not the “foundation” of AI as an entire field. They are only one component of an artificial intelligence system.
Importance: Benefits and Opportunities
Earlier examples of foundation models include pre-trained and transformer-based large language models including Bidirectional Encoder Representations from Transformers or BERT from Google and the Generative Pre-Trained Transformer or GPT from OpenAI. Meta Platforms has also released a model in February 2023 called Large Language Model Meta AI or LLaMA and it has been positioned as an open-source foundation language model.
Nvidia Corporation introduced its NVIDIA AI Foundations in March 2023 as a suite of cloud services designed to provide enterprise clients with a simplified approach to build and run their custom generative artificial intelligence applications across different use cases in domains such as text via NVIDIA NeMo, visual content via NVIDIA Picasso, and biology via NVIDIA BioNeMo. These services are based on the NVIDIA NeMo Framework.
The benefits of foundation models in the field of artificial intelligence are multifold. For starters, because they serve as groundwork for other researchers to build upon, they can help in advancing the field further and discovering novel and practical applications. The availability of these models also speeds up the development of other AI models while also encouraging knowledge sharing among researchers and developers.
Businesses can also benefit from foundational models. These models make it easier for them to adopt and deploy AI in a wide range of mission-critical situations because they reduce the time and cost spent on AI modeling. Remember that developing an AI model from the ground up is time-consuming and resource-intensive. Having access to models that can be tailorfitted to specific use cases provides cost savings and competitive advantages.
Note that the introduction of GPT-3 and GPT-4 has brought forth innovative applications that are revolutionizing how people use the internet and interact with their computers while creating novel productivity use cases. Foundation models have been considered a general-purpose technology with broad applications across different sectors and industries and positive spillover effects that can benefit economies and specific markets.