Foundation models, also known as?pre-trained models, represent a significant advancement in artificial intelligence (AI). These large-scale models are meticulously trained on vast amounts of data, enabling them to deeply understand various modalities, including language, images, and more.?
- Definition and Purpose: A?foundation model?is a machine learning model that undergoes training on broad and diverse data. This training typically involves self-supervision at scale. The primary purpose of foundation models is to serve as a versatile base that can be adapted (fine-tuned) for a wide range of downstream tasks. Unlike specialized models, which focus on specific domains or narrow use cases, foundation models aim to be general-purpose technologies.
- Transforming AI: Foundation models have ushered in a new era of AI development. They power prominent generative AI applications, such as?ChatGPT?(like the one you’re interacting with right now!). These models have transcended language barriers and extended their capabilities to other modalities, including images, music, coding, and mathematics.
- Resource-Intensive Creation: Building foundation models is no small feat. It requires substantial resources, both in terms of data and computational power. Considering the underlying data and compute requirements, developing the most expensive foundation models can cost hundreds of millions of dollars.
- Adaptability and Cost Efficiency: The beauty of foundation models lies in their adaptability. Once created, they can be fine-tuned for specific tasks or directly applied to various contexts. Adapting an existing foundation model is significantly less expensive than building one from scratch.
- Examples: Early foundation models included language models (LMs) like?Google’s BERT?and?OpenAI’s “GPT-n” series.
- Examples in Open Source: Mistral 7B?is a comprehensive?English text and code generation?foundation model. It excels in various applications, including text summarization, classification, text completion, and code completion. Designed for rapid inference and handling longer sequences (up to 8,000 tokens). Utilizes?grouped-query?and?sliding-window attention?mechanisms for lower latency and high throughput. It is less memory-intensive due to its 7B parameter size. Available under the?Apache 2.0 license, making it freely accessible. I created a notebook
in Google Colab[5] about how to use Mistral.?
- Examples in Foundation Model for Real-World Simulation
: Sora, developed by OpenAI, is a foundation model with remarkable capabilities. Sora is part of a groundbreaking effort to create models that understand and simulate the real world.OpenAI believes that this capability is a crucial milestone toward achieving Artificial General Intelligence (AGI)
- Beyond text, foundation models have expanded into other domains: DALL-E?and?Flamingo?for imagesMusicGen?for musicRT-2?for robotic control
- Legal Definitions: Legal definitions for foundation models have emerged as governments grapple with regulating AI. In the United States, an?Executive Order?defines foundation models as AI models trained on broad data, generally using self-supervision, and containing tens of billions of parameters applicable across diverse contexts. The proposed legislation further refines this definition, emphasizing high performance and potential risks.
Foundation models represent a broad shift in AI development, bridging gaps between domains and enabling powerful applications across various fields. Their adaptability and transformative impact make them a cornerstone of modern AI research and development[1-4].
