Lesson plan
Foundation model
In artificial intelligence, a foundation model (FM), also known as large x model (LxM, where "x" is a variable representing any text, image, sound, etc.), is a machine learning or deep learning model trained on vast datasets so that it can be applied across a wide range of use cases. Generative AI applications like large language models (LLMs) are common examples of foundation models. Building foundation models is often highly resource-intensive, with the most advanced models costing hundreds of millions of dollars to cover the expenses of acquiring, curating, and processing massive datasets, as well as the computational power required for training. These costs stem from the need for sophisticated infrastructure, extended training times, and advanced hardware, such as graphics processing units. In contrast, adapting an existing foundation model for a specific task or using it directly is far less costly, as it uses pre-trained capabilities and typically requires only fine-tuning on smaller, task-specific datasets. Early examples of foundation models are language models like OpenAI's GPT series. In addition to text, foundation models have been developed for visual images, including DALL-E and Stable Diffusion.
Article summary
In artificial intelligence, a foundation model (FM), also known as large x model (LxM, where "x" is a variable representing any text, image, sound, etc.), is a machine learning or deep learning model trained on vast datasets so that it can be applied across a wide range of use cases. Generative AI applications like large language models (LLMs) are common examples of foundation models. Building foundation models is often highly resource-intensive, with the most advanced models costing hundreds of millions of dollars to cover the expenses of acquiring, curating, and processing massive datasets, as well as the computational power required for training. These costs stem from the need for sophisticated infrastructure, extended training times, and advanced hardware, such as graphics processing units. In contrast, adapting an existing foundation model for a specific task or using it directly is far less costly, as it uses pre-trained capabilities and typically requires only fine-tuning on smaller, task-specific datasets. Early examples of foundation models are language models like OpenAI's GPT series. In addition to text, foundation models have been developed for visual images, including DALL-E and Stable Diffusion.
Key claims
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
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Key claims and bibliography are generated from the same tracked claims and sources as the article page.