Aman's AI Journal • Read List

Foundation models is a term first popularized by the Stanford Institute for Human-Centered Artificial Intelligence. This paradigm has a host of benefits including: (i) instead of requiring a large, well-labelled dataset for the specific task, foundation models need a great amount unlabeled data and only a limited set of unlabeled data to fine-tune it for different downstream tasks thereby reducing the labeled data requirements dramatically, (ii) since a foundation model can be shared for different downstream tasks, we can save on the resources needed to train task-specific models owing to the knowledge transfer that foundation models bring about (training a relatively large model with billions of parameters roughly has roughly the same carbon footprint as running five cars over their lifetime), and (iii) democratizing AI research by making it much easier for small businesses to deploy AI in a wider range of mission-critical situations owing to the reduced data labeling requirements. Foundation models is a term first popularized by the Stanford Institute for Human-Centered Artificial Intelligence. This paradigm has a host of benefits including: (i) instead of requiring a large, well-labelled dataset for the specific task, foundation models need a great amount unlabeled data and only a limited set of unlabeled data to fine-tune it for different downstream tasks thereby reducing the labeled data requirements dramatically, (ii) since a foundation model can be shared for different downstream tasks, we can save on the resources needed to train task-specific models owing to the knowledge transfer that foundation models bring about (training a relatively large model with billions of parameters roughly has roughly the same carbon footprint as running five cars over their lifetime), and (iii) democratizing AI research by making it much easier for small businesses to deploy AI in a wider range of mission-critical situations owing to the reduced data labeling requirements.