This page contains important concepts related to foundation models.
Foundation Model
Definition from Stanford:
“any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks”. https://arxiv.org/abs/2108.07258
Wikipedia link:
Answer from ChatGPT 3.5:
“A foundation model typically refers to a pre-trained machine learning model that serves as the basis or starting point for more specialized or task-specific models. These models are trained on large and diverse datasets to learn general patterns and representations of the data. Once trained, they can be fine-tuned or adapted for specific tasks or domains with smaller, task-specific datasets.
In the context of natural language processing (NLP), models like OpenAI’s GPT (Generative Pre-trained Transformer) can be considered foundation models. These models are trained on massive amounts of text data to understand the structure and relationships within language. Users can then fine-tune these models for specific tasks such as text classification, language translation, summarization, and more.
The advantage of using foundation models lies in their ability to capture general knowledge and language understanding, which can be beneficial for a wide range of applications. Fine-tuning allows developers to leverage the pre-learned representations for specific tasks without the need to train a model from scratch, saving computational resources and time.”
Generative AI
TBD
Neural Network
TBD