Foundational Pre-Trained AI Model

A Foundational Pre-Trained AI Model is a pre-trained AI model that is leveraged by AI systems (to solve AI tasks).

• Context:
  • It can typically be trained on massive foundational pre-trained neural model datasets using foundational pre-trained neural model self-supervised learning techniques.
  • It can typically learn foundational pre-trained neural model representations without requiring human-labeled data.
  • It can typically capture foundational pre-trained neural model patterns across diverse data domains.
  • It can typically encode foundational pre-trained neural model knowledge through foundational pre-trained neural model parameters.
  • It can typically serve as foundational pre-trained neural model infrastructure for AI ecosystem development.
  • ...
  • It can often demonstrate foundational pre-trained neural model emergent capabilities not explicitly foundational pre-trained neural model design goals.
  • It can often transfer foundational pre-trained neural model knowledge across different task domains.
  • It can often reduce downstream task development time through foundational pre-trained neural model transfer learning.
  • It can often exhibit foundational pre-trained neural model in-context learning without explicit parameter updates.
  • ...
  • It can range from being a Small-Scale Foundational Pre-Trained Neural Model to being a Large-Scale Foundational Pre-Trained Neural Model, depending on its foundational pre-trained neural model parameter count.
  • It can range from being a General-Purpose Foundational Pre-Trained Neural Model to being a Domain-Specific Foundational Pre-Trained Neural Model, depending on its foundational pre-trained neural model training data composition.
  • It can range from being a Unimodal Foundational Pre-Trained Neural Model to being a Multimodal Foundational Pre-Trained Neural Model, depending on its foundational pre-trained neural model input data type.
  • ...
  • It can incorporate foundational pre-trained neural model attention mechanisms for capturing long-range dependencies.
  • It can employ foundational pre-trained neural model architecture designed for efficient computation.
  • It can utilize foundational pre-trained neural model tokenization schemes for processing input data.
  • It can leverage foundational pre-trained neural model distributed training across computing clusters.
  • It can implement foundational pre-trained neural model regularization techniques to prevent overfitting.
  • ...
• Examples:
  • Foundational Pre-Trained Neural Model Modalities, such as:
    • Foundational Pre-Trained Neural Text Models, such as:
      • Foundational Pre-Trained Neural Encoder-Only Text Models, such as:
        • BERT (Bidirectional Encoder Representations from Transformers), a foundational pre-trained neural model for bidirectional language understanding.
        • RoBERTa (Robustly Optimized BERT Approach), a foundational pre-trained neural model with optimized training methodology.
        • ALBERT (A Lite BERT), a foundational pre-trained neural model with parameter-efficient architecture.
        • DistilBERT, a foundational pre-trained neural model using knowledge distillation techniques.
      • Foundational Pre-Trained Neural Decoder-Only Text Models, such as:
        • GPT (Generative Pre-trained Transformer) series, foundational pre-trained neural models for autoregressive text generation.
        • LLaMA (Large Language Model Meta AI), a foundational pre-trained neural model with efficient scaling properties.
        • Falcon, a foundational pre-trained neural model optimized for computational efficiency.
      • Foundational Pre-Trained Neural Encoder-Decoder Text Models, such as:
        • T5 (Text-to-Text Transfer Transformer), a foundational pre-trained neural model framing all tasks as text generation.
        • BART (Bidirectional and Auto-Regressive Transformer), a foundational pre-trained neural model for sequence-to-sequence tasks.
    • Foundational Pre-Trained Neural Vision Models, such as:
      • Foundational Pre-Trained Neural Image Classification Models, such as:
        • ResNet (Residual Network), a foundational pre-trained neural model using residual connections.
        • EfficientNet, a foundational pre-trained neural model with compound scaling methodology.
        • ViT (Vision Transformer), a foundational pre-trained neural model applying transformer architecture to images.
      • Foundational Pre-Trained Neural Object Detection Models, such as:
        • YOLO (You Only Look Once), a foundational pre-trained neural model for real-time object detection.
        • Faster R-CNN, a foundational pre-trained neural model using region proposal networks.
    • Foundational Pre-Trained Neural Multimodal Models, such as:
      • CLIP (Contrastive Language-Image Pre-training), a foundational pre-trained neural model connecting text and images.
      • DALL-E, a foundational pre-trained neural model generating images from text descriptions.
      • Flamingo, a foundational pre-trained neural model for visual and language understanding.
  • Foundational Pre-Trained Neural Model Training Approaches, such as:
    • Foundational Pre-Trained Neural Model Self-Supervised Learning Approaches, such as:
      • Masked Language Modeling, a foundational pre-trained neural model training approach predicting masked tokens.
      • Next Token Prediction, a foundational pre-trained neural model training approach predicting subsequent tokens.
      • Contrastive Learning, a foundational pre-trained neural model training approach using similarity comparisons.
    • Foundational Pre-Trained Neural Model Multi-Task Learning Approaches, such as:
      • Instruction Tuning, a foundational pre-trained neural model training approach using diverse task instructions.
      • Prompt-Based Learning, a foundational pre-trained neural model training approach using natural language prompts.
  • ...
• Counter-Examples:
  • Task-Specific Neural Models, which are trained directly for a particular application without the foundational pre-training phase.
  • Traditional Machine Learning Models, which lack the foundational pre-trained neural model transfer learning capabilities and neural network architecture.
  • Fine-Tuned Neural Models, which are derived from foundational pre-trained neural models but have been specialized for specific applications.
  • Ensemble Models, which combine multiple models rather than serving as a single foundational pre-trained neural model.
• See: Transfer Learning, Neural Network Architecture, Transformer Model, Self-Supervised Learning, Foundation Model Alignment.

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References

2023

• chat
  • A foundation model is a large pre-trained neural network model that serves as a starting point for various downstream artificial intelligence (AI) tasks. It is typically trained on massive amounts of diverse data (often from the Web), enabling it to learn and capture a wide range of language features and nuances. However, a foundation model is not typically fine-tuned for any specific task or application. If needed however, it is amenable to fine-tuning on a smaller dataset for some specific tasks. Examples of foundation models include BERT, GPT, and T5.

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Foundation_models Retrieved:2023-4-22.
  • A foundation model (also called base model) is a large artificial intelligence (AI) model trained on a vast quantity of data at scale (often by self-supervised learning or semi-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how AI systems are built, such as by powering prominent chatbots and other user-facing AI. The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) popularized the term.

    Early examples of foundation models were pre-trained large language models (LLMs) including Google's BERT and OpenAI's GPT-n series. Such broad models can in turn be used for task and/or domain specific models using sequences of other kinds of tokens, such as medical codes.

    Beyond text, several visual and multimodal foundation models have been produced— including DALL-E, Flamingo, Florence and NOOR. Visual foundation models (VFMs) have been combined with text-based LLMs to develop sophisticated task-specific models.

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