Summary
Instead of labeling data by hand and training one model for one application, you tell your foundation model to go read a bunch of unalbeled data and understand it, then with this ground knwoedge we can have one foundation model for several applicaitons. You end up with a large versatile model with more human-like capabilities.
A Strong AI Concept
Definition: Large-scale neural networks serving as a base for multiple applications. Instead of training models from scratch, foundation models are fine-tuned for specific applications.
Training Characteristics: - Huge Amount of Data: Gigabytes to petabytes. - Unsupervised Learning: No need for manual annotation. - Unstructured Data: Models learn from vast amounts of text and other unstructured data.
Paradigm Shift in AI
- Old Approach: Training one model per dataset for a specific function (Narrow AI).
- New Approach: Singular foundation models can drive multiple use cases and applications, replacing the need for several independently trained models (Strong AI).
Advantages
- Performance: Extensive data exposure provides contextual understanding.
- Efficiency: Saves time and resources.
- Accuracy: Better predictions.
- Versatility: Adaptable to various tasks like language translation, content generation, and image recognition.
- Small Data Training: Effective even with incomplete data (e.g., editing a choppy email).
Disadvantages
- Computing Cost: Expensive to train and run, requiring multiple GPUs.
- Trust Issues: Training data may contain unvetted, discriminatory, or false information. Efforts are ongoing to produce more reliable training data.
Types of Models
- Language Models: Large Language Models (LLMs) like ChatGPT.
- Vision Models: DALLE-2 for image generation.
- Scientific Models:
- Biology: Predicting protein folding for medicine development.
- Geology: Developing climate change technologies.
- Audio Models: For processing and generating audio.
- Code Models: Copilot for coding assistance.
Fine-Tuning
- Process: Foundation models can be fine-tuned with a small amount of labeled data to perform traditional supervised learning tasks.
Prompting
- Definition: Asking the model questions instead of training it on new data.