M0: Important Terms

0.1. Foundation model

The lecture discusses foundation models, which are a new paradigm for building AI systems. Here are the key points:

• Definition: A foundation model is trained on a vast amount of data and can be adapted for various applications. It is a large, general-purpose self-supervised model with billions of parameters.
• Capabilities: These models can handle multiple input types (text, image, audio, video) and perform complex tasks like answering questions, summarizing documents, and generating code.
• Examples:
  • Foundation Models: OpenAI's Dall-E and GPT-3/4 are examples, as they can perform a wide range of tasks.
  • Not Foundation Models: AlexNet, which is limited to image classification.
• Large Language Models (LLMs): When foundation models are trained on natural language data, they are referred to as LLMs, such as Google's Bert and Meta's Galactica.
• Adaptability: Foundation models can be customized for specific applications, making AI more accessible for businesses.
• Limitations: Potential biases in training data and the risk of generating inaccurate information (hallucination) are challenges that need to be addressed.

0.2. Hugging Face

The lecture discusses Hugging Face, an open-source AI platform that facilitates collaboration among scientists, developers, and businesses to create personalized machine learning tools. Key points include:

• Purpose: Hugging Face aims to democratize AI by providing access to models, datasets, and applications, making AI tools available to users without extensive resources.
• Capabilities: The platform hosts over 250,000 models, 50,000 datasets, and 1 million demos, focusing on natural language processing (NLP) and various machine learning tasks.

• Business Benefits: Companies can access pre-trained models, reducing costs and time while allowing customization for specific needs. Hugging Face also helps evaluate and improve model efficiency.
• Collaborations: Hugging Face partners with organizations like IBM's Watsonx.ai to enhance AI development and deployment.
• Community Focus: The platform supports an open-source community, fostering innovation and collaboration in AI.

This overview highlights Hugging Face's role in making AI accessible and beneficial for a wide range of users, including businesses and educational institutions.

0.3. Hugging Face Transformers