Experiments with Foundation Models.

"A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-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 since their introduction in 2018." - Foundation models - Wikipedia

"AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotic manipulation, reasoning, human interaction) and technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities, and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature." - On the Opportunities and Risks of Foundation Models

Today, foundation models such as GPT-3, PaLM, Flamingo, DALL-E, Stable Diffusion, Make-a-Video, and others are trained on broad data sets. They are, therefore, widely adaptable to a range of downstream tasks like text, image, and video generation, as well as text summarization. This technology pattern has produced some inspiring, even moving, early results. But the applications that sit on top of foundation models are often best described as proofs of concept. They are not becoming useful nearly as fast as the underlying models are improving. Developers are up against the labor, resources, and capital required to build a functioning application on top of a foundation model, which leaves startups facing a daunting climb to stay in the game.

-- Foundation Models: The future isn’t happening fast enough — Better tooling will make it happen faster

PROMPT: What are foundation models, exactly? Please generalize to multiple use-cases and not just neural networks trained on vast amounts of texts, such as Large Language Models (LLM).

CHATGPT: Foundation models are large and complex AI models that are trained on vast amounts of data to learn patterns and relationships within the data. These models can be applied to various fields and use cases, beyond just language processing. Here are some examples:

1. Computer vision: Foundation models can be used in computer vision tasks, such as object recognition or image segmentation. These models are trained on large datasets of labeled images and can accurately identify and classify objects within an image.

2. Natural Language Processing (NLP): As mentioned earlier, foundation models like Large Language Models (LLMs) are a significant advancement in NLP. These models can understand natural language and generate new text, such as completing sentences or generating articles.

3. Speech recognition: Foundation models can be used in speech recognition tasks to accurately transcribe spoken words into text. These models are trained on large datasets of speech data, allowing them to recognize and understand speech patterns.

4. Recommendation systems: Foundation models can be used in recommendation systems to predict user preferences based on their past behavior. These models are trained on large datasets of user data, allowing them to accurately predict what a user might like or need.

Overall, foundation models are powerful tools that enable machines to learn patterns and relationships within data, leading to accurate predictions and better decision-making in various fields and applications.

Ref. ChatGPT-generated response, accessed 03/06/2023

PROMPT: Define and describe foundation models, giving examples such as text-to-image model, Large Language Models (LLM), and other use-cases of these large artificial intelligence models trained on a vast quantity of unlabeled data at scale.

CHATGPT: Foundation models are large artificial intelligence models that are trained on vast quantities of unlabeled data to learn patterns and relationships within the data. These models serve as the building blocks or starting points for developing more specific AI applications. Here are some examples:

1. Text-to-Image Models: Text-to-image models are foundation models that can generate images from textual descriptions. These models are trained on large datasets of text and corresponding images to learn the relationship between language and visual information. One example of such a model is DALL-E, developed by OpenAI, which can generate highly realistic images from textual descriptions.

2. Large Language Models (LLMs): Large Language Models, also known as Pretrained Language Models, are foundation models that have been trained on vast amounts of text data, such as books, articles, and web pages. These models can understand natural language and generate new text based on the learned patterns and relationships between words and phrases. Examples of LLMs include GPT-3, developed by OpenAI, and BERT, developed by Google.

3. Speech Recognition Models: Speech recognition models are foundation models that can transcribe spoken words into text. These models are trained on large datasets of speech data to learn the patterns and relationships within speech. One example of such a model is DeepSpeech, developed by Mozilla, which can accurately transcribe spoken words into text.

4. Recommendation Models: Recommendation models are foundation models that can predict user preferences based on past behavior. These models are trained on large datasets of user data to learn the patterns and relationships within the data, allowing them to make accurate predictions. Examples of such models include Collaborative Filtering, Matrix Factorization, and Neural Networks based models.

Overall, foundation models are critical in the development of AI applications. They provide a starting point for building more specific models and allow for the creation of highly advanced AI systems that can perform complex tasks such as language translation, image generation, and speech recognition.

Ref. ChatGPT-generated response, accessed 03/06/2023