AI Foundation Models

- Overview

One of the breakthroughs with generative AI (GenAI) models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training. This has given organizations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models (FMs). 

Foundation models (FMs) are ML models that are trained on large amounts of data and can be used as a starting point for developing new ML models. They are also known as base models or large AI models. 

As the name suggests, FMs are designed to serve as a base for AI systems capable of handling a wide variety of tasks due to their pre-training on large amounts of diverse data, allowing them to be adapted (fine-tuned) for specific applications through further training on smaller datasets relevant to the desired task.

However, FMs can also be susceptible to inaccuracies and provide fictitious responses, known as hallucinations. These issues can be caused by a lack of context when prompting, biases in the training data, or low-quality training data. To mitigate these issues, deterministic controls, such as vector databases, can be added to ground the response in real data.  

While FMs bring new efficiencies and possibilities, they also amplify the ethical dilemmas and social implications of artificial intelligence (AI). From bias and fairness to job displacement, the debate surrounding AI is far from over.

FMs are not built for a single task but can be applied to many different use cases like text generation, image recognition, translation, and code writing. The knowledge gained from the large training data can be transferred to new tasks with relatively smaller datasets. To adapt a FM for a specific task, you can further train it on relevant data to specialize its capabilities.

- Some Unique Characteristics of Foundation Models

A unique feature of foundation models (FMs) is their adaptability. These models can perform a variety of different tasks with high accuracy based on input prompts. Some tasks include natural language processing (NLP), question answering, and image classification. 

The scale and general nature of FMs make it different from traditional ML models, which often perform specific tasks such as analyzing the sentiment of text, classifying images, and predicting trends.

You can use FMs as a base models to develop more specialized downstream applications. The models are the culmination of more than a decade of work, and they continue to grow in size and complexity.

Here are some characteristics of FMs:

• Trained on large datasets: FMs are trained on vast amounts of data using deep learning algorithms.
• Can perform a wide range of tasks: FMs can perform a variety of tasks, such as understanding language, generating text and images, and conversing in natural language.
• Can be fine-tuned for specific tasks: FMs can be customized for specific use cases by fine-tuning them.
• Can be used as a foundation for other applications: FMs can be used as a foundation for other applications or as standalone systems. For example, the LLM GPT is the foundation model of ChatGPT.
• Can be used to solve complex problems: FMs can provide an initial solution to complex problems, which can then be refined using other methods.

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- Examples of Foundation Models

Over the past few years, building and deploying successful AI applications has involved a lot of engineering. The team spent a lot of time and effort collecting, cleaning, and labeling the data, and iterations often took weeks.

With the emergence of powerful off-the-shelf basic models, AI builders have entered a new era of work. Instead of developing custom models from scratch, we can now simply adapt powerful, out-of-the-box models based on use cases and business needs.

Some examples of FMs include: 

• BERT: Developed by Google, BERT is good for language understanding tasks like sentiment analysis, question-answering, and named entity recognition.
• T5: Developed by Google, T5 is a versatile model that can be used for tasks like language translation, document summarization, and text classification.
• RoBERTa: An enhanced version of BERT, RoBERTa is good for natural language processing tasks.
• ELECTRA: ELECTRA is known for its efficient training process, which helps with language understanding and generation tasks.
• UniLM: A versatile model that can be used for tasks like machine translation, document classification, and text summarization.
• GPT-3: The original backbone of ChatGPT, GPT-3 is good for generating text on demand.

- Applications of Foundation Models

Foundation models (FMs) are large deep learning neural networks trained on massive datasets, changing the way data scientists approach ML. Rather than developing AI from scratch, data scientists use FMs as a starting point to develop ML models to power new applications faster and more cost-effectively. 

The term FM was coined by researchers to describe ML models that are trained on a wide range of generalized and unlabeled data and are capable of performing a variety of general tasks. FMs are considered the backbone of AI and are known for their adaptability and generality.

FMs can be used as standalone systems or as a base for other applications, including:

• Creating apps
• Text generation
• Image generation
• Audio generation
• Generative tasks, like drug discovery
• Healthcare
• Enhancing medical research by analyzing large data sets
• Creating more human-like chatbots