Generative AI, Large Language Models, and Foundation Models

- Overview

The technologies that support generative artificial intelligence (GenAI) have been developing at an unprecedented pace, thanks in large part to huge investments from big tech companies and research labs. In fact, GenAI appears to be unaffected by the overall slowdown in venture capital, with well-funded startups continuing to emerge and mature.

GenAI has been around since the 1960s, when it was introduced in chatbots. Recent interest in GenAI has been driven by the simplicity of new user interfaces that allow users to create high-quality content in seconds. It has a variety of applications, including: improving customer interactions, exploring unstructured data, and assisting with repetitive tasks. 

Generative AI can serve as effective brainstorming partners in research. These systems can – when used appropriately – help generate a variety of ideas, perspectives, and potential solutions, particularly useful during the initial stages of research planning.

A foundation model, also known as a large AI model, is a machine learning (ML) or deep learning model (DL) that is trained on a wide range of data so it can be applied to a wide range of use cases. The underlying model has transformed AI, powering famous GenAI applications like ChatGPT. 

Please refer to the following for more information:

• Wikipedia: Generative AI

• Wikipedia: Large Language Model

• Wikipedia: Foundation Model

- Generative AI

Generative artificial intelligence (GenAI) is a type of machine learning (ML) algorithm that can produce a variety of novel content, such as: Images, video, music, voice, text, software code, product design, and simulations. 

GenAI uses advanced algorithms and neural networks to mimic human creativity and generate new content. Some examples of GenAI tools include: GPT-4, ChatGPT, AlphaCode, GitHub Copilot, Bard. 

GenAI works by learning patterns and relationships in a dataset of human-created content, and then using those patterns to generate new content. The results can be indistinguishable from human-generated content, or they can seem uncanny, depending on the quality of the model and how well it matches the use case. 

GenAI uses various ML techniques, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs), to generate new content. One such technique is diffusion models, which slowly add random noise to training data during the forward diffusion process, and then reverse the noise to reconstruct the data samples during the reverse diffusion process. 

Novel data can then be generated by running the reverse denoising process starting from entirely random noise. However, one study warns that the influx of AI-generated content could rapidly devalue content, impacting eCommerce and marketing.

Some potential issues with GenAI include:

• Difficulty adapting to new circumstances
• Harmful bias
• Intellectual property rights
• Lack of transparency
• New cybersecurity dangers
• Potential problems with accuracy and appropriatenes

- Large Language Models (LLMs)

Generative AI (GenAI) is a broad category of AI that can create new content, such as text, images, music, and code. Large language models (LLMs) are a specific type of GenAI that focus on producing text that sounds like human writing. LLMs are trained on large amounts of text to understand existing content and generate original content. GenAI applications are built on top of LLMs and foundation models. 

LLMs are a type of AI program that can recognize and generate text. They are trained on huge sets of data, such as trillions of words, across many natural-language tasks. LLMs are built on ML, specifically a type of neural network called a transformer model. 

LLMs are proficient in generating text, producing fluent, succinct, and precise linguistic expressions. They are also proficient in language comprehension tasks, such as sentiment analysis, text categorization, and processing factual input. 

GenAI is a pinnacle achievement, particularly in the intricate domain of Natural Language Processing (NLP). 

LLMs provide context and memory capabilities, while GenAI enables the production of engaging responses. This results in more natural, humanlike, interactive conversations.

- AI Foundation Models

The foundation model (FM) is a general-purpose technology that can support a variety of use cases. Building basic models is often highly resource-intensive, with the most expensive models costing hundreds of millions of dollars to pay for the underlying data and computing required. In comparison, it is much less expensive to adapt an existing foundation model for a specific use case or to use it directly.

The term foundation model (FM) entered our lexicon when experts began to notice two trends in the field of ML:

• A small number of deep learning architectures are used to achieve results for a variety of tasks.
• Artificial intelligence (AI) models may emerge with new concepts that were not initially considered during training.

The underlying model has been programmed to function with a general contextual understanding of patterns, structures, and representations. This basic understanding of how to communicate and recognize patterns creates a baseline of knowledge that can be further modified or fine-tuned to perform domain-specific tasks in almost any industry.

Foundation models (FMs) are a type of ML model that can perform a variety of tasks and applications. AI FMs are large AI models that can generate a wide range of outputs, including text, images, or audio. They can be standalone systems or used as a base for other applications. 

FMs are a form of GenAI that can generate output from one or more inputs in the form of human language instructions. They are based on complex neural networks, including generative adversarial networks (GANs), transformers, and variational encoders. 

FMs are trained on large, unlabeled datasets and fine-tuned for an array of applications. They are pre-trained with extremely large data sets scraped from the public internet. 

The term "foundation model" was coined in August 2021 by the Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM).

Trained on massive datasets, AI FMs are large deep learning neural networks that have changed the way data scientists approach machine learning (ML). 

Rather than develop AI from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.

FMs can be very large, up to trillions of parameters in size, so adapting the entirety of a foundation model can be computationally expensive. Therefore, developers sometimes adapt only the last neural layer or only the bias vectors to save time and space. 

For particularly niche applications, specific data may also not be available to adapt the foundation model sufficiently. In such circumstances, data must be manually labeled, which is costly and can demand expert knowledge.

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- Generative AI vs. Predictive AI

Generative AI (GenAI) and predictive AI are two different approaches to AI. GenAI focuses on creating new content, while predictive AI focuses on making accurate predictions.

Predictive AI analyzes existing data to make predictions, recommendations, and decisions. It uses various AI and ML techniques. Predictive AI is commonly used in industries such as finance, healthcare, and marketing.

Predictive AI enables informed decision-making, cost reduction, and risk mitigation. It can also predict a company's future needs or events, such as foreseeing upcoming trends or predicting risks and their solutions.

GenAI and predictive AI are both types of AI that use ML algorithms, but they have different purposes and applications:

• Generative AI: Creates new content, such as images, videos, music, text, virtual worlds, artwork, or design concepts. It uses neural networks and deep learning to generate novel and creative outputs that mimic human-like patterns. Generative AI requires some initial creative input, such as a prompt, seed, or example, to start the creative process.
• Predictive AI: Analyzes patterns in existing data to make predictions about future outcomes, such as stock market trends, customer behavior, disease progression, financial forecasting, or demand forecasting. It uses statistical and machine learning models to forecast future scenarios and trends mathematically.

GenAI can build on predictive AI capabilities by harnessing data for insights and creating content. For example, predictive AI can help companies optimize inventory management and supply chain operations by anticipating future demand for products or services

- Generative Integration

The convergence of GenAI and data and application integration - aka generative integration – is a game-changer for enterprises, given its power to efficiently connect data across business streams and make it accessible to everyone. Generative Integration is a method of using GenAI and LLMs to automate the creation of integration pipelines and connect data sources and systems. 

Generative Integration is an advanced approach to data and application integration that leverages Gen AI and LLMs. This innovative method securely automates the creation of integration pipelines, streamlining the process of connecting disparate systems and data sources.

Generative integration can help reduce manual effort, improve the accuracy and efficiency of data integration tasks, and allow citizen developers to create integrations. 

Generative Integration can empower users to generate new connectors on-demand, further expanding the integration possibilities and catering to more specific needs.