An Introduction to Foundation Models

from | 19 April 2024 | Basics

Generative AI is a popular term in the business world, and the underlying technology, Foundation Models (FMs for short), is gaining traction due to its widespread use and adoption. The technology is of great value to organisations around the world as it enables them to gain a competitive advantage and accelerate business operations. However, this area is fraught with a lot of jargon as terms are often mixed up, confused and misused. Therefore, this blog post serves as a one-stop solution to clarify the basics of FM and determine its applications for your business.

What are foundation models?

Foundation models (also known as base models) are Universally applicable AI models that are trained on huge amounts of unlabelled data with the help of self-supervised learning become. Generative AI produces novel, contextualised and human-like results because Foundation Models are the underlying structure that understands and processes information.

Foundation models are so called for two reasons. Firstly, they form the Basis for countless use cases in the industry. For example, FMs help to gain insights from unstructured data and improve business efficiency by automating repetitive tasks, freeing up valuable time for strategic work. Secondly, the Foundation Models useful for the Fine tuning of models with user-defined Training data for area- and task-specific applications. Training FMs is expensive, so companies like to fine-tune a pre-trained model. LaMDA for example, can be fine-tuned with a company's customer support tickets to maximise the capabilities of the Chatbots to improve the response to customer complaints.

Foundation models already pass through the following phases before they are fine-tuned for further applications:

  1. Pre-trainingFMs undergo training with huge amounts of data records.
  2. GeneralisabilityFMs can perform well in a wide range of tasks.
  3. ScopeThe architecture of FMs and their extensive database give them a broad understanding and capabilities.
  4. AdaptabilityFMs are modifiable, and techniques such as fine tuning make them suitable for a wide range of tasks and applications.
  5. Self-supervising FMs are not given explicit instructions for their learning, but learn by making sense of the unlabelled data.

Using the learned patterns and relationships, Foundation Models predict the next element or elements in a sequence, such as the next word or image. They can generate many unique outputs from a single input because they create a probability distribution over all possible outputs that can follow the input and then randomly select the output from that distribution.

Companies are tuning FMs for robust applications, commonly known as generative AI. Generative AI is changing companies and reshaping industries. For example, rapid product development is possible as generative AI enables rapid prototyping and testing of new product designs. The Data analysis is now much more efficient as generative AI tools can extract patterns and insights from vast amounts of unstructured data. Data to promote strategic decision-making within a company.

Learn how large language models such as ChatGPT are improved through the use of Reinforcement Learning from Human Feedback (RLHF).

Reinforcement Learning from Human Feedback in the Field of Large Language Models

Differences between Foundation Models and Large Language Models

Foundation Models and large language models Large Language Models (LLMs for short) are two terms that are often used interchangeablywhich leads to the confusion that they are the same thing. An LLM is a type of foundation model that can only understand and generate text. FMs, on the other hand, can Process images, text, speech, videos, etc..

Although the two terms have some similarities, they are not identicalas can be seen by assessing their differences. Both have contributed significantly to progress in processing natural language processing (NLP) and speech processing. However, FMs are more general and less data-intensive than LLMs, which are more specialised and data-intensive.

The similarities and differences between the two are therefore summarised in the following table.

Foundation ModelsLarge Language Models
Both types of models can semantic relationship between words understand. The models use this ability to translate phrases from one language to another and to provide context-sensitive and relevant responses to prompts.

Word2vec represents words as vectors in a semantic space in order to interpret meaningful connections between them.
LLMs accelerate understanding semantic relationships between wordsby learning the co-occurrence of words and sentences through statistical learning and understanding the context of sentences based on the overall message.

GPT-3 can decode the context and meaning of sentences to provide understandable and context-dependent answers.
Managing FMs Sentiment analyses by decoding the positive, negative or neutral tone of texts.

Jurassic-1 Jumbo is useful for sentiment analysis, as the model can classify text based on labels or categories.
LLMs also demonstrate an advanced Sentiment analysisby understanding different tones such as sarcasm, hypocrisy, joy, etc.

BARD can analyse the feelings in a text and understand the emotions of customers towards products.
Foundation models make it possible to Chatbotsprocess user input and retrieve relevant information
PaLM enables the creation of chatbots with the help of its API.
LLMs make it possible to ChatbotsThe aim is to provide natural and human-like responses and thus improve the customer's dialogue experience.

The GPT-3 chatbot provides context-dependent answers and generates coherent responses.
FMs are for a Broad spectrum of tasks applicable.

LaMDA helps to create content, improve learning experiences through personalised content and respond to customer enquiries.
Large Language Models are exclusively for language texts used.

Google's T5 is useful for language tasks such as machine translation, keyword generation, summarisation and conversational Artificial intelligence.
FMs are not strictly trained only on voice data. Therefore, the answers are at a generic level.

DALL-E 2 has been trained on large quantities of text and image pairs. The model understands the user input through text input and outputs the desired images.
LLMs are trained exclusively on voice dataThis enables them to understand linguistic subtleties. This enables them to output grammatically correct, context-dependent and meaningful results.

Developed by NVIDIA, the Megatron-Turing NLG is able to generate dialogues and other language-related tasks while being grammatically correct.
Foundation models tend to do this, inaccurate results, but they are more innovative.

PaLM is a powerful FM with various applications. However, when it comes to historical or scientific information, the model's results are inaccurate.
LLMs are primarily Stable in their results and more sophisticated, making them a broad choice for business applications.

Megatron-Turing NLG has a wide range of applications in companies due to its interactive responses. It is therefore often used by companies for meetings, summaries and virtual support.
Similarities and differences between Foundation Models and Large Language Models
Top 14 LLMs in Business, a cubist collage of language

Large language models are transforming interaction with technology and expanding its application from content creation to customer service. Our overview presents 14 relevant representatives in detail:

The 14 Top Large Language Models: A Comprehensive Guide

Examples of foundation models

There are different FMs for different applications. To make it easier to understand, you will find the origins, functions and areas of application of these common foundation models below:

  • BERT: The BERT of Google AI can analyse the context of a word by taking into account the words that come before and after it. This helps the model decipher the intent behind search queries. BERT is used in business to improve search results, enhance chatbot functions by understanding user intent, provide contextualised answers and translate content.
  • DALL-E: DALL-E from Open AI can generate images from text input. In addition to the creation of images, DALL-E also enables the editing and refinement of existing images. The model is known for its applications in creating images for marketing campaigns, creating storyboards for games and films, generating ideas for physical products and creating a brand identity.
  • LLaMALLaMA by Meta AI is a basic large-scale language that takes a sequence of words as input and predicts the next word to recursively generate text. Like other models, LLaMA helps to generate and translate text, answer questions and generate code. Companies can use LLaMA for customer service, law firms can search for specific legal information, and e-commerce companies can use it to create product descriptions.
  • GPT-3GPT-3 from OpenAI is a language model that is primarily used as a creative writing assistant. It summarises texts by evaluating longer sections and providing informative responses to text input from the user. Companies like to use it to automate repetitive tasks, generate code, increase the productivity of software developers by assisting with code documentation and provide feedback for educational content.
  • SeamlessM4TSeamlessM4T from Meta AI is a multilingual, multimodal AI model for seamless language and text translations. Depending on the task, the model can perform translations for up to 100 languages. It can perform speech recognition, speech-to-text translation, speech-to-speech translation, text-to-text translation and text-to-speech translation.

Discover the impressive ability of new AI models to create realistic images from text that are almost indistinguishable from real works of art.

"Content is AI-NG - text-to-image generators at a glance" - Alexander Thamm GmbH

Choosing the right foundation model

The following is a suggested strategy for selecting FM for your organisation:

1. brainstorming about value and benefits

When deciding on Foundation Model for your organisation, consider the value it brings to your business operations. This will help you identify your rationale for choosing FM with benefits for your organisation. Below are some suggested motivations for choosing the right Foundation Model for your organisation:

  1. Accelerating efficiencyIncrease the efficiency of your organisation by automating tasks and enabling your employees to invest time and energy in strategic decisions.
  2. Improve the decision-making processImprove decision making by using FM to gain business insights.
  3. Improve the customer experienceBetter services for customers through the use of FM to personalise content.
  4. Developing better productsUtilise the possibilities of FM for the development and introduction of new products.

2. identification of business requirements and objectives

You need to determine what you need the Foundation Model for by assessing the resources and budget. Based on the current state and feasibility, you need to source and prepare accordingly. Below is a short list of key factors to consider when choosing the right Foundation Model for your organisation:

  1. Technical requirementsEvaluate the current state of your technical infrastructure and capabilities. FMs require large amounts of computing resources and data infrastructure. You need to assess whether you can provide or procure this type of data storage and processing. If not, you need to consider possible partnerships and collaborations with companies that specialise in such services.
  2. PersonnelIf you want a suitable FM for your company, you also need employees who can select, maintain and implement the FM. This could include Data Scientists, Data Engineers for machine learning or NLP specialists. Determine the employees within the company and hire more if necessary.
  3. Costs: FM training itself is expensive, so access to Foundation Models is expensive for an organisation. As with any other technology, the number of features and applications that FMs offer determines their price and value. FMs with more features are expensive, but they are also more generalised. Therefore, it is best to use large FMs to validate your Minimum Viable Product (MVP). Once your MVP is validated, you can use smaller models that are cheaper and tailored to your specific business application with more ease and profitability.
  4. Latency timeCompanies take a structured approach to product launches and often have limited time. FMs vary in the time they take to train and deliver the desired results. Choose a model that prioritises speed if you need a business application that requires quick responses.

3. define areas of application

You need to know what you need the Foundation Model for. While there are many uses for FMs, it's best to be clear about what you need the Foundation Model for so you can choose the right one for your organisation, depending on what purpose you want it to serve. Here are some possible uses for a Foundation Model:

  1. Content creationFMs are a powerful technology for creating business content through persuasive marketing copy, writing product descriptions for e-commerce websites or creating business reports based on meeting summaries.
  2. Provision of customer serviceFMs enhance the capabilities of chatbots by generating human-like responses, and with some fine-tuning, the model can improve sentiment analysis and provide empathetic responses for customers.
  3. Product developmentFMs can accelerate product development by analysing customer reviews from websites, research findings and social media data to improve products and bring new products to market.
  4. Research and developmentFMs improve data analysis by analysing huge amounts of data that can form the basis for scientific research.

The proposed strategy provides a guide for choosing the right Foundation Model for your organisation. Once you are clear about why you need FM and what you need it for, you can make an informed decision. Choosing the right FM for your organisation will lay the foundation for its value and help you gain a competitive advantage.

Fine-tuning the CLIP Foundation Model for Image Classification, Dr Bert Besser, Principal Data Engineer, Alexander Thamm GmbH, Tech Deep Dive

In our article, we analyse how the CLIP model performs after fine-tuning to specific datasets compared to traditional models such as ResNet50.

Fine-Tuning the CLIP Foundation Model for Image Classification

Basis for efficient business processes

Foundation Models are effective tools for generative AI, and their applications are transforming organisations and industries. By further fine-tuning FMs, companies can customise FM training data to automate repetitive tasks, develop products efficiently, improve customer support and create compelling business documents. Although both FMs and LLMs drive the business applications of generative AI, both contribute differently, as FMs are more general-purpose, while LLMs are specialised for text only. Foundation Models contribute significantly to efficient business processes. However, organisations need to be careful when using them by first developing a strategy for selecting the right foundation model for their use cases.



Pat has been responsible for Web Analysis & Web Publishing at Alexander Thamm GmbH since the end of 2021 and oversees a large part of our online presence. In doing so, he beats his way through every Google or Wordpress update and is happy to give the team tips on how to make your articles or own websites even more comprehensible for the reader as well as the search engines.

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