Foundation models (FMs) are characterized by their universal applicability and low-threshold entry – they gain their capabilities from enormous data sets that they can transfer to all kinds of problems. In this article, we will focus on the different areas of application of FMs and show you how to find the most suitable FM.
What are Foundation Models?
Foundation models (FM), also known as base models, are powerful and universally applicable AI models that have been trained on enormous amounts of data using self-monitoring training. The term foundation models was coined by researchers at the Stanford Institute for Human Centered Artificial Intelligence and arose from the increasingly broad capabilities of large language models. These capabilities now go far beyond the mere interpretation and generation of language, which means that they often serve as a starting point for more complex AI applications.
They can contain billions of parameters and enable different tasks to be accomplished.
FMs are the result of an intensive and elaborate training process and are based on an evolutionary stage of deep learning architectures.
Read our article „Machine learning vs. deep learning: finding the right concept for successful AI integration“.
FMs are often characterized by emergent behavior. It is often not easy to distinguish whether the behavior or response was already contained in the training data set, was actually newly created or was inferred from the interaction of the learned context. This wide range of capabilities and the potential to infer seemingly new things from a context qualifies FMs for use in various application scenarios characterized by flexibility and uncertainty – an area that has so far had to be played out predominantly by human actors.
Despite their flexibility, FMs can neither provide internal knowledge nor internal information. Therefore, the breadth of linguistic capabilities must be reduced and developed in depth by enriching them with internal data. This process is known as model grounding. Grounding makes it possible to adapt FMs to specific tasks.
One way of using Foundation Models is the use of Large Language Models (LLMs).
When companies decide to use LLMs, they can generally use them in 3 different ways:
Public models have online access via OpenAI, Microsoft, Google or similar.
There is usually only one chat interaction. The public models are well suited for brainstorming, although data protection and data security are rather mediocre. Integration into applications is not possible and if it is, then this is defined by the providers, as is the case with Gemini or CoPilot.
Partly adapted and individualized models are called custom models.
It is an advantage that the user can optimize such a model for a specific purpose.
However, data protection and data security can only be guaranteed to a limited extent. There is also a risk of a lock-in effect. In contrast to the public models, there is better integration into applications. One example of this is GPTs from OpenAI for answering first-level support emails.
Open source models such as Lamar are completely open and customizable models. As they can be managed and hosted in-house, they are considered secure and also offer maximum flexibility in terms of application integration. Another advantage is that there is no lock-in effect, as they are developed independently of providers thanks to the open source approach. In addition, the ability to react quickly to innovations in design and architecture results in a high level of efficiency.
However, there is a relatively large amount of work involved in managing and supporting these models – this is where the support of experts is needed.
The following diagram illustrates the relationship between generative AI, large language models and foundation models:
How to find the right foundation model for your project
When it comes to choosing the ideal foundation model, you should consider the following points:
- What tasks should the model solve?
What is important to me? This determines the type of model, whether public, custom or open source. When writing creative content such as copywriting or product descriptions to which you want to add your own touch, open source models are often helpful as they can be better adapted to your own needs. When it comes to using a “wizard” that is as general as possible, models from large providers are usually easier to use and more cost-effective. It also depends on the medium in which content is to be created. Models from specialized providers are the best choice for generating visual content in particular. Regardless of the respective use cases, flexibility in model selection is crucial. The highly dynamic market and the race between the various providers make low switching costs in solution design particularly important. - What are my general conditions and requirements for using the models?
Depending on the framework conditions and regulations, the requirements for transparency and security can influence the choice of model. Simple RAG systems, for example, are unsuitable for productively responding to customer inquiries in critical areas, as the selection of the information used for the response is not visible.
A smart selection of the architecture used is required to meet this challenge.
In addition, in the context of regulatory requirements, the locations of the model providers are important in the design. Depending on the level of integration, local deployment may be necessary. As a rule, only open source LLMs can be considered here, as they are not subject to any proprietary features or restrictions.
Possible areas of application
FMs can be used in a variety of ways: however, there is an accumulation of applications in the following areas:
- Knowledge management (RAG)
- Customer Support
- Copy Writing
- Data Curation
- Augmented AI Services
In customer service as chatbots or AI-supported FAQs, they can save a lot of time.
In the case of complaint e-mails, for example, 80% of the messages received can be correctly assigned with regard to the customer’s mood (classification). This enables more targeted approaches and more appropriate responses for customers.
It is possible to use LLMs in the area of data curation to recognize semantic correlations and thus merge data records independently. This saves a great deal of manual effort and reduces the error rate when merging data records.
In the area of augmented AI services, it is advantageous to use LLMs to take over extraction and control tasks in processes. In particular, they can supplement edge cases and exceptions from process automation so that these do not hold up the process flow any further and only a small part has to be reworked by humans.
Conclusion
The market for foundation models is constantly changing. It is uncertain who will win the competition for the best model. One thing is certain: FMs are more than just hype. Their versatility makes them a valuable tool for many applications that in the past would have required a great deal of effort and complexity to develop a solution for. This increases the usability and speed at which AI-driven applications are ready for production in companies. Exciting times are therefore ahead.
