Foundation Models

Foundation models for generative artificial intelligence (AI) act as the base layer on which other tasks, systems, or models can be built. These models are trained on enormous amounts of data, which has given data scientists a stronger head start for more effective and efficient analysis.

Foundational models work by training large-scale machine learning models from a broad range of datasets, and then filters the results to more specific downstream tasks and applications. In taking this many-to-one approach with AI, enterprises are able to do more with less by simplifying complex datasets, cataloging data, decreasing the time it takes to draw insights from the large data sets, and disseminating those insights faster to the organization as a whole.

Historically, AI was trained only on specific data sets for single use cases. As a result, there were limitations to what each function and system could achieve. Now, with foundation models, generative AI will expand its capabilities offering cross-functional capabilities, such as research-based text generation, image generation, and much more.

Foundation Models vs. Large Language Models

In the conference room, whether virtual or in corporate headquarters, you may hear the terms “foundation models” and “large language models” used interchangeably. While there are similarities between the two, there are also distinct differences that must be highlighted.

Large language models, such as Falcon and ChatGPT, focus on language-based datasets. These models are often part of a foundational or multimodal model.

Foundation models focus on broader data sets, and has the capability to expand to new types of systems in the future. For example, large language models can take swaths of text and perform a specific language-related task, such as writing a blog post or creating a written business plan. Foundation models can do the same, while also leveraging generative

Foundation Model Continues to Expand

Foundation models are in their infancy still, and continue to expand, evolve and elevate in capabilities. Rather than starting to leverage generative AI from nothing, many organizations are tapping into foundation models to get a head start on what’s possible with this technology.

Some of the most effective ways to use foundation models include processing different modalities, bridging the physical world with AI, performing cognitive reasoning, and interacting with humans. In using foundation models, teams can work alongside AI to become more productive and insights-centric while delivering a more personalized employee and customer experience.

Risks of Foundation Models

While foundation models offer a lot of promise, there are inherent risks to leaning too much on AI in enterprise. For example, foundation models are able to amplify implicit biases, which can be a tremendous risk when it comes to training robots alongside those biases. This risk is equally true when you consider how quickly foundation models could spread misleading or inaccurate information.

To mitigate these risks, organizations must lean on safeguards and be mindful of the foundational datasets they choose to use. Filters, frequent recalibrations of the models, and data hygiene are all paramount when using generative AI. Likewise, approaching an AI strategy through a business framework rooted in human behavioral psychology, like StoryVesting, can help keep the guardrails up while simultaneously simplifying the complex.

The Effect of Foundation Models on Generative AI

Ultimately, foundation models hold tremendous potential when wielded correctly. Here are just some of the possible benefits to using generative AI via foundation models in the enterprise space.

Boost Productivity

Foundation models can train robots to work alongside humans on the ground level of your organization. In pairing robots with humans, teams have the ability to work faster and more efficiently, ultimately boosting productivity and employee experience.

Take a Predictive Stance

With the cognitive reasoning and human interaction capabilities of foundation models, organizations are able to be more predictive with their marketing, product development and operations. In doing so, teams can take a more predictive stance and operate proactively rather than reactively.

Architect New Experiences

As new brand experience initiatives are designed and built, teams can continuously analyze and improve the experience based on the feedback from the CSAT score. This continual data looping allows teams to ensure their ideas align with the market demands.

Improve Path to Loyalty

The CSAT score can be continually measured throughout the path to loyalty to keep the customer moving confidently and methodically toward their end goals. In keeping a pulse on how the customer is feeling with a consistent metric, organizations can fine-tune those experiences for growth.

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Foundation Models

Foundation Models vs. Large Language Models

Foundation Model Continues to Expand

Risks of Foundation Models