Foundation Models: Definition, Applications & Challenges in 2024

Artificial intelligence is entering a new era defined by the rise of foundation models. As per Stanford University‘s Center for Research on Foundation Models, AI is undergoing a paradigm shift,1 primarily driven by breakthroughs in foundational AI models like BERT, CLIP, DALL-E, and GPT-3. However, there are ongoing debates around the challenges these models face, including unreliability and ingrained biases. In this comprehensive article, we will define foundation models, explain how they work, present real-world applications across industries, and analyze the main concerns around implementing these AI systems.

What is a Foundation Model?

A foundation model is a machine learning model trained on broad data at scale to serve as a foundation for downstream tasks. Unlike conventional AI models designed for narrowly defined purposes, foundation models learn general capabilities that can be adapted to various use cases.

foundation models paradigm shift in AI

As per the researchers who coined the term, "Instead of manually engineering features for each task, we learn foundations that capture patterns in enormous datasets across diverse tasks."1

Although the techniques behind foundation models have existed for years, their capabilities have dramatically increased recently. Leading examples include:

  • BERT: Language model pioneered by Google AI in 2018 for natural language processing. It marked a breakthrough in pre-trained models for NLP.
  • CLIP: Image-text model created by OpenAI in 2021 that matches images to relevant text captions. It demonstrated the viability of zero-shot learning.
  • DALL-E: Text-to-image generator unveiled by OpenAI in 2021 that creates realistic images from text prompts.
  • GPT-3: Language model introduced by OpenAI in 2020 with 175 billion parameters, allowing remarkably human-like text generation.

The "foundation" in foundation models refers to their ability to provide a basis for downstream tasks. Rather than training AI models from scratch for each new problem, foundation models enable efficient transfer learning.

How are Foundation Models Adapted?

A key advantage of foundation models is that they can be adapted for new uses without extensive retraining. The main techniques for adapting them include:


This involves additional training of the model on data specific to the target task. For instance, a BERT language model pre-trained on Wikipedia and news articles could be fine-tuned with legal documents to create an AI assistant for lawyers. Fine-tuning is much faster than training a custom model.

In-Context Learning

In this approach, the model is given task instructions and examples at runtime to infer how to perform the task. For instance, the GPT-3 model can be prompted with a few examples of translation or summarization before generating new translations or summaries. This allows adapting foundation models without separate fine-tuning.

In-context learning allows foundation models to display capabilities not observed during their original training. However, the reliability of this approach remains unproven compared to rigorous fine-tuning.

Real-World Applications of Foundation Models

Owing to their versatile capabilities, foundation models are being deployed across industries:

  • Healthcare: Clinical documentation, medical coding, personalized treatment plans
  • Education: Automated grading, personalized learning, lesson plan creation
  • Finance: Risk modeling, predictive analytics, automated reporting
  • Law: Contract review, legal research, case prediction
  • Government: Fraud detection, public policy analysis, automated form filling
  • Retail: Product recommendations, inventory forecasting, customer support bots
  • Media: Automatic text generation, fake image detection, augmented content creation
  • Transportation: Predictive fleet maintenance, real-time routing optimization, self-driving vehicles

applications of foundation models

Common use cases powered by foundation models include:

  • Email and content generation
  • Text summarization
  • Translation
  • Answering customer queries
  • Website creation
  • Image generation and classification

In summary, foundation models are generalizable platforms being adapted to specialized tasks across functions and industries. However, skeptics argue there are risks in deploying these models widely.

Concerns Around Implementing Foundation Models

While foundation models mark a major advancement for AI capabilities, researchers have identified pitfalls that must be navigated:

Unreliable Outputs

A common criticism is that foundation models frequently generate convincing but incorrect or nonsensical outputs. For instance, an object recognition model confidently labeled a physical apple with a "pizza" sticker as being a pizza.2 Unreliability poses risks if models are deployed to sensitive domains like medicine without rigorously measured reliability.

Lack of True Understanding

Models like GPT-3 display impressive fluency. However, some argue they have no real comprehension of the concepts they discuss, limiting their reasoning capabilities.3 This could lead to nonsensical or unethical outputs when deployed in sensitive contexts.

Perpetuating Biases

Foundation models trained on imperfect real-world data absorb societal biases around race, gender, and other attributes. Without careful design, they risk automating and propagating discriminatory decisions.4 Mitigating unfair biases in foundation models remains an open research problem.


Foundation models represent a seismic shift in AI capabilities, enabling versatile applications across industries. However, as with any technology, they come with risks around reliability, reasoning, and fairness. Rigorous testing, transparency, and accountability mechanisms will be vital as more high-stakes decisions rely on foundation models. If responsibly implemented, they offer immense potential for accelerating progress and augmenting human capabilities.

  1. Bommasani R.; Hudson D.; Adeli E; et al. (2022). "On the Opportunities and Risks of Foundation Models". Center for Research on Foundation Models (CRFM).

  2. OpenAI. (April 19, 2021). "Multimodal Neurons in Artificial Neural Networks". OpenAI Blog.

  3. Marcus, Gary and Davis, Ernest. (August 22, 2020). "GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about". MIT Technology Review.

  4. Johnson, Khari. (June 17, 2021). "The Efforts to Make Text-Based AI Less Racist and Terrible". WIRED.