Ready For It? The Rise of Foundation Models

I have a confession: I am obsessed with the Stanford Med AI YouTube channel. It’s my go-to rabbit hole when I want to feel both inspired and slightly inadequate about the state of my AI knowledge. And it was during one of these late-night binge sessions that I stumbled onto Vivek Natarajan ’s talk on foundational models in medical AI, a moment that left me genuinely rethinking everything I thought I understood about this field.

Vivek didn’t sugarcoat the state of medical AI today. Most models are narrow, rigid systems that need mountains of labeled data to do one thing decently. Do you want them to work in a different clinical context? Too bad—you’re starting from scratch. They’re like those old flip phones: functional, but limited, and not built for the complex demands of modern healthcare.

Then came, foundational models. Unlike traditional systems, these models are designed to generalize; they can adapt across tasks, require less data to perform well, and even interact in more intuitive ways. Tools like Med-PaLM and REMEDIS don’t just solve today’s problems; they reshape the blueprint for how AI can work in healthcare, tackling challenges like reliability, safety, and equity head-on.

That talk was a lightbulb moment for me. Not just because of the models themselves, but because of what they represent: a turning point where AI becomes less about rigid problem-solving and more about creating dynamic, versatile systems that can integrate into the complexities of real-world applications.

In the next few sections, we’re going to dive into the what, why, and how of foundation models, your one-stop TLDR blog for learning all about them. Whether you’re a beginner or the expert “Anti-Hero,” this should make understanding these powerful AI models crystal clear.

What’s in This Blog?

By the time you reach the end, you’ll:

• Grasp the mechanics behind foundational models in plain, no-nonsense terms.
• Explore their far-reaching applications and the ethical dilemmas they bring to the table.
• Learn why these models might not just be the present of AI but its defining blueprint for the future.

So let’s begin.

A Brief History of Foundation Models

To understand the rise of foundational models, let’s rewind a bit to where it all began. Back in the day, AI models were like your classic, single-album pop stars: they had one hit song (or in this case, one task), and that was it. You needed a separate model for every job: one to translate languages, another to detect spam, one to search answers for your homework and yet another to generate captions for your Instagram photos. These models were limited by the fact that they were trained on narrow, labeled datasets, requiring humans to painstakingly annotate data for each specific task.

Things began to change when researchers started experimenting with unsupervised learning: training models on massive amounts of unstructured data without needing explicit labels. A big breakthrough came with the introduction of word embeddings like Word2Vec, which transformed how machines understood language by mapping words into dense, meaningful vector spaces. This was like going from single-task singers to an artist who could suddenly write, produce, and perform across genres.

But the real turning point came in 2018 with the debut of BERT (Bidirectional Encoder Representations from Transformers). BERT was a game-changer: it wasn’t just trained to predict the next word in a sentence but to deeply understand context by processing text in both directions. It was versatile enough to handle a variety of tasks with just a little fine-tuning, and suddenly, the idea of a single model serving multiple purposes didn’t seem so far-fetched.

From there, things escalated quickly. Models like GPT-3 pushed the boundaries by scaling up data and compute, demonstrating that larger models trained on diverse datasets could perform astonishingly well across tasks. It was as if AI had moved from local indie fame to a sold-out stadium tour, no longer just good at one thing, but capable of reliable versatility.

What makes foundational models so special is that they’re trained on broad data at scale, often using self-supervised techniques. This allows them to develop a kind of “generalist” understanding of their domain, making them adaptable to a wide range of downstream tasks with minimal retraining. In AI terms, they’re the equivalent of 1989{Taylor's Version}: built with universal appeal but capable of resonating deeply with different audiences, whether you’re there for the synth pop anthems or the poetic deep cuts (have you heard the vault tracks)

The shift to foundational models has redefined how we think about AI development. Instead of building countless models for specific tasks, researchers are now focusing on building a few powerful general-purpose models that can adapt to a variety of use cases. It’s a shift that has fundamentally changed not just the capabilities of AI, but also how we approach its design and deployment.

What Are Foundation Models?

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks".

Let’s break this down piece by piece:

“Trained on broad data”: Imagine Taylor writing Folklore and Evermore, she drew from diverse stories, emotions, and genres during the pandemic to create something universal yet profoundly specific. Similarly, foundation models are trained on a wide, varied dataset; books, websites, code, and more, giving them the ability to understand patterns across multiple contexts.

“Self-supervision at scale”: Think of this as the Speak Now era, when you write your own songs, you don’t need anyone else to annotate or guide you. Foundation models learn in a similar way: they don’t rely on labeled data but instead teach themselves by predicting patterns in the vast data they’re exposed to.

“Fine-tuned for downstream tasks”: Once a foundation model is trained, it can be adapted, like re-recording an old album (Taylor’s Version) to resonate with a specific audience or purpose. The foundation remains the same, but with some tweaks, it can deliver exactly what’s needed.

Why are they called Foundation Models?

Researchers coined the term "foundation models" to describe a shift in AI that existing terms just can't capture. "Pretrained" and "self-supervised" models hit the technical marks but miss the bigger picture. Think of foundation models like the skeleton of AI, they’re not finished products but serve as the sturdy base from which we build everything else. While language models are often the poster child, foundation models go far beyond words. The term "foundation" was chosen to stress how crucial it is to get these models right, because a shaky foundation leads to disaster, but a solid one paves the way for something powerful. The catch?

We still don’t fully understand if these foundations are as solid as they need to be, which leaves a lot to figure out for everyone from developers to policymakers.

Now that we’ve defined foundational models, let’s peel back the layers to explore what makes them truly revolutionary: emergence, homogenization, and the magic of transfer learning.

Emergence, Homogenization, and Transfer Learning

Emergence in foundation models is like discovering that a song you’ve heard a hundred times holds new meaning each time, without it ever being intended that way. It’s not something explicitly programmed, but something that emerges from the sheer scale and complexity of the model. Think of it like the way a melody can evolve within a song, adding layers without an overt plan. For example, GPT-3, with its 175 billion parameters, wasn’t specifically taught how to perform tasks like few-shot learning, but it emerged as a capability once the model was scaled. It’s a bit like "tolerate it", where unexpected shifts in the mood and tone give the lyrics a deeper complexity, showing how even AI can reveal new insights when given enough data.

Now, homogenization in AI brings us to the consolidation of approaches. It’s like the rise of a signature sound that defines an entire genre. In the case of foundation models, nearly every NLP task uses the same foundational Transformer models. While this approach works across multiple domains, it’s a double-edged sword. Just as artists sometimes face the risk of sounding repetitive when they stick too closely to a signature style, foundation models face the risk of embedding systemic biases that can be amplified across different tasks.?

Transfer learning, on the other hand, is like borrowing lessons from one song to make another. Foundation models can adapt knowledge learned from one domain and apply it to another. For instance, a model trained on one type of data can be fine-tuned to work with different data entirely. This process allows for models to be “re-used”, much like how Swift repurposes certain narrative themes across albums. A model trained on language can adapt to vision tasks because the foundational knowledge can be transferred and fine-tuned for specific needs.

Why do we really foundation models?

Why are foundation models a game-changer in AI? They shift us from building single-use models for specific tasks to creating versatile systems that can adapt across a wide range of applications. This transforms AI development, making it faster, more scalable, and far more efficient.

Take this: Google Search, serving over 4 billion users globally, integrates foundation models like BERT to refine how it understands language. This isn’t just an incremental improvement; it’s a demonstration of how one model can power an entire ecosystem. GitHub Copilot, built on OpenAI ’s Codex, writes code with remarkable fluency, proving the versatility of these models in real-world applications.

But the need for foundation models goes deeper. They address scalability by leveraging massive datasets and transfer learning, these models accelerate development across sectors, from enhancing healthcare diagnostics to automating education tools. Without them, scaling AI for global impact would remain an uphill battle, marked by inefficiency and limited reach.

The brilliance of foundation models lies in their ability to bridge the gap between research and application. They democratize AI capabilities by providing a robust starting point, allowing researchers and developers to focus on innovating rather than building from scratch. This dual impact—scientific breakthroughs paired with tangible real-world benefits—is what makes them a cornerstone of modern AI development.

How Foundation Models Work: Breaking It Down

When you look at foundation models, there’s a lot happening in the background to make them work. Here’s a more technical glance at what goes into building these models and how they’re trained.

1. Architecture: The Building Blocks

The foundation of a good model starts with its architecture. Think of the architecture like the blueprint for a house; without it, the structure wouldn’t hold up. In the case of foundation models, most of them rely on a structure called transformers. These models are great at handling complex data, like language, images, or even music. What makes them so effective is their ability to understand long sequences of information, whether it’s a sentence or a video.

Key points about transformers:

• Expressivity: They can capture and understand diverse information.
• Scalability: They can handle huge datasets, which is crucial when dealing with large-scale applications.

But we’re not stopping at just transformers. Future models will need to incorporate new capabilities like:

• Multimodality: Being able to process different types of data (text, images, sound, etc.) together.
• Memory: Storing and recalling large amounts of information efficiently.
• Compositionality: The ability to adapt and solve new tasks that they haven’t seen before.

2. Training: How Models Learn

Once we have the architecture, the next step is training the model. Training is how the model learns from data to improve its performance. For example, when you teach a model to understand language, you might use something like masked language modeling (as seen in BERT). This involves hiding certain words in a sentence and asking the model to guess what’s missing. For images, techniques like SimCLR (learn more) help the model learn by comparing and contrasting different images.

However, most of the current training methods are built for specific tasks (text for one, images for another). But to make foundation models more powerful, we need domain-general training, where the model can learn from a variety of data at once, like text, images, and even audio. This is important for models that need to handle multiple types of inputs.

Some key considerations during training include:

• Generative vs. Discriminative Training: Do we want the model to generate new content (like writing a new article) or simply categorize existing content (like labeling an image)?
• Data Representation: How should the data be structured during training? This impacts how well the model learns and adapts. Large datasets improve performance but introduce challenges like data management, quality control, and privacy concerns. Public data is often used, but it can include biases or toxic content, impacting model behavior.

3. Challenges in Scaling and Deployment

As foundation models get larger, they require more resources, both in terms of computing power and data. Companies like OpenAI and Google Health have access to massive infrastructure that allows them to train these models on millions of data points. Smaller organizations or independent researchers don’t always have that luxury.?

Efforts like EleutherAI and Hugging Face ’s BigScience project are working to democratize the training of large foundation models. However, a significant disparity persists—and is likely to widen—between the resources available to private industry and those accessible to the broader community. Startups like OpenAI, Anthropic, and AI21 Labs, despite their agility and focus, still command more resources than academic institutions. Yet even these startups pale in comparison to the capabilities of major tech companies, which leverage unparalleled infrastructure, user bases, and vast amounts of proprietary data derived from their market dominance.

The inherently centralizing nature of foundation models means the barriers to entry for their development continue to rise, making it increasingly difficult for smaller players, including startups, to compete. This means that scaling up models will continue to be a challenge, especially when it comes to handling the growing complexity of the models.

To counteract this growing imbalance, government investment in public infrastructure could play a pivotal role. Historical examples like the Hubble Space Telescope and the Large Hadron Collider illustrate how substantial public funding can enable groundbreaking scientific advancements. Similarly, creating a robust computational infrastructure could empower academic research on foundation models.?

Training requires massive computational resources, typically GPUs, connected in parallel. Techniques like compression and distillation reduce the cost of inference but don’t eliminate the need for high compute power.

Foundation models improve with more data and larger sizes, following scaling laws. However, performance doesn’t always scale linearly, and “scaling breaks” may occur, making further improvements unpredictable.

4. The Goal: Robustness and Security

We want foundation models to be robust and secure. This means that they should be able to handle changes in the data they see (called distribution shifts) and be secure against attacks from malicious users. Imagine if a model trained on healthy social media posts suddenly encountered harmful or misleading information. It should be able to recognize and handle that shift in data appropriately.

Models are multi-purpose but require adaptation (fine-tuning or domain specialization) for specific tasks. This can be computationally expensive and may require manually labeled data for niche applications.

5. Evaluation: Ensuring Quality and Progress

Finally, the evaluation of foundation models plays a crucial role. Standardized benchmarks like MMLU and HumanEval help compare models and track progress. However, evaluating a model's general capabilities is just one part of the picture. To really assess a foundation model, you need to look at its downstream performance (how well it performs after being adapted) and the specific properties it exhibits.

Meta-benchmarks like BIG-Bench and HELM are emerging to provide a more holistic view, allowing stakeholders to evaluate not just raw performance but also how models interact with the tasks they're applied to. These evaluations help steer the development of foundation models, ensuring that they stay on track while being useful across a broad range of applications.

In conclusion, building and training foundation models is a multi-step process that requires the right architecture, careful training, and robust scaling. The end goal is to create models that can handle different types of data, perform well in various tasks, and stay secure and adaptable in real-world situations.

With the foundation in place, let’s break down what these models can actually do in practice.

Capabilities of Foundation Models - Where are they used?

Answering this question fully would take the space of a book, and even then, it wouldn't do justice to the vastness of foundation models' applications. Listing every field they touch would be impractical, trust me, this isn’t an exaggeration. So, for the sake of brevity (and sanity!), let's narrow the focus to one of the most exciting and impactful areas: the medical field.

If you're new here as a subscriber, you should know; clinical AI has a special place in my heart. So pardon the partiality XD

Healthcare and biomedicine aren’t just big; they’re massive. In fact:

• 17% of the U.S. GDP goes toward healthcare [Swensen et al., 2011; Keehan et al., 2020].
• Together, healthcare services and biomedical research require an enormous amount of time, money, and expert knowledge.

What if we could build AI that not only learns from vast amounts of medical data but also interacts with doctors and patients to improve care? This is where foundation models come into play.

• Central Knowledge Hub: Think of them as digital brains that learn from diverse medical data, constantly updated by professionals to stay relevant.
• Adaptable: These models can be customized for specific tasks, whether it's answering patient questions, suggesting treatments, or finding suitable clinical trials.
• Efficiency: By handling complex tasks, foundation models can speed up and improve medical processes [Elbattah et al., 2021].

1. Healthcare:

• For Providers:

Imagine cutting down on admin work and medical errors. These models can help generate patient summaries and even pull up relevant cases to guide decisions.

Surgical robots powered by AI could become the norm, offering super-precise operations, reducing human error.

• For Patients:

Foundation models can answer health questions, give reliable advice, and even assist care robots at home.

But for trust, accuracy is key. Medical information needs to be fact-checked and verified.

2. Biomedicine:

• Drug Discovery:

Think of AI helping scientists identify drug targets or design molecules faster than ever before, cutting the cost and time of traditional methods.

• Personalized Medicine:

AI can predict the best treatments for an individual by considering genetics and medical history, making healthcare more tailored and effective.

• Clinical Trials:

AI’s ability to match patients to the right trials and predict outcomes could accelerate the trial process and improve patient outcomes.

But It's Not All Smooth Sailing.

While the potential is huge, there are some major challenges to overcome:

1. Multimodal Data: Medical data comes in all shapes and sizes: text, images, gene sequences, and more. Current models struggle to integrate all of this information in a meaningful way.
2. Explainability: In healthcare, we need to understand why an AI made a certain decision; be it a diagnosis or a treatment recommendation. Transparency is a must for both trust and legal requirements.
3. Ethics and Legal Concerns: Privacy regulations (think HIPAA) and the need for fairness (avoiding biases related to race, gender, and income) are big hurdles that can’t be ignored.

Shifting away from biomedical applications, let's delve into the broader ethical concerns surrounding foundation models, particularly as their use spans multiple sectors.

AI Safety and Alignment

One of the primary ethical concerns is the safety and alignment of foundation models. As their capabilities grow, the focus must be on ensuring these models operate reliably, robustly, and align with human values. If misaligned, these models could pursue harmful or unintended objectives, particularly in high-stakes domains. The possibility of emergent behaviors, like deception or strategic planning, further complicates the situation, making it essential to monitor how these models behave as they are adapted for specific tasks.

Theoretical Gaps

Theoretical understanding of foundation models remains underdeveloped. While empirical methods have driven progress, existing theories of supervised learning don't fully explain how these models adapt and generalize across different tasks. There’s a significant gap between the initial training phase and the adaptation phase, highlighting the need for new theoretical approaches that can account for this transition. Bridging this gap will help clarify how foundation models operate and how they can be improved for safe deployment.

Interpretability and Explainability

Interpretability is another major ethical concern. The complex, opaque nature of foundation models makes it challenging to understand their decision-making processes. This lack of transparency can be problematic, especially when foundation models are applied to make critical decisions that affect individuals' lives. The more task-agnostic nature of foundation models, where a single model can be adapted to multiple tasks, further complicates the interpretability challenge. We must consider both interpretability, understanding the decision-making logic and explainability, ensuring users can comprehend why the model produces certain outcomes.

Inequity and Bias

Foundation models often inherit the biases present in their training data, which can lead to discriminatory outcomes when applied.

Cue “I had a marvellous time, ruining everything” except it isn’t so marvellous.

These biases can reflect or exacerbate societal inequities, impacting marginalized groups disproportionately. For example, a model trained on biased data may reinforce harmful stereotypes or perpetuate existing power imbalances. Addressing these issues requires identifying and mitigating both intrinsic biases (those inherent in the models themselves) and extrinsic harms (those arising from the specific ways models are applied in real-world contexts). Interventions should focus on proactive measures (e.g., bias mitigation techniques) and reactive measures (e.g., feedback loops and accountability mechanisms) to ensure fairness.

Misuse and Harmful Applications

The risk of misuse of foundation models is significant, particularly as they become more capable of generating realistic, personalized content. This could be exploited for malicious purposes, such as creating deep fakes, generating disinformation, or manipulating public opinion. The ease with which harmful content can be created may outpace existing detection methods, making it harder to track and counter such misuse.?

Environmental Impact

The environmental footprint of foundation models is another pressing ethical issue. The computational resources required to train these models are immense, leading to increased energy consumption and a larger carbon footprint. As models continue to scale, the environmental cost is likely to rise. To address this, we need to explore solutions such as energy-efficient models, optimized hardware, and sustainable energy grids. Additionally, the environmental costs should be an integral part of the evaluation process when comparing foundation models to more environmentally conscious alternatives.

Legal and Regulatory Considerations

Legally, the landscape surrounding foundation models remains unclear. Questions around liability for decisions made by these models, as well as protections against harmful behaviors, require urgent attention. Given that foundation models often serve as intermediary assets, adapted for specific tasks: their legal status differs from that of end-user applications, adding complexity to the determination of accountability. And when it comes to accountability, you can’t just “ shake it off ”.

Conclusion

Foundation models are reshaping how we approach AI, but with great power comes great responsibility. Think of Taylor Swift's "You Belong With Me" we've got something that is universally acclaimed, but only if we use it right. If we rush ahead without considering ethical concerns, we could end up feeling like the song "Look What You Made Me Do" where unintended consequences start to pile up. We need to make sure these models are safe, transparent, and fair from the beginning, or we risk letting things spiral.?

Just like in "Begin Again" (because 2025 indeed began on wednesday), we have a chance to build something better, but only if we learn from the past and keep the bigger picture in mind. This is for both, foundation models and the Dear Reader(s), starting the new year!

Resources

Base Report from Stanford - https://arxiv.org/abs/2108.07258

Types of Foundation Models - https://aws.amazon.com/what-is/foundation-models/

Centaur:? Foundational Model on Human Cognition - https://arxiv.org/abs/2410.20268?utm_source=tldrai

HuggingFace LxM Models: https://huggingface.co/models?other=foundation+model