Understanding Foundation Models and Their Potential

The AI landscape has rapidly evolved beyond just large language models. Today’s systems rely on a wide range of foundational model types—each designed for specific modalities, tasks, and constraints. This visual covers 12 foundational AI models and their core workflows. This is intended for engineers, researchers, and builders who want a structured view of the ecosystem. Here’s a breakdown of what’s included: → LLM (Large Language Models) – GPT, LLaMA Trained using transformer architecture to generate coherent, human-like text. The workflow involves data collection, tokenization, pattern learning, fine-tuning, and deployment. → SLM (Small Language Models) – Phi, TinyLLaMA Lightweight and efficient for on-device or low-resource environments. Focuses on model compression, compact training, and benchmarking. → VLM (Vision-Language Models) – CLIP, Flamingo Learns joint understanding between images and text. Ideal for tasks like image captioning and visual QA. → MLLM (Multimodal Large Language Models) – Gemini Designed to process and align multiple modalities such as text, image, audio, and video. → LAM (Large Action Models) – RT-2, InstructDiffusion Generates sequences of executable actions using behavioral and reinforcement learning data. → LRM (Large Reasoning Models) – DeepSeek-R1 Structured for tool use, chain-of-thought reasoning, and test-time modularity in logic-heavy tasks. → MoE (Mixture of Experts) – Mixtral Activates a subset of specialized models per input to reduce computation cost and improve performance. → SSM (State Space Models) – Mamba, RetNet Efficient at long-context sequence modeling using dynamic systems and parallelism. → RNN (Recurrent Neural Networks) – LSTM, GRU Uses hidden states to process time-dependent data, maintaining memory across input sequences. → CNN (Convolutional Neural Networks) – EfficientNet Learns spatial patterns in image data via convolution layers, pooling, and hierarchical stacking. → SAM (Segment Anything Model) – Meta Segments objects from images based on prompts (text, points, or boxes), making it useful for dynamic image understanding. → LNN (Liquid Neural Networks) – LFMs Leverages differential equations to adapt in real-time, supporting applications in time-sensitive environments. This chart is designed to help you understand not just what these models are, but how they work under the hood. If you're working in AI,  this foundational understanding is crucial for making informed architectural decisions.

🤖 Foundation Models in Radiology 🤔 At a conference I spoke at recently, someone asked me, "What are foundation models exactly? And, how do they differ from AI models we’ve seen so far?" Unlike narrow AI solutions that perform one specific task (e.g., detecting lung nodules on CT), foundation (not foundational 😉) models (FMs) are large AI models trained on vast amounts of diverse data that can be adapted for many different tasks. This Radiological Society of North America (RSNA) Radiology paper by Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari provides a good overview of foundation models in radiology. 🔍 Why do FMs matter? - Efficiency - Multimodal capabilities - Scalability - Automation 🩻 How can FMs be useful for radiology? - Enhanced patient communication: Patient-friendly reports and report translation into various languages - Radiology report generation: Report coding, summarization, automated impression generation, draft report generation, and error checking - Advanced diagnostic support - Automated follow-up care - Streamlined workflow (e.g., image quality monitoring and protocol adjustments) and data management - Unlocking population insights and disease prediction ⚠️ Challenges to consider: - Hallucinations/confabulations: AI-generated reports may include incorrect details ⚠️ - Bias & fairness: Models trained on skewed datasets can reinforce healthcare disparities 🏥 - Regulatory hurdles: Standardized evaluation and clinical validation are still lacking ⚖️ - Explainability: Can we trust AI decisions without transparency? 🤔 - Human-AI interactions: Expect to see more debates and research around this topic in 2025, where we will need to address not only automation bias but also algorithm aversion. 💡 What’s Next? We need: ✅ Real-world case studies demonstrating FM impact ✅ Improved model interpretability for clinical adoption ✅ Stronger data privacy and security frameworks ✅ Clearer regulations for AI deployment in radiology Although nascent, foundation models are rapidly progressing (expect to see more research and commercial focus), and those of us in radiology need to become familiar with this technology to understand its capabilities and limitations and ensure it is inclusive and effective. 🔗 to the paper is in the first comment 👇 #AI #Radiology #HealthcareInnovation #MedicalAI #FoundationModels #RadiologyAI #GenAI #LLMs #LMMs #VLMs

Foundation models (FMs) such as GPT, LLaMA, and CLIP are reshaping the landscape of recommender systems (RS), transforming how we personalize and interact with content across various domains like e-commerce, healthcare, and education. A recent comprehensive survey sheds light on this exciting convergence, identifying three powerful paradigms: 1. Feature-Based Paradigm: FMs enhance recommender systems by creating rich, semantic embeddings. For instance, BERT and CLIP help encode textual descriptions and multimodal data, dramatically improving feature representations and helping overcome challenges like data sparsity and cold-start scenarios. 2. Generative Paradigm: Leveraging models like GPT, this paradigm moves beyond mere recommendations, generating personalized content and explanations directly. It facilitates zero-shot/few-shot recommendations, personalized user experiences, and multimodal content generation, though it faces challenges around bias, control, and alignment with user intent. 3. Agentic Paradigm: Perhaps the most transformative, this approach uses autonomous FM-driven agents capable of real-time adaptation and interaction. Agentic systems integrate dynamic planning, reasoning, and user feedback loops to provide highly contextual and ethically aligned recommendations. Systems like AutoGPT illustrate how such agents proactively adapt to user preferences and environmental changes. The paper also discusses practical implementations across several recommendation tasks: - Top-N recommendations: Enhancing traditional ranking by incorporating semantic insights from FM embeddings. - Sequential recommendations: Leveraging FM's deep contextual understanding for accurate next-item predictions. - Conversational recommendations: Allowing more dynamic, natural dialogues between users and systems, significantly boosting user engagement. Despite substantial progress, the survey also highlights ongoing technical challenges such as efficiency, interpretability, fairness, and multimodal integration, offering a roadmap for future research directions. This comprehensive analysis by leading academic and industry institutions marks a critical step forward in our understanding of how Foundation Models can revolutionize recommender systems, paving the way for more sophisticated, user-centric, and intelligent recommendation platforms.

🧠 In scientific ML, we often assume that if a model is fast, accurate, and trained on vast simulation data, it must be a candidate for a “foundation model.” But here’s the crucial distinction 👉 Interpolation/mappings ⧣ Foundation A neural surrogate, no matter how sophisticated, learns mappings — not governing structure. Foundation status requires more than prediction quality: + Structural invariance across geometry & domain + Physical assembly (not just regression) + Cross-PDE reusability without retraining from scratch + Verifiability under real boundary/forcing variations + Auditability—the ability to inspect where physics lives, not just outputs 📌 This is why FEM/FVM were foundational: They didn’t just approximate solutions; they provided a governed mathematical framework that outlived problem classes. Our work on Data-Driven FEM (DD-FEM) follows the same principle: + physics in the global assembly + learning in the local operators + scalability in the modular interface + universality in the deployment footprint 🔥 A surrogate alone can interpolate, but a foundation model must generalize, assemble, and endure. 📖 Full paper: https://lnkd.in/gWSHPAqj What do you think? How should the community certify “foundation model” status in engineering & computational science?

As we've seen recently with the release of DeepSeek, there is substantial room for improvement in large scale foundation models, both in terms of architectural efficiency and unsupervised training techniques. While the discussion has been mostly about LLMs, there is also a strong need for improvement to the scalability of generative AI in other domains such as video and multi-sensor world models. In the last several months we have released multiple foundation models for video and multi-sensor generative simulation for the autonomous driving space: VidGen-1 and 2, WorldGen-1 and GenSim-2. These models were developed fully in-house (and not fine-tuned from any open-source models) using only ~100 H100 GPUs (inclusive of all the R&D and final training runs), which is a tiny percentage of the typical compute budgets associated with video foundation model development (thousands to tens of thousands of H100 GPUs). How did we achieve industry leading foundation models with much less compute? We combined DNN architecture innovation with advanced unsupervised learning techniques. By leveraging our Deep Teaching technology and improvements to generative AI DNN architectures, we were able to use smaller parameter/more efficient models and to simultaneously accelerate the unsupervised learning process, leading to superior scaling laws compared to industry-typical methods, which means higher accuracy per compute dollar spent, both during training and inference. We have verified that these scaling law advantages persist at larger scales of compute/data, and look forward to keep pushing the frontier of world models for autonomous driving and robotics by scaling up. In essence, combining Deep Teaching with generative AI architecture innovation, leads to a highly scalable form of generative AI for simulation.

This paper provides a comprehensive survey on the application of foundation models in healthcare, emphasizing their roles in enhancing diagnostics, predictive analytics, and decision support, while addressing their challenges and future directions. 1️⃣ Foundation models leverage large-scale pretraining to improve healthcare tasks such as clinical text analysis, medical imaging, and multimodal data integration. 2️⃣ Multimodal models integrate diverse healthcare data sources, including genomic information, imaging, and clinical notes, to enhance predictions and diagnostic precision. 3️⃣ Specialized healthcare models like BioBERT and ClinicalBERT are tailored for clinical text, enabling improved outcomes in tasks like EHR-based predictions and disease coding. 4️⃣ Medical imaging applications, such as X-rays, CT, and MRI analysis, benefit from foundation models, which enhance diagnostic accuracy while addressing data annotation challenges. 5️⃣ Structured data applications include EHR-based predictions (e.g., hospital readmission risks), with advancements in models like Med-BERT driving better performance. 6️⃣ Explainable AI (XAI) is critical to overcoming the "black box" nature of foundation models, ensuring trust and transparency in clinical decision-making. 7️⃣ Legal and ethical frameworks like GDPR and the EU AI Act are shaping the deployment of foundation models, emphasizing fairness, data privacy, and bias reduction. 8️⃣ Foundation models hold promise for augmented and virtual reality (AR/VR) applications, transforming medical training, therapy, and patient care with immersive technologies. 9️⃣ Crisis management and epidemic prediction applications demonstrate the potential of foundation models for real-time surveillance, resource allocation, and intervention strategies. ✍🏻 Mohan Timilsina, Samuele Buosi, Muhammad Asif Razzaq, Ph.D., Rafiqul Haque, Conor Judge, Edward Curry. Harmonizing Foundation Models in Healthcare: A Comprehensive Survey of Their Roles, Relationships, and Impact in Artificial Intelligence's Advancing Terrain. Preprint on SSRN. 2024. DOI: 10.2139/ssrn.4951028

From ChatGPT to ScienceGPT: AI is now learning the languages of physics, chemistry, biology, geology, and even nuclear science! A new preprint by Ameya D. Jagtap et al. offers a comprehensive review of foundation models across natural science domains. Beyond well-known chemistry models like ChemBERTa, MoLFormer, and MatterGen, it highlights breakthrough models across diverse fields, such as Aurora (climate forecasting), SeisT (earthquake detection), scFoundation (single-cell biology), RT-1 (robotics), and POSEIDON (solving partial differential equations). This broad overview reveals some key insights: 🔹Transformer architectures dominate across all disciplines 🔹Data scarcity remains the biggest bottleneck, not computing power 🔹Physics-informed models don't automatically outperform data-only approaches 🔹Domain-specific models may be more practical than universal ones (at least for now) 🔹Cross-domain transfer learning is still limited The field stands at an inflection point, shifting from narrow, task-specific tools toward AI systems that internalize scientific principles. While true universal scientific intelligence remains aspirational, we're steadily laying the groundwork. 📄 On Scientific Foundation Models: Rigorous Definitions, Key Applications, and a Survey, SSRN, August 27, 2025 🔗 https://lnkd.in/eMerEAXC

Do you know what foundation models are about? Five new studies show that foundation models are no longer limited to language or general imaging, because they are now being built directly on physiological data!  Researchers trained large self-supervised models on hundreds of thousands of sleep studies, millions of echocardiography videos, continuous glucose monitoring streams, tens of thousands of brain MRIs, and hundreds of thousands of CT scans.  Instead of training models for a single task, they first learned the underlying structure of sleep signals, cardiac motion, glucose dynamics, brain anatomy, or 3D organ volumes. After fine-tuning, these models predicted long-term risks such as dementia, heart failure, stroke, diabetes, cardiovascular death, genomic mutations, and multi-organ cancers. So briefly, a foundation model is a large AI system trained on massive amounts of mostly unlabeled data to learn general representations of a domain before being adapted to specific tasks. It first learns the “language” of physiology such as the patterns, structure, and relationships within signals or images. Once that representation is learned, the same model can be fine-tuned for many downstream applications with far less labeled data. Based on these models, it seems that we are moving from measuring isolated biomarkers (HbA1c, ejection fraction, sleep scores) to learning high-dimensional representations of human physiology. The studies can be found in the Doctor Penguin newsletter: https://lnkd.in/dtHqhZ6Q

🔬 Defining Foundation Models for Computational Science A must-read paper from Youngsoo Choi and team addresses a critical issue in SciML: https://lnkd.in/gjdw5jt8 Below is a short summary: 🎯 The Problem The term "foundation model" is being inconsistently and prematurely applied in computational science. What's missing is a precise, field-appropriate definition of what constitutes a foundation model in computational science. 🏗️ Revisiting Foundational Methods in Classical Computational Science Traditional methods like FEM offer valuable lessons through: • Universality and problem-independent structure - consistent frameworks across diverse problems • Mathematical rigor - supported by deep theoretical frameworks with proven convergence and stability • Implementation and software reusability - adapt to new problems by changing inputs, not architecture 📋 Definition: Foundation Models in Computational Science • Data-driven model - learns from scientific simulations, experiments, or observations • Trained on a broad distribution of scientific application types or physical systems • Exhibits wide generalization capability across scientific problems, computational domains, tasks, and physical conditions • Without requiring retraining from scratch or structural modification - easily adaptable to new tasks 🚀 Data-Driven Finite Element Method (DD-FEM) A promising framework that bridges classical methods with modern AI: - Generate training data with diverse geometries, meshes, boundary conditions, and schemes - Train local bases on small subdomains from the generated data - Assemble global domain using the trained data-driven elements - Solve governing equations on the global domain to enforce physical laws DD-FEM addresses key challenges like massive data sizes, expensive fine-tuning, and physics consistency while maintaining the mathematical rigor of traditional methods. #ComputationalScience #FoundationModels #AI4Science #FiniteElementMethod