Exploring the Different Types of Foundational Models in AI

Recent artificial intelligence (AI) breakthroughs have changed many fields across the globe aiding innovations and even enhancing efficiency. Its continuous evolution leads to the creation and popularization of the so-called foundation models. Foundation models (FMs) are large deep-learning neural networks that are used to develop various AI-powered applications. This article dives deeper into the types of foundational models, how they are utilized in industries, what are the challenges, and what the future looks like.

Foundation Models (FMs) are state-of-the-art deep learning neural networks that are trained on large datasets with the purpose of performing a number of general tasks such as language comprehension, text and image generation as well as free-form conversations. Rather than developing AI systems from scratch, data scientists rely on FMs to speed up and reduce the costs required to develop machine learning applications. Coined by researchers, the term “foundation model” emphasizes their ability to process broad, generalized, and often unlabeled data, making them an essential tool in modern AI development.

What sets foundation models apart is their adaptability and scale. Unlike traditional ML models designed for specific tasks, such as sentiment analysis or image classification, FMs can handle diverse tasks like natural language processing, question answering, and image generation with remarkable accuracy. Their evolution in size and complexity over the years is notable; for example, BERT in 2018 had 340 million parameters, while GPT-4 in 2023 used 170 trillion parameters. Modern FMs, including GPT-4, Claude 2, and Stable Diffusion, can perform out-of-the-box tasks across multiple domains, such as writing, image generation, math problem-solving, and document-based question answering. These models represent over a decade of innovation, with their computational power growing exponentially.

The image below provides a concise overview of foundation models at a glance.

Foundational models at a glance (source)

FMs are a type of generative AI (GenAI). They generate outputs based on one or more inputs (prompts), typically in the form of natural language instructions. FMs are created utilizing a variety of complicated neural networks, including generative adversarial networks (GANs), transformers, and variational encoders.

Although each type of FM acts differently, the fundamental principles underlying their operation are similar. FMs often employ learned patterns and relationships to predict the next item in a series. For example:

• Image generation: The model will analyze the image and generate a shaper, a more clearly defined version of the image.
• Text generation: The model will predict the next word in a string of text based on the previous word in the provided context using probability distribution techniques.

Let’s dive deeper into the different types of foundational models and their appropriate use cases.

Language models are a type of AI that are trained on text data and are capable of generating natural language responses to prompts or inputs. These models are trained on “text prediction” tasks, enabling them to predict the likelihood of a character, word, or string based on its preceding or surrounding context. For example, the autocorrect functionality we see in messaging applications utilizes simple language models to predict the correct word and provide suggestions.

Large Language Models (LLMs) generally refer to more advanced language models usually with hundreds of millions of parameters and, in recent times, even hundreds of billions. These models are trained on a massive volume of text data and utilize the transformer neural network architecture. LLMs are the most famous type of FM we see today as they are capable of performing a wide range of text-based tasks such as question-answering, translation, summarization, code generation, autocomplete, and natural language interpretation. The image below describes a high-level overview of the LLM landscape.

LLM Landscape (source)

Listed below are examples of some famous language models and their ideal use cases:

1. Generative Pre-Trained Transformer (GPT): GPT  is a family of LLMs developed by OpenAI that utilize the transformer architecture to carry out text generation tasks. ChatGPT is a famous implementation of GPT representing a conversational GenAI chatbot that’s capable of assisting humans in natural language. OpenAI also provides an API for easy consumption of the GPT models.
2. Pathways Language Model (PaLM): PaLM is a 540-billion-parameter LLM that was developed by the Google AI team. PaLM focuses more on generalizability and scale across domains while being highly efficient. It utilizes the Pathways system to achieve this scale. Google also offers the PaLM API for developers to quickly and safely experiment with its LLM making it more accessible and easy-to-use.
3. Large Language Model Meta AI (LLaMA): Open source collection of LLMs ranging from 7B to 65B parameters that can be fine-tuned, distilled, and deployed anywhere. LLaMA models are known for their exceptional performance while maintaining smaller sizes and demanding less computational power. This makes it perfect for developers and researchers to experiment with novel methodologies and validate the work of others. LLaMA is built on top of a standard API making it accessible for developers.

FMs have significantly improved the computer vision field transitioning from simple pattern recognition to sophisticated systems capable of complex visual analysis. Computer vision foundation models (VFMs) are advanced AI models designed for image generation and visual tasks. They often combine components from LLMs to enable text-based input prompts for creating images, relying on effective prompt engineering for high-quality outputs. Unlike traditional convolutional neural networks (CNNs), which focus on specific parts of an image, VFMs increasingly use transformer-based architectures like Vision Transformers (ViTs). These models employ self-attention mechanisms to analyze relationships between image segments, enabling deeper understanding and adaptability.

The image below describes a high-level overview of the computer vision model landscape.

Computer Vision Models Landscape (source)

Listed below are some examples of some famous vision models and their ideal use cases,

1. You Only Look Once – Neural Architecture Search (YOLO-NAS): Deci’s YOLO-NAS model offers cutting-edge performance in real-time object detection, striking an optimal balance between speed and accuracy. Applications include traffic monitoring and automated retail systems, where efficiency is a key.
2. EfficientNet: Widely used in healthcare, delivers exceptional accuracy in medical image analysis while minimizing computational requirements. It aids in diagnosing diseases through data from X-rays and MRIs, proving to be a critical asset in modern medicine.
3. Bootstrapping Language-Image Pre-training (BLIP): The BLIP-2 vision-language model enhances e-commerce platforms by automating product tagging and image indexing. By generating descriptive tags and improving searchability, it streamlines catalog management and enriches the online shopping experience.
4. Vision Transformer for Open-World Localization (OWL-ViT): The OWL-ViT model supports open-vocabulary object detection, playing a pivotal role in video summarization, scene recognition, and content moderation. It empowers digital platforms to efficiently organize and manage vast amounts of visual content.
5. Segment Anything Model (SAM): Meta’s SAM is a groundbreaking foundation model designed for universal image segmentation. By identifying and isolating objects or regions within images, SAM enables precise editing, object tracking, and content extraction. Its adaptability to various domains, from medical imaging to graphic design, makes it an invaluable tool for professionals seeking accuracy and efficiency.

Multimodal models are designed to generate contextually rich outputs by processing and integrating data from multiple modalities, such as text, images, audio, and video. Unlike unimodal models that handle a single type of data, multimodal frameworks combine information from different sources using components like encoders, fusion mechanisms, and decoders. This is depicted in the image below.

Unimodal vs Multimodal models (source)

Encoders transform raw data into feature vectors specific to each modality, such as CNNs for images, transformer models for text, and specialized audio encoders for sound. These feature vectors are then fused using strategies like early, intermediate, or late fusion, enabling the model to understand the relationships between modalities for enhanced accuracy and user experience. The next component is a fusion mechanism which is a key to multimodal models, employing techniques such as attention-based methods, concatenation, or dot-product operations to combine embeddings effectively. The last component, decoders, transforms the fused multimodal embeddings into meaningful outputs, such as text captions for images, video summaries, or audio transcriptions. Depending on the task, decoders often use architectures like transformer-based models, RNNs, or specialized generators to ensure accurate and contextually rich outputs.

Listed below are some examples of famous multimodal models and their ideal use cases,

1. Contrastive Language-Image Pre-training (CLIP): OpenAI’s CLIP is a multimodal model that links text descriptions to images for tasks like image classification, annotation, and retrieval. It also excels in identifying scene changes in videos by comparing consecutive frames for similarity. This capability is particularly useful in scenarios such as distinguishing between game segments and advertisements during sports broadcasts.
2. DALL·E: The OpenAI’s DALL·E multimodal model is capable of transforming textual descriptions into highly detailed and contextually accurate visuals. This capability supports a variety of applications, from marketing content creation to conceptual design. This enables users to visualize abstract ideas, prototype products, and enhance creative workflows easily.
3. Large Language and Vision Assistant (LLaVA): LLaVA integrates Vicuna and CLIP to handle text-image queries, excelling in visual question answering and chatbot creation for domains like e-commerce, where users interact using images and text.
4. Gemini: Google’s Gemini supports multimodal processing of text, images, audio, and video. Available in Ultra, Pro, and Nano versions, it serves applications ranging from complex coding tasks to on-device virtual assistants.
5. ImageBind: Meta AI’s ImageBind unifies six data modalities (text, audio, video, images, depth, and thermal data) into a single embedding space, enabling cross-modal tasks like audio-to-image or image-to-audio generation.

The capability of multimodal models allows for AI systems to perform complex tasks involving the simultaneous integration and interpretation of multiple modalities. Listed below are examples of such use cases:

• Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR): Multimodal models are evolving AR, MR, and VR systems by integrating sensory inputs in real time. These models utilize data from cameras, microphones, and sensors to process information and make sense of the surroundings. For example, MR headsets like the Apple Vision Pro and Meta Quest 3 can analyze visual information and respond to voice commands simultaneously. This enables various dynamic, interactive experiences.
• Autonomous Vehicles: Multimodal FMs in the transportation industry help make sense of data from sensors such as cameras, LiDAR, radar, and GPS. This understanding gives autonomous vehicles a comprehensive view of their surroundings, allowing them to make well-informed decisions in complex driving situations. These models can analyze both visual and contextual signals to make decisions in real time. Additionally, they help vehicles adapt to changes in the environment, such as weather patterns and road conditions, to ensure safe navigation.
  

  Autonomous Driving (source)

• Healthcare: Medical diagnostics can be enhanced further by Multimodal models through processing different types of data such as medical images, patient history, and test results, to generate more comprehensive diagnoses. By merging different data sources, such as MRI scans with patient medical records, these models can provide a deeper understanding of complex medical conditions like cancer. This approach helps doctors make more accurate assessments and treatment plans, enhancing patient well-being.
• Human-Robot Interaction: Multimodal models help in building robots that can interpret speech using NLP, recognize gestures through computer vision, and respond to commands via text or touch. This makes communication with robots more natural and adaptive. This also enables them to perform tasks that involve more complex and human-like interactions. Embedding AI into tangible entities, such as robots, equipping them with the capacity to perceive, learn from, and engage dynamically with their surroundings is often referred to as Embodied AI(EAI).
  

  Human-Robot Interaction (source)

FMs can coherently respond to prompts and inputs on subjects they haven’t been explicitly trained on, but they have come with certain limitations and challenges. Listed below are some challenges with foundation models:

• Infrastructure maintenance & Cost: Building FMs from scratch can be quite expensive and require extensive computational resources. Training can sometimes take months and the organization needs to be capable of handling such costs. The environmental sustainability of training these models can also be a concern due to the increased carbon footprint.
• Front-end development: Building just the FM isn’t quite enough for practical scenarios. Organizations need to invest time in integrating FMs into a software stack, including tools for prompt engineering, fine-tuning, and pipeline engineering. Continual fine-tuning and maintenance are crucial in many practical use cases of FMs.
• Lack of comprehension: FMs are not socially or psychologically aware. Although they can provide grammatically and factually correct answers, FMs can often misinterpret the context of prompts leading to unrealistic answers or hallucinations.
• Biased and Unreliable: FMs can pick up hate speech and inappropriate undertones from their training datasets and reflect them when generating outputs. This can lead to several legal, ethical, and security issues in practical applications. Developers need to take extra care to filter training data and define rules/ validations in their models.

FMs are the cutting-edge in modern AI-powered applications and it’s only expected to grow for the better in the years to come. Here are some potential future advancements that can be expected:

• Enhanced Model Architectures: Developments in model architectures and training objectives are expected to improve the capabilities of FMs to make them more adaptable and effective across various domains.
• Efficient Training Processes: Advances in training processes are expected to improve the efficiency of FMs. More efforts will be focused on reducing the training time and cost while maintaining good performance. Meta’s LLaMA 3 is a recent example of this as the newer version of the model is now capable of utilizing broader datasets more effectively and adapting to tasks more quickly.
• Larger models with increased Dependability: Researchers are continuously working on creating larger models to capture intricate data relationships. Moreover, there is a growing emphasis on understanding how these models arrive at decisions to enhance trust and dependability in their results.
• Open-Source Development and Collaboration: Democratizing access to advanced AI tools is crucial for the continued success of FMs. Collaborative efforts and open-source development can be a great driving factor to fuel further innovation in this field.

FMs have become a transformative force in the field of AI, enabling unprecedented advancements across industries and pushing the boundaries of what AI systems can achieve. From LLMs capable of handling complex text-based tasks to vision models that elevate computer vision capabilities, and multimodal models that integrate multiple types of data for enriched understanding, FMs are redefining the landscape of AI.

Despite the challenges, such as high infrastructure costs, integration complexities, and ethical considerations, ongoing innovations promise a brighter future. Continued advancements in model architecture, training efficiency, dependability, and open-source collaboration will pave the way for more adaptable, accessible, and responsible AI solutions.

As technology progresses, FMs are set to become even more embedded in daily life, offering powerful tools that enhance productivity, creativity, and decision-making, all while promoting ethical and inclusive practices. The evolution of FMs is a testament to the potential of AI to drive positive change and empower humanity in the years to come.