Jan 18 / Rahul Rai

The Power of Generative AI: A Deep Dive into Foundational Models

What is Generative AI?

What exactly is Generative AI, and what role do foundational models play? Generative AI (Gen AI) constitutes a subset of deep learning techniques characterized by models with numerous parameters. These models, often trained on large datasets, can be applied across a spectrum of tasks. While popular web interfaces like ChatGPT focus on natural language processing, it's crucial to recognize that generative AI extends beyond language models. These models, whether for language, images, or multimodal tasks, fall under the umbrella of generative AI. Large language models (LLMs), such as those used for natural language processing, represent just one facet of this expansive domain. 
It's important to clarify that while generative AI is often referred to as part of AI, a broader term encompassing machine learning, it essentially is large scale general purpose generative deep learning models. A better name to avoid confusion would have been “generative deep learning models.” Understanding this terminology sets the stage for answering the question “what generative AI is?” To begin to answer the question, let us focus on a broad classification of machine learning techniques. We can broadly categorize different types of machine learning models into two main groups
(1) Discriminative and
(2) Generative models.

Discriminative Models

Discriminative models focus on prediction or classification tasks, relying on labeled datasets. The core objective is to establish the relationship between input features and corresponding labels through deep neural networks. Discriminative models thrive on structured datasets, excelling in tasks where classifying or predicting outcomes is paramount. Discriminative models focus on predicting or classifying outcomes based on labeled data, typically represented as the relationship between input features (X) and labels (Y). This is the essence of building model F, where Y is conditioned on X.

Generative Models

In contrast, generative models take a different route. Generative models try to learn the underlying distribution of the data, enabling the model to generate new, realistic instances. Instead of being fixated on labeling existing data, they are geared towards creating new data points. Picture this: you have a cluster of data, and the goal is to learn the underlying distribution or probability function that enables the generation of new data points. Generative models shine in their ability to generate data points similar to the ones they were trained on. The emphasis is not on predicting specific labels but on understanding and replicating the distribution of the data. Imagine predicting the next word in a sequence or generating an entirely new word based on neighboring words – that's the essence of generative models. Now, let's highlight a crucial distinction. Discriminative models, as we've discussed, predict Y given X, while generative models go a step further. They aim to learn the joint distribution of X and Y, allowing them to generate new instances by sampling from this distribution. During training, what emerges is a statistical model denoted as F(x, y). This model encapsulates the joint distribution of both input (X) and output (Y) within a unified framework. It's about creating, not predicting.

Generative models, often labeled as generative AI, can be considered an advanced version of deep learning models. In summary, generative AI is a subset of deep learning that focuses on creating, not just predicting. It's a powerful paradigm that opens avenues for creativity and innovation, allowing machines to generate content autonomously.

To emphasize the difference, consider an image recognition scenario. A discriminative model would classify an image into predefined categories like cat or dog. On the other hand, a generative model could take an image, or even generate a new one, exhibiting similarities to the input provided. It's not about classification; it's about creating new instances within the learned distribution.

Figure 2. Difference between discriminative and generative machine learning models.

Discriminative models, as we discussed, output labels, either discrete for classification or continuous for regression. This output is essentially the model's prediction based on the input data and the learned relationship between X and Y. Now, consider the context of our generative focus in this course. Generative models operate in a distinct realm – a realm where you may not have labeled datasets or structured data. In this paradigm, we work with unstructured, unlabeled, or semi-labeled datasets, embracing the diversity of data at our disposal.

To comprehend the essence of generative AI, let's revisit the concept of mapping unknown functions from input (X) to output (Y). In the context of machine learning, our goal is to fit a highly nonlinear function to the data points we have. This function is not explicitly defined; it's embodied by the intricate architecture, hyperparameters, and activation functions within a deep neural network. To further differentiate between generative and discriminative models, consider the nature of the output of the function. When the model outputs a real number, it's a regression problem – a discriminative task. On the other hand, if the output is discrete, representing different classes or categories, it's a classification problem – another realm of discriminative models.

However, the defining characteristic of generative models lies in their output being more than just labels or numbers. When the output itself becomes a sample data point, like a piece of natural language, an image, or an audio segment, you've entered the realm of generative models. It's a model that learns the essence of the data and creates new instances within that learned distribution.

Different Classes of Generative AI Models

Within the realm of generative AI, a diverse array of models exists. One of them is LLMs designed for text-to-text interactions. These models thrive on input in the form of text and produce corresponding text outputs. Trained to unravel the intricacies of language, they excel in tasks such as language translation, predicting the next word in a sequence, and an array of language-related challenges. Their applications extend to generating new content, a capability we've witnessed in the ChatGPT interface. Its proficiency in understanding and manipulating text opens avenues for diverse applications, from content generation to classification tasks.  

Another important category of Gen AI models are text-to-image models. Here, the input (X) is textual, and the output (Y) is an image. These models, relatively new but immensely promising, undergo training on expansive datasets. Each image within the dataset is accompanied by a concise caption, serving as a textual description that encapsulates the essence of the visual content. One prominent technique employed in achieving this feat is diffusion.  The capabilities extend beyond mere image generation; these models empower also image editing. Picture this: you can instruct the model to "draw a sunset with a serene view and beautiful scenery." In response, it crafts a visual masterpiece that encapsulates your textual prompt.

You can request a drawing format, experiment with grayscale or watercolor versions, and witness the model seamlessly adapt to your creative vision. In the realm of image editing, the possibilities are boundless. Direct the model to remove specific elements from an image or add new components, and watch as it brings your creative directives to life. The marriage of text and image through generative AI opens up a realm of possibilities, with applications spanning creative expression to marketing endeavors.  

Another new frontier of generative AI embraces the burgeoning field of text-to-video models. The premise is captivating; these models aim to generate entire video sequences predicated on an initial text description. The text input, ranging from a single sentence to a comprehensive script, serves as the creative blueprint. The output manifests as a video that mirrors, or is inspired by, the input text. Imagine articulating a narrative, and witnessing it unfold dynamically in the form of a video – a convergence of storytelling and visual representation.  

The Gen AI progression extends to text-to-3D models, marking a pivotal stride in design applications. Picture a scenario where users can generate three-dimensional objects based on textual descriptions. This application holds immense promise for augmented reality (AR) and virtual reality (VR) experiences. Describing a scene through text prompts the model to automatically populate the environment with components, offering a seamless fusion of textual input and three-dimensional design. Moreover, the text-to-3D model landscape finds application in product design. Users can define the functionality of a product through text, and the model, with its generative prowess, automatically synthesizes the corresponding shape. This synthesis, driven by textual descriptions, opens avenues for innovative design methodologies.
Game development, traditionally a meticulous process of populating virtual environments with fixed geometry, sees a revolution. By leveraging text-to-3D models, the gaming industry is poised to create virtual worlds dynamically, responding to textual descriptions and automating the placement of assets. The implications are profound; the once tedious process of game development now stands at the precipice of a paradigm shift. Simulated environments can materialize with a mere textual prompt, offering an efficient and innovative approach to game design.

Another fascinating domain of Gen AI models is text-to-task models. Unlike their predecessors, these models are purpose-built for performing specific tasks or actions based on textual input. Users can issue textual commands that articulate a desired action or task. Consider a scenario where someone describes the contents of their fridge through text, listing items like carrots and various ingredients. Now, imagine asking the model to curate a new recipe based on these specified ingredients. In this instance, the text-to-task model excels at the specific task of generating a recipe tailored to the user's input.

Consider the scenario where a user instructs the model to navigate a web UI, extract relevant information, and compile a report based on the gathered data. The text-to-task model comprehends the textual input, interprets the underlying task, and seamlessly interacts with the web UI to fetch the required information. This streamlined interaction between textual prompts and tangible actions exemplifies the transformative potential of text-to-task models.

Foundation Models

Expanding upon the general domain generative AI, the concept of the foundation model emerges as a pivotal force shaping the landscape of machine learning. A foundation model is not merely a singular entity; rather, it is a colossal deep learning model constructed upon a diverse array of data types. Imagine a model that seamlessly integrates text, images, videos, and various other data formats into a unified framework. This amalgamation of heterogeneous data serves as the bedrock for a truly versatile and comprehensive unsupervised learning model.

The magnitude of a foundation model is reflected in its scale, often boasting an impressive billions parameters. Through unsupervised learning, this model engages in representation learning, absorbing insights from the myriad data sources encompassed within its training set. The result is a foundational representation that encapsulates the essence of diverse data modalities. The brilliance of the foundation model lies in its adaptability. Once established, this model becomes a cornerstone—a basic yet powerful framework that can be fine-tuned and customized for a myriad of tasks. Whether it's language modeling, vision tasks, or other specialized applications, the foundation model serves as the fundamental building block. Once the foundation model is in place, it becomes a versatile tool that can be harnessed for various purposes. The key lies in tweaking and adapting this foundational representation to cater to specific tasks, yielding different outputs tailored to distinct use cases.

So how does the foundation model that goes inside the GPT-type of architecture are developed? To understand this, let's delve into the mechanics.
In the context of a foundation model, various types of data—ranging from images to text—are utilized. This diverse dataset is then processed using a transformer model, a key component comprising encoder and decoder. Think of it as a mechanism that learns to represent data points in an abstract, latent semantic space through encoding and decoding. This transformative process results in a lower-dimensional yet abstract space, signifying representation learning. The outcome is what we call a Generative Pre-trained Transformer model, abbreviated as GPT. When you hear terms like GPT-3.5, GPT-4, and so forth, they refer to different versions of this generative pre-trained transformer model. Once this model is learned, it becomes the foundation model, serving as the basis for subsequent customization.
Now, this foundation model can be tailored for various tasks, such as classification, sentimental analysis, sentence generation, and more. The user interacts with this model, providing input and receiving an output, creating a dynamic process of interaction and response. The versatility of the foundation model allows it to be harnessed for diverse tasks beyond mere output generation. Whether it's sentence completion tasks, sentimental analysis, or other applications, the foundation model forms the backbone of the generative AI framework. This adaptability makes it a powerful tool for addressing a spectrum of tasks, showcasing the multifaceted capabilities inherent in generative AI.

What are the advantages of foundation models? 

Two critical aspects make them stand out. First and foremost, there's a remarkable enhancement in performance. When comparing to classical machine learning or specific deep neural networks for certain tasks, the foundation model outshines in terms of the data it is trained on. The sheer volume of data used to train the foundation model surpasses what we typically have in research settings. This abundance of data contributes to significantly better performance on any task, especially when fine-tuned. The foundation model operates on a multi-representational level, embedding various types of data—text, language, images—into a single representation.

The second advantage lies in productivity. Unlike the conventional approach of creating individual models for specific tasks, the foundation model introduces a novel concept. It minimizes the need for generating custom codes for each task and eliminates the hassle of frequent retraining. In contrast to the traditional models where any change in data demands retraining, the foundation model requires only minor fine-tuning for adaptation to specific purposes. This productivity gain stems from the fact that once the foundation model is established, it encapsulates a broad understanding of different data types and tasks. Subsequently, only slight adjustments or prompts are needed to tailor the model to a specific task. This streamlined process results in efficiency and versatility, making the foundation model a game-changer in the realm of generative AI.

What are the disadvantages of foundation models? 

Some of the disadvantages of foundation models revolve around the significant computational challenges they pose. One major hurdle is the immense computational power required for both training and execution. The foundation model, with its billions of parameters, demands a colossal amount of data ingestion and processing capabilities. Training such a model is beyond the reach of common resources. Unlike traditional deep neural networks that could be trained on a decent desktop, a foundation model necessitates a “super” computing facility. The sheer volume of data storage and model parameters makes it imperative to utilize extraordinary computing resources, adding complexity and maintenance challenges. This computational demand not only limits the accessibility of foundation models but also restricts their implementation to large corporations with extensive computing resources. Companies like Google, IBM and OpenAI, with their vast computational infrastructure, are among the few capable of creating and utilizing such models.

Moreover, the execution or runtime of foundation models also presents technological challenges. The rapid and efficient processing of input data to generate immediate output requires multiple high-performance GPUs. This computational intensity is beyond the capacity of lab or small-scale settings, making it feasible only in large organizational environments.
Additionally, foundation models often exhibit biases, posing a substantial challenge to trust in their outputs. The inherent bias in the training datasets requires careful consideration, and blindly trusting these models is discouraged. The blog emphasizes the critical need for awareness and caution, suggesting that users take the model's outputs with a level of skepticism. Addressing the bias issue is an active area of improvement, highlighting the ongoing efforts to enhance the trustworthiness of foundation models.
Some of the top Gen AI Products are GPT-4, Gemini, Github Copilot, Bard, Bing, Synthesia, DALL-E 2 and StyleGAN.
If you are intrigued after gaining a general idea Generative AI and foundation models, you may want to enhance your knowledge with a course program on the same by a renowned platform. AIBrilliance currently offers “ChatGPT Demystified: A Beginner’s Crash Course” in this domain.

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