Generative AI

Created: 10 Aug 2025.

Generative AI refers to a class of AI technologies, that is capable of generating various forms of content, including, but not limited to text, images, audio and video. Generative AI can create new content, based on their training data and input parameters, which usually includes text prompts or other forms of input such as images.

The recent buzz around Generative AI, comes from the simplicity with which new user interfaces powered by this technology, can create high-quality text, graphics and videos in a matter of seconds.

Deep Learning

Deep Learning, is considered a watershed moment for Generative AI. It transformed the field from simple experiments into systems that can create highly realistic, coherent and useful content. And here are the reasons why it is such a turning point:

Rules to Learning Patterns

Before Deep Learning, most AI generation was rule-based, or used shallow statistical models.

  • These systems couldn’t generalize beyond the data they were given.

  • Using neural networks that have many layers, Deep Learning enabled models to automatically learn features directly from the raw data, without relying on rules.

Capture Complex Representations

Generative AI needs to understand the structure of the training data it is given - grammar in language, composition in images, rhythm in music, etc.

  • Deep Learning, particularly with convolutional neural networks (CNNS) for images and recurrent/transformer networks for text, can model these complex dependencies. Unlike traditional neural networks that treat each pixel as an independent data point, CNNs are designed to recognize and learn features, by preserving the spatial relationships between pixels. They mimic the way the human visual cortex processes information.

  • This allows for fluid text, realistic images and coherent music generation by Generative AI.

Scale

Deep Learning shifted the paradigm from task-specific, rule-based systems to large, adaptable models that learn from massive and diverse datasets.

Traditional AI

Characteristics of earlier methods (Traditional AI) on older architectures were as follows:

  • Task specific: They were designed to perform a single specific task, such as classifying images of cats and dogs or predicting customer churn. If you wanted to do a different task, you would have had to build a new model from scratch.

  • Rule based: They learned methods that relied on explicit programming rules, which were not scalable because, creating and maintaining these rules for every possible scenario was prohibitively expensive and time-consuming.

  • Limited Data: These models were typically trained on smaller, curated data sets relevant to the specific task. They lacked the ability to generalize from a wide variety of information.

  • Scalability issues: Scaling these systems meant increasing the number of rules or the size of the task-specific dataset, which was a linear and difficult progress. They could not adapt to new information or unforseen circumstances without human intervention.

Modern AI

Deep Learning thrives on big data + big compute.

  • When models such as GPT, DALLE-E and Stable Diffusion are trained on billions of parameters, they learn rich, nuanced patterns.

  • The more parameters there are, the better the generative fidelity (sharper images, more convincing text).

  • Foundational Models: Modern Generative AI models, such as Large Language Models (LLMs), are often referred to as foundational models. Instead of being trained for one task, they are pre-trained on vast, diverse datasets from the internet. This allows them to learn the underlying patterns and structures of data in a way that allows them to perform a wide variety of tasks.

  • Self-Attenuation Mechanism: The Transformer Architecture introduced a “self-attenuation” mechanism that allows the model to weight the importance of different words in an input sequence. This enables the model to understand the context of an entire sentence or document, regardless of the position of the word. This is a massive leap from earlier methods, like Recurrent Neural Networks (RNNs), which processed data sequentially and struggled with long-range dependencies.

  • Parallelization: The self-attention or self-attenuation mechanism allows the model to process all parts of a sequence simultaneously, in parallel. This is a major efficiency improvement over sequential models like RNNs and Long Short-Term Memory (LSTM) networks, which process data one step at a time. Parallelization, makes it possible to train models on huge datasets using power hardware like GPUs.

  • Transfer Learning: A single, large, pre-trained model, can be fine-tuned with a small, task-specific dataset to perform a new task. This eliminates the need to train a new model from scratch for every application.

  • Parameter Scaling: The performance of transformer-based models often improves predictability, with an increase in the number of parameters (the size of the model) and the amount of training data. This encouraged a race to build larger and larger models, which have led to more capable generalized AI systems.

New Architectures for Creativity

Deep learning enabled generative-specific architectures, that were previously impossible with older AIs. Examples of these and what they can achieve are given below:

  • Generative Adversarial Networks (GANs): ultra-realistic images.

  • Variational Autoencoders (VAEs): smooth latent-space interpolation.

  • Diffusion Models: state of the art text to image synthesis.

  • Transformers: unlocked large language models.

Unification of Multi-model AI

With Deep Learning, we are able to use the same underlying techniques for text, images, audio, video, etc. - making multi-model generative systems possible.

In summary

Deep Learning thus:

  • removed the ceiling on complexity and realism for Generative AI.

  • enabled scale by introducing architectures that could handle massive, unstructured datasets and automatically learn complex features, which were significant limitations for earlier methods. Earlier methods were often limited by their reliance on specific pre-programmed rules and smaller task-specific datasets.

  • opened the door for entirely new applications - from chatbots to image generation, video synthesis and protein design.

Without Deep Learning, Generative AI today, would likely have still become a niche with low quality output, instead of the mainstream transformative technology that it is today.