Recently you might've come across the terms ChatGPT, dall-e and stable diffusion and more specifically the term "Generative AI".
Generative ai refers to the field of artificial intelligence that focuses on the development of algorithms and models capable of generating new and original content, mimicking human creativity and imagination.
Models built for this specific task are called "Generative Models" which we will explore in this and the upcoming (part-2) blog (so subscribe for that).
In Part-1 we will be covering what's generative model, it's working and its applications and in the second part we will be exploring its different types. So, let's dive in.
What are Generative models?
Generative models are a type of machine learning model designed for a unique purpose. They're designed to generate new instances that are similar to the data they were trained on. The process of generating new models are referred as "sample" or "generation".
Generative models can be categorized into two main types:
Density estimation
Sample generation
Density Estimation
Density estimation models aim to learn the underlying probability distribution that describes the origin of the data. These models estimate the likelihood of observing new data points within the same distribution. By representing how likely different data points occur in the distribution, these models capture the underlying patterns and structure of the data. Density estimation is a fundamental task in generative modeling, allowing AI systems to understand the data distribution and generate new samples that adhere to the learned distribution, making them valuable in anomaly detection, data imputation, and uncertainty estimation.
Sample Generation
Sample generation involves training a model to learn the underlying probability distribution of the training data. This learned model is then utilized to generate new samples that are similar to the data it was trained on. By capturing the patterns and relationships present in the training data, the generative model gains the ability to produce novel and diverse data instances that closely resemble the original data. Sample generation is a fundamental capability of generative models, enabling AI systems to create realistic images, texts, music, and more, thus expanding the horizons of artificial creativity.
Applications of Generative Models
Generative models have found numerous applications across various fields, such as:
a) Image Generation: GANs, VAEs, and autoregressive models have shown remarkable success in generating realistic images. They are used in art generation, deepfake creation, and even data augmentation for training other models.
b) Text Generation: Recurrent neural networks (RNNs) and language models like GPT (Generative Pre-trained Transformer) are used for text generation tasks, such as creative writing, chatbots, and language translation.
c) Music and Audio Generation: Generative models can compose original music, generate realistic sound effects, and even create human-like speech synthesis.
d) Drug Discovery: In pharmaceutical research, generative models are used to design new molecules with specific properties, potentially accelerating drug development.
Conclusion
Generative models are at the forefront of AI-driven creativity, enabling machines to generate novel and imaginative content across various domains. From art to drug discovery, these algorithms have already made a significant impact and continue to push the boundaries of what AI can achieve.
Hope you like this blog. Give your feedback in the comments and happy coding.
Thanks :)