AI Generative Models

Posted by: Dr. A. Bharathi

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AI Generative Models

Generative models have become potent tools in the field of artificial intelligence (AI) capable of producing fresh and inventive content. These models give computers the ability to produce realistic images, words, music, and even films that imitate human creativity by utilizing advanced algorithms and deep learning approaches.

Joseph Weizenbaum created the first generative AI in the 1960s as part of the Eliza chatbot. Ian Goodfellow demonstrated generative adversarial networks for generating realistic-looking and -sounding people in 2014. Subsequent research into LLMs from Open AI and Google ignited the recent enthusiasm that has evolved into tools like ChatGPT, Google Bard, and Dall-E.


Generative AI vs. AI

New material, chat responses, designs, synthetic data, or deepfakes are all products of generative AI. On the other hand, traditional AI has concentrated on finding patterns, making choices, improving analytics, classifying data, and spotting fraud. ChatGPT, Dall-E, and Bard are popular generative AI Interfaces.


Types of AI Generative Models

  1. Variational Auto-encoders(VAE): neural networks with a decoder and encoder — are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans.
  2. Generative Adversarial Networks (GANs): The generator aims to generate realistic samples, while the discriminator tries to distinguish between real and generated samples.
  1. Flow-based Models: Flow-based models directly model the data distribution by defining an invertible transformation between the input and output spaces.
  2. Transformer-based model: Transformer-based models are a type of deep learning architecture that has gained significant popularity and success in natural language processing (NLP) tasks.

The three key requirements of a successful generative AI model are:

  1. Quality: Having high-quality generated outputs is essential, especially for apps that interface directly with consumers.
  2. Diversity: A good generative model preserves generation quality while capturing the minority modes in its data distribution. As a result, the taught models have fewer unintended biases.
  3. Speed: Fast generation is necessary for many interactive applications, such as real-time image editing for use in content development workflows.


Use cases for generative AI

Generative AI tools


Generative AI Applications


Challenges of Generative AI



In conclusion, By enabling computers to produce realistic images, texts, music, and videos, AI generative models have transformed content creation and innovation. The use of AI generative models in art, design, storytelling, and entertainment has expanded thanks to techniques like VAEs, GANs, auto-regressive models, and flow-based models. To fully utilize generative modeling, however, problems like assessment, ethical issues, and responsible deployment must be resolved. AI generative models will continue to influence creativity and propel innovation in novel ways as we traverse the future.



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