Introduction to Generative AI_Engineering.pptx
- 1. Introduction to Generative AI Himanshu Shekhar Das
- 2. What is Generative AI? • Generative AI, in simple terms, is like a creative robot. It learns from examples and then creates new things on its own. These creations can be pictures, stories, music, or even videos. • Generative AI focuses on creating new content autonomously, such as images, text, music, or videos, based on patterns learned from existing data. • • Other types of AI, such as discriminative AI, primarily focus on classification tasks, distinguishing between different categories or classes within the data.
- 3. How does Generative AI work? • Generative AI works by learning patterns from existing data and then using this knowledge to generate new content. It uses algorithms to understand the features of the data and create similar but novel outputs. • • It utilizes algorithms, such as neural networks, to generate new data points that are similar to the examples it has been trained on.
- 4. Different Types of Generative AI • There are various types of Generative AI, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which are popular for their ability to generate realistic images, text, and other data.
- 5. How VAEs Work • Variational Autoencoders (VAEs) work by learning the underlying structure of the input data and then generating new data points by sampling from the learned distribution.
- 6. How GANs Work Generative Adversarial Networks (GANs) consist of two neural networks: a generator and a discriminator. The generator creates new data samples, and the discriminator evaluates how realistic they are. They compete with each other, resulting in the generation of highly realistic data. The generator creates new data samples, and the discriminator evaluates how realistic they are. They compete with each other, resulting in the generation of highly realistic data. This adversarial training process enables GANs to generate highly realistic data, distinguishing them from other types of Generative AI.
- 7. Applications of Generative AI • Generative AI has diverse applications such as: • Image Synthesis: Deepfakes • It can be used to create realistic images, such as in the synthesis of deepfakes or the generation of artistic content. • Generative AI can also be used for image-to-image translation, converting images from one domain to another. • Text Generation: Language Models, Chatbots • Chatbots use natural language processing (NLP) techniques coupled with Generative AI to understand user input and generate appropriate responses, enabling human-like interactions in various applications.
- 8. Applications in Industries • Generative AI can be helpful in various industries such as: • Entertainment: Creating realistic special effects and virtual characters • Healthcare: Assisting in drug discovery and medical image analysis • Generative AI can aid in medical image analysis, such as generating high-resolution images from low-resolution scans or synthesizing images to assist in diagnosis. • Generative AI can also be used in personalized medicine, creating patient-specific models for treatment planning. • Marketing: Personalizing content and generating advertisements
- 9. Future of Generative AI • As Generative AI continues to advance, we can expect even more remarkable developments. With improvements in algorithms and computing power, we may witness new applications and breakthroughs in fields such as art, entertainment, healthcare, and more.
- 10. Importance of Generative AI • Generative AI has the potential to revolutionize various industries by enabling creativity, innovation, and efficiency. It can assist in tasks ranging from content creation to drug discovery.
- 11. Conclusion • Generative AI is a powerful tool with numerous applications across various industries. Its ability to create new content autonomously opens up exciting possibilities for innovation and advancement.