Generative_AI_Presentation_INTRODUCTION.pptx
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Generative AI encompasses algorithms that create new content, such as images and text, by learning from extensive datasets. Its applications range from content creation and virtual assistants to drug discovery and gaming, leveraging models like GANs, VAEs, and transformers. The future of generative AI promises to revolutionize industries while raising ethical concerns regarding deepfakes and data privacy.
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Generative_AI_Presentation_INTRODUCTION.pptx
- 1. Generative AI Exploring the Future of Artificial Intelligence
- 2. What is Generative AI? • Generative AI refers to algorithms that can generate new content, such as images, text, music, and more. These models are trained on vast amounts of data and learn to create new, original outputs that mimic the data they were trained on.
- 3. Applications of Generative AI • - Content creation (art, music, writing) • - Image and video synthesis • - Drug discovery and design • - Virtual assistants and chatbots • - Gaming and virtual worlds
- 4. Generative Models • Popular models include: • - Generative Adversarial Networks (GANs) • - Variational Autoencoders (VAEs) • - Transformer models (e.g., GPT, DALL-E) • These models have revolutionized how machines can understand and generate human-like content.
- 5. Future of Generative AI • Generative AI is expected to transform industries by automating creative processes and enabling new forms of expression. Ethical considerations, such as deepfakes and data privacy, will play a crucial role in shaping the future development and deployment of these technologies.