🤖 What is AI?
Artificial Intelligence (AI) is a branch of computer science aimed at enabling machines to think, act, and behave like humans. Through AI, computers can learn from data, recognize patterns, and make informed decisions.
✨ What is Generative AI?
Generative AI is an advanced form of AI trained on vast datasets, often containing trillions of data points. It learns complex patterns, features, and relationships, allowing it to create new content, like text, images, or even music. Generative AI is far more advanced than traditional deep learning, a subset of machine learning, due to its ability to generate diverse and contextually relevant outputs.
🌍 Where is Generative AI Used?
Generative AI has become integral to various applications, including:
- 🌐 Text translation
- 🎨 Image, audio, and video generation
- 🗣️ Advanced Natural Language Processing (NLP) tasks, such as summarization and conversational AI
🔍 How is Generative AI Different from Deep Learning?
- Scale of Data and Parameters: Most deep learning models operate with millions of parameters, while generative AI models are trained on trillions.
- Context Range: Deep learning models, like RNNs and LSTMs, are designed to handle shorter sequences. They’re useful in sequential tasks but store only limited history, such as a brief context in NLP.
- Long-Range Understanding: Generative AI models, on the other hand, are capable of retaining extensive context, making them highly accurate and context-aware.
⚙️ Architecture of Generative AI
Generative AI models primarily rely on transformer architecture. Transformers process inputs in parallel, efficiently managing long-range dependencies. This approach enables generative AI to produce coherent, context-rich outputs across diverse applications.
📝 Stay tuned in this learning journey for more on generative AI architecture! I'd love to discuss this topic further – special thanks to Guvi for the course!
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