What is Generative AI? in Telugu

What is Generative AI?

• Definition: Generative AI is a branch of artificial intelligence that focuses on creating new content, rather than simply analyzing or classifying existing data. It can generate various forms of media like:

• Text (articles, poems, scripts)

• Images (realistic photos, original artwork)

• Code (software, scripts)

• Audio (music, sound effects)

• Video (clips, short animations)

• How it works: Generative AI models are trained on massive amounts of data. They learn complex patterns and relationships within the data, allowing them to produce new content that has similar characteristics to the training data. There are various techniques used, with the most common being:

• Generative Adversarial Networks (GANs): Two neural networks compete; one generates content, the other tries to distinguish "real" from "fake." This improves the generator's ability to create realistic results.

• Transformer Models: These excel at understanding and producing natural language and are behind tools like ChatGPT.

Examples of Generative AI Tools:

• Text:

• ChatGPT (conversational AI chatbot)

• Jasper.ai (writing assistant for marketing copy)

• Images:

• DALL-E 2

• Midjourney

• Stable Diffusion

• Code:

• GitHub Copilot (helps developers write code)

Applications of Generative AI:

• Creative Industries: Streamlines processes in areas like content creation, graphic design, video game design, and music production.

• Marketing and Advertising: Quickly generating ad copy variations, social media content, and personalized product images.

• Customer Service: Creating realistic chatbots and virtual assistants for enhanced customer interactions.

• Research and Development: Accelerating drug discovery, material design, and simulation generation.

• Education: Providing personalized learning content and interactive teaching materials.

What is Conventional AI?

Conventional AI, also known as rule-based or symbolic AI, follows a structured, logic-driven approach to problem-solving. It works by:

• Defining Knowledge: Expert knowledge is carefully coded into a system of rules and facts.

• Reasoning with Rules: The AI system uses these rules to process input data, make deductions, and arrive at conclusions.

Key Characteristics

• Predictable: Conventional AI behaves consistently based on the rules programmed into it.

• Explainable: The decision-making process can be clearly traced back to the specific rules followed.

• Limited: Its capabilities are constrained by the quality and scope of the rules it has been given.

Common Use Examples

• Expert Systems: Systems that emulate the decision-making capabilities of human experts in specialized fields. Examples:

• Medical diagnostic systems

• Financial risk assessment tools

• Game Playing AI: Systems that play games with defined rules and goals. Examples:

• Chess programs

• Checkers AI opponents

• Natural Language Processing (NLP) – Early Stages: NLP systems that heavily relied on defined grammatical rules and vocabularies for language understanding and processing.

Limitations of Conventional AI

While conventional AI was foundational, it has limitations that spurred the development of advanced techniques like machine learning:

• Difficulty Handling Ambiguity: It struggles with real-world data that's often imprecise, inconsistent, or incomplete.

• Knowledge Bottleneck: Creating and maintaining large rule bases becomes time-consuming and complex.

• Brittleness: These systems can fail unexpectedly when faced with scenarios outside their programmed knowledge.

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