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Knowledge
The chapter details the distinctions between Conventional AI, which operates based on predefined rules and logic, and Generative AI, which learns from data to create original content. Each type has unique benefits and challenges, leading to diverse applications across industries. Understanding the interplay between these AI forms is vital for grasping future technological developments.
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ch10.pdfClass Notes
Memorization
What we have learnt
- Conventional AI relies on r...
- Generative AI learns from d...
- Both AI types have specific...
Final Test
Revision Tests
What we have learnt
- Conventional AI relies on rule-based systems designed by humans.
- Generative AI learns from data to generate new and original content.
- Both AI types have specific applications, benefits, and challenges in real-world scenarios.
Key Concepts
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Term: Conventional AI
Definition: AI systems that are rule-based and explicitly designed by humans, often resulting in predictable and explainable outcomes.
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Term: Generative AI
Definition: AI systems that learn from large datasets to create unique content, often using Machine Learning techniques, and may operate in a less transparent manner.
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Term: Machine Learning
Definition: A subset of AI that enables systems to learn from data patterns without explicit programming.
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Term: Deep Learning
Definition: A type of Machine Learning that uses neural networks to interpret complex data inputs, often resulting in high levels of performance in tasks like image and speech recognition.
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Term: Generative Adversarial Networks (GANs)
Definition: A class of machine learning frameworks where two neural networks compete to generate new data that can mimic real data.
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Term: Large Language Models (LLMs)
Definition: AI models capable of understanding and generating human-like text based on learned patterns from extensive text data.