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Interactive Audio Lesson
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Introduction to Conventional AI
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Today, we're diving into Conventional AI, sometimes called symbolic AI. This type of AI is built on explicit rules that we program.
What does it mean for AI to be based on rules?
Great question! It means that all the 'thought process' of the AI is predetermined by humans. For instance, if we program a chess engine, it knows what moves are valid based on human-developed chess rules.
So, it's kind of like following a recipe?
Exactly, using the acronym 'RULES': R—Requires human input, U—Unchanging without updates, L—Logic-driven, E—Explainable outcomes, S—Structured environments. This captures the essence of Conventional AI.
Features of Conventional AI
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Let’s talk about the four main features we see in Conventional AI. First up, predictability and explainability.
Can you explain why being predictable is important?
Sure! AI that behaves predictably is easier to debug and understand. This is vital in areas like healthcare or finance, where decisions need to be transparent.
What about the need for human input? Does that slow down the process?
Yes, it can. Human designers must input the rules and logic, which takes time. We call these systems 'manual,' contrasting with more automated systems in Generative AI.
Applications of Conventional AI
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Let’s wrap up by looking at some examples of Conventional AI in action. Can anyone provide a specific application?
How about chess engines and how they help players?
Absolutely! Chess engines evaluate possible moves based on programmed rules. What about spam filters?
They check for fixed keywords, right?
Exactly! Remember the rule 'If it walks like a spam and quacks like spam, it gets filtered!' Each of these examples shows how Conventional AI simplifies complex decisions in our daily lives.
Introduction & Overview
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Quick Overview
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Key features of Conventional AI include its reliance on explicitly programmed rules, predictability, requirement for human inputs, and effectiveness in structured environments. The section provides specific examples that illustrate these characteristics.
Detailed
Detailed Summary
In this section, we explore Conventional AI, also known as symbolic AI. It is characterized by several key features:
- Logic and Rules-Based: Conventional AI operates under predefined logic and rules, with all knowledge being explicitly programmed by humans.
- Predictability and Explainability: Its operations are predictable, making it easier to understand how decisions are made compared to other types of AI.
- Human Input Requirement: Human designers must input decisions into the system, requiring significant upfront effort to create the decision-making processes.
- Structured Environment Efficiency: These AI systems perform best in structured environments where rules can be clearly defined.
Examples in Everyday Use: Common applications of Conventional AI include:
- Chess Engines: Evaluate the best possible moves by following specific rules of gameplay.
- Spam Filters: Identify unsolicited emails through fixed keywords or sender addresses.
- Navigation Systems: Utilize stored maps and programmed routes to provide directions.
Understanding these features highlights the strengths and limitations of Conventional AI, setting the stage for a comparison with Generative AI, which will be discussed in subsequent sections.
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Logic and Rules Based System
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- Based on logic and rules.
Detailed Explanation
Conventional AI operates on a framework of predefined rules and logical instructions. This means that every operation has specific guidelines designed by humans. For example, in a chess program, the moves allowed are determined by the established rules of chess.
Examples & Analogies
Think of it like following a recipe to bake a cake. You have specific steps to follow (rules) to achieve the final product. If you don’t follow the rules, the cake won’t turn out right.
Predictability and Explainability
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- Predictable and explainable.
Detailed Explanation
One of the strengths of Conventional AI is that its decision-making processes are transparent. Since the rules are predefined, it's easy to predict how the system will respond to different inputs, making it clear why a particular decision was made.
Examples & Analogies
Imagine a traffic light system that operates based on fixed rules. If the light is red, cars must stop. If it's green, they can go. Everyone understands the rules, making the system predictable.
Human Input Requirement
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- Requires human input to design its decision-making process.
Detailed Explanation
Conventional AI systems rely heavily on human expertise to set up their rules and logic. This means that for every unique situation or problem, a human must define how the AI should respond, limiting the AI's ability to adapt independently.
Examples & Analogies
Think of it as training a dog. A trainer must teach the dog commands and how to behave in various situations. Without the trainer's guidance, the dog wouldn't know what to do.
Effectiveness in Structured Environments
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- Works well in structured environments.
Detailed Explanation
Conventional AI is most effective in environments that are well-defined and predictable. Because these systems are based on clear rules, they perform best where there is little to no ambiguity, such as in standardized tests or specific applications like those used in banking or computing.
Examples & Analogies
Imagine a factory assembly line. Each machine and worker has a specific task to accomplish in a clearly defined sequence. This structured approach ensures things run smoothly and efficiently.
Examples of Conventional AI Applications
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- Examples:
• Chess engines: Follow specific rules to evaluate best moves.
• Spam filters: Use fixed keywords or sender addresses.
• Navigation systems: Use stored maps and programmed routes.
Detailed Explanation
Specific applications of Conventional AI showcase how it operates within set rules. Chess engines function by calculating potential moves based on chess rules, spam filters identify unwanted emails using keywords, and navigation systems rely on pre-mapped routes to direct users efficiently.
Examples & Analogies
Consider chess: a chess engine calculates moves similar to how students might solve math problems step-by-step based on the established rules of mathematics. Each choice leads to different outcomes based on the existing framework.
Definitions & Key Concepts
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Key Concepts
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Rule-Based Systems: Systems that operate based on a predefined set of rules and inputs.
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Human Input: Humans must design and input the rules that dictate an AI system's operations.
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Predictable and Explainable: Conventional AI's behavior can be anticipated and understood.
Examples & Real-Life Applications
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Examples
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Chess engines, which evaluate the best possible moves according to established rules.
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Spam filters that automatically categorize or block emails based on fixed criteria.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
🎵 Rhymes Time
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Conventional AI's rules are neat, decision-making people can't beat.
📖 Fascinating Stories
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Imagine a chef who only follows a recipe; this chef thinks exactly how to combine ingredients, just like Conventional AI follows its programmed logic.
🧠 Other Memory Gems
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Use 'RULES' - Requires human input, Unchanging, Logic-driven, Explainable, Structured for remembering Conventional AI.
🎯 Super Acronyms
C.A.R. - Conventional AI
- Consistent
- Automatically operated based on Rules.
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: Conventional AI
Definition:
A type of AI that relies on explicitly programmed rules and logic, also known as symbolic AI.
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Term: Predictability
Definition:
The characteristic of AI systems that allows their actions and decisions to be anticipated based on existing logic and rules.
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Term: Explainability
Definition:
The ability to understand how an AI system makes decisions and what factors it considers in its processing.
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Term: Rulebased System
Definition:
A system that uses a predefined set of rules to make deductions and decisions instead of learning from data.