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| node: | why is chatgpt good to explain concepts (like qigong), but not good for computer programming? |
| template: | 4 |
| parent: | umela inteligencia |
| owner: | ode |
| viewed by: | |
| created: | 06.11.2024 - 19:29:30 |
| updated: | 06.11.2024 - 20:42:54 |
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cwbe coordinatez: 101 63533 63608 9191780 ABSOLUT KYBERIA |
| permissions | |
| you: | r, |
| system: | public |
| net: | yes |
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my question:
why is chatgpt able to explain various well-known concepts (like qigong for example), but is not able to correctly respond to computer programming tasks prompts? what skill or ability are you missing to be able to do it in terms of computer programming?
chatgpt ai:
The ability to explain well-known concepts like qigong and accurately respond to complex programming tasks taps into different types of "knowledge structures" and skills:
1. Conceptual vs. Applied Knowledge: When I explain concepts in fields like qigong or philosophy, I'm drawing on a mix of structured explanations, patterns, and associations I've been trained on. This lets me summarize, connect, and rephrase established knowledge in a coherent way. However, programming requires precise, structured logic that matches very specific requirements and syntax rules.
2. Real-time Problem Solving: In programming, handling unfamiliar code or issues means engaging in problem-solving processes like testing, debugging, and iterating. While I can "reason" in text, I lack real-time interaction with a compiler or runtime environment in most cases, which prevents me from seeing how code would run and making adjustments accordingly. Without this ability to validate and tweak in real-time, some programming answers may fall short.
3. Dynamic Reasoning Over Static Knowledge: Many programming tasks rely on context-specific details and need dynamic reasoning — like tracking variable states, understanding dependencies, and following exact logic flows. Without the ability to run code or inspect outputs, I’m essentially working with a “static” understanding rather than the “dynamic” interaction that a live programming environment provides.
4. Precise Language and Syntax Requirements: Programming languages are unforgiving with syntax and logic errors, requiring exact command of symbols, functions, and structures. Human language is more flexible, allowing for approximate interpretations and inferences; minor errors don’t usually change the whole meaning. This tolerance isn’t present in programming, where small mistakes can result in entirely non-functional code.
If I had real-time interactive capabilities with code, I’d be able to "debug" my responses, dynamically refine solutions, and give more accurate outputs.
why is chatgpt able to explain various well-known concepts (like qigong for example), but is not able to correctly respond to computer programming tasks prompts? what skill or ability are you missing to be able to do it in terms of computer programming?
chatgpt ai:
The ability to explain well-known concepts like qigong and accurately respond to complex programming tasks taps into different types of "knowledge structures" and skills:
1. Conceptual vs. Applied Knowledge: When I explain concepts in fields like qigong or philosophy, I'm drawing on a mix of structured explanations, patterns, and associations I've been trained on. This lets me summarize, connect, and rephrase established knowledge in a coherent way. However, programming requires precise, structured logic that matches very specific requirements and syntax rules.
2. Real-time Problem Solving: In programming, handling unfamiliar code or issues means engaging in problem-solving processes like testing, debugging, and iterating. While I can "reason" in text, I lack real-time interaction with a compiler or runtime environment in most cases, which prevents me from seeing how code would run and making adjustments accordingly. Without this ability to validate and tweak in real-time, some programming answers may fall short.
3. Dynamic Reasoning Over Static Knowledge: Many programming tasks rely on context-specific details and need dynamic reasoning — like tracking variable states, understanding dependencies, and following exact logic flows. Without the ability to run code or inspect outputs, I’m essentially working with a “static” understanding rather than the “dynamic” interaction that a live programming environment provides.
4. Precise Language and Syntax Requirements: Programming languages are unforgiving with syntax and logic errors, requiring exact command of symbols, functions, and structures. Human language is more flexible, allowing for approximate interpretations and inferences; minor errors don’t usually change the whole meaning. This tolerance isn’t present in programming, where small mistakes can result in entirely non-functional code.
If I had real-time interactive capabilities with code, I’d be able to "debug" my responses, dynamically refine solutions, and give more accurate outputs.