AI is fundamentally different from traditional software.
Regular software is like a vending machine: put in a request, and you’ll always get the same predictable output. Type 2+2 into a calculator app, and you’ll always get 4.
AI, on the other hand, is more like chatting with a friend. They might respond differently to the same question on different days, AI can give varied answers to the same query. This is a feature that allows AI to be more adaptable and creative.
However, this variability comes with a caveat: you can’t always expect to get identical output from the same input.
This makes AI more flexible and human-like, but it also means you need to be aware that results may vary.
This is particularly important in business situations where consistency is crucial, such as in research or legal applications.
So while traditional software is consistent but rigid, AI is flexible and inventive. This difference is what truly sets AI apart from conventional software and makes it a unique and powerful tool.
But it also means we need to approach AI with a different mindset, understanding both its capabilities and its limitations.
Here’s a simple program that asks some questions. Try interacting with this basic system, and then copy the prompt below and try it in ChatGPT or another AI model to see the differences.
Rule-Based Conversation Simulator
This demo shows how a rule-based system responds to inputs, demonstrating the difference from AI language models.
If you can see this, the chat div is rendering correctly.
Hints for Testing:
- Try: "I'm feeling great" or "Not so good today"
- Try: "I love the color blue" or "Purple is my favorite"
- Try unexpected inputs and repeat inputs to see variations
Comparing with a Large Language Model (LLM):
After testing, use this prompt with an LLM (e.g., ChatGPT):
Compare the LLM's responses with the rule-based system above.
