How Traditional AI Systems Think

Traditional AI = Knowledge Base + Inference Engine

efore whole Buzz around CHatGPT and Generative AI .The AI was was built on below 2 parts:

Knowledge Base : Collection of domain specific knowledge and rules

Inference Engine: Its logical mind that takes rules define in knowledge base to reach conclusions and decisions step by step .

How did it work?

Traditional AI flow will begin with user input where user will ask questions .

System wouldn’t guess any answer rather will refer all respective domain knowledge and rules are already placed in knowledge base represented symbolically and logically.

Thereafter rules and and data are retrieved from knowledge base by inference engine and try to make sense of it using the rules defined.

It doesn’t guess ones , It keep checking rules again and again to become better each time till it finds good answer.

This is something like solving a big puzzle .End result is answer provided by system based on user input and whatever system learned from the rules.

Quick Example:

Lets understand with an example :

Knowledge Base: 

• Rain → Raincoat

• Cold → Jacket

• Sunny and warm —> Tshirt

User Input — > What should I wear its cold and rainy outside

Inference Engine: 

• System read user input and looks at rule defined in knowledge base

• Breaks down as user input into “Cold” and “Rainy”

• Matches the rules which it knows and start thinking step by step

• Cold =Jacket ? Rainy = Raincoat ?

• It has to come out with better choice.

• System replies to user” You should wear a waterproof Jacket”

Thus system didn’t gave any random output but used facts and rules defined in knowledge base and thought it over step by step and came with best output .

So traditional AI is slow careful but not creative .

We will talk about Traditional AI challenge in next post .

Smiles :)

Anurudh