This is non-standard and first-principal take on AI. I will try and avoid jargon and stick to intuition.
Let's ask much simpler and very old question
What does it mean to "know" something?
I am not looking for a philosophical answer. Let's say we simply ask
How does a calculator "know" 2 + 2 is 4 not 5?
Calculator does not need to know, It follows some rigid rules.
Now suppose I ask:
How does some recommendation engine recommend a movie or a book to you?
Rules and lots of data.
Now suppose I ask:
Which one the largest planet in solar system?
The last one seems different, doesn't it?
Which one is answered based on knowledge? All of them? last one? or none?
Let's build our very own intelligent machine. Let's not worry about how to build it, rather let's just focus only on what capabilities it will have.
For example, should it be able to: There is no right answer.
Let's see if I decide to choose just three
Let's stop and recheck the choices if I was onto something..
But,,, What is a fact?
For example, These are facts.
Now consider
Facts?
Well... mostly
Some cats have three
Some are born without one
The certainty has become fuzzy
Now consider:
Now consider:
Certainly not a fact..
So our intelligent machine can't store facts, isolated facts. It needs to understand
degreesof certainty and generalizations.
Second capability in the list, "Follow rules" is equally uncertain too. Rules change as per the situations.
Your intelligent car will follow rules like
Drive on the Left or Drive on the Right (within a region it typically is fixed)
But
Should I overtake now?
leads to rules that are purely situational.
Rules aren't changing,
applicationof the rule is.
If that is countered as Who decides what is important?
Let's pick following facts
Tomorrow...
Which one of these facts our intelligent system must remember?
Not all of them.
Why?
Because intelligence isn't just remembering. It is about what deserves to be remembered.
That is much harder than it seems.
Still circling around fact..
Suppose we built two systems
System A remembers everything forever.
System B forgets almost everything, keeping only what seems likely to matter later.
At first glance, System A sounds smarter.
It isn't.
Remembering everything eventually becomes a burden.
The real challenge is deciding what can be forgotten safely.
System A will become impractical soon while System B is weirdly difficult. For example, from the facts that we learned, My name is X, I am allergic to Peanuts seem important enough to remember tomorrow, they are not changing. Where my car is parked, may not be, car may have been moved for various unknown reasons and subsequent facts about the car and the parking lot itself.
That single statement alone holds a significant idea that appears over and over again in AI, databases, and even in human cognition.
Information has no intrinsic importance. Its importance depends on future use
Let me elaborate with an example. "My name is X"
It can be safely remembered as it does not change. Tomorrow the name is going to be X. But let's suppose the name is name of a waiter, you are talking to, in some cafe, you rarely frequent. Does this waiter or you need to remember each other's names?
Probably NO.
Now suppose the same setting but not a waiter, your family doctor.
Should your family doctor remember your name?
Absolutely. Same fact, different consumer, different retention needs.
Now about the Peanut allergy, that seemed important anyday, not just tomorrow, But why?
It is not because it is permanent. It's because, forgetting it has a very high cost
Importance isn't about permanence. It's about expected future utility
Now we are at a very important juncture where our intelligent systems is presented with just two facts.
One day X asks
"Can I eat Food Y?"
Nobody explicitly taught a new fact to the intelligent system that "X cannot eat food Y". yet it is expected from the would be intelligent system that it would advise X against Food Y.
Questions:
Where did that new knowledge came from?
It wasn't part of the training set.
It isn't a learned fact.
It wasn't stored in the system.
Essentially the questions itself is where the intelligence really begins. And, that's where modern AI starts to diverge from the traditional programming. The crux of the understanding is answer to the question
How does knowledge become connected?
That is exactly I will try to chase in this series.