[LOCKSUIT]

Search-Space Optimization

Say you have AGI, and you want it to recognize there is a cat in the 'image' that it takes a look at.
How do you know which output to output when you have such a large search-space?

Say you have AGI, and you want it to achieve goals in real life.
How do you know which output to output when you have such a large search-space?

Say you have AGI, and you want it to segment the image correctly.
How do you know which output to output when you have such a large search-space?

[AndyGoode]

What do you mean by "output to output"? An image is usually considered the input to a recognition system, and the recognition or identification is considered the output.

[LOCKSUIT]

An output is like an action, it may say "I SEE A CAT". So outputting this is returning it to the user, i.e. "to output" an output, is literally to give a gift to the user. To return an answer. To output an output.

I feel SSO is interesting because we want an understanding of what/why/how AI works, and a way to implement it on our computers.

I have good answers to the OP but I want to hear others inputs on my output :)

My questions are litterally asking you how does the AI find the answer when there is 99999999999 zillion possible answers!?

[HS]

It's got to know enough about cats to predict their potential forms. When the AI sees something unknown it starts fiddling around with it's mental model of a cat to see if it can make it match to what it sees.

[LOCKSUIT]

Also, as you may have noticed in my last post - AGI=heuristics, because it must eliminate many unlikely answers - exactly a way for us to implement it on our current computers !

[LOCKSUIT]

That's a start HS! Yes it has to be fed a lot of data, and start clustering it.

[LOCKSUIT]

You need hints, where to look in your memory. Votes to weigh in.

[HS]

Oh! If the AI has a body with joints or already knows about some other animals with similar anatomy to cats, then it can still predict the cat shapes without needing as much info specifically on the cat. If it encounters an unknown creature it can use broad knowledge to "triangulate" the creatures potential properties!

[LOCKSUIT]

quantinization!

[HS]

I think you only need a moderate amount of good quality knowledge/memory to deduce most things.

[HS]

Like mixing paints! Primary colors -----> Primary knowledge!  Hhehehehe I be genious!

[HS]

You'd think this primary knowledge would be physics, because you can deduce everything with it. But thats too processor intensive if you attempt to predict behaviors of anything more complex than a virus. This would be so much easier if the AI had goals. Then it could self select useful knowledge.

[goaty]

Think of it like chess.

If you test every single possibility, one must result in the kings death, the computer with enough search power is a perfect player, amazing and true. 
But theres an exponential amount of positions to check,  so the search space is too large.

But its actually not that bad...  if you think about it,  a lot of the board states will be invalid moves,  only 1 piece is allowed to move at a time,  and if you can look less than only 10 moves in front, its already better than the greatest human player just about.

Budgetting it,  is a bit like budgeting the lotto,  you have to work out the odds, of ending up at every state.

Binary probability trees are a good thing to study.

The thing that's exciting about this,  is if you had a quantum computer,  you would have no limits to the amount of states you need to check.  even a google states it could handle...  and could be a secret of how to get AGI to happen.

But if there is an optimization here,  its a tricky and top secret one...    like P=NP.

[LOCKSUIT]

Remember i said talk to 100 girls a day and 1 will hook with you? Everything works in #s. Teams. Spears. Etc.

If you send out 55 look scouting ants in your chess search tree, one will find a better path! Skip search space!? Just an idea, may not even work. Seems right though.

[HS]

Sounds like a job for parallel computing.