[LOCKSUIT]

What is labeling for?

Do not use the word data.

Hint: Supervision (rewards) are needed. Un-supervision (for memory searching) is needed. Supervision is Learning, which comes first. Un-supervision is for Reasoning, which comes next. What's missing? - How do you know to do a memory selection or generate guesses?

[kei10]

What is labeling for?

Do not use the word data.

Hint: Supervision (rewards) are needed. Un-supervision (for memory searching) is needed. Supervision is Learning, which comes first. Un-supervision is Reasoning, which comes next. What's missing? - How do you know to do a memory selection or generate guesses?

Hmm, I'm not sure what labeling is for. Since I don't have that kind of thing in my algorithm.

From what I understand, machine learning comprised of many types, but it seems that I can only think of two like yours, maybe? Except my viewpoint of those are different; Supervised learning is like someone teaching you with proper data. Unsupervised learning is learning by themselves, all by their own. Well, I'm sure that's how we learn as a human being.

Without your label thing -- if it's related, I indeed do know how to do make a proper memory selection and memory guessing, and -- hm, wait, do you? Interesting... I wonder how is yours like.

[LOCKSUIT]

Heh, the freaking current gen AI humans call memory assorting "labeling data". What it really is is storage of *senses into the outer-layer of the cortex and saving them near senses that look alike for *assorted memory i.e. KNN.

And this is all unsupervised can do really, sure it could make the most frequently sensed sense the reward, but that could be walls.

But this unsupervised memory assorting is important for selecting a memory by external/internal input entering.

What I call labels is giving each stored sense a either +/neutral/- label & rank by rewards, then when selection occurs by search&match or memory-self-ignition, + means do&tweak linked actions, n/-/0match means don't-do&guess.

Note that you can DO zero-action-actions, they are generated too hehe!

[ivan.moony]

Learning could be seen as comparing newly constructed theories to reality. Until it matches, new different theories are being constructed. When a match is recognized, we memorize it by attaching a label to it and move on.

So we'd need:
    1. semi-random theory builder, guided by some genetic algorithm
    2. comparing theory to reality, returning a percentage of matching

I think that learning is just a form of recognizing.

[LOCKSUIT]

Learning say actions is from guessing actions, and then ranking them by pre-installed rewards being matched to say lots/little.

When input matches rewards it adds a +/n/- label to it too. When input gets saved, it strengthens itself, and when it matches a memory, it strengthens it as long as attentively sensed.

[keghn]

+ and -  0ver many iteration and accumulated and remembered of each cycle.
MarI/O - Machine Learning for Video Games

[LOCKSUIT]

Yes I have already seen this video a whole year ago when I first began, the rewards are, well, obvious, and obviously are not the rewards we have - food, sex, girl, etc. The way we can still be mario masters is because we make mini-Rewards by linking NON-rewarded senses to the rewarded. You can make "the time you stay alive" bad or good! Cancer good/bad! Find gold=bad!

[Freddy]

How does your AI unlearn things ?

[LOCKSUIT]

Think about it, any AI can guess actions, and reward them by rewarding the sense that follows next such as food and *when see a neutral NON-rewarded sense such as a violin, meaning when it sees the violin it will be like seeing food. If it finds actions with a incorrect cue like this, or finds the wrong actions such as crawling in a circle, then a negative reward must happen to stop the strengthening of these, such as getting dizzy or not seeing the violin anymore. For crawling, visual input matches the highest ranked sense as long as similar and does so to a certain vision because this one is linked to the stronger than positive negDizzyRank, negative label means guess not tweak. As it guesses, neither acceleration nor dizzy sense is gotten nor strengthens the 2 memories linked to vision, so they weaken, and does so fast since not strong enough yet, then on the next input search&match, the NEG to make it guess is GONE, and so is the accel, so when it gets a accel reward now it will be the only one currently saved since all others vanished as it guessed, then it gets matched and done. It may be a curved crawl, but since positive labeled/marked senses mean do - not guess - it tweaks it instead & improves+zigzags! I demonstrated this with a doll, that's how it unlearns things. Use weakens faster if less strong, not de-increment if no reward. Also strength/rank can get high! Ex 97. No 1-3 lol.

Now play a violin and re-read this, just don't cry.

[Freddy]

How would you tell it to immediately unlearn something ?

[LOCKSUIT]

Well with a adult, most civilized and speaking humans have a stored phrase that says that OR simply have the sound "BAD!!" stored and linked to bad bad neg big time, then when you say woh Joey those are not the right moves to do on the tight-rope, then the first part of that input told exactly matches this bad memory and makes it get linked to just tried actions.

Nextly, importantly, this bad linked to the good cancels eachother out, else the adult would guess :#LOL or worse cancel the next best OLD actions and do the 2cddd best lol, this canceling happens for my demo at the wall and turning, simply put, the crawl and wall when seen cancel when wall is matched as approach, and next best turn actions previously guessed are done SINCE when it had previously guessed them it got ranked&labeled+ since had turned and seen the good far wall only linking to +crawl actions.

[Freddy]

Cool, okay last one for tonight. How would you get it to argue that what it thinks is correct when told otherwise ?

[LOCKSUIT]

Because the actions it has stored for explaining its AI blueprint are ranked so high that it says them, and for another's explaining to be accepted would mean for it to save and rank this paragraph higher than its own, which will only happen if you understand it, which clearly works on the ranking of senses and the strengthening of them since actually seeing a full fledged human robot made would link to a massively ranked sense (the video of such stored in brain) that's positive labeled.

Also some funny pics I drew:
http://advancessss.deviantart.com/art/7867887777-626916200?q=in%3Ascraps%20sort%3Atime%20gallery%3AADVANCESSSS&qo=3

http://advancessss.deviantart.com/art/78687777-626916330?q=in%3Ascraps%20sort%3Atime%20gallery%3AADVANCESSSS&qo=2

[Freddy]

Really time for me to sleep so final thought.

Okay, but that's just parrot fashion - it's not actually arguing a point using evidence or structured thought.

Isn't your system a more elaborate pattern matcher at the end of the day ?

I don't mean that as a bad thing, because I like the idea in general.

[kei10]

@Freddy
That's exactly my thought as well. That's the flawed part of the A.I.

Edit: Additionally, it isn't really possible for a human to unlearn something, unless they have amnesia. Every memory within us becomes experience, and that is the important part what makes us, us.

Even memory priority declines by itself, and something about this must be extremely important. So which means something else is must be happening. With this idea of degenerative priority, emotion like negative and positive, does not have ranking. It does not have "linked to bad bad neg big time".

My idea of memory priority goes for the idea of how "fast" neurons transfer information through the axon terminals. Since the general idea of how our information are encoded, is by the formation of new connections between the axons and dendrites, which means the memory priority is the strengthening of these connections. At the same time, these connections, gets weak, and sometimes fails due obstruction, making us forget temporarily.

Perhaps it's a good thing, for us humans, for example, we get something called "Ear worm" when we listen to music. The music will get stuck and loop inside us for hours. Perhaps this is the faulty side of our brain attempting to strengthen the music. We get bored as too many recurred memories were formed, the ear worm within our head will finally drift away -- oh, wait, this isn't a perfect example, never mind...

The same music can't repeat easily anymore, and can be easily stopped, even after a long time -- well that's what happened to me. Except, certain new situations makes you want to play that music in your head, as it forms new type of memory -- exciting you. I've used this method to keep me awake.

Either way, I'm not sure what's up with you, but the two pictures you drew aren't funny.