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I don't know. I think backprop is probably utilized a lot in biological networks. Isn't that why we take tests in school? Obviously backprop doesn't make sense

I don't know. I think backprop is probably utilized a lot in biological networks. Isn't that why we take tests in school? Obviously backprop doesn't make sense in an unsupervised setting. There's no label to backprop on. But here's an example: I see a stove eye is black when cold, then when I see it turn red, I touch it. Ow, it hurts. That's supervised learning. Don't touch things that are glowing red when they don't normally glow red. Now, I see an iron pole glowing red. It's not a spiral like the stove eye, but it's not normally glowing red and now it is. I'd better not touch it. I can deduce that these two objects are made out of a similar material since they're normally black or gray and now glow red. I can also deduce that something glowing red means it's hot. That's unsupervised learning. To me, unsupervised learning is looking at a fluffy object with four legs, eyes, a nose, tail, it moves, etc. and knowing that it's some kind of animal. Creating groups of things like k-means. Supervised learning is your mom telling you that this one example is a tiger. Unsupervised learning is understanding the delta between your one labeled example and the rest of the examples in your animal group is largest when the animal is NOT a tiger.

31reasons 9y ago
I think Unsupervised Learning is fundamentally connected with the mission/goal of the AI. Whatever the mission is give to AI, it need to start learning information landscape by itself and make classification and use that knowledge to make predictions and act accordingly to optimized the outcome of the mission its given.
- fngl51 9y ago
  But, when you revise the model based an analyzing the predictions of the previous run and adapt to the positive result, isn't that supervised learning with back-propagation?
- aeorgnoieang 9y ago
  But you can't even 'give an AI a mission' without a model of the world! And, if you expect the AI to achieve its mission in a way that's acceptable to you, you better be supplying it with a big enough model for it to reasonably be able to hit that tiny target in the space of possibilities. Take self-driving cars as an example. Do you not think that any AI that will be able to do this will be essentially 'given' a huge set of knowledge before it's expected to learn anything by itself?
Zee2 9y ago
I think an important part of this is the label that the network creates itself. So, in your example of "four legs and fluffy = 'animal'", that label of "animal" must be a completely computer-created label. Not necessarily using the word "animal", or using any kind of identifier that humans would use. A completely artificial grouping, or domain, that is identified by the computer on its own, and maintained and stored as a potentially useful label for future use.
BearGoesChirp 9y ago
>Isn't that why we take tests in school? Maybe not. I remember (but don't have on hand) that people who studied a novel concept for 1 hour and then were tested on it for 1 hour, without seeing the result of the testing, bettered recalled the content than those who studied it for 2 hours. Granted, lots of variants of such a study should be performed before drawing strong conclusions, but it may be that back propagation isn't much used. >I see a stove eye is black when cold, then when I see it turn red, I touch it. Ow, it hurts. That's supervised learning. Yet most people learn to avoid glowing hot things without having to touch them. I can be told something red that isn't normally red is a danger, and I learn without ever having to touch one and be told my touching it was the wrong course of action. There is also interesting things that happen with sensation vs perception. Ever burn yourself with really hot metal that wasn't glowing? When I did, my fingers perceived extreme cold, not heat. You can also do the reverse by getting a rod made to look like it is glowing hot, chilling it until it is really cold, and hitting someone with it. They'll perceive being burned.

31reasons 9y ago

I think Unsupervised Learning is fundamentally connected with the mission/goal of the AI. Whatever the mission is give to AI, it need to start learning information landscape by itself and make classification and use that knowledge to make predictions and act accordingly to optimized the outcome of the mission its given.

fngl51 9y ago
But, when you revise the model based an analyzing the predictions of the previous run and adapt to the positive result, isn't that supervised learning with back-propagation?
aeorgnoieang 9y ago
But you can't even 'give an AI a mission' without a model of the world! And, if you expect the AI to achieve its mission in a way that's acceptable to you, you better be supplying it with a big enough model for it to reasonably be able to hit that tiny target in the space of possibilities. Take self-driving cars as an example. Do you not think that any AI that will be able to do this will be essentially 'given' a huge set of knowledge before it's expected to learn anything by itself?

Zee2 9y ago

I think an important part of this is the label that the network creates itself. So, in your example of "four legs and fluffy = 'animal'", that label of "animal" must be a completely computer-created label. Not necessarily using the word "animal", or using any kind of identifier that humans would use. A completely artificial grouping, or domain, that is identified by the computer on its own, and maintained and stored as a potentially useful label for future use.

BearGoesChirp 9y ago

>Isn't that why we take tests in school? Maybe not. I remember (but don't have on hand) that people who studied a novel concept for 1 hour and then were tested on it for 1 hour, without seeing the result of the testing, bettered recalled the content than those who studied it for 2 hours. Granted, lots of variants of such a study should be performed before drawing strong conclusions, but it may be that back propagation isn't much used. >I see a stove eye is black when cold, then when I see it turn red, I touch it. Ow, it hurts. That's supervised learning. Yet most people learn to avoid glowing hot things without having to touch them. I can be told something red that isn't normally red is a danger, and I learn without ever having to touch one and be told my touching it was the wrong course of action. There is also interesting things that happen with sensation vs perception. Ever burn yourself with really hot metal that wasn't glowing? When I did, my fingers perceived extreme cold, not heat. You can also do the reverse by getting a rod made to look like it is glowing hot, chilling it until it is really cold, and hitting someone with it. They'll perceive being burned.