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>Human performance generally decreases with level of exposure Are you saying that as humans get more experience, they perform worse? I disagree, but irrespecti

by bumby 3y ago

>Human performance generally decreases with level of exposure Are you saying that as humans get more experience, they perform worse? I disagree, but irrespective of that point it’s wild that you can have this many responses while still completely bypassing the entire point I was making. I don’t think most would argue that performance increases with experience. The point is how well can the performance be maintained when there is little or no exposure. Because that implies principled reasoning rather than simple pattern mapping. That is the entire through line behind my comments regarding context dependent language, novel driving scenarios, etc. >Think on that In the context of the above, I don’t think this is nearly as strong of a point as you seem to think it is. There nothing novel about a text-based discussion.

famouswaffles 3y ago
1. We anchored this discussion on arithmetic so I stuck to that. If a child never learns (no exposure) how to do base 16 arithmetic then a test quizzing on base 16 arithmetic will result in zero performance. If that child had the basic teaching most children do (little exposure) then a quiz will result in much worse performance than a base 10 equivalent test. This is very simple. I don't know what else to tell you here. 2. You must understand that a human driver that stops because a kite suddenly comes across the road doesn't do so because of any kite>child>must not hurt reasoning. Your brain doesn't even process information that quickly. The human driver stops (or perhaps he/she doesn't) and then rationalizes a reason for the decision after the fact. Humans are very good at doing this sort of thing. Except that this rationalization might not have anything at all to do with what you believe to be "truth". Just because you think or believe it is so doesn't actually mean it is so. Choices shape preferences just as much as the inverse. For all anyone knows and indeed most likely, "child" didn't even enter the equation untill well after the fact. Now if you're asking whether LLMs a matter of principle can infere/grok these sort of casual relationships between different "objects" then yes as far as anyone is able to test.
- bumby 3y ago
  Your first statement seems to contradict your previous. Did you originally mistype what you meant when you said greater exposure leads to worse outcomes? Because now you’re implying more exposure has the opposite effect. Regardless, it still misses the point. I’ve never been explicitly exposed to base-72, yet I can reason my way through it. I would argue my performance wouldn’t be any different than base-82. So I can transfer basic principles. What the LLM result you referenced shows is that it is not learning basic principles. It sure seems like you just read the abstract and ran with it. As far as the psychology of decision making, again, I think you're speaking with greater confidence than is warranted. In time critical examples, I’m inclined to agree. And there’s certainly some notable psychologists who would expand it beyond snap judgments. But there are also some notable psychologists who tend to disagree. It’s not a settled science, despite your confidence. But again, that’s getting stuck in the limitations of the example and missing the forest for the trees. The point is not in whether decisions are made consciously or subconsciously, but rather how learning can be inferred from previous experience and transferred to novel experiences. Whether this happens consciously or not is besides the point. And you are further going down what I was explicitly taking against: confusing image/pattern recognition for contextual reason. You can see this in the relatively recent Go issue; any human could see what the issue was because they understand the contextual reasoning of the game but the AI could not and was fooled by a novel strategy. The points I’ve been making have completely flew over your head to the point where you’re shoehorning in a completely different conversation.
  - deleted 3y ago
    [deleted]
  - famouswaffles 3y ago
    >Did you originally mistype what you meant when you said greater exposure leads to worse outcomes? Because now you’re implying more exposure has the opposite effect. I guess so. I've never meant to imply greater exposure leads to worse outcomes. >I would argue my performance wouldn’t be any different than base-82. Even if that were true and i don't know that i agree, the authors of that paper make no attempt to test in circumstances that might make this true for LLMs as it might for people. So the paper is not evidence of the claim (no basic principles) either way. For example, i reckon your performance on the proceeding 82 test will be better if taken a immediately after than if taken weeks or months later. So surrounding context is important even if you're right. >What the LLM result you referenced shows is that it is not learning basic principles. I disagree here and i've explained why. >You can see this in the relatively recent Go issue; any human could see what the issue was because they understand the contextual reasoning of the game but the AI could not and was fooled by a novel strategy. You're talking about this ? https://www.zmescience.com/future/a-human-just-defeated-an-ai-in-go-heres-why-that-matters/ https://www.zmescience.com/future/a-human-just-defeated-an-a... KataGo taught itself to play go by explicitly deprioritizing “losing” strategies. This means it didn’t play many amateur strategies because they were lost early in the training. This is hard for a human to understand because humans all generally share a learning curve going from beginning to amateur to expert. So all humans have more experience with “losing” techniques. Basically what I’m saying is, it might be that the training scheme of this AI explicitly prioritized having little understanding of these specific tactics, which is different than not having any understanding. This circles back to the point I made earlier. Having failure modes humans don't or won't understand or have is not the same as a lack of "true understanding". We have no clue what "basic principles" actually are on the low level. The less inductive bias we try to shoehorn into models, the better performing they become. Models literally tend to perform worse the more we try to bake "basic principles" in. So presence of an odd failure mode we *think* belies a lack of "basic principles" is not necessarily evidence of a lack of it. >The points I’ve been making have completely flew over your head to the point where you’re shoehorning in a completely different conversation. You're convinced it's just "very good pattern matching", whatever that means. I disagree.

by bumby 3y ago

famouswaffles 3y ago

1. We anchored this discussion on arithmetic so I stuck to that. If a child never learns (no exposure) how to do base 16 arithmetic then a test quizzing on base 16 arithmetic will result in zero performance. If that child had the basic teaching most children do (little exposure) then a quiz will result in much worse performance than a base 10 equivalent test. This is very simple. I don't know what else to tell you here. 2. You must understand that a human driver that stops because a kite suddenly comes across the road doesn't do so because of any kite>child>must not hurt reasoning. Your brain doesn't even process information that quickly. The human driver stops (or perhaps he/she doesn't) and then rationalizes a reason for the decision after the fact. Humans are very good at doing this sort of thing. Except that this rationalization might not have anything at all to do with what you believe to be "truth". Just because you think or believe it is so doesn't actually mean it is so. Choices shape preferences just as much as the inverse. For all anyone knows and indeed most likely, "child" didn't even enter the equation untill well after the fact. Now if you're asking whether LLMs a matter of principle can infere/grok these sort of casual relationships between different "objects" then yes as far as anyone is able to test.

bumby 3y ago
Your first statement seems to contradict your previous. Did you originally mistype what you meant when you said greater exposure leads to worse outcomes? Because now you’re implying more exposure has the opposite effect. Regardless, it still misses the point. I’ve never been explicitly exposed to base-72, yet I can reason my way through it. I would argue my performance wouldn’t be any different than base-82. So I can transfer basic principles. What the LLM result you referenced shows is that it is not learning basic principles. It sure seems like you just read the abstract and ran with it. As far as the psychology of decision making, again, I think you're speaking with greater confidence than is warranted. In time critical examples, I’m inclined to agree. And there’s certainly some notable psychologists who would expand it beyond snap judgments. But there are also some notable psychologists who tend to disagree. It’s not a settled science, despite your confidence. But again, that’s getting stuck in the limitations of the example and missing the forest for the trees. The point is not in whether decisions are made consciously or subconsciously, but rather how learning can be inferred from previous experience and transferred to novel experiences. Whether this happens consciously or not is besides the point. And you are further going down what I was explicitly taking against: confusing image/pattern recognition for contextual reason. You can see this in the relatively recent Go issue; any human could see what the issue was because they understand the contextual reasoning of the game but the AI could not and was fooled by a novel strategy. The points I’ve been making have completely flew over your head to the point where you’re shoehorning in a completely different conversation.
- deleted 3y ago
  [deleted]
- famouswaffles 3y ago
  >Did you originally mistype what you meant when you said greater exposure leads to worse outcomes? Because now you’re implying more exposure has the opposite effect. I guess so. I've never meant to imply greater exposure leads to worse outcomes. >I would argue my performance wouldn’t be any different than base-82. Even if that were true and i don't know that i agree, the authors of that paper make no attempt to test in circumstances that might make this true for LLMs as it might for people. So the paper is not evidence of the claim (no basic principles) either way. For example, i reckon your performance on the proceeding 82 test will be better if taken a immediately after than if taken weeks or months later. So surrounding context is important even if you're right. >What the LLM result you referenced shows is that it is not learning basic principles. I disagree here and i've explained why. >You can see this in the relatively recent Go issue; any human could see what the issue was because they understand the contextual reasoning of the game but the AI could not and was fooled by a novel strategy. You're talking about this ? https://www.zmescience.com/future/a-human-just-defeated-an-ai-in-go-heres-why-that-matters/ https://www.zmescience.com/future/a-human-just-defeated-an-a... KataGo taught itself to play go by explicitly deprioritizing “losing” strategies. This means it didn’t play many amateur strategies because they were lost early in the training. This is hard for a human to understand because humans all generally share a learning curve going from beginning to amateur to expert. So all humans have more experience with “losing” techniques. Basically what I’m saying is, it might be that the training scheme of this AI explicitly prioritized having little understanding of these specific tactics, which is different than not having any understanding. This circles back to the point I made earlier. Having failure modes humans don't or won't understand or have is not the same as a lack of "true understanding". We have no clue what "basic principles" actually are on the low level. The less inductive bias we try to shoehorn into models, the better performing they become. Models literally tend to perform worse the more we try to bake "basic principles" in. So presence of an odd failure mode we *think* belies a lack of "basic principles" is not necessarily evidence of a lack of it. >The points I’ve been making have completely flew over your head to the point where you’re shoehorning in a completely different conversation. You're convinced it's just "very good pattern matching", whatever that means. I disagree.