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G
An amond tree is a TREE, by far more useful to anyone than some goofy ai image, …
ytr_UgzyDuDqA…
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This reminds me of people saying Moore's law was dead long before it died. A slo…
rdc_n7hbk3c
G
700 Billion and the only good thing I can name is alphFOLD. ERIC: "AI is major!.…
ytc_UgwHhkjMM…
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We will all exist at the mercy of emperor Bezoz, until the guns come out to lead…
ytc_UgxWZb5m_…
G
AI replacing software developers is basically hitting the same problem that ship…
ytc_UgxL5vie7…
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Artists post their work on internet without copyright protection. That is big mi…
ytc_UgwdlvmyN…
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One comment I read a few years back about alignment has stuck with me for a long…
ytc_UgxJWqd6y…
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I feel that the electronic house genre is still lacking. More importantly, my cr…
ytc_UgzD62Rf-…
Comment
The term "bias" has different, but related meanings in statistics and machine learning. Since a lot of people learn statistics before they learn machine learning, I thought I'd point out how to relate the statistical meaning to the machine learning meaning. However, regardless of what oder you learn the concepts, here they are.
In statistics, bias refers to consistently over estimating or consistently under estimating. A model with high bias will make predictions that are (consistently) way higher or (consistently) way lower than they should be. A model with low bias will only be off by little bit in either direction.
In machine learning, bias refers to how well the model fits the training data. A model with high bias will have a poorly fitting model, and its predictions will be way off - but maybe not way off in a consistent way like when we talk about things in a statistical sense. A model with low bias will fit the data pretty well and the predictions will only be off by a little bit.
NOTE: "over estimating" is different than "over fitting". In fact "over estimating" is more closely related to "under fitting". If we consistently over estimate something, then our model can not be over fitting the data.
youtube
AI Bias
2020-04-13T18:2…
♥ 1
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | unclear |
| Emotion | indifference |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
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{"id":"ytr_Ugy1Xyf8l5ei-znl26Z4AaABAg.9CWvWNurqny9CXpFzHBVsa","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"approval"},
{"id":"ytr_Ugy_FVI8-8up3Pn6Uwt4AaABAg.991xp5E2ooU99UvR31M7h_","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"mixed"},
{"id":"ytr_Ugw8-jz4mGyNdpgQX6h4AaABAg.97OjzA2wrnv97Ot9iNnLWh","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugw8-jz4mGyNdpgQX6h4AaABAg.97OjzA2wrnv97P4kjd3_CG","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgyBGSa7ZL_DpSXgnZB4AaABAg.96phYbPX3GN96q2sUb0j9_","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgzaZQogfBQPFKvKK4p4AaABAg.96ZWOXkyIkQ96ZiPX2NeAb","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgzpejckazGIdWMwAEp4AaABAg.94YA8CfU0hp94YkGJjRErS","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugyf4LmYOAFNEGVFgMV4AaABAg.94XRA_4kctd94_82WBUqEm","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgwMIA9cjPvOjrFTk0F4AaABAg.90-EtnredYt90-zcl2_hvD","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"}
]