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G Ai has much further reaching implications than that. Given enough information on… ytc_UgzH2KS-V… G ​@Seth9809 Except like the OP said, we don't need it. We have spell check, which… ytr_UgzPRZokB… G So all the tech bros worked themselves out of a job and they want to scare the w… ytc_UgzeW8Qu_… G Learnt a lot will go for Deep learning from here along with web development with… ytc_UgwFh-yHF… G Well business one way or another if u go majority AI you will be taxed at 75 per… ytc_UgyD2lWZq… G Here's something creepier. I questioned Bing about Sydney and it kept stopping t… ytc_UgwV6ABBZ… G From experience... I've seen plenty of younger people rely heavily on AI in orde… ytc_UgzaAJgwT… G lmao what's the point of learning maths and coding when ai can do it better than… ytc_UgwUj_H48…
Comment
​@sudeshnadutta5702 That's the idea. However, just like there are differences between population parameters and estimated parameters in Statistics, there are two different ways to think about bias and variance in Machine Learning. In a theoretical way, bias is the difference between the model we are using (like linear regression) and the true mechanism or process that creates the data. One way to estimate this bias by calculating the residual sum of squares or some other "loss function" measure. Likewise, for variance, in a theoretical setting, it is a measure of how our model will perform on an all kinds of testing datasets, including, but not limited to, the one that we actually have. Again, one way to estimate this by calculating the residual sum of squares for the testing dataset. Does that make sense?
youtube AI Bias 2020-02-03T11:4…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:26:44.938723
Raw LLM Response
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