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G Keep in mind the military is always 20+ years ahead in technology. However woul… ytc_UgyKWTk4N… G @ that makes a lot of sense actually, so I think ur point is that AI art would … ytr_UgyVE5UgR… G The thing is that a huge amount of jobs could have been already replaced 15 year… ytc_UgzfeO1A4… G Not to mention if an A.I. gets commercialized and a singular company cuts as man… ytc_UgwscNfB6… G Whose gonna have all the currency we use in the future? They'd lend it to peopl… ytc_UgxjjOP1w… G ChatGPT doesnt purely comes up with anything, its all piece by piece assambled f… ytc_UgzB-Jhps… G Great question! Sophia's design is meant to convey a sense of wisdom and experie… ytr_Ugx-XlGOD… G an real artist would take their time to make it breathtaking AI art fake shit … ytc_UgycAe9sd…
Comment
BIAS-VARIANCE TRADE OFF: Bias & Variance Bias: The inability of a machine learning model to capture a true relationship Bias means how well an ML Model fits to the training data Variance: Difference in fits between data sets (training and testing) Variance means how well an ML Model is able to predict the testing or unseen data Low bias, high variance/variability: Overfitting (great fit on training data but poor performance on testing data) High bias, low variance/variability: Underfitting (model cannot capture the pattern in data, poor performance) We need to find a sweet spot between simple and complex model to consistently make good predictions Common methods to find sweet spots: Bagging, Boosting & Regularization Ideal Model: Low bias: can accurately model the true relationship Low variability: producing consistent prediction across different datasets
youtube AI Bias 2025-10-22T09:5…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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