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G Facial recognition software isn't enough evidence to lock somebody up, they shou… ytc_UgyKw0VUB… G I always talk to ChatGPT like it's my pal. We shoot the shit and it's hilarious.… ytc_Ugy6U6n_1… G As someone who’s art style doesn’t go well with ai and poisons it by itself I us… ytc_UgyMrVAfx… G Just wait til people start to see Jesus in ChatGPT. Make it a religious thing… rdc_mlhlj2q G I aint scared about it. The other day i used chatGPT for a neuroanatomy class, a… ytc_UgyCipkt3… G Instrumental convergence. An artificially intelligent agent with unbounded goals… ytc_UgxoE_GzE… G there more then one form of artistic expression eyt then you see a new you fear … ytr_UgwOIAJRp… G all AI ceos are psychopathic criminals. and they are the ones setting the 'ethi… ytc_Ugw29efcp…
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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