Machine Learning Expertise Escalation

LINEAR REGRESSION, FITO GRADIENT DESCENT OVERFITTING VS UNDERFITTING HUGGINGFACE CAN YOU GIVE ME A DALL-E 2 INVITE? TUNING LEARNING RATES DATA LABELING HYPERPARAMETER SWEEPS BIAS/VARIANCE TRADEOFF PYTORCH EXPERIMENT TRACKING WHAT'S YOUR AGI TIMELINE? XGBOOST IS BETTER, ACTUALLY DISTRIBUTION SHIFT BAYESIAN HYPERPARAMETER OPTIMIZATION DATA PARALLELISM CUSTOM DATALOADERS FEATURE ENGINEERING MLOPS JAX REINFORCEMENT LEARNING AI SAFETY LAYERNORM MODEL PARALLELISM SCALING HYPOTHESIS SELU, MELU, ELU, GELU, PELU, PRELU, RELU6, RRELU, SRELU, SERLU CUSTOM CUDA KERNELS 2ND ORDER METHODS MPI VARIATIONAL INFERENCE SOFT VS HARD TAKEOFF DEEP BELIEF NETWORKS CAPSULE NETS BOLTZMANN MACHINES RADEMACHER COMPLEXITY PAC-BAYES BOUNDS JUST ADD MORE LAYERS TRANSFORMERS R ALL U NEED LANGUAGE MODELS ARE SENTIENT BITTER LESSON

A vertical progression shows increasingly disheveled figures alongside increasingly advanced machine-learning concepts, from basic linear regression to deep learning theory and AI alignment. The joke is that beginners casually discuss simple models and trendy tools, while supposed experts descend into dense jargon, obsessive technical details, and existential claims about sentient language models. It is sent as a reaction to the escalating complexity and absurdity of AI expertise culture.

• @profunctor_io / 8189 2022-08-23
• @dev_meme / 4826 2022-08-26