Bibliography (14):

1. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

2. Scaling Language Models: Methods, Analysis & Insights from Training Gopher

3. GPT-3: Language Models are Few-Shot Learners

4. Program Synthesis with Large Language Models

5. MMLU: Measuring Massive Multitask Language Understanding

6. Who Models the Models That Model Models? An Exploration of GPT-3’s In-Context Model Fitting Ability

7. The MovieLens Datasets: History and Context

8. Random_ai_poems.txt

9. https://tedunderwood.com/2021/02/02/why-sf-hasnt-prepared-us-to-imagine-machine-learning/

10. Wikipedia Bibliography:

    1. Google DeepMind

    2. Recommender systems

    3. Root-mean-square deviation

    4. https://en.wikipedia.org/wiki/MovieLens  :

       https://en.wikipedia.org/wiki/MovieLens

    5. https://en.wikipedia.org/wiki/Non-negative_matrix_factorization  :

       https://en.wikipedia.org/wiki/Non-negative_matrix_factorization