OpenAI（曾经）的首席科学家Ilya Sutskever给了John Carmack一份论文清单

江湖盛传……
OpenAI（曾经）的首席科学家Ilya Sutskever给了John Carmack一份论文清单，并说：「如果你真的把这些学会了，你就能理解当今人工智慧的90%重要内容。]
清单上的27篇研究论文（或课程）如下：
1. The Annotated Transformer
👉https://nlp.seas.harvard.edu/annotated-transformer/
2. The First Law of Complexodynamics
👉https://scottaaronson.blog/?p=762
3. The Unreasonable Effectiveness of RNNs
👉https://karpathy.github.io/2015/05/21/rnn-effectiveness/
4. Understanding LSTM Networks
👉https://colah.github.io/posts/2015-08-Understanding-LSTMs/
5. Recurrent Neural Network Regularization
👉https://arxiv.org/pdf/1409.2329
6. Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
👉https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
7. Pointer Networks
👉https://arxiv.org/pdf/1506.03134
8. ImageNet Classification with Deep CNNs
👉https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d…
9. Order Matters: Sequence to Sequence for Sets
👉https://arxiv.org/pdf/1511.06391
10. GPipe: Efficient Training of Giant Neural Networks
👉https://arxiv.org/pdf/1811.06965
11. Deep Residual Learning for Image Recognition
👉https://arxiv.org/pdf/1512.03385
12. Multi-Scale Context Aggregation by Dilated Convolutions
👉https://arxiv.org/pdf/1511.07122
13. Neural Quantum Chemistry
👉https://arxiv.org/pdf/1704.01212
14. Attention Is All You Need
👉https://arxiv.org/pdf/1706.03762
15. Neural Machine Translation by Jointly Learning to Align and Translate
👉https://arxiv.org/pdf/1409.0473
16. Identity Mappings in Deep Residual Networks
👉https://arxiv.org/pdf/1603.05027
17. A Simple NN Module for Relational Reasoning
👉https://arxiv.org/pdf/1706.01427
18. Variational Lossy Autoencoder
👉https://arxiv.org/pdf/1611.02731
19. Relational RNNs
👉https://arxiv.org/pdf/1806.01822
20. Quantifying the Rise and Fall of Complexity in Closed Systems
👉https://arxiv.org/pdf/1405.6903
21. Neural Turing Machines
👉https://arxiv.org/pdf/1410.5401
22. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
👉https://arxiv.org/pdf/1512.02595
23. Scaling Laws for Neural LMs
👉https://arxiv.org/pdf/2001.08361
24. A Tutorial Introduction to the Minimum Description Length Principle
👉https://arxiv.org/pdf/math/0406077
25. Machine Super Intelligence Dissertation
👉https://pdfs.semanticscholar.org/e758/b579456545f8691bbadaf26bcd3b536c7…
26. PAGE 434 onwards: Komogrov Complexity
👉https://www.lirmm.fr/~ashen/kolmbook-eng-scan.pdf
27. CS231n Convolutional Neural Networks for Visual Recognition
👉https://cs231n.github.io/
无论是真是假，疑似Ilya的秘密文件夹在此

👉https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE
就算被骗，你真搞懂了这些东西，应该也算半个神人吧？