[情報] QuakeCon 2026- 約翰. 卡馬克
有票的人 注意囉!
The original founders of id Software — John Carmack, John Romero, Adrian Carmac
k and Tom Hall — will reunite at QuakeCon 2026. Their appearance has been offic
ially announced as part of the convention's 30th-anniversary celebration.
The gathering of the original creators has naturally led to speculation about a
potential major announcement for one of id Software's classic franchises. Howeve
r, they might just be there to celebrate Quake's 30 years with the community.
QuakeCon 2026 is scheduled to take place from August 6–9, 2026, returning to th
e Gaylord Texan Resort & Convention Center in Grapevine, Texas.
Carmack在2026的新想法
AI真的需要dram?
https://www.techspot.com/news/111298-john-carmack-proposes-fiber-optic-loops-hig
h-speed.html
光的傳播需要時間- Carmack把這原本被視為限制的事情, 重新定義成儲存資源
Carmack履歷:
https://i.meee.com.tw/CXUajXO.jpg
徹底影響人類文明的3D圖形技術
https://i.meee.com.tw/HiAWt0b.jpg
火箭的垂直起降- 精確飛行控制與物理演算法
https://i.meee.com.tw/BS0rUmg.jpg
Oculus VR最關鍵的低延遲- 非同步時間扭曲ATW技術
https://i.meee.com.tw/PCxHFAz.jpg
未來AGI時代的顛覆性架構思維: 提出光纖環路儲存架構, 將光速延遲重新定義為超級數據
緩
衝池 (當然, 這是極有創造力的想法, 但Carmack承認這目前還需要改進, 未來有希望落地)
前去的網友, 別忘了閱讀一下Carmack的閱讀清單
27篇論文
1. The Annotated Transformer (nlp.seas.harvard.edu)
2. The First Law of Complexodynamics (scottaaronson.blog)
3. The Unreasonable Effectiveness of RNNs (karpathy.github.io)
4. Understanding LSTM Networks (colah.github.io)
5. Recurrent Neural Network Regularization (arxiv.org)
6. Keeping Neural Networks Simple by Minimizing the Description Length of the We
ights (cs.toronto.edu)
7. Pointer Networks (arxiv.org)
8.ImageNet Classification with Deep CNNs (proceedings.neurips.cc)
9. Order Matters: Sequence to sequence for sets (arxiv.org)
10. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelis
m (arxiv.org)
11. Deep Residual Learning for Image Recognition (arxiv.org)
12. Multi-Scale Context Aggregation by Dilated Convolutions (arxiv.org)
13. Neural Quantum Chemistry (arxiv.org)
14. Attention Is All You Need (arxiv.org)
15. Neural Machine Translation by Jointly Learning to Align and Translate (arxiv
.org)
16. Identity Mappings in Deep Residual Networks (arxiv.org)
17. A Simple NN Module for Relational Reasoning (arxiv.org)
18. Variational Lossy Autoencoder (arxiv.org)
19. Relational RNNs (arxiv.org)
20. Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Au
tomaton (arxiv.org)
21. Neural Turing Machines (arxiv.org)
22. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (arxiv.
org)
23. Scaling Laws for Neural LMs (arxiv.org)
24. A Tutorial Introduction to the Minimum Description Length Principle (arxiv.o
rg)
25. Machine Super Intelligence Dissertation (vetta.org)
26. PAGE 434 onwards: Komogrov Complexity (lirmm.fr)
27. CS231n Convolutional Neural Networks for Visual Recognition (cs231n.github.i
o)
(建議先閱讀: Attention Is All You Need, Deep Residual Learning, The Unreasonable
Effectiveness of RNNs)
以及準備AGI clusters/random access pattern的問題(詳見他的X)
八月在Q&A Session提出你的問題!
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※ 編輯: selfvalue (114.72.45.76 澳大利亞), 06/12/2026 04:28:53