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Y Combinator Paper Club：光学计算、神经形态与生物神经元等替代算力方向
Y Combinator 本期 Paper Club 聚焦替代算力。内容指出，现代 AI 由 transformer、反向传播和 GPU 的紧密耦合塑造，但随着算力效率开始趋于平台期，可能出现完全不同的机器构建方式。视频涵盖：为何探索替代算力、超越反向传播、零阶优化与 SOMA、用光进行计算、光学计算尚未普及的原因、用光构建扩散模型、神经形态计算的定义与现状、大脑对芯片设计的启发，以及用强化学习训练生物神经元玩 Doom、脑细胞如何从反馈中学习等主题，并附完整时间轴。
AI推荐理由 Y Combinator Paper Club 关于替代算力的视频，内容结构清晰，涵盖光学计算（用光执行计算、光学扩散模型）、神经形态架构（大脑对芯片的启发、当前进展）以及用强化学习训练生物神经元玩 Doom 等方向，并附完整时间轴。对算力硬件前沿探索有参考价值，但属于科普性讨论，未提供新的实验结果或可验证数据。
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This week’s Paper Club is all about alternative compute.
Modern AI has been shaped by a tight coupling between transformers, backpropagation, and GPUs. But as compute efficiency starts to plateau, there may be very different ways to build the machines that come next.
We explore what AI compute could look like beyond conventional digital hardware through optical systems that use light to perform computation, neuromorphic architectures inspired by the brain, and experiments teaching living brain cells to play Doom.
00:07 — [@FrancoisChauba1](https://x.com/FrancoisChauba1): Why Alternative Compute?
03:38 — Beyond Backpropagation
08:08 — Zero-Order Optimization and SOMA
15:06 — Ilker Oguz: Computing With Light
19:27 — Why Optical Computing Isn’t Everywhere Yet
22:23 — Building a Diffusion Model With Light
30:03 — Optical Computing Q&A
39:00 — [@AlokVasudev](https://x.com/AlokVasudev): What Is Neuromorphic Computing?
45:23 — What the Brain Can Teach Us About Chips
49:35 — Where Neuromorphic Computing Stands Today
54:24 — Neuromorphic Computing Q&A
1:04:24 — [@vytalow](https://x.com/vytalow): Teaching Brain Cells to Play Doom
1:07:48 — Training Biological Neurons With Reinforcement Learning
1:12:28 — How Brain Cells Learn From Feedback
1:15:40 — Biological Computing Q&A
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[🔗 View on Twitter](https://x.com/ycombinator/status/2106026006566055969/video/1)