开头：三言Pro
三言Pro音信 本年1月底，DeepSeek发布的R1模子对总共科技圈形成了普遍震撼，英伟达更是应声下降16.79%，市值挥发5900亿好意思元，创下好意思国金融史记载。
英伟达发言东谈主那时暗示：“DeepSeek是一项出色的东谈主工智能跳跃，亦然测试时辰缩放的齐全例子。”
尽管英伟达还是回血，不外其CEO黄仁勋一直未公开回报此事。
周四，黄仁勋在一场访谈中初次回报了DeepSeek，他暗示投资者对DeepSeek 在东谈主工智能领域获取的推崇存在扭曲，这导致了阛阓对英伟达股票的造作响应。
DeepSeek以低本钱高性能激发关注后，投资者运转质疑科技公司参增加量本钱建筑AI基础设的必要性。
黄仁勋暗示，阛阓的剧烈响应源于投资者的误读。尽管 R1 的开辟似乎减少了对算力的依赖，但东谈主工智能行业仍需强劲的算力来复古模子后检察处理门径，这些门径能让AI模子在后检察进行推理或商量。
“从投资者的角度来看，他们觉得寰宇分为预检察和推理两个阶段，而推理即是向 AI 发问独立即得到谜底。我不知谈这种扭曲是谁形成的，但赫然这种不雅念是造作的。”
黄仁勋指出，预检察仍然迫切，但后处理才是“智能最迫切的部分”，亦然“学习措置问题的要道门径”。
此外，黄仁勋还觉得R1开源后，环球范围内展现出的存眷令东谈主难以置信，“这是一件极其令东谈主抖擞的事情”。
黄仁勋访谈主要门径实录：
黄仁勋：
What‘s really exciting and you probably saw，what happened with DeepSeek．
The world‘s first reasoning model that’s open sourced，and it is so incredibly exciting the energy around the world as a result of R1 becoming open sourced，incredible．
实在令东谈主抖擞的是，你可能还是看到了，DeepSeek发生了什么。寰宇上第一个开源的推理模子，这太不成念念议了，因为R1变成了开源的，环球齐因此而充满了能量，真实不成念念议。
打听者：
Why do people think this could be a bad thing？I think it‘s a wonderful thing．
为什么东谈主们觉得这可能是一件赖事呢？我觉得这是一件好意思好的事情。
黄仁勋：
Well，first of all，I think from an investor from an investor perspective，there was a mental model that，the world was pretraining， and then inference.And inference was，you ask an AI question and it instantly gives you an answer，one shot answer．
I don‘t know whose fault it is，but obviously that paradigm is wrong.The paradigm is pre training，because we want to have foundation you need to have a basic level of foundational understanding of information.In order to do the second part which is post training.So pretraining is continue to be rigorous．
The second part of it and this is the most important part actually of intelligence is we call post training，but this is where you learn to solve problems.You have foundational information.You understand how vocabulary works and syntax work and grammar works，and you understand how basic mathematics work，and so you take this foundational knowledge you now have to apply it to solve problems．
率先，我觉得从投资者的角度来看，昔日存在一种念念维模子是，寰宇是事前检察好的，然后是推理。推理即是你问AI一个问题，它立即给你一个谜底，一次性回答。我不知谈这是谁的错，但赫然这种形式是造作的。
正确的形式应该是先进行预检察，因为咱们想要有一个基础，你需要对信息有一个基本的证明水平，以便进行第二个部分，也即是后期检察。是以预检察要不时保持严谨。第二部分实质上是智能最迫切的部分，咱们称之为后检察，但这是你学习措置问题的场地，你还是掌抓了基础常识，你理会词汇是若何职责的，句法是若何职责的，语法是若何职责的，你理会了基本数学是若何职责的，是以你现时必须旁边这些基础常识来措置实质问题……
So there‘s a whole bunch of different learning paradigms that are associated with post training，and in this paradigm，the technology has evolved tremendously in the last 5 years and computing needs is intensive.And so people thought that oh my gosh，pretraining is a lot less，they forgot that post training is really quite intense．
因而后检察有一系列许多不同的学习形式，在这种形式下，工夫在昔日五年里获取了普遍的跳跃，缱绻需求相等大，是以东谈主们觉得，哦天那，预检察要少得多。关联词他们健忘了后检察其实绝顶大。
And then now the 3rd scaling law is ，the more reasoning that you do，the more thinking that you do before you answer a question.And so reasoning is a fairly compute intensive part of.And so I think the market responded to R1 as ‘oh my gosh AI is finished’，you know it dropped out of the sky ，we don‘t need to do any computing anymore.It’s exactly the opposite．
现时第三条缩放定律是，你作念的推理越多，你在回答问题之前念念考得越多，推理就会越好，这是一个缱绻量绝顶大的进程。因此我觉得阛阓对R1的响应是“哦我的天哪，AI到头了“，就能够它从天而下，咱们不再需要进行任何缱绻了，但实质上统统相背。
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