Chetan Gupta
① 他说过的话
8 条
从某种意义上说，今天的 AI 是参差不齐的——意思是，有些任务 AI 真正擅长，有些并不。
指向原始笔记的链接
AI, in some sense today, is jagged. So so so meaning that there are some tasks for which AI is really good at.
—— Chetan Gupta · [19:30]
不要与任何模型家族联姻，因为它们会因为商业原因、地缘政治原因被换进换出。
指向原始笔记的链接
Don’t marry into any model family because they will swap in and out both for commercial reasons, geopolitical reasons, right?
—— Chetan Gupta · [25:42]
我会说：停止思考模型，开始思考架构——面向 AI 的企业架构。
指向原始笔记的链接
I would say stop thinking models and start thinking architectures, right, enterprise architectures for AI.
—— Chetan Gupta · [27:06]
从主权的角度来说，我不能被迫使用别人的宪法，而要用我自己的宪法。
指向原始笔记的链接
It is not someone else’s constitution that I have to use, but my own constitution as from a sovereign standpoint.
—— Chetan Gupta · [30:49]
主权的概念会一路延伸下来。它的核心是：我是这个实体，我有区别于他人利益的自身利益，而我需要保护它们。
指向原始笔记的链接
So this notion of sovereignty will come all the way down. And the idea is I am this entity, I have my own interests that are distinct from someone else’s interests, and I need to protect them.
—— Chetan Gupta · [31:27]
你拿同一个模型，构建两个不同的线束，你会得到两个不同的成果。
指向原始笔记的链接
So you take the same model and you build two different harnesses, you will get two different outcomes.
—— Chetan Gupta · [34:55]
那些基准测试不代表你的工作负载。正如我们之前说的，AI 的边界是参差不齐的——某个模型可能在基准测试上做得很好，但这并不必然意味着它对你的工作负载也会做得很好。
指向原始笔记的链接
They don’t represent your workloads. And as we said earlier, the boundary of AI is jagged, right? So although a certain model might do very well on a benchmark, it doesn’t necessarily translate into that it will do very well for your workloads as well.
—— Chetan Gupta · [38:19]
在这件事上，现在谁都有机会赢。因为在某些底层数学上，我们比其他所有人都强；在某些底层机械技术上，也许其他国家比我们强。
指向原始笔记的链接
And I think in that, there is I mean, it’s anyone’s game right now, I would say. Because some of the underlying math is where we are better than everyone else. Some of the underlying mechanics, maybe other countries are better than us.
—— Chetan Gupta · [14:02]
② 出现在这些集
1 集
③ 他谈到的
点进去有真内容 —— 本页主要出口
Daniel Whitenack · Chris Benson · Rackspace · Hitachi · Anthropic · Hugging Face · 工业 AI · 物理 AI · LLM · 生成式 AI