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indigo· @indigox · X·· 3 小时前AI 评分59
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indigo 引用并解读 Karpathy 的观点:约75%的人几乎没接触过大语言模型,约20%只把免费层当聊天机器人,约0.2%用前沿模型做数学和代码的专业人士见证数周项目被一个提示词完成,约5,000名接触内部系统的人看到成千上万 Agent 协作数周。

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对 AI 能力的认知正在变成一个陡峭的漏斗!绝大多数人几乎没接触过它,接触过的多数人看到的是一个有点笨的 Chat Bot,少数用前沿模型的专业人士正在经历加速的眩晕,而极少数接触内部系统的人认为:AGI 就在 2027 ✨这几种现实同时存在,彼此几乎无法沟通🤪

Karpathy 说这不是技术鸿沟,而是野心鸿沟!过去,能力是稀缺的,一个人有再大的想法,也受限于时间、人手和技能。现在,执行的门槛在迅速塌陷,真正稀缺的变成了“值得去做的大问题”本身。

那些说 AI 对自己没什么用的人,很多时候并没有说错,他们的生活里确实没有一个需要动用这种能力的问题。这不是他们的问题,而是这个时代悄悄换了一道考题:从你能做什么,变成了你想做什么。

往前看,访问权限会逐渐下沉,免费层和内部系统之间的差距会缩小,参差不齐的能力也会从代码和数学扩散到更多领域。但野心这一层不会自动弥合。将来决定一个人、一家公司乃至一个国家能从 AI 里拿到多少的,可能不是谁先拿到最强的模型,而是谁手里攒着最多值得解决的难题。漏斗的顶端,最终属于那些敢于提出大问题的人🤔

引用Andrej Karpathy@karpathy
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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来源:indigo · x.com