Karpathy 推演：1970 年代的人要猜中 LLM 需要哪几步
Karpathy 拆解了从 70 年代预测到 LLM 需要连跳六级，从 Shannon 到 ELIZA 到 Scaling Laws，思路很清晰。
Karpathy 在 X 上讨论 2000 年前的科幻为何很少猜中今天的 AI。他列出从 1970 年代推演到 LLM 所需的 6 个环节：数字化知识工作、互联网、大数据、统计学习（引 Shannon 1948 年的 n-gram 和 Rosenblatt 1958 年的 Perceptron）、LLM（类比 1966 年的 ELIZA）、以及 Scaling Laws。他认为最难跳过的一步是放弃手工编程机器人的思路。
@shostekofsky There are a few notable counter-examples (e.g. HAL) but this is basically right for a lot of pre-2000 sci-fi. I think getting AI right from e.g. somewhere in the ~1970s would require a fairly complicated set of inferences, probably the biggest things would be: Image the world in 50 years. 1. "Digital knowledge work / computing". The vast majority of knowledge work (which makes up a very large fraction of the economy) goes digital. As computers get cheaper, much of society’s work happens on screens, over billions of machines in homes and offices. 2. "Internet". Similar to ARPANET (and see Licklider’s “Intergalactic Computer Network” (1963)), this creates demand for a global network of computers where people work, argue, teach, joke, and socialize. 3. "Big data". This creates a very large, compounding repository of digital information, with traces of how people reason, how they create, what they know etc. 4. "Statistical learning". Trying to program intelligence by hand is extremely difficult. But Shannon's n-gram approximations of English (1948) show examples of how language has statistical structure that can be "learned". You could imagine doing so over extensions of cybernetic computing (e.g. in the style of Rosenblatt's Perceptron 1958). So instead of programming thought, we could fit a statistical model to the record. 5. "LLMs". This might lead to something along the lines of ELIZA (1966), but with enough data (per 1-3), and enough compute (see Moore's law 1965), the imitation might become of extreme hight fidelity to the point that it's economically extremely useful. 6. "Scaling laws". These networks may demand a huge amount of data / computing resources and expertise to train. It's plausible that the more of each you have, the better your ELIZA gets. The economics concentrate, where a few organizations build a few minds and then deploy copies of them digitally. Something like that. Basically, it's a lot of leaps. You have to start with computers, to internet, to big data, to big compute (Moore's law), which then allow large-scale statistical learning approach based on neural networks, and then posit scaling laws, and then imagine the form factor of language models as a lot more sophisticated ELIZA. And you'd have to short everything else, most predominantly I think the idea that we'd figure out how to program robots by hand. 💬 5 🔄 12 ❤️ 76 👀 4018 📊 17 ⚡
