Yann LeCun 在 ETH Zürich 最新演讲中称，靠扩展 LLM 实现 AGI 是"不可能的"。他指出 LLM 训练约 30 万亿 token（约 10^14 字节文本，人类需读约 40 万年），而 4 岁儿童仅靠视觉在约 1 年 10 个月内就接收同等数据量。他认为智能是快速学习新任务的能力，如青少年约 20 小时学会开车，扩展只增加存储知识，并不产生这种适应能力。
Yann LeCun's (@ylecun ) latest talk at ETH Zürich
Scaling LLMs to reach AGI is "impossible"
A large language model is trained on about 30 trillion tokens, which is roughly 10^14 bytes of text and would take a person about 400,000 years to read.
A 4-year-old child receives about the same amount of data, 10^14 bytes, through vision alone in about 1 year and 10 months.
In his view, intelligence is the ability to learn new tasks quickly or perform them without prior training, as a teenager learns to drive in about 20 hours. Scaling increases stored knowledge, but it does not produce this ability to adapt.
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From "Perfology Clips" YouTube channel, (link in comment)
来源：Rohan Paul · x.com