Yann LeCun 在 ETH Zürich 演讲：Scaling LLM 无法通向 AGI
LeCun 在 ETH Zürich 又开炮了，用 30 万亿 token 对比儿童视觉数据，解释为什么堆算力到不了 AGI，论据很具体。
Yann LeCun 在 ETH Zürich 的演讲中提出，靠 Scaling LLM 达到 AGI 是不可能的。他给出的数据对比是：LLM 训练消耗约 30 万亿 token（约 10^14 字节文本），而儿童仅凭视觉在约 1 年 10 个月内接收同样量级的数据。他定义智能为快速学习新任务的能力，例如青少年约 20 小时学会开车，而 Scaling 只增加存储知识，不带来这种适应能力。
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.
---- From "Perfology Clips" YouTube channel, (link in comment)
