swyx 认同一种关于 Transformer 当前擅长何种学习、为何会遇瓶颈的思维框架,并称年初与 Ankit 合写对抗世界模型时,描述的正是这些思维层级逐步逼近现实的 Kolmogorov 极限生成器。他认为在明显低效的范式上堆参数、算力只会被能假设并寻求真理的简单方案超越,但 bitter lesson 是扩展更简单,人类智能本就不够聪明也不够多,或许仍会撞上 AGI。
co-sign. a very handy mental framework for what kinds of learning transformers do well today, and why it runs into limitations. when @ankit2119 and i wrote about the need for adversarial world models earlier this year, we were describing a couple of the functions of these rungs of thinking that bring us ever closer to the kolmogorov-limit generator of reality. throwing more params, more power, more everything at a demonstrably inefficient paradigm will be outclassed by the simple solution that can hypothesize and seek truth rather than backfit a house of cards - although the bitter lesson is it is simpler to scale and we may hit agi anyway because human intelligence just isn’t that smart nor plentiful
Very well written blog. I think of RL as learning from interventions, and it kinda explains why it's more powerful as a paradigm than supervised learning. Now learning from counterfactuals is something we haven't been historically good at but maybe world modelling+ RL can get us there.在 X 查看被引用的帖子
来源:@swyx · x.com