语言学习者：从大模型到婴儿尺度

researchers in the United States largely adopted a rule-based framework influenced by Chomsky’s theories. They tried to teach language to computers by explicitly coding the rules into programs—think less immersion experience, more grammar class. This approach, part of a broader trend called symbolic AI, prevailed for decades. It also largely failed to produce models actually capable of handling human language at scale. Interest in natural-language processing chilled in the “AI winter” that began in the 1970s. In the aftermath, neural networks started to make a comeback. But it wasn’t until the 2010s, when computer hardware was getting cheap and capable and the internet was getting big, that their performance began turning heads. …