Kids outlearn AI—and we still don’t know why
Kids outlearn AI—and we still don’t know why
儿童的学习能力远超人工智能——而我们至今仍未参透原因
People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. Now there are two. 据我们所知，人类彼此交流的历史至少已有 10 万年。在漫长的岁月中，世界上只有一种事物能够将人类语言掌握到完美的流利程度，那就是人类的孩子。而现在，出现了第二种。
Four short years after the release of ChatGPT, many of us now take it for granted that we can converse naturally with our phones or computers. LLMs like Claude, DeepSeek, and OpenAI’s GPT models are fluent and flexible enough to masquerade convincingly as humans. But peek behind the computational curtain, and there’s a catch: Teaching a computer to use human language still requires an inhuman amount of data. 在 ChatGPT 发布短短四年后，我们许多人已经习以为常，认为可以与手机或电脑进行自然对话。Claude、DeepSeek 和 OpenAI 的 GPT 模型等大语言模型（LLM）已经足够流利和灵活，足以令人信服地伪装成人类。但掀开计算的幕布，你会发现一个问题：教计算机使用人类语言仍然需要海量的数据，其规模远超人类范畴。
An LLM can easily churn through a hundred thousand times more words than a person will experience in the process of mastering their mother tongue—and way more than children might hear by their first birthday, when they typically start to grab hold of language. “The progress recently has been amazing,” Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. “But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year.” 一个大语言模型处理的词汇量，可以轻松达到一个人掌握母语所需词汇量的十万倍——这远超孩子们在一岁生日前听到的词汇量，而那一阶段通常正是他们开始掌握语言的时候。“近期的进展令人惊叹，”斯坦福大学认知科学家迈克尔·C·弗兰克（Michael C. Frank）在谈到大语言模型时说，“但我们仍然需要‘烧毁一片森林’，并搜刮人类所有的知识总和，才能重现这个在我们的客厅里一年内就能达成的里程碑。”
This yawning divide between children and machines is called the data efficiency gap. And it raises a tantalizing question for cognitive scientists and a challenge for the architects of AI models: How is it that kids can still outperform the most linguistically sophisticated machines ever built? 这种儿童与机器之间巨大的鸿沟被称为“数据效率差距”。它为认知科学家提出了一个诱人的问题，也为人工智能模型的架构师带来了挑战：为什么孩子们仍然能胜过有史以来最复杂的语言机器？
Finding answers has stakes for both AI research and cognitive science. For the past decade, language models have mostly gotten better by getting bigger. Meta’s open-weight LLM Llama 3.1, released two years ago, chewed through 15 trillion tokens (word-like chunks of language) in pretraining—the main step of training a model that happens before it is fine-tuned for a specific task, like being a chatbot. Frontier models could be pretraining on 10 times more data, says Ethan Gotlieb Wilcox, a cognitive scientist and linguist at Georgetown University. But there’s only so much internet to train on, and eventually—perhaps as early as the 2030s—the well of easily available data could run dry. 寻找答案对人工智能研究和认知科学都至关重要。在过去十年中，语言模型主要通过“变大”来提升性能。Meta 两年前发布的开源权重模型 Llama 3.1 在预训练阶段处理了 15 万亿个 Token（类似单词的语言片段）——这是在模型针对特定任务（如聊天机器人）进行微调之前的主要训练步骤。乔治城大学的认知科学家兼语言学家伊桑·戈特利布·威尔科克斯（Ethan Gotlieb Wilcox）表示，前沿模型可能正在使用多出 10 倍的数据进行预训练。但互联网上的训练数据终究有限，最终——可能最早在 2030 年代——容易获取的数据源可能会枯竭。
Kids show that it could be possible to learn more with less. Far less. A preteen raised in a linguistically rich home may have heard something in the vicinity of 100 million words. Add literacy to the mix and you can boost that word count to maybe 300 million words by age 20. The difference in scale is something that can only really be gestured at in analogy. “Claude has seen the amount of language that an entire city will experience in one generation,” says Wilcox. 孩子们证明了用更少的数据学习更多知识是可能的。少得多。一个在语言环境丰富的家庭中长大的青春期前儿童，听到的词汇量大约在 1 亿左右。如果加上阅读能力，到 20 岁时，这个词汇量可能会增加到 3 亿左右。这种规模上的差异只能通过类比来粗略说明。威尔科克斯说：“Claude 所见过的语言量，相当于整座城市一代人所经历的语言总量。”
If you were to print out on paper all the words used to train a modern LLM, you could make a stack that would reach past the International Space Station. The human preteen’s 100 million words, meanwhile, would stack up just 20 meters. And we can make do with far less than that. 如果你把训练现代大语言模型所用的所有词汇打印在纸上，堆叠起来的高度将超过国际空间站。而人类儿童那 1 亿个词汇，堆起来只有 20 米高。而且，我们甚至不需要那么多就能学会语言。
By reverse-engineering the way kids learn, scientists hope to be able to create more data-efficient AI models, which could be useful for everything from training AI effectively on video to creating chatbots that serve minority language communities. Testing hypotheses about human learning in machine models could also settle enduring questions about language and children’s developing minds. Are we born with a language instinct, or would it be possible, even in principle, for a child to learn language purely from experience? Is the way we process language a quirk of our biology, or might at least some of it reflect universal constraints on how languages can be used and learned? 通过对儿童学习方式进行逆向工程，科学家们希望能够创造出数据效率更高的人工智能模型，这在从有效训练视频 AI 到为少数语言社区创建聊天机器人的各个领域都大有可为。在机器模型中测试关于人类学习的假设，也可能解决关于语言和儿童心智发展的长期疑问。我们是天生具有语言本能，还是说原则上一个孩子完全可以通过经验习得语言？我们处理语言的方式是生物学上的巧合，还是在一定程度上反映了语言使用和学习的普遍约束？
The essential elements
基本要素
Most of us realize language is hard only when we try to learn a new one after childhood. The past perfect tense, rolled rs and nasal vowels, the genitive case, phrasal verbs, grammatically masculine tables and feminine spoons—many are the instruments of linguistic torment for the adult language learner. It’s typically effortless to learn our mother tongues, however. Toddlers usually start producing grammatically correct sentences after hearing something like 10 million words, or 30 million on the high end. “It’s just totally miraculous,” says Frank. “If you train GPT-2 on 30 million words, you get a nonsense generator; you don’t get a kid.” 我们大多数人只有在成年后尝试学习新语言时，才会意识到语言有多难。过去完成时、颤音和鼻元音、属格、短语动词、语法上的阳性桌子和阴性勺子——对于成年语言学习者来说，这些都是折磨。然而，学习母语通常是毫不费力的。幼儿通常在听到约 1000 万个词汇（上限约为 3000 万）后，就开始能说出语法正确的句子了。“这简直是个奇迹，”弗兰克说，“如果你用 3000 万个词去训练 GPT-2，你只会得到一个胡言乱语的生成器，而不是一个孩子。”
Exactly how babies pull this off is a mystery. Researchers know a lot about what kids learn and how they use language at different stages in development, but there’s still a lot we don’t know. Perhaps the most enduring question is why babies can learn language at all. The syntax of human language—the rules for combining words into sentences—includes recursive, nested structures that allow us to express virtually infinite ideas with a finite lexicon of words and pieces of words. This seems like something that should be a problem for babies. They only splash about in the shallows of a fathomless ocean of language. And yet, somehow, that’s enough. From a drop, they infer the depths. 婴儿究竟是如何做到这一点的，至今仍是一个谜。研究人员对儿童在不同发育阶段学习什么以及如何使用语言了解颇多，但仍有许多未知之处。也许最持久的问题是：为什么婴儿能够学习语言？人类语言的句法——将词汇组合成句子的规则——包含递归和嵌套结构，使我们能够用有限的词汇和词素表达几乎无限的思想。这似乎应该是婴儿难以逾越的障碍。他们只是在语言深不可测的海洋浅滩中嬉戏。然而，不知何故，这就足够了。他们从一滴水中，推断出了深海的奥秘。
One solution, put forward in the 1950s by the MIT linguist Noam Chomsky, is that babies are born with hardwired knowledge of grammar. Chomsky was reacting to a rival view, championed by the psychologist B.F. Skinner, that language acquisition is entirely environmental. Skinner thought language was learned through conditioning and reinforcement, the way a dogifies out how to sit or shake for treats. Chomsky countered by citing the “poverty of the stimulus”—the idea that language, especially syntax, is too complex and children’s exposure to it too “impoverished” for them to learn entirely from experience. “His signature argument was, essentially, that language cannot be learned on the basis purely of statistics,” says Richard Futrell, a linguist and cognitive scientist at the University of California, Irvine. Instead, Chomsky posited that language is based on a set of logical rules and argued that children needed innate knowledge of those rules to deduce the grammar of their language. 麻省理工学院语言学家诺姆·乔姆斯基（Noam Chomsky）在 20 世纪 50 年代提出的一个解决方案是：婴儿天生就具备硬编码的语法知识。乔姆斯基当时是为了反驳心理学家 B.F. 斯金纳（B.F. Skinner）的观点，后者认为语言习得完全源于环境。斯金纳认为语言是通过条件反射和强化学习习得的，就像狗学会坐下或握手以换取零食一样。乔姆斯基则通过引用“刺激贫乏论”（poverty of the stimulus）进行反驳——即语言（尤其是句法）过于复杂，而儿童接触到的语言输入过于“贫乏”，不足以让他们完全从经验中学习。加州大学欧文分校的语言学家兼认知科学家理查德·富特雷尔（Richard Futrell）说：“他的核心论点本质上是：语言不能仅仅基于统计学来学习。”相反，乔姆斯基认为语言基于一套逻辑规则，并主张儿童需要对这些规则有先天的认知，才能推导出他们所学语言的语法。