AI Isn’t Smarter Than a Baby—Yet
AI Isn’t Smarter Than a Baby—Yet
人工智能还没婴儿聪明——至少目前如此
If you think an artificial intelligence model running on thousands of cutting-edge computer chips is smart, allow me to introduce you to the concept of a 1-year-old. 如果你认为运行在数千枚尖端计算机芯片上的AI模型很聪明，请允许我向你介绍一下“1岁婴儿”的概念。
OK, so babies might not be able to write computer programs, solve advanced math problems, or debate philosophical ideas. But unlike today’s AI models, which consume an ocean’s worth of training data and as much energy as a small country, babies learn to make sense of the world with amazing efficiency. They identify new objects after seeing them once or twice, and they learn through fleeting observation and physical interaction. 好吧，婴儿可能不会编写计算机程序、解决高等数学问题或辩论哲学思想。但与当今消耗海量训练数据和相当于一个小国能源的AI模型不同，婴儿以惊人的效率学习理解世界。他们只需看一两次就能识别新物体，并通过短暂的观察和身体互动进行学习。
When it comes to improving AI, babies—and the architecture of their brains—might hold crucial insights. Building a more baby-like version of AI could make frontier models less costly and less energy intensive, and it might also be valuable if AI-powered robots are to learn about their environments in a more natural way. 在改进AI方面，婴儿及其大脑结构可能蕴含着关键的见解。构建一个更像婴儿的AI版本，可以降低前沿模型的成本和能耗；如果AI驱动的机器人要以更自然的方式了解其环境，这也可能具有重要价值。
To explore this bold new frontier, researchers at Meta, Stanford University, the University of Tokyo, and France’s École Normale Supérieure developed a new test that highlights the learning skills of babies and pushes AI researchers to design algorithms that match them. 为了探索这一大胆的新前沿，Meta、斯坦福大学、东京大学和法国巴黎高等师范学院的研究人员开发了一项新测试，旨在突出婴儿的学习技能，并推动AI研究人员设计出与之匹配的算法。
The EgoBabyVLM Challenge judges how well vision language models, or VLMs, which learn from both text and imagery, can make sense of the world as a baby sees it. It requires a model to describe the world after ingesting about a thousand hours of video collected from cameras strapped to the heads of infants and toddlers. (Yes, really.) “EgoBabyVLM挑战赛”旨在评估视觉语言模型（VLM，即从文本和图像中学习的模型）在婴儿视角下理解世界的能力。它要求模型在摄入约一千小时的视频后描述世界，这些视频是由绑在婴儿和幼儿头上的摄像头采集的。（是的，没开玩笑。）
It turns out that the cutting-edge models fail miserably when fed this realistic and messy footage, which suggests there may be something different about the design of the baby brain that enables it to learn so rapidly from so little information. 事实证明，当这些尖端模型被输入这种真实且杂乱的影像时，表现得一塌糊涂。这表明婴儿大脑的设计可能存在某种特殊之处，使其能够从极少的信息中如此迅速地学习。
Instead of curated datasets, babies learn from a kaleidoscopic view of things: parents talking about objects that are no longer visible, indicating things using their gaze or a gesture, or discussing events from the past or in the future rather than whatever’s happening right then. Babies learn not just from language but also from a rich multimodal and tactile experience, says Michael Frank, a cognitive scientist at Stanford University who specializes in language learning and was involved with EgoBabyVLM’s development. 婴儿学习的不是精心策划的数据集，而是万花筒般的世界：父母谈论着不再可见的物体，用目光或手势指引事物，或者讨论过去或未来的事件，而不是仅仅关注当下发生的事情。斯坦福大学专门研究语言学习并参与了EgoBabyVLM开发的认知科学家迈克尔·弗兰克（Michael Frank）表示，婴儿不仅从语言中学习，还从丰富的多模态和触觉体验中学习。
The test shows that when it comes to AI, “it’s clear that there’s more [than just language] that’s needed,” Frank says. 弗兰克说，这项测试表明，对于AI而言，“显然需要的不仅仅是语言。”
Language Learning
语言学习
EgoBabyVLM is just the latest example of how scientists are using AI to explore human intelligence. A challenge called BabyLM, introduced in 2023, tasked AI models with learning the syntax of language using about the same amount of data a 10-year-old takes in—tens of millions of words, compared to trillions for AI models. Remarkably, it turns out that transformer-based AI models—which process language by paying attention to the relationship between words across different sentences—can do this quite well, a finding that challenges Noam Chomsky’s ideas concerning how syntax may be hardwired into the human brain. EgoBabyVLM只是科学家利用AI探索人类智能的最新案例。2023年推出的“BabyLM”挑战赛要求AI模型使用与10岁儿童所接触的相当的数据量（数千万字，而AI模型通常需要数万亿字）来学习语言语法。值得注意的是，基于Transformer的AI模型（通过关注不同句子中词语之间的关系来处理语言）能够很好地完成这项任务，这一发现挑战了诺姆·乔姆斯基（Noam Chomsky）关于语法可能如何“硬编码”在人脑中的观点。
Ryan Cotterell, a linguist at ETH Zurich who first developed BabyLM, says the situation is different when it comes to understanding the physical world. “There isn’t going to be a large corpus of human interactions—there’s no internet of human interactions,” he says. 苏黎世联邦理工学院的语言学家、BabyLM的最初开发者瑞安·科特雷尔（Ryan Cotterell）表示，在理解物理世界时，情况则不同。“不会有大量的人类互动语料库——不存在所谓的人类互动互联网，”他说。
Joshua Tenenbaum, a cognitive scientist at the Massachusetts Institute of Technology, notes that BabyLM showed models do not acquire “common sense” about the physical world, social dynamics, or theory of mind. 麻省理工学院的认知科学家约书亚·特南鲍姆（Joshua Tenenbaum）指出，BabyLM表明模型并未获得关于物理世界、社会动态或心智理论的“常识”。
“Transformers are very good at finding patterns in data,” says Tenenbaum. “But it does seem that just pure pattern learning systems are not able to take the kind of data that a baby or a child receives and learn all the things that they do.” “Transformer非常擅长在数据中寻找模式，”特南鲍姆说。“但纯粹的模式学习系统似乎无法利用婴儿或儿童所接收的那种数据，并学会他们所能学会的一切。”
An enduring question is whether evolution found a way to optimize certain learning skills in humans and other animals, or if simple learning algorithms can do everything we do. “There is a lot of debate in cognitive science and neuroscience about how much is built into the brain evolutionarily,” Tenenbaum says. “The brain is incredibly complex, and there’s a lot of built-in structure and architecture.” 一个长期存在的问题是，进化是否找到了一种优化人类和其他动物特定学习技能的方法，还是说简单的学习算法就能完成我们所做的一切。“在认知科学和神经科学领域，关于大脑中有多少是进化内置的，存在很多争论，”特南鲍姆说。“大脑极其复杂，其中有许多内置的结构和架构。”
In 2024, researchers showed that a basic VLM can learn simple things, like what a ball is, purely by consuming data recorded from the head of a single infant. But this is a ways away from reasoning about the world in sophisticated ways. “The mystery is how children get to the full capabilities that they have even at the age of 2,” says Brendan Lake, a cognitive scientist at Princeton University who was involved with the project. 2024年，研究人员展示了一个基础的VLM仅通过摄入从单个婴儿头部记录的数据，就能学会简单的东西，比如什么是球。但这距离以复杂方式推理世界还有很长的路要走。参与该项目的普林斯顿大学认知科学家布伦丹·莱克（Brendan Lake）说：“谜团在于，孩子们是如何在2岁时就获得他们所拥有的全部能力的。”
The authors of the EgoBabyVLM paper suggest that borrowing different ideas from cognitive science and neuroscience could enable progress toward more humanlike learning algorithms. This includes designing models that can pay attention over longer periods and can interpret social cues. EgoBabyVLM论文的作者建议，借鉴认知科学和神经科学的不同理念，可以推动向更具人类特征的学习算法迈进。这包括设计能够进行更长时间注意力分配并能解读社交线索的模型。
Stanford’s Frank has already shown that novel approaches can get us closer to baby-like AI. Earlier this year, he and colleagues tested a new kind of model that’s adept at learning causality and visual and temporal relationships—or how objects affect one another over time—using the same baby-head video data. They found the new model was able to learn about the dynamics of different objects, a foundation for physical reasoning, much more effectively. 斯坦福大学的弗兰克已经证明，新颖的方法可以让我们更接近婴儿般的AI。今年早些时候，他和同事测试了一种新模型，该模型擅长学习因果关系以及视觉和时间关系（即物体如何随时间相互影响），使用的是同样来自婴儿头部的视频数据。他们发现，新模型能够更有效地学习不同物体的动力学，这是物理推理的基础。
It’s a tantalizing possibility: Perhaps models that are biased to learn more rapidly about things like physics and social relationships could be more efficient learners overall. 这是一个诱人的可能性：也许那些倾向于更快学习物理和社会关系等事物的模型，在整体上能成为更高效的学习者。
“EgoBabyVLM is a wonderful challenge,” says Lake. “I’m excited to see what kinds of new architectures, approaches, and ingredients researchers come up with.” “EgoBabyVLM是一个很棒的挑战，”莱克说。“我很期待看到研究人员会提出什么样的架构、方法和要素。”
This is an edition of Will Knight’s AI Lab newsletter. Read previous newsletters here. 这是威尔·奈特（Will Knight）的AI实验室通讯。点击此处阅读往期通讯。