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Dr. Ullman noted that machine learning researchers have struggled over the past couple of decades to capture the flexibility of human knowledge in computer models. This difficulty has been a "shadow finding," he said, hanging behind every exciting innovation.
乌尔曼博士指出，过去几十年里，机器学习领域的研究人员一直在计算机模型中竭力捕捉人类知识的灵活性。他说，这一难题就像是在"找影子"，存在于每一项令人兴奋的创新背后。
Researchers have shown that language models will often give wrong or irrelevant answers when primed with unnecessary information before a question is posed; some chatbots were so thrown off by hypothetical discussions about talking birds that they eventually claimed that birds could speak.
研究人员已经表明，在提出问题之前，如果事先提示一些不必要的信息，语言模型往往会给出错误或不相关的答案；一些聊天机器人被关于会说话的鸟儿的假设性讨论给搞晕了，以至于它们最终声称鸟儿是会说话的。
Because their reasoning is sensitive to small changes in their inputs, scientists have called the knowledge of these machines "brittle."
因为它们在推理时对输入信息的微小变化很敏感，所以科学家们称这些机器的知识是"脆性易碎的"。
Dr. Gopnik compared the theory of mind of large language models to her own understanding of general relativity.
戈普尼克博士将大型语言模型的心智理论比作她自己对广义相对论的理解。
"I have read enough to know what the words are," she said. "But if you asked me to make a new prediction or to say what Einstein's theory tells us about a new phenomenon, I'd be stumped because I don't really have the theory in my head."
"我读了足够多的东西，知道这几个词是什么意思，"她说，"但如果你让我做一个新的预测，或者让我用爱因斯坦的理论去解释一个新现象，我就会被难住，因为我脑子里其实没有这个理论。"
By contrast, she said, human theory of mind is linked with other common-sense reasoning mechanisms; it stands strong in the face of scrutiny.
她说，与之形成对比，人类的心智理论与其他常识推理机制是联系在一起的，它经得起审视。
In general, Dr. Kosinski's work and the responses to it fit into the debate about whether the capacities of these machines can be compared to the capacities of humans -- a debate that divides researchers who work on natural language processing.
总体而言，科辛斯基博士的研究和对这一研究的各种回应属于一种辩论的范畴，即这些机器的能力是否可以与人类的能力相提并论，研究自然语言处理的人就这一问题分成了两派。
Are these machines stochastic parrots, or alien intelligences, or fraudulent tricksters?
这些机器是具有随机性的学舌鹦鹉，还是异于人类的智能，还是具有欺诈性的骗子？
A 2022 survey of the field found that, of the 480 researchers who responded, 51 percent believed that large language models could eventually "understand natural language in some nontrivial sense," and 49 percent believed that they could not.
2022年对该领域进行的一项调查发现，在做出回应的480名研究人员中，51%的人认为大型语言模型最终可以"在某种重大意义上理解自然语言"，49%的人认为它们不能做到这一点。
Dr. Ullman doesn't discount the possibility of machine understanding or machine theory of mind, but he is wary of attributing human capacities to nonhuman things.
乌尔曼博士并不否认机器有理解能力或机器有心智理论的可能性，但他对认为非人类事物有人类能力的这种想法持谨慎态度。
He noted a famous 1944 study by Fritz Heider and Marianne Simmel, in which participants were shown an animated movie of two triangles and a circle interacting. When the subjects were asked to write down what transpired in the movie, nearly all described the shapes as people.
他提到了弗里茨·海德和玛丽安· 西梅尔在1944年进行的一项著名研究，在这项研究中，参与者观看了一部两个三角形和一个圆形进行互动的动画影片。当受试者被要求写下电影中发生了什么时，几乎所有人都把这些形状当作人来描述。
"Lovers in the two-dimensional world, no doubt; little triangle number-two and sweet circle," one participant wrote. "Triangle-one (hereafter known as the villain) spies the young love. Ah!"
"毫无疑问，这是二维世界里的恋人，二号小三角和甜美的圆形。"一位参与者写道，"一号三角(以下简称反派)在暗中窥视这对年轻的恋人。啊!"
It's natural and often socially required to explain human behavior by talking about beliefs, desires, intentions and thoughts. This tendency is central to who we are -- so central that we sometimes try to read the minds of things that don't have minds, at least not minds like our own.
用信念、欲望、意图和想法来解释人类行为是很自然的，也经常是为社会所要求的。这种倾向是何以为人的核心所在，正因为它处于如此核心的地位，以至于我们有时候试图读懂那些无心智之物的心智，至少它们的心智和我们的并不相同。
