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Neuroscience: Great minds
神经科学：了不起的大脑
Five decades of research into artificial neural networks have earned Geoffrey Hinton the moniker of the Godfather of artificial intelligence (AI). Work by his group at the University of Toronto laid the foundations for today's headline-grabbing AI models, including ChatGPT and LaMDA.
五十年来对人工神经网络的研究为杰弗里·辛顿赢得了"人工智能教父"的称号。他在多伦多大学的研究小组的工作为ChatGPT和LaMDA等引人注目的人工智能模型奠定了基础。
These can write coherent (if uninspiring) prose, diagnose illnesses from medical scans and navigate self-driving cars. But for Dr Hinton, creating better models was never the end goal.
这些模型可以写出连贯（虽然平淡无奇）的文章，通过医学扫描诊断疾病，并驾驶自动驾驶汽车。但对于辛顿博士来说，创建更好的模型从来都不是最终目标。
His hope was that by developing artificial neural networks that could learn to solve complex problems, light might be shed on how the brain's neural networks do the same.
他希望通过开发能够学习解决复杂问题的人工神经网络，去揭示大脑神经网络如何做到同样的事情。
Brains learn by being subtly rewired: some connections between neurons, known as synapses, are strengthened, while others must be weakened.
大脑通过巧妙地重新连接来学习：一些神经元之间的连接（也叫突触）得到加强，而其他连接则必须削弱。
But because the brain has billions of neurons, of which millions could be involved in any single task, scientists have puzzled over how it knows which synapses to tweak and by how much.
但因为大脑有数十亿个神经元，其中的数百万个可能参与任何一项任务，所以科学家们一直在思索大脑如何知道要调整哪些突触以及调整到何种程度。
Dr Hinton popularised a clever mathematical algorithm known as backpropagation to solve this problem in artificial neural networks. But it was long thought to be too unwieldy to have evolved in the human brain.
辛顿博士推广了一种叫做反向传播的巧妙数学算法，来解决人工神经网络中的这个问题。但长期以来人们认为这种方法太过复杂笨拙，不可能在人脑中进化出来。
Now, as AI models are beginning to look increasingly human-like in their abilities, scientists are questioning whether the brain might do something similar after all.
现在，随着人工智能模型的能力开始越来越像人类，科学家们开始怀疑大脑是否还是能做类似反向传播的事情。
Working out how the brain does what it does is no easy feat. Much of what neuroscientists understand about human learning comes from experiments on small slices of brain tissue, or handfuls of neurons in a Petri dish.
弄清楚大脑的工作原理绝非易事。神经科学家对人类学习过程的大部分理解都来自对小片脑组织或培养皿中少量神经元的实验。
It's often not clear whether living, learning brains work by scaled-up versions of these same rules, or if something more sophisticated is taking place.
人们通常不清楚活的、学习中的大脑是否按照这些相同规则的放大版本工作，还是发生了更复杂的事情。
Even with modern experimental techniques, wherein neuroscientists track hundreds of neurons at a time in live animals, it is hard to reverse-engineer what is really going on.
即使运用现代实验技术，让神经科学家可以同时追踪活体动物的数百个神经元，也很难逆推真正发生了什么。
