剑桥大学校友杰弗里·辛顿（Geoffrey Hinton）与普林斯顿大学的约翰·霍普菲尔德（John Hopfield）共同获得2024年诺贝尔物理学奖。

Geoffrey Hinton, an alumnus of the University of Cambridge, was awarded the 2024 Nobel Prize in Physics, jointly with John Hopfield of Princeton University.

辛顿（国王学院，1967）和霍普菲尔德因“利用人工神经网络进行机器学习的基础性发现和发明”而获得该奖项。辛顿被誉为“AI（人工智能）教父”，为多伦多大学计算机科学名誉教授。

Hinton (King’s 1967) and Hopfield were awarded the prize ‘for foundational discoveries and inventions that enable machine learning with artificial neural networks.’ Hinton, who is known as the ‘Godfather of AI’ is Emeritus Professor of Computer Science at the University of Toronto.

我们说的人工智能，通常指的是基于人工神经网络的机器学习。该技术最初受到人脑结构的启发。在人工神经网络中，大脑的神经元由具有不同值的节点表示。这些节点通过类似于突触的连接相互影响，而连接可以变强或变弱。例如，在同时具有高数值的节点之间建立更强的连接，可以实现对人工神经网络的训练。今年的获奖者从20世纪90年代起就对人工神经网络开展了重要的研究。

When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through con­nections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward.

杰弗里·辛顿以约翰·霍普菲尔德发明的神经网络为基础，创建了新的神经网络：玻尔兹曼机。该网络可以学习识别给定类型数据中的特征元素。辛顿使用了统计物理学工具，统计物理学是研究大量相似组成元素的系统科学。通过输入机器运行时很可能出现的示例，以对机器进行训练。玻尔兹曼机可用于对图像进行分类，或创建训练模式类型的新示例。辛顿在此基础上，推动开启了当前机器学习的爆炸式发展。

Geoffrey Hinton used a network invented by John Hopfield as the foundation for a new network: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

“祝贺辛顿教授获得诺贝尔奖。我们的校友是剑桥群体的重要组成部分，他们许多人，如辛顿教授一样，完成了真正改变我们世界的发现和进步。我代表剑桥大学祝贺他取得这一巨大成就。”

“Many congratulations to Professor Hinton on receiving the Nobel Prize. Our alumni are a vital part of the Cambridge community, and many of them, like Professor Hinton, have made discoveries and advances that have genuinely changed our world. On behalf of the University of Cambridge, I congratulate him on this enormous accomplishment.”

“获奖者的工作已经产生了巨大的效益。在物理学许多领域，我们广泛应用了人工神经网络，例如开发具有特定性能的新材料。”

“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.” 

辛顿和霍普菲尔德分别是第122和第123位获得诺贝尔奖的剑桥大学成员。

Hinton and Hopfield are the 122nd and 123rd Members of the University of Cambridge to be awarded the Nobel Prize.

1980年至1982年，辛顿在英国医学研究理事会（MRC）应用心理学部（当时叫作英国医学研究理事会认知与脑科学研究所）担任科学官员，之后在匹兹堡的卡内基梅隆大学任职。

From 1980 to 1982, Hinton was a Scientific Officer at the MRC Applied Psychology Unit (as the MRC Cognition and Brain Sciences Unit was then known), before taking up a position at Carnegie Mellon University in Pittsburgh.

2023年5月，辛顿在剑桥大学的生存风险研究中心发表了题为“通往智能的两条道路”的公开演讲，他在演讲中指出，“大规模数字计算在获取知识方面可能比生物计算要好得多，而且很快就会比我们聪明得多”。

In May 2023, Hinton gave a public lecture at the University's Centre for the Study of Existential Risk entitled 'Two Paths to Intelligence', in which he argued that "large scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us".