揭晓！人工神经网络机器学习获得2024年诺贝尔物理学奖

2024年诺贝尔物理学奖得主

（图片来源：诺奖官网）

　　北京时间10月8日下午5点45分，瑞典皇家科学院宣布将2024年诺贝尔物理学奖授予：John J. Hopfield、Geoffrey E. Hinton。

　　获奖理由

　　2024年诺贝尔物理学奖授予“在人工神经网络机器学习方面的基础性发现和发明”（for foundational discoveries and inventions that enable machine learning with artificial neural networks）。

　　使用物理学训练人工神经网络

　　This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.

　　今年的两位诺贝尔物理学奖获得者使用了物理学的工具来开发方法，这些方法是当今强大的机器学习的基础。John Hopfield创造了一种联想记忆，可以存储和重建数据中的图像和其他类型的模式。Geoffrey Hinton发明了一种可以自主查找数据属性的方法，从而执行识别图片中特定元素等任务。

　　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.

当我们谈论人工智能时，我们通常是指使用人工神经网络的机器学习。这项技术最初的灵感来自于大脑的结构。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。这些节点通过连接相互影响，突触可以变强或变弱。该网络是经过训练的，例如，通过在同时具有高值的节点之间建立更强的连接。从20世纪80年代开始，今年的获奖者就在人工神经网络方面进行了重要的工作。

John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.

John Hopfield发明了一种用来保存和重建模式的网络。我们可以把这些节点想象成像素。Hopfield网络利用物理学来描述一种材料由于其原子自旋而产生的特性——这种特性使每个原子成为一个微小的磁铁。网络作为一个整体被描述为相当于物理学中发现的自旋系统中的能量，并通过寻找节点之间的连接值来训练，这样保存的图像具有较低的能量。当网络被输入一个扭曲或不完整的图像时，它有条不紊地通过节点并更新它们的值，使网络的能量下降。因此，网络逐步找到保存的最像它所提供的不完美的图像。

Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: 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.

Geoffrey Hinton使用 Hopfield网络作为一种新网络的基础，该网络使用了一种不同的方法：玻尔兹曼机。这可以学会识别给定类型的数据中的特征元素。Hinton使用了来自统计物理学的工具，统计物理学是由许多类似的组件组成的系统科学。机器通过提供机器运行时很可能出现的例子来训练。玻尔兹曼机器可以用来对图像进行分类，或创建它被训练的模式类型的新例子。Hinton已经建立在这项工作的基础上，帮助启动了当前机器学习的爆炸性发展。

“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,” says Ellen Moons, Chair of the Nobel Committee for Physics.

“获奖者的工作已经有了最大的好处。在物理学领域，我们在许多领域使用人工神经网络，比如开发具有特定特性的新材料，”诺贝尔物理学委员会主席 Ellen Moons说。

　　获奖人详细信息

　　John J. Hopfield, 1933年出生于美国伊利诺伊州芝加哥。1958年毕业于美国纽约州伊萨卡康奈尔大学博士。美国新泽西州普林斯顿大学教授。

　　Geoffrey E. Hinton, 1947年出生于英国伦敦。1978年获得英国爱丁堡大学博士学位。加拿大多伦多大学教授。