The Nobel Prize in Physics 2024 ｜ 2024 年诺贝尔物理学奖

The Nobel Prize in Physics 2024 ｜ 2024 年诺贝尔物理学奖 The Nobel Prize in Physics 2024 ｜ 2024 年诺贝尔物理学奖 Even very young children can point at different animals and confidently say whether it is a dog, a cat, or a squirrel. They might get it wrong occasionally, but fairly soon they are correct almost all the time. A child can learn this even without seeing any diagrams or explanations of concepts such as species or mammal . After encountering a few examples of each type of animal, the different categories fall into place in the child’s head. People learn to recognise a cat, or understand a word, or enter a room and notice that something has changed, by experiencing the environment around them. 即使非常年幼的儿童也可以指出不同的动物，并自信地说它是狗、猫还是松鼠。他们偶尔会错误，但很快就能正确识别几乎所有的动物。一个孩子即使没有看到任何关于 物种 或 哺乳动物 等概念的图表或解释，也能学会这一点。在接触了每种动物的几个例子之后，不同的类别就会在孩子的头脑中形成。人们通过身边的环境，学会识别猫、理解一个词语，或进入一个房间时发现有什么变化。 When Hopfield published his article on associative memory, Geoffrey Hinton was working at Carnegie Mellon University in Pittsburgh, USA. He had previously studied experimental psychology and artificial intelligence in England and Scotland and was wondering whether machines could learn to process patterns in a similar way to humans, finding their own categories for sorting and interpreting information. Along with his colleague, Terrence Sejnowski, Hinton started from the Hopfield network and expanded it to build something new, using ideas from statistical physics. 当霍普菲尔德发表其关于联想记忆的文章时，杰弗里·欣顿正在美国匹兹堡的卡内基梅隆大学工作。他此前曾在英格兰和苏格兰学习实验心理学和人工智能，他在思考机器是否能以类似人类的方式学习处理模式，找出自己的分类和解释信息的方法。欣顿和他的同事特伦斯·塞金斯基从霍普菲尔德网络出发，运用统计物理学的理念，拓展并建立了一些全新的东西。 Statistical physics describes systems that are composed of many similar elements, such as molecules in a gas. It is difficult, or impossible, to track all the separate molecules in the gas, but it is possible to consider them collectively to determine the gas’ overarching properties like pressure or temperature. There are many potential ways for gas molecules to spread through its volume at individual speeds and still result in the same collective properties. 统计物理描述由许多相似元素组成的系统，如气体中的分子。跟踪气体中所有单独的分子是很困难的，甚至是不可能的，但从整体上考虑它们，可以确定气体的压力或温度等宏观性质。气体分子可以以各种不同的个体速度在其体积中传播，但仍可以得到相同的整体特性。 The states in which the individual components can jointly exist can be analysed using statistical physics, and the probability of them occurring calculated. Some states are more probable than others; this depends on the amount of available energy, which is described in an equation by the nineteenth century physicist Ludwig Boltzmann. Hinton’s network utilised that equation, and the method was published in 1985 under the striking name of the Boltzmann machine . 个体组件可以共同存在的状态可以使用统计物理学来进行分析，并计算它们发生的概率。某些状态比其他状态更可能发生；这取决于可用能量的数量，这在十九世纪物理学家路德维希·波尔兹曼的一个方程中有描述。Hinton 的网络使用了这个方程，该方法于 1985 年发表，名称为 波尔兹曼机器 。 Recognising new examples of the same type 识别相同类型的新示例 The Boltzmann machine is commonly used with two different types of nodes. Information is fed to one group, which are called visible nodes. The other nodes form a hidden layer. The hidden nodes’ values and connections also contribute to the energy of the network as a whole. 玻尔兹曼机器通常使用两种不同类型的节点。输入信息的节点被称为可见节点。其他节点组成隐藏层。隐藏节点的值和连接也会对网络的整体能量产生贡献。 The machine is run by applying a rule for updating the values of the nodes one at a time. Eventually the machine will enter a state in which the nodes’ pattern can change, but the properties of the network as a whole remain the same. Each possible pattern will then have a specific probability that is determined by the network’s energy according to Boltzmann’s equation. When the machine stops it has created a new pattern, which makes the Boltzmann machine an early example of a generative model. 该机器通过逐一更新节点值的规则来运行。最终，机器将进入一个状态，节点的模式可以改变，但网络整体的属性保持不变。根据玻尔兹曼方程，每种可能的模式都有一个特定的概率。当机器停止时，它已创建了一个新的模式，这使得玻尔兹曼机成为生成模型的早期示例。 The Boltzmann machine can learn – not from instructions, but from being given examples. It is trained by updating the values in the network’s connections so that the example patterns, which were fed to the visible nodes when it was trained, have the highest possible probability of occurring when the machine is run. If the same pattern is repeated several times during this training, the probability for this pattern is even higher. Training also affects the probability of outputting new patterns that resemble the examples on which the machine was trained. 玻尔兹曼机器可以学习 不是从指令中学习，而是从给定的示例中学习。它通过更新网络连接中的值进行训练，以使在训练时输入可见节点的示例模式在机器运行时具有最高的概率出现。如果在此训练过程中重复相同的模式多次，则该模式的概率会更高。训练还会影响输出与机器接受训练的示例相似的新模式的概率。 A trained Boltzmann machine can recognise familiar traits in information it has not previously seen. Imagine meeting a friend’s sibling, and you can immediately see that they must be related. In a similar way, the Boltzmann machine can recognise an entirely new example if it belongs to a category found in the training material, and differentiate it from material that is dissimilar. 受过训练的玻尔兹曼机器可以在以前没有见过的信息中识别熟悉的特征。想象一下，当你遇到一个朋友的兄弟姐妹时，你可以立即看出他们是亲属关系。同样地，如果一个全新的例子属于训练材料中发现的某一类别，玻尔兹曼机器就可以识别它，并将其与不同的材料区分开来。 In its original form, the Boltzmann machine is fairly inefficient and takes a long time to find solutions. Things become more interesting when it is developed in various ways, which Hinton has continued to explore. Later versions have been thinned out, as the connections between some of the units have been removed. It turns out that this may make the machine more efficient. 在原有形式中，玻尔兹曼机器相当低效且需要很长时间才能找到解决方案。当它以各种方式被开发时，情况变得更加有趣，这是 Hinton 一直在探索的领域。后来的版本已经被精简，因为一些单元之间的连接已被移除。事实证明，这可能使机器更加高效。 During the 1990s, many researchers lost interest in artificial neural networks, but Hinton was one of those who continued to work in the field. He also helped start the new explosion of exciting results; in 2006 he and his colleagues Simon Osindero, Yee Whye Teh and Ruslan Salakhutdinov developed a method for pretraining a network with a series of Boltzmann machines in layers, one on top of the other. This pretraining gave the connections in the network a better starting point, which optimised its training to recognise elements in pictures. 在 1990 年代，许多研究人员失去了对人工神经网络的兴趣，但 Hinton 是继续在这个领域工作的人之一。他也帮助引发了令人兴奋的新结果；在 2006 年，他和他的同事 Simon Osindero、Yee Whye Teh 和 Ruslan Salakhutdinov 开发了一种用一系列玻尔兹曼机器逐层预训练网络的方法。这种预训练为网络的连接提供了更好的起点，优化了其识别图片中元素的训练。 预训练 The Boltzmann machine is often used as part of a larger network. For example, it can be used to recommend films or television series based on the viewer’s preferences. 玻尔兹曼机器通常作为更大网络的一部分使用。例如，它可用于根据观众的偏好推荐电影或电视剧。 Machine learning – today and tomorrow 机器学习 今天和明天 Thanks to their work from the 1980s and onward, John Hopfield and Geoffrey Hinton have helped lay the foundation for the machine learning revolution that started around 2010. 多亏了约翰·霍普菲尔德和杰弗里·辛顿从 1980 年代直到今天的努力，他们为 2010 年左右开始的机器学习革命奠定了基础。 The development we are now witnessing has been made possible through access to the vast amounts of data that can be used to train networks, and through the enormous increase in computing power. Today’s artificial neural networks are often enormous and constructed from many layers. These are called deep neural networks and the way they are trained is called deep learning. 我们现在目睹的发展是通过获取可用于训练网络的大量数据以及计算能力的巨大增长而实现的。如今的人工神经网络通常规模很大，由许多层构成。它们被称为深度神经网络，它们的训练方式称为深度学习。 A quick glance at Hopfield’s article on associative memory, from 1982, provides some perspective on this development. In it, he used a network with 30 nodes. If all the nodes are connected to each other, there are 435 connections. The nodes have their values, the connections have different strengths and, in total, there are fewer than 500 parameters to keep track of. He also tried a network with 100 nodes, but this was too complicated, given the computer he was using at the time. We can compare this to the large language models of today, which are built as networks that can contain more than one trillion parameters (one million millions). 快速浏览 1982 年霍普菲尔德（Hopfield）发表的有关联想记忆的文章，可以让我们了解到这一进展的背景。在该文章中，他使用了一个有 30 个节点的网络。如果所有节点都相互连接，那么就会有 435 个连接。节点有自己的值，连接有不同的强度，总共只有不到 500 个参数需要追踪。他还尝试了一个有 100 个节点的网络，但由于当时使用的计算机性能限制，这个网络已经太过复杂。我们可以将此与如今的大型语言模型进行比较，这些模型被构建为拥有超过一万亿个参数（一百万亿）的网络。 Many researchers are now developing machine learning’s areas of application. Which will be the most viable remains to be seen, while there is also wide ranging discussion on the ethical issues that surround the development and use of this technology. 许多研究人员目前正在开发机器学习的应用领域。哪个将成为最可行的仍有待观察，同时也广泛讨论了围绕这项技术的发展和使用的伦理问题。 Because physics has contributed tools for the development of machine learning, it is interesting to see how physics, as a research field, is also benefitting from artificial neural networks. Machine learning has long been used in areas we may be familiar with from previous Nobel Prizes in Physics. These include the use of machine learning to sift through and process the vast amounts of data necessary to discover the Higgs particle. Other applications include reducing noise in measurements of the gravitational waves from colliding black holes, or the search for exoplanets. 由于物理学为机器学习的发展提供了工具，我们很有趣地看到物理学作为一个研究领域也从人工神经网络中获益。机器学习长期用于我们从以前的物理学诺贝尔奖中可能熟悉的领域。这些包括使用机器学习来筛选和处理发现 希格斯 粒子所需的大量数据。其他应用包括降低测量碰撞黑洞引起的引力波的噪音，或者搜索系外行星。 In recent years, this technology has also begun to be used when calculating and predicting the properties of molecules and materials – such as calculating protein molecules’ structure, which determines their function, or working out which new versions of a material may have the best properties for use in more efficient solar cells. 近年来，在计算和预测分子和材料的性质时，这项技术也开始得到运用 比如计算蛋白质分子的结构，这决定了它们的功能，或者确定某种材料的新版本可能具有最佳性能，以用于更高效的太阳能电池。 The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Physics 2024 to 瑞典皇家科学院决定将 2024 年诺贝尔物理学奖授予 JOHN J. HOPFIELD 约翰·J·霍普菲尔德 Born 1933 in Chicago， IL， USA。 PhD 1958 from Cornell University, Ithaca, NY, USA. Professor at Princeton University, NJ, USA. 1933 年出生于美国伊利诺伊州芝加哥。1958 年获得美国纽约州伊萨卡康奈尔大学博士学位。现任美国新泽西州普林斯顿大学教授。 GEOFFREY E. HINTON 杰弗里·E·亨顿 Born 1947 in London， UK。 PhD 1978 from The University of Edinburgh, UK. Professor at University of Toronto, Canada. 1947 年出生于英国伦敦。1978 年从英国爱丁堡大学获得博士学位。现任加拿大多伦多大学教授。 “for foundational discoveries and inventions that enable machine learning with artificial neural networks” 对于实现人工神经网络机器学习的基础性发现和发明 Higgs 希格斯 Even very young children can point at different animals and confidently say whether it is a dog, a cat, or a squirrel. They might get it wrong occasionally, but fairly soon they are correct almost all the time. A child can learn this even without seeing any diagrams or explanations of concepts such as species or mammal . After encountering a few examples of each type of animal, the different categories fall into place in the child’s head. People learn to recognise a cat, or understand a word, or enter a room and notice that something has changed, by experiencing the environment around them. 即使非常年幼的儿童也可以指出不同的动物，并自信地说它是狗、猫还是松鼠。他们偶尔会错误，但很快就能正确识别几乎所有的动物。一个孩子即使没有看到任何关于 物种 或 哺乳动物 等概念的图表或解释，也能学会这一点。在接触了每种动物的几个例子之后，不同的类别就会在孩子的头脑中形成。人们通过身边的环境，学会识别猫、理解一个词语，或进入一个房间时发现有什么变化。 When Hopfield published his article on associative memory, Geoffrey Hinton was working at Carnegie Mellon University in Pittsburgh, USA. He had previously studied experimental psychology and artificial intelligence in England and Scotland and was wondering whether machines could learn to process patterns in a similar way to humans, finding their own categories for sorting and interpreting information. Along with his colleague, Terrence Sejnowski, Hinton started from the Hopfield network and expanded it to build something new, using ideas from statistical physics. 当霍普菲尔德发表其关于联想记忆的文章时，杰弗里·欣顿正在美国匹兹堡的卡内基梅隆大学工作。他此前曾在英格兰和苏格兰学习实验心理学和人工智能，他在思考机器是否能以类似人类的方式学习处理模式，找出自己的分类和解释信息的方法。欣顿和他的同事特伦斯·塞金斯基从霍普菲尔德网络出发，运用统计物理学的理念，拓展并建立了一些全新的东西。 Statistical physics describes systems that are composed of many similar elements, such as molecules in a gas. It is difficult, or impossible, to track all the separate molecules in the gas, but it is possible to consider them collectively to dete