揭曉！人工神經網絡機器學習獲得2024年諾貝爾物理學獎

2024年諾貝爾物理學獎得主

（圖片來源：諾獎官網）

　　北京時間10月8日下午5點45分，瑞典皇家科學院宣布將2024年諾貝爾物理學獎授予：John J. Hopfield、Geoffrey E. Hinton。

　　獲獎理由

　　2024年諾貝爾物理學獎授予“在人工神經網絡機器學習方面的基礎性發現和發明”（for foundational discoveries and inventions that enable machine learning with artificial neural networks）。

　　使用物理學訓練人工神經網絡

　　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.

　　今年的兩位諾貝爾物理學獎獲得者使用了物理學的工具來開發方法，這些方法是當今強大的機器學習的基礎。John Hopfield創造了一種聯想記憶，可以存儲和重建數據中的圖像和其他類型的模式。Geoffrey Hinton發明了一種可以自主查找數據屬性的方法，從而執行識別圖片中特定元素等任務。

　　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 con-nections 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.

當我們談論人工智能時，我們通常是指使用人工神經網絡的機器學習。這項技術最初的靈感來自于大腦的結構。在人工神經網絡中，大腦的神經元由具有不同值的節點表示。這些節點通過連接相互影響，突觸可以變強或變弱。該網絡是經過訓練的，例如，通過在同時具有高值的節點之間建立更強的連接。從20世紀80年代開始，今年的獲獎者就在人工神經網絡方面進行了重要的工作。

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發明了一種用來保存和重建模式的網絡。我們可以把這些節點想象成像素。Hopfield網絡利用物理學來描述一種材料由于其原子自旋而產生的特性——這種特性使每個原子成為一個微小的磁鐵。網絡作為一個整體被描述為相當于物理學中發現的自旋系統中的能量，并通過尋找節點之間的連接值來訓練，這樣保存的圖像具有較低的能量。當網絡被輸入一個扭曲或不完整的圖像時，它有條不紊地通過節點并更新它們的值，使網絡的能量下降。因此，網絡逐步找到保存的最像它所提供的不完美的圖像。

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網絡作為一種新網絡的基礎，該網絡使用了一種不同的方法：玻爾茲曼機。這可以學會識別給定類型的數據中的特征元素。Hinton使用了來自統計物理學的工具，統計物理學是由許多類似的組件組成的系統科學。機器通過提供機器運行時很可能出現的例子來訓練。玻爾茲曼機器可以用來對圖像進行分類，或創建它被訓練的模式類型的新例子。Hinton已經建立在這項工作的基礎上，幫助啟動了當前機器學習的爆炸性發展。

“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.

“獲獎者的工作已經有了最大的好處。在物理學領域，我們在許多領域使用人工神經網絡，比如開發具有特定特性的新材料，”諾貝爾物理學委員會主席 Ellen Moons說。

　　獲獎人詳細信息

　　John J. Hopfield, 1933年出生于美國伊利諾伊州芝加哥。1958年畢業于美國紐約州伊薩卡康奈爾大學博士。美國新澤西州普林斯頓大學教授。

　　Geoffrey E. Hinton, 1947年出生于英國倫敦。1978年獲得英國愛丁堡大學博士學位。加拿大多倫多大學教授。