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Researchers have developed an artificial intelligence (AI) system to help fill in missing words in ancient writings.
研究人员开发了一种人工智能系统，以帮助填补古代文字中缺失的单词。
The system is designed to help historians restore the writings and identify when and where they were written.
该系统旨在帮助历史学家恢复这些文字，并确定它们是在何时何地写的。
Many ancient populations used writings, also known as inscriptions, to document different parts of their lives.
许多古代居民使用文字，也被称为铭文，来记录他们生活的方方面面。
The inscriptions have been found on materials such as rock, ceramic and metal.
研究人员在岩石、陶瓷和金属等材料上发现了这些铭文。
The writings often contained valuable information about how ancient people lived and how they structured their societies.
这些文字通常包含有关古代人如何生活以及他们如何构建社会等有价值的信息。
But in many cases, the objects containing such inscriptions have been damaged over the centuries.
但在许多情况下，刻有铭文的物品在几个世纪的时间里已经损坏。
This left major parts of the inscriptions missing and difficult to identify and understand.
这使得铭文的大部分内容缺失，难以识别和理解。
In addition, many of the inscribed objects were moved from areas where they were first created.
此外，许多刻有铭文的物品都从起初创作的地方被人挪动过。
This makes it difficult for scientists to discover when and where the writings were made.
这使得科学家很难发现这些文字是在何时何地被创作的。
The new AI-based method serves as a technological tool to help researchers repair missing inscriptions and estimate the true origins of the records.
这种基于人工智能的新方法作为一种技术工具，可以帮助研究人员修复缺失的铭文并估计这些记录的真实来源。
The researchers, led by Alphabet's AI company DeepMind, call their tool Ithaca.
由Alphabet旗下的人工智能公司DeepMind领导的研究人员将他们的工具命名为伊萨卡。
In a statement, the researchers said the system is "the first deep neural network that can restore the missing text of damaged inscriptions."
研究人员在一份声明中表示，该系统是“首个能够修复受损铭文缺失文本的深度神经网络”。
A neural network is a machine learning computer system built to act like the human brain.
神经网络是一种机器学习计算机系统，其功能与人脑类似。
The findings were recently reported in a study in the publication Nature.
该研究结果最近发表在《自然》杂志上。
Researchers from other organizations--including the University of Oxford, Ca' Foscari University of Venice and Athens University of Economics and Business--also took part in the study.
来自其他组织的研究人员也参与了这项研究，其中包括牛津大学、威尼斯大学和雅典经济与商业大学。
The team said it trained Ithaca on the largest collection of data containing Greek inscriptions from the non-profit Packard Humanities Institute in California.
该研究小组表示，他们对伊萨卡进行了训练，收集了加州非营利性组织帕卡德人文研究所提供的最大规模的包含希腊文铭文在内的数据。
Feeding this data into the system is designed to help the tool use past writings to predict missing letters and words in damaged inscriptions.
将这些数据输入该系统的目的是帮助该工具利用以前的文字来预测损坏的铭文中缺失的字母和单词。
The researchers reported that in experiments with damaged writings, Ithaca was able to correctly predict missing inscription elements 62 percent of the time.
研究人员报告称，在对受损文字进行的实验中，伊萨卡预测缺失的铭文元素的准确率为62%。
In addition, the tool was 71 percent correct in identifying where the inscriptions first came from.
此外，该工具识别铭文最初来源的准确率为71%。
And, the system was able to effectively date writings to within 30 years, the team said.
而且，该研究小组表示，该系统能够有效地将文字的日期定在30年内。
Yannis Assael is a research scientist with DeepMind who helped lead the study.
扬尼斯·阿萨埃尔是DeepMind的一名研究科学家，他领导了这项研究。
He said in a statement that Ithaca was designed to "support historians to expand and deepen our understanding of ancient history."
他在一份声明中说，伊萨卡旨在“支持历史学家扩大和加深我们对古代历史的理解”。
When historians work on their own, the success rate for restoring damaged inscriptions is about 25 percent.
当历史学家独自工作时，修复受损铭文的成功率约为25%。
But when humans teamed up with Ithaca to assist in their work, the success rate jumped to 72 percent, Assael said.
阿萨埃尔说，但当伊萨卡协助他们工作时，成功率跃升至72%。
Thea Sommerschield was another lead researcher on the project.
西娅·萨默舍尔德是该项目的另一位首席研究员。
She is the Marie Curie Fellow at Ca' Foscari University of Venice.
她是威尼斯大学的玛丽·居里研究员。
Sommerschield said she hopes systems like Ithaca "can unlock the cooperative potential" between AI and humans in future restoration work involving important ancient inscriptions.
萨默舍尔德说，她希望像伊萨卡这样的系统能够在未来涉及重要古代铭文的修复工作中“释放人工智能和人类之间的合作潜力”。
She said the system had already provided new information to help researchers reexamine important periods in Greek history.
她说，该系统已经提供了新的信息，帮助研究人员重新审视希腊历史上的重要时期。
In one case, Ithaca confirmed new evidence presented by historians about the dating of a series of important Greek decrees.
在一个案例中，伊萨卡证实了历史学家提出的有关一系列重要希腊法令的日期的新证据。
The decrees were first thought to have been written before 446/445 BCE.
这些法令最初被认为是在公元前446或445年之前写的。
But the new evidence suggested a date in the 420s BCE.
但新的证据表明，它的日期是公元前420年。
Ithaca predicted a date of 421 BCE.
伊萨卡预测的日期是公元前421年。
Sommerschield said that the date change may seem small.
萨默舍尔德说，日期的变化可能看起来很小。
But it has "significant implications for our understanding of the political history of Classical Athens," she added.
但她还说，这“对我们理解古典雅典的政治史具有重大意义”。
The team is currently working on other versions of Ithaca trained on other ancient languages.
该研究小组目前正在开发其他版本的伊萨卡，这些版本的伊萨卡是针对其他古代语言进行训练的。
DeepMind has launched a free, interactive tool based on the system for use by researchers, educators, museum workers and the public.
DeepMind已经推出了一款基于该系统的免费互动工具，供研究人员、教育工作者、博物馆工作人员和公众使用。
I'm Bryan Lynn.
布莱恩·林恩为您播报。
译文为可可英语翻译，未经授权请勿转载！
