MIT Technology Review » Artificial Intelligence 07月23日 23:40
Google DeepMind’s new AI can help historians understand ancient Latin inscriptions
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Google DeepMind开发了一款名为Aeneas的新人工智能软件,旨在帮助历史学家解读古老的拉丁铭文。该工具能够分析风化石碑上的文字,推测其刻写时间和地点,并能填补缺失的文本。Aeneas通过将部分铭文转录与图像相结合,并参照包含约15万条铭文的数据库进行交叉比对,提供可能的年代、地点以及相似的铭文。与之前的工具Ithaca相比,Aeneas更侧重于为研究者提供进一步分析的切入点。研究表明,Aeneas能有效激发历史学家的研究思路,并提高对铭文来源地和年代的判断准确性。目前,Aeneas已开源,并计划整合到历史教育中。

🔬 **AI驱动的古籍解读新工具:** Google DeepMind推出的Aeneas是一款人工智能软件,专门用于帮助历史学家解读古老的拉丁铭文。它能够分析刻在风化石碑上的文字,推测铭文的刻写时间和地点,并能根据上下文智能填补缺失的文本。

💡 **深度学习与数据库结合:** Aeneas通过结合部分铭文的转录文本和其扫描图像,利用深度学习技术进行分析。关键在于它能够参照一个庞大的数据库,其中包含来自世界各地约15万条拉丁铭文,通过比对相似的词汇、短语和类比,为研究者提供有价值的线索。

🚀 **赋能历史研究的助手:** Aeneas并非旨在自动化古文字学研究,而是作为历史学家的辅助工具,将其整合到研究工作流程中。它能够提供多种假设和研究方向,节省研究者手动查阅资料的时间,并已被证明能激发研究者的灵感,提高对铭文来源和年代判断的准确性。

🌍 **开源共享与教育应用:** Aeneas目前已开源,其界面可供教师、学生、博物馆工作人员和学者免费使用。Google DeepMind正积极与学校合作,将Aeneas融入中学历史教育,让更多人有机会接触和学习古籍解读。

Google DeepMind has unveiled new artificial-intelligence software that could help historians recover the meaning and context behind ancient Latin engravings. 

Aeneas can analyze words written in long-weathered stone to say when and where they were originally inscribed. It follows Google’s previous archaeological tool Ithaca, which also used deep learning to reconstruct and contextualize ancient text, in its case Greek. But while Ithaca and Aeneas use some similar systems, Aeneas also promises to give researchers jumping-off points for further analysis.

To do this, Aeneas takes in partial transcriptions of an inscription alongside a scanned image of it. Using these, it gives possible dates and places of origins for the engraving, along with potential fill-ins for any missing text. For example, a slab damaged at the start and continuing with … us populusque Romanus would likely prompt Aeneas to guess that Senat comes before us to create the phrase Senatus populusque Romanus, “The Senate and the people of Rome.” 

This is similar to how Ithaca works. But Aeneas also cross-references the text with a stored database of almost 150,000 inscriptions, which originated everywhere from modern-day Britain to modern-day Iraq, to give possible parallels—other catalogued Latin engravings that feature similar words, phrases, and analogies. 

This database, alongside a few thousand images of inscriptions, makes up the training set for Aeneas’s deep neural network. While it may seem like a good number of samples, it pales in comparison to the billions of documents used to train general-purpose large language models like Google’s Gemini. There simply aren’t enough high-quality scans of inscriptions to train a language model to learn this kind of task. That’s why specialized solutions like Aeneas are needed. 

The Aeneas team believes it could help researchers “connect the past,” said Yannis Assael, a researcher at Google DeepMind who worked on the project. Rather than seeking to automate epigraphy—the research field dealing with deciphering and understanding inscriptions—he and his colleagues are interested in “crafting a tool that will integrate with the workflow of a historian,” Assael said in a press briefing. 

Their goal is to give researchers trying to analyze a specific inscription many hypotheses to work from, saving them the effort of sifting through records by hand. To validate the system, the team presented 23 historians with inscriptions that had been previously dated and tested their workflows both with and without Aeneas. The findings, which were published today in Nature, showed that Aeneas helped spur research ideas among the historians for 90% of inscriptions and that it led to more accurate determinations of where and when the inscriptions originated.

In addition to this study, the researchers tested Aeneas on the Monumentum Ancyranum, a famous inscription carved into the walls of a temple in Ankara, Turkey. Here, Aeneas managed to give estimates and parallels that reflected existing historical analysis of the work, and in its attention to detail, the paper claims, it closely matched how a trained historian would approach the problem. “That was jaw-dropping,” Thea Sommerschield, an epigrapher at the University of Nottingham who also worked on Aeneas, said in the press briefing. 

However, much remains to be seen about Aeneas’s capabilities in the real world. It doesn’t guess the meaning of texts, so it can’t interpret newly found engravings on its own, and it’s not clear yet how useful it will be to historians’ workflows in the long term, according to Kathleen Coleman, a professor of classics at Harvard. The Monumentum Ancyranum is considered to be one of the best-known and most well-studied inscriptions in epigraphy, raising the question of how Aeneas will fare on more obscure samples. 

Google DeepMind has now made Aeneas open-source, and the interface for the system is freely available for teachers, students, museum workers, and academics. The group is working with schools in Belgium to integrate Aeneas into their secondary history education. 

“To have Aeneas at your side while you’re in the museum or at the archaeological site where a new inscription has just been found—that is our sort of dream scenario,” Sommerschield said.

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Google DeepMind Aeneas 人工智能 古籍修复 历史学
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