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}} In 2023 it was shown that automatic high-quality translation of cuneiform languages like Akkadian can be achieved using [[Natural language processing|Natural Language Processing]] methods with [[convolutional neural network]]s.[{{Cite journal |last1=Gutherz |first1=Gai |last2=Gordin |first2=Shai |last3=Sáenz |first3=Luis |last4=Levy |first4=Omer |last5=Berant |first5=Jonathan |date=2023-05-02 |editor-last=Kearns |editor-first=Michael |title=Translating Akkadian to English with neural machine translation |url=https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgad096/7147349 |journal=PNAS Nexus |language=en |volume=2 |issue=5 |article-number=pgad096 |doi=10.1093/pnasnexus/pgad096 |doi-access=free|issn=2752-6542 |pmc=10153418 |pmid=37143863}}] |
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}} In 2023, it was shown that automatic high-quality translation of cuneiform languages like Akkadian can be achieved using [[Natural language processing|Natural Language Processing]] methods with [[convolutional neural network]]s.[{{Cite journal |last1=Gutherz |first1=Gai |last2=Gordin |first2=Shai |last3=Sáenz |first3=Luis |last4=Levy |first4=Omer |last5=Berant |first5=Jonathan |date=2023-05-02 |editor-last=Kearns |editor-first=Michael |title=Translating Akkadian to English with neural machine translation |url=https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgad096/7147349 |journal=PNAS Nexus |language=en |volume=2 |issue=5 |article-number=pgad096 |doi=10.1093/pnasnexus/pgad096 |doi-access=free|issn=2752-6542 |pmc=10153418 |pmid=37143863}}] |
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In November 2023, [[generative artificial intelligence]] managed to make accurate records of cuneiform writing with a three-dimensional scan and model capable of appreciating the depth of the impression left by the stylus in the clay and the distance between the symbols and the wedges. The [[Region Based Convolutional Neural Networks|Region Based Convolutional Neural Network]] was trained on 3D models of 1,977 cuneiform tablets, with detailed annotations of 21,000 cuneiform signs and 4,700 wedges.[{{cite book|url=https://thedebrief.org/5000-year-old-tablets-can-now-be-decoded-by-artificial-intelligence-new-research-reveals/|title=5,000-year-old tablets can now be decoded by artificial intelligence, new research reveals|date=2023 |doi=10.2312/gch.20231157|archive-url=https://archive.today/20231128093511/https://thedebrief.org/5000-year-old-tablets-can-now-be-decoded-by-artificial-intelligence-new-research-reveals/|archive-date=November 28, 2023|url-status=live |last1=Stötzner |first1=Ernst |last2=Homburg |first2=Timo |last3=Bullenkamp |first3=Jan Philipp |last4=Mara |first4=Hubert |publisher=The Eurographics Association |isbn=978-3-03868-217-2 }}] |
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In November 2023, researchers made more accurate records of cuneiform writing with three-dimensional scans and a [[Region Based Convolutional Neural Networks|Region Based Convolutional Neural Network]] capable of appreciating the depth of the impression left by the stylus in the clay and the distance between the symbols and the wedges. The neural network was trained on 3D models of 1,977 cuneiform tablets, with detailed annotations of 21,000 cuneiform signs and 4,700 wedges.[{{cite book|url=https://thedebrief.org/5000-year-old-tablets-can-now-be-decoded-by-artificial-intelligence-new-research-reveals/|title=5,000-year-old tablets can now be decoded by artificial intelligence, new research reveals|date=2023 |doi=10.2312/gch.20231157|archive-url=https://archive.today/20231128093511/https://thedebrief.org/5000-year-old-tablets-can-now-be-decoded-by-artificial-intelligence-new-research-reveals/|archive-date=November 28, 2023|url-status=live |last1=Stötzner |first1=Ernst |last2=Homburg |first2=Timo |last3=Bullenkamp |first3=Jan Philipp |last4=Mara |first4=Hubert |publisher=The Eurographics Association |isbn=978-3-03868-217-2 }}] |
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