A hymn dedicated to the ancient city of Babylon has been discovered after 2,100 years. Sung to the god Marduk, patron deity of the great city, the poem describes Babylon's flowing rivers, jewelled gates, and'bathed priests' in stunning detail. Although the song was lost to time after Alexander the Great captured the city, fragments of clay tablets survived in the ruins of Sippar, a city 40 miles to the North. In a process that would have taken'decades' to complete by hand, researchers used AI to piece together 30 different tablet pieces and recover the lost hymn. Originally 250 lines long, scientists have been able to translate a third of the original cuneiform text.

Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce TimeTravel, a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. Designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries, TimeTravel provides a structured dataset and robust evaluation framework to assess AI models' capabilities in classification, interpretation, and historical comprehension. By integrating AI with historical research, TimeTravel fosters AI-powered tools for historians, archaeologists, researchers, and cultural tourists to extract valuable insights while ensuring technology contributes meaningfully to historical discovery and cultural heritage preservation. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. Our goal is to establish AI as a reliable partner in preserving cultural heritage, ensuring that technological advancements contribute meaningfully to historical discovery. Our code is available at: \url{https://github.com/mbzuai-oryx/TimeTravel}.

Deep learning currently is used in LLMs (Large Language Models), for image identification, creation of deepfakes and analyses of astrophysical and financial information (Krizhevsky, 2012), (Sutskever, 2014), (Vaswani, 2017), (Wang, 2015), (George, 2018), (Li, 2010). When the instruments of deep learning became widely available, it was decided that the decipherment of all dead languages was only a matter of time (see (Xusen, 2019) and op.

Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE, represent one of humanity's earliest writing systems. Characterized by wedge-shaped marks on clay tablets, these artifacts provided insight into Mesopotamian civilization across various domains. Traditionally, the analysis and dating of these tablets rely on subjective assessment of shape and writing style, leading to uncertainties in pinpointing their exact temporal origins. Recent advances in digitization have revolutionized the study of cuneiform by enhancing accessibility and analytical capabilities. Our research uniquely focuses on the silhouette of tablets as significant indicators of their historical periods, diverging from most studies that concentrate on textual content. Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform Digital Library Initiative collection, we apply deep learning methods to classify cuneiform tablets, covering over 3,000 years of history. By leveraging statistical, computational techniques, and generative modeling through Variational Auto-Encoders (VAEs), we achieve substantial advancements in the automatic classification of these ancient documents, focusing on the tablets' silhouettes as key predictors. Our classification approach begins with a Decision Tree using height-to-width ratios and culminates with a ResNet50 model, achieving a 61% macro F1-score for tablet silhouettes. Moreover, we introduce novel VAE-powered tools to enhance explainability and enable researchers to explore changes in tablet shapes across different eras and genres. This research contributes to document analysis and diplomatics by demonstrating the value of large-scale data analysis combined with statistical methods. These insights offer valuable tools for historians and epigraphists, enriching our understanding of cuneiform tablets and the cultures that produced them.

The Moshiach came to Madison Avenue this summer. All over a not particularly Jewish neighborhood, posters of the bearded, Rembrandtesque Rebbe Schneerson appeared, mucilaged to every light post and bearing the caption "Long Live the Lubavitcher Rebbe King Messiah forever!" This was, or ought to have been, trebly astonishing. First, the rebbe being urged to a longer life died in 1994, and the new insistence that he was nonetheless the Moshiach skirted, as his followers tend to do, the question of whether he might remain somehow alive. Second, the very concept of a messiah recapitulates a specific national hope of a small and oft-defeated nation several thousand years ago, and spoke originally to the local Judaean dream of a warrior who would lead his people to victory over the Persians, the Greeks, and, latterly, the Roman colonizers.

There's an old belief that truth will always overcome error. Alas, history tells us something different. Without someone to fight for it, to put error on the defensive, truth may languish. It may even be lost, at least for some time. No one understood this better than the renowned Italian scientist Galileo Galilei.