A pioneering rule-based mechanical translation system (precursor of modern RBMTs) was first presented in December 1929 by its inventor, Federico Pucci, who later published the full method in a book titled "Il traduttore meccanico ed il metodo per corrispondersi fra Europei conoscendo ciascuno solo la propria lingua: Parte I", in Salerno (Italy), in 1931. This study illustrates how AI breathes new life into the system of international keys and ideograms devised by Pucci to translate from/into any Romance language (at least as a first step). The methodology involves having the AIs retranslate, following Pucci's method, the two text excerpts originally translated in 1931 and clearly documented in his publication: a passage from Dante's La Vita Nuova, translated from Italian into French, and a passage from Voltaire's Zadig, translated from French into Italian. The result is notable: the two texts, translated 94 years apart using the same method--by Pucci in 1931 and by AIs in 2025--show a low average difference, with only minor variations observed. With Pucci's system thus validated, it became feasible to have the AIs reproduce the excerpts in English, Spanish, and German according to his method. The results were consistent, and Pucci--via Artificial Intelligence--was tasked with translating more modern and technical texts, thereby reviving, nearly a century later, an invention that had remained almost entirely unknown and never applied beyond its creator, now brought to wider attention and opened to possible experimentation. Such a demonstration would not only affirm Pucci's historical status but also place him among the precursors and intellectual contributors to machine translation, whose work merits examination alongside figures such as Troyanskij, Booth, and Weaver, with possible consequences for how the history of the field is understood.

The Questio de aqua et terra is a cosmological treatise traditionally attributed to Dante Alighieri. However, the authenticity of this text is controversial, due to discrepancies with Dante's established works and to the absence of contemporary references. This study investigates the authenticity of the Questio via computational authorship verification (AV), a class of techniques which combine supervised machine learning and stylometry. We build a family of AV systems and assemble a corpus of 330 13th- and 14th-century Latin texts, which we use to comparatively evaluate the AV systems through leave-one-out cross-validation. Our best-performing system achieves high verification accuracy (F1=0.970) despite the heterogeneity of the corpus in terms of textual genre. The key contribution to the accuracy of this system is shown to come from Distributional Random Oversampling (DRO), a technique specially tailored to text classification which is here used for the first time in AV. The application of the AV system to the Questio returns a highly confident prediction concerning its authenticity. These findings contribute to the debate on the authorship of the Questio, and highlight DRO's potential in the application of AV to cultural heritage.

Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, especially since sentences are usually complex enough to include multiple units of meaning that merit separate treatment in the downstream task. We focus on the task of abstractive proposition segmentation: transforming text into simple, self-contained, well-formed sentences. Several recent works have demonstrated the utility of proposition segmentation with few-shot prompted LLMs for downstream tasks such as retrieval-augmented grounding and fact verification. However, this approach does not scale to large amounts of text and may not always extract all the facts from the input text. In this paper, we first introduce evaluation metrics for the task to measure several dimensions of quality. We then propose a scalable, yet accurate, proposition segmentation model. We model proposition segmentation as a supervised task by training LLMs on existing annotated datasets and show that training yields significantly improved results. We further show that by using the fine-tuned LLMs as teachers for annotating large amounts of multi-domain synthetic distillation data, we can train smaller student models with results similar to the teacher LLMs. We then demonstrate that our technique leads to effective domain generalization, by annotating data in two domains outside the original training data and evaluating on them. Finally, as a key contribution of the paper, we share an easy-to-use API for NLP practitioners to use.

What if Dante was not describing his redemption journey through Hell, Purgatory and Heaven, but he was poetically describing a Data Science journey through Data, Modelling and Deployment? The start of his first "Canto" in Inferno would have been written as… Midway upon my journey in the realm of data science, I found myself within a sea of algorithms and code, For the straightforward path of understanding had been lost. How hard a thing it is to say What was this chaos of machine learning models and techniques, Which in the very thought renews the confusion. So bitter is it, learning is little more; But of the good to treat, which there I found, Speak will I of the insights and discoveries I made there. Divina Commedia meets data science in this three-part series that explores the machine learning project lifecycle through the lens of Dante's epic journey through Hell, Purgatory, and Heaven.

When people think of artificial intelligence, the images that often come to mind are of the sinister robots that populate the worlds of "The Terminator," "i, Robot," "Westworld," and "Blade Runner." For many years, fiction has told us that AI is often used for evil rather than for good. But what we may not usually associate with AI is art and poetry -- yet that's exactly what Ai-Da, a highly realistic robot invented by Aidan Meller in Oxford, central England, spends her time creating. Ai-Da is the world's first ultra-realistic humanoid robot artist, and on Friday she gave a public performance of poetry that she wrote using her algorithms in celebration of the great Italian poet Dante. The recital took place at the University of Oxford's renowned Ashmolean Museum as part of an exhibition marking the 700th anniversary of Dante's death.

"The biggest change in human history will take place in the next decade," warns Aidan Meller, a Briton who ran an art gallery for 20 years until he became a pioneer by launching the world's first creative robot, Ai-Da. Introduced in 2019 as "the first humanoid artist," Ai-Da not only creates poems, paintings and sculptures, but also draws inspiration from the highest cultural references. Her name is not random either; it is a tribute to Ada Lovelace, a British mathematician considered the first computer programmer, also known for being the only legitimate daughter of the poet Lord Byron. Ai-Da's next action will be at the Giardini of the Venice Biennale on April 23. It will be the first time in the 120-year history of the Biennale that a robot artist will exhibit their work alongside that created by humans.

When people think of artificial intelligence, the images that often come to mind are of the sinister robots that populate the worlds of "The Terminator," "i, Robot," "Westworld," and "Blade Runner." For many years, fiction has told us that AI is often used for evil rather than for good. But what we may not usually associate with AI is art and poetry -- yet that's exactly what Ai-Da, a highly realistic robot invented by Aidan Meller in Oxford, central England, spends her time creating. Ai-Da is the world's first ultra-realistic humanoid robot artist, and on Friday she gave a public performance of poetry that she wrote using her algorithms in celebration of the great Italian poet Dante. The recital took place at the University of Oxford's renowned Ashmolean Museum as part of an exhibition marking the 700th anniversary of Dante's death.

When people think of artificial intelligence, the images that often come to mind are of the sinister robots that populate the worlds of "The Terminator," "i, Robot," "Westworld," and "Blade Runner." For many years, fiction has told us that AI is often used for evil rather than for good. But what we may not usually associate with AI is art and poetry -- yet that's exactly what Ai-Da, a highly realistic robot invented by Aidan Meller in Oxford, central England, spends her time creating. Ai-Da is the world's first ultra-realistic humanoid robot artist, and on Friday she gave a public performance of poetry that she wrote using her algorithms in celebration of the great Italian poet Dante. The recital took place at the University of Oxford's renowned Ashmolean Museum as part of an exhibition marking the 700th anniversary of Dante's death.

Dante's Divine Comedy has inspired countless artists, from William Blake to Franz Liszt, and from Auguste Rodin to CS Lewis. But an exhibition marking the 700th anniversary of the Italian poet's death will be showcasing the work of a rather more modern devotee: Ai-Da the robot, which will make history by becoming the first robot to publicly perform poetry written by its AI algorithms. The ultra-realistic Ai-Da, who was devised in Oxford by Aidan Meller and named after computing pioneer Ada Lovelace, was given the whole of Dante's epic three-part narrative poem, the Divine Comedy, to read, in JG Nichols' English translation. She then used her algorithms, drawing on her data bank of words and speech pattern analysis, to produce her own reactive work to Dante's. Ai-Da will perform the poems on Friday night at Oxford's Ashmolean Museum.

"Abandon all hope ye who enter here" was the inscription Dante read when passing through the gates of hell. Apparently, it's also true of anyone but the big cloud providers when it comes to artificial intelligence, according to an analysis by Bain & Company. "The CSPs [cloud service providers] are best positioned because of the significant head start they have in using AI on a large scale," the report authors stated. Given that FirstMark investor Matt Turck recently called out how well startups have done in the shadows of the cloud giants, it's worth diving deeper into the strengths the clouds bring to AI. "CSPs' cloud and digital services have given them access to the enormous amounts of data required to effectively train AI models," the authors concluded. Such economies of scale have been an asset to the cloud providers for years.