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.

In brief AI text-to-image generation models are all the rage right now. You give them a simple description of a scene, such as "a vulture typing on a laptop," and they come up with an illustration that resembles that description. But developers who have special access to OpenAI's text-to-image engine DALL·E 2 have found all sorts of weird behaviors – including what may be a hidden, made-up language. Giannis Daras, a PhD student at the University of Texas at Austin shared artwork produced by DALL·E 2 given the input: "Apoploe vesrreaitais eating Contarra ccetnxniams luryca tanniounons" – a phrase that makes no sense to humans. But to the machine, it seemed to generate images of birds eating bugs consistently.

Who will be liable for harmful speech generated by large language models? As advanced AIs such as OpenAI's GPT-3 are being cheered for impressive breakthroughs in natural language processing and generation -- and all sorts of (productive) applications for the tech are envisaged from slicker copywriting to more capable customer service chatbots -- the risks of such powerful text-generating tools inadvertently automating abuse and spreading smears can't be ignored. Nor can the risk of bad actors intentionally weaponizing the tech to spread chaos, scale harm and watch the world burn. Indeed, OpenAI is concerned enough about the risks of its models going "totally off the rails", as its documentation puts it at one point (in reference to a response example in which an abusive customer input is met with a very troll-esque AI reply), to offer a free content filter that "aims to detect generated text that could be sensitive or unsafe coming from the API" -- and to recommend that users don't return any generated text that the filter deems "unsafe". But, given the novel nature of the technology, there are no clear legal requirements that content filters must be applied.

Humans have been pondering the potential of artificial intelligence for thousands of years. Ancient Greeks believed, for example, that a bronze automaton named Talos protected the island of Crete from maritime adversaries. But AI only moved from the mythical realm to the real world in the last half-century, beginning with legendary computer scientist Alan Turing's foundational 1950 essay asked and provided a framework for answering the provocative question, "Can machines think?" At that time, the United States was in the midst of the Cold War. Congressional representatives decided to invest heavily in artificial intelligence as part of a larger security strategy.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. This article is part of Update or Die, a series from Future Tense about how businesses and other organizations keep up with technological change--and the cost of falling behind. Few passengers realize that in the airline industry, we exclusively train our pilots using simulators. When a new-hire pilot flies the real airplane for the first time, it's with paying customers in the back. To create one of our simulators, we hacksaw off the pointy end of a real airplane, put it on a 6-degree-of-freedom hexapod motion platform, and outfit it with video displays so pilots have something to look at out the front window.

A British computer expert who helped shut down the NHS'WannaCry' cyber attack has been charged in the US with creating banking malware. Marcus Hutchins, 23, has been charged with six counts of creating and distributing malware known as Kronos. Hutchins made a telephone call from jail hours after his arrest last August to an unidentified individual - which was recorded and filed by US prosecutors, according to court documents. He said he had written code as a youngster which was turned into malicious software that prosecutors say harvested banking details. According to court documents seen by The Washington Post, Hutchins said in the phone call: 'So I wrote code for a guy a while back who then incorporated it into a banking malware, so they have logs of that, and essentially they want to know my part of the banking operation or if I just sold the code on to some guy... once they found I sold the code to someone, they wanted me to give them his name, and I don't actually know anything about him.'

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.