Choosing 1930 as a cut-off date thus allows Talkie to sidestep the question of how to navigate copyright, an issue that has been blithely ignored by a problem for other LLMs. Which, OK, this is all interesting, but what is it for?<\/p>\n
In answering that question, Talkie\u2019s creators lean heavily on two sources: a talk<\/a> given by AI researcher Owain Evans<\/a>, from which the term \u201cvintage LLM\u201d originated, and a paper<\/a> on \u201ctemporal language models\u201d originating from a company called Calcifer Computing, whose business<\/a> apparently involves providing \u201cnon-recurring engineering services for clients with problems interesting enough to warrant our attention.\u201d<\/p>\n Evans\u2019 talk couches its ideas in the sort of hyperbole that seems compulsory for AI proponents: \u201cThe first humanistic motivation [for vintage LLMs] is time travel,\u201d he explains modestly. \u201cWhat would it be like to communicate with someone from 1700?\u201d To answer this question, he proposes the idea of models trained on data that cuts off at a certain time\u2014exactly the sort of thing Talkie is doing, in other words.<\/p>\n The Calcifer Computing paper is less violet-hued, discussing the challenge of how LLMs can account for the way that aspects of language\u2014words\u2019 meanings, speech patterns, vocabulary\u2014change over time. This is genuinely interesting, and apparently inspired one of the first uses for Talkie, which was to provide a subjective rating of the \u201csurprisingness\u201d of various post-1930 events.<\/p>\n The really interesting question, though, is how reliably an LLM trained on data that cuts off at a certain date can predict what will happen after that date. This feels like a scaled-down sociological analogue of the more fundamental question of determinism, which asks whether knowing everything about a system\u2019s initial state allows you to predict that system\u2019s future states.<\/p>\n Of course, you can feed an LLM information until the cows come home; it will never and could never know everything possible about the state of the world in 1930, or 69 BC, or 5:30pm yesterday. But still, the idea of giving an LLM a solid grounding in history along with a large amount of information about the state of the world in 1930, and then asking, \u201cWhat happens next?\u201d\u2026 even for someone generally skeptical of LLMs (like, y\u2019know, me), that\u2019s an interesting question. Being AI people, Talkie\u2019s creators aren\u2019t satisfied with predicting the future; they also allude to a question posed by Google DeepMind CEO Demis Hassabis, who once asked whether an LLM trained with data that cuts off at 1911 could discover general relativity.<\/p>\n Sadly, it doesn\u2019t appear that there\u2019s an answer to either question yet. The rest of the paper is devoted mostly to explaining the various challenges of getting Talkie to work reliably, foremost among them the lack of reliable training data. Talkie is trained on data scanned from physical sources, making reliable character recognition facilities extremely important. There\u2019s also the challenge of what the authors call \u201ccontamination,\u201d i.e., the leakage of post-1930s material into the training data.<\/p>\n At this point, Talkie falls into the \u201cpotentially interesting and apparently harmless\u201d category of LLM\/AI agent projects, which, honestly, these days feels like about the best we can hope for. The project\u2019s site contains a live feed of the LLM answering questions posed by \u2026 another LLM. As this post was being written, Talkie was describing an 1882 cricket match, and if you\u2019ll bear with me here:<\/p>\n ![\"Talkie](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-28-at-12.20.18.jpg\")
\u00a9 Talkie <\/p>\n