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Unlike most significant research milestones in AI, the lab won’t be sharing the dataset it used for training the algorithm or all of the code it runs on (though it has given temporary access to the algorithm to a number of media publications, including ).To put this work into context, it’s important to understand how challenging the task of language modeling really is.
The prowess of GPT-2, say Open AI, suggests there could be methods available to researchers right now that can mimic more generalized brainpower.
“What the new Open AI work has shown is that: yes, you absolutely can build something that really seems to ‘understand’ a lot about the world, just by having it read,” says Jeremy Howard, a researcher who was not involved with Open AI’s work but has developed similar language modeling programs “[GPT-2] has no other external input, and no prior understanding of what language is, or how it works,” Howard tells “Yet it can complete extremely complex series of words, including summarizing an article, translating languages, and much more.” But as is usually the case with technological developments, these advances could also lead to potential harms.
Older methods record information about words in only their most obvious contexts, while newer methods dig deeper into their multiple meanings.
So while a system like Predictive Text only knows that the word “sunny” is used to describe the weather, newer algorithms know when “sunny” is referring to someone’s character or mood, when “Sunny” is a person, or when “Sunny” means the 1976 smash hit by Boney M.
For decades, machines have struggled with the subtleties of human language, and even the recent boom in deep learning powered by big data and improved processors has failed to crack this cognitive challenge.
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Algorithmic moderators still overlook abusive comments, and the world’s most talkative chatbots can barely keep a conversation alive.But these systems are drawing on relatively simple types of language modeling, while algorithms like GPT-2 encode the same information in more complex ways.The difference between these two approaches is technically arcane, but it can be summed up in a single word: depth.Feed it the first line of a short story, and it’ll tell you what happens to your character next.It can even write fan fiction, given the right prompt. In each screenshot, the underlined text was generated by the algorithm in response to the sentence (or sentences) before it.The writing it produces is usually easily identifiable as non-human.Although its grammar and spelling are generally correct, it tends to stray off topic, and the text it produces lacks overall coherence.But new methods for analyzing text, developed by heavyweights like Google and Open AI as well as independent researchers, are unlocking previously unheard-of talents.Open AI’s new algorithm, named GPT-2, is one of the most exciting examples yet.In a world where information warfare is increasingly prevalent and where nations deploy bots on social media in attempts to sway elections and sow discord, the idea of AI programs that spout unceasing but cogent nonsense is unsettling.For that reason, Open AI is treading cautiously with the unveiling of GPT-2.