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action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home4/scienrds/scienceandnerds/wp-includes/functions.php on line 6114Source:https:\/\/techcrunch.com\/2023\/06\/14\/mechanical-turk-workers-are-using-ai-to-automate-being-human\/<\/a><\/br> File this one under inevitable but hilarious. Mechanical Turk is a service that from its earliest days seemed to invite shenanigans, and indeed researchers show that nearly half of its \u201cturkers\u201d appear to be using AI to do tasks that were specifically intended to be done by humans because AI couldn\u2019t. We\u2019ve closed the loop on this one; great job everybody!<\/p>\n Amazon\u2019s Mechanical Turk let users divide simple tasks into any number of small subtasks that take only a few seconds to do, and which pay pennies \u2014 but dedicated piecemeal workers would perform thousands and thereby earn a modest but reliable wage. It was, as Jeff Bezos memorably put it back then, \u201cartificial artificial intelligence.\u201d<\/p>\n These were usually tasks that were then difficult to automate \u2014 like a CAPTCHA, or identifying the sentiment of a sentence, or a simple \u201cdraw a circle around the cat in this image,\u201d things that people could do quickly and reliably. It was used liberally by people labeling relatively complex data and researchers aiming to get human evaluations or decisions at scale.<\/p>\n It\u2019s named after the famous chess-playing \u201cautomaton\u201d that actually used a human hiding in its base to make its plays \u2014 Poe wrote a great contemporary takedown of it<\/a>. Sometimes automation is difficult or impossible, but in such cases you can make a sort of machine out of humanity. One has to be careful about it, but it has proven useful over the years.<\/p>\n But a study from researchers at EPFL in Switzerland shows that Mechanical Turk workers are automating their work using large language models like ChatGPT: A snake biting its own tail or perhaps swallowing itself entirely.<\/p>\n The question emerged when they considered using a service like MTurk as a \u201chuman in the loop\u201d to improve or fact-check LLM responses, which are basically untrustable:<\/p>\n It is tempting to rely on crowdsourcing to validate LLM outputs or to create human gold-standard data for comparison. But what if crowd workers themselves are using LLMs, e.g., in order to increase their productivity, and thus their income, on crowdsourcing platforms?<\/p>\n<\/blockquote>\n To get a general sense of the problem, they assigned an \u201cabstract summarization\u201d task to be completed by turkers. By various analyses described in the paper (still not published or peer-reviewed)<\/a> they \u201cestimate that 33%-46% of crowd workers used LLMs when completing the task.\u201d<\/p>\n To some, this will come as no surprise. Some level of automation has likely existed in turking ever since the platform started. Speed and reliability are incentivized, and if you could write a script that handled certain requests with 90% accuracy, you stood to make a fair amount of money. With so little oversight of individual contributors\u2019 processes, it was inevitable that some of these tasks would not actually be performed by humans, as advertised. Integrity has never been Amazon\u2019s strong suit so there was no sense relying on them.<\/p>\n But to see it laid out like this, and for a task that until recently seemed like one only a human could do \u2014 adequately summarize a paper\u2019s abstract \u2014 it questions not just the value of Mechanical Turk but exposes another front in the imminent crisis of \u201cAI training on AI-generated data\u201d in yet another Ouroboros-esque predicament.<\/p>\n The researchers (Veniamin Veselovsky, Manoel Horta Ribeiro and Robert West) caution that this task is, as of the advent of modern LLMs, one particularly suited to surreptitious automation, and thus particularly likely to fall victim to these methods. But the state of the art is steadily advancing:<\/p>\n LLMs are becoming more popular by the day, and multimodal models, supporting not only text, but also image and video input and output, are on the rise. With this, our results should be considered the \u2018canary in the coal mine\u2019 that should remind platforms, researchers, and crowd workers to find new ways to ensure that human data remain human.<\/p>\n<\/blockquote>\n The threat of AI eating itself has been theorized for many years and became a reality almost instantly upon widespread deployment of LLMs: Bing\u2019s pet ChatGPT quoted its own misinformation<\/a> as support for new misinformation about a COVID conspiracy.<\/p>\n If you can\u2019t be 100% sure something was done by a human, you\u2019re probably better off assuming it wasn\u2019t. That\u2019s a depressing principle to have to adhere to, but here we are.<\/p>\n<\/p><\/div>\n <\/br><\/br><\/br><\/p>\n
\nMechanical Turk workers are using AI to automate being human<\/br>
\n2023-06-15 22:18:04<\/br><\/p>\n\n
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