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3 min read<\/p>\n3 min read<\/p>\n
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By one unique metric, we could approach technological singularity by the end of this decade, if not sooner.A translation company developed a metric, Time to Edit (TTE), to calculate the time it takes for professional human editors to fix AI<\/a>-generated translations compared to human ones. This may help quantify the speed toward singularity.An AI that can translate speech as well as a human could change society.<\/p>\n In the world of artificial intelligence, the idea of \u201csingularity<\/a>\u201d looms large. This slippery concept describes the moment AI exceeds beyond human control and rapidly transforms society. The tricky thing about AI singularity (and why it borrows terminology from black hole<\/a> physics) is that it\u2019s enormously difficult to predict where it begins and nearly impossible to know what\u2019s beyond this technological \u201cevent horizon.\u201d <\/p>\n However, some AI researchers are on the hunt for signs of reaching singularity measured by AI progress approaching the skills and ability comparable to a human. <\/p>\n One such metric, defined by Translated, a Rome-based translation company, is an AI\u2019s ability to translate speech at the accuracy of a human<\/a>. Language is one of the most difficult AI challenges, but a computer that could close that gap could theoretically show signs of Artificial General Intelligence (AGI).<\/p>\n Related Stories<\/p>\n \u201cThat\u2019s because language is the most natural thing for humans,\u201d Translated CEO Marco Trombetti said at a 2022 conference<\/a> in Orlando, Florida. \u201cNonetheless, the data Translated collected clearly shows that machines are not that far from closing the gap.\u201d<\/p>\n The company tracked its AI\u2019s performance from 2014 to 2022 using a metric called \u201cTime to Edit,\u201d or TTE, which calculates the time it takes for professional human editors to fix AI-generated translations compared to human ones. Over that 8-year period and analyzing over 2 billion post-edits, Translated\u2019s AI showed a slow, but undeniable improvement as it slowly closed the gap toward human-level translation quality.<\/p>\n Translated<\/p>\n On average, it takes a human translator roughly one second to edit each word of another human translator, according to Translated. In 2015, it took professional editors approximately 3.5 seconds per word to check a machine-translated (MT) suggestion\u2014today, that number is just 2 seconds. If the trend continues, Translated\u2019s AI will be as good as human-produced translation by the end of the decade (or even sooner).<\/p>\n \u201cThe change is so small that every single day you don\u2019t perceive it, but when you see progress \u2026 across 10 years, that is impressive,\u201d Trombetti said on a podcast<\/a>. \u201cThis is the first time ever that someone in the field of artificial intelligence did a prediction of the speed to singularity.\u201d<\/p>\n Although this is a novel approach to quantifying how close humanity is to approaching singularity, this definition of singularity runs into similar problems of identifying AGI more broadly<\/a>. And while perfecting human speech is certainly a frontier in AI research, the impressive skill doesn\u2019t necessarily make a machine intelligent (not to mention how many researchers don\u2019t even agree<\/a> on what \u201cintelligence\u201d is). <\/p>\n Whether these hyper-accurate translators are harbingers of our technological doom or not, that doesn\u2019t lessen Translated\u2019s AI accomplishment. An AI capable of translating speech as well as a human could very well change society, even if the true \u201ctechnological singularity\u201d remains ever elusive.<\/p>\n Our Most-Read Stories of the Year Darren lives in Portland, has a cat, and writes\/edits about sci-fi and how our world works. You can find his previous stuff at Gizmodo and Paste if you look hard enough.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"3 min read Here\u2019s what you\u2019ll learn when you read this story: By one unique metric, we could…\n","protected":false},"author":2,"featured_media":32030,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[6744,3013,9464,20549,66],"class_list":["post-32029","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agi","tag-agi","tag-artificial-general-intelligence","tag-core","tag-core2","tag-news"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/32029","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=32029"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/32029\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/32030"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=32029"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=32029"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=32029"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"speed](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/data-showing-speed-to-singularity-1674428505.jpeg\" "\\"speed")
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