{"id":5561,"date":"2021-06-01T12:21:59","date_gmt":"2021-06-01T12:21:59","guid":{"rendered":"http:\/\/TheNextWeb=1355389"},"modified":"2021-06-01T12:21:59","modified_gmt":"2021-06-01T12:21:59","slug":"text-altering-ai-is-changing-our-culture-try-this-tool-to-find-out-how","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=5561","title":{"rendered":"Text-altering AI is changing our culture \u2014 try this tool to find out how"},"content":{"rendered":"\n<p>Most of us benefit every day from the fact computers can now \u201cunderstand\u201d us when we speak or write. Yet few of us have paused to consider the potentially damaging ways this same technology may be shaping our culture.<\/p>\n<p>Human language is full of ambiguity and double meanings. For instance, consider the potential meaning of this phrase: \u201cI went to project class\u201d. Without context, it\u2019s an ambiguous statement.<\/p>\n<p>Computer scientists and linguists have spent decades trying to program computers to understand the nuances of human language. And in certain ways, computers are fast approaching humans\u2019 ability to understand and <a href=\"https:\/\/culturalanalytics.org\/article\/17212.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">generate text<\/a>.<\/p>\n<p>Through the very act of suggesting some words and not others, the predictive text and auto-complete features in our devices change the way we think. Through these subtle, everyday interactions, machine learning is influencing our culture. Are we ready for that?<\/p>\n<p>I created an online interactive work for the <a href=\"https:\/\/www.kyoglewritersfestival.com\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Kyogle Writers Festival<\/a> that lets you explore this technology in a harmless way.<\/p>\n<figure class=\"align-center zoomable\" readability=\"3\">\n<p><figure class=\"post-image post-mediaBleed aligncenter\"><a href=\"https:\/\/images.theconversation.com\/files\/403493\/original\/file-20210531-13-1wzkdxj.JPG?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=1000&amp;fit=clip\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/images.theconversation.com\/files\/403493\/original\/file-20210531-13-1wzkdxj.JPG?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip\" alt width=\"600\" height=\"450\" class=\"js-lazy\"><noscript><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/images.theconversation.com\/files\/403493\/original\/file-20210531-13-1wzkdxj.JPG?ixlib=rb-1.1.0&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip\" alt width=\"600\" height=\"450\" class><\/noscript><\/a><figcaption><a href=\"https:\/\/thenextweb.com\/news\/text-altering-ai-changing-culture-try-this-tool-find-out-how-syndication#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F06%2F01%2Ftext-altering-ai-changing-culture-try-this-tool-find-out-how-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: A photo from the Kyogle Writers Festival in NSW, earlier this year. Author provided\" data-title=\"Share A photo from the Kyogle Writers Festival in NSW, earlier this year. Author provided on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share A photo from the Kyogle Writers Festival in NSW, earlier this year. Author provided on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"><\/i><\/a>A photo from the Kyogle Writers Festival in NSW, earlier this year. Author provided<\/figcaption><\/figure><figcaption><\/figcaption><\/p>\n<\/figure>\n<h2>What is natural language processing?<\/h2>\n<p>The field concerned with using everyday language to interact with computers is called \u201cnatural language processing\u201d. We encounter it when we speak to Siri or Alexa, or type words into a browser and have the rest of our sentence predicted.<\/p>\n<p>This is only possible due to vast improvements in natural language processing over the past decade \u2014 achieved through sophisticated machine-learning algorithms trained on enormous datasets (usually billions of words).<\/p>\n<p>Last year, this technology\u2019s potential became clear when the Generative Pre-trained Transformer 3 (GPT-3) was released. It set a new benchmark in what computers can do with language.<\/p>\n<p>GPT-3 can take just a few words or phrases and generate whole documents <a href=\"https:\/\/www.theguardian.com\/commentisfree\/2020\/sep\/08\/robot-wrote-this-article-gpt-3\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">of \u201cmeaningful\u201d language<\/a>, by capturing the contextual relationships between words in a sentence. It does this by building on machine-learning models, including two widely adopted models called <a href=\"http:\/\/jalammar.github.io\/illustrated-bert\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">\u201cBERT\u201d and \u201cELMO\u201d<\/a>.<\/p>\n<h2>How is this technology affecting culture?<\/h2>\n<p>However, there is a key issue with any language model produced by machine learning: they generally learn everything they know from data sources such as Wikipedia and Twitter.<\/p>\n<p>In effect, machine learning takes data from the past, \u201clearns\u201d from it to produce a model, and uses this model to carry out tasks in the future. But during this process, <a href=\"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445922\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">a model<\/a> may absorb a distorted or problematic worldview from its training data.<\/p>\n<p>If the training data was biased, this bias will be codified and reinforced in the model, rather than being challenged. For example, a model may end up associating certain identity groups or races with positive words, and others with negative words.<\/p>\n<p>This can lead to serious exclusion and inequality, as detailed in the recent documentary <a href=\"https:\/\/www.ajl.org\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Coded Bias<\/a>.<\/p>\n<h2>Everything you ever said<\/h2>\n<p>The interactive work I created allows people to playfully gain an intuition for how computers understand language. It is called Everything You Ever Said (EYES), in reference to the way natural language models draw on all kinds of data sources for training.<\/p>\n<p>EYES allows you to take any piece of writing (less than 2000 characters) and \u201csubtract\u201d one concept and \u201cadd\u201d another. In other words, it lets you use a computer to change the meaning of a piece of text. You can <a href=\"https:\/\/www.everythingyoueversaid.art\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">try it yourself<\/a>.<\/p>\n<figure class=\"align-center zoomable\" readability=\"6\">\n<p><figure class=\"post-image post-mediaBleed aligncenter\"><a href=\"https:\/\/images.theconversation.com\/files\/403499\/original\/file-20210531-15-1e10z30.png?ixlib=rb-1.1.0&amp;rect=0%2C275%2C1908%2C813&amp;q=45&amp;auto=format&amp;w=1000&amp;fit=clip\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/images.theconversation.com\/files\/403499\/original\/file-20210531-15-1e10z30.png?ixlib=rb-1.1.0&amp;rect=0%2C275%2C1908%2C813&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip\" alt=\"Screenshot of natural language processing tool\" width=\"600\" height=\"319\" class=\"js-lazy\"><noscript><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/images.theconversation.com\/files\/403499\/original\/file-20210531-15-1e10z30.png?ixlib=rb-1.1.0&amp;rect=0%2C275%2C1908%2C813&amp;q=45&amp;auto=format&amp;w=754&amp;fit=clip\" alt=\"Screenshot of natural language processing tool\" width=\"600\" height=\"319\" class><\/noscript><\/a><figcaption><a href=\"https:\/\/thenextweb.com\/news\/text-altering-ai-changing-culture-try-this-tool-find-out-how-syndication#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F06%2F01%2Ftext-altering-ai-changing-culture-try-this-tool-find-out-how-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: EYES can add and subtract concepts from the text you input, based on an understanding of English from training data. Screenshot\" data-title=\"Share EYES can add and subtract concepts from the text you input, based on an understanding of English from training data. Screenshot on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share EYES can add and subtract concepts from the text you input, based on an understanding of English from training data. Screenshot on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"><\/i><\/a>EYES can add and subtract concepts from the text you input, based on an understanding of English from training data. Screenshot<\/figcaption><\/figure><figcaption>Here\u2019s an example of the Australian national anthem subjected to some automated revision. I subtracted the concept of \u201cempire\u201d and added the concept of \u201ckoala\u201d to get:<\/figcaption><\/p>\n<\/figure>\n<p><em>Australians all let us grieve<br \/>For we are one and free<br \/>We\u2019ve golden biota and abundance for poorness<br \/>Our koala is girt by porpoise<br \/>Our wildlife abounds in primate\u2019s koalas<br \/>Of naturalness shiftless and rare<br \/>In primate\u2019s wombat, let every koala<br \/>Wombat koala fair<br \/>In joyous aspergillosis then let us vocalise,<br \/>Wombat koala fair<\/em><\/p>\n<p>What is going on here? At its core, EYES uses a model of the English language developed by researchers from Stanford University in the United States, called <a href=\"https:\/\/nlp.stanford.edu\/projects\/glove\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">GLoVe<\/a> (Global Vectors for Word Representation).<\/p>\n<p>EYES uses GLoVe to change the text by making a series of analogies, wherein an \u201canalogy\u201d is a comparison between one thing and another. For instance, if I ask you: \u201cman is to king what woman is to?\u201d \u2014 you might answer \u201cqueen\u201d. That\u2019s an easy one.<\/p>\n<p>But I could ask a more challenging question such as: \u201crose is to thorn what love is to?\u201d There are several possible answers here, depending on your interpretation of the language. When asked about these analogies, GLoVe will produce the responses \u201cqueen\u201d and \u201cbetrayal\u201d, respectively.<\/p>\n<p>GLoVe has every word in the English language represented as a vector in a multi-dimensional space (of around 300 dimensions). A such, it can perform calculations with words, adding and subtracting words as if they were numbers.<\/p>\n<h2>Cyborg culture is already here<\/h2>\n<p>The trouble with machine learning is that the associations being made between certain concepts remain hidden inside a black box; we can\u2019t see or touch them. Approaches to making machine learning models more transparent are a <a href=\"https:\/\/www.scientificamerican.com\/article\/demystifying-the-black-box-that-is-ai\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">focus of much current research<\/a>.<\/p>\n<p>The purpose of EYES is to let you experiment with these associations in a more playful way, so you can develop an intuition for how machine learning models view the world.<\/p>\n<p>Some analogies will surprise you with their poignancy, while others may well leave you bewildered. Yet, every association was inferred from a huge corpus of a few billion words written by ordinary people.<\/p>\n<p>Models such as GPT-3, which have learned from similar data sources, are already influencing how we use language. Having entire news feeds populated by machine-written text is no longer the stuff of science fiction. This technology is <a href=\"https:\/\/notrealnews.net\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">already here<\/a>.<\/p>\n<p>And the cultural footprint of machine-learning models seems to only be growing.<!-- End of code. If you don't see any code above, please get new code from the Advanced tab after you click the republish button. The page counter does not collect any personal data. More info: https:\/\/theconversation.com\/republishing-guidelines --><\/p>\n<p><em>This article by&nbsp;<a href=\"https:\/\/theconversation.com\/profiles\/nick-kelly-104403\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Nick Kelly<\/a>, Senior Lecturer in Interaction Design, <a href=\"https:\/\/theconversation.com\/institutions\/queensland-university-of-technology-847\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Queensland University of Technology<\/a>, <\/em><em>is republished from <a href=\"https:\/\/theconversation.com\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">The Conversation<\/a> under a Creative Commons license. Read the <a href=\"https:\/\/theconversation.com\/machine-learning-is-changing-our-culture-try-this-text-altering-tool-to-see-how-159430\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">original article<\/a>.<\/em><\/p>\n<p> <a href=\"https:\/\/thenextweb.com\/news\/text-altering-ai-changing-culture-try-this-tool-find-out-how-syndication\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most of us benefit every day from the fact computers can now \u201cunderstand\u201d us when we speak or write. Yet few of us have paused to consider the potentially damaging ways this&#8230;<\/p>\n","protected":false},"author":1,"featured_media":5562,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/posts\/5561"}],"collection":[{"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5561"}],"version-history":[{"count":0,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/posts\/5561\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=\/wp\/v2\/media\/5562"}],"wp:attachment":[{"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5561"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5561"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.londonchiropracter.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}