{"id":2138,"date":"2019-12-17T02:21:52","date_gmt":"2019-12-16T23:21:52","guid":{"rendered":"http:\/\/kusuaks7\/?p=1743"},"modified":"2024-02-08T13:46:08","modified_gmt":"2024-02-08T13:46:08","slug":"the-bot-decade-how-ai-took-over-our-lives-in-the-2010s","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/the-bot-decade-how-ai-took-over-our-lives-in-the-2010s\/","title":{"rendered":"The Bot Decade: How AI Took Over Our Lives In The 2010s"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tThe Decade, Reviewed<\/a>\u00a0looks back at the 2010s and how it changed human society forever. From 2010 to 2019, our species experienced seismic shifts in science, technology, entertainment, transportation, and even the very planet we call home. This is how the past ten years have changed us.<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying … and a little\u00a0racist<\/a>. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn the 2010s, automation got better, cheaper, and way less avoidable. It\u2019s still mysterious, but no longer foreign; the most Extremely Online among us interact with\u00a0dozens of AIs throughout the day<\/u><\/a>. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from\u00a0fruit pickers<\/u><\/a>\u00a0to\u00a0hedge fund managers<\/u><\/a>\u00a0has suffered through headlines about being replaced.\n

Humans and tech have always coexisted and coevolved, but this decade brought us closer together\u2014and closer to the future\u2014than ever. These days, you don\u2019t have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as you\u2019re constantly offering your digital behavior to train AIs.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tSo here\u2019s how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We Made Them Smarter<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAll those little operational tweaks in our day come courtesy of a specific scientific approach to AI called\u00a0machine learning<\/a>, one of the most popular techniques for AI projects this decade. That\u2019s when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tDeep learning<\/a>, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn January,\u00a0Technology Review<\/em>\u2019s Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. \u201cMachine learning<\/u><\/a>\u00a0has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging\u00a0handwriting or video<\/u><\/a>,\u201d Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tHao\u2019s report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like\u00a0generative adversarial networks<\/u><\/a>\u00a0(GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordingly\u2014not unlike the way dogs and babies learn about the world.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a task\u2014especially if the task is to imitate a human in some capacity.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We Invited Them In<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasn\u2019t until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and\u00a0rapid worker disenfranchisement<\/u><\/a>\u00a0as increasingly complex service jobs are doubled up,\u00a0automated away<\/u><\/a>, or taken over by AI workers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term \u201csurveillance capitalism.\u201d This new economic system,\u00a0she wrote<\/u><\/a>, \u201cunilaterally claims human experience as free raw material for translation into behavioural data,\u201d in a bid to make profit from informed gambling based on predicted human behavior.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We Put Them in Charge<\/strong><\/h2>\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWe\u2019re already\u00a0using machine learning to make subjective decisions<\/u><\/a>\u2014even ones that have life-altering consequences. Medical applications are only some of the\u00a0least controversial<\/u><\/a>\u00a0uses of artificial intelligence; by the end of the decade, AIs were\u00a0locating stranded victims of Hurricane Maria<\/u><\/a>, controlling the\u00a0German power grid<\/u><\/a>, and\u00a0killing civilians<\/u><\/a>\u00a0in Pakistan.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci\u00a0pointed out<\/u><\/a>\u00a0the presence on the Obama reelection campaign of \u201can unprecedented number of data analysts and social scientists,\u201d bringing the traditional \u201cconfluence of marketing and politics\u201d into a new age.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIntelligence that relies on data from an unjust world suffers from the principle of \u201cgarbage in, garbage out,\u201d futurist Cory Doctorow observed in\u00a0a recent blog post<\/u><\/a>. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u201cGiven that a major application for facial recognition is totalitarian surveillance and control, maybe we should be thinking about limiting facial recognition altogether, rather than ensuring that it is equally good at destroying the lives of women and brown people.\u201d<\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt doesn\u2019t help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut the datasets of human faces that the program uses are sourced, according to a\u00a0report from March<\/u><\/a>, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been\u00a0arrested on suspicion of criminal activity<\/u><\/a>. None of the millions of faces in the program\u2019s data sets belonged to people who had consented to this use of their data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We Tried to Control Them<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tState-level efforts to regulate AI finally emerged this decade, with some success. The European Union\u2019s General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the \u201cdata subject\u201d (read: us); the GDPR also prohibits the \u201cblack box\u201d model for machine learning applications, requiring both\u00a0transparency and accountability<\/u><\/a>\u00a0on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI\u00a0ethics panel<\/u><\/a>\u00a0a week later, feigning shock at all the negative reception.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEven attempted regulation is a good sign. It means we\u2019re looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaigns\u2014essentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propaganda\u2014infiltrated the very mechanics of American democracy.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Next Up: The Second Bot Decade<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tKeeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural net\u2019s name is\u00a0Grover<\/u><\/a>, and it\u2019s\u00a0really good at this<\/u><\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

In the 2010s, automation got better, cheaper, and way less avoidable. It’s still mysterious, but no longer foreign. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. Humans and tech have always coexisted and coevolved, but this decade brought us closer together—and closer to the future—than ever. So here’s how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.<\/p>\n","protected":false},"author":692,"featured_media":3063,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[3489],"class_list":["post-2138","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":3489,"user_id":692,"is_guest":0,"slug":"lynne-peskoe-yang","display_name":"Lynne Peskoe-Yang","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Peskoe-Yang","first_name":"Lynne","job_title":"","description":"Lynne Peskoe-Yang is a Writer at the Society of Tribologists and Lubrication Engineers. Her reporting on civil engineering, machine learning, and artificial intelligence has appeared in Marketplace, IEEE Spectrum, Rewire.org, and Sludge; her essays on science and language live over at Popula. "}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2138","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/692"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2138"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2138\/revisions"}],"predecessor-version":[{"id":35911,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2138\/revisions\/35911"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3063"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2138"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2138"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2138"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2138"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}