{"id":1272,"date":"2020-01-15T01:53:49","date_gmt":"2020-01-15T01:53:49","guid":{"rendered":"https:\/\/www.danielparente.net\/en\/2020\/01\/15\/6-predictions-for-the-future-of-artificial-intelligence-in-2020-adweek\/"},"modified":"2020-01-15T01:53:49","modified_gmt":"2020-01-15T01:53:49","slug":"6-predictions-for-the-future-of-artificial-intelligence-in-2020-adweek","status":"publish","type":"post","link":"https:\/\/www.danielparente.net\/en\/2020\/01\/15\/6-predictions-for-the-future-of-artificial-intelligence-in-2020-adweek\/","title":{"rendered":"6 Predictions for the Future of Artificial Intelligence in 2020 \u2013 Adweek"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<p>The business world\u2019s enthusiasm for artificial intelligence has been building towards a fever pitch in the past few years, but those feelings could get a bit more complicated in 2020.<\/p>\n<div>\n<div class=\"kickout justify-left kickout-container ko-image-container\">\n<div class=\"ko-image\"> <a class=\"kickout-link kickout-image-link\" href=\"https:\/\/www.adweek.com\/category\/year-in-review\/\" target=\"_blank\" rel=\"noopener noreferrer\"><img loading=\"lazy\" decoding=\"async\" loading=\"lazy\" width=\"220\" height=\"232\" class=\"attachment-aw-kickout size-aw-kickout\" alt=\"says adweek 2019 in review in a blue sparkly diamond\" src=\"https:\/\/static.adweek.com\/adweek.com-prod\/wp-content\/uploads\/2019\/12\/2019-in-review-01-220.png\"\/><noscript><img fetchpriority=\"high\" decoding=\"async\" width=\"220\" height=\"232\" src=\"https:\/\/static.adweek.com\/adweek.com-prod\/wp-content\/uploads\/2019\/12\/2019-in-review-01-220.png\" class=\"attachment-aw-kickout size-aw-kickout\" alt=\"says adweek 2019 in review in a blue sparkly diamond\"\/><\/noscript><\/a><\/p>\n<p>Click here to read more.<\/p>\n<\/div>\n<\/div>\n<p>Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to come to terms with potential limitations in what AI can realistically achieve. Meanwhile, a growing movement is grappling with its ethics and social implications, and widespread business adoption <a href=\"https:\/\/www.adweek.com\/digital\/enterprise-companies-are-trying-to-automate-the-process-of-making-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">remains stubbornly low<\/a>.<\/p>\n<p>As a result, companies and organizations are increasingly pushing tools that commoditize existing predictive and image recognition machine learning, making the tech <a href=\"https:\/\/www.adweek.com\/digital\/spurred-by-bias-companies-are-trying-to-break-open-the-black-box-of-ai-algorithms\/\" target=\"_blank\" rel=\"noopener\">easier to explain<\/a> and <a href=\"https:\/\/www.adweek.com\/digital\/enterprise-companies-are-trying-to-automate-the-process-of-making-ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">use for non-coders<\/a>. Emerging breakthroughs, like the ability to create synthetic data and open-source language processors that require less training than ever, are aiding these efforts.<\/p>\n<p>At the same time, the use of AI for nefarious ends like deepfakes and the mass-production of spam are still in their earliest theoretical stages, and troubling reports indicate such dystopia may become more real in 2020.<\/p>\n<p>Here are six predictions for the tech in this new year:<\/p>\n<p> <span id=\"summary-section-1\" class=\"aw-article-summary\"\/><\/p>\n<h4>1. Machines will get better at understanding\u2014and generating their own\u2014speech and writing<\/h4>\n<p>A high-profile research org called OpenAI grabbed headlines in early 2019 when it proclaimed its latest news-copy generating machine learning software, GPT-2, was too dangerous to publicly release in full. Researchers worried the passably realistic-sounding text generated by GPT-2 would be used for the <a href=\"https:\/\/www.adweek.com\/digital\/new-ai-can-detect-fake-news-with-unprecedented-accuracy-and-generate-its-own\/\" target=\"_blank\" rel=\"noopener noreferrer\">mass-generation of fake news<\/a>.<\/p>\n<p>GPT-2 is the most sophisticated of a new type of language generation. It involves a base program trained on a massive dataset. In GPT-2\u2019s case, it trains on more than 8 million websites to understand the general mechanics of how language works. That foundational system can then be trained on a relatively smaller, more specific dataset to <a href=\"https:\/\/www.adweek.com\/digital\/can-you-tell-which-adweek-headlines-written-by-artificial-intelligence\/\" target=\"_blank\" rel=\"noopener noreferrer\">mimic a certain style<\/a> for uses like predictive text, <a href=\"https:\/\/www.adweek.com\/digital\/microsoft-releases-gpt-2-blueprint-advanced-chatbot-offensive\/\" target=\"_blank\" rel=\"noopener noreferrer\">chatbots<\/a> or even creative writing aids.<\/p>\n<p>OpenAI <a href=\"https:\/\/www.adweek.com\/digital\/openai-fake-news-machine-learning-too-dangerous-artificial-intelligence\/\" target=\"_blank\" rel=\"noopener noreferrer\">ended up publishing the full version<\/a> of the model in November. It called attention to the exciting\u2014if sometimes unsettling\u2014potential of a growing trend in a subfield of AI called natural language processing, the ability to parse and produce natural-sounding human language.<\/p>\n<p>The resource and accessibility breakthrough is analogous to <a href=\"https:\/\/qz.com\/1034972\/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world\/\" target=\"_blank\" rel=\"noopener noreferrer\">a similar milestone<\/a> in the subfield of computer vision around 2012, one widely credited with spawning the surge in image and facial recognition AI of the last few years. Some researchers think natural language tech is rumored to be <a href=\"https:\/\/ruder.io\/nlp-imagenet\/\" target=\"_blank\" rel=\"noopener noreferrer\">poised for a similar boom<\/a> in the next year or so. \u201cIt\u2019s now starting to emerge,\u201d Tsung-Hsien Wen, chief technology officer at a chatbot startup called PolyAI, said of this possibility.<\/p>\n<p> <span id=\"summary-section-2\" class=\"aw-article-summary\"\/><\/p>\n<h4>2. Synthetically produced data could make AI cheaper<\/h4>\n<p>Ask any data scientist or company toiling over a nascent AI strategy what their biggest headache is and the answer will likely involve data. Machine learning systems perform only as well as the data on which they\u2019re trained, and the scale at which they require it is massive.<\/p>\n<p>One reprieve from this insatiable need may come from an unexpected place: an emergent new machine learning model currently best known for its role in deepfakes and <a href=\"https:\/\/www.adweek.com\/creativity\/ai-powered-creativity-tools-are-now-easier-than-ever-for-anyone-to-use\/\" target=\"_blank\" rel=\"noopener\">AI-generated art<\/a>. <a href=\"https:\/\/www.adweek.com\/digital\/patent-filings-for-generative-ai-have-grown-500-this-year-as-brands-test-its-potential\/\" target=\"_blank\" rel=\"noopener noreferrer\">Patent applications indicate<\/a> that brands explored all kinds of uses for this tech, known as a generative adversarial network (GAN), in 2019. But one of its unsung, yet potentially most impactful, talents is its ability to pad out a dataset with mass-produced fake data that\u2019s similar but slightly varied from the original material.<\/p>\n<p>\u201cWhat happens here is that you try to complement a set of data with another kind of data that may not be exactly what you\u2019ve observed\u2013that could be made up\u2013but that are trustworthy enough to be used in a machine learning environment,\u201d said Gartner analyst Erick Brethenoux.<\/p>\n<p id=\"load-next-page-wrapper\"><button class=\"button\" id=\"load-next-page-btn\" data-btn-text=\"Continue Reading\">Continue Reading<\/button><\/p>\n<\/div>\n<p><script type='text\/javascript' src='https:\/\/connect.facebook.net\/en_US\/sdk.js'><\/script><br \/>\n<br \/>[ad_2]<br \/>\n<br \/><a href=\"https:\/\/www.adweek.com\/digital\/6-predictions-for-the-future-of-artificial-intelligence-in-2020\/\" target=\"_blank\" rel=\"noopener\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] The business world\u2019s enthusiasm for artificial intelligence has been building towards a fever pitch in the past few years, but those feelings could get a bit more complicated in 2020. Click here to read more. Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1273,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_post_was_ever_published":false},"categories":[94,92,98],"tags":[],"class_list":["post-1272","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-data-science","category-machine-learning"],"blocksy_meta":[],"jetpack_featured_media_url":"https:\/\/e928cfdc7rs.exactdn.com\/info\/uploads\/sites\/3\/2020\/01\/6-Predictions-for-the-Future-of-Artificial-Intelligence-in-2020.png?strip=all","jetpack_shortlink":"https:\/\/wp.me\/p2TFCd-kw","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts\/1272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/comments?post=1272"}],"version-history":[{"count":0,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts\/1272\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/media\/1273"}],"wp:attachment":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/media?parent=1272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/categories?post=1272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/tags?post=1272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}