{"id":419,"date":"2020-07-10T14:33:24","date_gmt":"2020-07-10T14:33:24","guid":{"rendered":"http:\/\/sites.rutgers.edu\/critical-ai\/?page_id=419"},"modified":"2025-09-02T18:08:44","modified_gmt":"2025-09-02T18:08:44","slug":"about-us","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/critical-ai\/about-us\/","title":{"rendered":"About Us"},"content":{"rendered":"
\n

Artificial Intelligence<\/i> (AI) is a common term for an emerging set of computer technologies that affect individuals, communities, societies, and the environment<\/span><\/a> at an increasing scale. Although the phrase \u201cAI\u201d was coined in the 1950s, the field of research to which it refers has undergone multiple transformations and \u201cwinters.<\/span><\/a>\u201d Moreover, until recently, \u201cAI\u201d was familiar to the general public largely as a theme for science fiction.<\/p>\n

\u201cAI\u201d returned to public discussion in the 2010s when a number of innovations in \u201cdeep learning\u201d became possible, largely because of the availability of massive stores of human-generated data on the internet and through networked devices. At around the same time, these technologies began to power widespread applications including voice assistants, recommendation systems, and grammar checks. When technologists speak of deep learning <\/i>(DL), which is a type of machine learning<\/i> (ML), the learning <\/i>in question denotes a computer model\u2019s ability to \u201coptimize\u201d for useful predictions while \u201ctraining\u201d on data (a process that involves adjusting the weights in an elaborate set of statistical calculations). The \u201clearning\u201d is deep <\/i>because of the multiple computational layers in the very large models that DL involves. Because AI researchers have used this anthropomorphic<\/span><\/a> language for many decades, today\u2019s DL and ML\u00a0 models are often said to \u201cunderstand,\u201d \u201clearn,\u201d \u201creason,\u201d \u201cexperience,\u201d and \u201cthink.\u201d Although most technologists recognize that products like OpenAI\u2019s ChatGPT or Microsoft’s Copilot are built on disembodied statistical models that do not \u201cunderstand, \u201clearn,\u201d or \u201cexperience\u201d the way that people do, this confusing vocabulary pervades the hype surrounding this resource-intensive<\/span><\/a> technology at the expense of public understanding. Teaching critical AI literacies in the current landscape begins with helping students to distinguish between the functionalities of actually existing technologies, and the fictional \u201cAI\u201d on view in popular media<\/span> such as Blade Runner <\/i>(1982), Ex Machina <\/i>(2014), <\/i>or Westworld <\/i>(2016-2022).<\/p>\n

Critical AI @ Rutgers is an interdisciplinary initiative, organized and led through a <\/span>steering committee<\/span><\/a> with support from the <\/span>Center for Cultural Analysis<\/span><\/a>, the <\/span>Rutgers Center for Cognitive Science<\/span><\/a>, and the Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory<\/a>. <\/span>To learn more about our past and upcoming events, click here<\/a>.<\/span>\u00a0\u00a0<\/span><\/p>\n

For links to the recordings of our 2024 DESIGN JUSTICE AI Global Humanities Institute at University of Pretoria, click here<\/a>.<\/p>\n

To learn more about our new journal, <\/span>Critical AI, <\/span><\/i>published with Duke University Press, see <\/span>Duke\u2019s website<\/span><\/a> and our <\/span>blog<\/span><\/a>.<\/span><\/p>\n

AFFILIATIONS<\/span><\/h5>\n

Rutgers Center for Cultural Analysis
\nhttps:\/\/cca.rutgers.edu\/<\/a><\/p>\n

Rutgers Center for Cognitive Science
\nhttps:\/\/ruccs.rutgers.edu\/<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Artificial Intelligence (AI) is a common term for an emerging set of computer technologies that affect individuals, communities, societies, and the environment at an increasing scale. 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