- Feb 2024
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- Jan 2024
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dl.acm.org dl.acm.org
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Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.
Sometimes, search is not rooted in discovery of a correct answer to a question. It's about exploration. Serendipity through search. Think Michael Lewis, Malcolm Gladwell, and Latif Nasser from Radiolab. The randomizer on wikipedia. A risk factor of where things trend with advanced AI in search is an abandonment of meaning making through exploration in favor of a knowledge-level pursuit that lacks comparable depth to more exploratory experiences.
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- Aug 2023
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Local file Local file
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Mills, Anna, Maha Bali, and Lance Eaton. “How Do We Respond to Generative AI in Education? Open Educational Practices Give Us a Framework for an Ongoing Process.” Journal of Applied Learning and Teaching 6, no. 1 (June 11, 2023): 16–30. https://doi.org/10.37074/jalt.2023.6.1.34.
Annotation url: urn:x-pdf:bb16e6f65a326e4089ed46b15987c1e7
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- Jul 2020
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www.nesta.org.uk www.nesta.org.uk
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Nesta, (2020, May 15). Invisible work: Nesta talks to John Howkins. https://www.nesta.org.uk/event/live-stream-invisible-work/
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- May 2020
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www.ft.com www.ft.com
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Multiple articles from Financial Times - Future of AI and Digital Healthcare
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- Apr 2020
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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- Oct 2019
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conference.nber.org conference.nber.org
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We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. Brynjolfsson, Rock, and Syverson describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, the researchers argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign
This is for anyone who is looking deep in economics of artificial intelligence or is doing a project on AI with respect to economics. This paper entails how AI might effect our economy and change the way we think about work. the predictions and facts which are stated here are really impressive like how people 30 years from now will be lively with government employment where everyone will get equal amount of payment.
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- Oct 2017
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adactio.com adactio.com
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I can’t go on
but I must go on!. Is this the future we are heading towards?
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