Artificial Intelligence/Small Data. Small Data’s Big AI Potential (Husanjot ChahalHelen Toner Ilya Rahkovsky, CSET)

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Conventional wisdom suggests that cutting-edge artificial intelligence is dependent on large volumes of data. An overemphasis on “big data” ignores the existence—and underestimates the potential—of several AI approaches that do not require massive labeled datasets. This issue brief is a primer on “small data” approaches to AI. It presents exploratory findings on the current and projected progress in scientific research across these approaches, which country leads, and the major sources of funding for this research.

Small Data’s Big AI Potential – Center for Security and Emerging Technology (georgetown.edu)

Marco Emanuele
Marco Emanuele è appassionato di cultura della complessità, cultura della tecnologia e relazioni internazionali. Approfondisce il pensiero di Hannah Arendt, Edgar Morin, Raimon Panikkar. Marco ha insegnato Evoluzione della Democrazia e Totalitarismi, è l’editor di The Global Eye e scrive per The Science of Where Magazine. Marco Emanuele is passionate about complexity culture, technology culture and international relations. He delves into the thought of Hannah Arendt, Edgar Morin, Raimon Panikkar. He has taught Evolution of Democracy and Totalitarianisms. Marco is editor of The Global Eye and writes for The Science of Where Magazine.

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