Reports of the AAAI 2019 spring symposium series

Antonio Chella, Clark Barrett, Jamie C. Macbeth, Knut Hinkelmann, David Gamez, Ranjeev Mittu, Shomir Wilson, Keiki Takadama, Takashi Kido, Andreas Martin, Donald Sofge, Ioana Baldini, Leilani H. Gilpin, Evan Patterson, Carlos Cinelli, Dylan Holmes, Murat Kocaoglu, Prasad Tadepalli, Alessio Lomuscio, William F. Lawless

Risultato della ricerca: Articlepeer review

Abstract

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates.
Lingua originaleEnglish
pagine (da-a)59-66
Numero di pagine8
RivistaAI Magazine
Volume40
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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