teaching machines
Recently Published Documents


TOTAL DOCUMENTS

228
(FIVE YEARS 20)

H-INDEX

8
(FIVE YEARS 1)

2021 ◽  
Vol 31 (1) ◽  
pp. 10-11
Author(s):  
Lee Skallerup Bessette
Keyword(s):  

2021 ◽  
Vol 118 (40) ◽  
pp. e2115605118
Author(s):  
Marcus Lapeyrolerie ◽  
Carl Boettiger
Keyword(s):  

Author(s):  
Garret Merriam

Artificial Emotional Intelligence research has focused on emotions in a limited “black box” sense, concerned only with emotions as ‘inputs/outputs’ for the system, disregarding the processes and structures that constitute the emotion itself. We’re teaching machines to act as if they can feel emotions without the capacity to actually feel emotions. Serous moral and social problems will arise if we stick with the black box approach. As A.I.’s become more integrated with our lives, humans will require more than mere emulation of emotion; we’ll need them to have ‘the real thing.’ Moral psychology suggests emotions are necessary for moral reasoning and moral behavior. Socially, the role of ‘affective computing’ foreshadows the intimate ways humans will expect emotional reciprocity from their machines. Three objections are considered and responded to: (1) it’s not possible, (2) not necessary, and (3) too dangerous to give machines genuine emotions.


Impact ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 9-11
Author(s):  
Lin-shan Lee

Spoken content refers to all content over the Internet which includes human voice, essentially those in multimedia, such As YouTube videos and online courses. Today such content is retrieved via Google primarily based on human-generated text labels, because Google can only retrieve text over the Internet. The goal of this project is to produce technologies to retrieve accurately and efficiently such spoken content directly based on the included audio sounds instead of text labels, because machines today can listen to human voice just as they can read the text. The long term goal is to create a spoken version of Google, which may revolutionize the ways in which humans access information and improve their knowledge. Professor Lin-shan Lee at National Taiwan University is leading this project. He has been a distinguished leader in the global scientific community for the area of teaching machines to speak and listen to human voice for many years.


2021 ◽  
Vol 09 (09) ◽  
pp. 143-152
Author(s):  
Yuan Sun ◽  
Sisi Liu ◽  
Chaofan Chen ◽  
Zhengcuo Dan ◽  
Xiaobing Zhao
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document