語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Towards More Task-Generalized and Explainable AI through Psychometrics.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Towards More Task-Generalized and Explainable AI through Psychometrics./
作者:
Braynen, Alec.
面頁冊數:
1 online resource (51 pages)
附註:
Source: Masters Abstracts International, Volume: 84-06.
Contained By:
Masters Abstracts International84-06.
標題:
Computer engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29993741click for full text (PQDT)
ISBN:
9798358463769
Towards More Task-Generalized and Explainable AI through Psychometrics.
Braynen, Alec.
Towards More Task-Generalized and Explainable AI through Psychometrics.
- 1 online resource (51 pages)
Source: Masters Abstracts International, Volume: 84-06.
Thesis (M.S.Cp.)--University of South Florida, 2022.
Includes bibliographical references
In this work, we propose that adopting the methods, principles, and guidelines of the field of psychometrics can help the Artificial Intelligence (AI) community to build more task-generalizable and explainable AI. Three arguments are presented and explored. These arguments are that psychometrics can help by providing 1) a framework for formulating better datasets, 2) psychometric AI data that can lead to models of generalization in AI, and 3) explainable AI through more informative evaluations.A review of psychometrics and psychological generalization is performed, along with an overview of evaluation, generalization, and explainability in AI. Various ideas are presented throughout for how psychometrics can lead to more task-generalizable and explainable AI. Additionally, in cases where there exists literature exemplifying the points, these works are presented and discussed.Furthermore, counterarguments to the thesis relevant to each argument, are also presented and discussed. Finally, we conclude the work with a summary and a brief discussion of future directions for research.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798358463769Subjects--Topical Terms:
621879
Computer engineering.
Subjects--Index Terms:
Dimensional spacesIndex Terms--Genre/Form:
542853
Electronic books.
Towards More Task-Generalized and Explainable AI through Psychometrics.
LDR
:02373nmm a2200373K 4500
001
2358918
005
20230830051519.5
006
m o d
007
cr mn ---uuuuu
008
241011s2022 xx obm 000 0 eng d
020
$a
9798358463769
035
$a
(MiAaPQ)AAI29993741
035
$a
AAI29993741
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Braynen, Alec.
$3
3699466
245
1 0
$a
Towards More Task-Generalized and Explainable AI through Psychometrics.
264
0
$c
2022
300
$a
1 online resource (51 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Masters Abstracts International, Volume: 84-06.
500
$a
Advisor: Licato, John.
502
$a
Thesis (M.S.Cp.)--University of South Florida, 2022.
504
$a
Includes bibliographical references
520
$a
In this work, we propose that adopting the methods, principles, and guidelines of the field of psychometrics can help the Artificial Intelligence (AI) community to build more task-generalizable and explainable AI. Three arguments are presented and explored. These arguments are that psychometrics can help by providing 1) a framework for formulating better datasets, 2) psychometric AI data that can lead to models of generalization in AI, and 3) explainable AI through more informative evaluations.A review of psychometrics and psychological generalization is performed, along with an overview of evaluation, generalization, and explainability in AI. Various ideas are presented throughout for how psychometrics can lead to more task-generalizable and explainable AI. Additionally, in cases where there exists literature exemplifying the points, these works are presented and discussed.Furthermore, counterarguments to the thesis relevant to each argument, are also presented and discussed. Finally, we conclude the work with a summary and a brief discussion of future directions for research.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2023
538
$a
Mode of access: World Wide Web
650
4
$a
Computer engineering.
$3
621879
650
4
$a
Quantitative psychology.
$3
2144748
650
4
$a
Philosophy.
$3
516511
653
$a
Dimensional spaces
653
$a
Explainable AI
653
$a
Psychometrics
655
7
$a
Electronic books.
$2
lcsh
$3
542853
690
$a
0464
690
$a
0632
690
$a
0422
710
2
$a
ProQuest Information and Learning Co.
$3
783688
710
2
$a
University of South Florida.
$b
Computer Science and Engineering.
$3
1682850
773
0
$t
Masters Abstracts International
$g
84-06.
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29993741
$z
click for full text (PQDT)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9481274
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入