國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Explainable AIinterpreting, explaini...

Samek, Wojciech.

Explainable AIinterpreting, explaining and visualizing deep learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explainable AIedited by Wojciech Samek ... [et al.].
其他題名:	interpreting, explaining and visualizing deep learning /
其他作者:	Samek, Wojciech.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xi, 439 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence.
電子資源:	https://doi.org/10.1007/978-3-030-28954-6
ISBN:	9783030289546$q(electronic bk.)

Explainable AIinterpreting, explaining and visualizing deep learning /
Explainable AIinterpreting, explaining and visualizing deep learning /[electronic resource] :edited by Wojciech Samek ... [et al.]. - Cham :Springer International Publishing :2019. - xi, 439 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,117000302-9743 ;. - Lecture notes in computer science ;4891..

The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intelligent" machines. Forsensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

ISBN: 9783030289546$q(electronic bk.)

Standard No.: 10.1007/978-3-030-28954-6doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .E86 2019

Dewey Class. No.: 006.3

Explainable AIinterpreting, explaining and visualizing deep learning /
LDR:02657nmm a2200337 a 4500 001 586845
003 DE-He213
005 20200629164439.0
006 m d
007 cr nn 008maaau
008 210326s2019 sz s 0 eng d
020 $a 9783030289546$q(electronic bk.)
020 $a 9783030289539$q(paper)
024 7 $a 10.1007/978-3-030-28954-6 $2 doi
035 $a 978-3-030-28954-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q335 $b .E86 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q335 $b .E96 2019
245 0 0 $a Explainable AI $h [electronic resource] : $b interpreting, explaining and visualizing deep learning / $c edited by Wojciech Samek ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xi, 439 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11700
490 1 $a Lecture notes in artificial intelligence
520 $a The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intelligent" machines. Forsensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Computing Milieux. $3 275270
650 2 4 $a Systems and Data Security. $3 274481
650 2 4 $a Computer Systems Organization and Communication Networks. $3 273709
700 1 $a Samek, Wojciech. $3 878347
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Lecture notes in artificial intelligence. $3 822012
856 4 0 $u https://doi.org/10.1007/978-3-030-28954-6
950 $a Computer Science (SpringerNature-11645)