Brain-Inspired Approaches to Natural Language Processing and Explainable Artificial Intelligence

被引:0
作者
Deussen, Erik [1 ]
Unger, Herwig [1 ]
Kubek, Mario M. [2 ]
机构
[1] Univ Hagen, Hagen, Germany
[2] Georgia State Univ, Atlanta, GA 30303 USA
来源
INNOVATIONS FOR COMMUNITY SERVICES, I4CS 2022 | 2022年 / 1585卷
关键词
Brain-inspired natural language processing; Explainable artificial intelligence; Attention;
D O I
10.1007/978-3-031-06668-9_2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Like no other medium, the World Wide Web became the major information source for many people within the last years, some even call it the brain of mankind. For any arising questions, any facts needed, or any multimedia content wanted, a web page providing the respective information seems to exist. Likewise, it seems that sometimes there is nothing that has not been thought, written, painted, or expressed in any other form before: most users simply feel overwhelmed by the flood of available information. Consequently, there is a need for new technologies for autonomous self-management, more timely information handling, processing, and the user's interaction with such huge amounts of data. Indeed, Einstein's saying Look deep into nature, then you will understand everything better is a big inspiration and challenge to find the required, new solutions. At this point, a short overview is given of existing organizational and functional principles, which have been derived from nature and in particular the human brain and which could be adapted to realize the desired, new methods for natural language processing. The methods mostly follow the strict natural design principle of locality, i.e. work without overseeing the whole system or full set of data, and exhibit a high degree of parallelism. Also, specific application fields for them will be discussed.
引用
收藏
页码:6 / 10
页数:5
相关论文
共 13 条
  • [1] [Anonymous], 2004, On Intelligence
  • [2] VQA: Visual Question Answering
    Antol, Stanislaw
    Agrawal, Aishwarya
    Lu, Jiasen
    Mitchell, Margaret
    Batra, Dhruv
    Zitnick, C. Lawrence
    Parikh, Devi
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, : 2425 - 2433
  • [3] Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth
    Baum, Graham L.
    Ciric, Rastko
    Roalf, David R.
    Betzel, Richard F.
    Moore, Tyler M.
    Shinohara, Russell T.
    Kahn, Ari E.
    Vandekar, Simon N.
    Rupert, Petra E.
    Quarmley, Megan
    Cook, Philip A.
    Elliott, Mark A.
    Ruparel, Kosha
    Gur, Raquel E.
    Gur, Ruben C.
    Bassett, Danielle S.
    Satterthwaite, Theodore D.
    [J]. CURRENT BIOLOGY, 2017, 27 (11) : 1561 - +
  • [4] Chomsky Noam, 1986, BARRIERS
  • [5] Deussen E., 2022, Studies in Big Data, V101, P149, DOI [10.1007/978-3-030-90936-9_11, DOI 10.1007/978-3-030-90936-9_11]
  • [6] Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
  • [7] Heyer G., 2006, Text Mining: Wissensrohstoff Text: Konzepte, Algorithmen, Ergebnisse
  • [8] Kubek M., 2012, 12 INT C INN INT COM, P202
  • [9] Kubek M., 2020, Concepts and Methods for a Librarian of the Web, DOI [10.1007/978-3-030-23136-1, DOI 10.1007/978-3-030-23136-1]
  • [10] Paass G., 2020, Kunstliche Intelligenz, P167, DOI [10.1007/978-3-658-30211-56, DOI 10.1007/978-3-658-30211-56]