Literature DB >> 31597977

Why deep-learning AIs are so easy to fool.

Douglas Heaven.   

Abstract

Keywords:  Computer science; Information technology

Mesh：

Year:  2019        PMID： 31597977     DOI： 10.1038/d41586-019-03013-5

Source DB:  PubMed          Journal:  Nature        ISSN： 0028-0836            Impact factor:   49.962


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  2 in total

1.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

Authors:  David Silver; Thomas Hubert; Julian Schrittwieser; Ioannis Antonoglou; Matthew Lai; Arthur Guez; Marc Lanctot; Laurent Sifre; Dharshan Kumaran; Thore Graepel; Timothy Lillicrap; Karen Simonyan; Demis Hassabis
Journal:  Science       Date:  2018-12-07       Impact factor: 47.728

2.  Adversarial attacks on medical machine learning.

Authors:  Samuel G Finlayson; John D Bowers; Joichi Ito; Jonathan L Zittrain; Andrew L Beam; Isaac S Kohane
Journal:  Science       Date:  2019-03-22       Impact factor: 47.728
  2 in total
  24 in total

Review 1.  Designing deep learning studies in cancer diagnostics.

Authors:  Andreas Kleppe; Ole-Johan Skrede; Sepp De Raedt; Knut Liestøl; David J Kerr; Håvard E Danielsen
Journal:  Nat Rev Cancer       Date:  2021-01-29       Impact factor: 60.716

Review 2.  Deep learning in single-molecule microscopy: fundamentals, caveats, and recent developments [Invited].

Authors:  Leonhard Möckl; Anish R Roy; W E Moerner
Journal:  Biomed Opt Express       Date:  2020-02-27       Impact factor: 3.732

3.  Novel-view X-ray projection synthesis through geometry-integrated deep learning.

Authors:  Liyue Shen; Lequan Yu; Wei Zhao; John Pauly; Lei Xing
Journal:  Med Image Anal       Date:  2022-01-29       Impact factor: 8.545

4.  Direct Human-AI Comparison in the Animal-AI Environment.

Authors:  Konstantinos Voudouris; Matthew Crosby; Benjamin Beyret; José Hernández-Orallo; Murray Shanahan; Marta Halina; Lucy G Cheke
Journal:  Front Psychol       Date:  2022-05-24

5.  Implementation and design of artificial intelligence in abdominal imaging.

Authors:  Hailey H Choi; Silvia D Chang; Marc D Kohli
Journal:  Abdom Radiol (NY)       Date:  2020-12

Review 6.  Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

Authors:  Xiaoxuan Liu; Samantha Cruz Rivera; David Moher; Melanie J Calvert; Alastair K Denniston
Journal:  Lancet Digit Health       Date:  2020-09-09

7.  Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

Authors:  Kyle Boone; Cate Wisdom; Kyle Camarda; Paulette Spencer; Candan Tamerler
Journal:  BMC Bioinformatics       Date:  2021-05-11       Impact factor: 3.169

8.  Deliberative Processes by Health Technology Assessment Agencies: A Reflection on Legitimacy, Values and Patient and Public Involvement Comment on "Use of Evidence-informed Deliberative Processes by Health Technology Assessment Agencies Around the Globe".

Authors:  Mireille Goetghebeur; Marjo Cellier
Journal:  Int J Health Policy Manag       Date:  2021-03-14

9.  Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension.

Authors:  Samantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; Alastair K Denniston; Melanie J Calvert
Journal:  BMJ       Date:  2020-09-09

Review 10.  Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

Authors:  Samantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; Alastair K Denniston; Melanie J Calvert
Journal:  Lancet Digit Health       Date:  2020-09-09
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