# pos

id: 07-kim-ai
pos: NNP
token:
  https://www.sowetanlive.co.za/news/south-africa/2019-06-04-meet-libby-the-new-robot-library-assistant-at-the-university-of-pretorias-hatfield-campus/

lemma:
  https://www.sowetanlive.co.za/news/south-africa/2019-06-04-meet-libby-the-new-robot-library-assistant-at-the-university-of-pretorias-hatfield-campus/


id: 07-kim-ai
pos: ADD
token:
  https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/

lemma:
  https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/


id: 07-kim-ai
pos: NNP
token:
  https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/

lemma:
  https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/


id: 07-kim-ai
pos: NNP
token:
  https://www.blog.google/products/assistant/interpreter-mode-brings-real-time-translation-your-phone/

lemma:
  https://www.blog.google/products/assistant/interpreter-mode-brings-real-time-translation-your-phone/


id: 07-kim-ai
pos: NNS
token:
  https://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case

lemma:
  https://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case


id: 11-prudhomme-taking
pos: NNP
token:
  https://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisited

lemma:
  https://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisited


id: 11-prudhomme-taking
pos: VBN
token:
[cell repeats an earlier one in this file: 116 characters]

lemma:
  https://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisite


id: 11-prudhomme-taking
pos: JJ
token:
  https://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/

lemma:
  https://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/


id: 11-prudhomme-taking
pos: NNP
token:
  https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063

lemma:
  https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063


id: 03-plumb-humanities
pos: NNP
token:
  https://critinq.wordpress.com/2019/04/01/computational-literary-studies-participant-forum-responses/

lemma:
  https://critinq.wordpress.com/2019/04/01/computational-literary-studies-participant-forum-responses/


id: 03-plumb-humanities
pos: NNP
token:
  https://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/

lemma:
  https://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/


id: 03-plumb-humanities
pos: NNS
token:
  https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/620caf9f-08a8-485e-a496-51400296ebcd#ch19

lemma:
  https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/620caf9f-08a8-485e-a496-51400296ebcd#ch19


id: 03-plumb-humanities
pos: NNP
token:
  https://critinq.wordpress.com/2019/04/01/computational-literary-studies-participant-forum-responses-5/

lemma:
  https://critinq.wordpress.com/2019/04/01/computational-literary-studies-participant-forum-responses-5/


id: 05-wiegand-cultures
pos: WRB
token:
  https://www.clevelandart.org/art-museums-and-technology-developing-new-metrics-measure-visitor-engagement

lemma:
  https://www.clevelandart.org/art-museums-and-technology-developing-new-metrics-measure-visitor-engagement


id: 05-wiegand-cultures
pos: NNP
token:
  https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov

lemma:
  https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov


id: 05-wiegand-cultures
pos: NNP
token:
  https://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872

lemma:
  https://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872


id: 05-wiegand-cultures
pos: .
token:
  https://medium.com/a-wikipedia-librarian/youre-a-researcher-without-a-library-what-do-you-do-6811a30373cd

lemma:
  https://medium.com/a-wikipedia-librarian/youre-a-researcher-without-a-library-what-do-you-do-6811a30373cd


id: 05-wiegand-cultures
pos: NNP
token:
  https://www.infotoday.com/OnlineSearcher/Articles/Features/Artificial-Intelligence-Tools-for-Information-Discovery-124721.shtml

lemma:
  https://www.infotoday.com/OnlineSearcher/Articles/Features/Artificial-Intelligence-Tools-for-Information-Discovery-124721.shtml


id: 05-wiegand-cultures
pos: NN
token:
[cell repeats an earlier one in this file: 127 characters]

lemma:
  https://www.infotoday.com/onlinesearcher/articles/features/artificial-intelligence-tools-for-information-discovery-124721.shtml


id: 09-lesk-fragility
pos: NNP
token:
  https://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processes

lemma:
  https://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processes


id: 09-lesk-fragility
pos: VBZ
token:
  https://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processes

lemma:
  https://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processe


id: 09-lesk-fragility
pos: NNP
token:
  https://www.forbes.com/sites/tomtaulli/2019/08/04/bias-the-silent-killer-of-ai-artificial-intelligence/#1cc6f35d7d87

lemma:
  https://www.forbes.com/sites/tomtaulli/2019/08/04/bias-the-silent-killer-of-ai-artificial-intelligence/#1cc6f35d7d87


id: 00-johnson-preface
pos: NNP
token:
  https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html

lemma:
  https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html


id: 14-hansen-can
pos: NNP
token:
  https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

lemma:
  https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47


id: 14-hansen-can
pos: NNP
token:
  https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence

lemma:
  https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence


id: 14-hansen-can
pos: NNP
token:
  https://www.microsoft.com/en-us/research/project/academic/articles/microsoft-academic-increases-power-semantic-search-adding-fields-study/

lemma:
  https://www.microsoft.com/en-us/research/project/academic/articles/microsoft-academic-increases-power-semantic-search-adding-fields-study/


id: 14-hansen-can
pos: NNP
token:
  http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

lemma:
  http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf


id: 14-hansen-can
pos: NNP
token:
  https://www.businessinsider.in/2017-is-the-year-of-machine-learning-heres-why/articleshow/56514535.cms

lemma:
  https://www.businessinsider.in/2017-is-the-year-of-machine-learning-heres-why/articleshow/56514535.cms


id: 06-jiang-cross
pos: NN
token:
  https://acuads.com.au/conference/article/challenges-and-opportunities-of-cross-disciplinary-design-education-and-research/

lemma:
  https://acuads.com.au/conference/article/challenges-and-opportunities-of-cross-disciplinary-design-education-and-research/


id: 02-harper-generative
pos: NNP
token:
  http://news.mit.edu/2019/mit-apollo-deepfake-art-installation-aims-to-empower-more-discerning-public-1125

lemma:
  http://news.mit.edu/2019/mit-apollo-deepfake-art-installation-aims-to-empower-more-discerning-public-1125


id: 02-harper-generative
pos: VBZ
token:
  https://www.washingtonpost.com/technology/2020/01/06/facebook-ban-deepfakes-sources-say-new-policy-may-not-cover-controversial-pelosi-video/

lemma:
  https://www.washingtonpost.com/technology/2020/01/06/facebook-ban-deepfakes-sources-say-new-policy-may-not-cover-controversial-pelosi-video/


# ent

id: 11-prudhomme-taking
type: PERSON
entity:
  https://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/ https://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/ https://blog-ica.org/2019/10/02/machine-learning-archives-and-special-collections-a-high-level-view/ https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063


id: 11-prudhomme-taking
type: PERSON
entity:
  https://www.nyu.edu/tisch/preservation/program/student_work/2019spring/19s_thesis_Schweikert.pdf https://www.nyu.edu/tisch/preservation/program/student_work/2019spring/19s_thesis_Schweikert.pdf https://github.com/tesseract-ocr/tesseract


id: 07-kim-ai
type: PERSON
entity:
  https://www.fatml.org/resources/principles-for-accountable-algorithms https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai


id: 07-kim-ai
type: PERSON
entity:
  https://www.icrc.org/en/document/what-are-jus-ad-bellum-and-jus-bello-0 https://www.icrc.org/en/document/what-are-jus-ad-bellum-and-jus-bello-0 https://www.technologyreview.com/2017/04/11/5113


id: 07-kim-ai
type: PERSON
entity:
  https://www.scientificamerican.com/article/how-the-computer-beat-the-go-master/ https://www.scientificamerican.com/article/how-the-computer-beat-the-go-master/


id: 07-kim-ai
type: PERSON
entity:
  https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/ https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/ https://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/


id: 07-kim-ai
type: EVENT
entity:
  https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/ https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/ https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/ https://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/


id: 07-kim-ai
type: WORK_OF_ART
entity:
  Moral Deskilling and Upskilling in a New Machine Age: Reflections on the Ambiguous Future of Character


id: 07-kim-ai
type: PERSON
entity:
  https://doi.org/10.1007/s13347-014-0156-9 https://doi.org/10.1007/s13347-014-0156-9 https://www.theverge.com/2018/5/8/17332070/google-assistant-makes-phone-call-demo-duplex-io-2018 https://www.theverge.com/2018/5/8/17332070/google-assistant-makes-phone-call-demo-duplex-io-2018


id: 12-cohen-machine
type: PERSON
entity:
  https://securegrants.neh.gov/publicquery/main.aspx?f=1&gn=AK-255299-17 https://securegrants.neh.gov/publicquery/main.aspx?f=1&gn=AK-255299-17 Cohen


id: 12-cohen-machine
type: ORG
entity:
  https://kdl.kyvl.org/ https://github.com/Tradd-Schmidt/PMSS_Scraper https://github.com/Tradd-Schmidt/PMSS_Scraper


id: 12-cohen-machine
type: PERSON
entity:
  https://xpmethod.plaintext.in/torn-apart/volume/2/ https://xpmethod.plaintext.in/torn-apart/volume/2/ https://reason.com/2017/11/25/the-noble-misguided-plan-to-tu/ https://reason.com/2017/11/25/the-noble-misguided-plan-to-tu/


id: 03-plumb-humanities
type: PERSON
entity:
  https://doi.org/10.1109/TKDE.2019.2946162 https://doi.org/10.1109/TKDE.2019.2946162 https://doi.org/10.1037/0033-295X.84.4.327 https://doi.org/10.1037/0033-295X.84.4.327


id: 05-wiegand-cultures
type: ORG
entity:
  https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov


id: 05-wiegand-cultures
type: WORK_OF_ART
entity:
  An Unsupervised Approach to Automatic Classification of Scientific Literature Utilising Bibliographic Metadata


id: 05-wiegand-cultures
type: PERSON
entity:
  https://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872 https://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872 https://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872


id: 05-wiegand-cultures
type: PERSON
entity:
  https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-galler https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-galler https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-galler https://doi.org/10.18352/lq.10170


id: 09-lesk-fragility
type: PERSON
entity:
  https://www.bbc.com/news/technology-41845878 https://www.bbc.com/news/technology-41845878 https://futureoflife.org/ai-principles/


id: 09-lesk-fragility
type: EVENT
entity:
  https://www.weforum.org/whitepapers/how-to-prevent-discriminatory-outcomes-in-machine-learning https://www.weforum.org/whitepapers/how-to-prevent-discriminatory-outcomes-in-machine-learning


id: 09-lesk-fragility
type: WORK_OF_ART
entity:
  A Comparison of Deep Learning Performance against Health-Care Professionals in Detecting Diseases from Medical Imaging: a Systematic Review


id: 09-lesk-fragility
type: PERSON
entity:
  https://www.sciencedirect.com/science/article/pii/S2589750019301232 https://www.sciencedirect.com/science/article/pii/S2589750019301232


id: 09-lesk-fragility
type: EVENT
entity:
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3317329 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3317329


id: 14-hansen-can
type: PERSON
entity:
  https://www.wired.com/2014/10/future-of-artificial-intelligence/ https://www.wired.com/2014/10/future-of-artificial-intelligence/ https://mathscinet.ams.org/mresubs/guide-reviewers.html


id: 14-hansen-can
type: PERSON
entity:
  https://www.microsoft.com/en-us/research/project/academic/articles/microsoft-academic-increases-power-semantic-search-adding-fields-study/ http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf


id: 06-jiang-cross
type: WORK_OF_ART
entity:
  History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 56


id: 06-jiang-cross
type: PERSON
entity:
  https://doi.org/10.1016/j.shpsc.2015.10.003 http://www.jmlr.org/papers/v12/pedregosa11a.html http://www.jmlr.org/papers/v12/pedregosa11a.html


id: 02-harper-generative
type: ORG
entity:
  https://arxiv.org/abs/2001.00179 https://doi.org/10.2352/ISSN.2470-1173.2019.15.AVM-048 https://doi.org/10.2352/ISSN.2470-1173.2019.15.AVM-048


# url

id: 11-prudhomme-taking
domain: library.stanford.edu
url:
  http://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisited


id: 11-prudhomme-taking
domain: towardsdatascience.com
url:
  http://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063


id: 03-plumb-humanities
domain: critinq.wordpress.com
url:
  http://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/


id: 03-plumb-humanities
domain: dhdebates.gc.cuny.edu
url:
  http://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/620caf9f-08a8-485e-a496-51400296ebcd#ch19


id: 03-plumb-humanities
domain: critinq.wordpress.com
url:
  http://critinq.wordpress.com/2019/04/01/computational-literary-studies-participant-forum-responses-5/


id: 07-kim-ai
domain: www.sowetanlive.co.za
url:
  http://www.sowetanlive.co.za/news/south-africa/2019-06-04-meet-libby-the-new-robot-library-assistant-at-the-university-of-pretorias-hatfield-campus/


id: 07-kim-ai
domain: www.forbes.com
url:
  http://www.forbes.com/sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-are-transforming-law-firms-and-the-legal-sector/


id: 07-kim-ai
domain: www.infodocket.com
url:
  http://www.infodocket.com/2019/06/13/library-of-congress-posts-solicitation-for-a-machine-learning-deep-learning-pilot-program-to-maximize-the-use-of-its-digital-collection-library-is-looking-for-r/


id: 07-kim-ai
domain: www.aclu.org
url:
  http://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case


id: 05-wiegand-cultures
domain: www.clevelandart.org
url:
  http://www.clevelandart.org/art-museums-and-technology-developing-new-metrics-measure-visitor-engagement


id: 05-wiegand-cultures
domain: www.who.int
url:
  http://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov


id: 05-wiegand-cultures
domain: www.campaignlive.com
url:
  http://www.campaignlive.com/article/recognizing-exclusion-key-inclusive-design-conversation-kat-holmes/1488872


id: 05-wiegand-cultures
domain: medium.com
url:
  http://medium.com/a-wikipedia-librarian/youre-a-researcher-without-a-library-what-do-you-do-6811a30373cd


id: 05-wiegand-cultures
domain: www.infotoday.com
url:
  http://www.infotoday.com/OnlineSearcher/Articles/Features/Artificial-Intelligence-Tools-for-Information-Discovery-124721.shtml


id: 14-hansen-can
domain: towardsdatascience.com
url:
  http://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47


id: 14-hansen-can
domain: www.bloomberg.com
url:
  http://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence


id: 14-hansen-can
domain: www.microsoft.com
url:
  http://www.microsoft.com/en-us/research/project/academic/articles/microsoft-academic-increases-power-semantic-search-adding-fields-study/


id: 14-hansen-can
domain: www.businessinsider.in
url:
  http://www.businessinsider.in/2017-is-the-year-of-machine-learning-heres-why/articleshow/56514535.cms


id: 09-lesk-fragility
domain: www.theguardian.com
url:
  http://www.theguardian.com/science/alexs-adventures-in-numberland/2015/jan/08/banking-forecasts-maths-weather-prediction-stochastic-processes


id: 09-lesk-fragility
domain: www.forbes.com
url:
  http://www.forbes.com/sites/tomtaulli/2019/08/04/bias-the-silent-killer-of-ai-artificial-intelligence/#1cc6f35d7d87


id: 00-johnson-preface
domain: www.oclc.org
url:
  http://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html


id: 06-jiang-cross
domain: acuads.com.au
url:
  http://acuads.com.au/conference/article/challenges-and-opportunities-of-cross-disciplinary-design-education-and-research/


id: 02-harper-generative
domain: www.washingtonpost.com
url:
  http://www.washingtonpost.com/technology/2020/01/06/facebook-ban-deepfakes-sources-say-new-policy-may-not-cover-controversial-pelosi-video/


# bib

id: 00-johnson-preface
title: Preface
author: Johnson
date: 2021
txt: ./txt/00-johnson-preface.txt
cache: ./cache/00-johnson-preface.pdf
extension: .pdf
mime: application/pdf
summary:
  The plan called for a survey and a series of workshops hosted across the country to explore, originally, "the national need for library based topic modeling tools in support of cross-disciplinary libraries ran concurrently with our grant — Cordell 2020 and Padilla 2019, which were commissioned by major players in the field, the Library of Congress and OCLC, respectively — and vi Machine Learning, Libraries, and Cross-Disciplinary Research We would like to thank the IMLS for providing essential funding support for the grant and the Thank you to the members of the Notre Dame IMLS grant team who, at of course, thanks to the 95 participants in our 2019 IMLS Grant Workshops (too many to enumerate here) and to the essay authors for sharing their expertise and perspectives in growing our collective knowledge of machine learning and its use in research, scholarship, and cultural heritage organizations. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html


id: 01-hintze-artificial
title: Artificial Intelligence in the Humanities: Wolf in Disguise, or Digital Revolution?
author: Hintze
date: 2021
txt: ./txt/01-hintze-artificial.txt
cache: ./cache/01-hintze-artificial.pdf
extension: .pdf
mime: application/pdf
summary:
  Artificial Intelligence, with its ability to machine learn coupled to an almost human-like understanding, sounds like the ideal tool to the humanities. But are these technologies imbued with intuition or understanding, and do they learn like humans? In the 80s and 90s, as home computers were becoming more common, Hollywood was sensationalizing the idea of smart or human-like Artificial Intelligent machines (AI) through movies Machine learning allows us to learn from these data sets in ways that exceed human capabilities, while an artificial brain will eventually allow us to objectively describe a subjective experience (through quantifying neural activations or positively and negatively associated memories). The following paragraphs will explore current Machine Learning and Artificial Intelligence learning, to the point where our whole identity as human could be generously defined as the Just because humans and machine learning are both black Currently, machines do not learn but must be trained, typically with human-labeled data.


id: 02-harper-generative
title: Generative Machine Learning
author: Harper
date: 2021
txt: ./txt/02-harper-generative.txt
cache: ./cache/02-harper-generative.pdf
extension: .pdf
mime: application/pdf
summary:
  Reddit have each issued their own bans on the category of machine-generated or -altered content that is commonly termed "deep fakes" (Cohen 2020; Romm, Harwell, and Stanley-Becker TV because of their dystopian implications, deep fakes are just one application of generative machine learning. Figure 2.2: Images generated with a simple statistical model appear as noise as the model is insufficient to capture the structure of the real data (Markov chains trained using wine bottles and 1In many examples, I have used the Google QuickDraw Dataset to highlight features of generative machine learning. (?iiTb,ff;Bi?m#X+QKf;QQ;H2+''2�iBp2H�#f[mB+F/''�r@/�i�b2i) shows the generator learning how to produce better sketches over time. built a GAN that generates high-quality photo-realistic images of people (Karras, Laine, and Aila Beyond medicine and autonomous vehicles, generative data augmentation will progressively impact other imaging-heavy fields (Shorten and Khoshgoftaar 2019) like GANs in Action: Deep Learning with Generative Adversarial Networks.


id: 03-plumb-humanities
title: Humanities and Social Science Reading through Machine Learning
author: Plumb
date: 2021
txt: ./txt/03-plumb-humanities.txt
cache: ./cache/03-plumb-humanities.pdf
extension: .pdf
mime: application/pdf
summary:
  Respondents such as Mark Algee-Hewitt pointed out that literary scholars employ computational statistical models in order to reveal something about texts that human readers Machine learning, and word embedding algorithms in particular, may have a unique ability to shift this conversation into new territory, where scholars Acknowledging this helps contextualize machine learning algorithms for text analysis tasks in the humanities, but also highlights data curation challenges This naturally raises questions about how machine learning algorithms like word embeddings are implemented for text analysis, and how they Based on the potential for word embeddings to model semantic spaces for different corpora and compare the distribution of terms, the next step was to build a corpus of non-canonical Designing humanities research with novel word embedding models stands to widen the territory where machine learning engineers look for conceptual concepts Systematic data curation, combined with word embedding algorithms, represent a new interpretive system for literary scholars.


id: 04-janco-machine
title: Machine Learning in Digital Scholarship
author: Janco
date: 2021
txt: ./txt/04-janco-machine.txt
cache: ./cache/04-janco-machine.pdf
extension: .pdf
mime: application/pdf
summary:
  Tools like RunwayML, the Teachable Machine, and Google AutoML allow researchers to train project-specific Since 2014, dramatic innovations in machine learning have occurred, providing new capabilities in computer vision, natural language processing, and other areas of applied artificial intelligence. deliberately and identify how machine learning methods can benefit a scholar''s research? for identifying basic tasks that can be completed by computers in ways that advance humanities research (2000). When working with texts or images, machine learning models are presently capable of making simple annotations and associations. Google''s Teachable Machine offers an intuitive web application that humanities faculty and students can use to train classification models for images, sounds, and poses. Machine learning models offer a variety of ways to identify similarity and difference with research materials. goals of academic researchers in the humanities with the technical possibilities of machine learning. "Scholarly Primitives: What Methods Do Humanities Researchers Have


id: 05-wiegand-cultures
title: Cultures of Innovation: Machine Learning as a Library Service
author: Wiegand
date: 2021
txt: ./txt/05-wiegand-cultures.txt
cache: ./cache/05-wiegand-cultures.pdf
extension: .pdf
mime: application/pdf
summary:
  traditional role, librarians in the 20th century added a new function—discovery—teaching people to find and use the library''s collected scholarship. learning in the library as the next step beyond collecting, with librarians instructing on information infrastructure with the goal of empowering library users to find, evaluate, and use scholarly go far beyond local library collections to a global perspective and normative practice of participation at scale in innovative emerging technologies such as Machine Learning. start by using Machine Learning tools to automate alerts of new content in a narrow area of interest and help researchers at all levels find and focus on problem-solving. A library that adapted Machine Learning as an innovation technology would improve its practices; add new services; choose, use, and license collections differently; utilize all spaces for learning; and role model innovative leadership. opening local collections to discovery and use in order to create new knowledge through digitization and semantic linking, with cross-disciplinary technologies to augment traditional research


id: 06-jiang-cross
title: Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples
author: Jiang
date: 2021
txt: ./txt/06-jiang-cross.txt
cache: ./cache/06-jiang-cross.pdf
extension: .pdf
mime: application/pdf
summary:
  Cross-disciplinary research matters, because (1) it provides an understanding of complex problems that require a multifaceted approach to solve; (2) it combines disciplinary breadth with the ability to collaborate One of the most popular cross-disciplinary research topics/programs is Machine Learning + top strengths of conducting cross-disciplinary ML research and give two examples based on my marriages, just like collaborators expect to have successful project outcomes (Robinson and Blanton 1993; Pettigrew 2000; Xu et al. The history professor Liang Cai and I have collaborated on an international research project titled "Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese We have enjoyed our collaboration and the power of cross-disciplinary research. Specifically, I presented the top strengths of producing successful cross-disciplinary ML research: (1) Partners are satisfied with communication. "The Challenges of Cross ǉ Disciplinary Research." Social Research Collaboration." Social Studies of Science 33, no. "Building Cross-Disciplinary Research Collaborations."


id: 07-kim-ai
title: AI and Its Moral Concerns
author: Kim
date: 2021
txt: ./txt/07-kim-ai.txt
cache: ./cache/07-kim-ai.pdf
extension: .pdf
mime: application/pdf
summary:
  does not provide an easy answer to the question of how one should program moral decisionmaking into intelligent machines. Described below are some of the significant ethical challenges that autonomous AI systems such as military robots present. 11Note that this moral decision-making process can be modeled with a rule-based symbolic AI approach, a machine 13(Kahn 2012) also argues that the resulting increase in the number of wars by the use of military robots will be morally 15This black-box nature of AI systems powered by machine learning has raised great concern among many AI researchers in recent years. agency in the AI -powered automated information environment presents an ethical challenge In this chapter, I discussed four significant ethical challenges that automating decisions and actions with AI presents: (a) moral desensitization; (b) unintended outcomes; (c) surrender of are at an early stage in developing AI applications and applying machine learning and deep learning techniques to improve library services, systems, and operations.


id: 08-altman-building
title: Building a Machine Learning Pipeline
author: Altman
date: 2021
txt: ./txt/08-altman-building.txt
cache: ./cache/08-altman-building.pdf
extension: .pdf
mime: application/pdf
summary:
  As you begin ingesting and preparing data, you''ll want to explore possible machine learning algorithms to perform on your dataset. Start by determining what general type of learning algorithm you need, and proceed from there to research and select one that While the final output of a machine learning workflow is some sort of intelligent model, The pipeline for a machine learning project generally comprises five stages: data acquisition, data preparation, model training and testing, evaluation and analysis, and application of results. good idea to save a copy in the rawest possible form and treat that copy as immutable, at least during the initial phase of testing different algorithms or configurations. algorithm uses the training data to "learn" a set of rules that it can subsequently apply to new, Immutable data storage can benefit the batch-processing ML pipeline, especially during the initial research and development phase.


id: 09-lesk-fragility
title: Fragility and Intelligibility of Deep Learning for Libraries
author: Lesk
date: 2021
txt: ./txt/09-lesk-fragility.txt
cache: ./cache/09-lesk-fragility.pdf
extension: .pdf
mime: application/pdf
summary:
  Machine learning systems have a set of data for training. of the real problem (if you train a machine translation program solely on engineering documents, there may be a lot of training data, including many noisy points, and the program may decide on Many popular magazines have discussed this problem; Forbes, for example, had an explanation of how the choice of datasets can produce a biased result without any deliberate attempt to used to suggest malicious creation of training data or examples of data designed to deceive machine learning systems. blood pressure, and lower blood pressure decreases the risk of heart attacks." Then I have to explain that the paper evaluates 32 possibilities (prior/current ownership ⇥ cats/dogs ⇥ 4 medical compare the performance of machine learning systems for medical diagnosis with actual doctors If a program is constantly learning from new data, there is no list of previously fixed failures to


id: 10-morgan-bringing
title: Bringing Algorithms and Machine Learning Into Library Collections and Services
author: Morgan
date: 2021
txt: ./txt/10-morgan-bringing.txt
cache: ./cache/10-morgan-bringing.pdf
extension: .pdf
mime: application/pdf
summary:
  advent of computers, the idea of sharing cataloging data as MARC (machine readable cataloging) the full text of its collections to enhance bibliographic description and resulting public service. ability to save, organize, and retrieve data; on the whole, the library profession does not understand the concept of a "data structure." For example, tab-delimited files, CSV (comma-separated the use of data structures, computers store and retrieve information. Libraries use computers to store, organize, preserve, and disseminate the gray literature of our time, and we call these systems "institutional repositories." In all Using such a process, there are really only four different types of machine learning: classification, clustering, regression, and dimension reduction. Given a set of previously classified menus, one could create a model There are many possible ways to enhance library collections and services through the use of machine learning. of plain text files and an integer, Topic Modeling Tool will create a weighted list of latent themes


id: 11-prudhomme-taking
title: Taking a Leap Forward: Machine Learning for New Limits
author: Prudhomme
date: 2021
txt: ./txt/11-prudhomme-taking.txt
cache: ./cache/11-prudhomme-taking.pdf
extension: .pdf
mime: application/pdf
summary:
  Combining automatic processes to assist in supporting inventory management with a focus on descriptive metadata, a machine learning solution could help alleviate time-consuming and relatively expensive metadata tagging tasks, Deep learning neural networks are more effective in feature detection as they are able to solve complex problems such as image classification with greater accuracy when trained with large datasets. For images, how can archives build a data-labeling pipeline into their digital curation workflow that enables machine learning of collections? machine learning is only good so long as value is added, archives and libraries will need to think As deep learning applications will only be as effective as the data, archives and libraries should expand their Along with greater computing capabilities, artificial intelligence could be an opportunity for libraries and archives to boost the discovery of their digital collections by pushing text and image


id: 12-cohen-machine
title: Machine Learning + Data Creation in a Community Partnership for Archival Research
author: Cohen
date: 2021
txt: ./txt/12-cohen-machine.txt
cache: ./cache/12-cohen-machine.pdf
extension: .pdf
mime: application/pdf
summary:
  archivally focused project that emerged from a partnership between the Pine Mountain Settlement School (PMSS)1 in Harlan County, Kentucky, and scholars and students at Berea College. a latent social network of historical families represented by the images held in one local archive, curricula for use in Kentucky public schools with PMSS archival materials. That decision led a team of Berea College undergraduate and faculty researchers to scrape the data from the PMSS archive site and supplement the images and transcriptions it contains with available textual metadata drawn from the site.9 Alongside the WordPress facial recognition software to identify the persons in historic photographs in the PMSS archives. We demonstrated to the local members at Pine Mountain how our use case and its constraints for digital archives fit with the current standards for the fair use of copyrighted materials


id: 13-lucic-towards
title: Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset
author: Lucic
date: 2021
txt: ./txt/13-lucic-towards.txt
cache: ./cache/13-lucic-towards.pdf
extension: .pdf
mime: application/pdf
summary:
  Reading Chicago Reading1 is a grant-supported digital humanities project that takes as its object the "One Book One Chicago" (OBOC) program2 of the Chicago Public Library. A related question is the focus of this paper: by associating place names with sentiment scores in Chicago-themed OBOC The HathiTrust research portal permits the extraction of non-consumptive features of the works included in the digital library, even those that are still under copyright. The place names extracted from our three Chicago-setting OBOC books allowed us to focus Our interest in creating a dataset of Chicago place names extracted from literature led us to Kaser''s book contains several indexes that can serve as sources of labeled data or instances in which Chicago locations are mentioned. the index as a source of already-labeled data for Chicago place names. associated sentiment scores for Chicago place names in the three OBOC selections centered on


id: 14-hansen-can
title: Can a Hammer Categorize Highly Technical Articles?
author: Hansen
date: 2021
txt: ./txt/14-hansen-can.txt
cache: ./cache/14-hansen-can.pdf
extension: .pdf
mime: application/pdf
summary:
  I would use the Mathematical Subject Classification (MSC) values assigned to the publications in MathSciNet1 to create a temporal citation network which would allow me to visualize Machine-learning-based categorization needs data to classify, which in our case automated categorization of mathematics, we were dilettantes in the world of machine learning. what happens when smarter and more capable minds tackle the problem of classifying mathematics and other highly technical subjects using advanced machine learning techniques. 9Mathematical Subject Classification (MSC) values in MathSciNet and zbMath are a particularly interesting categorization set to work with as they are assigned and reviewed by a subject area expert editor and an active researcher in the 16See ?iiTb,ff�+�/2KB+XKB+''QbQ7iX+QKf. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 One really interesting part of the machine learning method used by Microsoft was that it did not rely only on information from the article being replace the work of humans categorizing mathematics articles indexed in a database, which for


