# pos

id: prudhomme
pos: VBN
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-revisite


id: prudhomme
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: prudhomme
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: hansen
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: hansen
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: hansen
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: hansen
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: hansen
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: kim
pos: NNP
token:
  https://www.technologyreview.com/2020/04/01/974997/deepminds-ai-57-atari-games-but-its-still-not-versatile-enough/.

lemma:
  https://www.technologyreview.com/2020/04/01/974997/deepminds-ai-57-atari-games-but-its-still-not-versatile-enough/.


id: kim
pos: NNP
token:
  https://www.technologyreview.com/2019/04/08/103223/two-rival-ai-approaches-combine-to-let-machines-learn-about-the-world-like-a-child/.

lemma:
  https://www.technologyreview.com/2019/04/08/103223/two-rival-ai-approaches-combine-to-let-machines-learn-about-the-world-like-a-child/.


id: kim
pos: .
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: kim
pos: NNP
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: kim
pos: NNP
token:
  https://www.theverge.com/circuitbreaker/2018/10/15/17978298/pixel-buds-google-translate-google-assistant-headphones

lemma:
  https://www.theverge.com/circuitbreaker/2018/10/15/17978298/pixel-buds-google-translate-google-assistant-headphones


id: kim
pos: JJ
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: kim
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: lesk
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: lesk
pos: ADD
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: jiang
pos: NNS
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: harper
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: harper
pos: RB
token:
  http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=cat07006a&AN=cwru.b3647991&site=eds-live&custid=s8481523

lemma:
  http://search.ebscohost.com/login.aspx?direct=true&authtype=ip,shib&db=cat07006a&an=cwru.b3647991&site=eds-live&custid=s8481523


id: harper
pos: NNP
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/.


id: wiegand
pos: NNP
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: wiegand
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: wiegand
pos: RB
token:
  https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-gallery-scene/584134/?utm_source=share&utm_campaign=share

lemma:
  https://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-gallery-scene/584134/?utm_source=share&utm_campaign=share


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=126273987&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=126273987&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765240&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&an=133765240&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=108501392&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=108501392&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765244&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&an=133765244&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133042993&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&an=133042993&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=135423669&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&an=135423669&site=ehost-live


id: wiegand
pos: VB
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=138643455&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=138643455&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765245&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=lxh&an=133765245&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=98607738&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=98607738&site=ehost-live


id: wiegand
pos: NNP
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=127369436&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=127369436&site=ehost-live


id: wiegand
pos: JJ
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=134592659&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=134592659&site=ehost-live


id: wiegand
pos: RB
token:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=129632291&site=ehost-live

lemma:
  http://smcproxy1.saintmarys.edu:2048/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=bth&an=129632291&site=ehost-live


id: wiegand
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


# ent

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


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


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


# url

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


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


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


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


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


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


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


id: kim
domain: www.technologyreview.com
url:
  http://www.technologyreview.com/2020/04/01/974997/deepminds-ai-57-atari-games-but-its-still-not-versatile-enough/


id: kim
domain: www.technologyreview.com
url:
  http://www.technologyreview.com/2019/04/08/103223/two-rival-ai-approaches-combine-to-let-machines-learn-about-the-world-like-a-child/


id: kim
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: kim
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: kim
domain: www.theverge.com
url:
  http://www.theverge.com/circuitbreaker/2018/10/15/17978298/pixel-buds-google-translate-google-assistant-headphones


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


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


id: harper
domain: news.mit.edu
url:
  http://news.mit.edu/2019/mit-apollo-deepfake-art-installation-aims-to-empower-more-discerning-public-1125]


id: harper
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/


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


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


id: wiegand
domain: www.theatlantic.com
url:
  http://www.theatlantic.com/technology/archive/2019/03/ai-created-art-invades-chelsea-gallery-scene/584134/?utm_source=share&utm_campaign=share


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=126273987&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765240&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=108501392&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765244&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133042993&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=135423669&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=138643455&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=133765245&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=98607738&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=127369436&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=134592659&site=ehost-live


id: wiegand
domain: smcproxy1.saintmarys.edu:2048
url:
  http://smcproxy1.saintmarys.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=129632291&site=ehost-live


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


# bib

id: altman
title: altman
txt: ./txt/altman.txt
cache: ./cache/altman.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  I did most of my data cleanup by hand using spreadsheet software, and was not careful about preserving the formulas for each step of the process; instead, I deleted and wrote over many important intermediate computations, saving only the final results. 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. However you get your initial data, it is generally a 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. This is often the part of the process that requires the most work, and you should expect to iterate over your data preparations many times, even after you''ve started training and testing models. As you begin ingesting and preparing data, you''ll want to explore possible machine learning algorithms to perform on your dataset.


id: cohen-nakazawa
title: cohen-nakazawa
author: Jason E. Cohen
txt: ./txt/cohen-nakazawa.txt
cache: ./cache/cohen-nakazawa.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Consequently, our chapter describes the process we used to (1) generate technical and descriptive metadata for historical photographs as we pulled material from an extant blog website into a digital archives platform; (2) identify recurring faces in individual pictures as well as in photographs of groups of sometimes unidentified people in order to generate social networks as metadata; and (3) to help develop a controlled vocabulary for the institution''s future needs for object management and description. Similarly, as the ownership of historical images suddenly extended to include present-day community members, and as these questions of access and serving a local public were inextricably bound up with interactions with members of that shared public whose family names and faces appear in the images we were making available, we began to consider the ways in which our archival work was tied to what Ryan Calo calls the "historical validation" of primary source materials (2017, 424-5).


id: hansen
title: hansen
txt: ./txt/hansen.txt
cache: ./cache/hansen.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  [5: https://dml.cz/ ] [6: http://www.numdam.org/ ] [7: https://zbmath.org/ ] [8: Mathematical 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 same, or closely related, subfield as the article''s content before they are published. Now let us shift from mathematics-specific categorization to subject categorization in general and look at the work Microsoft has done assigning Fields of Study (FoS) in the Microsoft Academic Graph (MAG) which is used to create their Microsoft Academic article search product.[footnoteRef:15] While the MAG FoS project is also attempting to categorize articles for proper indexing and search, it represents the second path which is taken by automated categorization projects: using machine learning techniques to both create the taxonomy and to classify.


id: harper
title: harper
txt: ./txt/harper.txt
cache: ./cache/harper.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Figure 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 circles from Google''s QuickDraw dataset). Other types of generative statistical models, like Naive Bayes or a higher-order Markov chain,[footnoteRef:1] could perhaps capture a bit more information about the training data, but they would still be insufficient for real-world applications like this.[footnoteRef:2] Image, video, and audio are complicated; it is hard to reduce them to their essence with basic statistical rules in the way we were able to with the ordering of letters in English and Italian. Figure 4 A GAN being trained on wine bottle sketches from Google''s quickdraw dataset (https://github.com/googlecreativelab/quickdraw-dataset) shows the generator learning how to produce better sketches over time. GANs in Action: Deep Learning with Generative Adversarial Networks.


id: hintze-schossau
title: hintze-schossau
txt: ./txt/hintze-schossau.txt
cache: ./cache/hintze-schossau.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Artificial Intelligence, with its ability to machine learn coupled to an almost humanlike understanding, sounds like the ideal tool to the humanities. 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 technologies, explain how quantitative or qualitative they really are, and explore what the possible implications for future Digital Humanities could be. Currently, machines do not learn but must be trained, typically with human-labeled data. At the same time, memory formation (Marstaller, Hintze, and Adami 2013), information integration in the brain (Tononi 2004), and how systems evolve the ability to learn (Sheneman, Schossau, and Hintze 2019) are also being researched, as they are building blocks of general purpose intelligence.


id: jiang
title: jiang
txt: ./txt/jiang.txt
cache: ./cache/jiang.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Among the top strengths of happy marriages, at least five can be reflected in cross-disciplinary ML research, including "discuss problems well," "handle differences creatively," and "maintain a good balance of time alone and together." I use two examples of my personal experiences (as a computer scientist) of collaborating with researchers from multiple disciplines (e.g., historians, psychologists, IT technicians) to illustrate. 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 and synthesize varying expertise; (3) it enables researchers to reach a wider audience and communicate diverse viewpoints; (4) it encourages researchers to confront questions that traditional disciplines do not ask while opening up new areas of research; and (5) it promotes disciplinary self-awareness about methods and creative practices (Urquhart et al.


id: kim
title: kim
author: Bohyun Kim
txt: ./txt/kim.txt
cache: ./cache/kim.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  With their limited intelligence and fully deterministic nature, early rule-based symbolic AI systems raised few ethical concerns.[footnoteRef:4] AI systems that near or surpass human capability, on the other hand, are likely to be given the autonomy to make their own decisions without humans, even when their workings are not entirely transparent, and some of those decisions are distinctively moral in character. The Library of Congress has worked on detecting features, such as railroads in maps, using the convolutional neural network model, and issued a solicitation for a machine learning and deep learning pilot program that will maximize the use of its digital collections in 2019.[footnoteRef:18] Indiana University Libraries, AVP, University of Texas Austin School of Information, and the New York Public Library are jointly developing the Audiovisual Metadata Platform (AMP), using many AI tools in order to automatically generate metadata for audiovisual materials, which collection managers can use to supplement their archival description and processing workflows.[footnoteRef:19] [18: See Blewer, Kim, and Phetteplace 2018 and Price 2019.


id: lesk
title: lesk
txt: ./txt/lesk.txt
cache: ./cache/lesk.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Fragility errors here can arise from many sources for example, the training data may not be representative of the real problem (if you train a machine translation program solely on engineering documents, do not expect it to do well on theater reviews). Similarly, the New York Times discussed the way groups of primarily young white men will build systems that focus on their data, and give wrong or discriminatory answers in more general situations (Tugend 2019). Instead of trying to learn more about the characteristics of a system that is being modeled, the effort is driven by the dictum, "more data beats better algorithms." In a review of the history of speech recognition, Xuedong Huang, James Baker, and Raj Reddy write, "The power of these systems arises mainly from their ability to collect, process, and learn from very large datasets.


id: lucic-shanahan
title: lucic-shanahan
author: Microsoft Office User
txt: ./txt/lucic-shanahan.txt
cache: ./cache/lucic-shanahan.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  On its "Big Read" website, the Library of Congress includes information about One Book programs around the United States,[footnoteRef:2] and the American Library Association (ALA) also provides materials with which a library can build its own One Book program and, in this way, bring members of their communities together in a conversation.[footnoteRef:3] While community reading programs are not a new phenomenon and exist in various formats and sizes, the One Book One Chicago program is notable because of its size (the Chicago Public Library has 81 local branches) as well as its history (the program has been in existence for nearly 20 years). As part of ongoing work of the "Reading Chicago Reading" project, we used the secure data portal of the HathiTrust Research Consortium to access and pre-process the in-copyright novels in our set. The place names extracted from our three Chicago-setting OBOC books allowed us to focus on particular areas of the city such as Hyde Park, which is mentioned in each of them.


id: morgan
title: morgan
txt: ./txt/morgan.txt
cache: ./cache/morgan.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  Now, in a time of "big data," it is possible to go beyond mere automation and towards the more intelligent use of computers; the use of algorithms and machine learning is an integral part of future library collection building and service provision. Finally, this chapter outlines both a number of possible machine learning applications for libraries as well as a few real world use cases. Like the scale of computer input, the library profession has not really exploited computers'' 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 value) files, relational database schema, XML files, JSON files, and the content of email messages or HTTP server responses are all examples of different types of data structures.


id: prudhomme
title: prudhomme
txt: ./txt/prudhomme.txt
cache: ./cache/prudhomme.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  However, "the viability of machine learning and artificial intelligence is predicated on the representativeness and quality of the data that they are trained on," as Thomas Padilla, Interim Head, Knowledge Production at the University of Nevada Las Vegas, asserts (2019, 14). In this essay, I begin by placing artificial intelligence and machine learning in context, then proceed by discussing why AI matters for archives and libraries, and describing the techniques used in a pilot automation project from the perspective of digital curation at Oklahoma State University Archives. Artificial intelligence, and specifically machine learning as a subfield of AI, has direct applications through pattern recognition techniques that predict the labeling values for unlabeled data. 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 recognition machine learning techniques to new limits.


id: wiegand
title: wiegand
author: Sue Wiegand
txt: ./txt/wiegand.txt
cache: ./cache/wiegand.docx
extension: .docx
mime: application/vnd.openxmlformats-officedocument.wordprocessingml.document
summary:
  JSTOR, for example, will provide up to 25,000 documents (or more at special request) in a dataset for analysis.[footnoteRef:2] Clarivate''s Content as a Service provides Web of Science data to accommodate multiple purposes.[footnoteRef:3] Besides the many freely available bibliodata sources, researchers can sign up for developer accounts in databases such as Scopus to work with datasets for text mining and computational analysis.[footnoteRef:4] Using library-licensed collections as data could allow researchers to save time in reading a large corpus, stay updated on a topic of interest, analyze the most important topics at a given time period, confirm gaps in the research literature for investigation, and increase the efficiency of sifting through massive amounts of research in, for instance, the race to develop a vaccine (Ong 2020; Vamathevan 2019). By building out new services and tools, and instructing at all levels, libraries can reinvent themselves continuously by investing in creative and sustainable innovation, from digital and data literacy to assembling modules for a library-based Researchers'' Workstation that uses Machine Learning to enhance the efficiency of the scholars'' research cycle.


