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artificial_intelligence [2019/07/10 06:45]
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artificial_intelligence [2019/10/17 07:24] (current)
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 ====   ==== ====   ====
  
-Image from:AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence\\ +<font 8px/​inherit;;​inherit;;​inherit>​Image from:AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence\\ 
-Report of Session 2017–19 HL Paper 100 16 April 2018. [[https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf|https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf]]+Report of Session 2017–19 HL Paper 100 16 April 2018.</​font>​ 
 + 
 +[[https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf|https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf]]
  
 ==== General Introduction ==== ==== General Introduction ====
 +
 +**[[http://​www.kenchadconsulting.com/​wp-content/​uploads/​2019/​10/​AI-The-role-and-opportunity-in-libraries-ILI-Oct2019.pdf|AI-the role and opportunity for libraries**]]**. By Ken Chad\\
 +Data, analytics and artificial intelligence (AI) are becoming pervasive. However Kriti Sharma, VP Artificial Intelligence for Sage remarked (in Information Professional. March 2019), ‘today if you look at the very successful AI applications at scale they are in the field of making people click more ads". So what *is* the role AI in libraries? The presentation explores broad themes that are especially relevant to libraries: Data; Curation and Ethics. It also discuses sub themes around AI in regard to Collections;​ Research Teaching & Learning/​Student Success & Student Wellbeing. Presented at Internet Librarian International on 15th October 2019
  
 [[http://​www.kenchadconsulting.com/​wp-content/​uploads/​2019/​04/​Data_wars_management_info_-to_data_driven-_intelligence_UKSGconf_April2019.pdf|The data wars: moving from management information to data driven intelligence:​ ]] [[http://​www.kenchadconsulting.com/​wp-content/​uploads/​2019/​04/​Data_wars_management_info_-to_data_driven-_intelligence_UKSGconf_April2019.pdf|The data wars: moving from management information to data driven intelligence:​ ]]
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   * Big data   * Big data
 +
   * Analytics   * Analytics
 +
   * Machine learning   * Machine learning
 +
   * Natural language processing   * Natural language processing
 +
   * Data visualisation   * Data visualisation
 +
   * Decision logic"   * Decision logic"
  
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 ==== Components of AI ==== ==== Components of AI ====
  
-"A composite including: \\ • Big data \\ • Analytics \\ • Machine learning \\ • Natural language processing \\ • Data visualisation \\ • Decision logic"+"A composite including: \\ 
 +• Big data \\ 
 +• Analytics \\ 
 +• Machine learning \\ 
 +• Natural language processing \\ 
 +• Data visualisation \\ 
 +• Decision logic"
  
 Smith, A. (2016). Big Data Technology, Evolving Knowledge Skills and Emerging Roles. Legal Information Management, 16(4), 219-224. Smith, A. (2016). Big Data Technology, Evolving Knowledge Skills and Emerging Roles. Legal Information Management, 16(4), 219-224.
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 === Expert system === === Expert system ===
  
- \\ A computer system that mimics the decision-making ability of a human expert by following pre-programmed rules, such as ‘if this occurs, then do that’. These systems fuelled much of the earlier excitement surrounding AI in the 1980s, but have since become less fashionable,​ particularly with the rise of neural networks.+ \\ 
 +A computer system that mimics the decision-making ability of a human expert by following pre-programmed rules, such as ‘if this occurs, then do that’. These systems fuelled much of the earlier excitement surrounding AI in the 1980s, but have since become less fashionable,​ particularly with the rise of neural networks.
  
 === Machine learning === === Machine learning ===
  
- \\ One particular form of AI, which gives computers the ability to learn from and improve with experience, without being explicitly programmed. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems, such as identifying objects in pictures or winning at particular games, for example.+ \\ 
 +One particular form of AI, which gives computers the ability to learn from and improve with experience, without being explicitly programmed. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems, such as identifying objects in pictures or winning at particular games, for example.
  
 === Neural network === === Neural network ===
  
- \\ Also known as an artificial neural network, this is a type of machine learning loosely inspired by the structure of the human brain. A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Each node will receive data from several nodes ‘above ’it, and give data to several nodes ‘below’ it. Nodes attach a ‘weight’ to the data they receive, and attribute a value to that data. If the data does not pass a certain threshold, it is not passed on to another node. The weights and thresholds of the nodes are adjusted when the algorithm is trained until similar data input results \\ in consistent outputs.+ \\ 
 +Also known as an artificial neural network, this is a type of machine learning loosely inspired by the structure of the human brain. A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Each node will receive data from several nodes ‘above ’it, and give data to several nodes ‘below’ it. Nodes attach a ‘weight’ to the data they receive, and attribute a value to that data. If the data does not pass a certain threshold, it is not passed on to another node. The weights and thresholds of the nodes are adjusted when the algorithm is trained until similar data input results \\ 
 +in consistent outputs.
  
 === Deep learning === === Deep learning ===
  
- \\ A more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, as it is behind much of the wider interest in AI today. It is often used to classify information from images, text or sound+ \\ 
 +A more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, as it is behind much of the wider interest in AI today. It is often used to classify information from images, text or sound
  
-AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence \\ Report of Session 2017–19 HL Paper 100 16 April 2018. [[https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf|https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf]]+AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence \\ 
 +Report of Session 2017–19 HL Paper 100 16 April 2018. [[https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf|https://​publications.parliament.uk/​pa/​ld201719/​ldselect/​ldai/​100/​100.pdf]]
  
 ==== Adopting AI ==== ==== Adopting AI ====
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 ==== Why library analytics is on the rise ==== ==== Why library analytics is on the rise ====
  
-"​Hierarchy of analytics use in libraries \\ Level 1 - Any analysis done is library function specific. Typically ad-hoc analytics but there might be dashboard systems created for only one specific area (e.g. collection dashboard for Alma or web dashboard for Google analytics) \\ Level 2 - A centralised library wide dashboard is created covering most functional areas in the library \\ Level 3 - Library "shows value" runs correlation studies etc \\ Level 4 - Library ventures into predictive analytics or learning analytics \\ By the time you reach level 4, it would be almost impossible for the library to go it alone"​.+"​Hierarchy of analytics use in libraries \\ 
 +Level 1 - Any analysis done is library function specific. Typically ad-hoc analytics but there might be dashboard systems created for only one specific area (e.g. collection dashboard for Alma or web dashboard for Google analytics) \\ 
 +Level 2 - A centralised library wide dashboard is created covering most functional areas in the library \\ 
 +Level 3 - Library "shows value" runs correlation studies etc \\ 
 +Level 4 - Library ventures into predictive analytics or learning analytics \\ 
 +By the time you reach level 4, it would be almost impossible for the library to go it alone"​.
  
- \\ 5 reasons why library analytics is on the rise. Aaron Tay. Musings about librarianship [blog] [[http://​musingsaboutlibrarianship.blogspot.com/​2016/​11/​5-reasons-why-library-analytics-is-on.html|http://​musingsaboutlibrarianship.blogspot.com/​2016/​11/​5-reasons-why-library-analytics-is-on.html]]+ \\ 
 +5 reasons why library analytics is on the rise. Aaron Tay. Musings about librarianship [blog] [[http://​musingsaboutlibrarianship.blogspot.com/​2016/​11/​5-reasons-why-library-analytics-is-on.html|http://​musingsaboutlibrarianship.blogspot.com/​2016/​11/​5-reasons-why-library-analytics-is-on.html]]
  
 ==== How customer behaviour can drive intelligent library decision making ==== ==== How customer behaviour can drive intelligent library decision making ====
  
- \\ "Usage data on their own…give libraries and publishers very little insight into how content is being used or how much it is being looked at. \\ In spite of the huge amount of data that are now available to libraries, it feels as if little progress has been made in developing metrics that may give an indication of how resources are being used and the extent to which library users value the resources provided. These perceived shortcomings in conventional usage data led Nottingham Trent University and Alexander Street to partner in piloting an in-depth view of analytics, demonstrating user engagement and impact of use".+ \\ 
 +"Usage data on their own…give libraries and publishers very little insight into how content is being used or how much it is being looked at. \\ 
 +In spite of the huge amount of data that are now available to libraries, it feels as if little progress has been made in developing metrics that may give an indication of how resources are being used and the extent to which library users value the resources provided. These perceived shortcomings in conventional usage data led Nottingham Trent University and Alexander Street to partner in piloting an in-depth view of analytics, demonstrating user engagement and impact of use".
  
 Adey, H., & Eastman-Mullins,​ A. (2017). User engagement analytics case study: how customer behaviour can drive intelligent library decision making. Insights, 30(3), 138–147. DOI: [[http://​doi.org/​10.1629/​uksg.387|http://​doi.org/​10.1629/​uksg.387]]. [[https://​insights.uksg.org/​articles/​10.1629/​uksg.387/​|https://​insights.uksg.org/​articles/​10.1629/​uksg.387/​]] Adey, H., & Eastman-Mullins,​ A. (2017). User engagement analytics case study: how customer behaviour can drive intelligent library decision making. Insights, 30(3), 138–147. DOI: [[http://​doi.org/​10.1629/​uksg.387|http://​doi.org/​10.1629/​uksg.387]]. [[https://​insights.uksg.org/​articles/​10.1629/​uksg.387/​|https://​insights.uksg.org/​articles/​10.1629/​uksg.387/​]]
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 ==== Learning analytics in higher education ==== ==== Learning analytics in higher education ====
  
- \\ “Every time a student interacts with their university – be that going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. Learning analytics is the process of using this data to improve learning and teaching”+ \\ 
 +“Every time a student interacts with their university – be that going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. Learning analytics is the process of using this data to improve learning and teaching”
  
- \\ Learning analytics in higher education: A review of UK and international practice. Read our updated briefing on learning analytics and student success from January 2017. By Niall Sclater, Alice Peasgood & Joel Mullen. Jisc. 2016. [[https://​www.jisc.ac.uk/​reports/​learning-analytics-in-higher-education|https://​www.jisc.ac.uk/​reports/​learning-analytics-in-higher-education]]+ \\ 
 +Learning analytics in higher education: A review of UK and international practice. Read our updated briefing on learning analytics and student success from January 2017. By Niall Sclater, Alice Peasgood & Joel Mullen. Jisc. 2016. [[https://​www.jisc.ac.uk/​reports/​learning-analytics-in-higher-education|https://​www.jisc.ac.uk/​reports/​learning-analytics-in-higher-education]]
  
 ==== The potential of data and learning analytics in Higher Education ==== ==== The potential of data and learning analytics in Higher Education ====
  
- \\ The Higher Education Commission launched its fourth inquiry report, From Bricks to Clicks - The Potential of Data and Analytics in Higher Education, on 26 January 2016.+ \\ 
 +The Higher Education Commission launched its fourth inquiry report, From Bricks to Clicks - The Potential of Data and Analytics in Higher Education, on 26 January 2016.
  
 This report undertakes a review of the current data landscape across English higher education institutions,​ looking at data collections,​ learning analytics and the current barriers to implementing better data management and data analytics. It then looks ahead to how the HE sector may change in the next 5-10 years, how institutions can take advantage of the exciting opportunities that greater engagement with data and analytics offer, and how HE students stand to benefit. This report undertakes a review of the current data landscape across English higher education institutions,​ looking at data collections,​ learning analytics and the current barriers to implementing better data management and data analytics. It then looks ahead to how the HE sector may change in the next 5-10 years, how institutions can take advantage of the exciting opportunities that greater engagement with data and analytics offer, and how HE students stand to benefit.
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 "​Artificial intelligence has opened up strategic opportunities for tertiary institutions to fundamentally redesign their businesses to deliver premium personalized services. Education CIOs can learn how virtual personal assistants provide an important building block toward that objective."​ "​Artificial intelligence has opened up strategic opportunities for tertiary institutions to fundamentally redesign their businesses to deliver premium personalized services. Education CIOs can learn how virtual personal assistants provide an important building block toward that objective."​
  
- \\ Use AI to Take Student Success to the Next Level of Personalization in Higher Education. Nick Ingelbrecht & Jan-Martin Lowendahl. Gartner [report]. 14 February 2018 [[https://​www.gartner.com/​doc/​3857266/​use-ai-student-success-level|https://​www.gartner.com/​doc/​3857266/​use-ai-student-success-level]]+ \\ 
 +Use AI to Take Student Success to the Next Level of Personalization in Higher Education. Nick Ingelbrecht & Jan-Martin Lowendahl. Gartner [report]. 14 February 2018 [[https://​www.gartner.com/​doc/​3857266/​use-ai-student-success-level|https://​www.gartner.com/​doc/​3857266/​use-ai-student-success-level]]
  
 ==== Using Student data for educational analysis ==== ==== Using Student data for educational analysis ====
  
- \\ "​Northumbria University’s approach to the utilisation of Educational Analytics is directly linked to the University Strategy. \\ In the future, the use of Educational Analytics may be extended to personalised earning paths, adaptive learning, personalised feedback, visualisations of study journey, intelligent e- tutoring, intelligent peer support, etc. Furthermore,​ new technological innovations might allow for more targeted, measured approaches. \\ The following data, which is currently captured by the University, is initially in scope \\ for Educational Analytics: \\ • personal information provided by the student at registration \\ • student level study records held by the University including assessment marks \\ • details of a student’s assigned Personal Tutor system-generated data from Blackboard, such as the date and frequency of accessing pages \\ • student attendance data \\ • library borrowing logs \\ • smart card activity log on Campus \\ • Northumbria gym membership \\ This data will be used in line with the University’s Student and Applicant Privacy Notice"​.+ \\ 
 +"​Northumbria University’s approach to the utilisation of Educational Analytics is directly linked to the University Strategy. \\ 
 +In the future, the use of Educational Analytics may be extended to personalised earning paths, adaptive learning, personalised feedback, visualisations of study journey, intelligent e- tutoring, intelligent peer support, etc. Furthermore,​ new technological innovations might allow for more targeted, measured approaches. \\ 
 +The following data, which is currently captured by the University, is initially in scope \\ 
 +for Educational Analytics: \\ 
 +• personal information provided by the student at registration \\ 
 +• student level study records held by the University including assessment marks \\ 
 +• details of a student’s assigned Personal Tutor system-generated data from Blackboard, such as the date and frequency of accessing pages \\ 
 +• student attendance data \\ 
 +• library borrowing logs \\ 
 +• smart card activity log on Campus \\ 
 +• Northumbria gym membership \\ 
 +This data will be used in line with the University’s Student and Applicant Privacy Notice"​.
  
 Using Student data for educational analysis. Northumbria University. August 2018 [[https://​www.northumbria.ac.uk/​-/​media/​corporate-website/​new-sitecore-gallery/​services/​academic-registry/​documents/​qte/​student-engagement/​ethical-use-of-student-data-for-educational-analytics.pdf?​la=en&​hash=EEB8CF87D03669F66A935ECEA17D084F05947832|https://​www.northumbria.ac.uk/​-/​media/​corporate-website/​new-sitecore-gallery/​services/​academic-registry/​documents/​qte/​student-engagement/​ethical-use-of-student-data-for-educational-analytics.pdf?​la=en&​hash=EEB8CF87D03669F66A935ECEA17D084F05947832]] Using Student data for educational analysis. Northumbria University. August 2018 [[https://​www.northumbria.ac.uk/​-/​media/​corporate-website/​new-sitecore-gallery/​services/​academic-registry/​documents/​qte/​student-engagement/​ethical-use-of-student-data-for-educational-analytics.pdf?​la=en&​hash=EEB8CF87D03669F66A935ECEA17D084F05947832|https://​www.northumbria.ac.uk/​-/​media/​corporate-website/​new-sitecore-gallery/​services/​academic-registry/​documents/​qte/​student-engagement/​ethical-use-of-student-data-for-educational-analytics.pdf?​la=en&​hash=EEB8CF87D03669F66A935ECEA17D084F05947832]]
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 ==== Helping Research ==== ==== Helping Research ====
  
- \\ "​Yewno’s mission is ‘Knowledge Singularity’ and by that we mean the day when knowledge, not information,​ is at everyone’s fingertips. In the search and discovery space the problems that people face today are the overwhelming volume of information and the fact that sources are fragmented and dispersed. There’s a great T.S. Eliot quote ‘Where’s the knowledge we lost in information’ and that sums up the problem perfectly."​+ \\ 
 +"​Yewno’s mission is ‘Knowledge Singularity’ and by that we mean the day when knowledge, not information,​ is at everyone’s fingertips. In the search and discovery space the problems that people face today are the overwhelming volume of information and the fact that sources are fragmented and dispersed. There’s a great T.S. Eliot quote ‘Where’s the knowledge we lost in information’ and that sums up the problem perfectly."​
  
- \\ Ruth Pickering. Chief Business Development & Strategy Officer. Do You Know About Yewno? By ALICE MEADOWS 7 JUN 2017 [[https://​scholarlykitchen.sspnet.org/​2017/​06/​07/​do-you-know-about-yewno/​|https://​scholarlykitchen.sspnet.org/​2017/​06/​07/​do-you-know-about-yewno/​]]+ \\ 
 +Ruth Pickering. Chief Business Development & Strategy Officer. Do You Know About Yewno? By ALICE MEADOWS 7 JUN 2017 [[https://​scholarlykitchen.sspnet.org/​2017/​06/​07/​do-you-know-about-yewno/​|https://​scholarlykitchen.sspnet.org/​2017/​06/​07/​do-you-know-about-yewno/​]]
  
 === Making research more discoverable === === Making research more discoverable ===
  
- \\ "​Content is at the centre of everything a publisher does. Enriching that content delivers significant value across the whole content life cycle. One particular area where the need for content enrichment can add significant value is in enabling the researcher to find and discover the most relevant content to assist in the researcher'​s workflow. Features that can be enhanced using enrichment techniques are relating articles, subject and context navigation, categorisation of content and identification of entities to provide linking to other relevant content"​.+ \\ 
 +"​Content is at the centre of everything a publisher does. Enriching that content delivers significant value across the whole content life cycle. One particular area where the need for content enrichment can add significant value is in enabling the researcher to find and discover the most relevant content to assist in the researcher'​s workflow. Features that can be enhanced using enrichment techniques are relating articles, subject and context navigation, categorisation of content and identification of entities to provide linking to other relevant content"​.
  
- \\ Content Enrichment Industry Insights. Number 1 (2016). 67 Bricks. [[http://​www.67bricks.com/​index.php/​content-enrichment-industry-insights-1-2016|http://​www.67bricks.com/​index.php/​content-enrichment-industry-insights-1-2016]]+ \\ 
 +Content Enrichment Industry Insights. Number 1 (2016). 67 Bricks. [[http://​www.67bricks.com/​index.php/​content-enrichment-industry-insights-1-2016|http://​www.67bricks.com/​index.php/​content-enrichment-industry-insights-1-2016]]
  
 === Artificial intelligence for discovering emerging trends and relationships === === Artificial intelligence for discovering emerging trends and relationships ===
  
- \\ "The IET looked to Ontotext to deliver artificial intelligence technologies into its database for discovering emerging trends and relationships. This technology gives customers both a deeper understanding of current developments and more value from the data they have contributed so much towards"​. \\ Ontotext Press Release 24 August 2016 [[https://​www.ontotext.com/​company/​news/​ontotext-selected-unleash-power-institution-engineering-technologys-knowledge/​|https://​www.ontotext.com/​company/​news/​ontotext-selected-unleash-power-institution-engineering-technologys-knowledge/​]]+ \\ 
 +"The IET looked to Ontotext to deliver artificial intelligence technologies into its database for discovering emerging trends and relationships. This technology gives customers both a deeper understanding of current developments and more value from the data they have contributed so much towards"​. \\ 
 +Ontotext Press Release 24 August 2016 [[https://​www.ontotext.com/​company/​news/​ontotext-selected-unleash-power-institution-engineering-technologys-knowledge/​|https://​www.ontotext.com/​company/​news/​ontotext-selected-unleash-power-institution-engineering-technologys-knowledge/​]]
  
 ==== The future of AI and scholarly publishing ==== ==== The future of AI and scholarly publishing ====
artificial_intelligence.1562755509.txt.gz · Last modified: 2019/07/10 06:45 by 109.149.81.116