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Improving content discovery using AI and machine learning

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David Leeming 67 BricksAI and machine learning has been generating a lot of attention over the past couple of years, but they still raise a lot of questions for our industry. How should publishers, librarians and researchers engage with these technologies? Are these technologies a threat to the current scholarly ecosystem or an opportunity? Can these technologies really help us drive the discovery and dissemination of research? How have these technologies already become an essential part of the scholarly ecosystem? After a short introduction to the concepts of AI and machine learning we will address these questions by engaging the audience in a live interactive demonstration in which we work together to train a machine learning algorithm to work with scholarly content. We will share areas of opportunity we have uncovered from our experiences of working with these technologies within the industry and discuss how publishers, librarians and researchers might work with these technologies to further advance the future of scholarly communication.

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Improving content discovery using AI and machine learning

  1. 1. Improving content discovery using AI and machine learning
  2. 2. What is artificial intelligence?
  3. 3. “ Artificial Intelligence The use of computers to simulate human intelligent behaviour in order to tackle complex problems that are difficult to solve using traditional computational approaches
  4. 4. Experience data Improve task performance What is machine learning? Where a computer programme’s performance in a task improves with experience
  5. 5. “ “Those who can imagine anything, can create the impossible.” ― Alan Turing
  6. 6. Changing world
  7. 7. > 50,000,000 science papers published since 1665
  8. 8. 1999 2019 Go outside (in the cold)……………………………………. Take out your phone (from anywhere) Hail a cab……………………………………………………… Request an uber Don’t know how long it will take………………………….. Know how long it will take Don’t know your vehicle or what passengers think of your driver…………………………………………………… Know your vehicle and passenger feedback ratings about your driver Enter a (mostly) dirty taxi………………………………… Enter a (mostly) clean car Must converse with your driver about where you are going, etc. ………………………………………………….. Not necessary to speak with your driver, it’s up to you Upon arrival, pay driver via cash………………………. Upon arrival, payment including pre-determined tip Ask for and wait for hand written receipt………………. Receipt automatically sent via email THE TRANSPORTATION EXPERIENCE
  9. 9. 1999 2019 Research written up as human readable 'paper‘………. Research written up as human readable 'paper‘ (data and video in early stages) Painful non-user friendly submission process………… Painful non-user friendly submission process (mostly) Subjective untrained human peer review process ……. Subjective untrained human peer review process Research packaged in periodical journals (this structure dates from the 17th century)…………………… Research packaged in periodical journals Scientific research behind paywalls …………………….. Most scientific content still behind paywalls Researchers value mostly determined by journal impact factor……………………………………………….…. Researchers value still mostly determined by journal impact factor Powerful incentives to withhold results for months or years until research is published…………………………. Powerful incentives to withhold results for months or years until research is published (not all fields) THE SCHOLALRY PUBLISHING EXPERIENCE
  10. 10. “ AI will be “either the best, or the worst thing, ever to happen to humanity” — Stephen Hawking
  11. 11. Solving real world problems
  12. 12. AI empowers conservation biology
  13. 13. AI in the newsroom
  14. 14. First machine generated book
  15. 15. Opportunities
  16. 16. AI opportunities: research Ask questions instead of inputting search terms Identify researchers and institutions for collaboration Reveal trends that are important to your work Discover the research you should be reading Use images to diagnose medical conditions Improve the quality of science Automated or semi-automated content creation Understand the links between research objects Discover interdisciplinary connections Predicting emerging subject areas Automated labs Knowledge graphs and relationship maps Predict disease and drug combinations Generate scientific hypothesises Understand grant and funding trends Faster time to publication
  17. 17. AI opportunities: publishing Predicting high impact research Fighting plagiarism Delivering customised / personalised experiences Helping researchers discover content Delivering knowledge rather than documents Selling data to machine learning companies Automated or semi-automated content creation Detecting fraudulent image manipulation or data modification Augmenting / automating peer review Predicting emerging subject areas Automating internal processes Adaptive learning products Identifying flawed reporting and statistics Unlocking value in legacy content Improved and/or automated marketing Creating automated content collections
  18. 18. Al-BOT Einstein Physics article categorisation demo
  19. 19. Improving discovery
  20. 20. Sparrho: personalised discovery for researchers UNSILO: “Rethinking publishing with AI”
  21. 21. Artificial Intelligence and machine learning- the opportunities ◎Excels at well defined tasks ◎Supports researchers, does not replace them ◎Cost effective in resource intensive activities ◎Provide new insights previously hidden away ◎Free up workers to do more productive jobs ◎Provides consumers with access to better products and services
  22. 22. “ Data-Maturity: Your ability to store, manage, create and use data to deliver value to your users
  23. 23. Insights (predictive and prescriptive) Knowledge (what has happened) Personalisation (personalised search and discovery experience) Smart ‘granular’ content (improved search and discovery) Raw content assets (document based storage and access) Low High High Low
  24. 24. Product development data maturity model Level 1: Raw content assets 2: Smart ‘granular’ content 3: Personalisation 4: Knowledge 5: Insights Store and manage Document level metadata User access rights data Granular content ‘chunk’ level metadata Semantic fingerprints for users ‘Integrated’ usage data Data relationships (e.g. as triples) Large primary and supplementary data sets Extract and create Manual metadata creation during editorial processes Semantic fingerprints for content items User interest metadata User ‘cohort’ analysis Knowledge extraction - as relationships Predictions and recommendations Data use Access control Document collections Granular search Faceted search Proximity matching Usage trending Personalised proximity matching User type analytics Powerful knowledge query capabilities Predictive and prescriptive analytics Product value Find and access documents online Enhanced search and discovery Slice and dice content products Personalised discovery experience Answer questions Explain what has happened Explain what will happen and what to do about it
  25. 25.
  26. 26. Summary 1. Increase in data, speed of processors and AI is rapidly changing our world 2. We can use AI as an opportunity rather than a threat 3. Improving how researchers, librarians and publishers discover and use content and data for better results 4. Better understanding of your content and data (data- maturity) improves discovery and ability to remain relevant
  27. 27. David Leeming Head of client services 67 Bricks www.67bricks.com David.leeming@67bricks.com +44 (0) 7454 734401 @67bricks

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