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. Improving content discovery using AI and machine learning
- 4. “ 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
- 5. Experience data Improve task performance What is machine learning? Where a computer programme’s performance in a task improves with experience
- 6. “ “Those who can imagine anything, can create the impossible.” ― Alan Turing
- 10. > 50,000,000 science papers published since 1665
- 11. 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
- 12. 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
- 14. “ AI will be “either the best, or the worst thing, ever to happen to humanity” — Stephen Hawking
- 19. Opportunities
- 20. 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
- 21. 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
- 23. Al-BOT Einstein Physics article categorisation demo
- 25. Sparrho: personalised discovery for researchers UNSILO: “Rethinking publishing with AI”
- 26. 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
- 27. “ Data-Maturity: Your ability to store, manage, create and use data to deliver value to your users
- 28. 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
- 30. 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
- 31. “
- 32. 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
- 34. David Leeming Head of client services 67 Bricks www.67bricks.com David.leeming@67bricks.com +44 (0) 7454 734401 @67bricks
Notas del editor
- How many of you use AI today? – ask the audience for a show of hands. Expected that all do?
- Things that use AI: Personal assitsants in our home Financial institutes to check on bank fraud Google maps
- Its not new thinking and has been going on since the days of AlanTuring and his intelligent machines. In terms of how I like this quote and I think this is what makes this technology exciting. There are probably things we thought previously were impossible which are now possible.
- Single Jet engine - 20 TB per engine every hour Twitter - 4.8 TB in a year Single jet engine creates more data in an hour than twitter does in a year The jet engine industry is rapidly changing as a result GE started work on this problem to understand why some engines developed problems before others - they were able to solve this problem and in the process they have developed a whole new business - the new data business is more profitable than the original business of building engines
- We have seen a 1 trillion fold increase in the power of computer processors since 1956 In 1969 the Apollo Guidance Computer managed to land astronauts on the moon - it was 100 times slower than a gameboy Moore’s law states that the number of transistors in a processor doubles about every two years - this has held relatively true since it was first observed in 1965 We already have supercomputers that compute faster than the human brain and hold 10 times as much data It is predicted that in the next few years we will have a $1,000 computer that is as fast as the human brain More surprising is that by 2050 we expect to have a £1,000 computer that will exceed the processing power of the entire human race
- We have not been very good at focusing on the user experience An interesting contrast is the change in the transportation experience
- Minimal change
- It is time for scholarly publishing to evolve We need to move out of the water and onto land We need to understand what huge content and data assets that we have, we need to understand what is possible and we need to get better at using data We need to get ready for the coming changes
- Recent predictions claim that AI will contribute $15.7 trillion to the global economy by 2030. From self-driving cars, personal assistants and chatbots taking on roles as wide ranging as customer services, medicine, law and therapy - AI continues to generate hype, excitement and even fear. AI will clearly shape our future, but what will this future look like? And what - if anything - does it mean for scholarly publishing? Stephen Hawking told us that “AI will be either the best, or the worst thing, ever to happen to humanity”. There are certainly some big issues to be tackled, including privacy, transparency, security, ethical concerns around training data containing human bias, and even what jobs the future holds for humans in an automated world. While we need to keep these factors in mind, I personally believe that AI - if applied appropriately and diligently in the right places - will bring more positives to scholarly publishing than negatives. Although each organisation is unique there are many shared problems to solve and areas of opportunity.
- A test was carried out to see how an AI would compare to a human expert when diagnosing breast cancer It turns out that the AI was substantially better at correctly identifying breast cancer from scans What is interesting for us is that it was the data work that was the challenge in this activity The hard work was to turn the scans into data that the computer could use Google claims 99% accuracy in metastatic breast cancer detection Interesting work done on Nature
- Faced with mountains of image and audio data, researchers are turning to artificial intelligence to answer pressing ecological questions. When researchers collect audio recordings of birds, they are usually listening for the animals’ calls. But conservation biologist Marc Travers is interested in the noise produced when a bird collides with a power line. In 2011, Travers wanted to know how many of these collisions were occurring on the Hawaiian island of Kauai. With some 600 hours of audio collected — a full 25 days’ worth — counting the laser blasts manually was impractical. So, Travers sent the audio files (as well as metadata, such as times and locations) to Conservation Metrics, a firm in Santa Cruz, California, that uses artificial intelligence (AI) to assist wildlife monitoring. The company’s software was able to detect the collisions automatically and, over the next several years, Travers’ team increased its data harvest to about 75,000 hours per field season.
- Changes are happening As part of a three-month trial, PA will generate thousands of localised variations of stories sourced by reporters, on subjects ranging from health and crime to housing and employment. It marks the next stage in PA’s RADAR (Reporters and Data and Robots) project, looking at how artificial intelligence can be leveraged in the newsroom.
- Let’s take a look at some of the opportunities available to us when we have more data The Nobel Prize in Physics 2018 was awarded “for groundbreaking inventions in the field of laser physics” As a scholarly publisher with a back catalogue of laser content this is a great opportunity to share content on laser physics At the moment you’d probably look in a particular journal, perform searches of your own content to find relevant things, or use keywords assigned either by the author or a production editor. You are likely to miss things. It would be nice if you could push a button and see all your content that fits into this category instantly.
- demo : tripod This is a quick demo to show you how AI can support this activity All AI’s have a name - ours is called Al-bot Einstein, but unlike Albert Einstein he currently knows absolutely nothing. So let’s train him...
- Your ability to store, manage, create and use data to deliver value to your users
- There are different ways to think about the impact of data maturity - how it will impact you and your organisation We could consider how it will drive internal processes and increase efficiencies We could think about how it will impact the submission process, the researcher experience to find resources
- Primary data sources Secondary or supplementary data sources
- Let’s take a look at what data maturity is required to deliver these different levels of information product
- As an example let’s consider a research deciding where to focus his next piece of research 1. Find, read content from multiple sources 2. Improved search and discovery 3. Personal recommendations - you might be interested in this 4. Which organisations are doing research in this area, what has been trending, who else is doing research in this area, what are the most important papers in this area 5. What are the trends going to be over the next 3 years, what factors might impact that the success of my research output, which institution should I apply to, who should I contact because they have just finished some research...
- Increase in data, speed of processors and AI is rapidly changing our world There are substantial drivers for change in scholarly publishing Your data maturity will determine your ability to remain relevant in this new environment