AI - Artificial Intelligence - Implications for Libraries
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The webinar discusses the implications of artificial intelligence (AI) for libraries, exploring whether AI can replace library services or enhance them. It covers various AI concepts, such as machine learning and deep learning, and provides practical examples of AI applications in everyday life, including image and voice recognition, and smart homes. Furthermore, it emphasizes the importance of preparing for AI integration in library services and technology advancements.
AI - Artificial Intelligence - Implications for Libraries
- 1. AI: Implications for Libraries Brian Pichman | Evolve Project Education Institute Webinar | Ontario Library Association !
- 2. Evolve Project | Brian Pichman 2 AI – Implications for Libraries ! What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future. Today we are exploring… Welcome
- 3. What is Artificial Intelligence the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
- 4. Evolve Project | Brian Pichman 4 ! What makes up an intelligent system? AI Components " #$% Logic and Rules Based Computer makes decisions based on a decision tree, logic rules, or a predefined process with a calculated result. Pattern Based (Machine Learning) Computer learns overtime by using data and algorithms to detect patterns. Deep Learning Deep Learning is a subset of Machine Learning that enables the computer to make decisions on its own. Neural Networks A neural network allows an AI to make its own conclusions, where a simple pattern-only based AI must rely solely on data. A neural network allows deep learning to function.
- 5. Pattern Based Intelligence -> currently exists with self driving cars, language translations, movie recommendations etc. Strong Artificial Intelligence -> (doesn’t yet exist) • computers think at a level that meets or passes people (abstract thinking) Artificial Intelligence Exists
- 6. Evolve Project | Brian Pichman 6 Flash Light Examples Understanding AI ! If an ML algorithm makes an inaccurate prediction, then the engineer needs to correct. In DL, the algorithms can determine on their own if a prediction is accurate or not. Deep Learning Allow machines to make to their own accurate decisions without intervention from engineer Neural Networks" # If detects {dark} turn on {light} Logic Rules $ it’s performing a function with the data given and gets progressively better at that function Machine Learning Eventually, the system can turn on the light with other queues such as “I can’t see” DL “Code” Flashlight will turn on automatically as it learns other words for “dark” picking up on phrases that contains the word ML “Code”: % ♥ '
- 7. Evolve Project | Brian Pichman 7 create an algorithm that is able to teach itself without any external help Pattern Recognition ! " ! # $ Deep Learning Uses more complicated mathematical models to define pictures content and speech Self Learning The advance machine learning system makes decisions by analyzing its own data and making patterns Learning on Examples This method is used when a machine learns through examples. For instance, Google’s automatic spam filtering learns as users report spam. Learning on Experience The system learns from positive and negative experiences.
- 9. ! 9 From Patterns to Automation AI Models The idea is that an algorithm will sift through the data, learn from it, and apply it to make a decision. This can be seen in any recommendation type service. Machine Learning takes it a step farther by automating tasks; helping data security firms identify potential threats or finance looking for favorable deals. AI’s can be Transactional in which a question is asked and an answer is given, like a virtual assistant. AI’s can also be Automated in which routine tasks such automatically taking trash out on garbage day.
- 10. Evolve Project | Brian Pichman 10 • When editing or using filters in photos (do X to eyes and Y to ears) • Identification of license plates from an image in a toll violation • Facebook’s ability to identify and recommend faces in photos • iPhone users can have their phone categorize people by facial patterns – in which you then define their name • Google’s Image Recognition Examples How we see AI In Everyday Life Image Recognition ! Think of how we can use facial imaging to determine moods
- 12. Evolve Project | Brian Pichman 12 You probably see this everyday if you use Siri, Google Home, or an Echo Product. Overtime or with training, a system can tailored results based on identifying the user asking. For example, Google Home will provide my personal driving times to work if it hears me ask “how long will it take me to get to work” versus a friend asking who it has no data on. Examples How we see AI In Everyday Life Voice Recognition ! Think of how a system can respond and remember a user based solely on their voice
- 14. Evolve Project | Brian Pichman 14 And How We Use It Other Forms of AI ! Optical Character Recognition Think of how a picture of your license plate allows a machine to translate that to text and run a query to determine who violated a toll. Also see this in scanners that can take an image and convert this to text. Consider how you can take a photo of another language and have it translate to yours Advance User Preferences This is the concept of an AI providing solutions based on historic user’s preferences and comparing it to similar users. Compare how Amazon or Netflix makes recommendations based on your purchases or views – or even how Amazon guesses when you might run out of a specific product. Sensory Data Analysis Your wearables that detect heart rate for instance can determine without user intervention if you are working out and even what kind of work out such as jogging or bicycling.
- 17. Healthcare! Used in healthcare to identify and notice predictable trends – such as having a machine look at charts to recognize tumors sooner with more accuracy – or eyes to determine stage of glaucoma
- 19. Smart Homes! See how a home can alert when it sees a person versus an animal or know that its going to rain tomorrow so no need to water the grass today
- 21. Evolve Project | Brian Pichman 21 Inspiring AI’s ! AI: AlphaGo AlphaGo is the first AI to beat a human in arguably the most difficult game to master. AlphaGo now teaches moves to trainees. " # Client: ROSS ROSS is an AI tool to make legal research easier and faster " #
- 24. Evolve Project | Brian Pichman 24 Run-away AI’s Tay (Thinking About You) ! Released on March 23 2016 via Twitter, Tay (as TayTweets on Twitter) was designed to mimic the interactions of 19 year old girl through learned conversations on Twitter. Users began tweeting potlically incorrect phrases to Tay, and thus, Tay responded and answered with the learned inappropriate behavior – as it was it was not taught what the difference between Good Language and Bad Language was. Microsoft Artificial Chatter Bot
- 25. Evolve Project | Brian Pichman 25 Run-away AI’s Inspirobot.me ! I am an artificial intelligence dedicated to generating unlimited amounts of unique inspirational quotes for endless enrichment of pointless human existence. -- From their website Happy Accidents
- 26. Evolve Project | Brian Pichman 26 Logic and Rules Based Challenges for AI !" # Training Similar to having good data, an AI might need to learn the correct response for the correct situation or identify dangers or inappropriate interactions Precision The idea of garbage data in garbage data out. If you flood an AI with bad data and don’t set the proper syntax or thresholds you will get incoherent results Context AI’s can struggle with understanding context. For example, asking Siri ”call me an ambulance” may yield “OK, from now on, I will call you Ambulance” $
- 27. Evolve Project | Brian Pichman 27 Things to Expand Your Knowledge Cool Resources to Check Out ! IBM Watson Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. – Wikipedia Powered by the latest innovations in machine learning, Watson lets you learn more with less data. You can integrate AI into your most important business processes, informed by IBM’s rich industry expertise. You can build models from scratch, or leverage our APIs and pre-trained business solutions. No matter how you use Watson, your data and insights belong to you − and only you. --IBM Watson
- 28. By Pgr94 - Own work based on diagram found at http://www.aaai.org/Magazine/Watson/watson.php, CC0, https://commons.wikimedia.org/w/index.php?curid=14575947
- 29. Evolve Project | Brian Pichman 29 Things to Expand Your Knowledge Cool Resources to Check Out ! Kaggle Kaggle is an online community of data scientists and machine learners, owned by Google, Inc. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle got its start by offering machine learning competitions and now also offers a public data platform, a cloud-based workbench for data science, and short form AI education. -- Wikipedia
- 31. Evolve Project | Brian Pichman 31 Things to Expand Your Knowledge Cool Resources to Check Out ! TensorFlow TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applica=ons such as neural networks. It is used for both research and produc=on at Google. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open-source license on November 9, 2015. -- Wikipedia
- 32. Applying It In Your Library Thoughts, Ideas, and More !
- 33. How can you prepare people for these fields? 33 Skills For Learning ! Understanding data and how to read data sets is valuable https://dzone.com/articles/ten-machine- learning-algorithms-you-should-know-to Math and Algorithms Statistics Learning how inputs of code can interact physical parts Hardware + Software Robotics Learning to code at a basic level with syntax and flow; then move to Python (most common) https://www.geeksforgeeks.org/top-5-best- programming-languages-for-artificial- intelligence-field/ Coding Languages Coding Learning this is a huge skill to master, along with object recognition https://www.pyimagesearch.com/start- here-learn-computer-vision-opencv/ How Do Computers See Computer Vision
- 34. How can we use it now? 34 Automations and Community ! Receptionist Allow for an interaction that’s quick and frees up time for staff for more complex and human needed interactions “Where’s The Bathroom” Industry Risks If car automation takes hold, what does that do to the shipping and delivery industry? Preparing The Future People sometimes need help finding information on your website. A Chat bot that notices a user on a page for a long time can make recommendations or hand off to staff Online Support Chat Bots Futures What can we make with the technology to make the world a better place? What Can We Make
- 36. Evolve Project | Brian Pichman 36 Feel free to reach out! Questions / Contacts ! 815-534-0403 " www.evolveproject.org bpichman@evolveproject.org # Twitter: @bpichman linkedin.com/in/bpichman slideshare.net/bpichman $