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Intelligent System Scripting Language - Conceptual Presentation

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Intelligent System Scripting Language - Concepts

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Intelligent System Scripting Language - Conceptual Presentation

  1. 1. {ISSL} Intelligent System Scripting Language
  2. 2. CEDRIC POOTTAREN Researcher & Developer Artificial Intelligence Linux System Administration cedric@jcplaboratory.org @CedricPoottaren AKSHAY POKHUN Researcher & Developer Machine Learning Backend Development akshay@jcplaboratory.org @AkshayPokhun
  3. 3. Integration of Artificial Intelligence.
  4. 4. How A.I integrates our lives? Companion Robots: www.jcplaboratory.org
  5. 5. How A.I integrates our lives? Digital Assistants: www.jcplaboratory.org Microsoft Cortana Amazon Alexa EmoSpark AI
  6. 6. Do they achieve their intended purpose? www.jcplaboratory.org
  7. 7. Where to they fail: • No self-knowledge expansion/acquisition • Fail to integrate fully with their environment • Pre-programmed for basic tasks only Why to they fail: • Wrong development approach adopted? www.jcplaboratory.org
  8. 8. Introducing a new A.I Language.
  9. 9. {ISSL} Intelligent System Scripting Language
  10. 10. What is {ISSL}?  Intelligent System Scripting Language  A Scripting Programming Language  Uses Tag Elements such as in HTML and XML  NON XML-Compliant Language  It is read and executed by an Interpreter www.jcplaboratory.org
  11. 11. Why {ISSL}?
  12. 12. Why ISSL?  Dynamicity  Scripts can be altered non-programmatically  Scope of expansion is almost unlimited  Platform independent  ISSL Scripts can be shared among different platforms  Interpreted by a platform-specific interpreters www.jcplaboratory.org
  13. 13. The Concept Behind {ISSL}
  14. 14. How does {ISSL} work?  Knowledge are written into scripts  Scripts are interpreted and executed using an Interpreter.  Written using any programming language  e.g: C#, Java, Python etc.. www.jcplaboratory.org
  15. 15. {ISSL} Workflow www.jcplaboratory.org
  16. 16. AIML Artificial Intelligence Markup Language Dr. Richard S. Wallace We were inspired to create {ISSL}
  17. 17. What is AIML?  XML-Compliant Markup Language  Uses pattern and categories to define and set and conversion structure.  AIML was used to create the A.L.I.C.E Bot  Won the Loebner A.I Competition three times www.jcplaboratory.org
  18. 18. AIML Snippet www.jcplaboratory.org {category} Is a unit of knowledge {pattern} String of characters intended to match user’s input {template} The response to a match pattern
  19. 19. AIML v/s {ISSL}
  20. 20. Limitations of AIML • Designed to Chatterbots • No scope for self-learning • Poor integration into third-party systems www.jcplaboratory.org
  21. 21. ISSL / AIML • Non XML-Compliant • Self-Learning • Uses the Baby-Steps approach • [Feature] Compatible with AIML Knowledge Base • Integration ready • Uses a programming language-like structure www.jcplaboratory.org
  22. 22. Analyzing AIML 2.0 Knowledge Base AIMLAnalyzer https://github.com/cedroid09/AIMLAnalyzer
  23. 23. ISSL Snippet (Sample) www.jcplaboratory.org
  24. 24. ISSL Snippet (Explained) www.jcplaboratory.org  {lex} Is a unit of knowledge  {var} Variable  {vali} Specifies the minimum matched words accepted  {pattern} String of characters intended to match user’s input to a percentage  <idn> Exact corresponding pattern  {q} Question match structure  {pans} Randomized Possible answers
  25. 25. {ISSL} Interpreter
  26. 26. Functions of ISSL’s Interpreter • Interpret & execute ISSL Scripts • Library of Functions • To interact with the OS and other applications • I/O Operations, OS Control, Sending email etc… • Cognitive Services • Online search for information • Find images • Detect emotions within text • Speech recognition • Speech Synthesizer www.jcplaboratory.org
  27. 27. Using Text Analytics and Cognitive Services www.jcplaboratory.org User Input Interpreter: Processes Input Looks for matches in ISSL Docs Browse the Web for more information Processes new Information Response Information is turned into ISSL Knowledge ISSL Scripts are Updated
  28. 28. The Baby-Step Model
  29. 29. Baby-Steps Approach Is the concept of considering the A.I Entity as a Baby • Brain Programming • Learning anything from the beginning • Example: Its name and purpose • Meta-Cognition • Awareness of its know extents and limit of knowledge • Broaden its knowledge by: 1. Asking for questions 2. Looking for other sources www.jcplaboratory.org
  30. 30. www.jcplaboratory.org ISSL A.I Entity Learning Concept What is your name? I don’t know humanYour name is Bob Okay, :) my name is Bob Lookup for Knowledge Knowledge is Saved Knowledge Not Found in ISSL Knowledge Base Query to Enquire
  31. 31. {ISSL} Integration
  32. 32. Integrating ISSL into Existing/New Applications Why is Integration Important? • Application automation • The rise of Internet of Things How to integrate? • Using open sourced platform specific interpreters • Shared Class Libraries • Using cloud base API (?) www.jcplaboratory.org
  33. 33. www.jcplaboratory.org ISSL Integration with Interpreters ISSL Integration with Cloud API
  34. 34. {ISSL} – Common Knowledge Sharing
  35. 35. Common Knowledge Sharing (CKS) Hub Common place (Hub) for ISSL Entities around the world to share knowledge & acumen acquired over time. • Disadvantage of the Baby-Step Model Approach • A.I Entities will take a lot of time to mature up. • The CKS Hub • Sync and shares knowledge across all ISSL Entities • Affects only acumen and no personalized/self-form knowledge • You can opt-in and opt-out anytime • Newer ISSL Entities will be more intelligent as created www.jcplaboratory.org
  36. 36. www.jcplaboratory.org Visualizing Common Knowledge Sharing
  37. 37. ISSL Entities v/s Productivity
  38. 38. Productiveness of ISSL Entities • Smarter Systems • Better productivity • Helps you achieve more in limited time • Better integration • Provide technical support • Backup, problem solving etc. • Manual tasks are automated • Disk defrag, system monitoring, tasks management, file sync • Stress free computing www.jcplaboratory.org
  39. 39. ISSL Entities – Dream A.I • Learn to mimic human emotions • Using available cognitive services • Text Analytics with Sentiment Analysis • Keep that insight for references • Recurrence of events causing emotion • Self-Personality development • Learn conversations • Take cues from your likes to develop it’s own personality www.jcplaboratory.org
  40. 40. End of our concept round-up.
  41. 41. Thank you.

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