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Wolphram Alpha

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An introduction Wolphram Alpha's underlying technology.

Published in: Technology, Education

Wolphram Alpha

  1. 1. Wolfram AlphaAn introduction to the underlyingtechnology Pedro Gaspar SIGC 2010/2011
  2. 2. Outline Introduction History Technology – The “Four Pillars” Technology – Interesting Facts Conclusions Reference Wolfram Alpha - Pedro Gaspar 2
  3. 3. Introduction Real-time computational answering system Not a Search Engine like Google Not as static as Wikipedia or as an Encyclopedia Wolfram Alpha - Pedro Gaspar 3
  4. 4. Introduction Goal: “Wolfram|Alphas long-term goal is to make all systematic knowledge immediately computable and accessible to everyone.” Systematic knowledge: ◦ Objective Data ◦ Models ◦ Methods ◦ Algorithms ◦ Formulae Wolfram Alpha - Pedro Gaspar 4
  5. 5. Introduction Some of the explored areas:Mathematics Units & Measures Money & FinanceStatistics & Data Analysis Dates & Times Socioeconomic DataPhysics Weather Health & MedicineChemistry Places & Geography Food & NutritionMaterials People & History EducationEngineering Culture & Media OrganizationsAstronomy Music TransportationEarth Sciences Words & Linguistics Technological WorldLife Sciences Sports & Games Web & Computer SystemsComputational Sciences Colors Wolfram Alpha - Pedro Gaspar 5
  6. 6. HISTORYHow did the project start? Wolfram Alpha - Pedro Gaspar 6
  7. 7. History – Wolfram Alpha Project lead by Stephen Wolfram It is the culmination of 5 years of work, and 25 more years of previous development Stephen started Wolfram Research in 1987, focusing mainly on the Mathematica software Wolfram Alpha - Pedro Gaspar 7
  8. 8. History – Wolfram Alpha In 2002 Stephen publishes “A New Kind of Science” In 2004 the company tries to apply the concepts from the book to a real-world product and thus started developing Wolfram Alpha In May 18th, 2009 Wolfram Alpha is officially launched to the public Wolfram Alpha - Pedro Gaspar 8
  9. 9. History – ComputableKnowledge The history of Systematic Data and the Development of Computable Knowledge goes back to the 20,000 BC with the invention of arithmetic Scientific Books, Encyclopedias, Census, Maps and other sources of information have been collecting data since Ancient Mesopotamia Wolfram Alpha - Pedro Gaspar 9
  10. 10. TECHNOLOGYHow does it work? Wolfram Alpha - Pedro Gaspar 10
  11. 11. Technology – the “FourPillars” VisualizatioCuration Formalization NLP n Wolfram Alpha - Pedro Gaspar 11
  12. 12. Pillar1 - Curation Field Experts help the team find the best content sources and validate the data Community input is also accepted, but all the data has to go through a rigorous validation process before being used Almost none of their data comes from the Internet now It turned out that curation and data gathering was only 5% of the work Wolfram Alpha - Pedro Gaspar 12
  13. 13. Pillar1 - Curation Wolfram Alpha - Pedro Gaspar 13
  14. 14. Pillar 2 - Formalization Organizing the curated data so that it can be computable Figuring out its conventions, units, definitions and how it connects to other data All these are encoded algorithmically in Wolfram Alpha so that they’re available when needed All the algorithms, models and equations are encoded into functions in Mathematica, the programming language behind Wolfram Alpha Wolfram Alpha - Pedro Gaspar 14
  15. 15. Pillar 2 - Formalization Mathematica’s language is able to represent data of all kinds using arbitrarily structured symbolic expressions As a result, the code is much more compact than in a lower-level language like Java or Python Mathematica already includes a very big set of algorithms and functions, making it easier to implement new (usually more complex) algorithms Wolfram Alpha - Pedro Gaspar 15
  16. 16. Pillar 2 - Formalization This creates a recursive process, that makes implementing new algorithms easier and easier through software reutilization Wolfram Alpha - Pedro Gaspar 16
  17. 17. Pillar 2 - Formalization Wolfram Alpha - Pedro Gaspar 17
  18. 18. Pillar 2 - Formalization Wolfram Alpha - Pedro Gaspar 18
  19. 19. Pillar 3 – Natural LanguageProcessing How could users interact with the system and use its computing powers? Through human language is the most natural response The problem is not the one we are used to – instead of trying to make sense of a big set of words, the system has to map small pieces of human input (queries) into its large set of symbolic representations The implemented solutions generally achieve good results Wolfram Alpha - Pedro Gaspar 19
  20. 20. Pillar 3 – Natural LanguageProcessing Wolfram Alpha - Pedro Gaspar 20
  21. 21. Pillar 3 – Natural LanguageProcessing Wolfram Alpha - Pedro Gaspar 21
  22. 22. Pillar 3 – Natural LanguageProcessing Wolfram Alpha - Pedro Gaspar 22
  23. 23. Pillar 3 – Natural LanguageProcessing Wolfram Alpha - Pedro Gaspar 23
  24. 24. Pillar 4 – Visualization Wolfram Alpha’s ability to present results in formats other than text is one of its most visually appealing features Mathematica includes some functionality to deal with this challenge, through what they call “computational aesthetics” This automates, for a specific symbolic representation, what to present and how to present it Wolfram Alpha - Pedro Gaspar 24
  25. 25. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 25
  26. 26. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 26
  27. 27. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 27
  28. 28. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 28
  29. 29. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 29
  30. 30. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 30
  31. 31. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 31
  32. 32. Pillar 4 – Visualization Wolfram Alpha - Pedro Gaspar 32
  33. 33. Technology – InterestingFacts More than 10 trillion of data More than 50,000 types of algorithms and models Linguistic capacity for more than 1000 domains More than 8 million lines of symbolic Mathematica code Runs in clusters of supercomputers, including the 44th largest supercomputer in the world - R Smarr Hundreds of terabytes of storage Wolfram Alpha - Pedro Gaspar 33
  34. 34. Conclusions It is all a matter of representing data and mapping queries to the set of things they can compute about Uses an internal and pre-structured database to find the answers to the queries Computation brings a lot of value when comparing it to search engines like Google Little to no information available about how the system works internally Wolfram Alpha - Pedro Gaspar 34
  35. 35. References Wolfram Alphas website Wolfram Alphas blog The Story of the Making of Wolfram Alpha Opinion: Wolfram Alpha: How does it work? How the hell does Wolfram Alpha Work Wolfram Alpha Architecture Wolfram Data Summit 2010 Wolfram Alphas YouTube channel What is Mathematica? Wolfram Alpha - Pedro Gaspar 35
  36. 36. QUESTIONS? Pedro Gasparpgaspar@student.dei.uc.pt Wolfram Alpha - Pedro Gaspar 36

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