An introduction Wolphram Alpha's underlying technology.

1. Wolfram Alpha
An introduction to the underlying
technology
Pedro Gaspar
SIGC
2010/2011

2. Outline
 Introduction
 History
 Technology – The “Four Pillars”
 Technology – Interesting Facts
 Conclusions
 Reference
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3. Introduction
 Real-time computational answering system
 Not a Search Engine like Google
 Not as static as Wikipedia or as an
Encyclopedia
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4. Introduction
 Goal:
“Wolfram|Alpha's long-term goal is to make all systematic
knowledge immediately computable and accessible to
everyone.”
 Systematic knowledge:
◦ Objective Data
◦ Models
◦ Methods
◦ Algorithms
◦ Formulae
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5. Introduction
 Some of the explored areas:
Mathematics Units & Measures Money & Finance
Statistics & Data Analysis Dates & Times Socioeconomic Data
Physics Weather Health & Medicine
Chemistry Places & Geography Food & Nutrition
Materials People & History Education
Engineering Culture & Media Organizations
Astronomy Music Transportation
Earth Sciences Words & Linguistics Technological World
Life Sciences Sports & Games Web & Computer Systems
Computational Sciences Colors
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6. HISTORY
How did the project start?
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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
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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
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9. History – Computable
Knowledge
 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
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10. TECHNOLOGY
How does it work?
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11. Technology – the “Four
Pillars”
Visualizatio
Curation Formalization NLP n
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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
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13. Pillar1 - Curation
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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
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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
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16. Pillar 2 - Formalization
 This creates a recursive process, that makes
implementing new algorithms easier and easier
through software reutilization
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17. Pillar 2 - Formalization
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18. Pillar 2 - Formalization
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19. Pillar 3 – Natural Language
Processing
 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
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20. Pillar 3 – Natural Language
Processing
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21. Pillar 3 – Natural Language
Processing
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22. Pillar 3 – Natural Language
Processing
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23. Pillar 3 – Natural Language
Processing
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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
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25. Pillar 4 – Visualization
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26. Pillar 4 – Visualization
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27. Pillar 4 – Visualization
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28. Pillar 4 – Visualization
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29. Pillar 4 – Visualization
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30. Pillar 4 – Visualization
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31. Pillar 4 – Visualization
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32. Pillar 4 – Visualization
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33. Technology – Interesting
Facts
 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
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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
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35. References
 Wolfram Alpha's website
 Wolfram Alpha's 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 Alpha's YouTube channel
 What is Mathematica?
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36. QUESTIONS?
Pedro Gaspar
pgaspar@student.dei.uc.pt
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