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.
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.
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.
HISTORYHow did the project start? Wolfram Alpha - Pedro Gaspar 6
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.
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.
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.
TECHNOLOGYHow does it work? Wolfram Alpha - Pedro Gaspar 10
11.
Technology – the “FourPillars” VisualizatioCuration Formalization NLP n Wolfram Alpha - Pedro Gaspar 11
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.
Pillar1 - Curation Wolfram Alpha - Pedro Gaspar 13
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.
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.
Pillar 2 - Formalization This creates a recursive process, that makes implementing new algorithms easier and easier through software reutilization Wolfram Alpha - Pedro Gaspar 16
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
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
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.
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.
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.
QUESTIONS? Pedro Gasparpgaspar@student.dei.uc.pt Wolfram Alpha - Pedro Gaspar 36
Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.