Download Intro_to_Artificial_Intelligence - 91-514-s2011

Intro to Artificial Intelligence &
Google Prediction API
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Pawan Gupta
First definition of AI
"AI is the study of making computers do things, at which, at the
moment, people are better"
-- Elaine Rich (author of Artificial Intelligence book)
There are still many domains where its better to rely on human
to solve problems than computers.
For example : teaching programing course to novice
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Computers advantage
Computers are better than human beings in:
• Numeric Computation
• Information Storage
• Repetitive Operation
• Example
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Land aircraft safely
Ability to beat virtually any human being in the chess.
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Human advantages
Human beings are better than computers in:
• Common Sense/Judgement
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Crossing a road
Driving in busy city like New York and Boston
• Language, speech, understanding scene, emotions, and
expression situation
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Player emotions vs Video game
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Algorithm Vs Non-Algorithm
Algorithm is a systemic method in which a sequence of
steps/instructions are mentions, and each step/instruction can
takes finite amount of time.
Non-algorithm method, we may not have definite sequence of
steps to follow. Example crossing road.
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"Measure three times before you cut
once" - Proverbs
• Computers takes instructions from human and then do the
task. without thinking if instructions user gave is right or not.
• Garbage in and Garbage out
• Human can take garbage as an input, but may think it twice
before its provide output.
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Google Prediction API
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Making prediction based on data provided
Machine learning as a Web Service
It is a RESTfull HTTP service
Only HTTPS protocol is supported
Development Warning:
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"Not a crystal ball"
Not fully ready yet, a lot more work to be done.
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Process
Steps to use Prediction API
1. Prediction API Account access
2. Build a data model
3. Upload training data to Google cloud
4. Train the system with the data
5. Send a query
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Commands
• $ ./oauth-train.sh
• $ ./oauth-check-training.sh
• $ ./oauth-predict.sh
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Authentication Types
• clientLogin
• login credentials is validated only once per
session
• clientLogin is designed for desktop application
• oAuth
• Better user interface
• oAuth designed for web applications
• Secure user credential
• Required Account access: Google storage for dev &
Prediction API account
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Build a data Model
• Data format: comma separated value format(CSV)
• Two values –Answer and data column
• Cannot mix numbers and categorical data
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One data file per model
One example per line
Text is case-sensitive
Data can be numeric or unstructured text
Data cannot be more than 100M
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Numeric vs. Categorical model
• Numeric
• Return numeric value (Google example)
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Temperature City
Day_of_year Weather
52,
"Seattle“, 283,
"cloudy"
64,
"Seattle“,
295,
"sunny"
60,
"Seattle“, 287,
"partly_cloudy"
query "Seattle,288,sunny", return 63
Bad query “288, sunny, Seattle”
• Categorical
• Return string value
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Description
Subject line
"spam" ,
"Get her to love you"
"spam" ,
"Lose weight fast!"
query "You can lose weight now!" return “spam”
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Data Example
• "english" = "The patriots are the best team in the
league"
• "Hindi" = "आज बहुत अच्छा दिन है “
• "French", "Je ne vous parle pas, monsieur."
• "Spanish", "Soneto"
• "English", " * * * * * * *" (Bad data)
• "Spanish", "YO, EL REY."
• "French", "-- Ah! fit d'Artagnan."
• " Latin", " Non bene pro toto libertas venditur auro."
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Upload data
• Create a bucket
• gsutil mb gs://classexample (follow Google guidelines)
• Copy data to bucket
• $ gsutil cp ./language_id gs://classexample/language_id
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Train system
• To Train
• $ ./oauth-train.sh classexample/language_id
• To check
• $ ./oauth-check-training.sh classexample/language_id
• Return :
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{"data":{"data":"classexample/language_id","modelinfo":"Training has
not completed."}}
• Try again
• $ ./oauth-check-training.sh classexample/language_id
• Return :
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{"data":{"data":"classexample/language_id","modelinfo":"estimated
accuracy: 0.95"}}
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Send Queries
• Send queries
– $ ./oauth-predict.sh classexample/language_id "Today is Monday.
The New york City"
– return {data: {"input" : { "mixture" : [ " Today is Monday. The New
york City " ]}}}{"data":{"kind":"prediction#output",
"outputLabel":"english",}}
• outputLabel value shows best guess
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Languages
• Prediction API libraries available in following programming
languages:
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.Net
BASH
JavaScript
PHP
R (Google Prediction Client Library for R)
Ruby
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Google Recommendation
• Think carefully when uploading train data
• More examples (in millions), predict better results
• Query must match same number of arguments as features
• “english” , “Today is good day”
• Good query : “How is your car running”
• Bad query : “ How is your car running”, “What day is today”
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Summary
• When not following steps and using judgment to solve
problem, then its a field of AI.
• If series of steps can be follow to solve problem, its better to
use Algorithm. Its faster and cheaper way.
• Prediction API is just a proof of concept yet
• REMEMBER: Data model learn from the file you upload, NOT
from queries.
• Other examples that are equaling to Prediction API
• Email Rules or Filters
• Amazon recommendation
• Automation
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References
• http://code.google.com/apis/predict/
• http://en.wikipedia.org/wiki/Artificial_intelligence
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