barga didc'14 invited talk
TRANSCRIPT
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A Look into the Future by Learning from the PastRoger S. Barga
Cloud Machine Learning, Cloud and Enterprise
Microsoft Corporation
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This isn’t an academic talk…
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This isn’t an applied research talk…
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1 1 5 4 3
7 5 3 5 3
5 5 9 0 6
3 5 2 0 0
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1. Learn it when you can’t code it
2. Learn it when you can’t scale it
3. Learn it when you have to adapt/personalize
4. Learn it when you can’t track it
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• Distributed
computing and
storage
• Deep Neural
Networks
• Learning =
Scalable,
Adaptive
Computation for
Various Big
Data
2011 (“Big
Data, DNN”)
• Wide
application in
products
• Statistical
Modeling of
Data
• Learning =
Parameter
Estimation or
Inference
2005
(“Graphical
Models”)
• Statistical
Learning Theory
• Scoring Systems
• Learning =
Optimization of
Convex
Functions
2000
(“Kernel
Machines”)
• Expert Systems
• Decision-Tree
Learning (C4.5)
• Learning =
Methods to
automatically
build Expert
Systems
1990
(“Symbolic”)
• Neural
Networks
• Artificial
Intelligence
• Learning =
Adaptation of
Neurons based
on External
Stimuli
1980
(“Neuro”)
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• Distributed
computing and
storage
• Deep Neural
Networks
• Learning =
Scalable,
Adaptive
Computation for
Various Big
Data
2011 (“Big
Data, DNN”)
• Wide
application in
products
• Statistical
Modeling of
Data
• Learning =
Parameter
Estimation or
Inference
2005
(“Graphical
Models”)
• Statistical
Learning Theory
• Scoring Systems
• Learning =
Optimization of
Convex
Functions
2000
(“Kernel
Machines”)
• Expert Systems
• Decision-Tree
Learning (C4.5)
• Learning =
Methods to
automatically
build Expert
Systems
1990
(“Symbolic”)
• Neural
Networks
• Artificial
Intelligence
• Learning =
Adaptation of
Neurons based
on External
Stimuli
1980
(“Neuro”)
![Page 11: Barga DIDC'14 Invited Talk](https://reader031.vdocuments.site/reader031/viewer/2022022201/589bc59f1a28ab082b8b615b/html5/thumbnails/11.jpg)
• Distributed
computing and
storage
• Deep Neural
Networks
• Learning =
Scalable,
Adaptive
Computation for
Various Big
Data
2011 (“Big
Data, DNN”)
• Wide
application in
products
• Statistical
Modeling of
Data
• Learning =
Parameter
Estimation or
Inference
2005
(“Graphical
Models”)
• Statistical
Learning Theory
• Scoring Systems
• Learning =
Optimization of
Convex
Functions
2000
(“Kernel
Machines”)
• Expert Systems
• Decision-Tree
Learning (C4.5)
• Learning =
Methods to
automatically
build Expert
Systems
1990
(“Symbolic”)
• Neural
Networks
• Artificial
Intelligence
• Learning =
Adaptation of
Neurons based
on External
Stimuli
1980
(“Neuro”)
![Page 12: Barga DIDC'14 Invited Talk](https://reader031.vdocuments.site/reader031/viewer/2022022201/589bc59f1a28ab082b8b615b/html5/thumbnails/12.jpg)
• Distributed
computing and
storage
• Deep Neural
Networks
• Learning =
Scalable,
Adaptive
Computation for
Various Big
Data
2011 (“Big
Data, DNN”)
• Wide
application in
products
• Statistical
Modeling of
Data
• Learning =
Parameter
Estimation or
Inference
2005
(“Graphical
Models”)
• Statistical
Learning Theory
• Scoring Systems
• Learning =
Optimization of
Convex
Functions
2000
(“Kernel
Machines”)
• Expert Systems
• Decision-Tree
Learning (C4.5)
• Learning =
Methods to
automatically
build Expert
Systems
1990
(“Symbolic”)
• Neural
Networks
• Artificial
Intelligence
• Learning =
Adaptation of
Neurons based
on External
Stimuli
1980
(“Neuro”)
![Page 13: Barga DIDC'14 Invited Talk](https://reader031.vdocuments.site/reader031/viewer/2022022201/589bc59f1a28ab082b8b615b/html5/thumbnails/13.jpg)
• Distributed
computing and
storage
• Deep Neural
Networks
• Learning =
Scalable,
Adaptive
Computation for
Various Big
Data
2011 (“Big
Data, DNN”)
• Wide
application in
products
• Statistical
Modeling of
Data
• Learning =
Parameter
Estimation or
Inference
2005
(“Graphical
Models”)
• Statistical
Learning Theory
• Scoring Systems
• Learning =
Optimization of
Convex
Functions
2000
(“Kernel
Machines”)
• Expert Systems
• Decision-Tree
Learning (C4.5)
• Learning =
Methods to
automatically
build Expert
Systems
1990
(“Symbolic”)
• Neural
Networks
• Artificial
Intelligence
• Learning =
Adaptation of
Neurons based
on External
Stimuli
1980
(“Neuro”)
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The future will belong to those who can turn
their historical data into predictive models…
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Vision Analytics
Recommenda-
tion engines
Advertising
analysis
Weather
forecasting for
business planning
Social network
analysis
Legal
discovery and
document
archiving
Pricing analysis
Fraud
detection
Churn
analysis
Equipment
monitoring
Location-based
tracking and
services
Personalized
Insurance
Machine learning and predictive models are core new capabilities that will touch everything in the new enterprise
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training data (expensive) synthetic training data (cheaper)
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solve hard problems
value from Big Data
data analytics
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Machine learning enables nearly every
value proposition of web search.
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Hundreds of thousands of machines…
Hundreds of metrics and signals per machine…
Which signals correlate with the real cause of a problem?
How can we extract effective repair actions?
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solve hard problems
value from Big Data
data analytics
human intelligence
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
WER %
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Training
English training data
English words,
with some errors
English
speech input
More
with fewer errors
How much data? – About the same as a human needs…
Runtime
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Training
English training data
English words,
with some errors
English
speech input
with fewer errors
Runtime
Can we learn the internal representation of human speech?
French training data
or French words
or French
Chinese training data
or Chinese words
or Chinese
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Shetland Sheepdog (0.72) Shoe Store (0.56) Attack Aircraft Carrier (0.81)
Steel Arch Bridge (0.74) Ballplayer, Baseball Player (0.86) Catamaran (0.51)
Wood Rabbit, Cottontail, Cottontail Rabbit (0.18)
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The first image returned is Rajiv Gandhi (her husband) in the Answer.
An image of Lindsay Lohan appears in the Images Answer
not really
X
X
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solve hard problems
value from Big Data
data analytics
human intelligence
engineering practices
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intelligence will become ambient
intelligence from machine learning
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55
57
59
61
63
65
67
69
71
Overall NDCG
Bing NDCG Google NDCG
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The razor-toothed piranhas of the genera
Serrasalmus and Pygocentrus are the most
ferocious freshwater fish in the world. In
reality they seldom attack a human.
Template
matching
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The razor-toothed piranhas of the genera
Serrasalmus and Pygocentrus are the most
ferocious freshwater fish in the world. In
reality they seldom attack a human.
pypygygogoc
Pygocentrus
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The razor-toothed piranhas of the genera
Serrasalmus and Pygocentrus are the most
ferocious freshwater fish in the world. In
reality they seldom attack a human.
Sentence-level
decoding
The razor-toothed piranhas of the genera
Serrasalmus and Pygocentrus are the most
ferocious freshwater fish in the world. In
reality they seldom attack a human.
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Massive
The Intelligent Cloud
Machine
Learning &
Analytics
Crowd
Sourcing
Massive &
Diverse Data
The Cloud - Where Everything Comes Together
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