Intelligent Information Technology Research Lab, Acadia University, Canada1
Daniel L. Silver
Acadia University, Wolfville, NS, Canada
Intelligent Information Technology Research Lab, Acadia University, Canada2
Intelligent Information Technology Research Lab, Acadia University, Canada
Key Take Away
A major challenge in artificial intelligence has been how to develop common background knowledge
Machine learning systems are beginning to make head-way in this area
Taking first steps to capture knowledge that can be used for future learning, reasoning, etc.
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Intelligent Information Technology Research Lab, Acadia University, Canada
Outline
Learning – What is it? History of Machine Learning Framework and Methods ML Application Areas Recent and Future Advances Challenges and Open Questions
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Intelligent Information Technology Research Lab, Acadia University, Canada
What is Learning?
Animals and Humans① Learn using new experiences and prior
knowledge
② Retain new knowledge from what is learned
③ Repeat starting at 1.
Essential to our survival and thriving
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Intelligent Information Technology Research Lab, Acadia University, Canada
What is Learning?(A little more formally)
Inductive inference/modeling Developing a general model/hypothesis from
examples Objective is to achieve good generalization for
making estimates/predictions It’s like … Fitting a curve to data
Also considered modeling the data Statistical modeling
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Intelligent Information Technology Research Lab, Acadia University, Canada
What is Learning?
Generalization through learning is not possible without an inductive bias
= a heuristic beyond the data
Intelligent Information Technology Research Lab, Acadia University, Canada9
Inductive Bias
ASH ST
THI RDSEC OND
ELM ST
FIR ST
PINE ST
OAK ST
Inductive bias depends upon:• Having prior knowledge• Selection of most related knowledge
Human learners use Inductive Bias
Intelligent Information Technology Research Lab, Acadia University, Canada
What is Learning?
Requires an inductive bias
= a heuristic beyond the data
Do you know any inductive biases?
How do you choose which to use?
Intelligent Information Technology Research Lab, Acadia University, Canada
Inductive Biases
Universal heuristics - Occam’s Razor Knowledge of intended use – Medical
diagnosis Knowledge of the source - Teacher Knowledge of the task domain Analogy with previously learned tasks
Tom Mitchell, 1980
Intelligent Information Technology Research Lab, Acadia University, Canada
What is Machine Learning?
The study of how to build computer programs that: Improve with experience Generalize from examples Self-program, to some extent
Intelligent Information Technology Research Lab, Acadia University, Canada
History of Machine Learning
1950 20001980
PDP GroupMulti-layerPerceptrons,New apps
Renaissance
1990
AI Success
Data mining,Web mining,User models,New alg.,Google
Present
Big Data,Web Analytics,Parallel alg.,Cloud comp.,Deep learning
Advances
1890
WilliamJames,Neuronal learning
Origins
1940
Donald Hebb,Math models, The PerceptronLimited value
Promise
1960
Minsky &Papert paper,Researchwanes
Hiatus
1970
Genetic alg,Version spaces,Decision Trees
Exploration
Intelligent Information Technology Research Lab, Acadia University, Canada
Of Interest to Several Disciplines
Computer Science – theory of computation, new algorithms
Math - advances in statistics, information theory Psychology – as models for human learning, knowledge
acquisition and retention Biology – how does a nervous system learn Physics – analogy to physical systems Philosophy – epistemology, knowledge acquisition Application Domains – new knowledge extracted from
data, solutions to unsolved problems
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Intelligent Information Technology Research Lab, Acadia University, Canada
Classes of ML Methods Supervised – Develops models that predict the value of
one variable from one or more others: Artifical Neural Networks, Inductive Decision Trees, Genetic
Algorithms, k-Nearest Neighbour, Bayesian Networks, Support Vectors Machines
Unsupervised – Generates groups or clusters of data that share similar features K-Means, Self-organizing Feature Maps
Reinforcement Learning – Develops models from the results of a final outcome; eg. win/loss of game TD-learning, Q-learning (related to Markov Decision Processes)
Hybrids – eg. semi-supervised learning
Intelligent Information Technology Research Lab, Acadia University, Canada
Focus: Supervised Learning
Function approximation (curve fitting)
Classification (concept learning, pattern recognition)
x1
x2
AB
f(x)
x
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Intelligent Information Technology Research Lab, Acadia University, Canada23
Supervised Machine Learning Framework
Instance Space X
TrainingExamples
TestingExamples
(x, f(x))
Model ofClassifier
hInductive
Learning System
h(x) ~ f(x)
Intelligent Information Technology Research Lab, Acadia University, Canada
Supervised Machine Learning
Problem: We wish to learn to classifying two people (A and B) based on their keyboard typing.
Approach: Acquire lots of typing examples from each person Extract relevant features - representation!
M = number of mistakes T = typing time
Transform feature representation as needed Use an algorithm to fit a model to the data - search! Test the model on an independent set of examples of typing from
each person
Intelligent Information Technology Research Lab, Acadia University, Canada
Classification
Mistakes
Typing Speed
A
B
B
B
B
BB
B
BB
B
B
B
B
BB
B
B B
B
B
AA
AA
AA
AA
AA
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
BB
B
Logistic Regression
Y
Y=f(M,T)0
1
M T
Y
Intelligent Information Technology Research Lab, Acadia University, Canada
Classification
A
B
B
B
B
BB
B
BB
B
B
B
B
BB
B
B B
B
B
AA
AA
AA
AA
AA
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
BB
B
Artificial Neural Network
A
Mistakes
Typing Speed
M T
Y
…
Intelligent Information Technology Research Lab, Acadia University, Canada
Classification
A
B
B
B
B
BB
B
BB
B
B
B
B
BB
B
B B
B
B
AA
AA
AA
AA
AA
A
A
A
A
AA
A
A
B
B
B
B
B
B
BB
B
Inductive Decision Tree
AA
Mistakes
Typing Speed
M?
T? T?
Root
LeafAB
Blood Pressure Example
Intelligent Information Technology Research Lab, Acadia University, Canada
Application AreasData Mining: Science and medicine: prediction, diagnosis, pattern
recognition, forecasting Manufacturing: process modeling and analysis Marketing and Sales: targeted marketing, segmentation Finance: portfolio trading, investment support Banking & Insurance: credit and policy approval Security: bomb, iceberg, fraud detection Engineering: dynamic load shedding, pattern recognition
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Intelligent Information Technology Research Lab, Acadia University, Canada
Application Areas
Web mining – information filtering and classification, social media predictive modeling
User Modeling – adaptive user interfaces, speech/gesture recognition
Intelligent Personal Agents – email spam filtering, fashion consultant,
Robotics – image recognition, adaptive control, autonomous vehicles (space, under-sea)
Military/Defense – target acquisition and classification, tactical recommendations, cyber attack detection
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Intelligent Information Technology Research Lab, Acadia University, Canada
Recent and Future Advances
Robotics Neuroprosthetics Lifelong Machine Learning Deep Learning Architectures ML and Growing Computing Power NELL – Never-Ending Language Learner Cloud-based Machine Learning
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Intelligent Information Technology Research Lab, Acadia University, Canada
OASIS: Onboard Autonomous Science Investigation System
Since early 2000’s Goal: To evaluate,
and autonomously act upon, science data gathered by spacecraft
Including planetary landers and rovers
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Intelligent Information Technology Research Lab, Acadia University, Canada
Stanford’s Sebastian Thrun holds a $2M check on top of Stanley, a robotic Volkswagen Touareg R5
212 km autonomus vehicle race, Nevada Stanley completed in 6h 54m Four other teams also finished
Source: Associated Press – Saturday, Oct 8, 2005
DARPA Grand Challenge 2005
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Intelligent Information Technology Research Lab, Acadia University, Canada
The Competition
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Intelligent Information Technology Research Lab, Acadia University, Canada
Autonomous Underwater Vehicles
Arctic ExplorerAUV designed and built by International Submarine Engineering Ltd. (ISE) of Port Coquitlam, B.C.Used to map the sea floor underneath the Arctic ice shelf in support of Canadian land claims under the UN Convention on the Law of the Sea. Various military uses; e.g. mine detection, elimination
(Source: ISE, Mae Seto)
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Intelligent Information Technology Research Lab, Acadia University, Canada
Literally Extending Our Reach – Neuroprosthetic Decoders
Dec, 2012 Andy Schwart, Univ.
of Pittsburgh Jan Scheuermann,
quadriplegic Brain-machine
interface, 96 electrodes
13 weeks of training High-performance neuroprosthetic
control by an individual with tetraplegia, The Lancet, v381, p557-654, Feb 2013
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Intelligent Information Technology Research Lab, Acadia University, Canada40
Lifelong Machine Learning (LML)
Considers methods of retaining and using learned knowledge to improve the effectiveness and efficiency of future learning
We investigate systems that must learn: From impoverished training sets For diverse domains of tasks Where practice of the same task happens
Applications: Intelligent Agents, Robotics, User Modeling, DM
Intelligent Information Technology Research Lab, Acadia University, Canada41
Supervised Machine Learning Framework
Instance Space X
TrainingExamples
TestingExamples
(x, f(x))
Model ofClassifier
hInductive
Learning System
h(x) ~ f(x)
After model is developed and used it is thrown away.
Intelligent Information Technology Research Lab, Acadia University, Canada42
Lifelong Machine Learning Framework
Instance Space X
TrainingExamples
TestingExamples
(x, f(x))
Model ofClassifier
h
Inductive Learning Systemshort-term memory
h(x) ~ f(x)
DomainKnowledge
long-term memoryRetention &ConsolidationInductive
Bias SelectionKnowledgeTransfer
Intelligent Information Technology Research Lab, Acadia University, Canada43
Lifelong Machine Learning Framework
Instance Space X
TrainingExamples
TestingExamples
(x, f(x))
Model ofClassifier
h
Inductive Learning Systemshort-term memory
h(x) ~ f(x)
DomainKnowledge
long-term memoryRetention &ConsolidationInductive
Bias SelectionKnowledgeTransfer
Intelligent Information Technology Research Lab, Acadia University, Canada44
Lifelong Machine Learning One Implementation
Instance Space X
TrainingExamples
TestingExamples
(x, f(x))
Model ofClassifier
h
h(x) ~ f(x)
Retention &ConsolidationKnowledge
Transfer
f2(x)
x1 xn
f1(x) f5(x)
Multiple Task Learning (MTL)
InductiveBias Selection
f3(x)f2(x) … f9(x) fk(x)
Consolidated MTL
DomainKnowledge
long-term memory
Intelligent Information Technology Research Lab, Acadia University, Canada48
An Environmental Example
Stream flow rate prediction [Lisa Gaudette, 2006]
x = weather data
f(x) = flow rate
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12
13
14
15
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0 1 2 3 4 5 6Years of Data Transfered
MA
E (
m^
3/s)
No Transfer Wilmot Sharpe Sharpe & Wilmot Shubenacadie
Intelligent Information Technology Research Lab, Acadia University, Canada
Lifelong Machine Learning with csMTL
Example: Learning to Learn how
to transform images Requires methods of
efficiently & effectively Retaining transform
model knowledge Using this knowledge to
learn new transforms
(Silver and Tu, 2010)52
Intelligent Information Technology Research Lab, Acadia University, Canada
Lifelong Machine Learning with csMTL
55Demo
Intelligent Information Technology Research Lab, Acadia University, Canada
Deep Learning Architectures Hinton and Bengio (2007+)
Learning deep architectures of neural networks
Layered networks of unsupervised auto-encoders efficiently develop hierarchies of features that capture regularities in their respective inputs
Used to develop models for families of tasks
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Intelligent Information Technology Research Lab, Acadia University, Canada
Deep Learning Architectures
Consider the problem of trying to classify these hand-written digits.
Intelligent Information Technology Research Lab, Acadia University, Canada
Deep Learning Architectures
2000 top-level artificial neurons2000 top-level artificial neurons
00500 neurons
(higher level features)500 neurons
(higher level features)
500 neurons(low level features)
500 neurons(low level features)
Images of digits 0-9
(28 x 28 pixels)
Images of digits 0-9
(28 x 28 pixels)
11 22 33 44
55 66 77 88 99
Neural Network:- Trained on 40,000 examples - Learns: * labels / recognize images * generate images from labels- Probabilistic in nature- Demo
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3
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Intelligent Information Technology Research Lab, Acadia University, Canada
ML and Computing Power
Moores Law Expected to
accelerate as the power of computers move to a log scale with use of multiple processing cores
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Intelligent Information Technology Research Lab, Acadia University, Canada
ML and Computing Power
IBMs Watson – Jeopardy, Feb, 2011: Massively parallel data processing system capable
of competing with humans in real-time question-answer problems
90 IBM Power-7 servers Each with four 8-core processors 15 TB (220M text pages) of RAM Tasks divided into thousands of stand-alone
jobs distributed among 80 teraflops (1 trillion ops/sec)
Uses a variety of AI approaches including machine learning
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Intelligent Information Technology Research Lab, Acadia University, Canada
ML and Computing Power
Andrew Ng’s work on Deep Learning Networks (ICML-2012)Problem: Learn to recognize human faces, cats, etc from unlabeled dataDataset of 10 million images; each image has 200x200 pixels9-layered locally connected neural network (1B connections)Parallel algorithm; 1,000 machines (16,000 cores) for three days
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Building High-level Features Using Large Scale Unsupervised LearningQuoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean, and Andrew Y. NgICML 2012: 29th International Conference on Machine Learning, Edinburgh, Scotland, June, 2012.
Intelligent Information Technology Research Lab, Acadia University, Canada
ML and Computing Power
Results: A face detector that is 81.7%
accurate Robust to translation, scaling,
and rotation
Further results: 15.8% accuracy in recognizing
20,000 object categories from ImageNet
70% relative improvement over the previous state-of-the-art.
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Intelligent Information Technology Research Lab, Acadia University, Canada
Never-Ending Language Learner Carlson et al (2010)
Each day: Extracts information from the web to populate a growing knowledge base of language semantics
Learns to perform this task better than on previous day
Uses a MTL approach in which a large number of different semantic functions are trained together
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Intelligent Information Technology Research Lab, Acadia University, Canada
Cloud-Based ML - Google
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https://developers.google.com/prediction/
Intelligent Information Technology Research Lab, Acadia University, Canada
Machine Flight vs. Machine Learning
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Factor Machine Flight Machine Learning
Effectiveness Travel higher, father Learn more things, accurately
To places not reachable Model complex phenomena
Efficiency Travel faster Learn faster
Lower cost Lower cost
Satisfaction Safe travel, beauty Confidence, elegance
Reach the moon, and beyond
Reach new knowledge, solve new problems
Intelligent Information Technology Research Lab, Acadia University, Canada72
Thank You!
[email protected] http://plato.acadiau.ca/courses/comp/dsilver/ http://ML3.acadiau.ca
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