microsoft ai: at the intersection of people and society · site-specific data observations,...
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Stanford question & answer challenge
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Ethical, legal, societal influences
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Qualification problem
All preconditions?
Ramification problem
All effects of action?
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Knowing that you do not know is the best.
Not knowing that you do not know is an illness.
- Laozi, 500-600 BCE
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Fang, et al., 2015
Learn about abilities & failures
Performance
Successes & failures
p( fail | E, t)
Confidence
Image
H1
H2
H3
W1
W2
W3
W4
Input s
H3
Caption:
a man holding a tennis
racquet on a tennis court
H1
H2
H3
W1
W2
W3
Input t1
H3
W4
Deep learning about deep learning performance
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Reliable predictions of performance: Known unknowns
Grappling with Open-World Complexity
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Reliable predictions of performance: Known unknowns
Grappling with Open-World Complexity
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Grappling with Open-World Complexity
Reliable predictions of performance: Known unknowns
Challenge of unknown unknowns
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Expanded real-world testing
Algorithmic portfolios
Failsafe designs
People + machines
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M
training data
M
real-world concepts
x= (𝑓1, … , 𝑓𝑘)
wrong label
high confidence
Conceptual incompleteness
cats
dogs
Lakkaraju, Kamar, Caruana, H, 2017.
Identifying classifier blindspots
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M
training data
M
real-world concepts
x= (𝑓1, … , 𝑓𝑘)
wrong label
high confidence
cats
dogs
How to define & search regions of data space?
How to trade exploration and exploitation? Lakkaraju, Kamar, Caruana, H, 2017.
Identifying classifier blindspots
Conceptual incompleteness
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M
training data
cats
dogs
M
training data
x= (𝑓1, … , 𝑓𝑘)
wrong label
high confidence
Partition
space by
attributes
White CatsWhite Dogs Brown DogsBrown Cats Lakkaraju, Kamar, Caruana, H, 2017.
Identifying classifier blindspots
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Transfer learning
Learn from rich simulations
Learn generative models
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Hospital A Hospital B
Hospital C
Site-specific data
Observations, definitions
Patients, prevalencies
Covariate dependencies
Transfer learning opportunity
Hospital C
Hospital B
J. Wiens, J. Guttag, H, 2015.
A: Community hosp: 10k pts/yr
B: Acute care & teaching: 15k/yr
C: Major teaching & research: 40k/yr
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Site-specific data
Observations, definitions
Patients, prevalencies
Covariate dependencies
Transfer learning opportunity
Hospital A
Hospital C
Hospital B
J. Wiens, J. Guttag, H, 2015.
A: Community hosp: 10k pts/yr
B: Acute care & teaching: 15k/yr
C: Major teaching & research: 40k/yr
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M. Gabel, R. Caruana, M. Philipose, O. Dekel
Less data with better features
ImageNet 1000, 1M photos
Cut off top layer
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M. Gabel, R. Caruana, M. Philipose, O. Dekel
Less data with better features
ImageNet 1000, 1M photos
Cut off top layer
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M. Gabel, R. Caruana, M. Philipose, O. Dekel
Less data with better features
ImageNet 1000, 1M photos
Cut off top layer
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Raspberry Pi
Camera
Battery
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Trillions of sessions in complex scenarios
Learn & evaluate core competencies
Learn to optimize action plans
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Mapping
Planning
Next actions
Map
Plans
Stereo
algorithm
Depth Image
D. Dey, S. Sinha, S. Shah, A. Kapoor
CNN
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Mapping
Planning
Next actions
Map
Plans
Stereo
algorithmCNN
Depth Image
D. Dey, S. Sinha, S. Shah, A. Kapoor
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Learn expressive generative models
Generalize from minimal training sets
Harness physics
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Multilevel variational autoencoder
Learn disentangled representations
Groups of observations latent models
Learning generative models
Vary IDVary style
D. Buchacourt, R. Tomioka, S. Nowozin, 2017
Smooth control over learned latent space
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Inject physics to disentangle & generalize
Same?
Kulkarni, Whitney, Kohli & Tenenbaum, 2015
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Inject physics to disentangle & generalize
Kulkarni, Whitney, Kohli & Tenenbaum, 2015
Illumination Nod Shake
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Illumination Nod Shake
Inject physics to disentangle & generalize
Kulkarni, Whitney, Kohli & Tenenbaum, 2015
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AI attack surfaces
Adversarial machine learning
Self-modification
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Attacks on AI Systems
Goodfellow, et al.
Papernot, et al.
“Adverserial machine learning”
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EnvironmentAction
Environment
AI system
e.g., see: Amodei, Olah, et al., 2016
State Perception
ReinforcementReward
Adversarial Attacks & Self-Modification
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EnvironmentAction
Environment
AI system
e.g., see: Amodei, Olah, et al., 2016
State Perception
ReinforcementReward
Adversary
Adversarial Attacks & Self-Modification
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EnvironmentAction
Environment
AI system
e.g., see: Amodei, Olah, et al., 2016
State Perception
ReinforcementReward
Adversary
Action
Adversarial Attacks & Self-Modification
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EnvironmentAction
Environment
AI system
Amodei, Olah, et al., 2016H. 2016
State Perception
ReinforcementReward
Run-time verificationStatic analysis
Reflective analysisEnsure isolation * identify meddling * ensure operational faithfulness
Adversarial Attacks & Self-Modification
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Models of people & tasks
Models of complementarity
Coordination of initiative
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Models of people & tasks
Actions, services
E1 E2 E3
H1 H2
E4
Predictions about needs, goals
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Models of world & people
Predictions about user beliefs
E2 E3
H1 H2
E4
E1 E2 E3
H1 H2
E4
Predictions about world
Actions
H. Barry, 1995
H. , Apacible, Sarin, Liao, 2005
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H. Barry, 1995
Models of world & people
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Complementarity
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Complementarity
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Complementarity
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D. Wang, A. Khosla, R. Gargeya, H. Irshad, A.H. Beck, 2016
Identifying metastatic breast cancer
(Camelyon Grand Challenge 2016)
AI + Expert: 0.5%
85% reduction in errors.
Human is superior
Error: 3.4%
Complementarity
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Machine
perception
Human
perception
Machine learning & inference
Kamar, Hacker, H., AAMAS 2012
ComplementarityLabel galaxies in Sloan Digital Sky Survey
(Galaxy Zoo)
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~453 features
Machine learning & inference
Machine
perceptionHuman
perception
Kamar, Hacker, H., AAMAS 2012
ComplementarityLabel galaxies in Sloan Digital Sky Survey
(Galaxy Zoo)
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~453 features
Machine learning & inference
Ideal fusion, stopping
Machine
perceptionHuman
perception
Kamar, Hacker, H., AAMAS 2012
Complementarity Full accuracy: 47% of human effort
95% accuracy: 23% of human effort
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Designs for mix of initiatives
Machine learning & inference
Human
cognition
Machine
intelligence
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C.E. Reiley, et al.
Initiative: Recognizing human goals, stateRecognizing intention
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Padoy & Hager. ICRA 2011
van den Berg, et al, ICRA, 2010
Coordination of initiative
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Trustworthiness and safety
Fairness, accuracy, transparency
Ethical and legal aspects of autonomy
Jobs and economy
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Bernard Parker: rated high risk Dylan Fugett: rated low risk.
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March 2017
A. Howard, C. Zhang, H., 2017
Machine learning “contact lens” for children
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Science & engineering
Human-AI collaboration
AI, people, and society
Much to do