Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important: 1) Learning to interact with the world through reinforcement learning, 2) Learning while respecting everyone's privacy, and 3) Learning the causal relations in data (as opposed to discovering mere correlations). Together, they represent the "power tools" of the future machine learner.

1. Advances in Machine Learning
Max Welling

2. Overview
• Deep Learning
• Causality
• Reinforcement Learning
• Privacy
• Examples AI
• Conclusion
DeepDream

3. From Computer Science to Deep Learning
Computer Science
Data Science
Artificial Intelligence
Machine Learning
Deep Learning
3
econometry, mathematics

4. Explosive Growth
Moore's Law
Big Data
Deep Learning
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5. Types of Learning
• Supervised learning
– Learning from labeled data
• Unsupervised learning
– Learning from unlabeled data
• Reinforcement learning
– Learning from interactions and rewards from the world.
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6. Important New ML Developments
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• Deep Learning:
• powerful supervised predictors for high sampling rate signals.
• examples: speech recognition, image analysis.
• Causal discovery:
• prediciting causal relations between variables from observational data.
• examples: predictive maintenance, genomics
• Reinforcement learning:
• learning from interacting with the world
• examples: robotics, search engines, alphaGO
• Privacy preserving machine learning:
• learning from data such the privacy of individuals is guaranteed.
• examples: patient records, customer intelligence data

7. Deep Learning
GPUs
Data
billions of parameters
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8. Convolutional Neural Nets

9. Visual Object Classification
Annual "Image Net Challenge"
human performance
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10. CNN in Action
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(Andreiy Karpathy's blog)

11. Example in Healthcare:
Detection, segmentation, classification

12. Quality Control
critical
minor
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• Detect, segment and classify steel defects

13. Deep Learning & Art
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Gatys, Ecker, Bethge (arXiv 2015)
Extract style form paining and render a photo in that style

20. Fooling
Neural
Networks
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• This is bad news when you
need to make life or death decisions
• Know when you don't know:
uncertainty quantification!

21. Interpretation & Visualization
L. Zintgraf, T. Cohen & Welling 2016
HIV induced dimentia prediction
penguin
prediction
• How do we explain a prediction to a human?
• how do we anaylize an accident made by a self-driving car?
• How do we explain the diagnosis of Alzheimer's disease from an deep net?

22. Caption Generation
(Andrej Karpathy & Li Fei-Fei @ Stanford)
• Upload a picture
• Algorithm synthesises caption

23. Causality
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• Example:
• Insurance fees for black cars are higher…
• Mental disabilities in babies cause difficults births...
• Challenge: discovering causal relations without interventions

24. Predictive Maintenance
• "Predictive maintenance" : Predict if and when a part will fail.
• To fix the problem: predict what is the cause of the failure.
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25. Interacting with the World

26. Recommenders

27. The Argument For Private Data
• Data is becoming increasingly important as the "oil of our economy".
• The Googles and Facebooks are becoming "data-oligarchies"
• Private data in the hands of a few large corporations can be dangerous
• How can we democratize data, so everyone can benefit from it?
• How can we make sure data science is privacy preserving?

28. Re-Identifying Anonymized Data
MIT graduate student Latanya Sweeney was able to re-identify
Massachusetts Governor William Weld using some simple tactics and a voter
list.
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29. Re-Identifying Anonymized Data
• A five-digit zip code, date of birth, and gender are sufficient to identify an
individual uniquely about 87% of the time.
Name Zipcode Age Sex
Alice 47677 29 F
Bob 47983 65 M
Carol 47677 22 F
Dan 47532 23 M
Ellen 46789 43 F
Voter registration data
QID SA
Zipcode Age Sex Disease
47677 29 F Ovarian Cancer
47602 22 F Ovarian Cancer
47678 27 M Prostate Cancer
47905 43 M Flu
47909 52 F Heart Disease
47906 47 M Heart Disease
ID
Name
Alice
Betty
Charles
David
Emily
Fred
Microdata
(Table by Vitaly Shmatikov)
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30. Differential Privacy
• Differential privacy guarantees that any answer to a query will be only slightly
different for any individual if his/her data is in or out of the database
Cynthia Dwork
• DP adds just the right amount of noise to a query
to obfuscate private information.

31. Machine Translation
(Microsoft)
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Understand speechtranslate languagesynthesize speech

32. Transport
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In 10 years nobody will need a
driver's license.
In 10 years we will not need any
(physical) shops anymore.

33. Expert Systems
Natural Language Understanding
• Digital customer service assistent (Q&A)
• Digital doctors (AskADoctor)
• Digital lawyers
• Digital priest
• Digital professor ?
X1
X2
X3
+1 +1
+1
Input
Layer L1
Output
Layer L4
Layer L2
Layer L3
Deep learning, mul layer network
Machine Learning
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Information from Internet
• Business value: expensive employer is replaced by cheap AI system

34. Customer Intelligence
• Google Search
• Google Chrome
• Google+
• Google Maps
• Google Mail
• Google Now.
• Google Picasa
• Google Health?
• Google Car ?
User Profile (Mark Zuckerberg: "theory of mind")
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DATA

35. Conclusions
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Big Data, Big Brother?
smart city
profiling
autonomous weapons