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Artificial Intelligence. What machines can learn and how to implement Machine Learning project in you organization? Antons Misl ēvičs antonsm@microsoft. com
Artificial Intelligence. What machines can learn and how to implement Machine Learning
project in you organization?
Antons Mislēvičs
Agenda
1. What machines can do today?
2. How Machine Learning works?
3. How to implement Machine Learning project?
What machines can do today?
Chess: IBM Deep Blue 3 ½ - Gary Kasparov 2 ½ (1997)
Kasparov vs Deep Blue the rematch, 1997
https://www.research.ibm.com/deepblue/
Jeopardy: IBM Watson beats champions (2011)
Final Jeopardy! and the Future of Watson
http://www-03.ibm.com/marketing/br/watson/what-is-watson/the-future-of-watson.html
Go: Google AlphaGo 4 – Lee Sedol 1 (2016)
AlphaGo
https://deepmind.com/alpha-go
Image RecognitionError rates – human vs machine
1. Traffic Sign Recognition (IJCNN 2011):
– Human: 1.16%
– Machine: 0.54%
2. Handwritten Digits (MNIST):
– Human: approx. 0.2%
– Machine: 0.23% (2012)
The German Traffic Sign Recognition Benchmark: http://benchmark.ini.rub.de/?section=gtsrb&subsection=results
THE MNIST DATABASE of handwritten digits: http://yann.lecun.com/exdb/mnist/
Large Scale Visual Recognition Challenge (ILSVRC)
2015 challenge:
– Object detection - 200 categories
– Object recognition – 1000 categories
– Object detection from video – 30 categories
– Scene classification – 401 categories
Large Scale Visual Recognition Challenge 2015 (ILSVRC2015)
http://image-net.org/challenges/LSVRC/2015/index#maincomp
Large Scale Visual Recognition Challenge 2015 – Results: http://image-net.org/challenges/LSVRC/2015/results
Microsoft Researchers’ Algorithm Sets ImageNet Challenge Milestone, 2015: https://www.microsoft.com/en-us/research/microsoft-researchers-algorithm-sets-imagenet-challenge-milestone/
Microsoft Research Team:
“To our knowledge, our result is the first to surpass human-
level performance…on this visual recognition challenge”
Machines can understand the meaning…
Show and Tell: A Neural Image Caption Generator, O. Vinyals, A. Toshev, S. Bengio, D. Erhan, 2015: http://arxiv.org/abs/1411.4555v2
DenseCap: Fully Convolutional Localization Networks for Dense Captioning, J. Johnson, A. Karpathy, L. Fei-Fei, 2015: http://arxiv.org/abs/1511.07571
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, R. Kiros, R. Salakhutdinov, R. S. Zemel, 2014: http://arxiv.org/abs/1411.2539
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, R. Kiros, R. Salakhutdinov, R. S. Zemel, 2014: http://arxiv.org/abs/1411.2539
Self-Driving Cars
Google Self-Driving Car Project - How it drives: https://www.google.com/selfdrivingcar/how/
Autopilot Full Self-Driving Hardware (Neighborhood Long), Tesla Motors: https://vimeo.com/192179727
2. The Next Rembrandt [4]
1. A Neural Algorithm of Artistic Style, 2015: https://arxiv.org/abs/1508.06576
2. Supercharging Style Transfer, 2016: https://research.googleblog.com/2016/10/supercharging-style-transfer.html
3. Neural Doodle:, 2016: https://github.com/alexjc/neural-doodle
4. The Next Rembrandt: https://www.nextrembrandt.com/
5. Image Completion with Deep Learning in TensorFlow, 2016: https://bamos.github.io/2016/08/09/deep-completion/
6. Neural Enhance, 2016: https://github.com/alexjc/neural-enhance
Machines get creative…1. Reproduce artistic style [1, 2, 3]
4. Enhance images [6]
3. Complete images [5]
1. WaveNet: A Generative Model for Raw Audio: https://deepmind.com/blog/wavenet-generative-model-raw-audio/
2. WaveNet: A Generative Model for Raw Audio, 2016: https://arxiv.org/abs/1609.03499
3. Historic Achievement: Microsoft researchers reach human parity in conversational speech recognition: https://blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-researchers-reach-human-parity-conversational-speech-recognition/
4. Achieving Human Parity in Conversational Speech Recognition, 2016: http://arxiv.org/abs/1610.05256
Text to speech and voice recognition…
2. Recognize voice [3, 4]
1. Talk - text to speech (WaveNet) [1, 2]
2. Generate handwriting [2, 3]
3. Translate texts [4, 5]
1. Composing Music With Recurrent Neural Networks: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
2. Generating Sequences With Recurrent Neural Networks, A. Graves, 2014: http://arxiv.org/abs/1308.0850
3. Alex Graves’s RNN handwriting generation demo: http://www.cs.toronto.edu/~graves/handwriting.html
4. University of Montreal, Lisa Lab, Neural Machine Translation demo: http://lisa.iro.umontreal.ca/mt-demo
5. Fully Character-Level Neural Machine Translation without Explicit Segmentation, J.Lee, K. Cho, T. Hofmann, 2016: http://arxiv.org/abs/1610.03017
What else machines can do?
1. Compose music [1]
Playing Atari with Deep Reinforcement Learning, 2013: https://arxiv.org/abs/1312.5602
Google DeepMind's Deep Q-learning playing Atari Breakout: https://www.youtube.com/watch?v=V1eYniJ0Rnk
Playing Atari
Machine Learning is transforming businesses…
Predictive Maintenance for Aircraft Engines
Rolls-Royce and Microsoft collaborate to create new digital capabilities: https://www.youtube.com/watch?v=B3CZXp-RK0g
How Machine Learning works?
Machine Learning process
Introducing Azure Machine Learning, D. Chappell, 2015:
http://www.davidchappell.com/writing/white_papers/Introducing-Azure-ML-v1.0--Chappell.pdf
Machine Learning questions
1. How much / how many? Regression
2. Which category? Classification
3. Which groups? Clustering
4. Is it weird? Anomaly Detection
Regression: how much / how many?
$???
Housing prices by square feetPrice Square Feet
125,999 950
207,190 1125
227,555 1400
319,010 1750
345,846 1525
350,000 1690
437,301 2120
450,999 2500
605,000 3010
641,370 3250
824,280 3600
1,092,640 3700
1,187,550 4500
0
500
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3500
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4500
5000
0 100,000
200,000
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1,000,000
1,100,000
1,200,000
Input variable/FeatureOutput variable
Housing prices hypothesisPrice Square Feet
125,999 950
207,190 1125
227,555 1400
319,010 1750
345,846 1525
350,000 1690
437,301 2120
450,999 2500
605,000 3010
641,370 3250
824,280 3600
1,092,640 3700
1,187,550 4500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
Hypothesis
Using model to predict house pricePrice Square Feet
125,999 950
207,190 1125
227,555 1400
319,010 1750
345,846 1525
350,000 1690
437,301 2120
450,999 2500
??? 2700
605,000 3010
641,370 3250
824,280 3600
1,092,640 3700
1,187,550 4500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
Prediction “errors” & improving modelsPrice Square Feet
125,999 950
207,190 1125
227,555 1400
319,010 1750
345,846 1525
350,000 1690
437,301 2120
450,999 2500
??? 2700
605,000 3010
641,370 3250
824,280 3600
1,092,640 3700
1,187,550 4500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 100,000
200,000
300,000
400,000
500,000
600,000
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800,000
900,000
1,000,000
1,100,000
1,200,000
Cost function (sq. error function)
Try different algorithmPrice Square Feet
125,999 950
207,190 1125
227,555 1400
319,010 1750
345,846 1525
350,000 1690
437,301 2120
450,999 2500
??? 2700
605,000 3010
641,370 3250
824,280 3600
1,092,640 3700
1,187,550 4500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
Get more data
0
1000
2000
3000
4000
5000
6000
0 100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
1,300,000
1,400,000
1,500,000
1,600,000
1,700,000
1,800,000
1,900,000
2,000,000
2,100,000
Use more data variables (features)Price Square Feet # Bedrooms # Bathrooms Fireplaces Garage Size Floors
125,999 950 1 1 0 0 1
207,190 1125 1 1 0 1 1
227,555 1400 2 1.5 1 2 1
319,010 1750 2 1.5 0 2 2
345,846 1525 3 2 1 2 1
350,000 1690 3 1.5 1 2 1.5
437,301 2120 3 2.5 2 3 2
450,999 2500 3 2.5 1 2 1.5
605,000 3010 4 2.5 2 3 2
641,370 3250 3 3 1 3 2
824,280 3600 3 3 2 3 2
1,092,640 3700 5 4.5 2 3 2
1,187,550 4500 6 6 4 5 2
Classification: which category?
Years
driving Age Class
5 65 1
7 70 1
2 68 1
25 45 2
25 55 2
20 50 2
5 25 1
3 22 1
8 30 1
15 35 2
… … …
12 38 ???
Classification – 2 classes
Hypothesis / Classifier
Years driving
Age
Input variables/Features Output variable
WALL·E (2008): http://www.imdb.com/title/tt0910970/
1. Input variables / Features:
– Years driving
– Age
2. Output variable:
– Class: Yellow, Green, Blue
3. One-vs-rest approach:
– Train classifier for each class
– Select class that returned highest confidence score
Classification – more than 2 classes
Classifier 3
Years driving
Age
Classifier 2
Classifier 1
http://playground.tensorflow.org/
Why Machine Learning is developing rapidly?
1. Data
2. Algorithms – same approach in different domains (deep neural networks)
3. Computing power – cloud and GPUs
Introducing GeForce GTX TITAN Z: Ultimate Power, May 2014.
http://www.geforce.com/whats-new/articles/introducing-nvidia-geforce-gtx-titan-z
How to implement Machine Learning project?
1. Define business value and
how it can be measured
- How much / how many?
- Which category?
- Which groups?
- Is it weird?
3. Build models:
- “Black-box” – pure statistical analysis
of large amounts of data
- ”Soft-box” – heuristic insights from the
knowledge of experts
4. Integrate into production systems
- Adjust business processes
- Redesign existing systems
5. Drive adoption!
Implementing Machine Learning project2. Prepare data:
- Internal data sources
- External data sources
Understanding Data Analytics Maturity Model
Extend Your Portfolio of Analytics Capabilities, by L. Kart, A. Linden, W. R. Schulte. Gartner, 2013.
https://www.gartner.com/doc/2594822/extend-portfolio-analytics-capabilities
Understanding Data Analytics Maturity Model - 2
Extend Your Portfolio of Analytics Capabilities, by L. Kart, A. Linden, W. R. Schulte. Gartner, 2013.
https://www.gartner.com/doc/2594822/extend-portfolio-analytics-capabilities
Cortana Intelligence Suite services
Cortana Intelligence Suite: https://azure.microsoft.com/en-us/suites/cortana-intelligence-suite/
Typical Machine Learning scenarios
Customers
– Recommendations
– Customer Churn
– Customer Segmentation
Operations
– Predictive Maintenance
– Anomaly Detection
– Optimization
Security & Risk
– Credit Risk
– Fraud Detection
– Predict Security Threat
Cortana Intelligence Gallery: https://gallery.cortanaintelligence.com/
Cortana Intelligence Gallery - Industries: https://gallery.cortanaintelligence.com/industries/
Cortana Intelligence Gallery – Solutions: https://gallery.cortanaintelligence.com/solutions
Microsoft Cognitive Services: https://www.microsoft.com/cognitive-services
Recognize Emotions
Face Detection
Face Verification
Similar Face Searching Face Grouping
Key takeaways
1. Imagine how ML can change your business
2. Start small – look for quick ML wins
– Make goals measurable
– Focus on implementing models in production
3. Create data culture in your organization
– Improve data quality
– Invest in people
4. Describe business stakeholders what ML can do
