Artificial Intelligence. What machines can learn and how to implement Machine Learning

project in you organization?

Antons Mislēvičs

antonsm@microsoft.com

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

https://clarifai.com/demo

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

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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

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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

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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

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1000

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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

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3500

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Get more data

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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

https://quickdraw.withgoogle.com

https://quickdraw.withgoogle.com

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