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

project in you organization?

Antons Mislēvičs

antonsm@microsoft.com

antons.mislevics@hotmail.com

1. Over 9 years @ Microsoft Over 12 years of SharePoint experience

2. Projects: Portals, ECM, Social, Search, BPMS – Architect, Lead Developer

Architecture Design Sessions – Subject Matter Expert on SharePoint

Cloud Solutions: Office 365, SharePoint Online, Windows Azure, SharePoint / Office 365 Dev Patterns & Practices (PnP) Core Team member

3. Countries: Latvia, Lithuania, Estonia, Ukraine, Kazakhstan, Malta, Cyprus, Switzerland,

UK, Germany, France, Russia, Brunei, Azerbaijan, Georgia, Sweden, USA

4. PhD in Engineering of Computer Systems: Thesis: “Mobile Agents for Business Process Management Support in Cloud

Computing Environments”

5. Lead Researcher at Riga Technical University Department of Artificial Intelligence and Systems Engineering

Antons Mislēvičs

Senior Consultant

Microsoft Services EMEA

antonsm@microsoft.com

Agenda

1. What machines can do today?

2. How Machine Learning works?

3. How to implement Machine Learning in your project?

4. How the future looks like?

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

Poker: Libratus and DeepStack beat top pros (2017)

Carnegie Mellon Artificial Intelligence Beats Top Poker Pros: https://www.cmu.edu/news/stories/archives/2017/january/AI-beats-poker-pros.html

Brains Vs. AI Rematch: Why Poker?: https://www.youtube.com/watch?v=JtyA2aUj4WI

Tough poker player: Brains Vs. AI update: https://www.youtube.com/watch?v=CRiH8yCskAE

Safe and Nested Endgame Solving for Imperfect-Information Games, N. Brown, T. Sandholm, 2017: http://www.cs.cmu.edu/~sandholm/safeAndNested.aaa17WS.pdf

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker, 2017: https://arxiv.org/abs/1701.01724

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]

Machine Learning is transforming businesses…

– Different industries…

– Happens in Latvia as well…

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

5. Which action? Reinforcement Learning

Data Science for Rest of Us, B. Rohrer, 2015:

https://channel9.msdn.com/blogs/Cloud-and-Enterprise-Premium/Data-Science-for-Rest-of-Us

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

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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

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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

How Neural Network Learns?

http://playground.tensorflow.org/

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?

- Which action?

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

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

Microsoft Cognitive Services – QnA Maker: https://www.microsoft.com/cognitive-services/en-us/qnamaker

QnA Maker: https://qnamaker.ai/

Calculate cost savings and ROI

1. Generate confusion matrix for model

2. Calculate costs for each state

3. Choose the best model and

cut-off point

Cost of door maint proc/part replacement = 200 EUR

Cost of door maint proc/part replacement = 200 EUR

Cost of door repair = 600 EUR

Cost of cancelling door maint proc = - 150 EUR cost savings

What’s next?

A DARPA Perspective on Artificial Intelligence: https://www.youtube.com/watch?v=-O01G3tSYpU

1. Wave 1: Handcrafted Knowledge

2. Wave 2: Statistical Learning

3. Wave 3: ...

AI history

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

Critics…

Deep neural networks can be fooled…

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, 2015: https://arxiv.org/abs/1412.1897

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, 2015: https://arxiv.org/abs/1412.1897

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images: http://www.evolvingai.org/fooling

Machines do not always “understand” images…

Accelerating innovation and powering new experiences with AI: https://code.facebook.com/posts/310100219388873/accelerating-innovation-and-powering-new-experiences-with-ai/

CS231n: Convolutional Neural Networks for Visual Recognition, Lecture 10: Recurrent Neural Networks: http://cs231n.stanford.edu/slides/winter1516_lecture10.pdf

Machines do not “understand” how the world works…

Accelerating innovation and powering new experiences with AI: https://code.facebook.com/posts/310100219388873/accelerating-innovation-and-powering-new-experiences-with-ai/

RI Seminar: Yann LeCun : The Next Frontier in AI: Unsupervised Learning: https://www.youtube.com/watch?v=IbjF5VjniVE

A DARPA Perspective on Artificial Intelligence: https://www.youtube.com/watch?v=-O01G3tSYpU

1. Wave 1: Handcrafted Knowledge

2. Wave 2: Statistical Learning

3. Wave 3: Contextual Adaptation

AI future

RI Seminar: Yann LeCun : The Next Frontier in AI: Unsupervised Learning: https://www.youtube.com/watch?v=IbjF5VjniVE

1. Machines need to learn/understand how the world works (get

common sense)

2. Machines need to learn a very large amount of background

knowledge (through observation and action)

3. Unsupervised learning

The future…

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

Key takeaways

1. Imagine how ML can change your business?

2. Describe business stakeholders what ML can do

3. Start small – look for quick ML wins

– Make goals measurable – what is min required prediction accuracy?

– Focus on implementing models in production

4. Create data culture in your organization

– Improve data quality

– Invest in people

5. Do not sell ML as just “another cool thing” – define business value and calculate ROI

Slides: https://1drv.ms/f/s!AsXSX3Q3cMlAjfg_NxS_CCX6XmRBLg