This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.

1. Le mobile est-il soluble
dans l’intelligence
artificielle?
Bruno Schröder, National Technology Officer Microsoft BeLux
20ème #mforum. Nouveaux territoires mobiles2016

2. Computer pioneer Alan Kay:
“The best way to predict the future is to invent it.” In the A.I. context, it
basically means Stop predicting what the future will be like and create it
in a principled way.

3. Microsoft forms new 5,000-person AI division
Microsoft CEO Satya Nadella on how AI will transform
his company “AI is at the intersection of our
ambitions,” Nadella said, noting
how it will allow us “to reason
over large amounts of data and
convert that into intelligence.”
“We are on the cusp of a
paradigm shift in computing that
is unlike anything we have seen in
decades,”
He likened AI to the arrival of
books and the web and joked
that we will soon create so much
data that “we are getting to a
point where we don’t even know
what to name things.”

5. Current State of AI
• General AI ( Artificial General Intelligence)
• Kind of HAL in Movie “2001”
• Won’t come for decades
• Goal and/or Threat: Technological singularity (Superintelligence)
• Narrow AI ( addresses specific tasks )
• i.e language translation, self driving cars, assistants, image recognition, health
• Remarkable progress in last decade
• Broad societal benefits and economic agility
• Examples: Machine Learning, Deep Learning
• No magic black box. It is a statistical process
• All current products are here

6. Bing maps
launches
What’s the
best way
home?
Microsoft
Research
formed
Kinect
launches
What does
that motion
“mean”?
Azure Machine
Learning GA
What will
happen next?
Hotmail
launches
Which email is
junk?
Bing search
launches
Which
searches are
most relevant?
Skype
Translator
launches
What is that
person saying?
Microsoft & Machine Learning
Answering questions with experience
1991 201420091997 201520102008
Machine learning is pervasive throughout Microsoft products.

7. Inflection point: Why now?
• 2010 algorithmic breakthrough
• Cloud: Cheap compute (Quiz)
• Cloud: Cheap storage++ (Quiz)
• Huge data sets of various kinds for statistical learning
• Monetization opportunity & available business models
• Skills and tooling

8. What is the Issue with Algorithms?

9. Classic Programming vs. Machine Learning
2 + 3 = 5

10. Classic Programming vs. Machine Learning
2 + 3 = 5
Easy
Not Easy

11. Classic Programming vs. Machine Learning
2 + 3 = 5
Easy
Not Easy

12. Classic Programming vs. Machine Learning
For each photo: Cat ? Yes/No

13. Classic Programming vs. Machine Learning
Program = Algorithm
Written by humans
Specific to defined task
Algorithm is “fixed”
Algorithm “easy” to describe
Written by software
Goal: ability to generalize
Algorithm depends on training data
Algorithm can “morph” over time

14. A major paradigm shift
• Solutions based on logic and crafted by hand
Solutions based on probabilities and learned from data

15. Developing a Machine Learning Program
Learning
Experimenting
Testing
Deployment
Phase Process Learning Styles
Supervised
Unsupervised
Self reinforced
Hybrid /w humans

16. 28,2
25,8
16,4
11,7
7,3 6,7
5,1
3,5
ILSVRC 2010
NEC America
ILSVRC 2011
Xerox
ILSVRC 2012
AlexNet
ILSVRC 2013
Clarifi
ILSVRC 2014
VGG
ILSVRC 2014
GoogleNet
Human
Performance
ILSVRC 2015
ResNet
ImageNet Classification top-5 error (%)
8 layers
19 layers
22 layers
152 layers
8 layers
Fish or
stone???

18. Cognitive
Services
Give your solutions
a human side

19. Cognitive
Services
Give your solutions
a human side

20. Cognitive services
• Principles
• Augment human abilities & experiences
• Trustworthy
• Inclusive & respectful
• Participants
• People, Digital Assistants, Bots
• Platform
• Human language is the new UI
• Bots are the new apps; digital assistants are meta apps
• Intelligence infused into all interactions
• “Democratizing AI”

21. Azure
CNTK | Caffe | TensorFlow | Torch

22. Azure
Configurable Silicon in the Cloud
Building the first AI super computer

23. AI Playgrounds
• The race for Digital Assistants (MS, Google, Apple, FB, Amazon)
• Tapping the Enterprise Opportunity (MS, IBM, AWS)
Co-opetition mode: i.e. Partnership on AI (MS, Google, FB, IBM, Amazon)

24. Nouveaux champs d’action
• Fusionner les perceptions humaines et machines
• Modèles du monde et des humains
• Complémentarité de l’intelligence humaine et de la machine
• Coordination et initiative (de la machine)
• Les surprises probables
2016/12/15 24

25. 1. Empathy
2. Education (knowledge and skills)
3. Creativity
4. Judgment and accountability
Data Ethics Principles for Humanistic
Approach to AI

26. https://news.microsoft.com/cloudforgood/resources.html

27. Cloud for Global Good – AI Pillar
• Modernize laws and practices to enable AI
 Data access, copyright, trade secrets
 Safety and liability
 Transparency
 Publicly available data
• Assess privacy law in light of the benefits of AI
 Unlimited innovation
 Repurposing data
 AI’s predictive power
• Ethical principles
 Transparency
 Non-Discrimination
 Multi-stakeholder collaboration
 Industry standards
 Cloud-powered AI

28. Soluble, le mobile?
2016/12/15 28

29. Sans mobile, pas d’AI?
2016/12/15 29

30. Bringing it all together
The Seeing AI App
Computer Vision, Image, Speech Recognition, NLP,
and ML from Microsoft Cognitive Services
Watch Video HereRead Blog Here

31. Thank you!

32. Announcements
Partnership on AI
Sept 29th: AI & TnR reorg press release
Sept 29th: AI & TnR announcement (Harry Shum)
Oct 6th: 5m$ data science MSRC & The Alan Turing Institute
Nov 15th: MS and OpenAI (Harry Shum)
MS Research – AI and ML group
Papers
• Stanford – Artificial Intelligence and Life in 2030
• Whitehouse – Preparing for the future of AI
• Whitehouse – Responses
• Whitehouse – A Research agenda for AI
• Eric Horvitz – AI supporting people and society
• QMU – Machine Learning with Personal Data
• QMU – Responsibility and Machine Learning – Part
of a Process
• QMU - Responsibility, Autonomy and Accountability
– Liability for ML
Microsoft products
Azure ML
Cortana Intelligence Suite
Microsoft Cognitive Services
Language Understanding Intelligence Services (LUIS)
AI in Office 365
Office MyAnalytics
Projects
Captionbot.ai
How-Old.net
Mimicker Alarm
TwinsOrNot.net
Emotion Demo
Microsoft Pix
MileIQ
SwiftKey