Innovation report: Artificial Intelligence

1. Innovation Report:
Artificial Intelligence
Daniel Voignac • 08.2017

2. Overview
I. Definition
II. Issues
III. Applications
f

3. Motivation

4. Artificial Intelligence
Deep Learning
Self Driving cars
Cognitive Computing
Deep Neural Networks
Pattern Recognition
Machine Learning
Diagnostic assistanceChatbots
Intuition
Algorithms
Virtual assistant
Machine Translation
Recommendation systems
Robots
Search Engine
Spam Detection
Cancer detection
Games
Turing test

5. I. Definition

6. ● “The study and design of intelligent agents, where an
intelligent agent is a system that perceives its
environment and takes actions that maximize its chances
of success.”
Russel and Norvig
● “Just as the Industrial Revolution freed up a lot of
humanity from physical drudgery, I think AI has the
potential to free up humanity from a lot of the mental
drudgery.”
Andrew Ng

7. Definition
Thinking humanly Thinking rationally
“The exciting new effort to make
computers think... machines with
minds, in the full and literal sense.”
(Haugeland, 1985)
“The study of mental faculties
through the use of computational
models.” (Charniak and McDermott,
1985
Acting humanly Acting rationally
“The study of how to make
computers do things which, at the
moment, people are better.” (Rich
and Knight, 1991)
“Computational Intelligence is the
study of the design of intelligent
agents.” (Poole et al., 1998)

8. Definition
● AI is a combination of:
○ Philosophy
○ Mathematics
○ Economics
○ Neuroscience
○ Psychology
○ Computer Engineering
○ Control theory and cybernetics
○ Linguistics

9. Robotic Process Automation
(RPA)
● High-Volume repeatable tasks
● Different from IT automation because adaptable
● “Just as the Industrial Revolution freed up a lot of
humanity from physical drudgery, I think AI has the
potential to free up humanity from a lot of the mental
drudgery.”
Andrew Ng

10. Machine Learning
● Computer to act without programing
● Deep learning: automation of predictive analytics
● Three types:
○ Supervised learning
○ Unsupervised learning
○ Reinforcement learning
● Good read on current unefficiency of ML:
https://techcrunch.com/2017/08/08/the-evolution-of-machine-learni
ng
○ Some “old” AI algorithms are very good and suffice most
applications (“linear/logistic regression, random forests and boosted
decision trees”)
○ Deep Neural Networks is the real (“Getting deep learning to work
is hard “)

11. Machine Learning
● Supervised learning
○ Sets are labeled → pattern detection → label new data
sets
● Unsupervised learning
○ Unlabelled sets sortes according to = or ≠
● Reinforcement learning
○ Markov decision process - good for processes that
include long-term versus short-term reward trade-off
(agent performs action(s) and is given feedback) -
AlphaGo

12. Machine Learning
● https://www.engadget.com/amp/2017/08/10/google-and-blizzard-inv
ite-you-to-train-ai-with-starcraft-ii/

13. Computer Vision
● Analog-to-digital signal processing
● Compares digital processing to database
● YOLO (You Only Look Once) open source real-time
object detection
○ https://pjreddie.com/darknet/yolo/
○ https://www.ted.com/talks/joseph_redmon_how_a_computer_le
arns_to_recognize_objects_instantly/up-next
○ 20ms/image
● Google Image, self-driving cars ...

14. Natural Language Processing
● Processing of human language by a computer program
● Used very early in spam detection
● Current approach is based on machine learning
○ https://www.technologyreview.com/s/608382/to
-build-a-smarter-chatbot-first-teach-it-a-second-l
anguage/

15. Deep Neural Networks
● Algorithms designed after human brain
● Recognise patterns
● “Clustering and classification data on top of data you
store and manage”1
● Help group unclassified data
● Classify when given labelled data to train on
● “Deep” ⇔ stacked neural networks (multiple layers,
more than one hidden layer)
○ Layers are made of nodes
1
source: https://deeplearning4j.org/neuralnet-overview#define (date of access 14/08/17))

16. Deep Neural Networks
● Deeper the net, more complex features can nodes
recognize = feature hierarchy
● Google photos, Salesforce Einstein
● Three steps to neural networks
○ scoring input, calculating loss and applying an update to the model
Starts with linear regression model for binary
output but used logistic regression (sigmoïds,
Hyperbolic tangents)
1
source: https://deeplearning4j.org/neuralnet-overview#define (date of access 14/08/17))

17. Deep Neural Networks
● 1957: Rosenblatt’s perceptron - 1st artificial neuron
● 1969: Minsky’s doubts
● 1989: LeCun proposes convoluted network
● 1996: Banks start using them for cheque reading
● 2011: Watson wins Jeopardy
● 2012: 15.3% error percentage
● 2016: Uber’s autonomous vehicles in Pittsburgh
● 2017: Alpha Go beats Ke Jie
Source: Science & Vie 07/17

18. II. Issues

19. Prediction
● Predictive recommendation
● To simplify:
○ Consider all past events occurred at random
○ Group your data
○ Find a mathematical model that suits best
○ Iterate

20. Smarter objects
● Smart Home
○ Smart fridge
■ Samsung FamilyHub fridge (You can go
have a look at it at Boulanger down the street)
○ Apple HomePod, Amazon Alexa, Google
Home, Nest etc.
○ iRobot vacuum cleaner

21. Self-driving (cars?)
● Computer Vision
● Behavioural Prediction
● Influential companies include:
○ Waymo
○ Apple
○ Mobileye
○ Uber
○ Tesla
Level Name
0 No Driving Automation
1 Driver Assistance
2 Partial Driving Automation
3 Conditional Driving Automation
4 High Driving Automation
5 Full Driving Automation

22. (Chat)bots
● Human agent interaction platform
● Can be included in ready existing chats
● Or embedded in a website
● Most common uses:
○ Personalised customer experience
○ Virtual Buying assistant
○ Included in a CRM
○ News
○ Productivity tools (slack bots)

23. Chatbots
● A few useful links:
○ Designing a chatbot (no coding)
■ https://chatfuel.com/ or https://botsify.com/ (also
https://meya.ai/ )
○ https://chatbotsmagazine.com/
○ https://botlist.co/

24. III. Applications

25. Salesforce - Einstein
● Inbuilt AI module in the Salesforce CRM platform
● Combines and processes data from calendar,
email, social, news to make predictions on the
future
● Figures out competitor and recommends how to
interact with new opportunities
● Writes email
● https://www.salesforce.com/products/einstein/f
eatures/
See live demo

26. IBM - Deep Blue
● Chess playing computer
● Loses against Kasparov in 1996
● Wins (Deeper Blue) against Kasparov in 1997
● Kasparov’s latest book - Deep Thinking

27. Google DeepMind - Alpha GO
● Developed by DeepMind Technologies in
2010
● Bought by Google in 2014 (628 M$)
● Beats World Champion Ki Jie

28. IBM - Watson
● https://www.ibm.com/watson/
● First designed as a Question Answering computing
system
● Designed to play the Jeopardy TV game
● Today applications include:
○ Health: cancer detection
○ Éducation: TJ Bot
○ Recipe generation
○ Concierge
○ Strategy
○ Weather forecast

29. IBM - Watson
● Salesforce Integration
○ “Integrate IBM Watson APIs into Salesforce to bring
predictive insights from unstructured data inside or
outside an enterprise, together with predictive insights
from customer data delivered by Salesforce Einstein,
enabling smarter, faster decisions across sales, service,
marketing, commerce and more.”
● Hilton hotel Concierge robot

30. IBM - Watson
● Conversation
○ Conversation
○ Virtual Agent
● Vision
○ Visual Recognition
● Speech
○ Speech to text
○ Text to speech
● Empathy
○ Personality Insights
○ Tone Analyzer
● Discovery
○ NLP
○ Discovery News
○ Knowledge Studio
Document Conversion
● Language
○ Translator
○ Naturall Language Classifier
○ Retrieve and Rank

31. Google - Photos
● Smart Classification
● Image Processing
But also:
● Picture Enhancing - Street View
○ https://petapixel.com/2017/07/14/google-uses-ai-create-professional
-photos-street-view-shots/
● Image Generation
○ http://www.businessinsider.fr/us/these-trippy-images-show-how-googles-ai-sees-the-world-2015-6
/#the-engineers-found-that-the-ai-tended-to-populate-specific-features-with-the-same-object-for-e
xample-horizons-tended-to-get-filled-with-towers-and-pagodas-androcks-and-trees-turn-into-buildi
ngs-11110

32. Mobileye
● Not self driving car
● But device that makes any car smarter
● Even equipped a “self-pulling” suitcase
● Bought by Intel for 15.3 B$

33. Tesla - Autopilot
● Autopilot 2.0 - Level 4 ready
● Sadly famous accident involving white truck on
bright weather
● Towards level 5 with rumors of Autopilot 2.5
https://electrek.co/2017/08/09/tesla-autopilot-
2-5-hardware-computer-autonomous-driving/

34. Google - Brain
● https://research.google.com/teams/brain/
● Research team that sets its own research
goals
● Vastly focused on “Making machines
intelligent”

35. OpenAI
● “OpenAI is a non-profit AI research company,
discovering and enacting the path to safe artificial
general intelligence.”
● Recently announced developping an agent capable of
beating world class champions at Dota 2 (e-game)

36. Google - AI
● https://ai.google/
● Google assistant
● Cloud TPU (tensor processing unit)
● Platform to play with AI
○ https://aiexperiments.withgoogle.com/
○ https://quickdraw.withgoogle.com/
○ https://aiexperiments.withgoogle.com/auto
draw

37. Tensor Flow
● https://www.tensorflow.org/
● Developed by Google Brain teams
● Open Source ML library
● Helps deploying Neural Networks

38. MIT Media Lab - Deepmoji
● Understanding emotions with the use of emoji
● Countering e-bullying
● Monitoring social media
● Facebook announced 9000 job opening in social
monitoring early 2017

39. Other AI research teams
● https://labs.pinterest.com/projects/
● Uber Michelangelo

40. Appendices

41. References and useful links

42. References
● Patrick Winston. 6.034 Artificial Intelligence. Fall 2010. Massachusetts
Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu.
License: Creative Commons BY-NC-SA.
● S. Russell, P.Norvig, Artificial Intelligence: A Modern Approach, Third
Edition, 2010, Pearson Education, New Jersey

43. Useful links - Top 5
● https://www.coursera.org/learn/machine-learning
● https://deeplearning4j.org/neuralnet-overview#define
● https://chatbotsmagazine.com/chatbots-the-beginners-guide-
618e72599b55