This is the deck I used for a 20min talk at SopraSteria, on the 21st of June 2016.

1. AI, behind the scenes
Gwennael Gate
www.angus.ai
https://www.linkedin.com/in/gwennaelgate
gwennael.gate@angus.ai
@gwennoyel

2. AI
Future
Present
4 points
&
20 minutes
1. How it works ?
2. Machine learning / deep learning ?
3. When do I get a robot that grabs me beer ?
4. What are the AI opportunities today ?

3. #1
How it works ?

4. algorithm
outputinput
This is AI building block

5. algorithm
outputinput
Perception
Example 1

6. algorithm
outputinput
Say: « Do you want
to mary me? »
Decision
Example 2

7. algorithm
outputinput
Say: « Do you want
to mary me? »
Action
Example 3

8. A « would you mary me » robot !
…
…
All together:

9. Perception
Decision
Action
Most AI systems look like this today

10. Watson (IBM), 2011Deep Blue (IBM), 1997 BigDog (B. Dynamics), 2008
Google Car, 2012 AlphaGo (Google), 2016Pepper (Aldebaran), 2014

11. Perception
Decision
Action
a set of AI algorithms
plugged together by humans
to mimic some human capabilities
AI

12. #2
Where is Machine (Deep) Learning ?

13. Perception
Decision
Action
AILet’s zoom in…

14. algorithm
outputinput
Let’s take again that « woman detector » example…

15. algorithm
A model
An engineer will first pick a « mathematical model » for this algorithm…

16. That will look like this…

17. algorithm
A model
+
Parameters
He’ll close the box and expose a few parameters
* thanks Yann Lecun for the « mixing table » metaphor

18. algorithm
A model
+
Parameters
At first nothing will work…

19. algorithm
A model
+
Parameters
The old good way
fine tuning by an expert
These parameters can be tuned in 2 ways

20. algorithm
A model
+
Parameters
fine tuning by an expert
Machine Learning
a (supervised) training algorithm
+
training database
or
The old good way
These parameters can be tuned in 2 ways

21. Deep Learning ?
just a specific way of doing machine learning…

22. Deep Learning ?
model
=
neural network
The « mathematical model » is a (deep) neural net

23. Deep Learning ?
106
parameters
a (supervised) training algorithm
+
huge training databaseAnd there are a lot of
parameters to tune

24. AImachine learning based
tuned by experts
1. only a few algos are trainable
2. trainings are always « supervised »
3. today’s AI are « under control » by design

25. Mashable - Robot escapes lab and makes a break for freedom in Russia (June 2016)
That is probably not because of some revolutionary AI!

26. #3
When do i get a robot that…

27. When do I get a robot …
I can make love with?
who handles household duties?
who can grab me a beer?

28. robots
robots
NO robots
why ?

30. Perceive
Decide
Act

31. Perceive
Decide
Act
Difficult

32. Perceive
Decide
Act
Difficult

33. This is a nightmare

34. is a nightmare
Variability
Uncertainty }}Our World

35. Perception
Decision
Action
Real World
Complexity
Smart machines can hardly see today

36. much more incertain than
variable }}

37. Perception
Decision
Action
AlphaGo (Google), 2016
The Unimate in GM factory - 1961
Asimo, Honda (2008)

38. Perception
Decision
Action
Pepper (Aldebaran), 2014
Google Car, 2012
Amazon Echo (2015)
mapped streets or
standing humans or
human speech or

39. Perception
Decision
Action
Real World
Complexity
standing humans or
mapped streets or
human speech or
etc…
Today’s AI are highly task specific

40. Watson (IBM), 2011Deep Blue (IBM), 1997 BigDog (B. Dynamics), 2008
Google Car, 2012 AlphaGo (Google), 2016Pepper (Aldebaran), 2014
Today’s AI are highly task specific

41. #4
Task specific AI : today’s opportunities

42. Perception
Decision
Action
AI automates existing jobs
or is doing what no-one was doing before

43. Perception
Decision
Action
An AI in a human resources team
Incoming C.V.
interesting C.V.
people to set for an interview
email invitation
validation by humans

44. Perception
Decision
Action
An AI in a marketing team
e-shops
comments
bad products
corrective actions

45. A task that is specific enough
+
a large input/output database
=
AI can probably help !
Action
Perception
DecisionTo sum everything up…

46. www.angus.ai
Perception API (multimodal / hybrid / trainable)

47. the world
nb_people = 2
identity = « Julie » ; « Albert »
gender = female ; male
age = 28 ; 34
emotion = both smiling
talking? = Albert is
looking? = at me!
structured data
about human activity
What we do : perception algorithms

48. the world structured data
https://www.youtube.com/watch?v=LL-LwT-q6cQ&feature=youtu.be
An example

49. Robot / IOT / AI
Gwennael Gate
Thanks !
Gwennael Gate
www.angus.ai
https://www.linkedin.com/in/gwennaelgate
gwennael.gate@angus.ai
@gwennoyel