Edge-­AI  for Intelligent  User  ExperienceKal  Mos   VP,    Mercedes-­Benz  R&D  NA

Behavior Modeling

Action Automation

Adaptive UI

User Sensing

User Identification

Intelligence  on  the  Edge

Analytics for BigData

Connectivity is not stable: bandwidth, latency, availability..

Battery Power

Limited resources (Processing, Memory,…)

Algorithm in the wild (self training & decision making)

Challenges at the Edge

Cloud Edge  DeviceLab

Cloud  AI

Validation User Interaction

AI

Training

User Data

Cloud Edge  DeviceLab

Edge  AI

Validation

User Interaction

Validation Data

AI

AI

Training

User Data

Don’t  Trust  the  Model

Trust  The  Model

S1

a2-­‐n

a1

Deciding  to  Automate

S2

S3-­‐n

automate

don’t  automate

rc =  -­‐1*(C.L.+Annoyance)  =  0

rw =  <<0

E[r]!automate  =  <0

P(A=ac|S1)

1  -­‐ P(A=ac|S1)

Deciding  to  Automate

If                                                                                                                                                                                        then  automate  the  action

This  only  happens  when                                                                                is  high  enough  to  counteract  rw

Deciding  to  Automate

Requires  Model  Uncertainty

Drop  Out

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf

Uncertainty  with  Drop  Out

http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html

1. Apply  Drop  Out  at  inference  time

2. Doing  this  many  times  will  create  samples  from  the  learned  posterior  distribution

3. Estimate  the  moments  of  the  posterior  from  these  samples  (Monte  Carlo  Integration)

4. The  variance  represents  an  estimate  of  uncertainty

Many Independent Inference Passes

Highly Parallelizable

Scales well with hardware

• Having model uncertainty is important for instant decision making in the Edge

• Getting model uncertainty estimates in deep systems can be based on Monte Carlo sampling methods using dropout

• Doing this with parallelization works well on a GPU.

“We’re working on a new generation of vehicles that truly serve as digital companions. They…learn your habits,…adapt to your choices,…predict your

moves,…and interact with your social network.” Dr. Zetsche