Talk on Artificial Intelligence & Machine Learning in Defense Systems at ‘Tutorial cum workshop on AI&ML’ organized by IEEE Bombay Section in collaboration with the India Council during August 10-11, 2018.

1. Machine Learning in
Defence Applications
Part II
© Sunil Chomal | sunilchomal@gmail.com | www.linkedin/in/sunil-chomal

2. Current State of
Technology
Mature Technology or Art?

3. Mature field
 Text & Audio – Recurrent Neural Network (RNN)
 Image & Video – Convolutional Neural Network (CNN)
 Powerful frameworks such Tensorflow & Theano
 Pre-trained models reaching Human Level performance and
approaching “Bayes Optimal Error” in certain applications
 Powerful CPUs & GPUs and frameworks such as TensorRT for
optimization on target platforms
 Mature to the extent that it’s as simple as building using LEGO
blocks!

4. Lego Blocks
Azure Machine Learning Studio – “Fully-
managed cloud services that enables you to
easily build, deploy and share predictive
analytics solutions”
Amazon SageMaker – “Enables data scientists
and developers to quickly and easily build,
train, and deploy machine learning models …
and one-click training, tuning, and inference.”
Google Cloud AI – “Cloud AI provides modern
machine learning services, with pre-trained
models and a service to generate your own
tailored models.

5. New Domains - Art
Mature
 pre-trained models
 components based solutions,
with UI based drag & drop
Art
 neurons/activation function and
SGD
 low level plumbing in “ML” assembly
programming in frameworks such as
Tensorflow or Theano
 The pre-trained models, the LEGO
blocks, do not play well together
The father of modern neural networks, Geoffrey Hinton, who got neural
network in mainstream by introducing “back propagation” in 1986, has
recently been focusing on the fact that neurons are not the right
representation, and has been talking about a new high level concept
called “capsules”

6. ML application in Defence

7. Data
Data
What is deep
learning?
Input
Hand-designed
programs
Output
Summary
From: Deep Learning – Ian
Goodfellow et al.
Input
Hand-
designed
features
Mapping
from
features
Output
Input Features
Mapping
from
features
Output
Input
Simple
Features
Abstract
Features
Mapping
from
Features
Output
Rule based Systems
Classic Machine Learning
Machine Learning
Deep Learning

8. Challenges in Defence ML
Not Enough
Current advances are built on
of Data”
Unsupervised
Learning
Enormous Strides in ML have been
confined to Supervised Learning
Not enough
research in
Defence System
Current Research have been driven by
commercial applications

9. Generative
Adversarial Nets
(GAN)
Generative model G -
captures the data
distribution
Discriminative model D –
estimates the probability that
a sample came from the
training data rather than G
The training procedure for G
is to maximize the probability
of D making a mistake
Finally, G recovers the
training data distribution
Estimating generative models
via an adversarial process, in
which we simultaneously train
two models
Generative Adversarial Nets –
Ian Goodfellow et al, 2014.

10. Case Studies
Soft Error Predictions in Mission Critical
Systems
Tracking for Heterogeneous Sensors in a
Distributed Network
(Current Research at Tata Power SED)

11. Soft Error Predictions in
Mission Critical Systems

12. Soft Error Prediction using ML
Aim
• To predict errors in real-time so as to take pro-active
“maintenance” actions
Scope
• Software runtime errors, and “soft errors” in associated
components
Target
• Software Intensive System, with limited deployments

13. Approach
System
Data
(Text)
RNN
Trained
Model
Predict
Errors
System Data (Text)
GAN
A -> RNN
Trained
Model
Predict
Errors
Generative Network “Models the Errors”

14. Tracking for
Heterogeneous Sensors in
a Distributed Network

15. Tracking
1.
Alignm
ent
2.
Gating
3.
Associa
tion
4.
Filterin
g
5. Life
Cycle
Mgmt
6.
Present
ation
 Key steps are Filtering & Association
 Filtering is based on Kalman Filtering –
best linear state estimator
 Association – Optimisation Problem
 State Estimation depends on target
motion modeling, and often non linear
 Attribute fusion requires non-Bayesian
MLE approaches
 No of sensor & their nature exploding,
so is data
 Real time tracking a challenge
 Example: CERN challenges on Kaggle

16. Approach
 Deep Learning involves discriminative models, that map a high-
dimensional, rich sensory input to a class label
 They are based on the backpropagation and dropout algorithms,
using piecewise linear units
 Difficult in approximating many intractable probabilistic
computations that arise in Maximum Likelihood Estimation and non
linear Minimum Mean Square Estimation (MMSE), and due to
difficulty of leveraging the benefits of piecewise linear units in the
generative context.
 Generative Network ► Simulator
 Adversarial Network ► 4D CNN

17. Approach
Measurement
(Simulator)
Measurement
GAN
G ► Ground
Truth (Sim)
A ► 4D CNN
Trained Model
Association &
Filtering

18. Reading Recommendation
 Learning representations by back-propagation errors – Rumelhart &
Hinton (1986 – Nature)
 ADAM: A method for stochastic optimization – Diederik & Jimmy Lei Ba
(https: // arxiv.org/ pdf/1412.6980.pdf)
 Generative Adversarial Nets – Goodfellow et al (https: // arxiv.org/
pdf/1406.2661.pdf)
 Geoffrey Hinton talk "What is wrong with convolutional neural nets ?“
(https: //www. youtube.com/ watch?v=rTawFwUvnLE)
 Matrix capsules with EM routing – Hinton (https: // openreview.net/
pdf?id=HJWLfGWRb)
 Deep Learning – Ian Goodfellow, Yoshua Bengio & Aaron Courville

19. Sunil Chomal
Head Software
Tata Power Strategic Engineering Division
(proposed part of Tata Aerospace & Defence)
IEEE Computer Chapter  IEEE Computational Intelligence
Society  Agile Alliance  Open Geospatial Consortium
(OGC)
www.linkedin.com/in/sunil-chomal
sunilchomal@gmail.com