Revolutionise the human information relationship for Defence Webinar 27 February 2017

1. Challenge 2
Free up personnel through the application of
innovative use of machine learning algorithms
and artificial intelligence (AI) for military
advantage

2. Next generation Air Force
Informatio
n
collection
Human
analytic
capacity
People
TechnologyProcess
Challeng
e

3. Decision advantage
Manage, analyse and exploit
multiple information sources
…….at pace
Exponential data
Identify the right 1%
Constrained
human capacity

4. RAF ISTAR* Force*Intelligence Surveillance Target Acquisition and Reconnaissance
E-3D Sentry Shadow R1
Rivet JointSentinel R1
Reaper - Protector
1 ISR Wing P-8 PoseidonTornado Tac Recce
Space

5. Exponential data – ISR Services
Multi-Intelligence
Fusion & Cross-
Cue
Automation
AI
Analytics
Optimise
Intelligence
Analyst
Fusion /
Cross-Cue
Imagery
Multi-Spectral
Hyper-Spectral
Electronic
Communications
Foreign Intel Systems
Measurement &
Signatures
Cyber & EM
Acoustics
Human
Open Source
Historical /Archive
Direct
Collect
Process
Disseminate
PROCESS Information = Human /
Machine Partnership
Decision
Advantage

6. Human /
machine
analytics
Open source
activity
Ground
moving targets
Google
imagery
Cyber and
electromagnetic activity
Airborne
imagery
Synthetic
radar
imagery
Recognised
air picture

7. Wider opportunities
Engineering and logistics
• improve aviation safety
• keep aircraft in the air for longer
• environmental stress and trend analysis
• work closer to mandated tolerance limits
Cyber defence
• continuous activity on networks
• identify the anomalies

8. Conclusion
• decision advantage
• exponential data vs human
capacity – close the gap
• the right 1% ..... at pace
• human and machine in
partnership

9. OFFICIAL
Defence and
Security Accelerator
Defence and
Security Accelerator
Defence and
Security Accelerator
Challenge 2: Technical perspective
Leo Borrett, Capability Adviser, Data Science

10. OFFICIAL
UK OFFICIAL
LSVRC* classification challenge:
error rates by year
red line = human error rate
…
Face recognitionSpeech recognition Lip reading Machine translation
*Large Scale Visual Recognition Challenge

11. OFFICIAL
What do we want?
 
Over-fitting
Free and open-
source software
(where appropriate)
Solve one aspect of
the problem well

12. OFFICIAL
Automated activity classification
MOD requires methods for automated detection and classification of
activities and intents from multiple sensor types using state-of-the-art
machine learning and artificial intelligence (AI)
Fathom neural computer stick
Adversarial machine learning example
• beyond simple feature extraction
• ability to operate “at the edge”
• semi-supervised and un-
supervised methods
• approaches to enable robust
deployment (for example
adversarial machine learning)

13. OFFICIAL
Cognitive computing
UK OFFICIAL
Automated speech recognition
Knowledge graphs
Natural language question answering
Automation of manual
tasks
Flag adversary activity of
interest
Infer new “knowledge”
Identification of false
information

14. OFFICIAL
Combined human/machine derived models
UK OFFICIAL
MOD is interested in the combination of human-
derived models, exploiting domain knowledge using
a rules-based approach; with machine-derived
models, which require large volumes of data and
driven by machine learning technologies. How do
we:
• combine data and human derived models
• build more robust statistical models of subjective measures
(for example assessment of threat)
• ensure data-driven models are transparent and
understandable for analysts and operators?

15. OFFICIAL
Predictive analytics
Application of machine learning in support of predictive modelling to guide military decision
making. MOD requires solutions which go beyond enhancing military understanding of
current situations, but predicts future outcomes, including actions, anomalies, intent and
movements, to guide decision makers in support of operational planning.
UK OFFICIAL
Information
overload
Situation
understanding
Predictive
analytics
Prescriptive
analytics