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Innovation fund themed competition webinar - session 2

Revolutionise the human information relationship for Defence
Webinar
27 February 2017

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Innovation fund themed competition webinar - session 2

  1. 1. Challenge 2 Free up personnel through the application of innovative use of machine learning algorithms and artificial intelligence (AI) for military advantage
  2. 2. Next generation Air Force Informatio n collection Human analytic capacity People TechnologyProcess Challeng e
  3. 3. Decision advantage Manage, analyse and exploit multiple information sources …….at pace Exponential data Identify the right 1% Constrained human capacity
  4. 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. 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. 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. 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. 8. Conclusion • decision advantage • exponential data vs human capacity – close the gap • the right 1% ..... at pace • human and machine in partnership
  9. 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. 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. 11. OFFICIAL What do we want?   Over-fitting Free and open- source software (where appropriate) Solve one aspect of the problem well
  12. 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. 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. 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. 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

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