innovation fund themed competition webinar - session 2
Post on 20-Mar-2017
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Challenge 2Free up personnel through the application of innovative use of machine learning algorithms and artificial intelligence (AI) for military advantage
Next generation Air ForceInformatio
n collection
Human analytic capacity
People
TechnologyProcess
Cha
lleng
e
Decision advantage
Manage, analyse and exploit multiple information sources
…….at pace
Exponential data
Identify the right 1%
Constrained human capacity
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
Exponential data – ISR Services
Multi-Intelligence Fusion & Cross-
Cue
Automation AI Analytics
Optimise Intelligence
Analyst Fusion /
Cross-Cue
ImageryMulti-SpectralHyper-Spectral
ElectronicCommunications
Foreign Intel SystemsMeasurement &
SignaturesCyber & EMAcousticsHuman
Open SourceHistorical /Archive
Direct
Collect
Process
Disseminate
PROCESS Information = Human / Machine Partnership
Decision Advantage
Human / machine analytics
Open source activity
Ground moving targets
Google imager
y
Cyber and electromagnetic activity
Airborne imagery
Synthetic radar
imagery
Recognised air
picture
Wider opportunitiesEngineering 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
Conclusion• decision advantage
• exponential data vs human capacity – close the gap
• the right 1% ..... at pace
• human and machine in partnership
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Defence and Security Accelerator
Defence and Security AcceleratorDefence and Security Accelerator
Challenge 2: Technical perspectiveLeo Borrett, Capability Adviser, Data Science
OFFICIALUK 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
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What do we want?
Over-fitting
Free and open-source software (where appropriate)
Solve one aspect of the problem well
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Automated activity classificationMOD 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)
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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
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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?
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Predictive analyticsApplication 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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