artificial intelligence & aerospace · 2018-11-19 · a very brief history of artificial...
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Artificial Intelligence & Aerospace
THURSDAY OCTOBER 10TH – CTA SEMINAR
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XIXe: a century of prototyping
1830 1890 1850 1799
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XXe: a century of transformation
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Will AI drive a 4th industrial revolution ?
I’m smart !
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A very brief history of Artificial Intelligence
https://medium.com/machine-learning-for-humans/neural-
networks-deep-learning-cdad8aeae49b
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Digitization is a necessary prerequisite for Applied AI
Data
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Current Artificial Intelligence is far from matching Human Intelligence
Notional Intelligence Scale
Perceiving
Learning
Reasoning
Abstracting
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1st Wave of Artificial Intelligence (MBAI: Model Based AI)
https://www.darpa.mil/attachments/AIFull.pdf
Notional Intelligence Scale
Perceiving
Learning
Reasoning
Abstracting
Enables reasoning over
narrowly defined problems No learning capability and poor handling of uncertainty
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1st Wave of AI
https://www.darpa.mil/attachments/AIFull.pdf
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2nd Wave of Artificial Intelligence (DDAI: Data Driven AI)
Notional Intelligence Scale
Perceiving
Learning
Reasoning
Abstracting
Nuanced classification and
prediction capabilities No contextual capability and minimal reasoning ability
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2nd Wave of Artificial Intelligence (DDAI: Data Driven AI)
https://www.darpa.mil/attachments/AIFull.pdf
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Accuracy against Explainability
MBAI
DDAI
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Towards hybrid AI
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Aerospace: Digitization is a necessary perequesite for Applied AI
Data
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Data is the new Oil across the value chain
Operational Environment data In-service data Industrial data Supply chain data
OEM Aircraft operators & owners Airworthiness authorities Air Navigation service providers Component suppliers Service Providers
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Sense: A sky full of growing benefits and opportunities
Data Sources
Crews
Passengers
Pilots
Aircrafts
SATCOM
Radars
IoT Platform
Data Sharing
Co-develop Environment
SMEs
Start-Ups
OPTIMIZE
Performance
Logistics
Inventory
Reliability
Planning
Operations
PREDICT
Safety
Maintenance
Demand Management
Air Service on Demand
DECIDE
Fleet Management
Risk & Asset Management
Cyber Security
SUPERVISE
Operations
Flight & Fleet
Aircraft Environment
CONNECT SENSE
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Sense: Seamless Immersive In Flyt Passenger Experience
Data: Passenger Data &
Content & vendors Data
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. Sense: Man-AI teaming, building trust between the AI/Machine and the Human operator
Many data sources: Wearables, off-body sensors, physiological, behavioural, systems…
Very high cross subject variability: Gated models, Clustering, Active learning, context-aware models, multi modal fusion
Models running in real-time to identify human
physical and mental state
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Predict: Digitalized Risk fatigue management for civil aviation
Many data sources: Wearables, off-body sensors, physiological, behavioural, systems…
Very high cross subject variability: Gated models, Clustering, Active learning, context-aware models, multi modal fusion
Models running in real-time to identify human
physical and mental state
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Sense: Health and Usage Monitoring
Health and Usage data from equipment
and its environment
Evolutionary algorithms, Hidden Markov
Models, Multi-criteria decision
Machine Learning
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Predict: Predictive Maintenance for Equipment & sub-Component
• Data sources: raw data logs, legal/business framework,
contracts...
• Pattern Matching, Machine Learning
• Planning and logistics operations
• Multi-criteria optimization
▌Value proposition
Persistent and rare failure patterns
identification and RUL* estimation
Anticipation and optimization of
maintenance interventions
Reduction of maintenance manning
and cost
*RUL: Remaining Useful Life
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Optimize: Flight Operations Efficiencies
• Data from public/private, inflight/on ground
sources
• Flight pattern learning, anomaly detection,
prediction of disruptive event
• Passengers behaviour analysis
Estimated Time of Arrival : Standard
Deviation Error based on prediction
made at gate departure
Airlines SDE Thales SDE
13.0 min 3.3 min
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Supervise: Addressing Air Traffic Congestion potential risks
▌Value proposition
Flight Safety
Air Controller Workload
Air traffic Efficiencies
• Data Source: ADS-B
• Automatically detect abnormal flight path. Infer
additional flight information about an aircraft
• Machine Learning, Deep Learning, Recurrent
Neural Networks, Pattern Matching, Multi-
criteria Optimization, Operational research
Decide: Augmented automation or autonomy in the cockpit?
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A long journey with many stakeholders involved
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Towards a 3rd (Future) Wave of AI: Hybrid AI for Aerospace Industry
MBAI DDAI
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