Reveal uncertainty with Deep Learning

Benjamin HOURTE – EarthLab Luxembourg S.A.

benjamin.hourte@earthlab.lu

https://lu.linkedin.com/in/benjaminhourte

@bhourte

https://www.linkedin.com/company/earthlab-luxembourg-s.a./

“ an ever-changing world brings

new opportunities for businesses to make profits “

Franck Knight (1921)

Risk

Uncertainty

but UNCERTAINTY ≉ UNKNOWN

From Nassim Nicholas Taleb

RISK ORUNCERTAINTY?

RISK ORUNCERTAINTY?

Conjunctions of elements that was not possible at that

time…UNCERTAINTY

In the past 100 years, we covered the risk side:• Fine tune the probabilities• Better categorization of subscribers• Differentiating the hazards

Future challenges?

Artificial Intelligence

Machine Learning

Deep Learning

1950’s 1980’s1960’s 1970’s 1990’s 2000’s 2010’s

Deep learning is not guessing. It consists on applying the subjective probability:

1. Define “prior beliefs” as set of factorsno further assumptions

2. Use mathematical transformation projections and assimilation to detectinferences

3. Apply the computed model to new dataand overlook the parameters

Hum… looks complex… Pfff… Bullshit!

TRAININGDATASET

UNTRAINED NEURAL

NETWORK

TRAINED NEW

MODEL

NEW DATA

PROJECTION

TRAININGDATASET

UNTRAINED NEURAL

NETWORK

TRAINED NEW

MODEL

NEW DATA

PROJECTION

“EarthLab Luxembourg aims to develop a new generation of industrialand environmental risk monitoring services, with a view to proposingadvanced services related to risk exposure for insurance, strategiceconomic assessment and asset management concerns in the publicand private sector.”

Etienne Schneider - Deputy Prime Minister and Minister of the Economy of Luxembourg

Hazards and vulnerabilities are increasing more rapidly

than our ability to react. Our various sources of

information help to more precisely identify and define

risks

IMAGERY

High quality satellite, drone

and aerial

STREAMS

Open Data, networks,

structured data, internal history

PIPELINES

Real-time and continuous aggregation

MACHINE LEARNING

Simulation & impacts based on

tailored KPIs

Our capabilities allow us to explore various types of

data with the goal to provide proactive services and solutions backed by

evidence.

Exposure at one fixed point in time and permanent

monitoring to qualify the evolution. Combination of hazards, cumulative effects

and losses

Identification of related impacts on the Supply Chain

causing Business Interruption

INDICATORS

Performance, operational,

financial, environmental

ALERTS

Worldwide natural observatory,

Connected devices and sensors

Change of paradigms in the different areas:Don’t cope with risk management …… but anticipate current uncertainty

UNDERWRITING

Anticipate future hazards, impacts

and opportunitiesAdapt primes

CLAIMS

Help managing claims with reducing

response time, limiting fraud,

enhancing subscriber experience

OPERATIONS

Help reducing the operation load

Introduce parametric operations

Geo-Located Tweets

Semantic Analyzed Tweets

+

Intelligent overview of the situation, highlighting “ongoing events”

Credibility scoreTime distribution

Importance of the events

Detected events

One event timeline

WATCH: I owa Br aces f or Maj or

Fl oodi ng, Thousands Evacuat edht t ps: / / t . co/ 28HHY8ubUX #Fl oodi ng

Model

keras

Training of the model

218 samples

344 samples

Flood, sure at 90%

Converged accuracy: 90 %

Not a flood, sure at 73%

Application of the model

Integration of different indicators sources in the model

Training the model

Importance of the different neurons

Predictions

123 4 1 5 2 6

# #Advantage factor Disadvantage factor

Training of the model

keras

Searching for possible comportment or scenarios, the vibration consist on increasing/decreasing the input of the model.

NEW DATA

PROJECTION

NEW DATA

+/- 20 %

PROJECTION

n

[ … ]

Anticipated factor

Min

imu

mM

aximu

m

All the elements that influence the factor

Anticipated factor

The different models can also be chained to detect and anticipate domino effects between events

Output BOutput A Output C

Output Z

Infr

astr

uct

ure

Dedicated Server FarmCommutable slave computing

nodes on secured or public clouds

On premises slave computing nodes

CassandraCluster

Hadoop HDFS

Cluster

HBaseCluster

ElasticSearch

Cluster

Static Document

Storage

Private Data

Hybrid Storage

MesosMaster

MesosMaster

MesosMaster

MesosSlave

MesosSlave

MesosSlave

MesosSlave

MesosSlave

MesosSlave

MesosSlave

MesosSlave

MesosSlave

Projection

Expandable

Private Data and Application Processing

General Data and application ProcessesOn-Demand

processing extension

Projected Data

Expandable

Sto

rage

Pla

tfo

rmSo

ftw

are

Automatic Reverse-Proxy Configuration

Acc

ess

Projected Data

Projection

keras

Machine &deep learning

Current data, current computing power and current understanding allow a conversion of uncertainty to risk.

Requires a different approach, that can complement current focus on probabilistic risk.

Offers great perspective to support the development of new services, more customized, more responsive.

Anticipate upcoming change in the environment and market.