de onwaarschijnlijke evolutie van de informatietechnologie ...bdmdotbe/bdm2013... · 1910: atom...
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- From science to technology- The fourth paradigm- AI waves - Use Cases- Government action programs
The science
1880: Maxwell’s laws (electro-magnetism)
1905: Quanta: Planck and Einstein
1910: Atom model Bohr
1940: Computer (principle) of Turing and von Neumann
1948: Information theory of Shannon
1930: Quantummechanics of Heisenberg, Schrödinger,…
1950: Transistor of Shockley, Bardeen,…
3
0
1 109
2 109
3 109
4 109
5 109
6 109
1975 1980 1985 1990 1995 2000 2005 2010
Year
BookkeepingAudio
Video
3D games
LUI
Opera
tions/s
econd
‘Understand’ ?
Computational power x 2 every 18 months
Technology and Engineering Design: The third industrial revolution (1945…)
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Moore’s law:
computing power
doubles
every 18 months
Connectivity &bandwidth explosion
Bandwidth &
Connectivity
evolve
exponentially
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1 million = 1 000 0001 billion = 1 000 000 0001 trillion = 1 000 000 000 0001 quadrillion = 1 000 000 000 000 000
1 kB = 1 000 1 MB = 1 000 0001 GB = 1 000 000 0001 TB = 1 000 000 000 0001 PB = 1 000 000 000 000 000
1 TB = large university library= 212 DVD discs = 1430 CDs= 3 year music CD quality
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- From science to technology- The fourth paradigm- AI waves - Use Cases- Government action programs
The Fourth Paradigm
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Paradigm Time Ago Method
First A millenium Empirical
Second A few centuries Theoretical
Third A few decades Computational
Fourth Today Data-driven
Evolution
Data first!
From To
I have a hypothesis
I need data to check it
I have data
Which hypotheses can I check?
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- From science to technology- The fourth paradigm- AI waves - Use Cases- Government action programs
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040
Expert Systems Data Computations
EVIDENCE DATA-BASEDHYBRID AI REASONING BASED
AI enabled Decision Support Systems
SYSTEM ACTUATORS
DATAOBSERVATIONS
DISTURBANCES
A PRIORI INFORMATION- Objectives- Population based info - Expert’s expertise - Rules, syntaxis- Hypothesis, query, … - …..
ALGORITHMS / COMPUTATION- Modelling, simulation, prediction - State estimation , soft sensors - Data handling: Filtering, normalization - Segmentation: Clustering, classification- Relevance: Ranking, prioritization - Data fusion, ….
DECISION / ACTION- ‘Diagnosis’- Monitoring - Control action, correction - Learning- ….
SENSING DEVICESAudio-visual, Chemical, Mechanical , Electrio-Magnetic, Biological,….
HUMANIN-THE-LOOP
Main tasks
Prediction Segmentation Anomalies
Regression Clustering
Classification
Detect outliers
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Methodes om te clusteren
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zwart blond
oranje
blauw
lang
kort
haarkleur
lengte
Kleur kleren
KenmerkenClustersGelijkvormigheidBeslissing
Facial recognition
Objectives - ICT
Communication networks
Data center optimization
Home automation
Digital signing
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Objectives - Finance
Fraud detection
Just-in-time production
Credit worthiness
Portfolio management
Risk assessment
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Objectives - Education
Scientometrics
Student performance
Detecting plagiarism
Teacher performance
Grading
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Traffic management
Objectives – Smart Cities
Predictive maintenance
Electricity Demand
Smart lighting
Flood prediction
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Disease spreading
Tumour detection
Objectives – Health
Diagnostics
Medical fraud detection
Genome sequencing
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- From science to technology- The fourth paradigm- AI waves - Use Cases- Government action programs
Smart Cities DSS Environmental DSS: O3 and small particles Regional flood regulation DSS Nationwide electrical load DSS Security monitoring DSS Sports DSS
Industry 4.0 DSS Chemical processes DSS Mechanical structure monitoring DSS Fraud detection DSS
Mobility Traffic DSS
Precision Medicine DSS for patients, professionals, policy makers CDSS Ovarian Cancer, Biomarker detection CDSS Monitoring Glycemia, vital signals (brain, epilepsy,…) DSS Food DSS Fall detection 24
AI enabled Decision Support Systems
0 50 100 150 200 250 3000
20
40
60
80
100
120
140
Concentr
ation [
ug/m
3]
Time [Hours]
Measurements
Aurora (Model)
OI
DEnKF
O3 air-quality stations Average of the O3 concentration over the validation stations
- Validation stations
- Assimilation stations
Starting date: May 28th, 2005 at midnight 3 E 4 E 5 E 6 E
50 N
51 N
2
46
40
73
22
784
1667
56
19
25
Flanders O3/fine particle DSS
3/g m 3/g m
Average PM10 concentrationAverage PM10 concentration
vgl,
hgl
qbg
vopw,
hopw
qman
qopw
K7 A
v1,
h1
E
vs, hs
qK7
qA qE
vs2, hs2
qs
vs3, hs3
D
qs2 qs3
vvg, hvg
qhopw
vs4, hs4
qs4 qhs
vhopw,
hhopw
qvs qD
q2
qzbopw qzb1
qgopw
qgafw
v3,
h3
qvopw
hvopw
qK18
q3 q4
vw,
hw
qK19 qK30
qbgopw
qK7lg
qgl
vbg,
hbg
qK7bg
qzb3
q7 Demer
Zw
artew
ater
Zwartebeek
Vlootgracht
Schulensmeer
Webbekom
Herk
G
ete
Beg
ijn
eb
eek
Velp
e
Leu
geb
eek
Gro
te L
eig
ra
ch
t
Ho
uw
ersb
eek
q6 q5
qK31
qzb2
v4,
h4
vzb,
hzb
qzw
q1
vlg,
hlg
qK24B
hbgopw
qK24A
vzw,
hzw
qzwopw
qhs2
v2,
h2
qh
qsa
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Demer Flood Regulation DSS
Belgian smart electricity grid DSS
Power grid
250 transformer substationsEvery 15 min, 5 years
1 post, 1 week
1 post, four seasons
Seasonalities in the load: day, week, year, holidays
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6 posts, 1 yearSeasonalities, calender holidays !
1 month predictions depending on day, season and weather prediction
Customer profiling:Residential, business, industrial
Background detection
Goals:• traffic flow monitoring and control• security CCTV• face recognition• recommendation systems
(Netflix problem)• big data processing
Example:Separate background and foregroundobjects in a sequence of frames to count, identify, analyze objects on the scene.
Challenges:• handle very large scale problems
(e.g.: 10 sec, 25 FPS, small resolution (640x480): 150M variables)• minimize number of iterations, which are extremely costly
Security monitoring DSS
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Time Team Action Player Position0:00:00 1 Kick Off 9 (50,30)
0:00:01 1 Has Ball 10 (49,29)
0:00:04 1 Has Ball 8 (45,31)
,,,
,,,
,,,
,,,
,,,
0:12:25 1 Ball Out 6 (0,57)
0:12:46 2 Corner 3 (0,60)
0:12:47 2 Has Ball 4 (4,29)
0:12:49 2 Goal 4 (0,29)
0:13:38 1 Kick Off 10 (50,30)
,,,
,,,
,,,
,,,
,,,
Sport Analytics Decision Support Systems
Smart Cities DSS Environmental DSS: O3 and small particles Regional flood regulation DSS Nationwide electrical load DSS Security monitoring DSS Sports DSS
Industry 4.0 DSS Chemical processes DSS Mechanical structure monitoring DSS Fraud detection DSS
Mobility Traffic DSS
Precision Medicine DSS for patients, professionals, policy makers CDSS Ovarian Cancer, Biomarker detection CDSS Monitoring Glycemia, vital signals (brain, epilepsy,…) DSS Food DSS Fall detection 31
AI enabled Decision Support Systems
Top 99 %
Bottom 1 %
steam 15.2 T/h
Top 99.75 % (99.8%)
Bottom 0.25 % (0.2 %)
steam 20 T/h
Top 99.8 % (99.8%)
Bottom 0.31 % (0.2 %)
steam 20 T/h
Setpoint
changes
Idealrank top
3 -> 2
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Chemical process DSS
BatchManagement
Plant Information
Management
PlantPerformanceManagement
PlantMaintenanceManagement
DispatchingScheduling
BusinessModeling-
Trace &Tracking
Customer SpecificModules
EnterpriseAsset
Management
AdvancedProcessControl
Controls
Unit ModelUnit Model
DCS
Process
Model
Predictive
Control
Plant-Wide
Model Based
Optimizer
Optimal Reference
Signals
DCS
Process
Model
Predictive
Control
Model
Predictive
Control
DCS
Process
Market driven objectives
Plant-Wide
Model
Unit Model
Optimal Process
Conditions
Primary Control Signals
Economical
Benefits
€€
€€€€€€
€€€€€€€€€€€€
Chemical process DSS
Optimized Grade Change Trajectory
GRADE B
GRADE A
Off-spec
GRADE B
Grade transition with minimum off-spec
900
901
902
903
900
901
902
903
100 200 300 400 500 600 700 800 900 1000
900
901
902
903
No control, Quasi steady state and fast control
100
0 10 20 30 40 50 60 70 80 90 100
Histograms
No c
on
trol
Qu
asi s
tea
dy
co
ntro
lF
ast d
yn
am
ic
co
ntro
l
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Product quality optimization
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Short
Duration Long
Duration High
Frequency International Same
Destination Off
Peak Call
Forwarding Behaviour
Change Direct call
selling X X X X PABX fraud
X X X X X Freephone
fraud X X X X Premium
rate fraud X X X X Subscription
fraud X Handset
theft X X X X X
Call frequency
Average call duration
Fraud Detection DSS (phones, credit cards, tax declaration,…)
Smart Cities DSS Environmental DSS: O3 and small particles Regional flood regulation DSS Nationwide electrical load DSS Security monitoring DSS Sports DSS
Industry 4.0 DSS Chemical processes DSS Mechanical structure monitoring DSS Fraud detection DSS
Mobility Traffic DSS
Precision Medicine DSS for patients, professionals, policy makers CDSS Ovarian Cancer, Biomarker detection CDSS Monitoring Glycemia, vital signals (brain, epilepsy,…) DSS Food DSS Fall detection 39
AI enabled Decision Support Systems
Detector technology: inductive loops, Gatso-meters, camera’s
Traffic & Mobility DSS
0
1000
2000
3000
4000
5000
6000
7000
8000
0 50 100 150 200 250
density (#vehicles/km)
flo
w (
#veh
icle
s/h
)
Traffic jam prediction
Antwerp Ring
Density – Flow
Density per hour / day of the week
Smart Cities DSS Environmental DSS: O3 and small particles Regional flood regulation DSS Nationwide electrical load DSS Security monitoring DSS Sports DSS
Industry 4.0 DSS Chemical processes DSS Mechanical structure monitoring DSS Fraud detection DSS
Mobility Traffic DSS
Precision Medicine DSS for patients, professionals, policy makers CDSS Ovarian Cancer, Biomarker detection CDSS Monitoring Glycemia, vital signals (brain, epilepsy,…) DSS Food DSS Fall detection 42
AI enabled Decision Support Systems
Genome data
• Human genome project
– Initial draft: June 2000
– Final draft: April 2003
– 13 year project
– $300 million value with 2002 technology
• Personal genome
– June 1, 2007
– Genome of James Watson, co-discoverer of DNA double helix, is sequenced• $1.000.000
• Two months
• €1000-genome
– Expected 2012-2020
1,00E-07
1,00E-06
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
1,00E+01
1,00E+02
1,00E+03
1,00E+04
1,00E+05
1,00E+06
1,00E+07
1,00E+08
1,00E+09
1,00E+10
1,00E+11
1990 1995 2000 2002 2005 2007 2010 2015
Cost per base pair
Genome cost
YearΩ Cost per base pair Genome cost
1990 10 3E+10
1995 1 3.000.000.000
2000 0.2 600.000.000
2002 0.09 270.000.000
2005 0.03 90.000.000
2007 0.000333333 1.000.000
2010 3.33333E-06 10000
2015 0.0000001 300
Computer Tomography
Magnetic resonance
GS-FLX Roche Applied Science 454
Sequencers
ACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGA
Mass spectrometry
Microarrays (DNA chips)
Data tsunami
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- From science to technology- The fourth paradigm- AI waves - Use Cases- Government action programs
60 mio €, 2019-2020-…. AI (30 mio €) CS (20 mio €) PM (10 mio €)
AI (30 mio €) Luik I: Flankerend Beleid (5 mio €)
Kennis-/adviescentrum Ethiek en Maatschappelijke ImpactOpleidingen
Regulier kanaal (BaMa’s)Additionele opleidingskader
Luik II: Implementatie naar industrie (13 mio €)AI one stop shopVLAIO instrumentarium
Luik III: Strategisch Basisonderzoek (12 mio €) 2 a 4 programmalijnen
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Muyters initiatief AI/CS/PM