De onwaarschijnlijke evolutie van de informatietechnologie ...bdmdotbe/bdm2013... · 1910: Atom model Bohr 1940: Computer (principle) of Turing and von Neumann ... Objectives - ICT

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De onwaarschijnlijke evolutie van de informatietechnologie

Prof. Dr. Bart De MoorKU Leuven

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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,…

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1 109

2 109

3 109

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5 109

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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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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?

Big Data

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Data

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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

Main tasks

Filtering effects Assess relevance Combining info

Normalization Ranking Data fusion

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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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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


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

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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






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

Throughput increment of 11.81 t/h thanks to the DSS

Yield optimization

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

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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

-

Mechanical structure monitoring DSS

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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,…)






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

Speed harmonisation Ramp metering DRIP

Traffic & Mobility DSS: control






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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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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

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De onwaarschijnlijke evolutie van de informatietechnologie ...bdmdotbe/bdm2013... · 1910: Atom model Bohr 1940: Computer (principle) of Turing and von Neumann ... Objectives - ICT