Edinburgh 2020 digital changes everything v10

“Digital Changes Everything”

Why , How and Some Examples

Digital Agenda

Sustainability Agenda

Data is the New Natural Resource

Trend

The issues of sustainability , C02 emissions and climate change resilience

are to significant extent

information issues: Peter Williams

CTO: IBM Big Green Innovations

For CO2 & Climate Change: • What damage are we causing? • What might happen if we continue

that damage? • How can we mitigate the damage ? • Is our mitigation working?

For Climate Change Resilience : • What might happen? • What will break? • How can we mitigate the risk? • Are we on track with our plans? • What just broke? • Who/what needs help first?

Looking at Global CO2

Bad News Good News

2% 98% Use of IT accounts for 2% of C02 emissions

Use of IT can significantly control and reduce the other 98% emissions caused by other sources

What is driving the change?

It is estimate …

1 billion transistors per human

Each costing

1/10 millionth of a cent.

http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=TlDdajeaAJZ

IBM: SyNAPSE Chip: 5 Billion Transistor Inspired by the brain, very low power consumption






An opportunity to think and

act in new ways :

economically, socially and

environmentally.

+ + =

A Smarter Planet

Something profound is happening….

Instrumented

Interconnected

Intelligent

As a New Natural Resource Data is changing quickly

Vo

lum

e o

f D

ata

Perc

enta

ge o

f U

nce

rtai

n d

ata

9

Big Data

This is then amplified by a network effect, combining technologies in new ways

Source: Bill Chamberlin

Cloud

Virtualization

Social Business

Mobile

Data Visualization

Green IT

Analytics

Consumerization

BYOD

Digital Marketing

Cognitive Computing

IoT

Gamification

Security

10

Look for the triangles for new interesting innovations …

Source: Bill Chamberlin

Mobile

Data Visualization

Analytics

IoT

What makes a system smarter?

3 Patterns that enable C02 reduction AND increased performance

Operational Efficiency Incident/Event Management

Planned Maintenance Asset Failure Extreme

Weather GOAL:- Situational awareness to keep on track GOAL:- information & processes to better respond

Incidents

GOAL:- connecting BOTH people AND systems to drive collaboration

Intelligent Operations

Better Collaboration and Response

• Situational awareness • Analytics • Smarter response

Foresight examples: • Cave Man • Agricultural Revolution • Industrial Revolution • Digital Revolution • Environmental Revolution

What is it that drives the ability to change the system?

Foresight

Some Examples of the

Digital Agenda enabling improvements

in CO2 reduction

Using Biomimicry Self Assembly Molecular Chemistry to Increase

Energy Efficiency of Computer Chips By more precise insulation layers

IBM POWER Processor

“Air Gap” Chip Technology

Designing like snowflakes and sea shells

IBM & Airlight Energy

Solar Concentrator: IBM Zurich:

IBM & Airlight Energy

Poly-generation: Electricity, Heat, Cooling

Solar Concentrator = Sun x 2000 • Simple Materials: concrete structure, Plastic foil film mirrors • High efficiency solar cells • Supercomputer Cooling Output = 50% overall system efficiency 12 kwatts of electrical power 20 kwatts of heat (on a sunny day) Hot Water at over 85 DegC

http://www.ted.com/watch/ted-institute/ted-ibm/gianluca-ambrosetti-solving-the-energy-crisis-one-sunflower-at-a-time


























Visualising Sustainability

Data Visualisation

Insight and change happen here

with people in the system

Collaboration

1. Moving from guessing to knowing

2. Connecting a network of people, systems and data to create collective insight

3. Making sense of large amounts of data – in space and time

4. Evidence based decisions at pace

5. Ability to apply the right tools: • Automate the simple • Make sense of the complicated through analytics • Make sense of the complex & unpredictable through visualisation

5 Smarter City Essentials for making cities more efficient :

City in motion: monitoring using existing data to design better solutions

Istanbul:

Movement Analysis using

Vodafone network data • 2.4 million phones w. 156 million events/week

• Origin Destination analysis

• Accurate detection of meaningful locations

• Used for new metro line planning

Dubuque, USA:

Bus Route Optimization Optimized routes will:

• Reduce OPEX and CO2 by 40%

• Generated 37% more demand

• Reduce average commuter travel time by 60%

Seeing the Whole System: Dublin Bus Network

Public Transport Awareness Tool

used by citizens and planners

‘Open Data’ + ‘Big Data’

http://www.smartplanet.com/blog/business-brains/predictive-analytics-at-work-predicting-traffic-jams-before-they-occur/10543

Smarter transport: Singapore, San Francisco and Lyon

One hour ahead, 85% accuracy






















Peterborough Bedford Manchester

Out of city 90 000

Tonnes

Bedford Manchester

In to city Tonnes

Peterborough

Flow of Regulated Commercial Waste

Waste flowing out of Peterborough

Waste flowing into Peterborough

Hydro Electric: Situational Awareness, Analytics & Smarter Response Creating alternatives to coal fired power generation

Understanding the impact on the river flow

Putting the data in context: 3D landscape

Analytics to optimise the use of Water and Power:

• Environmental needs • Power generation, • Flood control, • Energy storage

Using open data about public building

Jump to Google Earth

Jump to Live system

Understanding city energy metabolism in 30 seconds

Visualising City Scale Energy Load:

Jump to short video

IoT: Internet of Things

Why the IoT will be important:

http://jackuldrich.com/blog/internet-information-tech/10-unexpected-ways-the-internet-of-things-will-open-up-a-future-of-opportunity/

Thanks to Jack Uldrich

































Fine Scale Environmental Sensing

High density data to create new insights (3D)

Fine Scale Thermal Management: IBM Data Centre Sites : All of our Strategic Data Centres use this (4M sq ft) Saving 300 MkWhours per year through data (10% Reduction)

• Tecomms Cell Towers • Hospitals • Public Building

https://www.youtube.com/watch?v=VxtHHaMuVMg Jump to 3 minute video

IoT is Cool

https://www.youtube.com/watch?v=VxtHHaMuVMg

What is needed in the future?

Data by itself doesn’t do anything

without some creativity…

this is where you come in.

Principles in Information Science will be of use:

1. Whole system design (not sub)

2. Caching (clever use of read ahead)

3. Pipelining (fine scale priority management)

4. Real-time awareness (always monitoring and optimising)

5. Distributed inputs (creating systemic resilience)

6. The importance of storage in the system

7. Keeping things pure / Design for disassembly

A B C From Linear To Network

We need to find the ‘Moore’s Law’ for CO2

relentless performance improvement ‘doing more with different’

A

E B

D C

Cognitive Computing:

People Compute power

Automation People Compute

power Programming

• Natural Language • Unstructured data • Confidence levels • Feedback by design

Computing is Changing: How could this be used to reduce CO2?

Programming:

Imagine: Doing 10,000 weeks of reading

in 15 seconds and understanding all the relationships !

Ingest Learn Test

Experience

Jump to One Example: • Watson Debater

• Watson Analytics • Watson Discovery • Watson Engagement Advisor • Watson Healthcare • Watson API

Replace Energy, Resources and CO2

With Information and Design

The grand challenge is to…

In summary:

“Digital Changes Everything” that’s when

44

Thank-you Ian Abbott-Donnelly

Augmenting Products with Data

Products

Data

Codifying a distinctive service capability (Products become services)

+ industry B

industry A

New insight & value

= Combining data within and across industries

New insight & value

trading Data Trading

DataAssets Digitising assets

Data replaces the use of product or service: • Less energy • Less Resources • Less cost (human intervention)

Data

Products

Composable components (Products, assets, services)

Finer scale products & services enabled by data: • Accuracy • Control • Feedback • Complexity

Data

Data Data

Homework: Using data to enable a new low carbon economy

1

2

3

4

7

6

5

8

Foresight creating a network of benefits Where projects in one domain create multiple benefits in other domains

Energy Management

Pipe Failure Prediction

Distributed incident management

Leakage management

Customer Portal

Situational Awareness

Meter data analytics

External Data

Weather Impact and

recovery

Understanding the system dynamics

using data

Example from Water Utility

Watson is the culmination of several cognitive technologies

© 2014 International Business

Machines Corporation 47

Visualizes with Supporting Evidence

Learns Through Expert Training

Understands Scientific Entities & Relationships

Integrates All Types of Big Data

Ingest

Learn

Test

Experience

Watson enables insights by connecting and analyzing hundreds of internal and external data sources in minutes rather than weeks



Learn

Test

Experience

Ingest

16M+ patents from

US, Europe, WIPO

23M+ abstracts

100+ journals

50+ books

11,000+ drug labels

20,000+ genes

12M+ chemical

structures Watson Corpus

Over 1TB of data

Over 40m

documents

Over 100m entities

and relationships

Internal Data

In vitro tests

In vivo studies

Compounds

Toxicology reports

Clinical trial data

Lab notes

Other

Available External Data

Chemical database

Public genomics

Medical textbooks

Medline

Other journals

FDA drugs/labels

Patents

Not just a search engine, Watson understands and interprets the language of science



Learn

Test

Experience

Ingest

Diagram

Formula

Names

(149)

Chemical ID

Valium, Dizapam Alboral,

Aliseum,AlupramAmiprol, Asiolin,

Ansiolisina Apaurin, Apoepam, etc.

CAS# 439-14-5

C16H13CIN2O Rich dictionaries

enable Watson

to link all entity

representations

H C3

O

CI N

N

More than mere text mining, Watson can identify relationships



Learn

Test

Experience

Ingest

Symptoms Arthritis

pain Chronic

pain Fever Headache

Drug class

Antiplatelet

NSAID

Analgesic

Adverse Effects

GI pain

Gastritis

GI bleeding

Nausea

Indications Reduce MI Reduce stroke

Reduce fever

Reduce pain

Anti-Inflammatory

Aspirin Illustrative Example

Ontologies: The relationship between any entity and other scientific domains

Annotators allow Watson to read and extract appropriate information



Learn

Test

Experience

Ingest

…doxorubicin results in extracellular signal-regulated kinase (ERK)2 activation, which in turn phosphorylates p53 on a previously uncharacterized site, Thr55…

Extracts Preposition Recognizes preposition location on Thr55

Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid

Extracts Verb Maps to domain of Post Translational Modification

Recognizes subject / object relationships



ERK2

phosphorylates

p53

on

Thr55

Machine learning enables Watson to teach itself over time



Learn

Test

Experience

Ingest

Aspirin

GI Pain

Valium

Depression

Annotator

Logic

Watson Applies

Annotators to Text

Watson Creates

Knowledge Graph

• Aspirin is an antiplatelet indicated to

reduce the risk of myocardial

infarction

• Known side effects include

Gastrointestinal (GI) pain, GI upset,

ulcers, GI bleeding, and nausea

• Valium or Diazepam is a

benzodiazepine derivative, indicated

for the treatment of anxiety, muscle

spasms

• Valium may cause depression,

suicidal ideation, hyperactivity,

agitation, aggression, hostility…

• Drug = entity

• Side effect = entity

association cause

• Cause = relating verb

• Rule = 1 drug to 1

side effect

Machine learning also enables Watson to learn from experts



Learn

Test

Experience

Ingest

Aspirin

GI Pain

Valium

Depression

Watson Creates

Knowledge Graph

Drugs can

have

more than

one side

effect

Expert

Interventio

n

Watson Applies Annotators &

Refines Knowledge Graph

Aspirin

GI Pain

GI Upset

Nausea

Ulcers

GI Bleed

Depression

Valium

Agitation

Aggression

Hostility

Hyperactivity

Beyond mere algorithms, Watson evaluates supporting evidence



Learn

Test

Experience

Ingest • Quantity

• Proximity

• Relationship

• Domain Truths/

Business Rules

What genes

contribute to

developing

colon cancer?

Search

Corpus

Extract

Evidence

Score &

Weigh Question

• Side Effects

• Lab Notes

• Genes

• Publications

• Drugs

• Animal Models

• Clinical Trial

Data

The Result: Watson enables breakthrough insights after analyzing thousands of articles and other corpus data in minutes



Learn

Test

Experience

Ingest

Gene Network

csnk1dros1 pdlim7

prkcg

aurka

nrgn

cdc20ugcg

hist1h1c

ca2

dach1

prb3

ccnb11

ppm1d

tp53inp1

mms

tpt1csnk2a1

mapk1

plk1

csnk1g2

ppp2r4

cdk7

gfm1

mapk14

mdm2hipk4

arl2

mapkapk2

cdk1

dyrk2

mapk8

chek1

tceal1

h2afx

brca1

jun

card16

atm

atr

stat3

cdk5

plk3

cdk9

mapk10chek2

ep300mapk9

nuak1mgst1

pdik1lptch1

tgm2

cdc25c

ccne1dnm1l

krt20kat2b

bbc3

stk11

nr1h2

cdk2

chmp1a

aldh1l1

slco6a1

e2f1

prrt2 csnk1a1tmprss11d

ephb2bard1

ptk2b

agt

cdkn2a

ccn2a

ptgs2

hdac6vhl

tbppin1

sgsm3

dyrk1aprkdc

des

dusp26

tp53

csnk1dros1 pdlim7

prkcg

aurka

nrgn

cdc20ugcg

hist1h1c

ca2

dach1

prb3

ccnb11

ppm1d

tp53inp1

mms

tpt1csnk2a1

mapk1

plk1

csnk1g2

ppp2r4

cdk7

gfm1

mapk14

mdm2hipk4

arl2

mapkapk2

cdk1

dyrk2

mapk8

chek1

tceal1

h2afx

brca1

jun

card16

atm

atr

stat3

cdk5

plk3

cdk9

mapk10chek2

ep300mapk9

nuak1mgst1

pdik1lptch1

tgm2

cdc25c

ccne1dnm1l

krt20kat2b

bbc3

stk11

nr1h2

cdk2

chmp1a

aldh1l1

slco6a1

e2f1

prrt2 csnk1a1tmprss11d

ephb2bard1

ptk2b

agt

cdkn2a

ccn2a

ptgs2

hdac6vhl

tbppin1

sgsm3

dyrk1aprkdc

des

dusp26

tp53

60534927591476718347480Proto-Oncogene Proteins

141062882603331334542169757Phosphorylation

19045070423056756308401Cell Cycle

75224756202167488821588076Cell Line

4106135439013003642528911125571728Humans

8943133032023216123305620060Apoptosis

137131016023957713092515820178Mice

27206382254401022216311004235507Animals

439241028910736465036465Tumor Suppressor Protein p53

2230471239721062969612327Aged

3942162138117609900130313252Mutation

268225416411321262984714728Middle Aged

623391893366485163922215559Signal Transduction

750

0

0

0

chek1

1745

255

0

198

cdk2MeSH Name Total pik3ca p53 braf chek2 epha2

Adult 12112 763 10291 928 144 47

Phosphatidylinositol 3-Kinases 11066 10726 0 271 0 0

Immunohistochemistry 10127 710 8930 309 38 57

Protein-Serine-Threonine Kinases 7413 2076 2600 162 1287 0

60534927591476718347480Proto-Oncogene Proteins

141062882603331334542169757Phosphorylation

19045070423056756308401Cell Cycle

75224756202167488821588076Cell Line

4106135439013003642528911125571728Humans

8943133032023216123305620060Apoptosis

137131016023957713092515820178Mice

27206382254401022216311004235507Animals

439241028910736465036465Tumor Suppressor Protein p53

2230471239721062969612327Aged

3942162138117609900130313252Mutation

268225416411321262984714728Middle Aged

623391893366485163922215559Signal Transduction

750

0

0

0

chek1

1745

255

0

198

cdk2MeSH Name Total pik3ca p53 braf chek2 epha2

Adult 12112 763 10291 928 144 47

Phosphatidylinositol 3-Kinases 11066 10726 0 271 0 0

Immunohistochemistry 10127 710 8930 309 38 57

Protein-Serine-Threonine Kinases 7413 2076 2600 162 1287 0

High

Affinity

Moderate

Affinity

Some

Affinity

no

Affinity

• Select entities from two different ontologies (i.e.

disease/gene)

• Visualize co-occurrence

• Use statistics to spot the intersections

• Drill down to see the evidence

• Select two or more genes of interest

• See network of relationships

• Show strength, nature & proximity of the relationship

• Colored vectors indicate the nature of the interaction

• Hover over connections to see the evidence

Co-occurrence Table

Edinburgh 2020 digital changes everything v10

Environment

Transcript of Edinburgh 2020 digital changes everything v10