Watson and the new era
of cognitive systems
Rui Coentro
University Relations Manager
Center for Advanced Studies (CAS) Manager
Global Technology Services Delivery Manager
Multi-Layer Neural Network
 Each simple building block is a connection of neurons which produces a
higher-order, more complex representation of the input
 Neurons in one layer are connected to neurons in the next layer
“bat”
2
© 2013 IBM Corporation
Define Objective Function
Error
ref=y
 For regression problems, sum-of-squared error is used
 For classification problems, cross-entropy is used
3
© 2013 IBM Corporation
Why Deep Networks Are Important
 In vision, each layer of the neural
network is mimicking how
humans process images
4
 In speech, layers are learning speaker
adaptation and discrimination, no need for
separate modules for each processing
stage as we previously did
© 2013 IBM Corporation
Deep Neural Network Applications
Pre-training, hardware improvements and parameter reduction
encourage deeper networks
Deep Neural Networks (DNNs) have been successfully applied
across a variety of pattern recognition tasks
–Acoustic Modeling for Speech Recognition
–Language Modeling for Speech Recognition
–Image Recognition
–Natural Language Processing
–Information Retrieval
–Multimodal Processing
–Regression Problems
5
© 2013 IBM Corporation
Experts build expertise through cognition
Observe
Interpret
&
Evaluate
© 2014 International Business Machines Corporation
Decide
Cognition
6
The volume, variety and
velocity of data is creating
an unprecedented opportunity.
2.5B
gigabytes of new data are
generated every day, 4/5ths of
which is unstructured.
© 2014 International Business Machines Corporation
7
8
Watson is creating a new
partnership between people
and computers that enhances,
scales and accelerates human
expertise.
© 2014 International Business Machines Corporation
8
IBM can put Watson to work for you
Engagement
Discovery
Decision
Exploration
Helps organizations
build stronger
relationships with
constituents
Help people create
new insights by
synthesizing
information
Help users make
more informed
evidence-based
decisions
Visually depict and
analyze data for
clear advice
© 2014 International Business Machines Corporation
9
Meaningful insights are only gained when data
reveals a universe of relationships
Plant
Biology
Genes
Diseases
FDA Orange
Book/Moieties
Drugs
Cells
Patents
Chemical
Compounds
®
™
Animal Models
Patients
© 2014 International Business Machines Corporation
10
How it works video – 8 min.
(https://www.youtube.com/watch?v=_Xcmh1LQB9I)
© 2014 International Business Machines Corporation
11
Watson is the culmination of several cognitive
technologies
Integrates All Types of Big Data
Understands Scientific Entities & Relationships
Learns Through Expert Training
Visualizes with Supporting Evidence
© 2014 International Business Machines Corporation
Ingest
Learn
Test
Experience
12
Watson enables insights by connecting and analyzing hundreds of
internal and external data sources in minutes rather than weeks
Available External Data
Internal Data
Ingest
In vitro tests
In vivo studies
Learn
Test
Compounds
Toxicology reports
Clinical trial data
Experience
Watson Corpus
Over 1TB of data
Over 40m
documents
Over 100m entities
and relationships
Chemical database
12M+ chemical
structures
Public genomics
20,000+ genes
Medical textbooks
Medline
Other journals
Lab notes
FDA drugs/labels
Other
Patents
© 2014 International Business Machines Corporation
50+ books
100+ journals
23M+ abstracts
11,000+ drug labels
16M+ patents from
US, Europe, WIPO
13
Not just a search engine, Watson understands and
interprets the language of science
H3C
O
N
Diagram
CI
N
Ingest
Learn
Test
Rich dictionaries
enable Watson
to link all entity
representations
Experience
© 2014 International Business Machines Corporation
Formula
C16H13CIN2O
Names
(149)
Valium, Dizapam Alboral,
Aliseum,AlupramAmiprol, Asiolin,
Ansiolisina Apaurin, Apoepam, etc.
Chemical ID
CAS# 439-14-5
14
More than mere text mining, Watson can identify
relationships
Ontologies: The relationship between any entity and other scientific domains
Symptom
s
Fever
Headache
Chronic
pain
Arthritis
pain
Ingest
Drug class
Adverse
Effects
Learn
AntiInflammatory
GI pain
Test
Aspirin
Antiplatelet
GI bleeding
Illustrative Example
NSAID
Nausea
Analgesic
Gastritis
Experience
Indications
© 2014 International Business Machines Corporation
Reduce MI
Reduce
stroke
Reduce
fever
Reduce
pain
15
Annotators allow Watson to read and extract appropriate
information
…doxorubicin results in extracellular signal-regulated kinase (ERK)2 activation,
which in turn phosphorylates p53 on a previously uncharacterized site, Thr55…
Ingest
ERK2
Learn
phosphorylates
Test
Experience
p53
on
Thr55
© 2014 International Business Machines Corporation
Extracts Entities
 ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
Extracts Verb
 Maps to domain of Post Translational Modification
 Recognizes subject / object relationships
Extracts Entities
 ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
Extracts Preposition
 Recognizes preposition location on Thr55
Extracts Entities
 ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
16
Machine learning enables Watson to teach itself over time
Annotator
Logic
Ingest
Learn
Test
• Drug = entity
• Side effect = entity
association cause
• Cause = relating verb
• Rule = 1 drug to 1
side effect
Experience
© 2014 International Business Machines Corporation
Watson Applies
Annotators to Text
• 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…
Watson Creates
Knowledge Graph
GI Pain
Aspirin
Valium
Depression
17
Machine learning also enables Watson to learn from
experts
Watson Creates
Knowledge Graph
Ingest
Learn
Expert
Intervention
GI Pain
GI Pain
Aspirin
Watson Applies Annotators &
Refines Knowledge Graph
Drugs can
have more
than one
side effect
GI Bleed
GI Upset
Nausea
Aspirin
Ulcers
Valium
Hyperactivity
Test
Valium
Experience
Agitation
Depression
Depression
Aggression
Hostility
© 2014 International Business Machines Corporation
18
Beyond mere algorithms, Watson evaluates supporting
evidence
Question
Ingest
Learn
What genes
contribute to
developing
colon cancer?
Test
Search
Corpus
Extract
Evidence
Score & Weigh
• Side Effects
• Quantity
• Lab Notes
• Proximity
• Genes
• Relationship
• Publications
• Domain Truths/
Business Rules
• Drugs
• Animal Models
Experience
© 2014 International Business Machines Corporation
• Clinical Trial
Data
19
The Result: Watson enables breakthrough insights after analyzing
thousands of articles and other corpus data in minutes
Co-occurrence Table
MeSH Name
Humans
Tumor Suppressor Protein p53
Gene Network
Total
pik3ca
p53
braf
chek2
epha2
chek1
cdk2
71728
11255
52891
3642
1300
390
1354
4106
36465
0
36465
107
289
0
241
439
csnk1d
High
Affinity
Ingest
35507
10042
21631
1022
440
225
638
2720
Mice
20178
5158
13092
577
239
160
310
1371
Apoptosis
20060
3056
16123
232
320
30
313
894
Signal Transduction
15559
9222
5163
648
336
89
391
623
Middle Aged
14728
847
12629
1132
164
54
22
268
Mutation
13252
1303
9900
1760
381
21
216
394
Aged
12327
696
10629
972
123
47
0
223
Adult
12112
763
10291
928
144
47
0
198
Test
tp53inp1
ca2
nrgn
gfm1
cdk7
jun
mms
prb3
tpt1
ccnb11
prrt2
mdm2
hipk4
atm
dach1
ppm1d
plk1
mapk14
no
Affinity
hist1h1c
ugcg
Phosphatidylinositol 3-Kinases
11066
10726
0
271
0
0
0
0
Immunohistochemistry
10127
710
8930
309
38
57
0
255
Phosphorylation
9757
4216
3345
331
603
82
628
1410
Cell Cycle
8401
630
5675
0
423
0
507
1904
Cell Line
8076
2158
4888
167
202
56
247
752
7480
1834
4767
591
27
9
34
605
h2afx
atr
ephb2
bard1
ptk2b
sgsm3
cdk1
tceal1
cdkn2a
7413
2076
2600
162
1287
0
750
1745
hdac6
tbp
pdik1l
des
mgst1
ep300
pin1
prkdc
nuak1
ptgs2
dyrk1a
dusp26
aldh1l1
stat3
mapk9
agt
vhl
ccn2a
tgm2
cdc25c
ptch1
chek1
tmprss11d
csnk1a1
mapkapk2
ccne1
dnm1l
cdk9
chek2
Protein-Serine-Threonine Kinases
csnk2a1
arl2
tp53
Proto-Oncogene Proteins
Experience
pdlim7
cdc20
csnk1g2
Some
Affinity
brca1
Learn
aurka
ppp2r4
card16
Animals
ros1
prkcg
mapk1
Moderate
Affinity
dyrk2
mapk10
cdk2
slco6a1
nr1h2
plk3
cdk5
mapk8
e2f1
bbc3
kat2b
krt20
chmp1a
stk11
• Select entities from two different ontologies (i.e.
disease/gene)
• Select two or more genes of interest
• Visualize co-occurrence
• Show strength, nature & proximity of the relationship
• Use statistics to spot the intersections
• Colored vectors indicate the nature of the interaction
• Drill down to see the evidence
• Hover over connections to see the evidence
© 2014 International Business Machines Corporation
• See network of relationships
20
Watson Discovery Advisor - Video
© 2014 International Business Machines Corporation
21
Watson Discovery Advisor:
Accelerating breakthrough insights across life science functions
Lead & Drug Discovery
Safety & Toxicity
Assessment
Pharmacovigilance
• What new ways could we target this
disease pathway?
• How can we quickly identify if this
compound has a toxicity issue?
• Are there reasons for the early safety
signals that we can quickly identify?
Let’s look at all the genes identified in
every disease that are activated by this
protein
Signals from internal toxicology reports
and published studies suggest this
compound may cause serious AEs
AE reports suggest that our drug is often
being taken with dairy foods when this
side effect is being reported
Drug Repurposing
Comparative Effectiveness /
Clinical Trial Design
Competitive Intelligence
• Does this drug have an effect on the
pathway of another disease?
• What populations are likely to benefit
most from this intervention?
• What do early studies of competitors
reveal about their efficacy and safety?
There are several diseases where the
same receptors that this compound
binds to exist
Looking at all known studies of similar
compounds, this is how this treatment
might perform in these populations
Animal models revealed early
effectiveness and faster onset,
differentiating from current products
© 2014 International Business Machines Corporation
22
Watson Explorer component view
Watson Explorer Applications
Search • Analyze • Interpret
Mobile
Security
Collaboration
Query
Routing
Application
Builder
Watson Developer
Cloud
Solution
Gallery
Content
Miner
Studio
Management and application development
Content
analytics
Indexing
Search
Text analytics
Indexing, search and analytics
…
File
systems
CMS
Hadoop
Email
DBMS
Support
Social
SCM
Connectivity
Wikis
CRM
Public Cloud
Cognitive and information
analysis services
Question & Answer
Relationship Extraction
Concept Expansion
Personality Insights
Language Translation
Tradeoff Analytics
Message Resonance
AlchemyAPI
AlchemyVision
… more …
External
Private Cloud
= available ith
Advanced Edition
23
Reusable services form the basis for all Watson
cognitive solutions
Advisors
App Store
Cross Industry Editions
Engagement
Discovery
Decision
Policy
Core Offerings
Target Industry Editions
Oncology
Wealth Mgmt.
Intelligence
Cooking
Watson Analytics
Powered by Watson Offerings
Industry Aligned
Healthcare
Travel
...
Watson Explorer
Market Aligned
Call Center
Research
...
Financial Svc.
User Profiling
Developers Cloud
Specialties
Cognitive Services (APIs)
Tooling
Assemble
Models
Design
Visualize
Train
Deploy
Content
Admin
Data Services
Extract
Ingest
Curate
Annotate
The same services are used by business partners, customers, and IBM Developers.
© 2014 International Business Machines Corporation
24
Watson Platform built on Bluemix
•
•
•
•
Build your application using callable Watson Service
APIs
Question Answering
Language Identification
Speech to text / Text to speech
Visual Recognition
Machine Translation
Personality Insights
Message Resonance
Relationship Extraction
Concept Expansion
Concept Insights
Tradeoff Analytics
Visualization Rendering (library)
Built on Bluemix
(http://www.ibm.com/smarterplanet/us/en/ibmwatson/
developercloud/services-catalog.html)
Can be combined with the 100s of other available
services on Bluemix
Pre-ingested content for health and travel
© 2014 International Business Machines Corporation
Ecosystem Development
Announcing the IBM Academic Initiative for Cloud
Free Trial
Build unlimited apps and
access services like Watson,
IoT, Big Data, and more!
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ISEP – October 2015
Smarter Cities:
Capitalizing on new insights, creating system-wide efficiencies, collaborating in new ways
© 2014 IBM Corporation
Vibrant cities are realizing their full potential by integrating across functions, capitalizing
on new insights, creating system-wide efficiencies and collaborating in new ways
29
© 2014 IBM Corporation
And our Smarter Cities solution portfolio is expansive
Prioritized Industries
Public Safety
Government Transportation
Energy
Healthcare
Water
Solutions
Planning and Management
Infrastructure
People
 Intelligent Operations Center
 Law enforcement, public safety,
intelligence, counter fraud
 Emergency management
 Building management
 Campus management
 Transportation management
 Social program
management
 Water management
 Utility network management
 Asset management
 Smarter care
 Health management
 Educational outcomes
Consulting and Services
Smarter City Operations
Business Partner Solutions
30
© 2014 IBM Corporation
IBM Intelligent Operations for Water (IOW)
and IBM’s Intelligent Water Portfolio
The Water market is complex and comprises 5 broad segments. This release
addresses key specific needs across segments
Use Cases that may be
delivered using IOW 1.5
capabilities
TOP CROSS-SEGMENT NEEDS
Analytics & Optimization +What-if
 End to End Water Lifecycle View
Multi-stakeholder Collaboration
Asset Management
Planning, Regulatory Compliance & Audit
Environmental impact
Natural Ecosystem
• Usage optimization
• Blend optimization
• Run-off monitoring
5.Treated
Wastewater Other Use
1.Water
Sourcing
1
• Monitor source levels
(above & below ground
• Optimize source
“blending”
• Resource Mapping
• Land use analysis
• Water intake flow monitor
• Raw Water quality
• Flood / levee Mgmnt
• Contamination
monitoring
• Ecosystem services
value
Recycled/Treated
2.Water
Treatment
3.Water
Storage &
Distribution
HIGH-LEVEL TOP SEGMENT NEEDS
2
• Monitor concentration
of chemicals
• Water quality
• Audit-ready reporting
• Asset performance
• Resource scheduling
3
• Asset condition monitoring
• Work Mgmnt optimization
• Predictive asset management
(including failure prediction)
• Cust. / Usage segments
• Leak detection
• Pump & Pressure
Optimization
• Water Quality
• Theft & tampering
• Meter outage / failure
• Demand mgmnt /
conservation
• Budget, Price analyses
4.Waste/Storm
water
Collect &
Discharge
4
• Sewer discharge /
overflow
• Flood monitoring &
modeling
• Treated Water quality
• Wastewater potential
(chemical & energy
recovery, new water)
Natural Ecosystem
5
KPIs, Reports, Graphs, GIS capabilities
 Energy Consumption & Carbon Footprint
Impact of Weather events
Link to other domains (Grid, Buildings)
Water value accounting
Water Risk Management
Water and wastewater agencies are focusing on several key
imperatives to manage water and ensure sustainability
Sustainable
Operations
Operational
Visibility
Ageing
infrastructure
 Optimize water and wastewater operations
 Proactive operational stance versus a reactive one
 Ensure accurate data on water is accessible
 Revitalize water delivery infrastructure; extend asset life
 Drive service quality, conservation in water-stressed areas
Quality of
Services
Engaging
Citizens
Grey
Tsunami
33
 Capture contextual knowledge as codified business rules
 Collaborate across silos
 Encourage citizen participation in monitoring & reporting
 Document operational expertise via work flows
© 2013 IBM Corporation
Water efficiency management business value
Enables water operators to reduce water loss, minimize network disruptions,
make more informed decisions, drive holistic leak management
Water Efficiency Management: Non-Revenue Water




Save money - lower repair costs, extended asset life & reduced energy bills
Lower business risk with preventative maintenance
Become more proactive - change operational stance
Improve quality of service to end users
Pipe Failure
Prediction
Asset
Management
Non Revenue
Water
Pressure
Management
34
Energy
Reduction
© 2013 IBM Corporation
Water efficiency management solutions for water operators
Enables water operators to reduce water loss, minimize network disruptions,
make more informed decisions, drive holistic leak management
1) Pressure Management. Explicitly manage to
achieve network pressure targets with possibly
conflicting goals. LOWER COSTS, RECAPTURE
REVENUE
2) Pipe Failure Prediction. Focus on system reliability,
preventive maintenance effectiveness. LOWER COSTS
AND OPERATIONAL RISK
3) Asset Management + Operational Information.
Proactively / Effectively manage incidents and repairs.
LOWER REPAIR / MAINTENANCE EXPENSE
4) Situational Awareness. Leverage data holistically to
create insights, improve water management. LOWER
NETWORK RISK, IMPROVE EFFICIENCY
35
© 2013 IBM Corporation
Water efficiency management - pressure management
Optimize network pressure – Lower energy costs, Decrease leak and burst
incidence and extend life of assets
 Data visualization
– Consolidates data from a variety of
sources, e.g. SCADA, billing records…
– Provides continual visibility and
understanding of pressure status
 Monitoring, insight
– Generates real-time anomaly alerts
– Provides detailed trend information
 Decision making
– Accepts input via intuitive user interface:
desired targets at pressure critical points
– Provides recommendations for detailed
equipment operational settings
36
© 2013 IBM Corporation
Water efficiency management - pipe failure prediction
Identify riskiest pipes and drive preventative maintenance plans to reduce leaks
and bursts. Lowers cost of expensive disruptions and improves Quality of service
 Data visualization
– Pipe network, failure risk hotspots, risk
factors distribution
 Pattern analysis, modeling
– Analyzes seasonal patterns, spatial pattern,
factor correlation, feature selection
– Advanced data mining (e.g. decision tree,
regression, neural network)
 Prediction and planning
– Failure prediction: which pipe sections are
most at risk of failure
– Generate preventative maintenance plan
37
© 2013 IBM Corporation
Water efficiency management – asset management and
operational insight
Enhance situational awareness of operations and infrastructure by integrating
and visualizing asset and work order information
 Visualization and correlation
– Synchronize asset details from an
enterprise asset management
– Combine with operational information
(e.g. pressure, flow, temperature)
 Analysis and estimation
– Estimate cost of repair based on data
(e.g. material, age, diameter, location)
 EAM integration
– Create work order in EAM system
– View status and details, GIS map view
38
© 2013 IBM Corporation
Water efficiency management – situational awareness
Leverage data holistically to find hidden patterns, correlations - create insights
to improve water management: improve decision making, enhance efficiency
and reduce risk
 Visibility
– Visualize near real time data, status and
performance of water systems
Non-revenue
water
• SCADA systems, sensors, meters, video, etc.
– Visualize real-time / near real-time data
feeds from external data sources
• GIS / geographic information system
• EAM / enterprise asset management
• ERP / enterprise resource planning, etc.
 Situational awareness
– View relationships, patterns, correlations
– Leverage key performance indicators,
business rules, standard operating procedures
– Bridge gap between physical world of control
systems and realm of business decisions
39
….and beyond …
Water
conservation
Water
sustainability
Wastewater
management
Urban flood
management
© 2013 IBM Corporation
Water Maturity continuum chart
Advanced analytics
Leak / Theft Detection
Dynamic Pressure Optimization
Weather prediction and modeling
Time
Integrated view of Operations & Infrastructure
•Situational Intelligence for better management
•Basic correlation & Reporting
•Demand trend & Patterns  Forecasts
•Workflows, KPIs
EAM, CRM, ERP & GIS
• Asset and Workforce Management
•Customer Management systems
•Financial management
•Workforce Management
SCADA and basic sensor systems
• Basic IT applications and SCADA Security
• Ph, turbidity, chemical sensors etc.
Customer Value
40
© 2012 IBM Corporation
Complementary solutions
Enterprise Asset Management:
Integration with an Enterprise Management System provides a “closed loop”
to identify, mitigate and quickly address any disruptive events – by linking
predictive analytics, pressure optimization and asset management.
Video Analytics – Infrastructure Physical Security:
Video is another data feed for increased situation awareness with ability to
search for events and analyze patterns. It can help secure high value assets in
critical operations, widely dispersed assets – by providing alerts in real time.
41
© 2013 IBM Corporation
Use Case 1
42
© 2012 IBM Corporation
 Smarter Water Landing Page: ibm.com/smarterplanet/water
 GIO on Water: ibm.com/ibm/gio/water
 Desert Mountain video - http://www.youtube.com/watch?v=LepjT1j9wcA
 City of South Bend video – http://www.youtube.com/watch?v=ZvA8q6cU2jw
© 2012 IBM Corporation