watson and the new era of cognitive systems - ipp.pt - ruicoentro.pdf · watson and the new era of...
TRANSCRIPT
Rui Coentro
University Relations Manager
Center for Advanced Studies (CAS) Manager
Global Technology Services Delivery Manager
Watson and the new era
of cognitive systems
© 2013 IBM Corporation
Multi-Layer Neural Network
2
“bat”
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
© 2013 IBM Corporation
For regression problems, sum-of-squared error is used
For classification problems, cross-entropy is used
Define Objective Function
3
Error
ref=y
© 2013 IBM Corporation 4
In vision, each layer of the neural
network is mimicking how
humans process images
Why Deep Networks Are Important
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
© 2014 International Business Machines Corporation 6
Experts build expertise through cognition
Observe
Interpret
&
Evaluate
Decide Cognition
© 2014 International Business Machines Corporation 7
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.
8
© 2014 International Business Machines Corporation 8
Watson is creating a new partnership between people
and computers that enhances, scales and accelerates human
expertise.
© 2014 International Business Machines Corporation 9
IBM can put Watson to work for you
Exploration Visually depict and
analyze data for
clear advice
Decision Help users make
more informed
evidence-based
decisions
Discovery Help people create
new insights by
synthesizing
information
Engagement Helps organizations
build stronger
relationships with
constituents
© 2014 International Business Machines Corporation 10
Meaningful insights are only gained when data
reveals a universe of relationships
Genes
Chemical
Compounds
Diseases
Patients Animal Models
FDA Orange
Book/Moieties
Cells Patents Drugs
Plant
Biology
®™
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
© 2014 International Business Machines Corporation 12
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
© 2014 International Business Machines Corporation 13
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
© 2014 International Business Machines Corporation 14
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
© 2014 International Business Machines Corporation 15
Learn
Test
Experience
Ingest
Symptom
s
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
© 2014 International Business Machines Corporation 16
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
Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
Extracts Entities ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
ERK2
phosphorylates
p53
on
Thr55
Machine learning enables Watson to teach itself over time
© 2014 International Business Machines Corporation 17
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
© 2014 International Business Machines Corporation 18
Learn
Test
Experience
Ingest
Aspirin
GI Pain
Valium
Depression
Watson Creates
Knowledge Graph
Drugs can
have more
than one
side effect
Expert
Intervention
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
© 2014 International Business Machines Corporation 19
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
© 2014 International Business Machines Corporation 20
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
© 2014 International Business Machines Corporation 22
Watson Discovery Advisor: Accelerating breakthrough insights across life science functions
• What new ways could we target this
disease pathway?
Let’s look at all the genes identified in
every disease that are activated by this
protein
Lead & Drug Discovery
• How can we quickly identify if this
compound has a toxicity issue?
Signals from internal toxicology reports
and published studies suggest this
compound may cause serious AEs
Safety & Toxicity
Assessment
• Are there reasons for the early safety
signals that we can quickly identify?
AE reports suggest that our drug is often
being taken with dairy foods when this
side effect is being reported
Pharmacovigilance
• Does this drug have an effect on the
pathway of another disease?
There are several diseases where the
same receptors that this compound
binds to exist
Drug Repurposing
• What populations are likely to benefit
most from this intervention?
Looking at all known studies of similar
compounds, this is how this treatment
might perform in these populations
Comparative Effectiveness /
Clinical Trial Design
• What do early studies of competitors
reveal about their efficacy and safety?
Animal models revealed early
effectiveness and faster onset,
differentiating from current products
Competitive Intelligence
Watson Explorer component view
Security
Management and application development
Query
Routing
Mobile Collaboration Application
Builder
Solution
Gallery
Content
Miner
Studio
Connectivity
… CMS Email DBMS External CRM Wikis Support Social SCM File
systems
Public Cloud Hadoop
Indexing, search and analytics
Indexing Search
Content
analytics Text analytics
= available ith
Advanced Edition
Watson Explorer Applications Search • Analyze • Interpret
Watson Developer
Cloud Cognitive and information
analysis services
Question & Answer
Relationship Extraction
Concept Expansion
Personality Insights
Language Translation
Tradeoff Analytics
Message Resonance
AlchemyAPI
AlchemyVision
… more …
Private Cloud 23
© 2014 International Business Machines Corporation 24
Advisors
Developers Cloud
Specialties
Models
Content
Tooling
Assemble
Train
Deploy
Admin
Data Services Ingest Extract Annotate Curate
Design
Engagement Discovery
Decision Policy
Cross Industry Editions
Oncology Wealth Mgmt.
Intelligence Cooking
Target Industry Editions Powered by Watson Offerings
App Store
Healthcare
Financial Svc.
Travel . . .
Call Center
User Profiling
Research . . . Core Offerings Watson Analytics Watson Explorer
Industry Aligned Market Aligned
Visualize
Cognitive Services (APIs)
The same services are used by business partners, customers, and IBM Developers.
Reusable services form the basis for all Watson
cognitive solutions
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
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Ecosystem Development
Announcing the IBM Academic Initiative for Cloud
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Start today with three simple steps
© 2014 IBM Corporation
Smarter Cities: Capitalizing on new insights, creating system-wide efficiencies, collaborating in new ways
ISEP – October 2015
© 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
Energy Government Healthcare Public Safety
Prioritized Industries
Transportation Water
Solutions
Smarter City Operations
Consulting and Services
And our Smarter Cities solution portfolio is expansive
Infrastructure Planning and Management People
Intelligent Operations Center
Law enforcement, public safety, intelligence, counter fraud
Emergency management
Building management
Campus management
Transportation management
Water management
Utility network management
Asset management
Social program management
Smarter care
Health management
Educational outcomes
30
Business Partner Solutions
1.Water
Sourcing
2.Water
Treatment
3.Water
Storage &
Distribution
4.Waste/Storm
water
Collect &
Discharge
Recycled/Treated
• 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
• Monitor concentration of chemicals • Water quality • Audit-ready reporting • Asset performance • Resource scheduling
• 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
• Sewer discharge / overflow • Flood monitoring & modeling • Treated Water quality • Wastewater potential (chemical & energy recovery, new water)
5.Treated
Wastewater -
Other Use
• Usage optimization • Blend optimization • Run-off monitoring
HIGH-LEVEL TOP SEGMENT NEEDS
Analytics & Optimization +What-if End to End Water Lifecycle View Multi-stakeholder Collaboration Asset Management Planning, Regulatory Compliance & Audit Environmental impact
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
TOP CROSS-SEGMENT NEEDS
Natu
ral E
co
syste
m
1 2 3 4
5
Na
tura
l E
co
syste
m
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
© 2013 IBM Corporation 33
Engaging Citizens
Operational Visibility
Water and wastewater agencies are focusing on several key imperatives to manage water and ensure sustainability
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
Capture contextual knowledge as codified business rules
Collaborate across silos
Encourage citizen participation in monitoring & reporting
Document operational expertise via work flows
Sustainable Operations
Ageing infrastructure
Quality of Services
Grey Tsunami
© 2013 IBM Corporation 34
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
Pressure Management
Pipe Failure Prediction
Non Revenue Water
Asset Management
Energy Reduction
© 2013 IBM Corporation 35
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
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
© 2013 IBM Corporation 36
Water efficiency management - pressure management
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
Optimize network pressure – Lower energy costs, Decrease leak and burst incidence and extend life of assets
© 2013 IBM Corporation 37
Water efficiency management - pipe failure prediction
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
Identify riskiest pipes and drive preventative maintenance plans to reduce leaks and bursts. Lowers cost of expensive disruptions and improves Quality of service
© 2013 IBM Corporation 38
Water efficiency management – asset management and operational insight
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
Enhance situational awareness of operations and infrastructure by integrating and visualizing asset and work order information
© 2013 IBM Corporation 39
Water efficiency management – situational awareness
Visibility
– Visualize near real time data, status and performance of water systems
• 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
Leverage data holistically to find hidden patterns, correlations - create insights to improve water management: improve decision making, enhance efficiency and reduce risk
Non-revenue water
Water conservation
Water sustainability
Wastewater management
Urban flood management
….and beyond …
© 2012 IBM Corporation
Water Maturity continuum chart
40
Integrated view of Operations & Infrastructure •Situational Intelligence for better management
•Basic correlation & Reporting
•Demand trend & Patterns Forecasts
•Workflows, KPIs
Advanced analytics Leak / Theft Detection
Dynamic Pressure Optimization Weather prediction and modeling
SCADA and basic sensor systems • Basic IT applications and SCADA Security
• Ph, turbidity, chemical sensors etc.
EAM, CRM, ERP & GIS • Asset and Workforce Management
•Customer Management systems
•Financial management
•Workforce Management
Tim
e
Customer Value
© 2013 IBM Corporation 41
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.