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1January, 2017CSC Proprietary and Confidential
Cognitive Systems
M. Sc. Lukas Ott
Wintersemester 2016/17
Hochschule Aschaffenburg
2January, 2017CSC Proprietary and Confidential
Agenda
• Gartner Hypecycle
• Watson & other Services
• What is cognitive? - Basics
• Influence of the “Cognitive Era” on
our professional life (Discussion)
3January, 2017CSC Proprietary and Confidential
Agenda
• Gartner Hypecycle
• Watson & other Services
• What is cognitive? - Basics
• Influence of the “Cognitive Era” on
our professional life (Discussion)
4January, 2017CSC Proprietary and Confidential
Gartner Hype Cycle - Explained
Innovations-
Trigger
Peak of Inflated
Expect
Trough of
Disillusionment
Slope of
Enlightment
Plateau of
Productivity
time
expectations
5January, 2017CSC Proprietary and Confidential
Gartner Hype Cycle - Explained
6January, 2017CSC Proprietary and Confidential
7January, 2017CSC Proprietary and Confidential
Gartner Hype Cycle 2016
8January, 2017CSC Proprietary and Confidential
CSC Trends 2017
9January, 2017CSC Proprietary and Confidential
Agenda
• Gartner Hypecycle
• Watson & other Services
• What is cognitive? - Basics
• Influence of the “Cognitive Era” on
our professional life (Discussion)
10January, 2017CSC Proprietary and Confidential
What are cognitive services?
11January, 2017CSC Proprietary and Confidential
What are cognitive systems?
• Cognitive systems understand (partly)
human expressions.
–Text, verbal and visual through extraction of
the actual intent of problem
•They learn through known patterns.
–For the meaning they need examples and
feedback.
–They interact with humans with their own
words and expressions.
12January, 2017CSC Proprietary and Confidential
What are the possibilities a cognitive system
like Watson can do?
Cognitive
• Natural Language Processing
• Translation
• Speech to text
• Confidence
• Personality insights
• Machine learning
• Trend analysis
• Context analysis
• Image analysis
Analytics
• Structuring unstructured Data
• Building dashboards
• Creating a corpus of knowledge
• Combining multiple data sources
• Knowledge classification
• Data integration
13January, 2017CSC Proprietary and Confidential
Watson
Jeopardy Relationship
Extraction
Questions
&
Answers
Language
Detection
Personality
Insights
Keyword
Extraction
Image Link
Extraction
Feed
Detection
Visual
Recognition
Concept
Expansion
Concept
Insights
Dialog
Sentiment
Analysis
Text to
Speech
Tradeoff
Analytics
Natural
Language
Classifier
Author
Extraction
Speech to
Text
Retrieve
&
Rank
Watson
News
Language
Translation
Entity
Extraction
Tone
Analyzer
Concept
Tagging
Taxonomy
Text
Extraction
Message
Resonance
Image
Tagging
Face
Detection
Answer
Generation
Usage
Insights
Fusion
Q&A
Video
Augmentation
Decision
Optimization
Knowledge
Graph
Risk
Stratification
Policy
Identification
Emotion
Analysis
Decision
Support
Criteria
Classification
Knowledge
Canvas
Easy
Adaptation
Knowledge
Studio Service
Statistical
Dialog
Q&A
Qualification
Factoid
Pipeline
Case
Evaluation
Natural Language
Processing
Machine Learning
Question Analysis
Feature
Engineering
Ontology Analysis
Jeopardy
Watson
Watson
Explorer
Jeopardy
Watson
Watson
Knowledge
Studio
Jeopardy
Watson
Watson
Engagement
Advisor
Jeopardy
Watson
Watson
Company
Analyser
Jeopardy
Watson
Watson
Analytics
IBM Watson is a growing platform and set of solutions & tooling
Industries
14January, 2017CSC Proprietary and Confidential
IBM Watson Services
15January, 2017CSC Proprietary and Confidential
16January, 2017CSC Proprietary and Confidential
Amazon Alexa – Homemade Skills for the Smart Home
17January, 2017CSC Proprietary and Confidential
• Azure ML Studio
• Cognitive Services (extract):
• Speech Recognition
• Knowledge Extraction
• Language Recognition
Cortana Intelligence Suite
18January, 2017CSC Proprietary and Confidential
Cortana skills
19January, 2017CSC Proprietary and Confidential
Agenda
• Gartner Hypecycle
• Watson & other Services
• What is cognitive? - Basics
• Influence of the “Cognitive Era” on
our professional life (Discussion)
20January, 2017CSC Proprietary and Confidential
Steps for the semantic intelligence of machines
21January, 2017CSC Proprietary and Confidential
How does it work?
22January, 2017CSC Proprietary and Confidential
Named Entity Recognition - NER
Format
Analyses
Tokenizer
Named Entity
Recognition
Text
23January, 2017CSC Proprietary and Confidential
NLP – Part-of-speech Tagging and Treebank
24January, 2017CSC Proprietary and Confidential
Named Entity Recognition - NER
Format
Analyses
Tokenizer
Named Entity
Recognition
Relationship
detection and
Classification
Text
25January, 2017CSC Proprietary and Confidential
NLP – Semantic nets
26January, 2017CSC Proprietary and Confidential
Device
Function
ality
Service
hasService hasFunctionality
Operation
hasOperation
Input
isExposedBy
consistsOf
consistsOf ?
Method
hasMethod
Output
hasInput hasOutput
Target
hasTarget
Aspect
refersTo
Measuring Controlling
Thing
Location
hasLocation
quantifies
Thing
Property
hasProperty
hasAspect
is-a
is-a
is-a
is-a
Ontology – Domaine – Knowledge Graph
Reference material from
TS-0012: oneM2M Base Ontology
27January, 2017CSC Proprietary and Confidential
Linked data
28January, 2017CSC Proprietary and Confidential
Neural networks
29January, 2017CSC Proprietary and Confidential
•Supervised (Regression / Classification)
Types of Machine Learning
SVM (Classification)•Unsupervised (Clustering)
K- Means (Clustering)
30January, 2017CSC Proprietary and Confidential
Software – Algorithms for decision making
•Rule-based
decisions
–If
condition
then action 1
or
action 2
–Examples:
•threshold
•Simple machine
programming
•Statistical
reasoning
–Simple
regression
–Examples:
•Outlier
detection
•Interpolation
•Predictive
Maintenance
•Machine
Learning
–Classification
tasks
–Examples:
•Identification of
relevant features
from a huge
dataset
•Quality
assurance with
different metrics
•Artificial
Intelligence
–Dynamic
Adaption
–Examples:
•Self-driving cars
•Human-like
conversations
•Intelligent
assistance
every programmer
Data Scientist
Complex System Engineer
31January, 2017CSC Proprietary and Confidential
From Big Data to smart Applications
• Big Data
– Web content (Blogs)
– Social Networks
– Online Activities (Search and Buy)
– Enterprise applications (ERP, CRM)
– Internet of Things (sensor data)
– Processes
– Text based content (reports)
– Knowledge representation
• Smart
Applications
–Predictions
–Planning
–Analyses
–Discover
–Detection
–Comparison
• Data acquisition
–Data preparation
–Natural Language
Processing
•Entity- Extraction
•Relation -
Extraction
•Taxonomy -
Generation
•Semantic
Graph
• Machine based
reasoning
–Intents
–Recommendations
–Context
–Semantic search
–Rules
–Machine learning
32January, 2017CSC Proprietary and Confidential
33January, 2017CSC Proprietary and Confidential
•People – Understand what kind of people are interacting
with your application
•Interact – Understand the context and the intent
•Naturally - Understand and respond like people normally
would do
Interact naturally with people
34January, 2017CSC Proprietary and Confidential
Find
evidence to
support
responses
Cognitive Computing and Analytics
Trained to Understand,
Discover & Learn
“Ingest”
relevant data
across a broad
domain to create
a repository
Understand
an ambiguous
English language
inquiry
Generate
potentially relevant
responses
Rank
responses with
confidance factors
and adapt
from machine
learning
Hello Mickey,
Welcome to
ABC Insurance
Train
35January, 2017CSC Proprietary and Confidential
36January, 2017CSC Proprietary and Confidential
Pepper the first robot that read emotions–
Already in use for shops and trade fairs
37January, 2017CSC Proprietary and Confidential
Agenda
• Gartner Hypecycle
• Watson & other Services
• What is cognitive? - Basics
• Influence of the “Cognitive Era” on
our professional life (Discussion)
38January, 2017CSC Proprietary and Confidential
Influence of the
“Cognitive Era”
on our professional life
(Discussion)
39January, 2017CSC Proprietary and Confidential
Outlook and abstract
Cognitive
Services
Watson/
Cortana/ Alexa
Services
Future of
Artificial
Intelligence
40January, 2017CSC Proprietary and ConfidentialCSC Proprietary and Confidential 40
Thank you!
Questions?
Feedback!
41January, 2017CSC Proprietary and Confidential
Quellen
• Gartner Hype Cycle
• http://www-05.ibm.com/de/watson/index.html
• IBM Watson Academy
• https://www.ibm.com/watson/developercloud/project-intu.html
• IBM Watson Developer Conference, November 2016, San Francisco
• How to Make Intelligent Robots That Understand the World | Danko
Nikolić | TED
• http://www.slideshare.net/DankoNikolic1/how-data-science-works-
and-how-can-customers-help
• https://www.microsoft.com/cognitive-services/en-us/apis
• http://www.economist.com/technology-quarterly/2017-01-07
42January, 2017CSC Proprietary and Confidential

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Cognitive Systems

  • 1. 1January, 2017CSC Proprietary and Confidential Cognitive Systems M. Sc. Lukas Ott Wintersemester 2016/17 Hochschule Aschaffenburg
  • 2. 2January, 2017CSC Proprietary and Confidential Agenda • Gartner Hypecycle • Watson & other Services • What is cognitive? - Basics • Influence of the “Cognitive Era” on our professional life (Discussion)
  • 3. 3January, 2017CSC Proprietary and Confidential Agenda • Gartner Hypecycle • Watson & other Services • What is cognitive? - Basics • Influence of the “Cognitive Era” on our professional life (Discussion)
  • 4. 4January, 2017CSC Proprietary and Confidential Gartner Hype Cycle - Explained Innovations- Trigger Peak of Inflated Expect Trough of Disillusionment Slope of Enlightment Plateau of Productivity time expectations
  • 5. 5January, 2017CSC Proprietary and Confidential Gartner Hype Cycle - Explained
  • 6. 6January, 2017CSC Proprietary and Confidential
  • 7. 7January, 2017CSC Proprietary and Confidential Gartner Hype Cycle 2016
  • 8. 8January, 2017CSC Proprietary and Confidential CSC Trends 2017
  • 9. 9January, 2017CSC Proprietary and Confidential Agenda • Gartner Hypecycle • Watson & other Services • What is cognitive? - Basics • Influence of the “Cognitive Era” on our professional life (Discussion)
  • 10. 10January, 2017CSC Proprietary and Confidential What are cognitive services?
  • 11. 11January, 2017CSC Proprietary and Confidential What are cognitive systems? • Cognitive systems understand (partly) human expressions. –Text, verbal and visual through extraction of the actual intent of problem •They learn through known patterns. –For the meaning they need examples and feedback. –They interact with humans with their own words and expressions.
  • 12. 12January, 2017CSC Proprietary and Confidential What are the possibilities a cognitive system like Watson can do? Cognitive • Natural Language Processing • Translation • Speech to text • Confidence • Personality insights • Machine learning • Trend analysis • Context analysis • Image analysis Analytics • Structuring unstructured Data • Building dashboards • Creating a corpus of knowledge • Combining multiple data sources • Knowledge classification • Data integration
  • 13. 13January, 2017CSC Proprietary and Confidential Watson Jeopardy Relationship Extraction Questions & Answers Language Detection Personality Insights Keyword Extraction Image Link Extraction Feed Detection Visual Recognition Concept Expansion Concept Insights Dialog Sentiment Analysis Text to Speech Tradeoff Analytics Natural Language Classifier Author Extraction Speech to Text Retrieve & Rank Watson News Language Translation Entity Extraction Tone Analyzer Concept Tagging Taxonomy Text Extraction Message Resonance Image Tagging Face Detection Answer Generation Usage Insights Fusion Q&A Video Augmentation Decision Optimization Knowledge Graph Risk Stratification Policy Identification Emotion Analysis Decision Support Criteria Classification Knowledge Canvas Easy Adaptation Knowledge Studio Service Statistical Dialog Q&A Qualification Factoid Pipeline Case Evaluation Natural Language Processing Machine Learning Question Analysis Feature Engineering Ontology Analysis Jeopardy Watson Watson Explorer Jeopardy Watson Watson Knowledge Studio Jeopardy Watson Watson Engagement Advisor Jeopardy Watson Watson Company Analyser Jeopardy Watson Watson Analytics IBM Watson is a growing platform and set of solutions & tooling Industries
  • 14. 14January, 2017CSC Proprietary and Confidential IBM Watson Services
  • 15. 15January, 2017CSC Proprietary and Confidential
  • 16. 16January, 2017CSC Proprietary and Confidential Amazon Alexa – Homemade Skills for the Smart Home
  • 17. 17January, 2017CSC Proprietary and Confidential • Azure ML Studio • Cognitive Services (extract): • Speech Recognition • Knowledge Extraction • Language Recognition Cortana Intelligence Suite
  • 18. 18January, 2017CSC Proprietary and Confidential Cortana skills
  • 19. 19January, 2017CSC Proprietary and Confidential Agenda • Gartner Hypecycle • Watson & other Services • What is cognitive? - Basics • Influence of the “Cognitive Era” on our professional life (Discussion)
  • 20. 20January, 2017CSC Proprietary and Confidential Steps for the semantic intelligence of machines
  • 21. 21January, 2017CSC Proprietary and Confidential How does it work?
  • 22. 22January, 2017CSC Proprietary and Confidential Named Entity Recognition - NER Format Analyses Tokenizer Named Entity Recognition Text
  • 23. 23January, 2017CSC Proprietary and Confidential NLP – Part-of-speech Tagging and Treebank
  • 24. 24January, 2017CSC Proprietary and Confidential Named Entity Recognition - NER Format Analyses Tokenizer Named Entity Recognition Relationship detection and Classification Text
  • 25. 25January, 2017CSC Proprietary and Confidential NLP – Semantic nets
  • 26. 26January, 2017CSC Proprietary and Confidential Device Function ality Service hasService hasFunctionality Operation hasOperation Input isExposedBy consistsOf consistsOf ? Method hasMethod Output hasInput hasOutput Target hasTarget Aspect refersTo Measuring Controlling Thing Location hasLocation quantifies Thing Property hasProperty hasAspect is-a is-a is-a is-a Ontology – Domaine – Knowledge Graph Reference material from TS-0012: oneM2M Base Ontology
  • 27. 27January, 2017CSC Proprietary and Confidential Linked data
  • 28. 28January, 2017CSC Proprietary and Confidential Neural networks
  • 29. 29January, 2017CSC Proprietary and Confidential •Supervised (Regression / Classification) Types of Machine Learning SVM (Classification)•Unsupervised (Clustering) K- Means (Clustering)
  • 30. 30January, 2017CSC Proprietary and Confidential Software – Algorithms for decision making •Rule-based decisions –If condition then action 1 or action 2 –Examples: •threshold •Simple machine programming •Statistical reasoning –Simple regression –Examples: •Outlier detection •Interpolation •Predictive Maintenance •Machine Learning –Classification tasks –Examples: •Identification of relevant features from a huge dataset •Quality assurance with different metrics •Artificial Intelligence –Dynamic Adaption –Examples: •Self-driving cars •Human-like conversations •Intelligent assistance every programmer Data Scientist Complex System Engineer
  • 31. 31January, 2017CSC Proprietary and Confidential From Big Data to smart Applications • Big Data – Web content (Blogs) – Social Networks – Online Activities (Search and Buy) – Enterprise applications (ERP, CRM) – Internet of Things (sensor data) – Processes – Text based content (reports) – Knowledge representation • Smart Applications –Predictions –Planning –Analyses –Discover –Detection –Comparison • Data acquisition –Data preparation –Natural Language Processing •Entity- Extraction •Relation - Extraction •Taxonomy - Generation •Semantic Graph • Machine based reasoning –Intents –Recommendations –Context –Semantic search –Rules –Machine learning
  • 32. 32January, 2017CSC Proprietary and Confidential
  • 33. 33January, 2017CSC Proprietary and Confidential •People – Understand what kind of people are interacting with your application •Interact – Understand the context and the intent •Naturally - Understand and respond like people normally would do Interact naturally with people
  • 34. 34January, 2017CSC Proprietary and Confidential Find evidence to support responses Cognitive Computing and Analytics Trained to Understand, Discover & Learn “Ingest” relevant data across a broad domain to create a repository Understand an ambiguous English language inquiry Generate potentially relevant responses Rank responses with confidance factors and adapt from machine learning Hello Mickey, Welcome to ABC Insurance Train
  • 35. 35January, 2017CSC Proprietary and Confidential
  • 36. 36January, 2017CSC Proprietary and Confidential Pepper the first robot that read emotions– Already in use for shops and trade fairs
  • 37. 37January, 2017CSC Proprietary and Confidential Agenda • Gartner Hypecycle • Watson & other Services • What is cognitive? - Basics • Influence of the “Cognitive Era” on our professional life (Discussion)
  • 38. 38January, 2017CSC Proprietary and Confidential Influence of the “Cognitive Era” on our professional life (Discussion)
  • 39. 39January, 2017CSC Proprietary and Confidential Outlook and abstract Cognitive Services Watson/ Cortana/ Alexa Services Future of Artificial Intelligence
  • 40. 40January, 2017CSC Proprietary and ConfidentialCSC Proprietary and Confidential 40 Thank you! Questions? Feedback!
  • 41. 41January, 2017CSC Proprietary and Confidential Quellen • Gartner Hype Cycle • http://www-05.ibm.com/de/watson/index.html • IBM Watson Academy • https://www.ibm.com/watson/developercloud/project-intu.html • IBM Watson Developer Conference, November 2016, San Francisco • How to Make Intelligent Robots That Understand the World | Danko Nikolić | TED • http://www.slideshare.net/DankoNikolic1/how-data-science-works- and-how-can-customers-help • https://www.microsoft.com/cognitive-services/en-us/apis • http://www.economist.com/technology-quarterly/2017-01-07
  • 42. 42January, 2017CSC Proprietary and Confidential