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The future of Artificial Intelligence
Dr. Dirk MichelsenManaging Consultant IBM Watson & AI
TimeToAct, Köln, 11.4.2018
Agenda
1. History & future of AI
2. Success factors of AI projects
3. Examples of AI Services & AI projects
4. Recommendation & Summary
Agenda
1. History & future of AI (Man Machine Interaction)
2. Success factors of AI projects
3. Examples of AI Services & AI projects
4. Recommendation & Summary
5
The first chatbot came to life
1966
by Joseph Weizenbaum
6
IBM‘s Deep Blue chess computer defeats the reigning world champion Gary Kasparov
1997
by IBM
7
IBM‘s Watson defeats the two champions Ken Jennings and Brad Rutter in Jeopardy!
2011
by IBM
8
Soul Machines & IBM present Rachel
2017
by IBM & Soul Machines
Artificial IntelligenceEntire research area(Learn + NLP + Expert systems + …)
Machine LearningLearning computer systemsunsupervised vs. supervised
Deep Learning as a main component of Machine Learning is a part of Artificial Intelligence. It provides the basis for Cognitive Computing.
Deep LearningAlgorithms in neural networks with multiple levels that automatically recognize concepts and similarities
We are at the beginning of a new era: The cognitive era of AI systems that understand, reason, learn & interact
TabulatingSystems Era
ProgrammableSystems Era
CognitiveSystems Era
1900s 1950s Today...
11
A glimpse into the future of Artificial Intelligence and new technologies
Time
Expe
ctat
ions
Gartner Hype Cycle for Emerging Technologies, as of July 2017
InnovationTrigger
Peak of inflatedExpectations
Trough ofDisillusionment
Slope ofEnlightenment
Plateau ofProductivity
more than 10 years
Smart Dust
4D Printing
Artificial GeneralIntelligence
5 to 10 years
Deep ReinforcementLearning
Neuromorphic HardwareHuman Augmentation
5G
2 to 5 years
Serverless PaaS
Digital Twin
Quantum ComputingVolumetric Displays
Brain Computer InterfaceConversational UserInterface
Smart Workspace
Augmented Data Discovery
Edge ComputingSmart Robots
IoT Platforms
Virtual Assistants
Connected HomeDeep Learning
Machine Learning
Autonomous Vehicles
Nanotube Electronis
Cognitive Computing
Blockchain
Commercial UAVs (Drones)
Cognitive Expert Advisors
Enterprise Taxonomy andOntology Management
Software-DefinedSecurity
AugmentedReality
VirtualReality
less than 2 years
Plateau will be reached in
12
A glimpse into the future of Artificial Intelligence and new technologies
Time
Expe
ctat
ions
Gartner Hype Cycle for Emerging Technologies, as of July 2017
InnovationTrigger
Peak of inflatedExpectations
Trough ofDisillusionment
Slope ofEnlightenment
Plateau ofProductivity
more than 10 years
Smart Dust
4D Printing
Artificial GeneralIntelligence
5 to 10 years
Deep ReinforcementLearning
Neuromorphic HardwareHuman Augmentation
5G
2 to 5 years
Serverless PaaS
Digital Twin
Quantum ComputingVolumetric Displays
Brain Computer InterfaceConversational UserInterface
Smart Workspace
Augmented Data Discovery
Edge ComputingSmart Robots
IoT Platforms
Virtual Assistants
Connected HomeDeep Learning
Machine Learning
Autonomous Vehicles
Nanotube Electronis
Cognitive Computing
Blockchain
Commercial UAVs (Drones)
Cognitive Expert Advisors
Enterprise Taxonomy andOntology Management
Software-DefinedSecurity
AugmentedReality
VirtualReality
less than 2 years
Plateau will be reached in
AI related
Agenda
1. History & future of AI
2. Success factors of AI projects
3. Examples of AI Services & AI projects
4. Recommendation & Summary
Data for AI
14
Health datawill grow
99%
88%unstructured.
Electronics data will grow
99%
84%Unstructured
Connected DevicesInternet of ThingsSmart Home
Manufacturing data will grow
99%
82%unstructured.
Automotivedata will grow
94%
84%unstructured.
Autonomous CarConnected CarElectric Car
Wir sind mitten im Zeitalter der Digitalisierung und Artifical IntelligenceUnstrukturierte Daten wachsen rapide und sind die Grundlage von disruptiven Geschäftsmodellen
44 Zetabytes
Sensors & Devices
Images / Multimedia
Text
Enterprise Data
unstructured
structured
You are here
2010 2020
Data
Beispiele für disruptiveGeschäftmodelle
People for AI
16
Data ScientistComputer Linguist
KI-Trainer
Augmented Reality Designer
In the age of artificial intelligence thousands of new jobs occur that support the shift of technologies
Artificial Intelligence will help us to use our natural capabilitiesin a new and better way
Avatar Designer
Machine Learning Expert
Security Expert
UIMA Modeler
Deep Learning Specialist
Robotics Engineer
Software for AI
18
19
IBM Cloud - the architecture engineered for disruption
Cloud InfrastructureA highly scalable, security enabled infrastructure
DataTools to prepare data for cognitive
AICognitive building blocks for developers
Applications, solutions and servicesTargeted solutions for enterprise businesses
Conversation
Visual Recognition
Discovery
Speech
Compare and Comply
Document Conversion
DLaaS
Nat Language Understanding
Nat Language Classifier
ToneAnalyzer
PersonalInsight
KnowledgeQuery
Cloud Integration
Networking Security Core Enterprise Infrastructure
CognitiveSystems
Virtual Servers File StorageObject Storage
Cognitive Micro-services DevOps Tooling
WatsonOncology
WatsonCyber Security
Weather
IBM Services and Industry Solutions
WatsonVirtual Agent
Watson Explore and Discover
IBM Risk and Compliance
Asset Management (Maximo)
Storage Analytics Deployment GovernanceIngestion
Agenda
1. History & future of AI
2. Success factors of AI projects
3. Examples of AI Services & AI projects
4. Recommendation & Summary
Artificial Intelligence will support us with enhanced super forces around our abilities to see, listen, think, talk, and interact. Available through Watson Services.
Read & WriteWatson Conversation Service
1
See & RecognizeVisual Recognition Service
2
Speak & Listen
Text to Speech Service
3
Generate & Navigate Knowledge
Watson Discovery Service
4
Read & WriteWatson Conversation Service
1
Vodafone Gigacube | Virtual Product Advisor in GermanWatson Conversation Servicehttps://www.facebook.com/VodafoneGigaCube/
Read & WriteWatson Conversation Service
1
Dr. Wolfgang Hildesheim | Dennis Scheuer
AskMercedes | Personal Car AssistantWatson Conversation Service + Augmented Realityhttps://ibm.biz/BdjL6Nhttps://www.ibm.com/de-de/blogs/think/2017/11/30/ask-mercedes-chatbot-statt-betriebsanleitung/
Read & WriteWatson Conversation Service
1
See & RecognizeVisual Recognition Service
2
Carglass UK | Automatic Quote for RepairWatson Visual Recognitionhttps://autoglassbodyrepair.co.uk/book/v2/ See & Recognize
Visual Recognition Service
2
See & RecognizeVisual Recognition Service
2
Dr. Wolfgang Hildesheim | Dennis Scheuer
AskMercedes | Personal Car AssistantWatson Conversation Service + Augmented Realityhttps://ibm.biz/BdjL6N https://www.ibm.com/de-de/blogs/think/2017/11/30/ask-mercedes-chatbot-statt-betriebsanleitung/
See & RecognizeVisual Recognition Service
2
Dr. Wolfgang Hildesheim | Dennis Scheuer
AskMercedes | Personal Car AssistantWatson Conversation Service + Augmented Realityhttps://ibm.biz/BdjL6N https://www.ibm.com/de-de/blogs/think/2017/11/30/ask-mercedes-chatbot-statt-betriebsanleitung/
See & RecognizeVisual Recognition Service
2
Pre-built Image RecognitionOut of the Box!
40-45
See & RecognizeVisual Recognition Service
2
Speak & ListenText to Speech Service
3
Integrated AI Reference: ElectronicsPanasonic Smart Home & Smart Mirror
Speak & Listen
Text to Speech Service
3
Generate & Navigate Knowledge
Watson Discovery Service
4
4/12/18
Generate & Navigate Knowledge
Watson Discovery Service
4
Agenda
1. History & future of AI
2. Success factors of AI projects
3. Examples of AI Services & AI projects
4. Recommendation & Summary
36
Pick a challenge
Leverage agile approaches to identify meaningful use cases
Identify business outcome for use case
Prove it fast
Identify right data sets to solve the challenge
Demonstrate benefits and outcomes early and often
Gain expertise in pilot projects and prototypes
Plan the program
Build an AI team to centralize competencies and experiences
Develop an AI center of competencies to build awareness, skills & best practice
Set milestones for success
Measure the outcome
Listen, iterate, learn and course correct while continuously learning
Guidelines for your AI Journey
“Customers have control over their Data” with different deployment models
Fully Multi-TenantTypical Data, POCs, POTs,
Demos, etc.
Tenant
Cognitive Services
Runtime
Storage (DB)
Virtualization (Cloud Mgt)
Hardware
Bluemix(IBM SoftLayer Data Center)
Tenant
Confidential Data,Encrypted Data at Rest and In Transit, Production SLAs, Services deliver VM
or Container level isolation
Tenant
Cognitive Services
Runtime
Storage (DB)
Virtualization (Cloud Mgt)
Hardware
Bluemix(IBM SoftLayer Data Center)
Tenant
Watson Premium Plans
Dedicated
Shared
Cognitive Services
Runtime
Storage (DB)
Virtualization (Cloud Mgt)
Hardware
IBM Softlayer Data Center
Tenant
Watson Standard Plans
Watson Dedicated Plans
Single-Tenant Software and Hardware in Shared Data Center.Strict Isolation Needs,
Data Location Requirements,Enterprise Integration Needs
Summary and Call to Action
38
• AI is the hottest trend in computer science.
• AI helps to reduce cost and allows new business models.
• Therefore you need to start or accelerate your AI business now:• Consult AI experts and run AI projects
• Detail your AI data strategy and AI use case
• Invest in a IBM Cloud Public Subscription and IBM Expert Services
• Learn to use the IBM Watson & AI Platform and test the Watson Studio offering
• Engage with TimeToAct & IBM to identify opportunities
Backup
40
© 2017 IBM Corporation
AI Decision Tree for Data Driven Projects(Goal: Better insights)
Time
Is there a lot ofunused data ?
Is the classificationknown?
Is there (probably)value in the data?
Is a complexclassificationof the data needed?
SupervisedLearningProject
Will the classificationneeded very often?
Yes Yes
Yes
No Data=No Project
No Business Case=No Project
ClassicalAnalyticsProject
One-TimeOutsourcing
to AI&Data Scientist
UnsupervisedLearningProject
Yes Yes
Yes
NoNoNo No No
© 2017 IBM Corporation
AI Qualitative Benefits Factors for Data Driven Projects(Goal: Better insights)
Better Insights=
Better Decisions
Increased numberof positive decisions
decreased numberof negative decisions
Increased positiveimpact of decisions
decreased negativeimpact of decisions
© 2017 IBM Corporation
AI Decision Tree for Interaction Driven Projects(Goal: Reduced costs)
Are there a lot ofIntellectually simpleinteractions ?
Is the classificationknown?
Is there enoughData to decideautomatically?
Is a complexclassificationof the data needed?
SupervisedLearningProject
Is the classification working all the time?
Yes Yes
NoNo
BusinessCase=No Project
Do Data=
NoProject
ClassicalAutomation
Project
NoNo
UnsupervisedLearning Project
Yes
Yes
Yes
AI + HumanProject
No
© 2017 IBM Corporation
AI Qualitative Benefits Factors for Interaction Driven Projects(Goal: Reduced costs)
Reduced Coststhrough
Cognitive Automatization
Reduced number ofhuman activities
Reduced failure rate of human activities
Reduced effort ofhuman activities
45
You have to act now in order to keep up the pace of AI
Make AI part of your own strategy Make AI part of your customer’s journey
Build up a center of competency for Artificial Intelligence and implement AI in your business.
Hire a set of AI Experts.
Pick some challenges, start with small prototypes to gain experience and scale fast.
Implement MVP jointly with IBM Expert Services.
Establish a factory approach to ensure consistency and stability for new AI solutions and adoption.
Invest in a first IBM Cloud Subscription in February
Train a team of business analysts, developers, and AI specialists to support your client’s use cases.
Prototype must be running after days
Realize a lot of smaller initiatives to quickly gain experience and ensure trust of your clients
Customer Care & ChatBot factories are a good start
Build a coherent and holistic AI Program to engage in transformational and innovative projects
Create jointly with your client a AI CoC
And most important beside measuring the outcomes, knowing the chances of AI, and gaining experience
is having fun in exploring new technologies that are a blast and bring huge business potential!
Personal Assistantto chat with
Read & WriteWatson Conversation Service
1
“Computers analyze texts”
Computers are able to read
They understand natural language
They can extract the meaning from large documents in a speed where no one could compete
Read & WriteWatson Conversation Service
1
48
The sociable humanoid robot Kismet is able to express emotion and recognize cues from interactions with humans
2000
by Cynthia Breazeal
49
Paralyzed woman moves robotic arm with help of a brain sensor
2012
by DLR
50
Soul Machines have partnered with Autodesk to create AVA -Autodesk's Virtual Agent
2018
Four capabilities differentiate Watson from traditional programmed computing systems
51
Reason
Watson reasons. It understands underlying ideas and concepts. It forms hypotheses. It infers and extracts concepts.
InteractWith abilities to see, talk and hear, Watson interacts with humans in a natural way.
LearnWatson never stops learning,getting more valuable with time. Advancing with each new piece of information, interaction and outcome, it develops “expertise.”
Understand
Watson understands like humans do.
EMBODIED COGNITION
IBM SERVICE ORCHESTRATOR
INGESTIONFRAMEWORK
Knowledge Source
REST API‘S
INTERFACE
ANSWER STORE
Frontend
WATSON IOT 3rd Party Service
“Enterprise Ready” cognitive architectureOrchestration Layer
WATSON DISCOVERY SERVICES
VISUAL RECOGNITION
CONVERSATION
Speech-To-TextText-To-Speech
Speak & ListenText to Speech Service
3
Generate & Navigate Knowledge
Watson Discovery Service
4
See & RecognizeVisual Recognition Service
2
Read & WriteWatson Conversation Service
1
Orange Bank | Mobile Only Bank & Banking AdvisorWatson Conversation Servicehttps://www.finextra.com/newsarticle/30460/orange-bank-preps-july-launch
Personal CustomerService Assistant
The Royal Bank of Scotland | Customer Service AssistantWatson Conversation Servicehttp://personal.rbs.co.uk/personal/support-centre.html
EVA – Inter Versicherung | Product Advisor, ZahnzusatzversicherungWatson Conversation Servicehttps://www.inter.de/produkte/gesundheit/krankenzusatzversicherung/inter-qualimed-zr-zahnzusatzversicherung/
Ask MercedesPersonal Car AssistantWatson Conversation Service + Augmented Realityhttps://ibm.biz/BdjL6N
Dr. Wolfgang Hildesheim / Marian Zoll *IBM Confidental*
Early Trend Detection
Challenge• Fraunhofer needs to detect and analyze innovation trends
• <100 scientists need to follow several hundred Tec Trends
• Flexible research based on different focus areas and buyers
Solution• IBM Watson Explorer serves as research tool to „read“
• A role based work place allows superior knowledge
management
Business Value• Detect and understand early important Tec Trends
• Provide leading tool for market research
• „Read“ and evaluate large volumes of unstructured data like for
example technical publications, patent DB`s and product description
60
Small initiatives already increase the productivity drastically and open the chance for huge automation potentials
FAQ self-service is trained relatively fastThe more sophistication is needed, the more services to train and implement are required• Design Thinking for user experience design• Systems integration• Cognitive process automation
Chatbot FAQ
Self-service for simple back-end processes
Cognitive Call Center assistant
Digital Virtual Assistant(next best action)
Semi-automated mid-complex processing (e.g. E-mail response, claims handling, ...)
61
Definitions and categories of AI
Narrow Artificial Intelligence(AI used for specific topics and
processes)
General Artificial Intelligence(AI applicable to any topic)
Super Artificial Intelligence(AI superior to humans)
Time
Inte
llige
nce
Industrial Use
Current Research
Philosophical Speculation
Watson / Presentation Title / Date62
The point in time when Artificial Intelligence becomes bigger than Human Intelligence (AI > HI) and its implications is subject of philosophical discussions.
Nick Bostrom: „Eine Superintelligenz wäre die größte Herausforderung, vor der die Menschheit je gestanden hat.“
63