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| Date post: | 21-Jan-2018 |
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Technology |
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• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
Businesses will require ROI
from AI
• Investment increased 10 times recent 5
years (2011-2016), but commercial cases are
limited
• Drastic changes of views last 2 years
(AI: from enemies to partners)
Faster development on
Conversational Interface
• Game-changing innovations
(AI learns human languages)
• Natural language search from Google and
Bing, DeepText from Facebook (Personal
Pattern Recognition), Changes on Chat
Bots/Digital Assistants/Messenger Apps
Designs evolve to increase
Credibility of AI
• Reflects onto AI design the knowledge on
how human earns credibility between
people
• AI NLP integrated with Communication
components such as tone, emotion, timing,
visual perception, and word selection
Begin discussion on how AIs
will talk to each other
• Protocols between AIs
• How to evade collision between AI
systems operating as silos
• Consider collisions between AI systems of
different purposes
Imbedded bias will be a big
blocker for AI dev
• Cases from Google/Microsoft
• Gender, Racial imbalance
• Different sources of bias
• Training data, user interactions, lack
of diversity, conflicting purposes
InteractionsComputer – Computer
Human – Computer
Human – Human
5 predictions for artificial intelligence in 2017, Stuart Frankel, CEO, Narrative Science, Dec 2016
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
Transform data into intelligent action
Intelligence
Dashboard /
Visualization
Info Mgmt Big Data Store Machine Learning /
Advanced Analytics
CortanaIoT Hub
Event Hub
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Intelligence Action
People
Automated Systems
Apps
Web
Mobile
Bots
Bot
FrameworkSQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory Machine
Learning
Data Lake
StoreCognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
Advanced Analytics Cycle
Act: Score,
Visualize
Deploy Apps,
Services &
Visualizations
Measure
Preparation Modeling
Feature &
Algorithm
Selection
Model Testing &
Validation
Models
Visualizations
Ingest
Profile
Explore
Visualize
Transform
Cleanse
Denormalize
Prepare Model
OperationalizeModels
Visualizations
Input1 Input2 … Actual Predicted
• Classification example – Confusion Matrix
Demo
Rolls-Royce case studyhttps://customers.microsoft.com/en-US/story/rollsroycestory
Rolls-Royce demohttp://rolls-royce.azurewebsites.net/#/fleetlocation
Solutions –Predictive Maintenance for Aerospacehttps://gallery.cortanaintelligence.com/Solution/Predictive-Maintenance-for-Aerospace-4
Tutorial –Simulating phenotypes from genomic datahttps://gallery.cortanaintelligence.com/Experiment/Simulating-phenotypes-from-genomic-data-2
https://github.com/Azure/Cortana-Intelligence-Gallery-Content/tree/master/Resources/Phenotype-Prediction
Solutions –Vehicle Telemetry (IoT)https://gallery.cortanaintelligence.com/Solution/Vehicle-Telemetry-Analytics-9
https://docs.microsoft.com/en-us/azure/machine-learning/cortana-analytics-playbook-vehicle-telemetry
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
{ Your Code }
Direct Line Protocol
REST Endpoint
• Bots are UX, Conversations as a Platform (CaaP)
• Contents are important as well: From simple information delivery to actionable insights
1.Microsoft R • Statistical Analysis, Data Preparation, Predictive Modeling
Big Data • Hadoop, Spark, Data Lake Analytics
Machine Learning • Predictive Analysis, Deep Learning
Cognitive Services • Image Recognition, Natural Language Understanding
Bot Framework • Dev Framework, Different service channels
Technologies around Bots
Extended Scenarios
Big Data Analytics
Spark on HDInsight
Data Lake Analytics
Real Time Processing
Stream Analytics
Personalized Offer
Machine Learning
SQL Server R Services
On-premises Integration
SQL Server
Data Management Gateway
Visualization enabled
Power BI Embedded
Demo
Bots are communication interfaces with natural language processing capabilities
Hi Shelly! I see
you’re not
satisfied. How
can I help?
Call Schedule
Customer satisfaction
call scheduled with
Shelly Smith.
08:23 AM
AccountShelly Smith
Primary Contact
Account
Filt
er:
All Incl
ude:
Related *Regarding” RecordsCase Number Title
Portal
timesheets
Call Status
Active
Created on
6.14.2013 9:18
AM
DFC Support CasesCase Associated View
W X Y Z
Contact center of the future
Deepen engagement Hidden insights beyond
your data
Infusing business processes
with intelligence
Business Systems
Integration
Machine
Learning
Big Data
Deepen engagement
Smart recommendations,
personalization,
immersive experiences
Infusing business processes
with intelligence
Dynamics CRM
Hidden insights beyond
your data
Advanced analytics,
Azure Machine Learning,
data enrichment
Demo
Democratizing AITo empower every person and organization to achieve more with AI
• What is AI and why now?
• Digital Transformation and AI
• Microsoft Approach to AI
• One perspective on AI : Reasoning
• Another perspective on AI : Understanding and Interacting
• Discussions and Q&A
Food for thoughts
– How retailers can drive digital transformation with AI
Delight your customers with personalized experiences
Empower your workforce to provide differentiated customer experiences
Transform your products and services
Optimize your supply chain with intelligent operations
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
Digital assistant
Product expert alert
It looks like Jane might need help
CUSTOMERHISTORY
Women’s Clothing
Intelligent Customer Service
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
Digital assistant
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
Digital assistant
Product expert alert
It looks like Jane might need help
CUSTOMERHISTORY
Women’s Clothing
Intelligent Customer Service
Proposed production based on forecasted trends
High-performing attributes
Blue
Leather
Cross-
body
Recommendation: More blue, leather, and cross-body styles
Demand forecasting
1 2 3 4 5 6 7
Green clutc
Floral hand
Leather cro
Cross-body
Demand forecasting
Blue
Leather
Cross-
body
Recommendation: More blue, leather, and cross-body styles
WHERE TO BUYHow can I help you, Jane?
Can you help me find the
size I’m looking for?
Sure. What size are you?
INTERACTIVE
KIOSK
MODERN STORE
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
MODERN STORE
Size 7.5
WHERE TO BUY
MODERN STORE
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
Shoe style XYZ
In-stock at Modern Store!
BUY NOW SHOPSIMILAR
CHAT
MODERN STORE
Shoe style XYZ
In-stock at Modern Store!
BUY NOWWITH OFFER
SHOPSIMILAR
CHAT
MODERN STORE
Analyze customer behavior and sentiment and
automatically deliver highly relevant product and
service offers
Transform the way customers begin the buying journey
and use automated engines to help them take the next
best step
Provide customers with a digital personal assistant
to guide their decision-making and shorten the
conversion cycle
Product expert alertIt looks like Jane might need help
CUSTOMERHISTORY
Women’s Clothing
Sent to…
Sales Associate
Scanning foraccurate pricing. . .
.
OPTIMAL STORE LAYOUT
Ch
ild
ren
’s’
Ap
pare
l
Accessories
Men
’sWomen’s
Counter
Men
’s
Women’s
Counter
MODERN STORE HEAT MAP OPTIMAL STORE LAYOUT
Ch
ild
ren
’s’
Ap
pare
l
Accessories
Men
’sWomen’s
Counter
Men
’s
Women’s
Counter
MODERN STORE HEAT MAP
Sent to…
Sales Associate
Scanning foraccurate pricing. . . .
Optimize shelf space and ensure items are always
labeled with accurate prices and promotions
Develop heatmaps based on in-store customer
behavior to understand which store layouts drive
conversions and increase sales
Alert expert staff automatically when customers have
specific concerns about a product or service
Blue
Leather
Cross-body
Recommendation:
More blue, leather,
and cross-body
styles
High-performing attributes
Projected handbag demand
Demand forecasting
Restock now
Low leather handbag stock
Inventory
Alert
DELIVERY ROUTE OPTIMIZATION
Order 10295
Optimizing local delivery route
1 2 3 4 5 6 7
Green clutc
Floral hand
Leather cro
Cross-body
Projected handbag demand 1 2 3 4 5 6 7
Green clutc
Floral hand
Leather cro
Cross-body
Blue
Leather
Cross-body
Recommendation:
More blue, leather,
and cross-body
styles
High-performing attributes
Demand forecastingDELIVERY ROUTE OPTIMIZATION
Order 10295
Optimizing local delivery route
Restock now
Low leather handbag stock
Inventory
Alert
Optimize order deliveries and cut costs by using
customer location and predicted traffic to plan
distributed order fulfillment
Generate more accurate demand forecasting
and pricing insights based on public and customer
data automatically
Automate stock replenishment processes and
optimize inventory management
Based on this
data, we should
stock more boot
colorways
Based on this
data, we should
stock more boot
colorways
Spring Favorites
Spring Favorites
Searching
Inventory
Jane’s Favorites
Searching
Inventory
Jane’s Favorites
Enable customers to test, model, and customize
products on the sales floor
Identify customer preferences from multiple
sources and match them to the most relevant
piece of inventory
Aggregate and analyze sentiment collected
throughout the buying process to further fine-tune the
customer journey
