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June 9th, 2018
A 21st century lunch bell
Thomas Gölles & Stephan Bisser
Thomas Gölles
Microsoft MVP (O365)
http://www.modernworkplacesolutions.rocks
@thomyg
Head of Modern Workplace Solutions
SOLVION
Stephan Bisser
Microsoft MVP (AI)
https://www.cloudguy.pro
@cloudguy_pro
#AskCloudguy
SOLVION
Gold sponsors ______________
Silver sponsors
Bronze sponsors
Collaborate
Platinum sponsor
Agenda
1 Background 2 “Bussiness Problem”
3 Tech geek out 4 Demo
Our Office in Graz, Austria
Lunchtime at our office (around 10:45)
Yummy
Possible solutions
 Create a recurrent event in Outlook
 “Ping” a colleague everyday
 Stand up and have a look
 Use technology to solve the problem
Penny: Well, you know, it’s the Cheesecake Factory. People order
cheesecake, and I bring it to them.
Leonard: So, you sort of act as a Carbohydrate Delivery System.
Penny: Yeah, call it whatever you want, I get minimum wage.
MOCADESYMO was born
Mobile
Monitor
Hi, I‘m MOCADESYMO – your 21st century lunchbell
MOCADESYMO‘s view
Raspberry Pi + camera module
Parts
Runs Raspbian Jessie
Lan/Wlan connection
raspistill for taking images
mogrify for cropping the image
Azure CLI 2.0
curl
Pi Setup/Tools
Shell script
Azure components
Blob
Storage
Azure
Function
Custom
Vision API
Storage
Tables
Blob Storage
Azure Function
• Min. Visual Studio 2017
Update 2
• Usage of custom library
• Including PnP library
• Gets triggered by the
curl request from the Pi
• Takes the image from
the blob storage
Azure Function
• Calls Custom Vision API
• Gets back the prediction
result from http request
• Stores data to log and
state tables
• Informs users in Teams
by calling a connector if
prediction is above a
certain threshold
• uploads pictures to a
SharePoint Portal
Custom Vision API
• Project with sample data
• Start with at least 30
images
• Train the models
• Use live data to learn in
iterations
• Be prepared to
understand prediction
results
• Getting above 95% with
~450 images now
1. Raspberry PI camera takes photo of food truck, after it has arrived
2. Paspberry PI uploads the taken image into an Azure Blob Storage
3. Raspberry PI triggers an Azure Function after the image has been uploaded successfully
4. Azure Function calls the Custom Vision API in order to check wether the food truck is present or not
5. If Custom Vision API responds with a high probability that the truck has arrived, the Azure Function sends a notification to Teams
6. Users can ask the Lunchbell Bot in Teams wether the food truck has arrived or not or if it is still present
7. Azure Bot Service checks the entries in an Azure Table Storage where the arrival and departure time is stored
The future: Conversations
• Natural language between people
and technology
• Conversational canvas
• Bots and agents
2000s: Mobile
• Social
• User download apps
from App Stores
1990s: Internet
• Search
• User “visits” websites
1980s: PC
• Desktop
The evolution of computers and IT
Bots 101
“a computer program designed to have a
conversation with a human being, especially over
the internet” *
* https://dictionary.cambridge.org/dictionary/english/chatbot
Bots 101
 It’s one thing: it’s an app that performs an automated task
 It solves the user’s needs in the quickest/easiest way compared to any other
option... like an app, or a website
 What makes a bot great:
• It is not how much AI it has
• It is not how much natural language it offers
• It is not whether it uses voice or not
Bots 101
{ Your Code }
REST Endpoint
Microsoft Bot Framework
Modelling your conversationFlexibility
Effort to implement
Dialogs
FormFlow
QnA
Bespoke
FAQs,
command & control
Data capture,
“Web forms” scenarios
Multi layered conversations
Roll your own
state management etc.
Conversational mechanisms
• Text, with optional media attachments
• Traditional chat, can contain media attachments
(e.g., image, video, audio, file)
• Input prompts
• Suggested actions: Buttons, numbered items in a list, etc.
• Rich cards, rendered as a list or carousel
• Images, buttons, audio, animations, video, user sign-in, etc.
• Hero | Audio | Animation | Thumbnail | Receipt | Sign In |
Video | Adaptive | Purchase
• Speech
• Text-based chat using Speech Recognition & Synthesis (TTS)
Continuous Improvement
• Instrumentation provided
by App Insights; added by
default in Bot Service
• Extend instrumentation
through AI SDK
• If you are not building
bots that will actually be
used, then you are not
building bots
Custom Vision Service
 Build a custom image classifier in 10
minutes or less
 Diversity of images is key: angles,
lighting, backgrounds
 Not for object detection and is robust
to subtle differences
 Handles tuning model for edge cases
(like misses)
Custom Vision Service
Normal State98,53 % True100 % True99,9 % True
Yes
Similar
image
Query
image
 Researchers took a traditional machine learning approach
• Example: HoG Detectors
- Histogram of oriented
gradients (HoG) features
- Sliding window detector
- SVM Classifier
- Very fast OpenCV
implementation (<100ms)
Deep Neural Network for Computer Vision
cat? YES
dog? NO
car? NO
Convolutional Layers
Fully
Connected
Layers
Complex Objects
& Scenes
(people, animals,
cars, beach
scene, etc.)
Image
Low-Level Features
(lines, edges,
color fields, etc.)
High-Level Features
(corners, contours,
simple shapes)
Object Parts
(wheels, faces,
windows, etc.)
Language Understanding
[ $LunchBell.Object ] [ $LunchBell.Operation ]
„Has the food truck arrived?“
www.luis.ai
„Has the food truck arrived?“
Intent = CheckArrivalState
Microsoft Teams
SharePoint Online
Demo Case
Please, fill your SP & Office 365
Saturday Madrid passport if you
want to participate.
You can win one of these gifts:
Raffle
10
9
8
Odor Odor@winterfell.com
Please, fill your SP & Office 365
Saturday Madrid passport if you
want to participate.
You can win one of these gifts:
Office 365 for IT Pros
Get a discount of $10 buying the
book here:
https://gumroad.com/l/O365IT/
spsspain
Gold sponsors ______________
Silver sponsors
Bronze sponsors
Collaborate
Platinum sponsor

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SharePoint Saturday Madrid 2018 21st century lunchbell

  • 1. June 9th, 2018 A 21st century lunch bell Thomas Gölles & Stephan Bisser
  • 2. Thomas Gölles Microsoft MVP (O365) http://www.modernworkplacesolutions.rocks @thomyg Head of Modern Workplace Solutions SOLVION Stephan Bisser Microsoft MVP (AI) https://www.cloudguy.pro @cloudguy_pro #AskCloudguy SOLVION
  • 3. Gold sponsors ______________ Silver sponsors Bronze sponsors Collaborate Platinum sponsor
  • 4. Agenda 1 Background 2 “Bussiness Problem” 3 Tech geek out 4 Demo
  • 5.
  • 6. Our Office in Graz, Austria
  • 7.
  • 8. Lunchtime at our office (around 10:45)
  • 10.
  • 11.
  • 12. Possible solutions  Create a recurrent event in Outlook  “Ping” a colleague everyday  Stand up and have a look  Use technology to solve the problem
  • 13.
  • 14. Penny: Well, you know, it’s the Cheesecake Factory. People order cheesecake, and I bring it to them. Leonard: So, you sort of act as a Carbohydrate Delivery System. Penny: Yeah, call it whatever you want, I get minimum wage. MOCADESYMO was born Mobile Monitor
  • 15.
  • 16. Hi, I‘m MOCADESYMO – your 21st century lunchbell
  • 18.
  • 19. Raspberry Pi + camera module
  • 20. Parts
  • 21. Runs Raspbian Jessie Lan/Wlan connection raspistill for taking images mogrify for cropping the image Azure CLI 2.0 curl Pi Setup/Tools
  • 25. Azure Function • Min. Visual Studio 2017 Update 2 • Usage of custom library • Including PnP library • Gets triggered by the curl request from the Pi • Takes the image from the blob storage
  • 26. Azure Function • Calls Custom Vision API • Gets back the prediction result from http request • Stores data to log and state tables • Informs users in Teams by calling a connector if prediction is above a certain threshold • uploads pictures to a SharePoint Portal
  • 27. Custom Vision API • Project with sample data • Start with at least 30 images • Train the models • Use live data to learn in iterations • Be prepared to understand prediction results • Getting above 95% with ~450 images now
  • 28. 1. Raspberry PI camera takes photo of food truck, after it has arrived 2. Paspberry PI uploads the taken image into an Azure Blob Storage 3. Raspberry PI triggers an Azure Function after the image has been uploaded successfully 4. Azure Function calls the Custom Vision API in order to check wether the food truck is present or not 5. If Custom Vision API responds with a high probability that the truck has arrived, the Azure Function sends a notification to Teams 6. Users can ask the Lunchbell Bot in Teams wether the food truck has arrived or not or if it is still present 7. Azure Bot Service checks the entries in an Azure Table Storage where the arrival and departure time is stored
  • 29.
  • 30. The future: Conversations • Natural language between people and technology • Conversational canvas • Bots and agents 2000s: Mobile • Social • User download apps from App Stores 1990s: Internet • Search • User “visits” websites 1980s: PC • Desktop The evolution of computers and IT
  • 31. Bots 101 “a computer program designed to have a conversation with a human being, especially over the internet” * * https://dictionary.cambridge.org/dictionary/english/chatbot
  • 32. Bots 101  It’s one thing: it’s an app that performs an automated task  It solves the user’s needs in the quickest/easiest way compared to any other option... like an app, or a website  What makes a bot great: • It is not how much AI it has • It is not how much natural language it offers • It is not whether it uses voice or not
  • 33. Bots 101 { Your Code } REST Endpoint
  • 35. Modelling your conversationFlexibility Effort to implement Dialogs FormFlow QnA Bespoke FAQs, command & control Data capture, “Web forms” scenarios Multi layered conversations Roll your own state management etc.
  • 36. Conversational mechanisms • Text, with optional media attachments • Traditional chat, can contain media attachments (e.g., image, video, audio, file) • Input prompts • Suggested actions: Buttons, numbered items in a list, etc. • Rich cards, rendered as a list or carousel • Images, buttons, audio, animations, video, user sign-in, etc. • Hero | Audio | Animation | Thumbnail | Receipt | Sign In | Video | Adaptive | Purchase • Speech • Text-based chat using Speech Recognition & Synthesis (TTS)
  • 37. Continuous Improvement • Instrumentation provided by App Insights; added by default in Bot Service • Extend instrumentation through AI SDK • If you are not building bots that will actually be used, then you are not building bots
  • 38.
  • 39. Custom Vision Service  Build a custom image classifier in 10 minutes or less  Diversity of images is key: angles, lighting, backgrounds  Not for object detection and is robust to subtle differences  Handles tuning model for edge cases (like misses)
  • 40. Custom Vision Service Normal State98,53 % True100 % True99,9 % True
  • 41.
  • 43.  Researchers took a traditional machine learning approach • Example: HoG Detectors - Histogram of oriented gradients (HoG) features - Sliding window detector - SVM Classifier - Very fast OpenCV implementation (<100ms)
  • 44. Deep Neural Network for Computer Vision cat? YES dog? NO car? NO Convolutional Layers Fully Connected Layers Complex Objects & Scenes (people, animals, cars, beach scene, etc.) Image Low-Level Features (lines, edges, color fields, etc.) High-Level Features (corners, contours, simple shapes) Object Parts (wheels, faces, windows, etc.)
  • 45. Language Understanding [ $LunchBell.Object ] [ $LunchBell.Operation ] „Has the food truck arrived?“ www.luis.ai „Has the food truck arrived?“ Intent = CheckArrivalState
  • 46.
  • 47.
  • 50.
  • 51.
  • 53.
  • 54. Please, fill your SP & Office 365 Saturday Madrid passport if you want to participate. You can win one of these gifts: Raffle 10 9 8 Odor Odor@winterfell.com Please, fill your SP & Office 365 Saturday Madrid passport if you want to participate. You can win one of these gifts:
  • 55. Office 365 for IT Pros Get a discount of $10 buying the book here: https://gumroad.com/l/O365IT/ spsspain
  • 56. Gold sponsors ______________ Silver sponsors Bronze sponsors Collaborate Platinum sponsor

Editor's Notes

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