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FABBRICA INTELLIGENTE
Matteo Valoriani & Antimo Musone
@MatteoValoriani
Linkedin: https://it.linkedin.com/in/matteovaloriani
GitHub: https://github.com/mvaloriani
Blog: http://fifthingenium.com/blog
Matteo Valoriani
PhD
CEO of FifthIngenium
matteo.valoriani@fifthingenium.com
@AntimoMusone
Linkedin: https://it.linkedin.com/in/antimo-musone
GitHub: https://github.com/mrcarbook
Antimo Musone
Senior Manger EY Italia
Co-Founder of FifthIngenium
antimo.musone@fifthingenium.com
antimo.musone@it.ey.com
2
HYPE CYCLE FOR EMERGING
TECHNOLOGIES, 2018
4
5
THE INDUSTRY
4.0
H1 2018
AR INDUSTRY
LANDSCAPE
6
BRAIND40 – La Soluzione7
LESS PAPER,
MORE DIGITAL
3D MANUAL
Visualizzazione
del Manuale di
istruzioni in
3D/AR
MR CHECKLIST
Checklist per
la manutenzione
in 3D/AR
MAINTENANCE
3D MODEL
VIEWER
Visualizzazione
3D
di prototipi e/o
modelli virtuali
PLANNING&
PROTOTYPING 7
8
SAFETY & WORK
ENVIRONMENT
PLANNING
RELAYOUT
Progettazione
e
organizzazion
e dello spazio
di lavoro
PLANNING&
PROTOTYPING
BRAIND40 – La Soluzione
TRAINING &
DEVELOPMENT
10
TRAINING
Training
virtuale del
personale per
l’uso delle
macchine
Pianificazione della
formazione del
personale in
conformità con gli
standard OCRA
TRAINING
SKILLS
10
11
11
1212
13
ML
14
GARTNERS VS FORRESTER
ARTIFICIAL INTELLIGENCE
NELL’INDUSTRIA 4.0
15
Collaborazione uomo-macchina
Bilanciare la necessità di automatizzare il
lavoro con quella di valorizzare le capacità
delle persone
Autoconfigurazione e
auto-ottimizzazione
Un sistema intelligente controlla la propria
produzione. Autoconfigurazione e auto-
ottimizzazione dei sistemi, fino ad una
completa auto-organizzazione
Processi intelligenti
I sistemi di assistenza aprono nuove
possibilità per una efficiente creazione del
valore nei processi
Machine Learning al servizio di
automazione e ottimizzazione
FRAMEWORKS & AUTOML SERVICES
16
ONNX
17
Installing ONNX
ONNX can be installed from binaries, Docker or source at
https://github.com/onnx/onnx
Model Zoo
It is a collection of pre-trained models for state-of-the-art models in deep learning
at https://github.com/onnx/models
The Open Neural Network eXchange (ONNX) is an open format
to represent deep learning models
Frameworks Runtimes
MIXED REALITY & MACHINE LEARNING SOLUTION
18
Model Evaluation
on Cloud
Model Evaluation
on Device
Model Evaluation on
Local Network with dedicated
hardware
MOVIDIUS
19
19
NEURAL COMPUTE STICK 2 ( NCS)
20
Mustang-V100-MX8
Computing Accelerator Card with 8 x
Movidius Myriad X MA2485 VPU, PCIe
gen2 x4 interface, RoHS
UP AI CORE X
MiniCard/mPCIe/M.2
form factor.
Second-gen Intel Neural Compute Stick
Intel® Distribution of OpenVINO™ toolkit
write once, deploy everywhere software.intel.com/openvino-toolkit
Strong Adoption + Rapidly Expanding Capability
Agnostic, Complementary to
major frameworks
Cross-platform flexibility
Supports >100 Public
Models, incl. 30+
Pretrained Models
D E E P L E A R N I N G C O M P U T E R V I S I O N
OpenCV* OpenCL™
CV
Algorithms
Model
Optimizer
Inference
Engine
CV Library
(Kernel & Graphic APIs)
Over 20 Customer Products Launched based on
Intel® Distribution of OpenVINO™ toolkit
Breadth of vision product portfolio12,000+ Developers
High Performance, high
Efficiency
Optimized media
encode/decode functions
Optimization NoticeAn open source version is available at 01.org/openvinotoolkit
21
YOLO
22
Capture Input Images
You only look once (YOLO) is an object detection system targeted for real-time processing.
Divides the input image into an S×S grid
Predicts boundary boxes and class probabilities
23
GRAZIE
@MatteoValoriani
Linkedin: https://it.linkedin.com/in/matteovaloriani
GitHub: https://github.com/mvaloriani
Blog: http://fifthingenium.com/blog
Matteo Valoriani
PhD
CEO of FifthIngenium
matteo.valoriani@fifthingenium.com
@AntimoMusone
Linkedin: https://it.linkedin.com/in/antimo-musone
GitHub: https://github.com/mrcarbook
Antimo Musone
Senior Manger EY Italia
Co-Founder of FifthIngenium
antimo.musone@fifthingenium.com
antimo.musone@it.ey.com
25
LINKS & REFERENCES
• https://mtaulty.com/2018/03/11/first-experiment-with-image-classification-on-windows-ml-from-uwp/
• https://mtaulty.com/2018/03/29/second-experiment-with-image-classification-on-windows-ml-from-uwp-on-hololens/
• https://mtaulty.com/2018/03/29/third-experiment-with-image-classification-on-windows-ml-from-uwp-on-hololens-in-unity/
• https://elbruno.com/2018/03/15/machinelearning-windows-ml-hello-world-or-how-to-create-and-uwp-app-which-use-a-local-onnx-model/
• https://github.com/mtaulty/WindowsMLExperiment
• https://blogs.msdn.microsoft.com/appconsult/2018/11/15/23535/
• https://elbruno.com/2018/04/05/windowsml-create-native-ai-apps-for-hololens-windows10/
• https://meulta.com/en/2018/05/18/experimenting-with-windows-machine-learning-and-mixed-reality/
• https://github.com/Microsoft/mixedreality-azure-samples/tree/master/Standalone-Samples/WindowsML-CustomVision-Hololens
• https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
• https://elbruno.com/2018/03/21/winml-gamingml-created-for-windows10-gamers-unity3d-and-hololens-v2-included/
• https://pjreddie.com/darknet/yolo/
• https://gallery.azure.ai/Model/Tiny-YOLOv2
• https://blogs.msdn.microsoft.com/appconsult/2018/05/23/add-a-bit-of-machine-learning-to-your-windows-application-thanks-to-winml/
• https://blogs.msdn.microsoft.com/appconsult/2018/11/06/upgrade-your-winml-application-to-the-latest-bits/
• https://github.com/Microsoft/Windows-AppConsult-Samples-MixedReality/tree/master/HoloLensWinML/Assets/_Scripts
• https://github.com/lutzroeder/netron
27
Intel Movidius™ Neural Compute Stick
Redefining the AI developer kit
28
Neural network accelerator in USB stick form factor
TensorFlow™ and Caffe frameworks supported, along with many
popular networks
Source is available for the SDK, which allows you to compile for
other platforms
Features the same Intel Movidius vision processing unit (Intel
Movidius VPU) used in drones, surveillance cameras, VR headsets,
and other low-power intelligent and autonomous products

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La Fabbrica Intelligente (Antimo Musone, EY - Matteo Valoriani, Fifth Ingenium)

  • 2. @MatteoValoriani Linkedin: https://it.linkedin.com/in/matteovaloriani GitHub: https://github.com/mvaloriani Blog: http://fifthingenium.com/blog Matteo Valoriani PhD CEO of FifthIngenium matteo.valoriani@fifthingenium.com @AntimoMusone Linkedin: https://it.linkedin.com/in/antimo-musone GitHub: https://github.com/mrcarbook Antimo Musone Senior Manger EY Italia Co-Founder of FifthIngenium antimo.musone@fifthingenium.com antimo.musone@it.ey.com 2
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  • 4. HYPE CYCLE FOR EMERGING TECHNOLOGIES, 2018 4
  • 7. BRAIND40 – La Soluzione7 LESS PAPER, MORE DIGITAL 3D MANUAL Visualizzazione del Manuale di istruzioni in 3D/AR MR CHECKLIST Checklist per la manutenzione in 3D/AR MAINTENANCE 3D MODEL VIEWER Visualizzazione 3D di prototipi e/o modelli virtuali PLANNING& PROTOTYPING 7
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  • 10. BRAIND40 – La Soluzione TRAINING & DEVELOPMENT 10 TRAINING Training virtuale del personale per l’uso delle macchine Pianificazione della formazione del personale in conformità con gli standard OCRA TRAINING SKILLS 10
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  • 12. 1212
  • 13. 13 ML
  • 15. ARTIFICIAL INTELLIGENCE NELL’INDUSTRIA 4.0 15 Collaborazione uomo-macchina Bilanciare la necessità di automatizzare il lavoro con quella di valorizzare le capacità delle persone Autoconfigurazione e auto-ottimizzazione Un sistema intelligente controlla la propria produzione. Autoconfigurazione e auto- ottimizzazione dei sistemi, fino ad una completa auto-organizzazione Processi intelligenti I sistemi di assistenza aprono nuove possibilità per una efficiente creazione del valore nei processi Machine Learning al servizio di automazione e ottimizzazione
  • 16. FRAMEWORKS & AUTOML SERVICES 16
  • 17. ONNX 17 Installing ONNX ONNX can be installed from binaries, Docker or source at https://github.com/onnx/onnx Model Zoo It is a collection of pre-trained models for state-of-the-art models in deep learning at https://github.com/onnx/models The Open Neural Network eXchange (ONNX) is an open format to represent deep learning models Frameworks Runtimes
  • 18. MIXED REALITY & MACHINE LEARNING SOLUTION 18 Model Evaluation on Cloud Model Evaluation on Device Model Evaluation on Local Network with dedicated hardware
  • 20. NEURAL COMPUTE STICK 2 ( NCS) 20 Mustang-V100-MX8 Computing Accelerator Card with 8 x Movidius Myriad X MA2485 VPU, PCIe gen2 x4 interface, RoHS UP AI CORE X MiniCard/mPCIe/M.2 form factor. Second-gen Intel Neural Compute Stick
  • 21. Intel® Distribution of OpenVINO™ toolkit write once, deploy everywhere software.intel.com/openvino-toolkit Strong Adoption + Rapidly Expanding Capability Agnostic, Complementary to major frameworks Cross-platform flexibility Supports >100 Public Models, incl. 30+ Pretrained Models D E E P L E A R N I N G C O M P U T E R V I S I O N OpenCV* OpenCL™ CV Algorithms Model Optimizer Inference Engine CV Library (Kernel & Graphic APIs) Over 20 Customer Products Launched based on Intel® Distribution of OpenVINO™ toolkit Breadth of vision product portfolio12,000+ Developers High Performance, high Efficiency Optimized media encode/decode functions Optimization NoticeAn open source version is available at 01.org/openvinotoolkit 21
  • 22. YOLO 22 Capture Input Images You only look once (YOLO) is an object detection system targeted for real-time processing. Divides the input image into an S×S grid Predicts boundary boxes and class probabilities
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  • 25. @MatteoValoriani Linkedin: https://it.linkedin.com/in/matteovaloriani GitHub: https://github.com/mvaloriani Blog: http://fifthingenium.com/blog Matteo Valoriani PhD CEO of FifthIngenium matteo.valoriani@fifthingenium.com @AntimoMusone Linkedin: https://it.linkedin.com/in/antimo-musone GitHub: https://github.com/mrcarbook Antimo Musone Senior Manger EY Italia Co-Founder of FifthIngenium antimo.musone@fifthingenium.com antimo.musone@it.ey.com 25
  • 26. LINKS & REFERENCES • https://mtaulty.com/2018/03/11/first-experiment-with-image-classification-on-windows-ml-from-uwp/ • https://mtaulty.com/2018/03/29/second-experiment-with-image-classification-on-windows-ml-from-uwp-on-hololens/ • https://mtaulty.com/2018/03/29/third-experiment-with-image-classification-on-windows-ml-from-uwp-on-hololens-in-unity/ • https://elbruno.com/2018/03/15/machinelearning-windows-ml-hello-world-or-how-to-create-and-uwp-app-which-use-a-local-onnx-model/ • https://github.com/mtaulty/WindowsMLExperiment • https://blogs.msdn.microsoft.com/appconsult/2018/11/15/23535/ • https://elbruno.com/2018/04/05/windowsml-create-native-ai-apps-for-hololens-windows10/ • https://meulta.com/en/2018/05/18/experimenting-with-windows-machine-learning-and-mixed-reality/ • https://github.com/Microsoft/mixedreality-azure-samples/tree/master/Standalone-Samples/WindowsML-CustomVision-Hololens • https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier • https://elbruno.com/2018/03/21/winml-gamingml-created-for-windows10-gamers-unity3d-and-hololens-v2-included/ • https://pjreddie.com/darknet/yolo/ • https://gallery.azure.ai/Model/Tiny-YOLOv2 • https://blogs.msdn.microsoft.com/appconsult/2018/05/23/add-a-bit-of-machine-learning-to-your-windows-application-thanks-to-winml/ • https://blogs.msdn.microsoft.com/appconsult/2018/11/06/upgrade-your-winml-application-to-the-latest-bits/ • https://github.com/Microsoft/Windows-AppConsult-Samples-MixedReality/tree/master/HoloLensWinML/Assets/_Scripts • https://github.com/lutzroeder/netron
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  • 28. Intel Movidius™ Neural Compute Stick Redefining the AI developer kit 28 Neural network accelerator in USB stick form factor TensorFlow™ and Caffe frameworks supported, along with many popular networks Source is available for the SDK, which allows you to compile for other platforms Features the same Intel Movidius vision processing unit (Intel Movidius VPU) used in drones, surveillance cameras, VR headsets, and other low-power intelligent and autonomous products