Machine Learning for computer vision, logistics and streaming analytics - SAS Italy

Copyright © SAS Institute Inc. All rights reserved.
Point of View: Machine Learning for...
Computer Vision, Logistics and Streaming Analytics
Cristina Conti, Artificial Intelligence Manager EMEA at SAS
Cristina.Conti@sas.com
@mcristinac

Company Confidential – For Internal Use Only
Largest privately owned business analytics company
Key Investment Areas
Analytics for EveryoneAnalytics EverywhereArtificial Intelligence

Employees: 7,400+
Offices: 52
Customers: 10,990
Employees: 500+
Offices: 17
Customers: 852
Employees: 2,500+
Offices: 36
Customers: 3,919
Employees: 3,600+
Offices: 60
Customers: 7,998

Forces Shaping Analytics
Cloud enabled
analytics
Everyone wants to
be a data scientist
Machine learning
& Artificial
Intelligence
Analytics of ThingsChanging data
landscape
Analytics embraces
open

Understand
Context
Learn
Patterns
Recognize
Objects
Artificial Intelligence
is the science of training systems to
emulate human tasks through
Learning and Automation

What Artificial Intelligence
is NOT
• A crystal ball
• Intelligent, autonomous robots
• Self-creating/correcting models
• “Out-of-the-box” everything
• Perfect model prediction
• Artificial general intelligence

Chatbots
Smart Q&A and Search System
Deep Learning Algorithms
Roboadvisor
Natural Language Processing
Self Learning Models
Reinforcement Algorithms
Machine Learning
Facial recognition Conversational system
What falls under AI?
CAPABILITIES
CAPABILITIES
APPLICATIONS
APPLICATIONS

Artificial Intelligence at SAS
Machine Learning Natural Language Computer Vision Forecasting and
Optimization
Visualization Decision Support Deployment
CoreAddedValue
Data
Management

A Simple Example: Understanding What We See
Identify things in a
picture or video.
Relate items to
each other.
Evaluate and predict
future performance.

Top AI use cases we’re working on
53%
fewer customer
complaints1
Improved
liver & brain
tumor diagnosis
with AI & analytics
2.7x
increase in client
purchase rates4
90%
accuracy for ID of
wildlife using tracks5
Continuous learning
and Insight from
clients to improve
design & quality3
Rogers
Telecom
VUmc
Healthcare
WildTrack
Data for Good
Honda
Manufacturing
Daiwa
Financial

Manufacturing
Defect
Detection Logistics
Workplace
Safety
Artificial
Intelligence

Workplace Safety - Image detection on IoT device (@ the Edge)

Workplace
Safety
practical
example

Defect Detection - High Volume Classification using Deep Learning
Defect
Classification

Logistics – Sort Parcel (conveyable or not) / Detect Tampering / Damage
So many parcels.. What if I say one of
them have something dangerous.
Would you know which?

How does this all work behind the scenes?
Machine Learning
Machine Learning is a branch
of artificial intelligence based
on the idea that systems can
learn from data, identify
patterns and make decisions
with minimal human
intervention
Deep Learning
Deep Learning is a type of
machine learning, based on
neural network algorithms,
used in recognizing speech,
identifying objects in images
and more
Computer Vision
Computer Vision, often
called image processing, is
an application of artificial
intelligence which uses
specific image processing
capabilities to detect colors,
borders, lines and ultimately,
combined with deep
learning, to detect objects

The elephant in the room:
how does all this execute?

Deploying artificial intelligence
At the edge as part of the connected world
Stream Data
Prep for AI
Text, Data, Images
Train & Score In-
Stream
Machine Learning
Train At-Rest &
Score In-Stream
Machine/Deep Learning,
Text Analytics

IoT Data
Intelligent Data
Management
Streaming Analytics
Execution
Deploy
Automated
Response
Machine Learning and Artificial Intelligence in IOT
DeployETL
Data
Alerts / Reports /
Decisioning
Data Storage

GLOBAL
OPERATIONS
I can see my
production line status
and recommend
adjustments to better
manage operational
cost.
I know when to deploy
the right resources for
predictive
maintenance to
minimize equipment
failures and reduce
service cost.
I gain insight into
usage patterns from
multiple customers
and track equipment
deterioration,
enabling me to
reengineer products
for better
performance.
MANUFACTURING PLANT
Aggregate product data,
customer sentiment, and
other third-party syndicated
data to identify and correct
quality issues.
Manage equipment remotely, using
temperature limits and other settings to
conserve energy and reduce costs.
Monitor production flow in near-real time
to eliminate waste and unnecessary work
in process inventory.
GLOBAL FACILITY INSIGHT
Implement condition-
based maintenance
alerts to eliminate
machine down-time and
increase throughput.
THIRD-PARTY LOGISTICS
Provide cross-channel visibility into inventories to
optimize supply and reduce shared costs in the
value chain.
CUSTOMER SITE
Transmits operational information to the
partner (e.g. OEM) and to field service
engineers for remote process automation
and optimization.
Management
R&D
Field Service
In the context of plants and logistics…

HydroPower Plant
• 3 Power Groups
• 5,50 MW
• 3.000 TAGS
The installation of a component directly on the device at-
the-edge allows the execution of AI on the device itself

Interpretability
Transition
Open
Governance
Automation and Intelligence
Some ChallengesAI FacesToday

What is behind the scenes?
• Picture of brain or crystal ball? Fancy named Stetson? Watson? Archimedes?
Terminator? Musk? Zuckenberg? Google?
• Essentially each application has model behind. An algorithm.
• An algorithm that is suitable for that perticular problem. It can solve that problem. The
same algortighm that plays GO does not classify fraudulant claims or read a single
article.
• Sure you can utilize the same algorighm or arhchitecture to a new SIMILAIR problem. But you
need to retrain. You will most likely need to adjust thigns around the algorithm.
• Combining these algorighms is possible but at the moment it is nowhere near General
AI – AI that learns like humans. AI that learns different problems.
• Quote ”It just one model” Netflix GO document
• Quote Siitä artikkelista – fuss blaa blaa
• Quote Jim (or Oliver) ”It ain’t nothing the machine learned. It’s a model”
“Despite all the hype and
excitement about AI, it’s still
extremely limited today relative to
what human intelligence is.”
Andrew Ng,
Adjunct Professor at Stanford University

What is behind the scenes?
• Picture of brain or crystal ball? Fancy named Stetson? Watson? Archimedes?
Terminator? Musk? Zuckenberg? Google?
• Essentially each application has model behind. An algorithm.
• An algorithm that is suitable for that perticular problem. It can solve that problem. The
same algortighm that plays GO does not classify fraudulant claims or read a single
article.
• Sure you can utilize the same algorighm or arhchitecture to a new SIMILAIR problem. But you
need to retrain. You will most likely need to adjust thigns around the algorithm.
• Combining these algorighms is possible but at the moment it is nowhere near General
AI – AI that learns like humans. AI that learns different problems.
• Quote ”It just one model” Netflix GO document
• Quote Siitä artikkelista – fuss blaa blaa
• Quote Jim (or Oliver) ”It ain’t nothing the machine learned. It’s a model”
“It’s a model, it ain’t
nothing the machine
learned”
Jim Goodnight,
CEO of SAS

sas.com
Grazie per l’attenzione
cristina.conti@sas.com
@mcristinac

Machine Learning for computer vision, logistics and streaming analytics - SAS Italy

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