lidenskap for moderne teknologi
TRANSCRIPT
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LIDENSKAPFORMODERNETEKNOLOGI
VI LEVERER MED
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Machine learning:From hype to industrial applications
Vegard Flovik: Lead Data Scientist, Axbit
Background:Automation technician
Physicist (Master + PhD)
Computational neuroscience
Main focus:• AI/Machine learning and data analytics
About me:
AI : Beyond the hype
Machine learning in practice:
Use case examples
Advanced analytics: From
technology to business value
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https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
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McKinsey Discussion Paper
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Gartner’s Hype Cycle for Emerging Technologies
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Machine Learning Timeline
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Drivers behind Machine Learning:Connectivity & Data
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Drivers behind Machine Learning:Computing Power
2004 2020
35.86 TFLOPS
Worlds fastest Supercomputer 2002-2004
>500kW
14 TFLOPS
$1000 GPU for use in Workstation
250W
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Why now? Summary
01Datasets
Connected devices, systems and
user-generated content have
provided enormous datasets.
03Computing Platforms
Cloud computing are commoditized
enabling technologies available to
anyone
02Research & Collaboration
Decades of research, combined with
open collaboration and open-source
software reduce barriers of entry.
04Economic Effects
AI has potential to reduce labor
costs and increase quality above
human capability. Computing cost is
falling.
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Machine Learning Algorithms Extracting Information from Data
Data
Text
Measurements
Images
Video
Speech
Sensors
Machine Learning
Model
Labels
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Distribution of effort
https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#a284206f637d
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Cross-Functional Collaboration
Data Science
Building models, processing
data and extracting information
are the core of the system
Domain Knowledge
Industrial expertise is necessary to
identify goals, limitations and
possibilities in a system
Software Engineering
Software development for
products and applications is the
final step
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Getting startedWhere to begin?
• What problem are you trying to solve?Business problem
• Do you have available data? (sensors, images, video, text, …)Data availability
• From business problem to data science problem
• Start simple: Data visualizationFormulate hypothesis
• Key learnings from analyzing your data?
• Static analysis tool or software solution for deployment? Insight or product?
Getting startedEvaluating opportunities for machine learning projects
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Machine learning:Use case examples
• Quality assurance
• Sales forecasting
• Condition monitoring
• Image recognition
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• Historical data: Machine learning algorithm «learns» which process parameters affect production quality
• Predict whether the produced unit will be «OK» or «Not OK», given process conditions during production.
Pressure Humidity Temperature Flow ......... Status
2 bar 50 % 16 C 1.2 m3/min .... OK
2.1 bar 66 % 18 C 1.1 m3/min .... Not OK
1.8 bar 60 % 14 C 1.1 m3/min .... OK
: : : : .... ....
Historical data: Known status
Pressure Humidity Temperature Flow ......... Status
2.1 bar 55 % 13 C 1 m3/min .... ?
1.9 bar 69 % 20 C 0.95 m3/min .... ?
1.95 bar 57 % 15 C 1.3 m3/min .... ?
: : : : .... ....
Status
OK
OK
Not OK
....
New datapoints: Unknown status
Prediction
Classification model
Machine learning alg.
Logged process-variables
Logged process-variables
Machine learning for production: Quality assurance
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Machine learning for production: Optimization
1. Prediction:
• Historical data: Model learns connections between process-variables and production rate/efficiency etc...
2. Optimization:
• Perform a multidimensional optimization with aim of improving production.
3. Actionable output:
• Advice on recommended changes in order to optimize production, as well as estimates of expected improvement.
Control variable Pressure Temperature Flow rate Control valve Pump-RPM
Old setpoint: 1.2 bar 57C 1.6 m3/h 35% 970 o/min
New setpoint 1.1 bar 55C 1.55 m3/h 33% 970 o/min
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Machine learning for condition monitoring
Example case: Monitoring of a compressor• Changes in process variables over time (temperature, pressure, flow, vibration, etc.)
• Deviations from «normal» triggers warnings/alarms: Anomaly detection
• Planned maintenance and repair rather than uncontrolled breakdowns.
Model warns of upcoming failure several days before actual event
Bearing failure
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Machine learning for sales forecasting
Data:
• Historical sales records from 2013-2017
Challenge:
• Estimating the sales during last quarter of 2017?
Solution:
• Use machine learning to predict future sales
based on historical records.
???
Data 2013-2017
Utsnitt: Data 2017
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Machine learning for sales forecasting
Solution:
Average error of approximately 3% for predicted
sales
Value:
Useful information for planning of logistics and
distribution
• Optimize distrubution of goods to each
location
• Warehouse optimization based on demand
forecasting
Predicted vs. Real sales
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Artificial Neural Network for image recognition: “Deep Learning”
• Mathematical model that mimics how information is processed in the brain.
• Principles similar to the visual cortex of our brain: Layered network structure.
• Advanced optimization methods “train” the model to perform the desired task
Car
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Artificial Neural Network for image recognition: “Deep Learning”
Style transfer learning to produce artificial intelligence “art”
Edward Munch: «The Scream»
Romsdalen Valley (Close to Molde) Artificial Intelligence generated art
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Image recognition in healthcare
Using deep learning to detect pneumonia from X-ray images
• Accuracy > 95% : Comparable or better than human radiology experts
PneumoniaHealthy
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Image recognition for quality assurance
Image analysis of equipment, inspect characteristics such as e.g. corrosion, cracks, weld quality etc...
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Image recognition in aquaculture, fish health
Deformed fish
Lice detection/countingLice detection/counting
Automatic image analysis, extracting information on lice, decease, deformities, ++
• Allows for real-time monitoring of fish health on large fish farms
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Machine Learning & Advanced AnalyticsFrom technology to business value: Main takeaway
Lice detection/countingLice detection/counting
• Data is the fuel behind machine learning
• Collaboration between domain experts, data expertise and software engineering
is key for building business value using emerging technologies.
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The futureis here
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Prepared tutorials:
IoT Sensors
IoT Gateway Cloud Applications
Tutorials have been prepared for a two selected example cases:
1) «Image classification»: Building a «deep learning» model using Keras/Tensorflow to classify images oftraffic signs
2) «Condition monitoring»: Build machine learning models to predict «health state» of equipment. (This tutorial is slightly more technical)
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• Image classification vs. Object detection
• Object detection more complex task, and requires more data preparation
• Example case: Build image classification model using Keras/Tensorflow
Deep learning for image classification
Computer: Image = Numbers
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• Use pre-trained models from Google, trained on millions of images
• Use «basic features» learned from these models, and adapt them to our own specific task: Transfer learning
Deep learning for image classification
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• Example case: Classify traffic signs
• Prepared training set: < 200 images pr. class
Deep learning for image classification
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• Common problem: Few training images
• Solution: Image augmentation!
• Artificially increase size of dataset.Flip/rotate/zoom images etc.
• Improves generalization of image classifiermodels
Deep learning for image classificationOriginal
Augmentation
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• The power of transfer learning!
• Even with few training images of low quality:Classify correct traffic sign with 99% accuracy
Deep learning for image classification
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Machine learning for condition monitoring:
Rotation speed: 2000 RPM
Accelerometers
Radial load: 6000 lbs
• Sensors used: Accelerometers mounted on each bearing
• Failures accured after exceeding designed life time of the bearing (more than 100 million revolutions)
• Challenge: Detect bearing failure before breakdown
• Deviations from «normal» triggers warnings/alarms: Anomaly detection
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Machine learning for condition monitoring:• Sensors used: Accelerometers mounted on each bearing
• Failures accured after exceeding designed life time of the bearing (more than 100 million revolutions)
• Challenge: Detect bearing failure before breakdown
• Deviations from «normal» triggers warnings/alarms: Anomaly detection
• Here: Anomaly detection model generates warning3 days ahead of actual bearing failure
Bearing failureWarning3 daysAnomaly score
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First: Brief introduction to Google Colaboratory
IoT Sensors
IoT Gateway Cloud Applications
Then: Let`s get to the fun part and start building models!
Traffic sign Classification: http://bit.ly/2SN3eIO
Anomaly detection: http://bit.ly/2SF9q5j