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How ML technology helps
solving specific business
problems
Borys Pratsiuk
CTO, Pawa
Who am I?
C, embedded
2004
2006
2013
2012
Ph.D
2019
2009
2007
b_pratsiuk
bp@pawa.ai
Borys Pratsiuk,
Ph.D.
2019 - 2021
CTO
2021 …
CTO
What is AI?
ML basics
Case studies
Q&A
Agenda
IT evolution
Scientists
Brilliant people
60-th 80-th 2000
Systems
(IBM, Microsoft,
etc)
Application
(Web, Mobile, etc)
Systems
2010
Eco-System
- Outsourcing
-Freelance
PM / BA / QA / …
Application
now
Systems
Data Science
Brilliant people
Machine learning
Reinforcement learning
Unsupervised learning Supervised learning
Classification
Regression
Types of machine learning
Linear regression
Case: Predict house prices
Predict your house by similar houses Use a linear regression model
Model
Cost function
Unsupervised learning
Unsupervised machine learning
is the machine learning task of
inferring a function to describe hidden
structure from "unlabeled" data.
Clusterization! No apriori information
about which classes there are in the
data and how many classes.
AI cases
What is scientific research?
Scientific research is a fundamental background to test
any revolutionary business ideas
Zhang, W., Yu, Q., Siddiquie, B., Divakaran, A., & Sawhney,
H. (2015). "Snap-n-Eat": food recognition and nutrition
estimation on a smartphone. Journal of Diabetes Science and
Technology, 9(3), 525-533. doi: 10.1177/1932296815582222
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224860/
Parasite detection
CHALLENGE:
There are around 4500 salmon farms in Norway. Every week each
farm should submit ecology report to the government, otherwise
should pay high penalty. Sometimes it’s not possible due to weather
and limited access of biologist to the cages.
SOLUTION:
• Underwater capsula with 2 cameras for 24/7 monitoring and for
6k resolution images.
• Image processing algorithm for fish detection and parasites
detection on early stages.
• Infrastructure for centralized processing and analytics.
BUSINESS VALUE:
• 10% cost reduction for report generation due to data collection
optimization.
• up to 25% reduction of pesticide use
due to parasite detection on early stages.
Багато апельсин
Порахуте апельсини
98
AI for fruits detection
CHALLENGE:
Client needed to quantify the volume of ripe fruits in the
garden using the automatic collection of images. They
required the scalable solution showing the applicability of DL-
inspired approach.
Accurately locate the fruits in the images with different scales,
perspectives, angles etc. and provide robust scalable
solution.
SOLUTION:
• Developed object detection algorithm based on Faster R-
CNN architecture
• Built AI system that identifies fruit in an image in a nearly
human-level performance
BUSINESS VALUE:
• More accurate counting 83% versus 75% (human
performance)
• AI framework able to automate counting
on 1.6 million of acres orange fields.
AI for car claim cost evaluation
CHALLENGE:
The Client needed to automatically evaluate the car repair
costs using DL-inspired approach and eliminate manual
evaluation errors. Each claim contained images with
different types of damages, different perspectives, scale,
amount of dirt, sun glare etc.
SOLUTION:
Developed system classifies damaged parts of a сar,
segments the damages in the images and automatically
estimates a car claim cost.
BUSINESS VALUE:
Reduce up to 30% costs associated with needs to send
commissary to the car accident with not significant
damages
Large production deployment of AI & IoT Applications
CHALLENGE:
To increase efficiency, develop new services, and spread a
digital culture across the organization, Enel is executing an
enterprise-wide digitalization strategy. Central to achieving the
Fortune 100 company’s goals is the large-scale deployment of
the C3 AI Suite and applications. Enel operates the world’s
largest enterprise IoT system with 20 million smart meters
across Italy and Spain.
SOLUTION:
Analyzed 50Tb unified federated cloud image with 25+
connected data sources. Time line of development 5+
years
BUSINESS VALUE:
• 2X performance Increase in identifying Unbilled Energy
• 2.5B USD cumulative economic benefit target from
digitalization by 2021
Retail automation
Conversations in Retail – Smart assistant
CHALLENGE:
The Client ask for innovation project to find a way how to
leverage from conversation commerce in retail.
SOLUTION:
Smart IoT shell with directed microphone and direct
speaker covered a selected zone. Designed smart
assistant that convert speech to text and advice customer
in his need. Additional camera help to identify products and
find similar in a database.
BUSINESS VALUE:
• Increase client satisfaction in pilot shop on 7%
• Enable buying experience for people with disabilities.
Blind people could be guided buy sound and their
movements controlled with a camera.
• First step in Amazon competition strategy.
Drones inspection of harbors
https://news.develop
er.nvidia.com/ai-
drones-help-inspect-
industrial-equipment/
Thanks for watching

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Borys Pratsiuk: How ML technology helps solving specific business problems

  • 1. How ML technology helps solving specific business problems Borys Pratsiuk CTO, Pawa
  • 2. Who am I? C, embedded 2004 2006 2013 2012 Ph.D 2019 2009 2007 b_pratsiuk bp@pawa.ai Borys Pratsiuk, Ph.D. 2019 - 2021 CTO 2021 … CTO
  • 3. What is AI? ML basics Case studies Q&A Agenda
  • 4. IT evolution Scientists Brilliant people 60-th 80-th 2000 Systems (IBM, Microsoft, etc) Application (Web, Mobile, etc) Systems 2010 Eco-System - Outsourcing -Freelance PM / BA / QA / … Application now Systems Data Science Brilliant people
  • 5. Machine learning Reinforcement learning Unsupervised learning Supervised learning Classification Regression Types of machine learning
  • 7. Case: Predict house prices Predict your house by similar houses Use a linear regression model
  • 10. Unsupervised learning Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data. Clusterization! No apriori information about which classes there are in the data and how many classes. AI cases
  • 11. What is scientific research? Scientific research is a fundamental background to test any revolutionary business ideas Zhang, W., Yu, Q., Siddiquie, B., Divakaran, A., & Sawhney, H. (2015). "Snap-n-Eat": food recognition and nutrition estimation on a smartphone. Journal of Diabetes Science and Technology, 9(3), 525-533. doi: 10.1177/1932296815582222 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224860/
  • 12.
  • 13.
  • 14. Parasite detection CHALLENGE: There are around 4500 salmon farms in Norway. Every week each farm should submit ecology report to the government, otherwise should pay high penalty. Sometimes it’s not possible due to weather and limited access of biologist to the cages. SOLUTION: • Underwater capsula with 2 cameras for 24/7 monitoring and for 6k resolution images. • Image processing algorithm for fish detection and parasites detection on early stages. • Infrastructure for centralized processing and analytics. BUSINESS VALUE: • 10% cost reduction for report generation due to data collection optimization. • up to 25% reduction of pesticide use due to parasite detection on early stages.
  • 16. AI for fruits detection CHALLENGE: Client needed to quantify the volume of ripe fruits in the garden using the automatic collection of images. They required the scalable solution showing the applicability of DL- inspired approach. Accurately locate the fruits in the images with different scales, perspectives, angles etc. and provide robust scalable solution. SOLUTION: • Developed object detection algorithm based on Faster R- CNN architecture • Built AI system that identifies fruit in an image in a nearly human-level performance BUSINESS VALUE: • More accurate counting 83% versus 75% (human performance) • AI framework able to automate counting on 1.6 million of acres orange fields.
  • 17. AI for car claim cost evaluation CHALLENGE: The Client needed to automatically evaluate the car repair costs using DL-inspired approach and eliminate manual evaluation errors. Each claim contained images with different types of damages, different perspectives, scale, amount of dirt, sun glare etc. SOLUTION: Developed system classifies damaged parts of a сar, segments the damages in the images and automatically estimates a car claim cost. BUSINESS VALUE: Reduce up to 30% costs associated with needs to send commissary to the car accident with not significant damages
  • 18. Large production deployment of AI & IoT Applications CHALLENGE: To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain. SOLUTION: Analyzed 50Tb unified federated cloud image with 25+ connected data sources. Time line of development 5+ years BUSINESS VALUE: • 2X performance Increase in identifying Unbilled Energy • 2.5B USD cumulative economic benefit target from digitalization by 2021
  • 20. Conversations in Retail – Smart assistant CHALLENGE: The Client ask for innovation project to find a way how to leverage from conversation commerce in retail. SOLUTION: Smart IoT shell with directed microphone and direct speaker covered a selected zone. Designed smart assistant that convert speech to text and advice customer in his need. Additional camera help to identify products and find similar in a database. BUSINESS VALUE: • Increase client satisfaction in pilot shop on 7% • Enable buying experience for people with disabilities. Blind people could be guided buy sound and their movements controlled with a camera. • First step in Amazon competition strategy.
  • 21. Drones inspection of harbors https://news.develop er.nvidia.com/ai- drones-help-inspect- industrial-equipment/