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Main machine learning systems and their business usage
About me
Illarion Khlestov
Researcher at Ring Ukraine, computer vision department
GitHub: https://github.com/ikhlestov
Blog: https://medium.com/@illarionkhlestov
Facebook: https://www.facebook.com/i.khlestov
- Machine learning is just a tool.
- The tool that may help you and your business.
- ML may not be easy, but at least it’s possible.
- It’s interesting.
- And in any case ML is very popular.
Main ideas
Agenda
- Industry overview
- Closer look at:
- Chatbots
- Healthcare
- Autonomous driving
What is machine learning?
What is machine learning?
Industry Overview. What is the reason of ML?
- ML market - 1.41 Billion in the end of 2017.
- Expected on 2022 - 8.81 Billion (report)
- Company engaged:
- Toyota, VAG group, Daimler AG
- Walmart, Target, Amazon
- AIG, PayPal, Zappos
- ...
- How?
- Personalize
- Automate
- Predict
- Improve
- ...
Chatbots
The easiest bot
A little bit better example
ML solution
Available tools and approaches
Words to vectors
Word-to-vec example
You may try it online:
http://projector.tensorflow.org/
DialogFlow
Business Values
- Reduced costs
- Customers happiness
- Response rate
- 24/7 availability
- Scalability
- Additional training
What’s next? VoiceBots?
- Customers intention understanding
- Complicated actions
- Speech recognition
- Voice generation
Healthcare
What does exist now?
- Digital medical records
- Disease identification/Diagnosis
- Drugs discovery/Manufacturing
- Epidemic outbreak prediction
What can be done?
- Wearable continuous monitoring devices
- Single database
- Personalized medicine
- Automatic treatment or recommendation
- Automated handling of medical records
- Treatment of disabled people
- People modifications
How is it possible?
- Objects classification
- Objects detection
- Prediction systems
- Speech and text recognition
Business values
- Increased life expectancy
- Reduction of insurance payments
- Improvements in the one of the most huge markets
Potential problems
- Data availability
- Personal data handling and
protecting
- False positive or false negative
results
- Certification, medical clearance
- Bureaucracy and conservatism
Autonomous Driving
Current state of the field
Grounding
- Safety
- Traffic improvements
- Costs reducing
- Cargo transportation
Blockers
- Legal issues
- Opaque decision system
- People
- Privacy
- Other...
Adversarial Attack
Adversarial Attack
Adversarial Attack
Moral issues: what should car do?http://moralmachine.mit.edu/
Job losses
- Approximate 3.5 million of truck drivers
- Abt .5 million of taxi drivers
- Support staff
What is mainly used
- Objects detection
- Segmentation
- Tracking
- Reinforcement learning
- Usual SGD
- SLAM
SLAM - Simultaneous localization and mapping
Existed resources
- Udacity Self Driving Cars nanodegree
- Open Source Self Driving Car Initiative
- MIT 6.S094: Deep Learning for Self-Driving Cars
- Autonomous Driving CookBook
- Nvidia end-to-end training paper
General Overview
Are you need it?
- What benefit?
- What are implementation costs?
Take a look at the possible blockers:
- Is such task implementable with the help of ML at all?
- Legal issues
- Datasets existence
First steps:
- Consult with domain expert
- Define clear requirements(minimum and maximum)
- Speed
- Accuracy
- What should be considered as "done"?
- Check available open sourced solutions
Later:
- Measure real profit
- Decide, should your solution be updated or not
Thank you!Questions?
GitHub: https://github.com/ikhlestov
Blog: https://medium.com/@illarionkhlestov
Facebook: https://www.facebook.com/i.khlestov
UDS Community: https://www.facebook.com/groups/udsclub/
Bonus: another fields with ML
- Recommendation systems.
- Market analysis. Market prediction and targeting.
- Security systems.
- Content adjusting.
- Agriculture usage. Diseases detection, harvest prediction…
- Generative models. Routes planning, development and arts.
- Physical world modelling.
- Virtual Reality.
