The main types of machine learning and their practical application

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Illarion Khlestov (Research Engineer at Ring Ukraine). Fan of PyTorch. Interested in CNC devices and off-road cars. Topic: The main types of machine learning and their practical application. Recently, the term of "machine learning" is used very often. At the same time, it is not always obvious what can be done with its help, and what is not. We will analyze various business problems and see through what methods they are being solved, as well as tasks that are not solved or are difficult to solve.

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

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
- ...

Word-to-vec example
You may try it online:
http://projector.tensorflow.org/

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

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

Grounding
- Safety
- Traffic improvements
- Costs reducing
- Cargo transportation

Blockers
- Legal issues
- Opaque decision system
- People
- Privacy
- Other...

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

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.

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