SlideShare a Scribd company logo
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.

More Related Content

Similar to The main types of machine learning and their practical application

MLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseMLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
BigML, Inc
 
Industrial revolution 4.0
Industrial revolution 4.0 Industrial revolution 4.0
Industrial revolution 4.0
Aditya Randika
 
An informed definition
An informed definitionAn informed definition
An informed definition
Anju Vallabhaneni
 
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Ed Fernandez
 
5 examples that show how machine learning is changing modern advertising indu...
5 examples that show how machine learning is changing modern advertising indu...5 examples that show how machine learning is changing modern advertising indu...
5 examples that show how machine learning is changing modern advertising indu...
USM Systems
 
Slave to the Algorithm 2016
Slave to the Algorithm  2016 Slave to the Algorithm  2016
Slave to the Algorithm 2016
Lilian Edwards
 
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
Steve Omohundro
 
XMANAI Technical Project Overview
XMANAI Technical Project OverviewXMANAI Technical Project Overview
XMANAI Technical Project Overview
XMANAI
 
Generative AI in Transportation for Connected Future Transport System July 20...
Generative AI in Transportation for Connected Future Transport System July 20...Generative AI in Transportation for Connected Future Transport System July 20...
Generative AI in Transportation for Connected Future Transport System July 20...
Sudha Jamthe
 
Introduction to AI & ML
Introduction to AI & MLIntroduction to AI & ML
Introduction to AI & ML
Jai Porje
 
Slave to the Algo-Rhythms?
Slave to the Algo-Rhythms?Slave to the Algo-Rhythms?
Slave to the Algo-Rhythms?
Lilian Edwards
 
Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1
Harsh Khatke
 
Jakarta presentation
Jakarta presentationJakarta presentation
Jakarta presentation
Gil Brown
 
Taking advantageofai july2018
Taking advantageofai july2018Taking advantageofai july2018
Taking advantageofai july2018
Yves Caseau
 
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
akira-ai
 
Technology in Cars PowerPoint-Team 5
Technology in Cars PowerPoint-Team 5Technology in Cars PowerPoint-Team 5
Technology in Cars PowerPoint-Team 5
ajohns19
 
The Ultimate Guide to Machine Learning (ML)
The Ultimate Guide to Machine Learning (ML)The Ultimate Guide to Machine Learning (ML)
The Ultimate Guide to Machine Learning (ML)
RR IT Zone
 
Overview of Next Generation IT trends
Overview of Next Generation IT trendsOverview of Next Generation IT trends
Overview of Next Generation IT trends
Yuvaraj Ilangovan
 
1 introduction to data science
1 introduction to data science1 introduction to data science
1 introduction to data science
Dr Nisha Arora
 
Using the Open Source VS Code Editor with the HPCC Systems Platform
Using the Open Source VS Code Editor with the HPCC Systems PlatformUsing the Open Source VS Code Editor with the HPCC Systems Platform
Using the Open Source VS Code Editor with the HPCC Systems Platform
HPCC Systems
 

Similar to The main types of machine learning and their practical application (20)

MLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseMLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
 
Industrial revolution 4.0
Industrial revolution 4.0 Industrial revolution 4.0
Industrial revolution 4.0
 
An informed definition
An informed definitionAn informed definition
An informed definition
 
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
 
5 examples that show how machine learning is changing modern advertising indu...
5 examples that show how machine learning is changing modern advertising indu...5 examples that show how machine learning is changing modern advertising indu...
5 examples that show how machine learning is changing modern advertising indu...
 
Slave to the Algorithm 2016
Slave to the Algorithm  2016 Slave to the Algorithm  2016
Slave to the Algorithm 2016
 
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...
 
XMANAI Technical Project Overview
XMANAI Technical Project OverviewXMANAI Technical Project Overview
XMANAI Technical Project Overview
 
Generative AI in Transportation for Connected Future Transport System July 20...
Generative AI in Transportation for Connected Future Transport System July 20...Generative AI in Transportation for Connected Future Transport System July 20...
Generative AI in Transportation for Connected Future Transport System July 20...
 
Introduction to AI & ML
Introduction to AI & MLIntroduction to AI & ML
Introduction to AI & ML
 
Slave to the Algo-Rhythms?
Slave to the Algo-Rhythms?Slave to the Algo-Rhythms?
Slave to the Algo-Rhythms?
 
Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1
 
Jakarta presentation
Jakarta presentationJakarta presentation
Jakarta presentation
 
Taking advantageofai july2018
Taking advantageofai july2018Taking advantageofai july2018
Taking advantageofai july2018
 
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages...
 
Technology in Cars PowerPoint-Team 5
Technology in Cars PowerPoint-Team 5Technology in Cars PowerPoint-Team 5
Technology in Cars PowerPoint-Team 5
 
The Ultimate Guide to Machine Learning (ML)
The Ultimate Guide to Machine Learning (ML)The Ultimate Guide to Machine Learning (ML)
The Ultimate Guide to Machine Learning (ML)
 
Overview of Next Generation IT trends
Overview of Next Generation IT trendsOverview of Next Generation IT trends
Overview of Next Generation IT trends
 
1 introduction to data science
1 introduction to data science1 introduction to data science
1 introduction to data science
 
Using the Open Source VS Code Editor with the HPCC Systems Platform
Using the Open Source VS Code Editor with the HPCC Systems PlatformUsing the Open Source VS Code Editor with the HPCC Systems Platform
Using the Open Source VS Code Editor with the HPCC Systems Platform
 

More from Provectus

Choosing the right IDP Solution
Choosing the right IDP SolutionChoosing the right IDP Solution
Choosing the right IDP Solution
Provectus
 
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
Provectus
 
Choosing the Right Document Processing Solution for Healthcare Organizations
Choosing the Right Document Processing Solution for Healthcare OrganizationsChoosing the Right Document Processing Solution for Healthcare Organizations
Choosing the Right Document Processing Solution for Healthcare Organizations
Provectus
 
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionMLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in Production
Provectus
 
AI Stack on AWS: Amazon SageMaker and Beyond
AI Stack on AWS: Amazon SageMaker and BeyondAI Stack on AWS: Amazon SageMaker and Beyond
AI Stack on AWS: Amazon SageMaker and Beyond
Provectus
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine Learning
Provectus
 
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerMLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
Provectus
 
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMRCost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
Provectus
 
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
Provectus
 
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K..."Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
Provectus
 
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ..."How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
Provectus
 
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky..."Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
Provectus
 
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2..."Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
Provectus
 
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma..."Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
Provectus
 
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ..."Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
Provectus
 
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
Provectus
 
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
Provectus
 
"Integrate your front end apps with serverless backend in the cloud", Sebasti...
"Integrate your front end apps with serverless backend in the cloud", Sebasti..."Integrate your front end apps with serverless backend in the cloud", Sebasti...
"Integrate your front end apps with serverless backend in the cloud", Sebasti...
Provectus
 
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
Provectus
 
How to implement authorization in your backend with AWS IAM
How to implement authorization in your backend with AWS IAMHow to implement authorization in your backend with AWS IAM
How to implement authorization in your backend with AWS IAM
Provectus
 

More from Provectus (20)

Choosing the right IDP Solution
Choosing the right IDP SolutionChoosing the right IDP Solution
Choosing the right IDP Solution
 
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
Intelligent Document Processing in Healthcare. Choosing the Right Solutions.
 
Choosing the Right Document Processing Solution for Healthcare Organizations
Choosing the Right Document Processing Solution for Healthcare OrganizationsChoosing the Right Document Processing Solution for Healthcare Organizations
Choosing the Right Document Processing Solution for Healthcare Organizations
 
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionMLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in Production
 
AI Stack on AWS: Amazon SageMaker and Beyond
AI Stack on AWS: Amazon SageMaker and BeyondAI Stack on AWS: Amazon SageMaker and Beyond
AI Stack on AWS: Amazon SageMaker and Beyond
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine Learning
 
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerMLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
 
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMRCost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
Cost Optimization for Apache Hadoop/Spark Workloads with Amazon EMR
 
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...
 
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K..."Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...
 
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ..."How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
"How to build a global serverless service", Alex Casalboni, AWS Dev Day Kyiv ...
 
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky..."Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
"Automating AWS Infrastructure with PowerShell", Martin Beeby, AWS Dev Day Ky...
 
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2..."Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
"Analyzing your web and application logs", Javier Ramirez, AWS Dev Day Kyiv 2...
 
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma..."Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
"Resiliency and Availability Design Patterns for the Cloud", Sebastien Storma...
 
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ..."Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
"Architecting SaaS solutions on AWS", Oleksandr Mykhalchuk, AWS Dev Day Kyiv ...
 
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
"Developing with .NET Core on AWS", Martin Beeby, AWS Dev Day Kyiv 2019
 
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
"How to build real-time backends", Martin Beeby, AWS Dev Day Kyiv 2019
 
"Integrate your front end apps with serverless backend in the cloud", Sebasti...
"Integrate your front end apps with serverless backend in the cloud", Sebasti..."Integrate your front end apps with serverless backend in the cloud", Sebasti...
"Integrate your front end apps with serverless backend in the cloud", Sebasti...
 
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019
 
How to implement authorization in your backend with AWS IAM
How to implement authorization in your backend with AWS IAMHow to implement authorization in your backend with AWS IAM
How to implement authorization in your backend with AWS IAM
 

Recently uploaded

Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
Mariano Tinti
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
shyamraj55
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
danishmna97
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceAI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
IndexBug
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
Jason Packer
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
Claudio Di Ciccio
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Matthew Sinclair
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
DianaGray10
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 

Recently uploaded (20)

Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceAI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 

The main types of machine learning and their practical application

  • 1. Main machine learning systems and their business usage
  • 2. 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
  • 3. - 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
  • 4. Agenda - Industry overview - Closer look at: - Chatbots - Healthcare - Autonomous driving
  • 5. What is machine learning?
  • 6. What is machine learning?
  • 7. 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 - ...
  • 10. A little bit better example
  • 12. Available tools and approaches
  • 14. Word-to-vec example You may try it online: http://projector.tensorflow.org/
  • 16. Business Values - Reduced costs - Customers happiness - Response rate - 24/7 availability - Scalability - Additional training
  • 17. What’s next? VoiceBots? - Customers intention understanding - Complicated actions - Speech recognition - Voice generation
  • 19. What does exist now? - Digital medical records - Disease identification/Diagnosis - Drugs discovery/Manufacturing - Epidemic outbreak prediction
  • 20. 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
  • 21. How is it possible? - Objects classification - Objects detection - Prediction systems - Speech and text recognition
  • 22. Business values - Increased life expectancy - Reduction of insurance payments - Improvements in the one of the most huge markets
  • 23. Potential problems - Data availability - Personal data handling and protecting - False positive or false negative results - Certification, medical clearance - Bureaucracy and conservatism
  • 25. Current state of the field
  • 26. Grounding - Safety - Traffic improvements - Costs reducing - Cargo transportation
  • 27. Blockers - Legal issues - Opaque decision system - People - Privacy - Other...
  • 31. Moral issues: what should car do? http://moralmachine.mit.edu/
  • 32. Job losses - Approximate 3.5 million of truck drivers - Abt .5 million of taxi drivers - Support staff
  • 33. What is mainly used - Objects detection - Segmentation - Tracking - Reinforcement learning - Usual SGD - SLAM
  • 34. SLAM - Simultaneous localization and mapping
  • 35. 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
  • 37. 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
  • 38. 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/
  • 39. 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.

Editor's Notes

  1. N: about me
  2. N: main ideas
  3. N: agenda
  4. N: ML demotivator
  5. N: Matrices image - dangerous AI
  6. Q: Who believe that ML will rule the world? N: Market overview
  7. N: chatbots
  8. N: easy example
  9. A: where I get it and what is troubles N: privat example
  10. A: how it’s maybe works and what troubles N: Better ML solution
  11. A: One component added N: solution with frameworks
  12. N: Vectors explanation
  13. N: word to vec online example
  14. N: dialogflow example
  15. N: business values
  16. N: chatbot from the future
  17. N: healthcare
  18. N: already existed solutions
  19. N: what can be done?
  20. N: possible approaches
  21. N: business values
  22. N: troubles
  23. N: autonomous driving
  24. N: market players
  25. A: about uber N: grounding of such interest
  26. N: blockers
  27. N: people Adv.Attack example
  28. Q: is it real? N: people Adv.Attack example one more
  29. Q: are lines parallel to each other? N: Computer Adv attack example
  30. N: Moral machine
  31. Q: what will you decide? N: negative outcome: job losses
  32. N: if you still interested - how you may implement this
  33. N: slam explanation
  34. Constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. N: available resources
  35. N: general overview
  36. Again repeat main ideas: easy, can get some benefit, interesting and hyped. And for those who decided to dive into ML -> N: checklist
  37. N: final slide
  38. N: coffee and networking).. Or bonus slide.