Questions and Insights
Table of Contents
- 1. Machine Learning Questions
- 2. Reading
- 3. Nation States Sponsors
- 4. Current Uses
- 4.1. ML: Products
- 4.2. Reinforcement Learning: Text Prediction
- 4.3. Reinforcement Learning: Games
- 4.4. Algorithms: Games
- 4.5. Reinforcement Learning: Video Games
- 4.6. Reinforcement Learning: Evolution
- 4.7. Reinforcement Learning: Example
- 4.8. Classification and Linear Regression
- 4.9. Classification: Digit Recognizer
- 4.10. Classification: Hotdog (Silicon Valley)
- 4.11. Classification: Cat or Dog
- 4.12. Classification: Face detection
- 4.13. Classification: Facial Recognition
- 4.14. Classification: Lie Detection
- 4.15. Classification: Stress
- 4.16. Classification: Depression
- 4.17. AdTech
- 4.18. FinTech
- 4.19. Auctions
- 4.20. Autoencoders and Generative Adversarial Network
- 4.21. Generation: Text
- 4.22. Generation: Music
- 4.23. Generation: Horror Imagery
- 4.24. Generation: Painting Styles
- 4.25. Generation: Image-to-image
- 4.26. Generation: Colorizing Images
- 4.27. Generation: Faces
- 4.28. Generation: Games
- 4.29. Generation: Physical Models
- 4.30. Training
- 4.31. Diagnosis
- 4.32. Recommendations
- 4.33. Natural Language Processing (NLP)
- 5. Approaches
- 6. MLaaS vs. Hosted
- 7. Machine Learning Use Cases
- 8. Product Areas
- 9. Hiring and Skillsets
- 10. Summarizing Entities
- 11. Creating Data Pipelines
- 12. Cleaning Data
- 13. Finding Features
- 14. Evaluating Features
- 15. Feature Importance and Correlated Features
- 16. Evaluating Models
- 17. Maintaining Models
- 18. DataLab and Experimentation
- 19. DataLab vs. Production
- 20. Outlier Management
- 21. Tracking Technical Debt
- 22. Design and Code Review
- 23. Documentation
- 24. Outliers technical_debt
- 25. Monitoring Correlated Features
- 26. Causation technical_debt
- 27. Boundary Conditions technical_debt
- 28. Monitoring Outcomes
- 29. Escalation
- 30. Scenarios
1. Machine Learning Questions
- What are the factors in selecting new features
- Selecting models
- How does one test new features
- How does one produce new features
- How does one deploy new features
- What is the process for regression
- What visualization can be provided
http://www.scikit-yb.org/en/latest/tutorial.html https://docs.aws.amazon.com/machine-learning/latest/dg/ml-model-insights.html
https://aws.amazon.com/aml/faqs/
https://medium.freecodecamp.org/a-beginners-guide-to-training-and-deploying-machine-learning-models-using-python-48a313502e5a https://engineering.quora.com/Avoiding-Complexity-of-Machine-Learning-Systems
These are some of the areas we would want to consider (as well as areas that we don't want to consider):
2. Reading
3. Nation States Sponsors
- https://www.reuters.com/article/us-france-tech/france-to-spend-1-8-billion-on-ai-to-compete-with-u-s-china-idUSKBN1H51XP
- https://www.forbes.com/sites/ninaxiang/2018/10/05/chinas-ai-industry-has-given-birth-to-14-unicorns-is-it-a-bubble-waiting-to-pop/#679a9c9c46c3
- https://www.forbes.com/sites/gilpress/2018/09/24/the-thriving-ai-landscape-in-israel-and-what-it-means-for-global-ai-competition/#105ae46b30c5
- https://slate.com/technology/2018/08/the-u-k-wants-to-be-the-world-leader-in-ethical-a-i.html
4. Current Uses
4.1. ML: Products
- Resume matching: https://ideal.com/resume-matching-software/
- https://www.marutitech.com/how-can-artificial-intelligence-help-fintech-companies/
- Doctors https://venturebeat.com/2018/10/30/98point6-raises-50-million-for-ai-virtual-doctor-visits/
- Education https://www.forbes.com/sites/bernardmarr/2018/07/25/how-is-ai-used-in-education-real-world-examples-of-today-and-a-peek-into-the-future/#2f32b384586e
4.2. Reinforcement Learning: Text Prediction
https://skymind.ai/wiki/deep-reinforcement-learning#code https://skymind.ai/wiki/markov-chain-monte-carlo
This is effectively Markov chains with reward functions.
4.3. Reinforcement Learning: Games
- Tic-Tac-Toe https://www.youtube.com/watch?v=yMRuYeOLf0o
- Chess https://www.youtube.com/watch?v=0g9SlVdv1PY
- Jeopardy https://www.youtube.com/watch?v=P0Obm0DBvwI
- AlphaGo https://medium.freecodecamp.org/explained-simply-how-an-ai-program-mastered-the-ancient-game-of-go-62b8940a9080
- Deepstack https://www.youtube.com/watch?v=jLXPGwJNLHk
- Libratus https://www.youtube.com/watch?v=2dX0lwaQRX0
- Rock, paper, scissors https://github.com/flesler/neural-rock-paper-scissors
4.4. Algorithms: Games
4.5. Reinforcement Learning: Video Games
- https://www.youtube.com/watch?v=Yo2SepcNyw4
- https://www.youtube.com/watch?v=Aut32pR5PQA
- Super Mario Bros https://thenextweb.com/artificial-intelligence/2018/01/03/this-live-stream-of-ai-learning-to-play-super-mario-bros-is-awesome/
- MarI/O https://www.youtube.com/watch?v=qv6UVOQ0F44
- Mario Kart (MariFlow) https://www.youtube.com/watch?v=Ipi40cb_RsI
- OpenAI DOTA 2 https://www.youtube.com/watch?v=eaBYhLttETw
4.7. Reinforcement Learning: Example
https://becominghuman.ai/reinforcement-learning-step-by-step-17cde7dbc56c https://github.com/rfeinman/tictactoe-reinforcement-learning
- Create Environment (agent_state, agent_move)
- Create State
- Specify reward
- Create game.py https://inventwithpython.com/chapter10.html
- Create agent.py
- Create Actions list
- Create game visualization
- Create match visualizations
https://skymind.ai/wiki/deep-reinforcement-learning#code
This would also look at game boards.
https://github.com/topics/tic-tac-toe?l=javascript&o=desc&s=stars
4.8. Classification and Linear Regression
- wines
- flowers
4.15. Classification: Stress
4.17. AdTech
https://www.spotx.tv/resources/blog/product-pulse/artificial-intelligence-vs-machine-learning-disrupting-ad-tech/ https://www.teads.tv/machine-learning-teads-4-use-cases-adtech-industry/
View-through rate prediction (VTR) Broken creative detection Bid-request relevancy prediction Look-alike Modeling (on-going)
4.18. FinTech
https://rubygarage.org/blog/machine-learning-in-fintechFraud prevention Risk management Investment predictions Customer service Digital assistants Marketing Network security Loan underwriting Algorithmic trading Process automation Document interpretation Content creation Trade settlements Money-laundering prevention Custom machine learning solutions
4.22. Generation: Music
4.28. Generation: Games
4.30. Training
4.32. Recommendations
5. Approaches
- What is Supervised Learning?
- What is Unsupervised Learning?
- What is Reinforcement Learning?
8. Product Areas
https://machinelearningmastery.com/machine-learning-checklist/
- predictive analytics
- classification
9. Hiring and Skillsets
Focus on executives, designers, and business analysts.
12. Cleaning Data
13. Finding Features
23. Documentation
27. Boundary Conditions technical_debt
One sense in which all machine learning algorithms incur a technical debt is through the erosion of boundaries
https://www.kdnuggets.com/2015/01/high-cost-machine-learning-technical-debt.html