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Ask HN: Control Theory a Superior Paradigm over AI for Plant Performance?

model predictive control (MPC) framework as the dominant paradigm for the control and adaptation of asset-based facilities, and examine how specific AI techniques may be selectively used within this framework for performance improvement. Critical control functions, including observability and controllability, where AI/ML methods perform poorly in real-world applications.

PaulHoule 4y ago
My take is this. Most people who work with machine learning and AI follow the herd using certain methods of evaluation that don't necessary lead to working systems. I'm more familiar with applications to business, text analysis, and a bit about finance such as algo trading. This is one of the missing links https://scikit-learn.org/stable/modules/calibration.html https://scikit-learn.org/stable/modules/calibration.html if you have a prediction that says "this is in class A" that's not very useful in itself. If your predictor is calibrated and says "there is a 55% chance that this is in class A" you probably don't want to take action that prediction but if it says "there is a 97% chance this is in class A" you will take action. IBM Watson won at Jeopardy because it was calibrated and could make a rational decision of whether or when it should hit the button. From an algo trading point of view the other thing you need to turn predictions into actions is https://en.wikipedia.org/wiki/Kelly_criterion https://en.wikipedia.org/wiki/Kelly_criterion I bet there is some similar way to turn predictions into actions based on control theory. Speaking of betting there is a practical application of this in this book https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0688072216 https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0... which is a gambling system that really makes money. The book is not very mathematical, Dr. Ziemba has written a lot about hedge funds, algo trading, etc. and probably in his body of work there is something that covers the same ground and is more mathematically rigorous.
- BOOSTERHIDROGEN 4y ago
  Really good insight is there any books other than Beat Racetrack ? Thanks

PaulHoule 4y ago

My take is this. Most people who work with machine learning and AI follow the herd using certain methods of evaluation that don't necessary lead to working systems. I'm more familiar with applications to business, text analysis, and a bit about finance such as algo trading. This is one of the missing links https://scikit-learn.org/stable/modules/calibration.html https://scikit-learn.org/stable/modules/calibration.html if you have a prediction that says "this is in class A" that's not very useful in itself. If your predictor is calibrated and says "there is a 55% chance that this is in class A" you probably don't want to take action that prediction but if it says "there is a 97% chance this is in class A" you will take action. IBM Watson won at Jeopardy because it was calibrated and could make a rational decision of whether or when it should hit the button. From an algo trading point of view the other thing you need to turn predictions into actions is https://en.wikipedia.org/wiki/Kelly_criterion https://en.wikipedia.org/wiki/Kelly_criterion I bet there is some similar way to turn predictions into actions based on control theory. Speaking of betting there is a practical application of this in this book https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0688072216 https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0... which is a gambling system that really makes money. The book is not very mathematical, Dr. Ziemba has written a lot about hedge funds, algo trading, etc. and probably in his body of work there is something that covers the same ground and is more mathematically rigorous.

BOOSTERHIDROGEN 4y ago
Really good insight is there any books other than Beat Racetrack ? Thanks