The future of ML (and OR!) lies in the hybridization of ML and OR.
One of our main mottos is:
Where machine learning meets operations research
and this truly is where Funartech shines and distinguishes itself from other companies. We strongly advocate for the combination of ML and OR for all industrial projects.
OR and ML might not be sufficient but they should be considered in any analytical solution.
There are basically 4 ways to combine ML and OR:
This is the most common approach whenever companies propose to combine ML and OR. Often, ML is used to predict and OR is then used to optimize on those predictions.
As an example, let's say you are a train company and you want to repair/replace your tracks. Of course, you'll want to do this for the minimum cost. How can you do this? Decouple this problem in two steps:
You might want to have a look at our use cases as they all use that approach.
OR can be used to optimize, i.e. minimize or maximize some function in ML. But wait, isn't this what ML is about? Exactly, ML is strongly based on OR optimization when it optimizes its predictions.
It also goes the opposite way. The strong point of ML is to predict. Some research is conducted in order to predict on what variable and how to branch in search trees.
We are developing new algorithms that are a complete and total hybridization of both ML and OR. These new algorithms don't treat ML and/or OR as separate black boxes but are really a new combination of the strengths of both fields. We are convinced that these new algorithms are the way to go to solve any problem and that in the future OR and ML will be merged into a new field.
We use this approach for our Open door to AGI project.