We are frequently asked if what we’re doing is AI or not.
From an engineer’s perspective it’s a very uninteresting question – if you’re sitting in a thing with four wheels that runs on petrol and transports people to work, then it doesn’t really matter what you call it, you’ll end up at the office anyway.
On the other hand, the categories, where we put things, do really matter. They’re part of our mental landscape and guide our preconceptions about what we can expect from a thing belonging to a certain reference group. If you put a thing in the wrong category, people end up confused.
So in what category does optimization belong?
– It helps automate something previously manual
– It is self-learning
– It requires a lot of computation resources
– It gets better the more (high-quality) data you have
– Everyone talks about it
– It is implemented by engineers with some heavy math background
So 1-0 for the non-AI crowd!
Looks like the game is tied again at 1-1.
- In machine learning the machine learns and produces insights; In optimization the business logic comes from humans.
- Machine learning models are inherently black boxes (although the insights can be explained, see e.g. https://christophm.github.io/interpretable-ml-book/). Optimization models are inherently transparent, i.e. it’s easy to track back to first principles on why the model outputs a certain result.
- Machine learning is predictive analytics; optimization is prescriptive analytics (suggesting decisions)
If you have good ideas on what to call it, do let us know! In the meantime we’ll continue helping companies do more with less.