[orasis]

I disagree with your conclusion.

In my app, 7 Second Meditation, I am constantly authoring new content. It uses improve.ai to show that new content to some small number of users and learn how they react to that content.

The learning happens from the users themselves. In my particular case, within about 300-500 views the system can tell if a new piece of content is total crap and will basically stop showing it. If the content is amazing, within 100-200 views it has a strong idea and will immediately prioritize this content.

The results speak for themselves, before I started using machine learning I was getting 4 star ratings and usage was flat. Now I'm getting 5 star ratings and the app is growing.

Will it ever take off at large scale? I have no clue, but I've seen such strong benefits from the machine learning that I'm now working on developing improve.ai full time.

[arekkas]

Your example isn't even real statistical/machine learning, it's basic A/B testing. This has nothing in common with orchestrating code.

[orasis]

I would contend that bayesian reinforcement learning is statistical machine learning.

The difference between A/B testing and reinforcement learning is that reinforcement learning does not use a control and does not wait for high confidence to continue to improve. This allows much more data-efficient learning than A/B testing because at every step it is making its best guess as to whether to continue to explore or exploit what its already learned.

As to it having nothing to do with orchestrating code:

Most code is logic reacting to data. Reinforcement learning replaces hand-coded logic with goal-driven logic. If you can use a declarative approach to specify your goals, much of the logic can be moved to reinforcement learning.