[phantasilide]

I’ve worked at a startup providing a similar solution. Even for an ad piece, the article is wildly underestimating the difficulty in getting this to work and the problem is not lack of state of the art methods. Most customers don’t have the type of data to control these systems, even if they think they do. While industry 4.0 has taught companies to measure everything, there is rarely any notion of data integrity or quality in place, and even your smart new ML technique will not be able to control system with very low correlation between observations and controls. The reality for companies providing these ML solutions is that they end up spending all of their resources on fixing each clients’ data problems rendering their smart solution irrelevant.

[vonnik]

It's true that a lot of companies are at different stages of what we would call "the journey of digital transformation." Some of them do not know what's happening inside their plants. The problem before the problem -- the upstream chokepoint of ML -- is gathering, collecting and cleaning the right data. That's true across the industry and it's the main reason why ML is not moving faster, and why we don't see more huge ML-based startups. Scale, one of the unicorns, is chiefly devoted to data annotation... But there are a lot of smaller, well defined problems with machine scheduling and/or inventory management where they have exactly the data they need, and they are already feeding it to an optimizer. Deep RL can often outperform against those existing technologies.