[benjamta]

Here’s a bit more about the Rainbird platform for those interested…

The platform has two main parts:

1. The studio (an area where you can make visual decision-making models in a process similar to mind mapping)

2. The inference engine (which uses your models to automate decision making)

The platform is designed so that computer scientists and automation specialists can overcome the challenges of machine learning (in particular, our inability to stay in control of algorithms that find their own patterns in data) and RPA (in particular, a decision tree’s inability to juggle many variables at a massive scale). And that’s why we’ve focussed on two principles as we improve Rainbird:

1. Human-down structure: that is, we start with human knowledge and apply it to data (so that humans can always understand and explain what the machines are doing)

2. Non-linear automation: that is, we focus on capturing and codifying knowledge that can apply to multiple scenarios (rather than on steps to be followed in a one-off, isolated situation)